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Processes, Volume 8, Issue 2 (February 2020) – 130 articles

Cover Story (view full-size image): Self‐synchronizing oscillatory metabolism was revealed in continuous culture of Clostridium pasteurianum by the real-time measurement of gas production (CO2 and H2) and redox potential, explaining the variable product profile. The oscillations in CO2 and H2 production and redox potential were in sync with glycerol in the CSTR, providing strong evidence that the mechanism is involved in the regulation of the oxidative pathway of glycerol metabolism, involving cycles of enzyme inhibition and activation by pathway intermediates and/or redox equivalents. The importance of these findings lies both in understanding regulation and in developing a productive industrial biobutanol production process from biodiesel-derived crude glycerol.View this paper.

 

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12 pages, 1320 KiB  
Article
Dominance Conditions for Optimal Order-Lot Matching in the Make-To-Order Production System
by Jae-Gon Kim, June-Young Bang, Hong-Bae Jun and Jong-Ho Shin
Processes 2020, 8(2), 255; https://doi.org/10.3390/pr8020255 - 24 Feb 2020
Cited by 1 | Viewed by 2381
Abstract
Order-lot matching is the process of assigning items in lots being processed in the make-to-order production system to meet the due dates of the orders. In this study, an order-lot matching problem (OLMP) is considered to minimize the total tardiness of orders with [...] Read more.
Order-lot matching is the process of assigning items in lots being processed in the make-to-order production system to meet the due dates of the orders. In this study, an order-lot matching problem (OLMP) is considered to minimize the total tardiness of orders with different due dates. In the OLMP considered in this study, we need to not only determine the allocation of items to lots in the production facility but also generate a lot release plan for the given time horizon. We show that the OLMP can be considered as a special type of machine scheduling problem with many similarities to the single machine total tardiness scheduling problem ( 1 | | T i ). We suggest dominance conditions for the OLMP by modifying those for 1 | | T i and a dynamic programming (DP) model based on the dominance conditions. With two example problems, we show that the DP model can solve small-sized OLMPs optimally. Full article
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<p>Order-lot matchings using the compact pegging method.</p>
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<p>The effect of interchanging two orders in an order sequence.</p>
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<p>The effect of moving an order after another one in an order sequence</p>
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<p>The optimal order-lot matching for Example 1</p>
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<p>The optimal order-lot matching for Example 2.</p>
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19 pages, 3643 KiB  
Article
Fluid–Solid Coupling Model and Simulation of Gas-Bearing Coal for Energy Security and Sustainability
by Shixiong Hu, Xiao Liu and Xianzhong Li
Processes 2020, 8(2), 254; https://doi.org/10.3390/pr8020254 - 24 Feb 2020
Cited by 12 | Viewed by 3025
Abstract
The optimum design of gas drainage boreholes is crucial for energy security and sustainability in coal mining. Therefore, the construction of fluid–solid coupling models and numerical simulation analyses are key problems for gas drainage boreholes. This work is based on the basic theory [...] Read more.
The optimum design of gas drainage boreholes is crucial for energy security and sustainability in coal mining. Therefore, the construction of fluid–solid coupling models and numerical simulation analyses are key problems for gas drainage boreholes. This work is based on the basic theory of fluid–solid coupling, the correlation definition between coal porosity and permeability, and previous studies on the influence of adsorption expansion, change in pore free gas pressure, and the Klinkenberg effect on gas flow in coal. A mathematical model of the dynamic evolution of coal permeability and porosity is derived. A fluid–solid coupling model of gas-bearing coal and the related partial differential equation for gas migration in coal are established. Combined with an example of the measurement of the drilling radius of the bedding layer in a coal mine, a coupled numerical solution under negative pressure extraction conditions is derived by using COMSOL Multiphysics simulation software. Numerical simulation results show that the solution can effectively guide gas extraction and discharge during mining. This study provides theoretical and methodological guidance for energy security and coal mining sustainability. Full article
(This article belongs to the Special Issue Green Technologies for Production Processes)
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<p>Structural deformation.</p>
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<p>Deformation relationship of coal particles.</p>
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<p>Coal and gas coupling geometric model.</p>
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<p>Mathematical model input.</p>
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<p>Gas pressure profile at different times.</p>
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<p>Gas pressure profile at different times.</p>
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<p>Contour maps of gas pressure at different times.</p>
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<p>Evolution curves of gas pressure.</p>
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<p>Evolution curves of porosity.</p>
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<p>Evolution curves of permeability.</p>
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16 pages, 20873 KiB  
Article
Synthesis and Research of Rare Earth Nanocrystal Luminescent Properties for Security Labels Using the Electrohydrodynamic Printing Technique
by Chinh Dung Trinh, Thuan Van Doan, Phuong Hau Thi Pham, Dung My Thi Dang, Pham Van Quan and Chien Mau Dang
Processes 2020, 8(2), 253; https://doi.org/10.3390/pr8020253 - 24 Feb 2020
Cited by 3 | Viewed by 2849
Abstract
YVO4:Eu3+ nanoparticles were successfully synthesized by two methods, namely the sonochemical method and hydrothermal method. The X-ray diffraction (XRD) patterns showed the tetragonal phase of YVO4 (JCPDS 17-0341) was indexed in the diffraction peaks of all samples. The samples [...] Read more.
