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Processes, Volume 8, Issue 10 (October 2020) – 128 articles

Cover Story (view full-size image): The aqueous extraction process (AEP) enables the concurrent extraction of oil and protein from almond flour without the use of flammable solvents. However, the majority of the extracted oil is entrapped in an emulsion that needs to be demulsified to free the extracted oil. The effects of scaling up the AEP of almond flour and the efficiency of enzymatic and chemical cream demulsification strategies were evaluated. Oil and protein extraction yields of 61.9% and 66.6% were achieved, respectively. At optimum conditions, enzymatic and chemical demulsification strategies increased the recovery of the extracted oil from 8% to 66%, compared with the control. However, enzymatic demulsification resulted in significant changes in the physicochemical properties of the cream protein and faster demulsification compared with the chemical approach. View this paper
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14 pages, 13097 KiB  
Article
Biomass Pretreatment with the Szego Mill™ for Bioethanol and Biogas Production
by Merlin Raud, Kaja Orupõld, Lisandra Rocha-Meneses, Vahur Rooni, Olev Träss and Timo Kikas
Processes 2020, 8(10), 1327; https://doi.org/10.3390/pr8101327 - 21 Oct 2020
Cited by 14 | Viewed by 4412
Abstract
Results from an investigation of the mechanical size reduction with the Szego Mill™ as a pretreatment method for lignocellulosic biomass are presented. Pretreatment is a highly expensive and energy-consuming step in lignocellulosic biomass processing. Therefore, it is vital to study and optimize different [...] Read more.
Results from an investigation of the mechanical size reduction with the Szego Mill™ as a pretreatment method for lignocellulosic biomass are presented. Pretreatment is a highly expensive and energy-consuming step in lignocellulosic biomass processing. Therefore, it is vital to study and optimize different pretreatment methods to find a most efficient production process. The biomass was milled with the Szego Mill™ using three different approaches: dry milling, wet milling and for the first time nitrogen assisted wet milling was tested. Bioethanol and biogas production were studied, but also fibre analysis and SEM (scanning electron microscope) analysis were carried out to characterize the effect of different milling approaches. In addition, two different process flows were used to evaluate the efficiency of downstream processing steps. The results show that pretreatment of barely straw with the Szego Mill™ enabled obtaining glucose concentrations of up to 7 g L−1 in the hydrolysis mixture, which yields at hydrolysis efficiency of 18%. The final ethanol concentrations from 3.4 to 6.7 g L−1 were obtained. The lowest glucose and ethanol concentrations were measured when the biomass was dry milled, the highest when nitrogen assisted wet milling was used. Milling also resulted in an 6–11% of increase in methane production rate during anaerobic digestion of straw. Full article
(This article belongs to the Special Issue Sustainable Development of Waste towards Green Growth)
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Graphical abstract
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<p>A schematic describing the working principle of the Szego Mill™. Reproduced with permission from [Gravelsins, R. J.; Trass, O.], [Powder Technology]; published by [Elsevier], [2013].</p>
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<p>A flow chart of the studied routes of biomass for biofuel and biogas production. Reproduced with permission from [Merlin Raud and Timo Kikas], [Environmental and Climate Technologies]; published by [Sciedo], [2020].</p>
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<p>Size distribution of barley straw after grinding with cutting mill and Szego mill.</p>
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<p>SEM (scanning electron microscope) pictures of milled biomass samples by cutting mill (<b>A</b>) and the Szego Mill ((<b>B</b>)—dry milling, (<b>C</b>)—wet milling, (<b>D</b>)—nitrogen assisted wet milling).</p>
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<p>Concentration of glucose and ethanol in hydrolysis and fermentation mixtures in case of different milling methods and fermentation approaches.</p>
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<p>Biomethane production in time during experiments and corresponding best-fit curves (according to Equation (1)) for material milled with cutting mill (untreated) and samples that were dry milled, wet milled and nitrogen assisted wet milled using the Szego Mill. Inset shows the starting slope of the respective anaerobic digestion processes.</p>
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<p>Maximum biogas and biomethane yields for untreated material and samples from the different stages of bioethanol production pretreated with dry milling, wet milling and nitrogen assisted wet milling.</p>
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<p>Kinetic rate constant (k) for untreated material and samples from the different stages of bioethanol production pretreated with dry milling, wet milling and nitrogen assisted wet milling.</p>
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<p>The mass balance of bioethanol and anaerobic digestion process using different milling methods with the Szego Mill<sup>TM</sup> when liquid and solid fractions (dry matter) are separated after hydrolysis and before fermentation.</p>
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<p>The mass balance of bioethanol and anaerobic digestion process using different milling methods with the Szego Mill<sup>TM</sup> when liquid and solid fractions (dry matter) are separated after fermentation.</p>
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17 pages, 2151 KiB  
Article
A Novel Consensus Fuzzy K-Modes Clustering Using Coupling DNA-Chain-Hypergraph P System for Categorical Data
by Zhenni Jiang and Xiyu Liu
Processes 2020, 8(10), 1326; https://doi.org/10.3390/pr8101326 - 21 Oct 2020
Cited by 4 | Viewed by 3016
Abstract
In this paper, a data clustering method named consensus fuzzy k-modes clustering is proposed to improve the performance of the clustering for the categorical data. At the same time, the coupling DNA-chain-hypergraph P system is constructed to realize the process of the clustering. [...] Read more.
In this paper, a data clustering method named consensus fuzzy k-modes clustering is proposed to improve the performance of the clustering for the categorical data. At the same time, the coupling DNA-chain-hypergraph P system is constructed to realize the process of the clustering. This P system can prevent the clustering algorithm falling into the local optimum and realize the clustering process in implicit parallelism. The consensus fuzzy k-modes algorithm can combine the advantages of the fuzzy k-modes algorithm, weight fuzzy k-modes algorithm and genetic fuzzy k-modes algorithm. The fuzzy k-modes algorithm can realize the soft partition which is closer to reality, but treats all the variables equally. The weight fuzzy k-modes algorithm introduced the weight vector which strengthens the basic k-modes clustering by associating higher weights with features useful in analysis. These two methods are only improvements the k-modes algorithm itself. So, the genetic k-modes algorithm is proposed which used the genetic operations in the clustering process. In this paper, we examine these three kinds of k-modes algorithms and further introduce DNA genetic optimization operations in the final consensus process. Finally, we conduct experiments on the seven UCI datasets and compare the clustering results with another four categorical clustering algorithms. The experiment results and statistical test results show that our method can get better clustering results than the compared clustering algorithms, respectively. Full article
(This article belongs to the Special Issue Modeling, Simulation and Design of Membrane Computing System)
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<p>Consensus clustering framework.</p>
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<p>An example of the hypergraph which has vertices <math display="inline"><semantics> <mrow> <mi>v</mi> <mo>=</mo> <mo>{</mo> <msub> <mi>v</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>4</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>5</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>6</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>7</mn> </msub> <mo>}</mo> </mrow> </semantics></math>, and hyper-edges <math display="inline"><semantics> <mrow> <mi>e</mi> <mo>=</mo> <mo>{</mo> <msub> <mi>e</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>e</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>e</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>e</mi> <mn>4</mn> </msub> <mo>}</mo> <mo>=</mo> <mo>{</mo> <mo>{</mo> <msub> <mi>v</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>3</mn> </msub> <mo>}</mo> <mo>,</mo> <mo>{</mo> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>3</mn> </msub> <mo>}</mo> <mo>,</mo> <mo>{</mo> <msub> <mi>v</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>5</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>6</mn> </msub> <mo>}</mo> <mo>}</mo> </mrow> </semantics></math>.</p>
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<p>Flow chart of the proposed DNA-chain-hypergraph P System for consensus clustering (DCHP-FCC) algorithm.</p>
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<p>Chain membrane structure.</p>
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<p>Hyper-membrane structure of the P system.</p>
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<p>The membrane structure of the DCHP system.</p>
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<p>Boxplots of Adjusted Rank Index (ARI), Clustering Accuracy (ACC), and F_measure by DCHP-FCC algorithm on Soybean-small dataset with basic partitions (BPs) equal to 30, 60 and 90, respectively.</p>
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29 pages, 5075 KiB  
Article
Digital Twin for Lyophilization by Process Modeling in Manufacturing of Biologics
by Leon S. Klepzig, Alex Juckers, Petra Knerr, Frank Harms and Jochen Strube
Processes 2020, 8(10), 1325; https://doi.org/10.3390/pr8101325 - 21 Oct 2020
Cited by 18 | Viewed by 5815
Abstract
Lyophilization stabilizes formulated biologics for storage, transport and application to patients. In process design and operation it is the link between downstream processing and with final formulation to fill and finish. Recent activities in Quality by Design (QbD) have resulted in approaches by [...] Read more.
Lyophilization stabilizes formulated biologics for storage, transport and application to patients. In process design and operation it is the link between downstream processing and with final formulation to fill and finish. Recent activities in Quality by Design (QbD) have resulted in approaches by regulatory authorities and the need to include Process Analytical Technology (PAT) tools. An approach is outlined to validate a predictive physical-chemical (rigorous) lyophilization process model to act quantitatively as a digital twin in order to allow accelerated process design by modeling and to further-on develop autonomous process optimization and control towards real time release testing. Antibody manufacturing is chosen as a typical example for actual biologics needs. Literature is reviewed and the presented procedure is exemplified to quantitatively and consistently validate the physical-chemical process model with aid of an experimental statistical DOE (design of experiments) in pilot scale. Full article
(This article belongs to the Section Biological Processes and Systems)
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<p>Freeze drying process shown in p-T phase diagram. Adapted from [<a href="#B24-processes-08-01325" class="html-bibr">24</a>].</p>
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<p>Product temperature for the critical vials. Highest heat input (<b>corner vial</b>)-red: defines maximum shelf temperature; minimum heat input (<b>center vial</b>)-blue: defines minimum process time.</p>
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<p>Quality-by-Design based process development strategy [<a href="#B55-processes-08-01325" class="html-bibr">55</a>].</p>
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<p>Part of the risk assessment for lyophilization: (<b>a</b>) Ishikawa diagram and (<b>b</b>) Occurrence-Impact diagram.</p>
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<p>Schematic representation of the (<b>a</b>) one-dimensional two-phases with uniform sublimation front (adapted from [<a href="#B42-processes-08-01325" class="html-bibr">42</a>]) and of a (<b>b</b>) two-dimensional freeze-drying model showing the sublimation interface and axial and radial heat transfer (adapted from [<a href="#B59-processes-08-01325" class="html-bibr">59</a>]).</p>
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<p>Workflow for process- and model development, adapted from [<a href="#B68-processes-08-01325" class="html-bibr">68</a>].</p>
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<p>Model depth for some models from literature (Pictures adapted from [<a href="#B42-processes-08-01325" class="html-bibr">42</a>,<a href="#B62-processes-08-01325" class="html-bibr">62</a>,<a href="#B72-processes-08-01325" class="html-bibr">72</a>]).</p>
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<p>Balance volumes–energy and mass.</p>
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<p>(<b>a</b>) Freeze-dryer Epsilon 2-6D [<a href="#B79-processes-08-01325" class="html-bibr">79</a>] and (<b>b</b>) WTMplus sensors.</p>
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<p>Model parameter determination concept overview.</p>
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<p>Workflow summary for parameter determination.</p>
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<p>Comparison of measured data for product temperature and simulated temperature. Taken from [<a href="#B59-processes-08-01325" class="html-bibr">59</a>].</p>
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<p>Pareto chart of standardized effects for residual moisture after secondary drying. Blue bars: absolute value of t-ratio; red dashed line: significance boundary; green line: separation of significant (<b>top</b>) and non-significant (<b>bottom</b>) parameters.</p>
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<p>Comparison of experimental (<b>red</b>) and simulated (<b>blue</b>) product temperatures. The average shelf temperature is represented by the black dashed line.</p>
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<p>Correlation-Loading plot visualizing the dependencies between each variable as found for the Monte Carlo simulations.</p>
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22 pages, 962 KiB  
Article
A Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks
by Cheng Gong, Chao Guo, Haitao Xu, Chengcheng Zhou and Xiaotao Yuan
Processes 2020, 8(10), 1324; https://doi.org/10.3390/pr8101324 - 21 Oct 2020
Cited by 8 | Viewed by 2432
Abstract
Wireless Sensor Networks (WSNs) have the characteristics of large-scale deployment, flexible networking, and many applications. They are important parts of wireless communication networks. However, due to limited energy supply, the development of WSNs is greatly restricted. Wireless rechargeable sensor networks (WRSNs) transform the [...] Read more.
Wireless Sensor Networks (WSNs) have the characteristics of large-scale deployment, flexible networking, and many applications. They are important parts of wireless communication networks. However, due to limited energy supply, the development of WSNs is greatly restricted. Wireless rechargeable sensor networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a two-phase scheme is proposed to improve the energy management efficiency for WRSNs. In the first phase, we designed an annulus virtual force based particle swarm optimization (AVFPSO) algorithm for area coverage. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a queuing game-based energy supply (QGES) algorithm was designed. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution strategy can be obtained. The simulation results show that our scheme improves the efficiency of coverage and energy supply, and then extends the lifetime of WSN. Full article
(This article belongs to the Special Issue Smart Systems and Internet of Things (IoT))
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<p>A coverage and power supply system model of WRSNs.</p>
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<p>The relationship between antenna angle parameters and sampling point position.</p>
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<p>Energy supply system model.</p>
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<p>The azimuth distribution of the antenna on a power supply node (PSN).</p>
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<p>The flow of the TPEM scheme.</p>
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<p>The optimal allocation rate with a changing value of <span class="html-italic">q</span>.</p>
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<p>The trend of the system’s minimum cost while the value of <span class="html-italic">q</span> changes.</p>
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<p>The actual network deployment map.</p>
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<p>The coverage rate with 10 particles.</p>
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<p>The coverage rate with 20 particles.</p>
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<p>The electric quantity of the nodes with two algorithms.</p>
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<p>The electric quantity of the system with two algorithms.</p>
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27 pages, 18911 KiB  
Article
CFD Hydrodynamics Investigations for Optimum Biomass Gasifier Design
by Emanuele Fanelli
Processes 2020, 8(10), 1323; https://doi.org/10.3390/pr8101323 - 21 Oct 2020
Cited by 5 | Viewed by 3473
Abstract
Biomass gasification is nowadays considered a viable option for clean energy production. Furthermore, still more efforts need to be spent to make this technology fully available at commercial scale. Drawbacks that greatly limit the full-time plant availability—and so its economically feasibility—mainly concerns syngas [...] Read more.
Biomass gasification is nowadays considered a viable option for clean energy production. Furthermore, still more efforts need to be spent to make this technology fully available at commercial scale. Drawbacks that greatly limit the full-time plant availability—and so its economically feasibility—mainly concerns syngas purification by contaminants such as tars. Different technological approaches were investigated over last two decades with the aim to increase both the plant availability and the overall efficiency by keeping, at the same time, CAPEX and OPEX low. Among technologies, fluidized beds are surely the most promising architectures for power production at thermal scale above 1 MWth. Gasifier can be surely considered the key component of the whole power plant and its proper design, the main engineering effort. This process involves different engineering aspects: thermo-structural, heat, and mass transfer, and chemical and fluid-dynamic concerns being the most important. In this study, with the aim to reach an optimal reaction chamber design, the hydrodynamics of a bubbling fluidized bed reactor was investigated by using a CFD approach. A Eulerian–Eulerian multiphase model, supported by experimental data, was implemented to describe the interactions between the solid and fluid phases inside the reactor while a discrete dense phase model (DDPM) model was considered to investigate momentum exchange among continuous phases and solid particles simulating char. Different process parameters, such as the bed recirculation rate and the particles circulation time inside the bed, were at least analyzed to characterize the hydrodynamics of the reactor. Results indicate that the recirculation time of bed material is in the order of 6–7 s at bench scale and, respectively, of 15–20 s at full scale. Information about solid particles inside the bed that should be used to avoid elutriation and agglomeration phenomenon, suggest that the dimension of the mother fuel particles should not exceed the value of 5–10 mm. Full article
(This article belongs to the Special Issue Biomass to Renewable Energy Processes)
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<p>ENEA’s 1 MWth Internally Circulating Bubbling Fluidized Bed (ICBFB) pilot plant: (<b>a</b>) Gasifier details; (<b>b</b>) Whole pilot plant layout: it is visible the biomass feeding system, the gasifier, the heat exchanger and the syngas cleaning system.</p>
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<p>Schematic representation of the experimental apparatus used by Deza et al. [<a href="#B44-processes-08-01323" class="html-bibr">44</a>], considered for the numerical validation of the mathematical model. In the table, characterizing properties of solid particles composing the inert bed material are collected.</p>
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<p>ENEA’s ICBFB pseudo-2D cold model test ring implementing LaVision PIV system for velocity field evaluations. Images of test conducted with: (<b>a</b>) 0.14 mm copper powder particles; (<b>b</b>) 0.55 mm glass beads.</p>
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<p>Pressure drop through the bed as function of the superficial gas velocity: comparison between theoretical pressure drop (red dashed line), experimental (red dot), and CFD calculation (black triangle). CFD calculations were obtained by implementing the parametric model of Syamlal-O’Brien: parameters of this model, are shown in the table.</p>
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<p>Instantaneous solid phase volume fraction at time t = 20 s: images are referred to calculations carried out by using Fluent code implementing user defined function (UDF) considering three different grid refinement: (<b>a</b>) 19 × 80; (<b>b</b>) 38 × 160; (<b>c</b>) 76 × 320.</p>
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<p>Averaged void fraction profiles as function of column diameter at (<b>a</b>) 4 cm and (<b>b</b>) 8 cm above flow distribution plate; (<b>c</b>) function of column height (calculated as mean values of all grids at the same height). Comparison between experimental and numerical data averaged between 5 s and 40 s.</p>
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<p>Schematic representation of the cold model used to investigate the hydrodynamic behavior of the ENEA’s ICBFB reactor. In the table, characterizing properties of solid particles composing the inert bed material and fluidizing condition for the two chambers are collected.</p>
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<p>Pressure drop through the bed as function of the superficial gas velocity: comparison between theoretical pressure drop (red dashed line) and CFD calculation (black triangle). CFD calculations were obtained by implementing the parametric model of Syamlal-O’Brien: parameters of this model are shown in the table.</p>
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<p>Solid phase volume fraction and field velocity comparison between CFD solutions with different grid refinement and at various simulation time.</p>
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<p>Main CFD results. Comparison between solutions obtained considering the coarsest and finest mesh: solid phase velocity profiles. Plotted data were averaged over simulation time between 1 s and 20 s.</p>
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<p>Main CFD results. Comparison between solutions obtained by considering the coarsest and finest mesh: (<b>a</b>) void fraction profiles at centerline in the two chambers; void fraction profiles as function of the bed height in the down-flowing bed (DFB) (<b>b</b>) and up-flowing bed (UFB) chamber (<b>c</b>). Plotted data were averaged over simulation time between 1 s and 20 s.</p>
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<p>3D-CFD visual representation at simulation time t = 10 s: (<b>a</b>) solid phase volume fraction and solid velocity field visualization on main section planes (colors of arrows are only delimiting the cutting plane where vector field is displayed); (<b>b</b>) granular solid phase iso-surfaces (red <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>0.35</mn> </mrow> </semantics></math>, blue <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>0.15</mn> </mrow> </semantics></math>) delimiting bubbles formation inside the two chambers.</p>
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<p>Visual representation of equivalent char particles position inside cold model reactor at different simulation time: white dot for particle of diameter <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>d</mi> <mo>¯</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <mn>0.22</mn> <mo> </mo> <mi>mm</mi> </mrow> </semantics></math>; black dot for particle of diameter <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>d</mi> <mo>¯</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <mn>0.27</mn> <mo> </mo> <mi>mm</mi> </mrow> </semantics></math>. Images also show the instantaneous granular solid phase volume fraction and the solid velocity field.</p>
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16 pages, 10386 KiB  
Article
Motor Fault Detection Using Wavelet Transform and Improved PSO-BP Neural Network
by Chun-Yao Lee and Yi-Hsin Cheng
Processes 2020, 8(10), 1322; https://doi.org/10.3390/pr8101322 - 21 Oct 2020
Cited by 47 | Viewed by 4036
Abstract
This paper proposes a motor fault detection method based on wavelet transform (WT) and improved PSO-BP neural network which is combined with improved particle swarm optimization (PSO) and a back propagation (BP) neural network with linearly increasing inertia weight. First, this research used [...] Read more.
This paper proposes a motor fault detection method based on wavelet transform (WT) and improved PSO-BP neural network which is combined with improved particle swarm optimization (PSO) and a back propagation (BP) neural network with linearly increasing inertia weight. First, this research used WT to analyze the current signals of the healthy motor, bearing damage motor, stator winding inter-turn short circuit motor, and broken rotor bar motor. Second, features after completing the signal analysis were extracted, and three types of classifiers were used to classify. The results show that the improved PSO-BP neural network can effectively detect the cause of failure. In addition, in order to simulate the actual operating environment of the motor, this study added white noise with signal noise ratios of 30 dB, 25 dB, and 20 dB to verify that this model has a better anti-noise ability. Full article
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<p>Schematic diagram of multiple resolution analysis (MRA).</p>
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<p>Flow chart of particle swarm optimization (PSO) algorithm.</p>
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<p>Flow chart of improved PSO algorithm.</p>
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<p>Construct of neural network.</p>
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<p>Flow chart of improved particle swarm optimization–back propagation (PSO-BP) neural network.</p>
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<p>Experimental structure.</p>
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<p>Schematic diagram of bearing inner ring defect.</p>
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<p>Stator winding inter-turn short circuit.</p>
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<p>Broken rotor bar.</p>
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<p>MRA of current signal of (<b>a</b>) healthy motor; (<b>b</b>) bearing damage motor; (<b>c</b>) stator short circuit motor; (<b>d</b>) broken rotor bar motor.</p>
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<p>The feature extraction process of MRA.</p>
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<p>MRA feature energy diagram (signal noise ratio (SNR) = ∞ dB).</p>
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<p>MRA feature energy diagram (SNR = 30 dB).</p>
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<p>MRA feature energy diagram (SNR = 25 dB).</p>
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<p>MRA feature energy diagram (SNR = 20 dB).</p>
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26 pages, 4171 KiB  
Article
High Cell Density Cultivation of Saccharomyces cerevisiae with Intensive Multiple Sequential Batches Together with a Novel Technique of Fed-Batch at Cell Level (FBC)
by Kwanruthai Malairuang, Morakot Krajang, Jatuporn Sukna, Krongchan Rattanapradit and Saethawat Chamsart
Processes 2020, 8(10), 1321; https://doi.org/10.3390/pr8101321 - 21 Oct 2020
Cited by 34 | Viewed by 15119
Abstract
High cell density cultivation (HCDC) is developed for the production of microbial biomasses and their products. They must be produced from high concentrations of substrate, e.g., glucose or sucrose. In batch culture, a high concentration of those sugars >40–50% (w/v) cannot [...] Read more.
High cell density cultivation (HCDC) is developed for the production of microbial biomasses and their products. They must be produced from high concentrations of substrate, e.g., glucose or sucrose. In batch culture, a high concentration of those sugars >40–50% (w/v) cannot efficiently be utilized because of a dissolved O2 limitation causing the Crabtree effect that produces toxic by-products, i.e., ethanol and/or acetate, that inhibit cell growth. To prevent this effect, the HCDC is conducted with the fed-batch strategies. However, it has many disadvantages, i.e., complicated operations. To overcome those problems, this study was designed to use a new, efficient C-source (carbon source) substrate, namely dextrin, an oligomer of glucose. It can be utilized by yeast at a very high concentration of ~100 g/L although using just batch cultivation. As it is gradually hydrolyzed to release glucose molecules and gradually assimilated into the cells as “fed-batch at the cell level” (FBC), it prevents the yeast cell system from undergoing the Crabtree effect. In this research, the types of medium, the types of sugar compared with dextrin, and the concentrations of yeast extract (YE) were studied. The batch production medium (BPM) with dextrin and YE performed very good results. The concentrations of dextrin for yeast cultivation were studied in the aerobic batch 5-L bioreactors. Its optimum concentration was at 90 g/L with 9 g/L of YE in 3× BPM. It was operated at 3 W/kg energy dissipation rate per unit mass (ε¯T) and 3 vvm airflow rate. Further, the intensive multiple sequential batch (IMSB) technique of high intensities of agitation speed and airflow was developed to achieve higher yield and productivity. The maximum values of cell biomass, specific growth rate, yield coefficient, productivity, and efficiency were at 55.17 g/L, 0.21 h−1, 0.54 g/g, 2.30 g/L/h, and 98.18%, respectively. The studies of cell growth kinetics, biochemical engineering mass balances, and fluid dynamics for the design of impeller speeds of the 5-L bioreactors during the cultivations of yeast using dextrin at the high concentrations were successful. The results can be used for the scale-up of bioreactor for the industrial production of yeast cell biomass at high concentrations. Full article
(This article belongs to the Special Issue Advances in Microbial Fermentation Processes)
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Graphical abstract
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<p>Growth (<b>a</b>) OD<sub>600 nm</sub>; (<b>b</b>) dry cell weight, and (<b>c</b>) glucose (reducing sugar) used during cultivations of <span class="html-italic">S. cerevisiae</span> in shake flasks with different media, YPD (Yeast Peptone Dextrose) a standard medium for yeast culture, BPM (Batch Production Medium) with yeast extract (YE) and BPM without YE as a control treatment. The BPM was formulated here for economic reasons and potent industrial use. Note: As standard deviation values are very small, error bars in Figures (<b>a</b>) and (<b>b</b>) are somewhat invisible.</p>
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<p>Growth (<b>a</b>) OD<sub>600 nm</sub>; (<b>b</b>) dry cell weight, and (<b>c</b>) glucose utilization during cultivations of <span class="html-italic">S. cerevisiae</span> in shake flasks using BPM with 20 g/L of glucose, supplemented with yeast extract (YE) at different concentrations (% <span class="html-italic">w/v</span>).</p>
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<p>Growth (<b>a</b>) OD<sub>600 nm</sub>; (<b>b</b>) dry cell weight, and (<b>c</b>) substrates utilized during cultivations of <span class="html-italic">S. cerevisiae</span> in shake flasks using BPM with 20 g/L of different carbon sources, each supplemented with 0.3% (<span class="html-italic">w/v</span>) of yeast extract to compare with the use of glucose and select the most potential C-source for larger-scale and industrial cultivations; Note: As standard deviation values are very small, error bars in Figures might be invisible.</p>
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<p>Growth (<b>a</b>) OD<sub>600 nm</sub> and (<b>b</b>) dry cell weight; substrate utilizations (<b>c</b>) reducing sugar (residual glucose) and (<b>d</b>) total sugar, during cultivations of <span class="html-italic">S. cerevisiae</span> in 5-L bioreactors using BPM medium with dextrin at different concentrations of 30, 60, 90, and 120 g/L, and operating at the impeller speeds of 500, 600, 700, and 800 rpm, (equivalent to <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>ε</mi> <mo stretchy="false">¯</mo> </mover> <mi>T</mi> </msub> </mrow> </semantics></math> = 1, 2, 3, and 4 W/kg) with the airflow rates at 1, 2, 3, and 4 vvm where they were proportional to the concentrations of dextrin, respectively.</p>
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<p>Growth (<b>a</b>) OD<sub>600 nm</sub> and dry cell weight; (<b>b</b>) substrate utilizations, reducing sugar (residual glucose), G<sub>l</sub> and total sugar, S<sub>T</sub> during cultivations of <span class="html-italic">S. cerevisiae</span> with the intensive multiple sequential batches for 5-cycle runs using 3× BPM with 90 g/L of dextrin and 9 g/L (0.9% <span class="html-italic">w/v</span>) of yeast extract, operating at the impeller speed of 700 rpm (<math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>ε</mi> <mo stretchy="false">¯</mo> </mover> <mi>T</mi> </msub> </mrow> </semantics></math> = 3 W/kg) with the airflow rate at 3 vvm.</p>
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<p>(<b>a</b>) Glucose uptake into the cells (green line) and (<b>b</b>) dextrin hydrolysis to release glucose (blue line), and their rates (slopes of <span class="html-italic">rG<sub>u</sub> = dG<sub>u</sub>/dt</span> and <span class="html-italic">rDx = dDx/dt</span>) during cultivation of <span class="html-italic">S. cerevisiae</span> with the intensive multiple sequential batches for 5-cycle runs using 3× BPM with 90 g/L of dextrin and 9 g/L (0.9% <span class="html-italic">w/v</span>) of yeast extract, operating at the impeller speed of 700 rpm (<math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>ε</mi> <mo stretchy="false">¯</mo> </mover> <mi>T</mi> </msub> </mrow> </semantics></math> = 3 W/kg) with the airflow rate at 3 vvm.</p>
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<p>The relationship of the three sugar components during the cultivations of <span class="html-italic">S. cerevisiae</span> with the multiple sequential batches for 5-cycle runs; in each batch, the glucose uptake (<span class="html-italic">G<sub>u</sub></span>) is the sum of dextrin (<span class="html-italic">Dx</span>) hydrolyzed to release glucose plus residual glucose (<span class="html-italic">G<sub>l</sub></span>) in the culture system. Thus, <span class="html-italic">G<sub>u</sub> = Dx + G<sub>l</sub></span>. This obviously shows the “fed-batch at cell level” (FBC) phenomenon. It is the compromised utilization of glucose from both sources during the gradual hydrolysis of dextrin to release glucose.</p>
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16 pages, 3751 KiB  
Article
Insulin Release from NPH Insulin-Loaded Pluronic® F127 Hydrogel in the Presence of Simulated Tissue Enzyme Activity
by Muhammad H. Sultan, Wael A. Mahdi and Young M. Kwon
Processes 2020, 8(10), 1320; https://doi.org/10.3390/pr8101320 - 21 Oct 2020
Cited by 8 | Viewed by 3265
Abstract
Background: Despite the widespread use of newer basal insulins, Natural Protamine Hagedorn (NPH) insulin still represents a well-established basal formulation with its long history of use, featuring the native form of human insulin. However, NPH insulin exhibits an undesirable peak within hours after [...] Read more.
Background: Despite the widespread use of newer basal insulins, Natural Protamine Hagedorn (NPH) insulin still represents a well-established basal formulation with its long history of use, featuring the native form of human insulin. However, NPH insulin exhibits an undesirable peak within hours after a single subcutaneous (s.c.) injection, which may lead to hypoglycemia followed by insufficient basal insulin delivery. This may be attributed to the s.c. enzyme activities degrading the protamine in NPH microcrystals. Methods: A thermogelling block copolymer Pluronic® F127 (PF127) was utilized as a protective carrier for NPH microcrystals and as a modulator for insulin release from NPH. NPH insulin-loaded PF127 gel was prepared with varying concentrations of the polymer (15–25%) under mild conditions. The formulations were characterized for their gelling temperature, morphology, gel erosion, and in vitro insulin release, with trypsin concentrations up to 5 U/mL. Results: Scanning electron microscopy (SEM) showed that the integrity of NPH microcrystals was maintained after preparation. The burst release of insulin from NPH was significantly attenuated over the course of ~16h in the presence of PF127 with or without enzyme activity. Conclusion: NPH-PF127 successfully resisted the acceleration of NPH crystal dissolution and insulin release in vitro in the presence of protamine-degrading enzyme activity, warranting further testing. Full article
(This article belongs to the Section Pharmaceutical Processes)
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Figure 1

Figure 1
<p>Insulin recovery percentage after incubation (3 h) with 0, 1, 5, 10 Unit/mL of trypsin at 37 °C, pH 7.4. Data are expressed as means ± S.D. (<span class="html-italic">n</span> = 3), * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Phase diagram of the lower sol-gel transition of PF127 in phosphate-buffered saline (PBS) (pH 7.4) with or without NPH. Data are expressed as means ± S.D. (<span class="html-italic">n</span> = 3).</p>
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<p>FTIR spectra of NPH, NPH/PF127, and PF127.</p>
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<p>SEM images for (<b>A</b>) NPH only, (<b>B</b>): NPH-PF127 by direct dissolution in PF127, (<b>C</b>) NPH-PF127 by the centrifugation of NPH, (<b>D</b>) NPH in the presence of trypsin (1 U/mL), (<b>E</b>) NPH-PF127 in the presence of trypsin.</p>
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<p>Degradation and erosion behavior of different concentrations of NPH-PF127 using the dialysis method at 37 °C. Data are expressed as means ± S.D. (<span class="html-italic">n</span> = 3).</p>
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<p>In vitro insulin release profile of NPH, NPH-PF127 (20%), and NPH-PF127 (25%) using PBS at pH 7.4 as a dialysis medium.</p>
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<p>In vitro insulin release profile from NPH, NPH-PF127 (20%), and NPH-PF127 (25%) using PBS (pH 7.4) with trypsin concentrations of (<b>a</b>) 1 U/mL and (<b>b</b>) 5 U/mL.</p>
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<p>Comparison of the insulin release in vitro at <span class="html-italic">t</span> = 6 h: NPH, NPH-PF127 (20%), and NPH-PF127 (25%) without and with (1–5 U/mL) trypsin. Values represent the mean ± S.D. (<span class="html-italic">n</span> = 3). Statistically significant differences (**** <span class="html-italic">p</span> &lt; 0.0001, ** <span class="html-italic">p</span> &lt; 0.001, and * <span class="html-italic">p</span> &lt; 0.01) in the % release values (at <span class="html-italic">t</span> = 6 h) between each paired group were obtained using Tukey’s post hoc test.</p>
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<p>In vitro release of insulin (NPH- PF127 25%) at day 1, 7, and 21 upon storage at 4 °C. Data are expressed as means ± S.D. (<span class="html-italic">n</span> = 3).</p>
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13 pages, 8582 KiB  
Article
The Effects of Different Slurry Concentrations and Wire Speeds for Swinging and Non-Swinging Wire-Saw Machining
by Yao-Yang Tsai, Yi-Chian Chen, Yunn-Shiuan Liao, Chia-Chin Hsieh, Chung-Chen Tsao and Chun-Yao Hsu
Processes 2020, 8(10), 1319; https://doi.org/10.3390/pr8101319 - 20 Oct 2020
Cited by 2 | Viewed by 2402
Abstract
Slurry concentration and wire speed affect the yield and machining quality of ceramics (Al2O3) that are produced using wire-saw machining (WSM). This study determines the effect of slurry concentration and wire speed on the material removal rate (MRR), the [...] Read more.