YVO4:Eu3+ nanoparticles were successfully synthesized by two methods, namely the sonochemical method and hydrothermal method. The X-ray diffraction (XRD) patterns showed the tetragonal phase of YVO4 (JCPDS 17-0341) was indexed in the diffraction peaks of all samples. The samples synthesized by the sonochemical method had a highly crystalline structure (X-ray diffraction results) and luminescence intensity (photoluminescence results) than those synthesized by the hydrothermal method. According to the results of field emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM), the average size of YVO4:Eu3+ nanoparticles was around 25–30 nm for the sonochemical method and 15–20 nm for the hydrothermal method. YVO4:Eu3+ nanoparticles in the case of the sonochemical method had a better crystalline structure and stronger emissivity at 618 nm. The Eu3+ ions’ average lifetime in YVO4:Eu3+ at 618 nm emission under 275 nm excitation were at 0.955 ms for the sonochemical method and 0.723 ms for the hydrothermal method. The security ink for inkjet devices contained YVO4:Eu3+ nanoparticles, the binding agent as polyethylene oxide or ethyl cellulose and other necessary solvents. The device used for security label printing was an inkjet printer with an electrohydrodynamic printing technique (EHD). In the 3D optical profilometer results, the width of the printed line was ~97–167 µm and the thickness at ~9.1–9.6 µm. The printed security label obtained a well-marked shape, with a size at 1.98 × 1.98 mm. Full article
(This article belongs to the Section Materials Processes)
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<p>FE-SEM micrographs of YVO<sub>4</sub>:Eu<sup>3+</sup> nanoparticles synthesized by the sonochemical (<b>a</b>) and hydrothermal (<b>b</b>) methods. TEM micrographs of YVO<sub>4</sub>:Eu<sup>3+</sup> nanoparticles synthesized by the sonochemical method (<b>c</b>). XRD patterns of YVO<sub>4</sub>:Eu<sup>3+</sup> nanoparticles synthesized by different methods (<b>d</b>).</p>
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<p>FE-SEM micrographs of YVO<sub>4</sub>:Eu<sup>3+</sup> nanoparticles synthesized by the sonochemical (<b>a</b>) and hydrothermal (<b>b</b>) methods. TEM micrographs of YVO<sub>4</sub>:Eu<sup>3+</sup> nanoparticles synthesized by the sonochemical method (<b>c</b>). XRD patterns of YVO<sub>4</sub>:Eu<sup>3+</sup> nanoparticles synthesized by different methods (<b>d</b>).</p>
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<p>EDX spectra of YVO<sub>4</sub>:Eu<sup>3+</sup> nanoparticles synthesized by sonochemical and hydrothermal methods.</p>
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<p>Raman spectra of undoped and Eu-doped YVO<sub>4</sub> nanoparticles synthesized by the sonochemical and hydrothermal methods (<b>a</b>). The Raman active mode shift at 388 cm<sup>−1</sup> for undoped and Eu-doped YVO<sub>4</sub> nanoparticles (<b>b</b>). The Raman peak intensity ratio diagram at 839 and 893 cm<sup>−1</sup> (<b>c</b>).</p>
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<p>Raman spectra of undoped and Eu-doped YVO<sub>4</sub> nanoparticles synthesized by the sonochemical and hydrothermal methods (<b>a</b>). The Raman active mode shift at 388 cm<sup>−1</sup> for undoped and Eu-doped YVO<sub>4</sub> nanoparticles (<b>b</b>). The Raman peak intensity ratio diagram at 839 and 893 cm<sup>−1</sup> (<b>c</b>).</p>
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<p>Energy levels and transitions scheme of Eu<sup>3+</sup>, vertical arrows: absorption and emission transitions (<b>a</b>). Photoluminescence excitation (PLE) spectra (<b>b</b>) and photoluminescence emission (PL) spectra of samples with different methods (<b>c</b>).</p>
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<p>Energy levels and transitions scheme of Eu<sup>3+</sup>, vertical arrows: absorption and emission transitions (<b>a</b>). Photoluminescence excitation (PLE) spectra (<b>b</b>) and photoluminescence emission (PL) spectra of samples with different methods (<b>c</b>).</p>
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<p>The luminescence decay curves for the <sup>5</sup>D<sub>0</sub> excited state of Eu<sup>3+</sup> under 275 nm excitation for YVO<sub>4</sub>:Eu<sup>3+</sup> samples synthesized with different methods.</p>
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<p>The diagram of analyzed ink surface tension.</p>
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<p>Schematic of the electrohydrodynamic (EHD) technology mechanism.</p>
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<p>Image taken by high speed camera of an ink droplet jetted onto a glass substrate, performing of ink II (<b>a</b>) and ink I (<b>b</b>).</p>
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<p>The micrographs from a 3D optical profilometer of a security label’s lines printed by ink I (<b>a</b>) and ink II (<b>b</b>). The micrograph of the security label printed by ink I (<b>c,d</b>)</p>
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<p>The height–width diagram of the printed lines by ink I and II taken by a Sensofar Metrology 3D Optical Profilometer (<b>a</b>). The image of a label under daylight and UV lamp (<b>b</b>).</p>
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8 pages, 1167 KiB  
Letter
Syngas Production Improvement of Sugarcane Bagasse Conversion Using an Electromagnetic Modified Vacuum Pyrolysis Reactor
by Muhammad Djoni Bustan, Sri Haryati, Fitri Hadiah, Selpiana Selpiana and Adri Huda
Processes 2020, 8(2), 252; https://doi.org/10.3390/pr8020252 - 24 Feb 2020
Cited by 6 | Viewed by 4287
Abstract
The trends and challenges of pyrolysis technology nowadays have shifted to low-temperature pyrolysis, which provides low-cost processes but high-yield conversion, with suitable H2/CO ratios for performing gas-to-liquid technology in the future. The present study has developed a modified vacuum pyrolysis reactor [...] Read more.
The trends and challenges of pyrolysis technology nowadays have shifted to low-temperature pyrolysis, which provides low-cost processes but high-yield conversion, with suitable H2/CO ratios for performing gas-to-liquid technology in the future. The present study has developed a modified vacuum pyrolysis reactor to convert sugarcane bagasse to gas products, including H2, CO2, CH4, and CO in the low-temperature process. The experimental design includes the effects of pyrolysis time, pyrolysis temperature, and applying a current as a function of the electromagnetic field. The result showed that 0.12 ng/µL, 0.85 ng/µL, and 0.31 ng/µL of hydrogen (H2), carbon dioxide (CO2), and carbon monoxide (CO) gases, respectively, started forming in the first 20 min at 210 °C for the pyrolysis temperature, and the gas product accumulated in the increase of pyrolysis time and temperature. In the absence of electromagnetic field, the optimum condition was obtained at 60 min and 290 °C of pyrolysis time and temperature, respectively, in which 20.98, 14.86, 14.56, and 15.78 ng/µL of H2, CO2, CH4, and CO were generated, respectively. However, this condition did not meet the minimum value of Fischer–Tropsch synthesis, since the minimum requirement of the H2/CO ratio is 2. Furthermore, applying the electromagnetic field performed a significant improvement, in which applying current ≥3A improved the gas product to 33.76, 8.71, 18.39, and 7.66 ng/µL of H2, CO2, CH4, and CO, respectively, with an H2/CO ratio above 2. The obtained result showed that applying electric current as an electromagnetic field provides a significant improvement, not only in boosting yield product, but also in performing the standard ratios of H2/CO in the gas–liquid conversion of syngas to liquid hydrocarbon. The result proves that applying an electromagnetic approach could be used as an alternative way to obtain efficiency and as a better process to convert biomass as a future energy source. Full article
(This article belongs to the Special Issue Biomass Processing and Conversion Systems)
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<p>Modified vacuum pyrolysis diagram for converting bagasse to syngas. CC-01: biomass chamber; CP-01: control panel; CD-01: condenser; EC-01: electromagnetic induction source; FT-01: flash tank; GS-01: gas storage; LS-01: liquid storage; RP-01: pyrolysis reactor; VP-01: vacuum pump; WD-01: water drum; and WP-01: centrifugal pump.</p>
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<p>The effect of pyrolysis time in gas production. Temperature: 210 °C, with vacuum pressure in the range of 5–20 kPa.</p>
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<p>Total yield syngas production at 60 min of pyrolysis time as a function of pyrolysis temperature.</p>
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<p>Syngas production improvement in the increasing of an applied electromagnetic current. Pyrolysis time and temperature were controlled at 60 min and 210 °C, respectively (control).</p>
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14 pages, 3864 KiB  
Article
Size-Selected Graphene Oxide Loaded with Photosensitizer (TMPyP) for Targeting Photodynamic Therapy In Vitro
by Kateřina Bartoň Tománková, Ariana Opletalová, Kateřina Poláková, Sergii Kalytchuk, Jana Jiravová, Jakub Malohlava, Lukáš Malina and Hana Kolářová
Processes 2020, 8(2), 251; https://doi.org/10.3390/pr8020251 - 24 Feb 2020
Cited by 9 | Viewed by 3403
Abstract
Targeted therapies of various diseases are nowadays widely studied in many biomedical fields. Photodynamic therapy (PDT) represents a modern treatment of cancer using a locally activated light. TMPyP is an efficient synthetic water-soluble photosensitizer (PS), yet with poor absorption in the visible and [...] Read more.
Targeted therapies of various diseases are nowadays widely studied in many biomedical fields. Photodynamic therapy (PDT) represents a modern treatment of cancer using a locally activated light. TMPyP is an efficient synthetic water-soluble photosensitizer (PS), yet with poor absorption in the visible and the red regions. In this work, we prepared size-selected and colloidally stable graphene oxide (GO) that is appropriate for biomedical use. Thanks to the negative surface charge of GO, TMPyP was easily linked in order to create conjugates of GO/TMPyP by electrostatic force. Due to the strong ionic interactions, charge transfers between GO and TMPyP occur, as comprehensively investigated by steady-state and time-resolved fluorescence spectroscopy. Biocompatibility and an in vitro effect of GO/TMPyP were confirmed by a battery of in vitro tests including MTT, comet assay, reactive oxygen species (ROS) production, and monitoring the cellular uptake. PDT efficiency of GO/TMPyP was tested using 414 and 740 nm photoexcitation. Our newly prepared nanotherapeutics showed a higher PDT effect than in free TMPyP, and is promising for targeted therapy using clinically favorable conditions. Full article
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Graphical abstract

Graphical abstract
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<p>Atomic force microscopy image of graphene oxide (GO) captured on mica substrate together with height profile along the red line of interest on GO particle where the height is about 17 nm and lateral dimension about 200 nm in length.</p>
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<p>UV–vis absorbance spectrum of GO/TMPyP compared to TMPyP and GO alone. There is an obvious red shift of the Soret band from 417 nm (TMPyP) to 4375 nm (GO/TMPyP 1:1).</p>
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<p>Normalized excitation–emission color maps of TMPyP (<b>left</b>) and GO/TMPyP (1:1) (<b>right</b>) samples.</p>
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<p>PL emission of TMPyP and GO/TMPyP under the excitation of 375 nm (<b>left</b>) and PL decays for TMPyP and GO/TMPyP (<b>right</b>). Experimental data are represented by symbols, whereas lines are single or multiexponential fits. Correspondent extracted PL lifetimes are indicated.</p>
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<p>Confocal image of Hela cells after 60 min, 120 min and 24 h of TMPyP (<b>A</b>–<b>C</b>) and GO/TMPyP nanocarrier (<b>D</b>–<b>F</b>) at concentration of 25 µM. Red (pink) areas depict TMPyP or GO/TMPyP inside nucleus. Note: (<b>A</b>)—60 min TMPyP, (<b>B</b>)—120 min TMPyP, (<b>C</b>)—24 h TMPyP, (<b>D</b>)—60 min GO/TMPyP, (<b>E</b>)—120 min GO/TMPyP, (<b>F</b>)—24 h GO/TMPyP. Magnification is 40×.</p>
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<p>Confocal image 24 h after photodynamic therapy (PDT) treatment (after irradiation with light dose 30 J/cm<sup>2</sup> of 740 nm + light dose 1 J/cm<sup>2</sup> of 414 nm). (<b>A</b>)—TMPyP and (<b>B</b>)—GO/TMPyP nanocarrier at concentration of 25 µM. Magnification is 40×.</p>
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<p>Kinetic production of reactive oxygen species in concentrations of 0.25 μM, 2.5 μM, and 25 μM of TMPyP (<b>A</b>,<b>C</b>) or GO/TMPyP (<b>A</b>,<b>B</b>) nanocarrier on the HeLa cell line. Notes: The linear regression of the reactive oxygen species (ROS) rate expressed the ROS amount created at each minute. Data represent mean and standard error from three independent measurements.</p>
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<p>Percentage of DNA in tail (<b>A</b>) and tail length (<b>B</b>) determined by comet assay in concentrations of 25 μM, 2.5 μM, and 0.25 μM of TMPyP and GO/TMPyP nanocarrier on the HeLa cell lines. Data represent mean and standard error from three independent measurements.</p>
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22 pages, 5468 KiB  
Article
Triangulum City Dashboard: An Interactive Data Analytic Platform for Visualizing Smart City Performance
by Mina Farmanbar and Chunming Rong
Processes 2020, 8(2), 250; https://doi.org/10.3390/pr8020250 - 24 Feb 2020
Cited by 24 | Viewed by 8608
Abstract
Cities are becoming smarter by incorporating hardware technology, software systems, and network infrastructure that provide Information Technology (IT) systems with real-time awareness of the real world. What makes a “smart city” functional is the combined use of advanced infrastructure technologies to deliver its [...] Read more.