Slurry concentration and wire speed affect the yield and machining quality of ceramics (Al2O3) that are produced using wire-saw machining (WSM). This study determines the effect of slurry concentration and wire speed on the material removal rate (MRR), the machined surface roughness (SR), the kerf width, the wire wear and the flatness for swinging and non-swinging WSM. The experiments show that swinging WSM results in a higher machining efficiency than non-swinging WSM. WSM with swinging also achieves a peak MRR at a medium slurry concentration (25 wt%) and a higher wire speed (5.6 m/s) using the cutting conditions for the experimental region. However, slurry concentration and wire speed have no significant effect on the machined SR, the kerf width, the wire wear or the flatness for WSM with swinging mode. Full article
(This article belongs to the Section Advanced Digital and Other Processes)
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Graphical abstract

Graphical abstract
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<p>Setup for the single wire-saw system.</p>
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<p>(<b>a</b>) Model of swinging wire-sawing and (<b>b</b>) contact between the wire and the workpiece.</p>
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<p>(<b>a</b>) Model of swinging wire-sawing and (<b>b</b>) contact between the wire and the workpiece.</p>
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<p>Effect of different wire speeds and slurry concentrations on material removal rate (MRR).</p>
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<p>Comparison of swinging and non-swinging processes in terms of the MRR for different slurry concentrations and swinging frequencies.</p>
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<p>Effect of different swinging angles and frequency on MRR.</p>
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<p>Effect of wire speed and swinging frequency on MRR.</p>
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<p>Comparison of MRR for #600 and #1000 grains.</p>
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<p>Effect of slurry concentration on machined SR for Al<sub>2</sub>O<sub>3.</sub></p>
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<p>The effect swinging and non-swinging processes on the machined SR for Al<sub>2</sub>O<sub>3</sub> for different slurry concentrations.</p>
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<p>Fracture cross section for Al<sub>2</sub>O<sub>3.</sub></p>
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<p>Machined surface using swinging WSM and a slurry of green silicon carbide (GC).</p>
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<p>Machined surface using non-swinging WSM and a slurry (GC).</p>
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<p>Machined surface for WSM using a fixed abrasive (diamond).</p>
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<p>SEM photo of Al<sub>2</sub>O<sub>3</sub> that is machined using no slurry.</p>
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<p>The energy-dispersive spectrometer (EDS) results for zone A.</p>
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<p>Effect of wire speed on kerf width for different slurry concentrations.</p>
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<p>Effect of swinging and non-swinging processes on the kerf width for different slurry concentrations.</p>
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<p>Effect of wire speed and concentration on wire wear.</p>
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<p>Effect of swinging and non-swinging processes on wire wear for different slurry concentrations and wire speeds.</p>
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<p>Effect of wire speed and swinging frequency on wire wear.</p>
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<p>No corner chipping with swinging for WSM.</p>
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<p>Images of subsurface regions using swinging WSM.</p>
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17 pages, 5160 KiB  
Article
The Brooks and Corey Capillary Pressure Model Revisited from Pore Network Simulations of Capillarity-Controlled Invasion Percolation Process
by Xiang Lu, Abdolreza Kharaghani, Hadi Adloo and Evangelos Tsotsas
Processes 2020, 8(10), 1318; https://doi.org/10.3390/pr8101318 - 20 Oct 2020
Cited by 15 | Viewed by 21128
Abstract
Relating the macroscopic properties of porous media such as capillary pressure with saturation is an on-going problem in many fields, but examining their correlations with microstructural traits of the porous medium is a challenging task due to the heterogeneity of the solid matrix [...] Read more.
Relating the macroscopic properties of porous media such as capillary pressure with saturation is an on-going problem in many fields, but examining their correlations with microstructural traits of the porous medium is a challenging task due to the heterogeneity of the solid matrix and the limitations of laboratory instruments. Considering a capillarity-controlled invasion percolation process, we examined the macroscopic properties as functions of matrix saturation and pore structure by applying the throat and pore network model. We obtained a relationship of the capillary pressure with the effective saturation from systematic pore network simulations. Then, we revisited and identified the microstructure parameters in the Brooks and Corey capillary pressure model. The wetting phase residual saturation is related to the ratio of standard deviation to the mean radius, the ratio of pore radius to the throat length, and pore connectivity. The size distribution index in the Brooks and Corey capillary pressure model should be more reasonably considered as a meniscus size distribution index rather than a pore size distribution index, relating this parameter with the invasion process and the structural properties. The size distribution index is associated with pore connectivity and the ratio of standard deviation to mean radius (σ0/r¯), increasing with the decline of σ0/r¯ but the same for networks with same σ0/r¯. The identified parameters of the Brooks and Corey model might be further utilized for correlations with other transport properties such as permeability. Full article
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Figure 1

Figure 1
<p>Pore and throat radius distribution for the random network with throat mean radius 250 µm and the standard deviation 25 µm. The pore radius is assumed larger or at least equal with the largest radius of neighbor throats.</p>
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<p>Schematic of the pore network used for capillarity-controlled invasion percolation (IP) simulations. The pore network is connected to air reservoir at the top and liquid reservoir at the bottom. Throats/pores filled with liquid are shown in black, whereas gas throats/pores are shown in white.</p>
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<p>Residual saturation as a function of (<b>a</b>) ratio of the standard deviation of throat size distribution (TSD) over mean throat radius. Data denoted by (‘o’) have been obtained for the networks with <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 1/10 (µm), 2/20 (µm), 5.2/11.8 (µm), 9.1/14.6 (µm). Other data have been recorded: ‘Δ’ with <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 10/100 (µm), ‘□’ with <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 20/200 (µm) and ‘◊’ with <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 25/250 (µm). The center distance between two pores was <span class="html-italic">L</span> = 1000 µm. <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>r</mi> <mo>¯</mo> </mover> <mi>p</mi> </msub> </mrow> </semantics></math> is the mean radius of pores in the network. Throat length (<span class="html-italic">L<sub>t</sub></span>) is calculated based on neighbor pores and averaged throat length (<math display="inline"><semantics> <mover accent="true"> <mi>L</mi> <mo>¯</mo> </mover> </semantics></math><sub>t</sub>) is averaged over all throats in the network. The connectivity (Z) was 6; (<b>b</b>) connectivity at constant ratio <math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 25/250 (µm). The connectivity presents the averaged connectivity of pores for the whole network. The simulation values are averaged over 40 realizations.</p>
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<p>The volume distribution of liquid-filled throats and pores in the network at the beginning of the capillarity-controlled IP process and at <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>r</mi> </msub> </mrow> </semantics></math> for a single simulation: (<b>a</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 1/10 (µm), <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>r</mi> <mo>¯</mo> </mover> <mi>p</mi> </msub> <mo>/</mo> <msub> <mover accent="true"> <mi>L</mi> <mo>¯</mo> </mover> <mi>t</mi> </msub> </mrow> </semantics></math> = 0.01 (<b>b</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 10/100 (µm), <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>r</mi> <mo>¯</mo> </mover> <mi>p</mi> </msub> <mo>/</mo> <msub> <mover accent="true"> <mi>L</mi> <mo>¯</mo> </mover> <mi>t</mi> </msub> <mo>=</mo> <mn>0.15</mn> </mrow> </semantics></math> (<b>c</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 20/200 (µm), <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>r</mi> <mo>¯</mo> </mover> <mi>p</mi> </msub> <mo>/</mo> <msub> <mover accent="true"> <mi>L</mi> <mo>¯</mo> </mover> <mi>t</mi> </msub> <mo>=</mo> <mn>0.42</mn> </mrow> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 25/250 (µm), <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>r</mi> <mo>¯</mo> </mover> <mi>p</mi> </msub> <mo>/</mo> <msub> <mover accent="true"> <mi>L</mi> <mo>¯</mo> </mover> <mi>t</mi> </msub> <mo>=</mo> <mn>0.66</mn> </mrow> </semantics></math>. The network connectivity is 6.</p>
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<p>The function <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>r</mi> </msub> <mo>/</mo> <mi>ε</mi> </mrow> </semantics></math> against pore connectivity at constant <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 25/250 (µm). The connectivity presents the average of whole pore connectivity for the whole network. Simulated values have been averaged over 40 realizations.</p>
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<p>Relationship of entry pressure (<span class="html-italic">P<sub>e</sub></span>) and mean capillary pressure (<span class="html-italic">P<sub>c,mean</sub></span>) with specific properties of the porous medium: (<b>a</b>) influence of mean radius with identical standard deviation (5 µm) and a connectivity of 6; (<b>b</b>) influence of standard deviation with constant mean radius (250 µm) and a connectivity of 6; (<b>c</b>) influence of connectivity with the constant ratio <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 25/250 (µm). Simulation results have been averaged over 40 realizations.</p>
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<p>Influence of the mean radius on capillary pressure profile with effective saturation: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 5/50 (µm), (<b>b</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 5/150 (µm) and (<b>c</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 5/250 (µm). The network connectivity is 6. The simulation values are averaged over 40 realizations.</p>
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<p>The capillary pressure profile with effective saturation, influenced by (<b>a</b>) the standard deviation at constant pore connectivity (Z = 6) and (<b>b</b>) the pore connectivity with <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 25/250 (µm). The simulation values are averaged over 40 realizations.</p>
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<p>The exponent λ related with (<b>a</b>) and (<b>b</b>) the ratio of standard deviation over the mean radius <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> </mrow> </semantics></math>/<math display="inline"><semantics> <mover accent="true"> <mi>r</mi> <mo>¯</mo> </mover> </semantics></math> with pore connectivity Z = 6, and (<b>c</b>) pore connectivity at <math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 25/250 (µm). The values are averaged over 40 realizations.</p>
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<p>The ratio of standard deviation of the meniscus throat/pore radius distribution (<span class="html-italic">σ<sub>m</sub></span>) to the given standard deviation of the radius size distribution of the network (<span class="html-italic">σ<sub>0</sub></span>) changes with effective network saturation; (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> <mo>/</mo> <mover accent="true"> <mrow> <mi>r</mi> <mo> </mo> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> = 5/50 (µm), Z = 6).</p>
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14 pages, 2240 KiB  
Article
Effect of Short Blade Circumferential Position Arrangement on Gas-Liquid Two-Phase Flow Performance of Centrifugal Pump
by Biaobiao Wang, Haoyang Zhang, Fanjie Deng, Chenguang Wang and Qiaorui Si
Processes 2020, 8(10), 1317; https://doi.org/10.3390/pr8101317 - 20 Oct 2020
Cited by 12 | Viewed by 2458
Abstract
In order to study the internal flow characteristics of centrifugal pumps with a split impeller under gas-liquid mixed transportation conditions, this paper conducted a steady calculation of the flow field in the centrifugal pump under the conditions of different inlet gas volume fractions [...] Read more.
In order to study the internal flow characteristics of centrifugal pumps with a split impeller under gas-liquid mixed transportation conditions, this paper conducted a steady calculation of the flow field in the centrifugal pump under the conditions of different inlet gas volume fractions based on the Eulerian-Eulerian heterogeneous flow model, using air and water as the working media and the Schiller Nauman model for the interphase resistance. This paper takes a low specific speed centrifugal pump as the research object, through the controlling variables, using the same pump body structure and pump body geometric parameters and setting three different arrangements of long and short blades (each plan uses the same long and short blades) to explore the influence of the short blade arrangement on the low specific speed centrifugal pump performance under a gas-liquid two-phase flow. The research results show that, under pure water conditions, the reasonable arrangement of the short blade circumferential position can eliminate the hump of the centrifugal pump under low-flow conditions, can make the flow velocity in the impeller more uniform, and can optimize the performance of the pump. Under the design conditions and the gas-liquid two-phase inflow conditions, when the circumferential position of the short blades is close to the suction surface of the long blades, some of the bubbles on the suction surface of the long blade can be broken under the work of the pressure surface of the short blade and flow out of the impeller with the liquid, which improves the flow state of the flow field in the impeller. Full article
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<p>Impeller domains of the flow field calculation.</p>
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<p>Mesh of the centrifugal pump.</p>
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<p>The structure of the impeller and volute of scheme 2.</p>
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<p>External characteristics under different schemes.</p>
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<p>Comparison of the external characteristics. EXP stands for experimental data and CFX represents numerical simulation data.</p>
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<p>The efficiency of each scheme under different inlet gas volume fractions.</p>
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<p>Velocity streamline diagram of a cross-section of the impeller flow field under different flows in pure water conditions.</p>
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<p>Turbulent kinetic energy distribution cloud map in a cross-section of the impeller under different flow rates in pure water conditions.</p>
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<p>Gas distribution cloud map of the cross-section of the impeller flow field under different flow rates in water with various gas volume fractions.</p>
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<p>Liquid phase velocity streamline diagram of the impeller flow field cross-section for each inflow gas volume fraction of each scheme under the design conditions.</p>
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19 pages, 3492 KiB  
Article
Efficient Simulation of Chromatographic Processes Using the Conservation Element/Solution Element Method
by Valentin Plamenov Chernev, Alain Vande Wouwer and Achim Kienle
Processes 2020, 8(10), 1316; https://doi.org/10.3390/pr8101316 - 20 Oct 2020
Cited by 3 | Viewed by 3609
Abstract
Chromatographic separation processes need efficient simulation methods, especially for nonlinear adsorption isotherms such as the Langmuir isotherms which imply the formation of concentration shocks. The focus of this paper is on the space–time conservation element/solution element (CE/SE) method. This is an explicit method [...] Read more.
Chromatographic separation processes need efficient simulation methods, especially for nonlinear adsorption isotherms such as the Langmuir isotherms which imply the formation of concentration shocks. The focus of this paper is on the space–time conservation element/solution element (CE/SE) method. This is an explicit method for the solution of systems of partial differential equations. Numerical stability of this method is guaranteed when the Courant–Friedrichs–Lewy condition is satisfied. To investigate the accuracy and efficiency of this method, it is compared with the classical cell model, which corresponds to a first-order finite volume discretization using a method of lines approach (MOL). The evaluation is done for different models, including the ideal equilibrium model and a mass transfer model for different adsorption isotherms—including linear and nonlinear Langmuir isotherms—and for different chromatographic processes from single-column operation to more sophisticated simulated moving bed (SMB) processes for the separation of binary and ternary mixtures. The results clearly show that CE/SE outperforms MOL in terms of computational times for all considered cases, ranging from 11-fold for the case with linear isotherm to 350-fold for the most complicated case with ternary center-cut eight-zone SMB with Langmuir isotherms, and it could be successfully applied for the optimization and control studies of such processes. Full article
(This article belongs to the Special Issue Advanced Methods in Process and Systems Engineering)
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<p>Computational schemes of the conservation element/solution element (CE/SE) method: (<b>a</b>) standard CE/SE method; (<b>b</b>) reversed CE/SE method.</p>
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<p>Single-column binary chromatographic process with Langmuir isotherms—ideal equilibrium model. (<b>a</b>) Concentration profiles along the column calculated using the two numerical methods and the analaytical solution. (<b>b</b>) Comparison of the computational times for each of the numerical methods.</p>
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<p>Single-column binary chromatographic process with Langmuir isotherms—LDF model. (<b>a</b>) Concentration profiles along the column calculated by two numerical methods and the analaytical solution. (<b>b</b>) Comparison of the computational times for each of the numerical methods.</p>
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<p>Binary SMB chromatographic process with Langmuir isotherms—LDF model. (<b>a</b>) Process configuration. (<b>b</b>) Comparison of the computational times for each of the methods. (<b>c</b>) Concentration profiles along the SMB plant calculated using the CE/SE method. (<b>d</b>) Concentration profiles along the SMB plant calculated using MOL. Dashed curves in (<b>c</b>,<b>d</b>) are at the beginning of each cycle, while solid curves are at the end.</p>
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<p>Ternary center-cut eight-zone SMB chromatographic process with raffinate recycle with linear isotherms—ideal equilibrium model. (<b>a</b>) Process configuration. (<b>b</b>) Comparison of the computational times for each of the methods. (<b>c</b>) Concentration profiles along the SMB plant calculated using CE/SE method. (<b>d</b>) Concentration profiles along the SMB plant calculated using MOL. Dashed curves in (<b>c</b>,<b>d</b>) are at the beginning of each cycle, while solid curves are at the end.</p>
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<p>Ternary center-cut eight-zone SMB chromatographic process with raffinate recycle with Langmuir isotherms—LDF model. (<b>a</b>) Concentration profiles along the SMB plant calculated using CE/SE method. (<b>b</b>) Concentration profiles along the SMB plant calculated using MOL. (<b>c</b>) Comparison of the computational times for each of the methods. Dashed curves in (<b>a</b>,<b>b</b>) are at the beginning of each cycle, while solid curves are at the end.</p>
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<p>Ternary center-cut eight-zone SMB chromatographic process with raffinate recycle with Langmuir isotherms—LDF model. (<b>a</b>) Concentration profiles along the SMB plant calculated using CE/SE method. (<b>b</b>) Concentration profiles along the SMB plant calculated using MOL. (<b>c</b>) Comparison of the computational times for each of the methods. Dashed curves in (<b>a</b>,<b>b</b>) are at the beginning of each cycle, while solid curves are at the end.</p>
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12 pages, 4855 KiB  
Article
Evolution and Physical Characteristics of a Raceway Based on a Transient Eulerian Multiphase Flow Model
by Xing Peng, Jingsong Wang, Haibin Zuo and Qingguo Xue
Processes 2020, 8(10), 1315; https://doi.org/10.3390/pr8101315 - 20 Oct 2020
Cited by 5 | Viewed by 2572
Abstract
In industrial processes, a semi-cavity area formed by airflow wherein the particles circulate is called a “raceway”. In a blast furnace, the role of the raceway is particularly important. To understand and predict the evolution and physical characteristics of the raceway, a three-dimensional [...] Read more.