Cities are becoming smarter by incorporating hardware technology, software systems, and network infrastructure that provide Information Technology (IT) systems with real-time awareness of the real world. What makes a “smart city” functional is the combined use of advanced infrastructure technologies to deliver its core services to the public in a remarkably efficient manner. City dashboards have drawn increasing interest from both city operators and citizens. Dashboards can gather, visualize, analyze, and inform regional performance to support the sustainable development of smart cities. They provide useful tools for evaluating and facilitating urban infrastructure components and services. This work proposes an interactive web-based data visualization and data analytics toolkit supported by big data aggregation tools. The system proposed is a cloud-based prototype that supports visualization and real-time monitoring of city trends while processing and displaying large data sets on a standard web browser. However, it is capable of supporting online analysis processing by answering analytical queries and producing graphics from multiple resources. The aim of this platform is to improve communication between users and urban service providers and to give citizens an overall view of the city’s state. The conceptual framework and architecture of the proposed platform are explored, highlighting design challenges and providing insight into the development of smart cities. Moreover, results and the potential statistical analysis of important city services offered by the system are introduced. Finally, we present some challenges and opportunities identified through the development of the city data platform. Full article
(This article belongs to the Special Issue Clean Energy Conversion Processes)
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<p>Screenshots of some example dashboards. (<b>a</b>) London CityDashboard, one page with row, (<b>b</b>) Skopje dashboard, drill-down with menu, (<b>c</b>) Dublin dashboard, drill-down with the menu.</p>
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<p>Conceptual three-tier architecture of the Triangulum City Dashboard.</p>
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<p>Data collection system.</p>
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<p>Data acquisition pipeline.</p>
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<p>Sample screenshots of the Triangulum City Dashboard. (<b>a</b>) The main page, drill-down layout with menu, (<b>b</b>) Parking management dashboard, (<b>c</b>) Energy consumption management dashboard, (<b>d</b>) Data analytic toolkit, (<b>e</b>) Tabular data presentation, (<b>f</b>) Integrated maps.</p>
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<p>Sample screenshots of the Triangulum City Dashboard. (<b>a</b>) The main page, drill-down layout with menu, (<b>b</b>) Parking management dashboard, (<b>c</b>) Energy consumption management dashboard, (<b>d</b>) Data analytic toolkit, (<b>e</b>) Tabular data presentation, (<b>f</b>) Integrated maps.</p>
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<p>Sample screenshots of the Triangulum City Dashboard. (<b>a</b>) The main page, drill-down layout with menu, (<b>b</b>) Parking management dashboard, (<b>c</b>) Energy consumption management dashboard, (<b>d</b>) Data analytic toolkit, (<b>e</b>) Tabular data presentation, (<b>f</b>) Integrated maps.</p>
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<p>Sources of urban data.</p>
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<p>All busses traveling in the area.</p>
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<p>Hourly parking lot availability.</p>
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<p>Parking lot availability for a selected period.</p>
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<p>Energy consumption of all the households in 2017.</p>
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<p>Hourly energy consumption of house labeled “gw_0”.</p>
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<p>Distribution of whole data samples in terms of day.</p>
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<p>Monthly energy usage of the selected devices.</p>
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14 pages, 11263 KiB  
Article
Numerical Analysis on Velocity and Temperature of the Fluid in a Blast Furnace Main Trough
by Yao Ge, Meng Li, Han Wei, Dong Liang, Xuebin Wang and Yaowei Yu
Processes 2020, 8(2), 249; https://doi.org/10.3390/pr8020249 - 22 Feb 2020
Cited by 10 | Viewed by 3707
Abstract
The main trough is a part of the blast furnace process for hot metal and molten slag transportation from the tap hole to the torpedo, and mechanical erosion of the trough is an important reason for a short life of a campaign. This [...] Read more.
The main trough is a part of the blast furnace process for hot metal and molten slag transportation from the tap hole to the torpedo, and mechanical erosion of the trough is an important reason for a short life of a campaign. This article employed OpenFoam code to numerically study and analyze velocity, temperature and wall shear stress of the fluids in the main trough during a full tapping process. In the code, a three-dimensional transient mass, momentum and energy conservation equations, including the standard k-ε turbulence model, were developed for the fluid in the trough. Temperature distribution in refractory is solved by the Fourier equation through conjugate heat transfer with the fluid in the trough. Change velocities of the fluid during the full tapping process are exactly described by a parabolic equation. The investigation results show that there are strong turbulences at the area of hot metal’s falling position and the turbulences have influence on velocity, temperature and wall shear stress of the fluid. With the increase of the angle of the tap hole, the wall shear stress increases. Mechanical erosion of the trough has the smallest value and the campaign of the main trough is estimated to expand over 5 days at the tap hole angle of 7°. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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<p>Schematic diagram of the main trough in front view: 1-1 cross-section view at the main trough; 2-2 cross-section view at the outlet.</p>
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<p>Computational grid: (<b>a</b>) the grids at the inlet and the upper wall, (<b>b</b>) the grids at the outlet.</p>
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<p>Locations of 5 faces and 6 lines in the main trough for post-processing analysis: (<b>a</b>) planes in the full main trough, (<b>b</b>) lines in the mixture fluid of the main trough.</p>
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<p>Monitoring residual error curves of pressure, velocity and temperature in the simulation.</p>
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<p>Velocity distribution on the center plane (cf. <a href="#processes-08-00249-f003" class="html-fig">Figure 3</a>a) at 5, 30, 55 and 80 s.</p>
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<p>Velocity distribution of the mixture fluid on plane 4 (cf. <a href="#processes-08-00249-f003" class="html-fig">Figure 3</a>a) at 5, 30, 55 and 80 s.</p>
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<p>Wall shear stress on the line 1 (blue curve, cf. <a href="#processes-08-00249-f003" class="html-fig">Figure 3</a>b) and line 2 (red curve, cf. <a href="#processes-08-00249-f003" class="html-fig">Figure 3</a>b).</p>
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<p>Temperature distribution of the center plane (cf. <a href="#processes-08-00249-f003" class="html-fig">Figure 3</a>a) at 5, 30, 55 and 80 s.</p>
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<p>Temperature distribution of the mixture fluid on plane 4 (cf. <a href="#processes-08-00249-f003" class="html-fig">Figure 3</a>a) at 5, 30 and 55 s.</p>
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<p>Temperature fluctuations of four lines during tapping ((<b>a</b>) line 3, (<b>b</b>) line 4, (<b>c</b>) line 5 and (<b>d</b>) line 6, cf. <a href="#processes-08-00249-f003" class="html-fig">Figure 3</a>b).</p>
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<p>Temperature distributions from the central plane to plane 3 (cf. <a href="#processes-08-00249-f003" class="html-fig">Figure 3</a>a).</p>
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<p>Wall shear stress distribution at different angles of the tap hole on line 2 (cf. <a href="#processes-08-00249-f003" class="html-fig">Figure 3</a>b).</p>
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<p>Temperature fluctuations on line 3 (cf. <a href="#processes-08-00249-f003" class="html-fig">Figure 3</a>b) during tapping with different tap hole angles, (<b>a</b>) 7°, (<b>b</b>) 10° and (<b>c</b>) 12°.</p>
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23 pages, 1088 KiB  
Review
Alkaline Water Electrolysis Powered by Renewable Energy: A Review
by Jörn Brauns and Thomas Turek
Processes 2020, 8(2), 248; https://doi.org/10.3390/pr8020248 - 21 Feb 2020
Cited by 456 | Viewed by 98807
Abstract
Alkaline water electrolysis is a key technology for large-scale hydrogen production powered by renewable energy. As conventional electrolyzers are designed for operation at fixed process conditions, the implementation of fluctuating and highly intermittent renewable energy is challenging. This contribution shows the recent state [...] Read more.
Alkaline water electrolysis is a key technology for large-scale hydrogen production powered by renewable energy. As conventional electrolyzers are designed for operation at fixed process conditions, the implementation of fluctuating and highly intermittent renewable energy is challenging. This contribution shows the recent state of system descriptions for alkaline water electrolysis and renewable energies, such as solar and wind power. Each component of a hydrogen energy system needs to be optimized to increase the operation time and system efficiency. Only in this way can hydrogen produced by electrolysis processes be competitive with the conventional path based on fossil energy sources. Conventional alkaline water electrolyzers show a limited part-load range due to an increased gas impurity at low power availability. As explosive mixtures of hydrogen and oxygen must be prevented, a safety shutdown is performed when reaching specific gas contamination. Furthermore, the cell voltage should be optimized to maintain a high efficiency. While photovoltaic panels can be directly coupled to alkaline water electrolyzers, wind turbines require suitable converters with additional losses. By combining alkaline water electrolysis with hydrogen storage tanks and fuel cells, power grid stabilization can be performed. As a consequence, the conventional spinning reserve can be reduced, which additionally lowers the carbon dioxide emissions. Full article
(This article belongs to the Special Issue Electrolysis Processes)
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<p>The number of publications per year from 1990 to 2019 containing the specified keywords. Around 2010, the publication rate increases due to greater interest in the energy turnaround. While the topic is often discussed technology-independently (unspecified), more publications for low-temperature technologies, like alkaline water electrolysis (AEL) and proton exchange membrane electrolysis (PEMEL), are available than for the high-temperature technology solid oxide electrolysis (SOEL) [<a href="#B26-processes-08-00248" class="html-bibr">26</a>].</p>
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<p>A schematic flow diagram of an alkaline water electrolyzer. The electrolyte is pumped through the electrolysis cell where the gas evolution takes place. Adjacent gas separators split both phases, and the liquid phase flows back to the electrolysis stack. Heat exchangers ensure that the optimal temperature is maintained, and the product gases can be purified afterward.</p>
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<p>Different cell designs for alkaline water electrolysis. Whereas (<b>a</b>) shows a conventional assembly with a defined distance between both electrodes, (<b>b</b>) depicts a zero-gap assembly where the electrodes are directly pressed onto the separator [<a href="#B38-processes-08-00248" class="html-bibr">38</a>].</p>
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<p>The calculated cell voltage of an atmospheric alkaline water electrolyzer at a temperature of 60 °C according to Equation (<a href="#FD8-processes-08-00248" class="html-disp-formula">8</a>). The overall cell voltage consists of the reversible cell voltage <math display="inline"> <semantics> <msub> <mi>U</mi> <mi>rev</mi> </msub> </semantics> </math>, ohmic losses <math display="inline"> <semantics> <mrow> <mi>I</mi> <mo>·</mo> <msub> <mi>R</mi> <mi>ohm</mi> </msub> </mrow> </semantics> </math>, and activation overvoltages <math display="inline"> <semantics> <msub> <mi>η</mi> <mi>act</mi> </msub> </semantics> </math> [<a href="#B39-processes-08-00248" class="html-bibr">39</a>,<a href="#B40-processes-08-00248" class="html-bibr">40</a>].</p>
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<p>The calculated specific electrolyte conductivity as a function of the electrolyte concentrations of sodium hydroxide (NaOH) and potassium hydroxide (KOH) solutions at different temperatures obtained by Equations (<a href="#FD73-processes-08-00248" class="html-disp-formula">A2</a>) and (<a href="#FD74-processes-08-00248" class="html-disp-formula">A3</a>). The correlation parameters can be found in <a href="#processes-08-00248-t0A2" class="html-table">Table A2</a> [<a href="#B52-processes-08-00248" class="html-bibr">52</a>,<a href="#B57-processes-08-00248" class="html-bibr">57</a>].</p>
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<p>Anodic gas impurity (<math display="inline"> <semantics> <msub> <mi mathvariant="normal">H</mi> <mn>2</mn> </msub> </semantics> </math> in <math display="inline"> <semantics> <msub> <mi mathvariant="normal">O</mi> <mn>2</mn> </msub> </semantics> </math>) in relation to the current density at different pressure levels for (<b>a</b>) separated and (<b>b</b>) mixed electrolyte cycles, at a temperature of 60 °C, with an electrolyte concentration of approximately 32 wt.% and an electrolyte volume flow of 0.35 L min<sup>−1</sup> [<a href="#B64-processes-08-00248" class="html-bibr">64</a>].</p>
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<p>Typical time-related profiles for (<b>a</b>) solar radiation and (<b>b</b>) wind velocity, measured by the weather station of the Clausthal University of Technology. Though solar radiation peaks around noon, wind velocity shows sinusoidal oscillations.</p>
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<p>Schematic of alkaline water electrolysis powered by solar energy. Photovoltaic panels convert the solar radiation into electricity, which can be used for the operation. The implementation of a DC/DC power converter is optional, as direct and indirect coupling is possible [<a href="#B70-processes-08-00248" class="html-bibr">70</a>,<a href="#B78-processes-08-00248" class="html-bibr">78</a>,<a href="#B79-processes-08-00248" class="html-bibr">79</a>].</p>
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<p>Example calculation results of the (<b>a</b>) current–voltage characteristics of a photovoltaic panel at different solar radiation levels and the corresponding (<b>b</b>) power–voltage curve. Additionally, a current–voltage characteristic of an alkaline water electrolyzer (AEL) is implemented. The intersections determine the possible operation points. For an efficient operation, the distance to the maximal power points (MPP) should be minimal [<a href="#B29-processes-08-00248" class="html-bibr">29</a>,<a href="#B72-processes-08-00248" class="html-bibr">72</a>,<a href="#B73-processes-08-00248" class="html-bibr">73</a>].</p>
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<p>Schematic of alkaline water electrolysis powered by wind energy. Wind turbines convert the available wind power into electricity, which can be used for the operation. The implementation of a suitable AC/DC converter is mandatory [<a href="#B74-processes-08-00248" class="html-bibr">74</a>,<a href="#B79-processes-08-00248" class="html-bibr">79</a>].</p>
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<p>Example calculation results of (<b>a</b>) the performance coefficient for various rotor blade pitch angles using Equation (17) and (<b>b</b>) the wind turbine power for different wind velocities using Equation (16). The maximum power point (MPP) trajectory is marked [<a href="#B74-processes-08-00248" class="html-bibr">74</a>,<a href="#B79-processes-08-00248" class="html-bibr">79</a>].</p>
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<p>The schematic process scheme of a hydrogen energy system. Photovoltaic panels and wind turbines generate renewable energy to power alkaline water electrolyzers, and stored hydrogen can be converted back into electricity by fuel cells. Therefore, either oxygen or air can be utilized. Additional energy storage devices can damp fluctuations, and the complete hydrogen energy system can be used for power grid stabilization [<a href="#B25-processes-08-00248" class="html-bibr">25</a>,<a href="#B28-processes-08-00248" class="html-bibr">28</a>,<a href="#B82-processes-08-00248" class="html-bibr">82</a>,<a href="#B87-processes-08-00248" class="html-bibr">87</a>].</p>
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12 pages, 2148 KiB  
Article
Application of a Liquid Biphasic Flotation (LBF) System for Protein Extraction from Persiscaria Tenulla Leaf
by Hui Shi Saw, Revathy Sankaran, Kuan Shiong Khoo, Kit Wayne Chew, Win Nee Phong, Malcolm S.Y. Tang, Siew Shee Lim, Hayyiratul Fatimah Mohd Zaid, Mu. Naushad and Pau Loke Show
Processes 2020, 8(2), 247; https://doi.org/10.3390/pr8020247 - 21 Feb 2020
Cited by 9 | Viewed by 5412
Abstract
Persiscaria tenulla, commonly known as Polygonum, is a plant belonging to the family Polygonaceae, which originated from and is widely found in Southeast Asia countries, such as Indonesia, Malaysia, Thailand, and Vietnam. The leaf of the plant is believed to have [...] Read more.