In industrial processes, a semi-cavity area formed by airflow wherein the particles circulate is called a “raceway”. In a blast furnace, the role of the raceway is particularly important. To understand and predict the evolution and physical characteristics of the raceway, a three-dimensional transient Eulerian multiphase flow model in a packed particle bed was developed. In the model, it was assumed that the gas and solid (particle) phases constitute an interpenetrating continuum. The gas-phase turbulence was described as a k–ε dispersed model. The gas-phase stress was considered in terms of the effective viscosity of the gas. The solid-phase constitutive relationship was expressed in terms of solid stress. It was found that the evolution process of the raceway can be divided into three stages: (1) rapid expansion, (2) slow contraction, and (3) gradual stabilization. When the blast velocity was increased from 150 m/s to 300 m/s, the surface area of the raceway increased from 0.194 m2 to 1.644 m2. The depth and height of the raceway increased considerably with velocity, while the width slightly increased. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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<p>Blast furnace (BF) schematic and geometry model of the calculation domain.</p>
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<p>Evolution of penetration depth in the raceway: (<b>a</b>) 0–2 s and (<b>b</b>) 2–50 s.</p>
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<p>Time evolution of the solid volume fraction for Case 3.</p>
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<p>Raceway shapes after they stabilize at different blast velocities: (<b>a</b>) 150 m/s; (<b>b</b>) 200 m/s; (<b>c</b>) 250 m/s; (<b>d</b>) 300 m/s.</p>
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<p>Effect of blast velocity on the raceway size.</p>
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<p>(<b>a</b>) Gas pressure and (<b>b</b>) solid granular pressure for Case 3 in the symmetry plane at 40 s.</p>
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<p>Gas pressure and solid granular pressure for Case 3 on the axis of the tuyere at 40 s.</p>
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<p>The symmetry plane of Case 3 at 40 s: (<b>a</b>) gas velocity streamline; (<b>b</b>) solid velocity streamline.</p>
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<p>The tuyere level plane of Case 3 at 40 s: (<b>a</b>) gas velocity streamline; (<b>b</b>) solid velocity streamline.</p>
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<p>Gas and solid velocity along the axis of the tuyere for Case 3 at 40 s.</p>
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15 pages, 254 KiB  
Article
Some Physical Properties and Mass Modelling of Pepper Berries (Piper nigrum L.), Variety Kuching, at Different Maturity Levels
by Puteri Nurain Megat Ahmad Azman, Rosnah Shamsudin, Hasfalina Che Man and Mohammad Effendy Ya’acob
Processes 2020, 8(10), 1314; https://doi.org/10.3390/pr8101314 - 19 Oct 2020
Cited by 14 | Viewed by 3146
Abstract
Pepper berry (Piper nigrum L.) is known as the king of spices and has sharp, pungent flavour and aroma. In this study, the physical properties (weight, dimensions, sphericity, volume, surface area, and projected area) were measured, and the mass of pepper berries [...] Read more.
Pepper berry (Piper nigrum L.) is known as the king of spices and has sharp, pungent flavour and aroma. In this study, the physical properties (weight, dimensions, sphericity, volume, surface area, and projected area) were measured, and the mass of pepper berries of the Kuching variety at different maturity levels (immature, mature, and ripe) was predicted using four models: linear, quadratic, s-curve, and power. When the models were based on volume and projected area, the mass could be predicted with maximum precision. The Quadratic model was best fitted for mass prediction at all mass maturity levels (immature, mature, and ripe). The results showed that mass modelling based on the actual volume of pepper berries was more applicable compared to other properties with the highest determination coefficient, 0.995, at the 1% probability level. From an economical point of view, mass prediction based on actual volume in the Quadratic form, M= 0.828 − 0.015 V + 7.376 ×105V2, is recommended. The findings of physical properties and mass modelling of the berries would be useful to the scientific knowledge base, which may help in developing grading, handling, and packaging systems. Full article
(This article belongs to the Special Issue Advances in Postharvest Process Systems)
25 pages, 3743 KiB  
Article
NMPC-Based Workflow for Simultaneous Process and Model Development Applied to a Fed-Batch Process for Recombinant C. glutamicum
by Philipp Levermann, Fabian Freiberger, Uma Katha, Henning Zaun, Johannes Möller, Volker C. Hass, Karl Michael Schoop, Jürgen Kuballa and Ralf Pörtner
Processes 2020, 8(10), 1313; https://doi.org/10.3390/pr8101313 - 19 Oct 2020
Viewed by 3102
Abstract
For the fast and improved development of bioprocesses, new strategies are required where both strain and process development are performed in parallel. Here, a workflow based on a Nonlinear Model Predictive Control (NMPC) algorithm is described for the model-assisted development of biotechnological processes. [...] Read more.
For the fast and improved development of bioprocesses, new strategies are required where both strain and process development are performed in parallel. Here, a workflow based on a Nonlinear Model Predictive Control (NMPC) algorithm is described for the model-assisted development of biotechnological processes. By using the NMPC algorithm, the process is designed with respect to a target function (product yield, biomass concentration) with a drastically decreased number of experiments. A workflow for the usage of the NMPC algorithm as a process development tool is outlined. The NMPC algorithm is capable of improving various process states, such as product yield and biomass concentration. It uses on-line and at-line data and controls and optimizes the process by model-based process extrapolation. In this study, the algorithm is applied to a Corynebacterium glutamicum process. In conclusion, the potency of the NMPC algorithm as a powerful tool for process development is demonstrated. In particular, the benefits of the system regarding the characterization and optimization of a fed-batch process are outlined. With the NMPC algorithm, process development can be run simultaneously to strain development, resulting in a shortened time to market for novel products. Full article
(This article belongs to the Special Issue Fermentation Optimization and Modeling)
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<p>Workflow for the Nonlinear Model Predictive Control (NMPC) cycle. Followed by a model parameter estimation, the process input (here, the feed profile) is optimized (adapted from [<a href="#B6-processes-08-01313" class="html-bibr">6</a>,<a href="#B9-processes-08-01313" class="html-bibr">9</a>,<a href="#B43-processes-08-01313" class="html-bibr">43</a>]).</p>
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<p>Schematic overview of the NMPC-assisted workflow for process development.</p>
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<p>Schematic overview of the used control system setup. Model system and process control are realized together in one control system.</p>
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<p>Schematic workflow to apply the NMPC control to a fed-batch process with a cycle time of one hour.</p>
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<p>Quality of fit (Model A) for the shaking flask experiments with varying initial glucose levels of 5, 8, 12, and 16 g L<sup>−1</sup> for cell dry weight (<b>a</b>) and glucose (<b>b</b>) (conversion OD<sub>600</sub> 1 ~ 0.39 g L<sup>−1</sup>).</p>
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<p>Time course of the glucose and biomass concentration for fed-batch 1; symbols mark samples; the samples were measured as three technical replicates; lines mark the adapted simulated cultivation course (Model B); constant feeding rate of 12 mL h<sup>−1</sup>.</p>
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<p>Time course of the glucose and biomass concentration for fed-batch 2. Left side of the vertical dotted lines indicate the estimation horizon (EH): symbols mark samples, the samples were measured as three technical replicates, lines mark the adapted simulated cultivation course (Model B+), the dashed line shows the calculated feed profile, the dotted line marks show the simulated reactor volume. Between the vertical dotted lines indicating the prediction horizon (PH): the dashed line shows the optimized feed profile, the lines mark the extrapolated cultivation course, the dotted line marks the simulated reactor volume. (<b>a</b>) Prediction of the starting time point for feed and the calculation of the optimized first feed rate with extrapolation starting time point τ = 9 h. (<b>b</b>) Calculation of the optimized second feed rate at τ = 10 h. (<b>c</b>) Calculation of the optimized third feed rate at τ = 11 h. (<b>d</b>,<b>e</b>) Calculation of the optimized sixth and seventh feed rate at τ = 14 h and τ = 15 h. (<b>f</b>) Finished process. For the parameter estimation results and the feed optimization results, see <a href="#processes-08-01313-t0A8" class="html-table">Table A8</a>, <a href="#processes-08-01313-t0A9" class="html-table">Table A9</a> and <a href="#processes-08-01313-t0A10" class="html-table">Table A10</a>. For model equations, see <a href="#processes-08-01313-t0A7" class="html-table">Table A7</a> or <a href="#processes-08-01313-t002" class="html-table">Table 2</a>.</p>
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<p>Time course of the glucose and biomass concentration for fed-batch 2. Left side of the vertical dotted lines indicate the estimation horizon (EH): symbols mark samples, the samples were measured as three technical replicates, lines mark the adapted simulated cultivation course (Model B+), the dashed line shows the calculated feed profile, the dotted line marks show the simulated reactor volume. Between the vertical dotted lines indicating the prediction horizon (PH): the dashed line shows the optimized feed profile, the lines mark the extrapolated cultivation course, the dotted line marks the simulated reactor volume. (<b>a</b>) Prediction of the starting time point for feed and the calculation of the optimized first feed rate with extrapolation starting time point τ = 9 h. (<b>b</b>) Calculation of the optimized second feed rate at τ = 10 h. (<b>c</b>) Calculation of the optimized third feed rate at τ = 11 h. (<b>d</b>,<b>e</b>) Calculation of the optimized sixth and seventh feed rate at τ = 14 h and τ = 15 h. (<b>f</b>) Finished process. For the parameter estimation results and the feed optimization results, see <a href="#processes-08-01313-t0A8" class="html-table">Table A8</a>, <a href="#processes-08-01313-t0A9" class="html-table">Table A9</a> and <a href="#processes-08-01313-t0A10" class="html-table">Table A10</a>. For model equations, see <a href="#processes-08-01313-t0A7" class="html-table">Table A7</a> or <a href="#processes-08-01313-t002" class="html-table">Table 2</a>.</p>
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<p>Time course of the glucose and biomass concentrations for fed-batch 3. Left side of the vertical dotted lines indicates the estimation horizon (EH): symbols mark samples, the samples were measured as three technical replicates, the lines mark the adapted simulated cultivation course (Model B+), the dashed line shows the calculated feed profile, the dotted line marks the simulated reactor volume. Between the vertical dotted lines, indicating the prediction horizon (PH), the dashed line shows the optimized feed profile, the lines mark the extrapolated cultivation course, the dotted line marks the simulated reactor volume. (<b>a</b>) Prediction of the starting time point for the feed and calculation of the optimized first feed rate with the extrapolation starting time point τ = 12 h. (<b>b</b>) Finished process. For the parameter estimation results and the feed optimization results, see <a href="#processes-08-01313-t0A12" class="html-table">Table A12</a>, <a href="#processes-08-01313-t0A13" class="html-table">Table A13</a> and <a href="#processes-08-01313-t0A14" class="html-table">Table A14</a>. For model equations, see <a href="#processes-08-01313-t0A11" class="html-table">Table A11</a> or <a href="#processes-08-01313-t002" class="html-table">Table 2</a>.</p>
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<p>Time course of the glucose and biomass concentration for fed-batch 4. Left side of the vertical dotted lines indicates the estimation horizon (EH): symbols mark samples, the samples were measured as three technical replicates, the lines mark adapted the simulated cultivation course (Model B+), the dashed line shows the calculated feed profile, the dotted line marks the simulated reactor volume. Between the vertical dotted lines indicating the prediction horizon (PH): the dashed line shows the optimized feed profile, the lines mark the extrapolated cultivation course, the dotted line marks the simulated reactor volume. (<b>a</b>) Prediction of the starting time point for the feed and the calculation of the optimized first feed rate with extrapolation starting time point τ = 21 h. (<b>b</b>–<b>d</b>) Calculation of the optimized feed rate for τ = 25.66 h, τ = 29.75 h, and τ = 32.75 h. For the parameter estimation results and the feed optimization results, see <a href="#processes-08-01313-t0A16" class="html-table">Table A16</a>, <a href="#processes-08-01313-t0A17" class="html-table">Table A17</a> and <a href="#processes-08-01313-t0A18" class="html-table">Table A18</a>. For the model equations, see <a href="#processes-08-01313-t0A15" class="html-table">Table A15</a> or <a href="#processes-08-01313-t002" class="html-table">Table 2</a>.</p>
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<p>Time course of the glucose and biomass concentration for fed-batch 4. Left side of the vertical dotted lines indicates the estimation horizon (EH): symbols mark samples, the samples were measured as three technical replicates, the lines mark adapted the simulated cultivation course (Model B+), the dashed line shows the calculated feed profile, the dotted line marks the simulated reactor volume. Between the vertical dotted lines indicating the prediction horizon (PH): the dashed line shows the optimized feed profile, the lines mark the extrapolated cultivation course, the dotted line marks the simulated reactor volume. (<b>a</b>) Prediction of the starting time point for the feed and the calculation of the optimized first feed rate with extrapolation starting time point τ = 21 h. (<b>b</b>–<b>d</b>) Calculation of the optimized feed rate for τ = 25.66 h, τ = 29.75 h, and τ = 32.75 h. For the parameter estimation results and the feed optimization results, see <a href="#processes-08-01313-t0A16" class="html-table">Table A16</a>, <a href="#processes-08-01313-t0A17" class="html-table">Table A17</a> and <a href="#processes-08-01313-t0A18" class="html-table">Table A18</a>. For the model equations, see <a href="#processes-08-01313-t0A15" class="html-table">Table A15</a> or <a href="#processes-08-01313-t002" class="html-table">Table 2</a>.</p>
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<p>Time course of the product concentration 2′-FL for fed-batch 4: symbols mark the measured product concentration, the line marks adapted simulated cultivation course for the product (Model D). For the parameter estimation results, see <a href="#processes-08-01313-t0A20" class="html-table">Table A20</a>, <a href="#processes-08-01313-t0A21" class="html-table">Table A21</a> and <a href="#processes-08-01313-t0A22" class="html-table">Table A22</a>. For model equations, see <a href="#processes-08-01313-t0A19" class="html-table">Table A19</a> or <a href="#processes-08-01313-t002" class="html-table">Table 2</a>.</p>
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18 pages, 3820 KiB  
Article
Exploring the Function of Ion-Exchange Membrane in Membrane Capacitive Deionization via a Fully Coupled Two-Dimensional Process Model
by Xin Zhang and Danny Reible
Processes 2020, 8(10), 1312; https://doi.org/10.3390/pr8101312 - 19 Oct 2020
Cited by 8 | Viewed by 3248
Abstract
In the arid west, the freshwater supply of many communities is limited, leading to increased interest in tapping brackish water resources. Although reverse osmosis is the most common technology to upgrade saline waters, there is also interest in developing and improving alternative technologies. [...] Read more.