Persiscaria tenulla, commonly known as Polygonum, is a plant belonging to the family Polygonaceae, which originated from and is widely found in Southeast Asia countries, such as Indonesia, Malaysia, Thailand, and Vietnam. The leaf of the plant is believed to have active ingredients that are responsible for therapeutic effects. In order to take full advantage of a natural medicinal plant for the application in the pharmaceutical and food industries, extraction and separation techniques are essential. In this study, an emerging and rapid extraction approach known as liquid biphasic flotation (LBF) is proposed for the extraction of protein from Persiscaria tenulla leaves. The scope of this study is to establish an efficient, environmentally friendly, and cost-effective technology for the extraction of protein from therapeutic leaves. Based on the ideal conditions of the small LBF system, a 98.36% protein recovery yield and a 79.12% separation efficiency were achieved. The upscaling study of this system exhibited the reliability of this technology for large-scale applications with a protein recovery yield of 99.44% and a separation efficiency of 93.28%. This technology demonstrated a simple approach with an effective protein recovery yield and separation that can be applied for the extraction of bioactive compounds from various medicinal-value plants. Full article
(This article belongs to the Special Issue Biotechnology for Sustainability and Social Well Being)
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<p>Schematic diagram illustrating the apparatus set-up of liquid biphasic flotation (LBF) system for protein extraction. 1: Air pump; 2: flowmeter; 3: sintered disk; 4: LBF column; 5: top alcohol phase; 6: bottom salt phase.</p>
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<p>Effect of different conditions on the protein recovery and separation efficiency: (<b>a</b>) Effect of the alcohol type, (<b>b</b>) Effect of the types of salt, (<b>c</b>) Effect of the ethanol concentration, and (<b>d</b>) Effect of the ammonium salt concentration.</p>
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<p>Effect of the kesum biomass amount on the protein recovery yield and separation efficiency.</p>
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<p>Effect of pH on the protein recovery yield and separation efficiency.</p>
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<p>Effect of the the flotation time on the protein recovery yield and separation efficiency.</p>
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22 pages, 7815 KiB  
Article
Synthesis and Characterization of New Schiff Base/Thiol-Functionalized Mesoporous Silica: An Efficient Sorbent for the Removal of Pb(II) from Aqueous Solutions
by Moawia O. Ahmed, Ameen Shrpip and Muhammad Mansoor
Processes 2020, 8(2), 246; https://doi.org/10.3390/pr8020246 - 21 Feb 2020
Cited by 12 | Viewed by 5000
Abstract
A new type of silica hybrid material functionalized with Schiff base-propyl-thiol and propyl-thiol groups (adsorbents 1 and 2, respectively) was synthesized using a co-condensation method. The synthesized materials and their starting materials were successfully characterized using a variety of techniques such as [...] Read more.
A new type of silica hybrid material functionalized with Schiff base-propyl-thiol and propyl-thiol groups (adsorbents 1 and 2, respectively) was synthesized using a co-condensation method. The synthesized materials and their starting materials were successfully characterized using a variety of techniques such as Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD), nitrogen adsorption–desorption isotherms, the Brunauer–Emmett–Teller (BET) surface area calculation method, the Barrett, Joyner, and Halenda (BJH) pore size calculation method, thermogravimetry analysis (TGA), and 1H and 13C nuclear magnetic resonance (NMR) spectra. The results indicate that the new material (adsorbent 1) has a large surface and possesses different functional groups (-SH, -OH, -C=O and –N=C). The newly synthesized hybrid materials (1 and 2) were investigated as potential adsorbents for removal of toxic heavy metals, such as Pb(II) from aqueous solutions. The adsorption results show that materials 1 and 2 have different sorption properties and were found to be effective adsorbents for Pb(II) removal from aqueous solutions. In addition, compound 1 exhibited a higher adsorption capacity for Pb(II) compared to compound 2. The results showed that the optimum pH for Pb(II) sorption was 6.5. Contact time was observed to occur after 30 min for 25 mg L−1 Pb(II) concentration and adsorbent dose of 0.4 g L−1 at 25 °C. Full article
(This article belongs to the Special Issue Gas, Water and Solid Waste Treatment Technology)
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<p>FTIR spectra of (a) free Schiff base and (b) adsorbent <b>1</b>.</p>
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<p>FTIR absorption spectrum of adsorbent <b>1</b>, (a) before absorption and (b) after adsorption.</p>
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<p>FTIR absorption spectrum of adsorbent (2), (a) before absorption and (b) after adsorption.</p>
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<p>The X-ray diffraction (XRD) patterns of (a) adsorbent <b>1</b> and (b) adsorbent <b>2</b>.</p>
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<p>Scanning electron microscopy (SEM) images of pure adsorbent <b>1</b> before adsorption.</p>
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<p>Energy-dispersive X-ray spectroscopy (EDX) spectrum of adsorbent <b>1</b> before adsorption.</p>
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<p>SEM images of adsorbent <b>1</b> after adsorption.</p>
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<p>(EDX) spectrum of adsorbent <b>1</b> after adsorption.</p>
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<p>Field emission scanning electron microscopy (FESEM) images of adsorbent <b>2</b> before adsorption.</p>
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<p>EDX images of adsorbent <b>2</b> (inset is the EDX data of adsorbent <b>1</b> before adsorption).</p>
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<p>SEM images of adsorbent <b>2</b> after adsorption.</p>
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<p>EDX spectrum of adsorbent <b>2</b> after adsorption.</p>