In the arid west, the freshwater supply of many communities is limited, leading to increased interest in tapping brackish water resources. Although reverse osmosis is the most common technology to upgrade saline waters, there is also interest in developing and improving alternative technologies. Here we focus on membrane capacitive deionization (MCDI), which has attracted broad attention as a portable and energy-efficient desalination technology. In this study, a fully coupled two-dimensional MCDI process model capable of capturing transient ion transport and adsorption behaviors was developed to explore the function of the ion-exchange membrane (IEM) and detect MCDI influencing factors via sensitivity analysis. The IEM enhanced desalination by improving the counter-ions’ flux and increased adsorption in electrodes by encouraging retention of ions in electrode macropores. An optimized cycle time was proposed with maximal salt removal efficiency. The usage of the IEM, high applied voltage, and low flow rate were discovered to enhance this maximal salt removal efficiency. IEM properties including water uptake volume fraction, membrane thickness, and fixed charge density had a marginal impact on cycle time and salt removal efficiency within certain limits, while increasing cell length and electrode thickness and decreasing channel thickness and dispersivity significantly improved overall performance. Full article
(This article belongs to the Special Issue Design, Control and Optimization of Desalination Processes)
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<p>Schematic diagrams of MCDI, (<b>a</b>) desalination process, (<b>b</b>) regeneration process.</p>
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<p>Two-dimensional MCDI assembly in this model.</p>
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<p>Simulated salt adsorption per cycle curve with this model and experimental data from ref. [<a href="#B25-processes-08-01312" class="html-bibr">25</a>].</p>
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<p>Transient effluent concentration curves of CDI and MCDI. The applied voltage is 0.8 V, the flow rate is 10 mL/min, and the feed water concentration is 20 mol/m<sup>3</sup>.</p>
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<p>(<b>a</b>) Sodium ion flux distribution and (<b>b</b>) chloride ion flux distribution along the cross-sectional line of CDI and MCDI at <span class="html-italic">t</span> = 50 s. The applied voltage is 0.8 V, the flow rate is 10 mL/min, and the feed water concentration is 20 mol/m<sup>3</sup>.</p>
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<p>Adsorption isotherms of CDI and MCDI. The flow rate is 10 mL/min, and the applied voltage is 0.8 V.</p>
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<p>Transient effluent concentration, transient average effluent concentration, and transient salt removal efficiency curves of MCDI. The cross mark represents cycle time with the maximum salt removal efficiency. The applied voltage is 0.8 V, the flow rate is 10 mL/min, and the feed water concentration is 20 mol/m<sup>3</sup>.</p>
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<p>The maximum salt removal efficiency and the corresponding cycle time of CDI and MCDI under (<b>a</b>) flow rate of 10 mL/min and feed water concentration of 20 mol/m<sup>3</sup>, (<b>b</b>) applied voltage of 0.8 V and flow rate of 10 mL/min, and (<b>c</b>) feed water concentration of 20 mol/m<sup>3</sup> and applied voltage of 0.8 V. Lines are used for guiding the eyes.</p>
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<p>The maximum salt removal efficiency and the corresponding cycle time of CDI and MCDI under (<b>a</b>) flow rate of 10 mL/min and feed water concentration of 20 mol/m<sup>3</sup>, (<b>b</b>) applied voltage of 0.8 V and flow rate of 10 mL/min, and (<b>c</b>) feed water concentration of 20 mol/m<sup>3</sup> and applied voltage of 0.8 V. Lines are used for guiding the eyes.</p>
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21 pages, 6351 KiB  
Article
Hydrodynamics and Mass Transfer Analysis in BioFlow® Bioreactor Systems
by Marian Kordas, Maciej Konopacki, Bartłomiej Grygorcewicz, Adrian Augustyniak, Daniel Musik, Krzysztof Wójcik, Magdalena Jędrzejczak-Silicka and Rafał Rakoczy
Processes 2020, 8(10), 1311; https://doi.org/10.3390/pr8101311 - 19 Oct 2020
Cited by 6 | Viewed by 3689
Abstract
Biotechnological processes involving the presence of microorganisms are realized by using various types of stirred tanks or laboratory-scale dual-impeller commercial bioreactor. Hydrodynamics and mass transfer rate are crucial parameters describing the functionality and efficiency of bioreactors. Both parameters strictly depend on mixing applied [...] Read more.
Biotechnological processes involving the presence of microorganisms are realized by using various types of stirred tanks or laboratory-scale dual-impeller commercial bioreactor. Hydrodynamics and mass transfer rate are crucial parameters describing the functionality and efficiency of bioreactors. Both parameters strictly depend on mixing applied during bioprocesses conducted in bioreactors. Establishing optimum hydrodynamics conditions for the realized process with microorganisms maximizes the yield of desired products. Therefore, our main objective was to analyze and define the main operational hydrodynamic parameters (including flow field, power consumption, mixing time, and mixing energy) and mass transfer process (in this case, gas–liquid transfer) of two different commercial bioreactors (BioFlo® 115 and BioFlo® 415). The obtained results are allowed using mathematical relationships to describe the analyzed processes that can be used to predict the mixing process and mass transfer ratio in BioFlo® bioreactors. The proposed correlations may be applied for the design of a scaled-up or scaled-down bioreactors. Full article
(This article belongs to the Section Advanced Digital and Other Processes)
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Graphical abstract

Graphical abstract
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<p>Sketch of BioFlo<sup>®</sup> 115 bioreactor: 1—tube for temperature sensor; 2—sampling tube; 3—thermostatic circuits; 4—blowdown connection; 5—sparger; 6—shaft; 7—impeller.</p>
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<p>Sketch of BioFlo<sup>®</sup> 415 bioreactor: 1—tube for temperature sensor; 2—sampling tube; 3—batching tube; 4—sparger; 5—shaft; 6—impeller.</p>
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<p>Typical simulated results of velocity profiles for the tested mixing systems.</p>
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<p>Typical simulated results of velocity profiles for the tested mixing systems.</p>
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<p>Power consumption per unit volume versus impeller rotational speed (<b>a</b>) and power characteristics (<b>b</b>) for the tested bioreactors.</p>
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<p>Experimental (symbols) and fitted (lines) relative power consumption values obtained for the tested bioreactors.</p>
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<p>The graphical presentation of the proposed dimensionless ratio as a function of operational parameters (speed rotation and gas flow rate).</p>
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<p>Dependence Θ = <span class="html-italic">f</span> (Re) for the tested bioreactors.</p>
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<p>The dimensionless mixing number variation with Reynolds number and flow number for: (<b>a</b>) BioFlo<sup>®</sup>115 (<span class="html-italic">R</span><sup>2</sup> = 97.28%; <span class="html-italic">S<sub>e</sub></span> = 2.99 × 10<sup>−5</sup>); (<b>b</b>) BioFlo<sup>®</sup>415 (<span class="html-italic">R</span><sup>2</sup> = 95.37%; <span class="html-italic">S<sub>e</sub></span> = 5.31 × 10<sup>−5</sup>); (<b>c</b>) comparison of the calculated variation of <math display="inline"><semantics> <mrow> <msub> <mrow> <mrow> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mo>Θ</mo> <mi>g</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> </mrow> <mrow> <mi>B</mi> <mi>i</mi> <mi>o</mi> <mi>F</mi> <mi>l</mi> <mi>o</mi> <mi>w</mi> <mo> </mo> <mn>115</mn> </mrow> </msub> <msup> <mrow> <mrow> <mo>[</mo> <mrow> <msub> <mrow> <mrow> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mo>Θ</mo> <mi>g</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> </mrow> <mrow> <mi>B</mi> <mi>i</mi> <mi>o</mi> <mi>F</mi> <mi>l</mi> <mi>o</mi> <mi>w</mi> <mo> </mo> <mn>415</mn> </mrow> </msub> </mrow> <mo>]</mo> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> with the selected flow number.</p>
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<p>Influence of specific power consumption on the mixing time for (<b>a</b>) ungassed conditions; (<b>b</b>) gassed conditions (<math display="inline"><semantics> <mrow> <mover> <mrow> <msub> <mi>V</mi> <mi>g</mi> </msub> </mrow> <mo>•</mo> </mover> <mo>=</mo> <mn>0.083</mn> <mo> </mo> <mi mathvariant="normal">d</mi> <msup> <mi mathvariant="normal">m</mi> <mn>3</mn> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>).</p>
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<p>The comparison of the dimensional mixing energy characteristics for the tested bioreactors under ungassed and gassed conditions.</p>
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<p>Comparison of mixing energy for different mixing systems (<b>a</b>) and mixing energy for tested BioFlow systems at <span class="html-italic">n</span> = 8.33 s<sup>−1</sup> (<b>b</b>).</p>
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<p>The comparison of the volumetric mass transfer coefficient at various gas flow rates for the tested BioFlo<sup>®</sup> 115 (<b>a</b>) and BioFlo<sup>®</sup> 415 (<b>b</b>) bioreactors.</p>
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45 pages, 2320 KiB  
Review
Process Strategies for the Transition of 1G to Advanced Bioethanol Production
by Ana Susmozas, Raquel Martín-Sampedro, David Ibarra, María E. Eugenio, Raquel Iglesias, Paloma Manzanares and Antonio D. Moreno
Processes 2020, 8(10), 1310; https://doi.org/10.3390/pr8101310 - 19 Oct 2020
Cited by 75 | Viewed by 11963
Abstract
Nowadays, the transport sector is one of the main sources of greenhouse gas (GHG) emissions and air pollution in cities. The use of renewable energies is therefore imperative to improve the environmental sustainability of this sector. In this regard, biofuels play an important [...] Read more.
Nowadays, the transport sector is one of the main sources of greenhouse gas (GHG) emissions and air pollution in cities. The use of renewable energies is therefore imperative to improve the environmental sustainability of this sector. In this regard, biofuels play an important role as they can be blended directly with fossil fuels and used in traditional vehicles’ engines. Bioethanol is the most used biofuel worldwide and can replace gasoline or form different gasoline-ethanol blends. Additionally, it is an important building block to obtain different high added-value compounds (e.g., acetaldehyde, ethylene, 1,3-butadiene, ethyl acetate). Today, bioethanol is mainly produced from food crops (first-generation (1G) biofuels), and a transition to the production of the so-called advanced ethanol (obtained from lignocellulosic feedstocks, non-food crops, or industrial waste and residue streams) is needed to meet sustainability criteria and to have a better GHG balance. This work gives an overview of the current production, use, and regulation rules of bioethanol as a fuel, as well as the advanced processes and the co-products that can be produced together with bioethanol in a biorefinery context. Special attention is given to the opportunities for making a sustainable transition from bioethanol 1G to advanced bioethanol. Full article
(This article belongs to the Special Issue Bioethanol Production Processes)
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<p>Production of fuel ethanol by country (share of global production) in 2019. Adapted from: 2020 Ethanol Industry Outlook. Renewable Fuel Association (RFA) [<a href="#B11-processes-08-01310" class="html-bibr">11</a>].</p>
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<p>Predictable Renewable Fuels Association (RFA) volumes by year in the USA. Source: U.S. Department of Energy (<a href="https://afdc.energy.gov/laws/RFS.html" target="_blank">https://afdc.energy.gov/laws/RFS.html</a>).</p>
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<p>Different bioethanol production pathways (adapted from [<a href="#B55-processes-08-01310" class="html-bibr">55</a>]).</p>
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<p>Different thermochemical bioethanol production pathways (adapted from [<a href="#B122-processes-08-01310" class="html-bibr">122</a>]).</p>
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<p>Possible configurations for the retrofitting of a starch-based 1G facility.</p>
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<p>Possible configurations for the retrofitting of a sugar-based 1G facility.</p>
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<p>Descriptive flow sheet of the scenario 1.</p>
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<p>Descriptive flow sheet of the scenario 2.</p>
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<p>Schematic diagram of D3MAX technology (adapted from [<a href="#B143-processes-08-01310" class="html-bibr">143</a>]).</p>
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<p>Schematic diagram of ICM technology (adapted from [<a href="#B146-processes-08-01310" class="html-bibr">146</a>]).</p>
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17 pages, 7487 KiB  
Article
Construction and Automation of a Microcontrolled Solar Tracker
by Juliano da Rocha Queiroz, Anacreone da Silva Souza, Maurício Klein Gussoli, Júlio César Dainezi de Oliveira and Cid Marcos Gonçalves Andrade
Processes 2020, 8(10), 1309; https://doi.org/10.3390/pr8101309 - 19 Oct 2020
Cited by 12 | Viewed by 5533
Abstract
A solar tracker can be defined as an electromechanical system capable of following the apparent path of the Sun, in order to orient an array of solar panels and/or collectors directly to the solar rays, maximizing the collected energy. Accordingly, the present work [...] Read more.
A solar tracker can be defined as an electromechanical system capable of following the apparent path of the Sun, in order to orient an array of solar panels and/or collectors directly to the solar rays, maximizing the collected energy. Accordingly, the present work describes the process of building and automating a micro-controlled solar tracker. Two mobile structures were built, one equipped with high-precision step motors and four luminosity sensors separated in quadrants by a cross structure, and the other equipped with DC motors and the 275 Wp solar panel, allowing the design and evaluation of the behavior of each structure separately. The control and automation system is centralized in an Arduino MEGA2560 microcontroller, which runs the tracking and positioning algorithms. The built prototype allows us to carry out studies of solar tracking strategies based on sensor and control systems applied to DC motors. Full article
(This article belongs to the Special Issue Process System Engineering-Brazil (PSE-BR))
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<p>Sun’s apparent position annual variation at latitude −24.25° [<a href="#B19-processes-08-01309" class="html-bibr">19</a>].</p>
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<p>Sun’s apparent position annual variation at equator line [<a href="#B19-processes-08-01309" class="html-bibr">19</a>].</p>
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<p>Prototype of two-axis active solar tracker.</p>
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<p>Constructive details of the tracking structure: (<b>a</b>) structure overview; (<b>b</b>) gear and engine detail.</p>
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<p>Constructive details of the solar panel structure: (<b>a</b>) structure overview; (<b>b</b>) motor, reduction box and chain detail.</p>
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<p>Electronic system block diagram.</p>
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<p>Designed full bridge inverter.</p>
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<p>Constructive details of angle sensors.</p>
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<p>Dissipation circuit and power measurement.</p>
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<p>Block diagram of the closed loop control system.</p>
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<p>Azimuth angle sensor tracking method.</p>
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<p>Flowchart of the sensor-tracking algorithm.</p>
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<p>Block diagram of the on-off controller.</p>
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<p>Flowchart of the solar panel positioning algorithm.</p>
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<p>Response of the sensor-tracking algorithm.</p>
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<p>Tracker elevation angle compared to SunEarthTools database.</p>
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<p>Tracker azimuth angle compared to SunEarthTools database.</p>
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<p>Positioning algorithm response for azimuth angle.</p>
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<p>Positioning algorithm response for elevation angle.</p>
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<p>Comparison between fixed (blue) and movable (red) panel power on February 1st.</p>
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<p>Irradiation and generated power comparison on the movable panel.</p>
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<p>Energy comparison between the movable panel (red), fixed panel (blue) and power source (green).</p>
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<p>Solar tracker system efficiency on the experiment period.</p>
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13 pages, 9280 KiB  
Article
Chaotic Mixing Analyzing in Continuous Mixer with Tracing the Morphology Development of a Polymeric Drop
by Tao Chen, Hao Guo, Guo Li, Huajian Ji, Linsheng Xie and Yu Yang
Processes 2020, 8(10), 1308; https://doi.org/10.3390/pr8101308 - 18 Oct 2020
Cited by 2 | Viewed by 2619
Abstract
The chaotic mixing process in a continuous mixer plays an important role and has an essential influence on the performance of prepared materials. To reveal how a polymeric drop experienced the chaotic mixing and give more specific analysis about the chaotic mixing, the [...] Read more.
The chaotic mixing process in a continuous mixer plays an important role and has an essential influence on the performance of prepared materials. To reveal how a polymeric drop experienced the chaotic mixing and give more specific analysis about the chaotic mixing, the morphology development of a single drop was traced and recorded with an on-line visualization system. The drop would undergo elongation deformation, reorientation, and folding process, which were the typical signs of chaotic mixing. The elongation deformation was an important precondition for drop experiencing the reorientation and folding process and mainly existed in the region near the barrier, rotor tip clearance, and wedgelike region. The reorientation and folding process mostly appeared in the region near the rotor flat and interaction window. Besides, the erosion process of the drop was observed at the initial stage under lower rotor rotation speed. The chaotic mixing always held the dominant place in continuous mixer although the rotor rotation speed and drop viscoelasticity were adjusted. In this work, the chaotic mixing in a continuous mixer was dynamically presented. The dynamical results would give a more real and visual understanding of the chaotic mixing. Full article
(This article belongs to the Section Materials Processes)
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<p>The cross-section view of the mixing chamber and rotors.</p>
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<p>Shear rate versus viscosity for linear-low-density-polyethylene (LLDPE) at 200 °C.</p>
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<p>The initial situation and the erosion process of acrylonitrile-butadiene-styrene (ABS) drop: (<b>a</b>) initial situation; (<b>b</b>) erosion process of ABS drop at t = 11.58 s.</p>
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<p>Morphology development of ABS drop passing through the rotor tip clearance: (<b>a</b>) t = 14.98 s; (<b>b</b>) t = 15.15 s; (<b>c</b>) t = 16.24 s.</p>
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<p>ABS drop deformation in the interaction window: (<b>a</b>) t = 17.18 s; (<b>b</b>) t = 17.63 s; (<b>c</b>) t = 18.06 s; (<b>d</b>) t = 18.37 s; (<b>e</b>) t = 21.37 s.</p>
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<p>The dispersion and distribution situation of ABS drop at (<b>a</b>) t = 31.31 s and (<b>b</b>) t = 52.43 s when rotor rotation speed was 28.7 rpm.</p>
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<p>The morphology development of ABS drop when rotor rotation speed was 114.8 rpm: (<b>a</b>) t = 4.25 s; (<b>b</b>) t = 4.52 s; (<b>c</b>) t = 4.85 s; (<b>d</b>) t = 4.94 s.</p>
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<p>The dispersion and distribution situation of ABS drop at (<b>a</b>) t = 9.65 s and (<b>b</b>) t = 16.74 s when rotor rotation speed was 114.8 rpm.</p>
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<p>The morphology development of LLDPE/CaCO<sub>3</sub> drop when rotor rotation speed was 28.7 rpm: (<b>a</b>) t = 4.90 s; (<b>b</b>) t = 5.37 s; (<b>c</b>) t = 6.16 s; (<b>d</b>) t = 7.16 s; (<b>e</b>) t = 7.92 s.</p>
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<p>The dispersion and distribution situation of LLDPE/CaCO<sub>3</sub> drop at (<b>a</b>) t = 16.65 s and (<b>b</b>) t = 30.74 s when rotor rotation speed was 28.7 rpm.</p>
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<p>The chaotic mixing in circumferential flow for a continuous mixer.</p>
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14 pages, 1355 KiB  
Article
Adaptive Monitoring of Biotechnological Processes Kinetics
by Velislava Lyubenova, Maya Ignatova, Olympia Roeva, Stefan Junne and Peter Neubauer
Processes 2020, 8(10), 1307; https://doi.org/10.3390/pr8101307 - 17 Oct 2020
Cited by 6 | Viewed by 2609
Abstract
In this paper, an approach for the monitoring of biotechnological process kinetics is proposed. The kinetics of each process state variable is presented as a function of two time-varying unknown parameters. For their estimation, a general software sensor is derived with on-line measurements [...] Read more.