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<p>Thermogravimetric (TGA) curve of (a) adsorbent <b>1</b> and (b) adsorbent <b>2</b>.</p>
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<p>Effects of pH on the adsorption of Pb(II) (mean ± RSD) by adsorbent <b>1</b> and adsorbent <b>2</b>. (Experimental conditions: C<sub>o</sub> = 25.52 mg/L, dosage = 0.01 g per 50 mL, shaking time 2 h, mixing rate = 300 rpm; T = 25 °C). Each pH measurement was done in triplicate.</p>
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<p>Kinetic adsorption of Pb(II) on adsorbent <b>1</b> (a) and adsorbent <b>2</b> (b) as a function of contact time (pH 6.5 ± 0.2, initial metal concentration = 20 mg/L, dosage = 50 mg/50 mL, mixing rate = 50 rpm, T = 25 °C).</p>
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<p>Lagergren pseudo-second-order kinetics adsorption for Pb(II) onto adsorbents <b>1</b> and <b>2</b> (Experimental conditions: pH 6.5 ± 0.2, initial metal concentration = 20 mg/L, dosage = 50 mg/50 mL, mixing rate = 50 rpm, T = 25 °C).</p>
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<p>Effect of the temperature on Pb (II) ion adsorbtion (mean ± RSD) on adsorbent <b>1</b> and adsorbent <b>2</b>. (pH 6.5 ± 0.2, initial metal concentration = 20 mg/L Pb(II), dosage = 50 mg/50 mL, at different temperatures for 60 min). Each measurement has been done in triplicate.</p>
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<p>The Langmuir Isotherm Model adsorption for Pb(II) onto (<b>A</b>) adsorbent <b>1</b> and (<b>B</b>) adsorbent <b>2</b>. (Experimental conditions: dosage = 0.015 g (adsorbent <b>1</b>) and 0.1 (adsorbent <b>2</b>) per 50 mL; T = 25 ± 1 °C; contact time = 60 min.; pH = 6.5 ± for Pb(II).</p>
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<p>The Freundlich Isotherm Model adsorption for Pb(II) onto (<b>C</b>) adsorbent <b>1</b> and (<b>D</b>) adsorbent <b>2</b>. (Experimental conditions: dosage = 0.015 g (adsorbent <b>1</b>) and 0.1 (adsorbent <b>2</b>) per 50 mL; T = 25 ± 1 °C; contact time = 60 min.; pH = 6.5 ± for Pb(II).</p>
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<p>Pb(II) ion adsorbent <b>1</b> complex formation.</p>
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<p>Schiff base synthesis.</p>
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<p>The synthesis of adsorbent 1.</p>
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<p>Preparation of adsorbent <b>2</b>.</p>
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22 pages, 4465 KiB  
Article
A Three-Stage Coordinated Optimization Scheduling Strategy for a CCHP Microgrid Energy Management System
by Yan Xu, Zhao Luo, Zhendong Zhu, Zhiyuan Zhang, Jinghui Qin, Hao Wang, Zeyong Gao and Zhichao Yang
Processes 2020, 8(2), 245; https://doi.org/10.3390/pr8020245 - 21 Feb 2020
Cited by 17 | Viewed by 3531
Abstract
With renewable generation resources and multiple load demands increasing, the combined cooling, heating, and power (CCHP) microgrid energy management system has attracted much attention due to its high efficiency and low emissions. In order to realize the integration of substation resources and solve [...] Read more.
With renewable generation resources and multiple load demands increasing, the combined cooling, heating, and power (CCHP) microgrid energy management system has attracted much attention due to its high efficiency and low emissions. In order to realize the integration of substation resources and solve the problems of inaccurate, random, volatile and intermittent load forecasting, we propose a three-stage coordinated optimization scheduling strategy for a CCHP microgrid. The strategy contains three stages: a day-ahead economic scheduling stage, an intraday rolling optimization stage, and a real-time adjustment stage. Forecasting data with different accuracy at different time scales were used to carry out multilevel coordination and gradually improve the scheduling plan. A case study was used to verify that the proposed scheduling strategy can mitigate and eliminate the load forecasting error of renewable energy (for power balance and scheduling economy). Full article
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<p>A structural diagram of a CCHP (combined cooling, heating, and power) microgrid.</p>
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<p>The relationship between the three scheduling stages.</p>
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<p>Rolling optimization time window.</p>
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<p>Three-stage optimization energy management structure.</p>
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<p>Data of the system. (<b>a</b>) System cold load. (<b>b</b>) System thermal load. (<b>c</b>) System electrical output. (<b>d</b>) System photovoltaic output. (<b>e</b>) System wind turbine output. (<b>f</b>) System time-of-use electricity price.</p>
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<p>Electric power output of the microgas turbine at multiple time scales.</p>
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<p>Battery charge and discharge power at multiple time scales.</p>
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<p>Interactions with the power grid at multiple time scales.</p>
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<p>Refrigeration power of the electric refrigerator at multiple time scales.</p>
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<p>Refrigeration power of the adsorption refrigerator at multiple time scales.</p>
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<p>Heat absorption power of heat exchangers at multiple time scales.</p>
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21 pages, 6709 KiB  
Article
Improvement of Small Wind Turbine Control in the Transition Region
by Mario L. Ruz, Juan Garrido, Sergio Fragoso and Francisco Vazquez
Processes 2020, 8(2), 244; https://doi.org/10.3390/pr8020244 - 21 Feb 2020
Cited by 7 | Viewed by 2856
Abstract
Wind energy conversion systems are very challenging from the control system viewpoint. The control difficulties are even more challenging when wind turbines are able to operate at variable speed and variable pitch. The contribution of this work is focused on designing a combined [...] Read more.