In this paper, an approach for the monitoring of biotechnological process kinetics is proposed. The kinetics of each process state variable is presented as a function of two time-varying unknown parameters. For their estimation, a general software sensor is derived with on-line measurements as inputs that are accessible in practice. The stability analysis with a different number of inputs shows that stability can be guaranteed for fourth- and fifth-order software sensors only. As a case study, the monitoring of the kinetics of processes carried out in stirred tank reactors is investigated. A new tuning procedure is derived that results in a choice of only one design parameter. The effectiveness of the proposed procedure is demonstrated with experimental data from Bacillus subtilis fed-batch cultivations. Full article
(This article belongs to the Special Issue Modelling and Optimal Design of Complex Biological Systems)
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<p>General structure of kinetics software sensor.</p>
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<p>Software sensor scheme for the considered case.</p>
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<p>Inputs of software sensor: (<b>a</b>): biomass concentration, (<b>b</b>): glucose concentration, (<b>c</b>): specific oxygen consumption rate, (<b>d</b>): respiratory coefficient. Experiment I measurements (red lines); Experiment II measurements (black lines).</p>
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<p>Tuning using Experiment I measurements: Comparison between experimental data (points) and estimates (red lines): (<b>a</b>): yield coefficient <math display="inline"><semantics> <mrow> <msub> <mi>Y</mi> <mrow> <mi>X</mi> <mo>/</mo> <msub> <mi>S</mi> <mn>1</mn> </msub> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>): yield coefficient <math display="inline"><semantics> <mrow> <msub> <mi>Y</mi> <mrow> <msub> <mi>S</mi> <mn>2</mn> </msub> <mo>/</mo> <msub> <mi>S</mi> <mn>1</mn> </msub> </mrow> </msub> </mrow> </semantics></math>; (<b>c</b>): yield coefficient <math display="inline"><semantics> <mrow> <msub> <mi>Y</mi> <mrow> <mi>R</mi> <mi>Q</mi> <mo>/</mo> <msub> <mi>S</mi> <mn>1</mn> </msub> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>): glucose consumption rate.</p>
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<p>Verification using Experiment II measurements: Comparison between experimental data (points) and estimates (red lines). (<b>a</b>): yield coefficient <math display="inline"><semantics> <mrow> <msub> <mi>Y</mi> <mrow> <mi>X</mi> <mo>/</mo> <msub> <mi>S</mi> <mn>1</mn> </msub> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>): yield coefficient <math display="inline"><semantics> <mrow> <msub> <mi>Y</mi> <mrow> <msub> <mi>S</mi> <mn>2</mn> </msub> <mo>/</mo> <msub> <mi>S</mi> <mn>1</mn> </msub> </mrow> </msub> </mrow> </semantics></math>; (<b>c</b>): yield coefficient <math display="inline"><semantics> <mrow> <msub> <mi>Y</mi> <mrow> <mi>R</mi> <mi>Q</mi> <mo>/</mo> <msub> <mi>S</mi> <mn>1</mn> </msub> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>): glucose consumption rate.</p>
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21 pages, 1896 KiB  
Article
Denitrification Control in a Recirculating Aquaculture System—A Simulation Study
by Pedro Almeida, Laurent Dewasme and Alain Vande Wouwer
Processes 2020, 8(10), 1306; https://doi.org/10.3390/pr8101306 - 17 Oct 2020
Cited by 8 | Viewed by 4963
Abstract
The recirculating aquaculture system (RAS) is a land-based water treatment technology, which allows for farming aquatic organisms, such as fish, by reusing the water in the production (often less than 5%). This technology is based on the use of filters, either mechanical or [...] Read more.
The recirculating aquaculture system (RAS) is a land-based water treatment technology, which allows for farming aquatic organisms, such as fish, by reusing the water in the production (often less than 5%). This technology is based on the use of filters, either mechanical or biological, and can, in principle, be used for any species grown in aquaculture. Due to the low recirculation rate, ammonia accumulates in the system and must be converted into nitrate using nitrification reactors. Although less toxic for fish, nitrate can also be further reduced into nitrogen gas by the use of denitrification biofilters which may create several issues, such as incomplete denitrification, resulting in toxic substances, such as nitrite and nitric oxide, or a waste of carbon source in excess. Control of the added quantity of carbon source in the denitrification biofilter is then mandatory to keep nitrate/nitrite concentrations under toxic levels for fish and in accordance with local effluent regulations, and to reduce costs related to wasted organic carbon sources. This study therefore investigates the application of different control methodologies to a denitrification reactor in a RAS. To this end, a numerical simulator is built to predict the RAS behavior and to allow for the comparison of different control approaches, in the presence of changes in the operating conditions, such as fish density and biofilter removal efficiency. First, a classical proportional-integral-derivative (PID) controller was designed, based on an SIMC tuning method depending on the amount of ammonia excreted by fish. Then, linearizing and cascade controllers were considered as possible alternatives. Full article
(This article belongs to the Special Issue Wastewater Treatment Processes)
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<p>Industrial scale RAS.</p>
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<p>Simulated response of the industrial scale RAS to a step increase in an inert soluble component production rate by 1 gCOD/(m<sup>3</sup>·h). First order model presented for comparison purpose.</p>
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<p><math display="inline"><semantics> <msub> <mi>S</mi> <mrow> <mi>N</mi> <mi>O</mi> <mn>3</mn> </mrow> </msub> </semantics></math> response following several step variations in acid flowrate.</p>
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<p><math display="inline"><semantics> <msub> <mi>S</mi> <mrow> <mi>N</mi> <mi>O</mi> <mn>3</mn> </mrow> </msub> </semantics></math> evolution following a step increase in acid flowrate.</p>
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<p>Variation of <math display="inline"><semantics> <mrow> <mo>(</mo> <mfrac> <mi>y</mi> <msub> <mi>y</mi> <mi>s</mi> </msub> </mfrac> <mo>)</mo> </mrow> </semantics></math> as a function of the time constant.</p>
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<p>Application of the PI controller with and without anti-windup following a nitrate setpoint variation. A <math display="inline"><semantics> <msub> <mi>K</mi> <mrow> <mi>a</mi> <mi>w</mi> </mrow> </msub> </semantics></math> value of −1 is used.</p>
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<p>Selection of the PID derivative gain <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>D</mi> </msub> </semantics></math>.</p>
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<p>Closed-loop system input/output evolution following (<b>left</b>) several setpoint changes and (<b>right</b>) 5 to <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math> step variations in waste production (<math display="inline"><semantics> <msub> <mi>ϑ</mi> <mrow> <mi>N</mi> <mi>H</mi> </mrow> </msub> </semantics></math>).</p>
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<p>Closed-loop system input/output evolutions following (<b>left</b>) an increase in biomass decay rate from <math display="inline"><semantics> <mrow> <mn>2.6</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>2</mn> </msup> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>7.8</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math> h<sup>−1</sup> and (<b>right</b>) 5 to <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math> step variations applied to the bypass flowrate (<math display="inline"><semantics> <msub> <mi>F</mi> <mrow> <mi>D</mi> <mi>e</mi> <mi>n</mi> <mi>i</mi> <mi>t</mi> <mi>r</mi> <mi>i</mi> </mrow> </msub> </semantics></math>).</p>
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<p>Nitrate concentration and acid flowrate evolutions for an increase in biomass decay rate from <math display="inline"><semantics> <mrow> <mn>2.6</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>2</mn> </msup> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>7.8</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <mspace width="3.33333pt"/> <msup> <mi mathvariant="normal">h</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> while using two PID controllers with different proportional gains (<math display="inline"><semantics> <msub> <mi>K</mi> <mi>C</mi> </msub> </semantics></math>)—Gaussian noise with zero mean and <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math> relative standard deviation is added to the measured variable (<math display="inline"><semantics> <msub> <mi>S</mi> <mrow> <mi>N</mi> <mi>O</mi> <mn>3</mn> </mrow> </msub> </semantics></math>).</p>
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<p>Linearizing control scheme.</p>
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<p>Simulation results obtained for the linearizing, adaptive and cascade controllers when the biomass death rate is increased and a 10% measurement noise on all measured variables (<math display="inline"><semantics> <msub> <mi>S</mi> <mrow> <mi>N</mi> <mi>O</mi> <mn>3</mn> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>S</mi> <mi>S</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>S</mi> <mi>O</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>X</mi> <mrow> <mi>B</mi> <mi>H</mi> </mrow> </msub> </semantics></math>) is added. <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>e</mi> <mi>t</mi> <mi>P</mi> <msub> <mi>t</mi> <mrow> <mi>C</mi> <mi>a</mi> <mi>s</mi> <mi>c</mi> <mi>a</mi> <mi>d</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> represents the corrected setpoint curve and <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>e</mi> <mi>t</mi> <mi>p</mi> <mi>o</mi> <mi>i</mi> <mi>n</mi> <mi>t</mi> </mrow> </semantics></math> the desired setpoint.</p>
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<p>Linearizing controller and PI in cascade.</p>
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<p>Nitrate concentration and acid flowrate evolutions for a setpoint step variation using different controllers: cascade (<math display="inline"><semantics> <mrow> <mi>C</mi> <mi>a</mi> <mi>s</mi> <mi>c</mi> <mi>a</mi> <mi>d</mi> <mi>e</mi> </mrow> </semantics></math>), classical linearizing (<math display="inline"><semantics> <mrow> <mi>L</mi> <mi>i</mi> <mi>n</mi> </mrow> </semantics></math>) and adaptive linearizing controllers (<math display="inline"><semantics> <mrow> <mi>A</mi> <mi>d</mi> <mi>a</mi> <mi>p</mi> <mi>t</mi> <mi>i</mi> <mi>v</mi> <mi>e</mi> </mrow> </semantics></math>).</p>
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<p>Nitrate concentration and acid flowrate evolutions for 5 to <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math> variations in ammonia (waste) production (<math display="inline"><semantics> <msub> <mi>ϑ</mi> <msub> <mi>S</mi> <mrow> <mi>N</mi> <mi>H</mi> </mrow> </msub> </msub> </semantics></math>) using different controllers: cascade (<math display="inline"><semantics> <mrow> <mi>C</mi> <mi>a</mi> <mi>s</mi> <mi>c</mi> <mi>a</mi> <mi>d</mi> <mi>e</mi> </mrow> </semantics></math>), classical linearizing (<math display="inline"><semantics> <mrow> <mi>L</mi> <mi>i</mi> <mi>n</mi> </mrow> </semantics></math>) and adaptative linearizing controllers (<math display="inline"><semantics> <mrow> <mi>A</mi> <mi>d</mi> <mi>a</mi> <mi>p</mi> <mi>t</mi> <mi>i</mi> <mi>v</mi> <mi>e</mi> </mrow> </semantics></math>).</p>
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<p>Nitrate concentration and acid flowrate evolutions for 5 to <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math> variations in bypass flowrate (<math display="inline"><semantics> <msub> <mi>F</mi> <mrow> <mi>D</mi> <mi>e</mi> <mi>n</mi> <mi>i</mi> <mi>t</mi> <mi>r</mi> <mi>i</mi> </mrow> </msub> </semantics></math>) using different controllers: cascade (<math display="inline"><semantics> <mrow> <mi>C</mi> <mi>a</mi> <mi>s</mi> <mi>c</mi> <mi>a</mi> <mi>d</mi> <mi>e</mi> </mrow> </semantics></math>), classical linearizing (<math display="inline"><semantics> <mrow> <mi>L</mi> <mi>i</mi> <mi>n</mi> </mrow> </semantics></math>) and adaptative linearizing controllers (<math display="inline"><semantics> <mrow> <mi>A</mi> <mi>d</mi> <mi>a</mi> <mi>p</mi> <mi>t</mi> <mi>i</mi> <mi>v</mi> <mi>e</mi> </mrow> </semantics></math>).</p>
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<p>Nitrate concentration and acid flowrate evolutions for 5 to <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math> variations in fresh water flowrate (<math display="inline"><semantics> <msub> <mi>F</mi> <mrow> <mi>f</mi> <mi>r</mi> <mi>e</mi> <mi>s</mi> <mi>h</mi> </mrow> </msub> </semantics></math>) using different controllers: cascade (<math display="inline"><semantics> <mrow> <mi>C</mi> <mi>a</mi> <mi>s</mi> <mi>c</mi> <mi>a</mi> <mi>d</mi> <mi>e</mi> </mrow> </semantics></math>), classical linearizing (<math display="inline"><semantics> <mrow> <mi>L</mi> <mi>i</mi> <mi>n</mi> </mrow> </semantics></math>) and adaptative linearizing controllers (<math display="inline"><semantics> <mrow> <mi>A</mi> <mi>d</mi> <mi>a</mi> <mi>p</mi> <mi>t</mi> <mi>i</mi> <mi>v</mi> <mi>e</mi> </mrow> </semantics></math>).</p>
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<p>Simulation results obtained for the linearizing, adaptive and cascade controllers when the biomass death rate is increased and a 10% measurement noise on all measured variables (<math display="inline"><semantics> <msub> <mi>S</mi> <mrow> <mi>N</mi> <mi>O</mi> <mn>3</mn> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>S</mi> <mi>S</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>S</mi> <mi>O</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>X</mi> <mrow> <mi>B</mi> <mi>H</mi> </mrow> </msub> </semantics></math>) is added and when a higher steady-state nitrate concentration and setpoint are considered. <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>e</mi> <mi>t</mi> <mi>P</mi> <msub> <mi>t</mi> <mrow> <mi>C</mi> <mi>a</mi> <mi>s</mi> <mi>c</mi> <mi>a</mi> <mi>d</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> represents the corrected setpoint curve and <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>e</mi> <mi>t</mi> <mi>p</mi> <mi>o</mi> <mi>i</mi> <mi>n</mi> <mi>t</mi> </mrow> </semantics></math> is the desired setpoint.</p>
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15 pages, 7214 KiB  
Article
Thermal Performance of T-shaped Obstacles in a Solar Air Heater
by Seung-Yong Ahn and Kwang-Yong Kim
Processes 2020, 8(10), 1305; https://doi.org/10.3390/pr8101305 - 17 Oct 2020
Cited by 6 | Viewed by 3018
Abstract
This paper proposes T-shaped ribs as obstacles attached to the heat absorber plate in a rectangular solar air heater to promote heat transfer. The thermal and aerodynamic performance of the solar heater was numerically evaluated using three-dimensional Reynolds-averaged Navier–Stokes equations with the shear [...] Read more.
This paper proposes T-shaped ribs as obstacles attached to the heat absorber plate in a rectangular solar air heater to promote heat transfer. The thermal and aerodynamic performance of the solar heater was numerically evaluated using three-dimensional Reynolds-averaged Navier–Stokes equations with the shear stress transport turbulence model. A parameter study was performed using the ratios of rib height to channel height, rib width to channel width, and rib width to rib height. The area-averaged Nusselt number and friction factor were selected as the performance parameters of the solar air heater to evaluate the heat transfer and friction loss, respectively. In addition, the performance factor was defined as the ratio of the area-averaged Nusselt number to the friction factor. The maximum area-averaged Nusselt number was found at h/e = 0.83 for a fixed rib area. Compared with triangular ribs, the T-shaped ribs showed up to a 65 % higher area-averaged Nusselt number and up to a 49.7% higher performance factor. Full article
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Figure 1

Figure 1
<p>Computational domain of the solar air heater with T-shaped ribs. (<b>a</b>) Computational domain, (<b>b</b>) Test section.</p>
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<p>Example of grid system.</p>
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<p>Validation of numerical results for smooth duct.</p>
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<p>Validation of numerical results for a solar air heater (SAH) with triangular ribs using experimental data of Bekele et al. [<a href="#B8-processes-08-01305" class="html-bibr">8</a>].</p>
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<p>Variation of the performance parameter related to heat transfer (F<sub>Nu</sub>) with the ratio of the rib height to the channel height (h/H) (the ratio of the rib width to the channel width (e/W) = 0.1).</p>
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<p>Variation of the performance parameter related to the pressure drop (F<sub><span class="html-italic">f</span></sub>) with h/H (e/W = 0.1).</p>
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<p>Variation of performance factor (PF) with h/H (e/W = 0.1).</p>
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<p>Streamlines on x-y plane (z = 0) between fifth and seventh ribs (dark region in <a href="#processes-08-01305-f001" class="html-fig">Figure 1</a>b) for different h/H. (<b>a</b>) h/H = 0.3, (<b>b</b>) h/H = 0.5, (<b>c</b>) h/H = 0.7.</p>
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<p>Nusselt number distributions on the heated surface for different h/H. (<b>a</b>) h/H = 0.3, (<b>b</b>) h/H = 0.5.</p>
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<p>Variation of F<sub>Nu</sub> with e/W (h/H = 0.5).</p>
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<p>Variation of F<sub><span class="html-italic">f</span></sub> with e/W (h/H = 0.5).</p>
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<p>Variation of PF with e/W (h/H = 0.5).</p>
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<p>Streamlines on x-y plane (z = 0) between fifth and seventh ribs for different e/W. (<b>a</b>) e/W = 0.067, (<b>b</b>) e/W = 0.10, (<b>c</b>) e/W = 0.133.</p>
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<p>Nusselt distributions on the heated surface between fifth and seventh ribs for different e/W. (<b>a</b>) e/W = 0.067; (<b>b</b>) e/W = 0.10; (<b>c</b>) e/W = 0.133.</p>
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<p>Variation of F<sub>Nu</sub> with the ratio of the rib height to the rib width (h/e) (rib area = 375 mm<sup>2</sup>).</p>
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<p>Variation of F<sub><span class="html-italic">f</span></sub> with h/e (rib area = 375 mm<sup>2</sup>).</p>
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<p>Variation of PF with h/e (rib area = 375 mm<sup>2</sup>).</p>
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<p>Nusselt number distributions on the heated surface between fifth and seventh ribs for different h/e. (<b>a</b>) h/e = 0.38; (<b>b</b>) h/e = 0.83; (<b>c</b>) h/e = 1.75.</p>
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<p>Comparison of performance parameters between T-shaped and triangular ribs.</p>
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<p>Comparison of PF between T-shaped and triangular ribs.</p>
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18 pages, 2080 KiB  
Article
Mathematical Model of COVID-19 Transmission Dynamics in South Korea: The Impacts of Travel Restrictions, Social Distancing, and Early Detection
by Byul Nim Kim, Eunjung Kim, Sunmi Lee and Chunyoung Oh
Processes 2020, 8(10), 1304; https://doi.org/10.3390/pr8101304 - 17 Oct 2020
Cited by 25 | Viewed by 6102
Abstract
The novel coronavirus disease (COVID-19) poses a severe threat to public health officials all around the world. The early COVID-19 outbreak in South Korea displayed significant spatial heterogeneity. The number of confirmed cases increased rapidly in the Daegu and Gyeongbuk (epicenter), whereas the [...] Read more.
The novel coronavirus disease (COVID-19) poses a severe threat to public health officials all around the world. The early COVID-19 outbreak in South Korea displayed significant spatial heterogeneity. The number of confirmed cases increased rapidly in the Daegu and Gyeongbuk (epicenter), whereas the spread was much slower in the rest of Korea. A two-patch mathematical model with a mobility matrix is developed to capture this significant spatial heterogeneity of COVID-19 outbreaks from 18 February to 24 March 2020. The mobility matrix is taken from the movement data provided by the Korea Transport Institute (KOTI). Some of the essential patch-specific parameters are estimated through cumulative confirmed cases, including the transmission rates and the basic reproduction numbers (local and global). Our simulations show that travel restrictions between the epicenter and the rest of Korea effectively prevented massive outbreaks in the rest of Korea. Furthermore, we explore the effectiveness of several additional strategies for the mitigation and suppression of Covid-19 spread in Korea, such as implementing social distancing and early diagnostic interventions. Full article
(This article belongs to the Section Biological Processes and Systems)
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Figure 1

Figure 1
<p>The spatial distribution of cumulative incidence is displayed in the South Korea map; a total of 10,512 confirmed cases in South Korea and 8217 confirmed cases in Daegu and Gyeongbuk as of 29 April 2020.</p>
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<p>The time series of daily incidence in Daegu and Gyeongbuk (grey bar) is compared with the daily incidence in the rest of South Korea (green bar). Administrative countermeasures are implemented by the Korean government (Daegu and Gyeongbuk).</p>
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<p>The flow chart of a single-patch model for COVID-19 transmission dynamics is displayed.</p>
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<p>The flow chart of a two-patch model for COVID-19 transmission dynamics is displayed; patch 1 represents Daegu and Gyeongbuk and patch 2 represents the rest of South Korea.</p>
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<p>(<b>A</b>) Model calibration to Daegu-Gyeongbuk incidence data (blue solid line: model prediction, red dots: data. (<b>B</b>) Model calibration to South Korea incidence data except Daegu-Gyeongbuk area.</p>
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<p>A global sensitivity analysis is displayed; left panel of patch 1 and right panel of patch 2 under various patch-specific parameters.</p>
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<p>Effect of mobility (<math display="inline"><semantics> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </semantics></math>) on the global <math display="inline"><semantics> <msub> <mi>R</mi> <mn>0</mn> </msub> </semantics></math>. The global <math display="inline"><semantics> <msub> <mi>R</mi> <mn>0</mn> </msub> </semantics></math> is displayed as a function of <math display="inline"><semantics> <msub> <mi>m</mi> <mn>12</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>m</mi> <mn>21</mn> </msub> </semantics></math>. The left panel with the transmission rate <math display="inline"><semantics> <mrow> <msub> <mi>β</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.338</mn> </mrow> </semantics></math> in Daegu and Gyeongbuk and a transmission rate <math display="inline"><semantics> <mrow> <msub> <mi>β</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.357</mn> </mrow> </semantics></math> in the rest of Korea. The right panel with the transmission rate <math display="inline"><semantics> <mrow> <msub> <mi>β</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math> in Daegu and Gyeongbuk and a transmission rate <math display="inline"><semantics> <mrow> <msub> <mi>β</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.357</mn> </mrow> </semantics></math> in the rest of Korea.</p>
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<p>The impact of different motility on daily incidences and cumulative incidence. (<b>A</b>) 1% of population is traveling between two patches. (<b>B</b>) 1% of population in patch 2 migrates to the patch 1, while 10% of population in patch 1 migrates to the patch 2. (<b>C</b>) 10% of population in both patches are traveling between two patches.</p>
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<p>The impacts of different social distancing on daily incidences and cumulative incidences. (<b>A</b>) Effect of not strict social distancing assuming estimated transmission rates on the incidences. (<b>B</b>) Effect of more strict social distancing assuming 50% of transmission rates on the incidences.</p>
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<p>The impact of different early detection rate on daily incidences and cumulative incidences. (<b>A</b>) Early detection rate is assumed as estimated <math display="inline"><semantics> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> </semantics></math>. (<b>B</b>) The effect of 5 fold increase of the detection rate on the incidences.</p>
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<p>The impacts of three different patch-specific interventions on cumulative incidence are shown; the leftmost panel of travel restriction, the middle panel of social distancing, the rightmost panel of early detection rate (measured at day 60).</p>
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23 pages, 24742 KiB  
Article
Improving the Inner Surface State of Thick-Walled Tubes by Heat Treatments with Internal Quenching Considering a Simulation Based Optimization
by Fabian Mühl, Moritz Klug, Stefan Dietrich and Volker Schulze
Processes 2020, 8(10), 1303; https://doi.org/10.3390/pr8101303 - 16 Oct 2020
Cited by 2 | Viewed by 2645
Abstract
Internal Quenching is an innovative heat treatment method for difficult to access component sections. Especially, the microstructure, as well as the residual stress state at inner surfaces, of thick-walled tubes can be adjusted with the presented flexible heat treatment process. Based on multiphysical [...] Read more.
Internal Quenching is an innovative heat treatment method for difficult to access component sections. Especially, the microstructure, as well as the residual stress state at inner surfaces, of thick-walled tubes can be adjusted with the presented flexible heat treatment process. Based on multiphysical FE-models of two different steels, a simulative optimization study, considering different internal quenching strategies, was performed in order to find the optimal cooling conditions. The focus hereby was on the adjustment of a martensitic inner surface with high compressive residual stresses. The simulatively determined optimal cooling strategies were carried out experimentally and analyzed. A good agreement of the resulting hardness and residual stresses was achieved, validating the presented Fe-model of the Internal Quenching process. The shown results also indicate that the arising inner surface state is very sensitive to the transformation behavior of the used steel. Furthermore, the presented study shows that a preliminary simulative consideration of the heat treatment process helps to evaluate significant effects, reducing the experimental effort and time. Full article
(This article belongs to the Section Materials Processes)
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Graphical abstract

Graphical abstract
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<p>Internal Quenching device and schematical temperature evolution during the process overlaid on a schematic TTT-diagram.</p>
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<p>Geometry of the specimen used for Internal Quenching heat treatment.</p>
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<p>FE-model for the Internal Quenching heat treatment, including the mesh and boundary conditions (<b>a</b>) and 3D model for the simulation of the cutting process and electrolytic removal (<b>b</b>).</p>
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<p>Experimental and modeled bainite volume fractions at different isothermal temperatures for AISI 4140.</p>
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<p>Experimental and modeled ferrite/perlite (<b>a</b>) and bainite (<b>b</b>) volume fractions at different isothermal temperatures for AISI 1045.</p>
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<p>Experimentally measured hardness as a function of the isothermal temperatures for AISI 4140 (<b>a</b>) and AISI 1045 (<b>b</b>).</p>
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<p>Varied process parameters (<b>left</b>) and the investigated temperature fields of the internal and the external surface in the case of a continuous (<b>bottom right</b>) and a discontinuous (<b>top right</b>) cooling of the external surface for AISI 4140.</p>
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<p>Simulated axial (<b>a</b>) and tangential (<b>b</b>) residual stresses at the inner surface in dependence of the internal and external quenching temperature.</p>
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<p>Simulated stress development during the Internal Quenching heat treatment in tangential direction (<b>a</b>) and the resulting bainite content in the sample (<b>b</b>) at different external quenching temperatures (<b>b</b>).</p>
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<p>Simulated axial (<b>a</b>) and tangential (<b>b</b>) residual stresses at the inner surface in dependence of the internal quenching temperature and internal cooling rate during a continuous cooling of the external surface with <math display="inline"> <semantics> <mrow> <mn>3</mn> <mo> </mo> <mrow> <mi mathvariant="normal">K</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics> </math>.</p>
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<p>Influence of two different outer cooling rates on the resulting bainite content and residual stresses as a function of distance from surface using the same cooling conditions at the inner surface.</p>
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<p>Simulated axial (<b>a</b>) and tangential (<b>b</b>) residual stresses at the inner surface in dependence of the heat transfer coefficient <math display="inline"> <semantics> <mi>α</mi> </semantics> </math> and the outer cooling rate.</p>
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<p>Simulated martensite distribution and the corresponding development of the tangential stresses in dependence of the heat transfer coefficient <math display="inline"> <semantics> <mi>α</mi> </semantics> </math> using an outer cooling rate of <math display="inline"> <semantics> <mrow> <mn>3</mn> <mo> </mo> <mrow> <mi mathvariant="normal">K</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics> </math>.</p>
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<p>Experimental measured and simulated temperatures at the inner and outer surface of the optimal heat treatment strategies for both investigated steels in the corresponding TTT-diagrams ((<b>a</b>): AISI 4140; (<b>b</b>): AISI 1045) [<a href="#B27-processes-08-01303" class="html-bibr">27</a>].</p>
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<p>Simulated phase, hardness and residual stress distribution after the described heat treatments for both AISI 4140 (<b>a</b>) and AISI 1045 (<b>b</b>).</p>
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<p>Comparison between measured and simulated hardness as a function of the surface distance after the described optimal heat treatments for AISI 4140 (<b>a</b>) and AISI 1045 (<b>b</b>).</p>
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<p>AISI 4140: Comparison between measured and simulated residual stresses at the inner surface as a function of the surface distance (<b>a</b>) and the outer surface along the height (<b>b</b>) of the sample after the described heat treatment.</p>
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<p>AISI 1045: Comparison between measured and simulated residual stresses at the inner surface as a function of the surface distance (<b>a</b>) and the outer surface along the height (<b>b</b>) of the sample after the described heat treatment.</p>
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<p>Comparison of the Sachs EDM measurements and the simulated residual stress development for AISI 4140 (<b>a</b>) and AISI 1045 (<b>b</b>).</p>
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12 pages, 4140 KiB  
Article
Real-Time Nanoplasmonic Sensor for IgG Monitoring in Bioproduction
by Thuy Tran, Olof Eskilson, Florian Mayer, Robert Gustavsson, Robert Selegård, Ingemar Lundström, Carl-Fredrik Mandenius, Erik Martinsson and Daniel Aili
Processes 2020, 8(10), 1302; https://doi.org/10.3390/pr8101302 - 16 Oct 2020
Cited by 18 | Viewed by 4750
Abstract
Real-time monitoring of product titers during process development and production of biotherapeutics facilitate implementation of quality-by-design principles and enable rapid bioprocess decision and optimization of the production process. Conventional analytical methods are generally performed offline/at-line and, therefore, are not capable of generating real-time [...] Read more.
Real-time monitoring of product titers during process development and production of biotherapeutics facilitate implementation of quality-by-design principles and enable rapid bioprocess decision and optimization of the production process. Conventional analytical methods are generally performed offline/at-line and, therefore, are not capable of generating real-time data. In this study, a novel fiber optical nanoplasmonic sensor technology was explored for rapid IgG titer measurements. The sensor combines localized surface plasmon resonance transduction and robust single use Protein A-modified sensor chips, housed in a flexible flow cell, for specific IgG detection. The sensor requires small sample volumes (1–150 µL) and shows a reproducibility and sensitivity comparable to Protein G high performance liquid chromatography-ultraviolet (HPLC-UV). The dynamic range of the sensor system can be tuned by varying the sample volume, which enables quantification of IgG samples ranging from 0.0015 to 10 mg/mL, without need for sample dilution. The sensor shows limited interference from the sample matrix and negligible unspecific protein binding. IgG titers can be rapidly determined in samples from filtered unpurified Chinese hamster ovary (CHO) cell cultures and show good correlation with enzyme-linked immunosorbent assay (ELISA). Full article
(This article belongs to the Special Issue Measurement Technologies for up- and Downstream Bioprocessing)
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Figure 1

Figure 1
<p>(<b>A</b>) Schematic illustration of the sensor system setup. Samples with IgG were injected into the flow cell containing the sensor chip. (<b>B</b>) Nanoplasmonic sensor chips were functionalized with Protein A for specific recognition of IgG. Binding of IgG to the sensor chip results in a concentration-dependent redshift of the LSPR peak maximum. The sensor signal is transmitted to the detector using fiber optics and was monitored in real-time using a dedicated software.</p>
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<p>Unspecific binding evaluation using bovine serum albumin (BSA) chips and cell culture medium (HyClone ActiPro<sup>TM</sup> + GlutaMax) as negative controls. (<b>A</b>) Representative sensorgrams showing the binding of IgG (1 mg/mL) to a ProtA chip and a BSA chip, and the binding of cell culture medium to a ProtA chip. (<b>B</b>) Comparison of specific and unspecific binding responses. Relative binding responses at t = 250 s were averaged (n = 3 sensor chips), and error bars show standard deviations. Injection volume was 1 mL.</p>
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<p>(<b>A</b>) Binding and regeneration profiles of IgG to a ProtA chip using three IgG concentrations: 0.0156, 0.0625 and 1 mg/mL. (<b>B</b>) Baseline shift after first and second regeneration pulse. (<b>C</b>) Sensorgrams obtained after eight cycles of IgG injections (1 mg/mL) each followed by two regeneration pulses using 10 mM glycine buffer pH 2.5. (<b>D</b>) Change in absolute and relative binding responses after each binding and regeneration cycle. The relative binding response is the difference between the absolute responses and the baseline before each IgG injection. The sample volume was 1 mL in all experiments.</p>
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<p>Evaluation of reproducibility of sensor chips. Mean responses from six runs using n = 3 different chips and two injection replicates per chip using three different IgG concentrations, 0.0156, 0.0625 and 2 mg/mL. The data show 95% confidence intervals. Injection volume was 1 mL.</p>
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<p>Binding responses at t = 250 s versus IgG concentrations fitted to sigmoidal curves when using various sample injection volumes, 1 μL, 20 μL, and 1 mL.</p>
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<p>Quantification of IgG in filtrated upstream samples from a bioreactor cell culture. The sample has an IgG concentration of 2.3 mg/mL determined by traditional Protein G-HPLC-UV. (<b>A</b>) A full run of nine IgG standards with various concentrations starting from 0.125 to 15 mg/mL and two injections of the bioreactor sample. 1 µL of samples was used for measurements. Concentrations are displayed on top of each individual sensorgram. The sensor chip was regenerated using 10 mM glycine buffer pH 2.5 after each sample injection. (<b>B</b>) Extracted and normalized sensorgrams of the sample and six IgG standards (0.5, 1, 2, 3, 5, 10 mg/mL) used for making the calibration curve. (<b>C</b>) Calibration curve and determined concentration of sample based on two replicates.</p>
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<p>LSPR measurement of filtrated cell culture samples collected daily over two weeks of cultivation. (<b>A</b>) A full run of six IgG standards (diluted from downstream monomeric IgG sample 15 mg/mL) with different concentrations marked on top of each sensorgram and 15 cell culture samples collected from day 0 to day 14 (D0–D14). Injection volume was 150 µL. (<b>B</b>) Calibration curve. (<b>C</b>) Binding responses of samples over cultivation time.</p>
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<p>(<b>A</b>) Comparison of IgG titers over cultivation time obtained by ELISA, SDS-PAGE and the developed LSPR sensor. Error bars represent standard deviations (n = 3 and 2 for ELISA and LSPR, respectively. (<b>B</b>) Scatter plot and linear regression analysis for comparison of LSPR versus ELISA and LSPR versus SDS-PAGE.</p>
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<p>pH scouting for surface functionalization using glass sensor chips that have the same surface chemistry as stainless-steel chips. Protein A was immobilized to the sensor surface using 10 mM sodium acetate pH 4.0, 10 mM MES pH 6.0 and 10 mM phosphate buffer pH 7.0.</p>
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<p>Binding responses of IgG sample (1 mg/mL) at different injection volumes ranging from 1 μL to 1 mL. For injection volumes &gt; 200 μL, the responses reached maximum binding capacity of the sensor chip. Sensorgrams of two replicates of each injection volumes are depicted with the same color.</p>
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12 pages, 7345 KiB  
Article
Mathematical Modeling of Hydrodynamics in Bioreactor by Means of CFD-Based Compartment Model
by Agnieszka Krychowska, Marian Kordas, Maciej Konopacki, Bartłomiej Grygorcewicz, Daniel Musik, Krzysztof Wójcik, Magdalena Jędrzejczak-Silicka and Rafał Rakoczy
Processes 2020, 8(10), 1301; https://doi.org/10.3390/pr8101301 - 16 Oct 2020
Cited by 12 | Viewed by 4411
Abstract
This study presents the procedure of deriving a compartmental model (CM) based on an analysis obtained from the computational fluid dynamics (CFD) model of a bioreactor. The CM is composed of two parts, a structural (that takes into account the architecture of the [...] Read more.
This study presents the procedure of deriving a compartmental model (CM) based on an analysis obtained from the computational fluid dynamics (CFD) model of a bioreactor. The CM is composed of two parts, a structural (that takes into account the architecture of the mathematical model), and a parametric part (which contains the extrinsic parameters of the model). The CM is composed of the branches containing the set of perfectly mixed continuous stirred-tank reactors (CSTRs) in a configuration that matches the bioreactor’s flow patterns. Therefore, this work’s main objective was to develop a mathematical model that incorporated the flow field obtained by CFD technique. The proposed mathematical model was validated by means of the experimental data in the form of the residence time distribution (RTD) measurements. Full article
(This article belongs to the Section Process Control and Monitoring)
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Figure 1
<p>Sketch of the BioFlo 415 bioreactor: 1—tube for the temperature sensor; 2—sampling tube; 3—batching tube; 4—sparger; 5—shaft; 6—impeller.</p>
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<p>The view of the bioreactor BioFlo<sup>®</sup>415 (<b>a</b>) with the batching system of the tracer (<b>b</b>). The batching system of the tracer consisted of 1—the batcher; 2—the water supply hose to the mixing system; 3—the cut-off valve for the water supply hose to the mixing system; 4—the dosage valve for the tracer; 5—the power drive of the bioreactor; 6—the connector pipe for the water supply to the mixing system; and 7—the liquid level indicator in the mixing system.</p>
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<p>The typical example of residence time distribution (RTD) curves at different flow rates: (<b>a</b>) 10 dm<sup>3</sup>∙h<sup>−1</sup>; and (<b>b</b>) 60 dm<sup>3</sup>∙h<sup>−1</sup>.</p>
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<p>The typical example of the velocity field and overall flow pattern for the tested bioreactor in vertical and horizontal planes: (<b>a</b>) <math display="inline"><semantics> <mrow> <mover> <mi>V</mi> <mo>•</mo> </mover> </mrow> </semantics></math> = 10 dm<sup>3</sup>∙h<sup>−1</sup> and <span class="html-italic">N =</span> 100 RPM; (<b>b</b>) <math display="inline"><semantics> <mrow> <mover> <mi>V</mi> <mo>•</mo> </mover> </mrow> </semantics></math> = 10 dm<sup>3</sup>∙h<sup>−1</sup> and <span class="html-italic">N =</span> 600 RPM; (<b>c</b>) <math display="inline"><semantics> <mrow> <mover> <mi>V</mi> <mo>•</mo> </mover> </mrow> </semantics></math> = 60 dm<sup>3</sup>∙h<sup>−1</sup> and <span class="html-italic">N =</span> 100 RPM; (<b>d</b>) <math display="inline"><semantics> <mrow> <mover> <mi>V</mi> <mo>•</mo> </mover> </mrow> </semantics></math>= 60 dm<sup>3</sup>∙h<sup>−1</sup> and <span class="html-italic">N =</span> 600 RPM.</p>
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<p>Mapping of the CFD results (for <math display="inline"><semantics> <mrow> <mover> <mi>V</mi> <mo>•</mo> </mover> </mrow> </semantics></math> = 30 dm<sup>3</sup>∙h<sup>−1</sup> and <span class="html-italic">N =</span> 300 RPM) into a comaprtmental model (<span class="html-italic">V</span><sub>1</sub>—inlet subdomain; <span class="html-italic">V</span><sub>2</sub>—upper turbine subdomain; <span class="html-italic">V</span><sub>3</sub>—intensive mixing subdomain; <span class="html-italic">V</span><sub>4</sub>—bottom turbine subdomain; <span class="html-italic">V</span><sub>5</sub>—outlet subdomain). The parameters of the mathematical model: <span class="html-italic">α</span>, <span class="html-italic">β</span>, <span class="html-italic">γ;</span> volumetric flow rate: 10–60 dm<sup>3</sup>∙s<sup>−1</sup>, compartment volume: <span class="html-italic">V</span><sub>1</sub> = 0.1 dm<sup>3</sup>; <span class="html-italic">V</span><sub>2</sub> = 2.2 dm<sup>3</sup>; <span class="html-italic">V</span><sub>3</sub> = 1.6 dm<sup>3</sup>; <span class="html-italic">V</span><sub>4</sub> = 2.2 dm<sup>3</sup>; <span class="html-italic">V</span><sub>5</sub> = 0.9 dm<sup>3</sup>.</p>
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<p>The mathematical model in the Matlab/SimulinkTM environment (where: <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>1</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>1</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>2</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>1</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mi>γ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>3</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>1</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <mrow> <mn>1</mn> <mo>+</mo> <mi>γ</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>4</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>2</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <mrow> <mi>α</mi> <mo>+</mo> <mi>γ</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>5</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>2</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mi>γ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>6</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>2</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <mrow> <mi>α</mi> <mo>+</mo> <mn>2</mn> <mi>γ</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>7</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>3</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <mrow> <mi>α</mi> <mo>+</mo> <mi>γ</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>8</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>3</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mi>γ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>9</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>3</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <mrow> <mi>α</mi> <mo>+</mo> <mn>2</mn> <mi>γ</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>10</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>4</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <mrow> <mi>α</mi> <mo>+</mo> <mi>γ</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>11</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>4</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mi>β</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>12</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>4</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mi>γ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>13</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>4</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <mrow> <mi>α</mi> <mo>+</mo> <mi>β</mi> <mo>+</mo> <mn>2</mn> <mi>γ</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>14</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>5</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <mrow> <mn>1</mn> <mo>−</mo> <mi>α</mi> <mo>−</mo> <mi>β</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>15</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>5</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <mrow> <mi>α</mi> <mo>+</mo> <mi>β</mi> <mo>+</mo> <mi>γ</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mn>16</mn> <mo>=</mo> <mfrac> <mover accent="true"> <mi>q</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi>V</mi> <mn>5</mn> </msub> <mi>ρ</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <mrow> <mn>1</mn> <mo>+</mo> <mi>γ</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>).</p>
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<p>The typical examples of the experimental data fit by using the proposed mathematical model (Equation (2)).</p>
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20 pages, 2471 KiB  
Article
Group Key Management Scheme for Multicast Communication Fog Computing Networks
by Mai Trung Dong and Haitao Xu
Processes 2020, 8(10), 1300; https://doi.org/10.3390/pr8101300 - 16 Oct 2020
Cited by 3 | Viewed by 3008
Abstract
In group key management, the implementation of encryption often fails because multicast communication does not provide reliable linkage. In this paper, a new group key management scheme is proposed for multicast communication in fog computing networks. In the proposed scheme, any legal fog [...] Read more.
In group key management, the implementation of encryption often fails because multicast communication does not provide reliable linkage. In this paper, a new group key management scheme is proposed for multicast communication in fog computing networks. In the proposed scheme, any legal fog user belonging to a fog node will be able to decrypt a ciphertext encrypted by a secret shared key. The shared secret key is divided into key segments. In the rekeying operation process, each key segment is split into two factors with its shared production mechanism. The key updates are required to belong to the fog provider or the group management device. Fog users will have independent key segments unchanged. Then, the cost, the message of rekeying, and the dependence on credible channels will be decreased. This method can resist collusion attacks and ensure backward security and forward security, even if the number of users leaving is larger than the threshold value. Our scheme is also suitable for untrusted affiliate networks. Full article
(This article belongs to the Special Issue Smart Systems and Internet of Things (IoT))
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<p>Network model of fog structure.</p>
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<p>Compare 1-affect-n phenomenon.</p>
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<p>Compare the time latency of the group key management scheme for multicast communication fog computing networks (GKMSFC) and Scheme [<a href="#B45-processes-08-01300" class="html-bibr">45</a>,<a href="#B46-processes-08-01300" class="html-bibr">46</a>].</p>
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<p>Rekeying success rate under different connectivity probability.</p>
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<p>Compare time latency between GKMSFC and Scheme [<a href="#B45-processes-08-01300" class="html-bibr">45</a>,<a href="#B46-processes-08-01300" class="html-bibr">46</a>].</p>
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14 pages, 6732 KiB  
Article
A Method of Bending Shrinkage Groove on Vortex Suppression and Energy Improvement for a Hydrofoil with Tip Gap
by Zanao Hu, Chuibing Huang, Zhenwei Huang and Jinsong Zhang
Processes 2020, 8(10), 1299; https://doi.org/10.3390/pr8101299 - 16 Oct 2020
Cited by 20 | Viewed by 2302
Abstract
Hydro energy is a kind of typical renewable energy, which can be converted by hydraulic machinery. However, tip leakage vortex (TLV) has a significant negative influence on the flow pattern and energy performance of hydraulic machinery. In this paper, a bending shrinkage groove [...] Read more.
Hydro energy is a kind of typical renewable energy, which can be converted by hydraulic machinery. However, tip leakage vortex (TLV) has a significant negative influence on the flow pattern and energy performance of hydraulic machinery. In this paper, a bending shrinkage groove (BSG) is proposed to suppress the TLV and improve the energy performance of a hydrofoil first, and then a parametric optimization design method is briefly introduced and applied to determine the optimal configuration of the groove. The main geometric parameters of the groove are selected as optimized variables and three different groove configurations are selected from the optimization result. Finally, the performance improvement of the hydrofoil with groove, the sensitivity analysis of the optimization variables, and the groove impacts on the TLV and flow patterns are investigated. The results demonstrate that the preferred groove reduces the non-dimensional Q criterion vortex isosurfaces area (Qarea = 2 × 107) by 5.13% and increases the lift drag ratio by 17.02%, comparing to the origin hydrofoil. Groove depth d and groove width w are proved to have more significant impacts on the hydrofoil energy performance. The TLV and flow patterns are greatly affected by the different BSG configurations, and the wider BSG contributed to reducing the area of TLV, at the cost of energy performance deterioration. Full article
(This article belongs to the Section Energy Systems)
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<p>Stereoview with measurement plane.</p>
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<p>Parametric bending shrinkage groove (BSG).</p>
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<p>Comparison of the axial velocity contour at cut-plane of z/c = 1 between experiment [<a href="#B27-processes-08-01299" class="html-bibr">27</a>] and numerical simulation. (<b>a</b>) Experimental results. (<b>b</b>) Numerical results.</p>
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<p>Grid arrangement: (<b>a</b>) refined grid in tip clearance; (<b>b</b>) refined grid around the foil; (<b>c</b>) refined grid in the groove.</p>
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<p>The flow chart of the parametric design of the BSG.</p>
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<p>The optimization results.</p>
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<p>Effects of design parameters on foil performances. (<b>a</b>) Q votex isosurface(Q<sub>area</sub> = 2 × 10<sup>7</sup>), (<b>b</b>) lift drag ratio.</p>
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<p>Global effects of design parameters. (<b>a</b>) Q votex isosurface(Q<sub>area</sub> = 2 × 10<sup>7</sup>), (<b>b</b>) lift drag ratio.</p>
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<p>The vortex around the foil for different cases. (<b>a</b>) Origin case, (<b>b</b>) Case A, (<b>c</b>) Case B, (<b>d</b>) Case C.</p>
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<p>Velocity swirling strength contour on different planes. (<b>a</b>) Origin Case, (<b>b</b>) Case A, (<b>c</b>) Case B, (<b>d</b>) Case C.</p>
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<p>Streamline at the blade tip. (<b>a</b>) Origin Case, (<b>b</b>) Case A, (<b>c</b>) Case B, (<b>d</b>) Case C.</p>
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<p>Velocity vector of the foil tip. (<b>a</b>) Origin Case, (<b>b</b>) Case A, (<b>c</b>) Case B, (<b>d</b>) Case C.</p>
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<p>Velocity vector of the foil tip. (<b>a</b>) Origin Case, (<b>b</b>) Case A, (<b>c</b>) Case B, (<b>d</b>) Case C.</p>
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22 pages, 3885 KiB  
Article
Water Salinity as Potential Aid for Improving the Carbon Dioxide Replacement Process’ Effectiveness in Natural Gas Hydrate Reservoirs
by Alberto Maria Gambelli, Beatrice Castellani, Andrea Nicolini and Federico Rossi
Processes 2020, 8(10), 1298; https://doi.org/10.3390/pr8101298 - 16 Oct 2020
Cited by 44 | Viewed by 3251
Abstract
Natural gas hydrates represent a valid opportunity to counteract two of the most serious issues that are affecting humanity this century: climate change and the need for new energy sources, due to the fast and constant increase in the population worldwide. The energy [...] Read more.
Natural gas hydrates represent a valid opportunity to counteract two of the most serious issues that are affecting humanity this century: climate change and the need for new energy sources, due to the fast and constant increase in the population worldwide. The energy that might be produced with methane contained in hydrates is greater than any amount of energy producible with known conventional energy sources; being widespread in all oceans, they would greatly reduce problems and conflicts associated with the monopoly of energy sources. The possibility of extracting methane and simultaneously performing the permanent storage of carbon dioxide makes hydrate an almost carbon-neutral energy source. The main topic of scientific research is to improve the recovery of technologies and guest species replacement strategies in order to make the use of gas hydrates economically advantageous. In the present paper, an experimental study on how salt can alter the formation process of both methane and carbon dioxide hydrate was carried out. The pressure–temperature conditions existing between the two respective equilibrium curves are directly proportional to the effectiveness of the replacement process and thus its feasibility. Eighteen formation tests were realized at three different salinity values: 0, 30 and 37 g/L. Results show that, as the salinity degree increases, the space between CO2 and CH4 formation curves grows. A further aspect highlighted by the tests is how the carbon dioxide formation process tends to assume a very similar trend in all experiments, while curves obtained during methane tests show a similar trend but with some significant differences. Moreover, this tendency became more pronounced with the increase in the salinity degree. Full article
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Graphical abstract

Graphical abstract
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<p>Comparison between P–T diagrams of hydrate formation carried out with methane (in blue) and carbon dioxide (in red).</p>
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<p>Image of the lab-scale apparatus used for hydrate formation tests.</p>
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<p>Schematization of the completely assembled experimental apparatus.</p>
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<p>Comparison between Test 1, carried out using CH<sub>4</sub>, and Test 4, performed with CO<sub>2</sub>.</p>
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<p>Comparison between Test 2, carried out using CH<sub>4</sub>, and Test 5, performed with CO<sub>2</sub>.</p>
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<p>Comparison between Test 3, carried out using CH<sub>4</sub>, and Test 6, performed with CO<sub>2</sub>.</p>
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<p>Comparison between Test 7, carried out using CH<sub>4</sub>, and Test 10, performed with CO<sub>2</sub>.</p>
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<p>Comparison between Test 8, carried out using CH<sub>4</sub>, and Test 11, performed with CO<sub>2</sub>.</p>
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<p>Comparison between Test 9, carried out using CH<sub>4</sub>, and Test 12, performed with CO<sub>2</sub>.</p>
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<p>Comparison between Test 13, carried out using CH<sub>4</sub>, and Test 16, performed with CO<sub>2</sub>.</p>
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<p>Comparison between Test 14, carried out using CH<sub>4</sub>, and Test 17, performed with CO<sub>2.</sub></p>
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<p>Comparison between Test 15, carried out using CH<sub>4</sub>, and Test 18, performed with CO<sub>2.</sub></p>
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