Wind energy conversion systems are very challenging from the control system viewpoint. The control difficulties are even more challenging when wind turbines are able to operate at variable speed and variable pitch. The contribution of this work is focused on designing a combined controller that significantly alleviates the wind transient loads in the power tracking and power regulation modes as well as in the transition zone. In a previous work, the authors studied the applicability of different multivariable decoupling methodologies. The methodologies were tested in simulation and verified experimentally in a lab-scale wind turbine. It was demonstrated that multivariable control strategies achieve a good closed-loop response within the transition region, where the interaction level is greater. Nevertheless, although such controllers showed an acceptable performance in the power tracking (region II) and power regulation (region IV) zones, appreciable improvement was possible. To this end, the new proposed methodology employs a multivariable gain-scheduling controller with a static decoupling network for the transition region and monovariable controllers for the power tracking and power regulation regions. To make the transition between regions smoother, a gain scheduling block is incorporated into the multivariable controller. The proposed controller is experimentally compared with a standard switched controller in the lab-scale wind turbine. The experiments carried out suggest that the combination of the proposed multivariable strategy for the transition region to mitigate wind transient loads combined with two monovariable controllers, one dedicated to region II and other to region IV, provide better results than traditional switched control strategies. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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<p>Wind turbine operation modes.</p>
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<p>Multivariable control scheme.</p>
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<p>Lab-scale small wind turbine.</p>
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<p>Polynomial fitting of the power coefficient <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mrow> <mi>λ</mi> <mo>,</mo> <mi>β</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> in four cases of constant pitch angle.</p>
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<p>Experimental power coefficient <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mrow> <mi>λ</mi> <mo>,</mo> <mi>β</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> surface.</p>
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<p>Open-loop step responses of the experimental data (solid line) and the identified model (dashed-dotted line) in the transition zone (wind speed of 8 m/s).</p>
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<p>General structure of a gain-scheduling controller.</p>
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<p>Variables values for each operational point (<b>blue</b>) and the corresponding polynomial regression (<b>red</b>).</p>
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<p>Decentralized multivariable controller with static simplified decoupling network and gain scheduling.</p>
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<p>Experiment 1: ascending wind profile and control switch.</p>
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<p>Evolution of the tip speed ratio and <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>P</mi> </msub> </mrow> </semantics></math> in experiment 1.</p>
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<p>Experimental test 2: ascending wind profile and classic switched control.</p>
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<p>Tip speed ratio and power coefficient for test 2.</p>
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<p>Test 3: ascending wind profile and combined control.</p>
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<p>Experiment 4: proposed combined control strategy.</p>
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<p>Experiment 5: standard switched control test.</p>
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<p>Tip speed ratio and power coefficient for experiment 4: proposed combined strategy.</p>
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<p>Tip speed ratio and power coefficient for experiment 5: standard switched strategy.</p>
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20 pages, 579 KiB  
Review
Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data
by Iftikhar Ahmad, Ahsan Ayub, Manabu Kano and Izzat Iqbal Cheema
Processes 2020, 8(2), 243; https://doi.org/10.3390/pr8020243 - 20 Feb 2020
Cited by 38 | Viewed by 6286
Abstract
Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge [...] Read more.
Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated. Full article
(This article belongs to the Special Issue Advanced Methods in Process and Systems Engineering)
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<p>Generalized framework grey-box (GB) modeling [<a href="#B34-processes-08-00243" class="html-bibr">34</a>].</p>
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<p>GB models in process industries in term of percentage share; (<b>a</b>) collective, (<b>b</b>) miscellaneous.</p>
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<p>Percentage share of types of GB sensors, i.e., parallel, serial and combined.</p>
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<p>Percentage share of black-box (BB) methods used in the GB design.</p>
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<p>Percentage in terms of types of application.</p>
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13 pages, 2609 KiB  
Article
Isolated Taylor Bubbles in Co-Current with Shear Thinning CMC Solutions in Microchannels—A Numerical Study
by Ana I. Moreira, Luís A. M. Rocha, João Carneiro, José D. P. Araújo, João B. L. M. Campos and João M. Miranda
Processes 2020, 8(2), 242; https://doi.org/10.3390/pr8020242 - 20 Feb 2020
Cited by 10 | Viewed by 4026
Abstract
Slug flow is a multiphase flow pattern characterized by the occurrence of long gas bubbles (Taylor bubbles) separated by liquid slugs. This multiphase flow regime is present in many and diversified natural and industrial processes, at macro and microscales, such as in eruption [...] Read more.
Slug flow is a multiphase flow pattern characterized by the occurrence of long gas bubbles (Taylor bubbles) separated by liquid slugs. This multiphase flow regime is present in many and diversified natural and industrial processes, at macro and microscales, such as in eruption of volcanic magmas, oil recovery from pre-salt regions, micro heat exchangers, and small-sized refrigerating systems. Previous studies in the literature have been mostly focused on tubular gas bubbles flowing in Newtonian liquids. In this work, results from several numerical simulations of tubular gas bubbles flowing in a shear thinning liquid in microchannels are reported. To simulate the shear thinning behavior, carboxymethylcellulose (CMC) solutions with different concentrations were considered. The results are compared with data from bubbles flowing in Newtonian liquids in identical geometric and dynamic conditions. The numerical work was carried out in computational fluid dynamics (CFD) package Ansys Fluent (release 16.2.0) employing the volume of fluid (VOF) methodology to track the volume fraction of each phase and the continuum surface force (CSF) model to insert the surface tension effects. The flow patterns, the viscosity distribution in the liquid, the liquid film thickness between the bubble and the wall, and the bubbles shape are analyzed for a wide range of shear rates. In general, the flow patterns are similar to those in Newtonian liquids, but in the film, where a high viscosity region is observed, the thickness is smaller. Bubble velocities are smaller for the non-Newtonian cases. Full article
(This article belongs to the Special Issue Flow, Heat and Mass Transport in Microdevices)
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<p>Domain of the simulations in a microchannel with a diameter of 100 μm.</p>
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<p>Viscosity of CMC solutions as a function of the shear rate, showing the main constrains to the study. Viscosity curves were calculated using Equation (20).</p>
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<p>Numerical results of the velocity vectors and streamlines for systems with bubbles flowing through a 0.1% CMC solution (<b>left</b>) and a 0.5% CMC solution (<b>right</b>). The domain under consideration has a <span class="html-italic">D</span> = 100 μm.</p>
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<p>Viscosity fields along the characteristic viscosity curve of a 0.5% CMC solution obtained for microchannels with a 100 μm and 1000 μm diameter. The viscosity curve was calculated using Equation (20).</p>
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<p>Bubble velocity normalized by the liquid velocity as a function of the liquid capillary number. The numerical results are for bubbles flowing in CMC solutions (0.1% and 0.5%) and in a Newtonian liquid with a viscosity corresponding to the characteristic shear rate. The theoretical predictions were based on the Liu et al. [<a href="#B37-processes-08-00242" class="html-bibr">37</a>] correlation. The presented numerical data concerns the systems with a channel of <span class="html-italic">D</span> = 100 μm.</p>
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<p>Bubble velocity normalized by the liquid velocity as a function of the bubble capillary number. The numerical results are for bubbles flowing in CMC solutions (0.1% and 0.5%) and in a Newtonian liquid with a viscosity corresponding to the characteristic shear rate. The theoretical predictions are based on the stagnant film model and on the Abiev and Lavretsov [<a href="#B38-processes-08-00242" class="html-bibr">38</a>] correlation. The numerical data concerns the systems with a channel of <span class="html-italic">D</span> = 100 μm.</p>
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<p>Normalized film thickness as a function of the bubble capillary number. Numerical results are for bubbles flowing in CMC solutions (0.1% and 0.5%) and Newtonian liquid with a viscosity corresponding to the characteristic shear rate. The theoretical predictions are based on the Han and Shikazono [<a href="#B36-processes-08-00242" class="html-bibr">36</a>] correlation. The numerical data concerns the systems with a channel of <span class="html-italic">D</span> = 100 μm.</p>
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29 pages, 13115 KiB  
Article
Influence of Soil Particle Size on the Temperature Field and Energy Consumption of Injected Steam Soil Disinfection
by Zhenjie Yang, Adnan Abbas, Xiaochan Wang, Muhammad Ameen, Haihui Yang and Shakeel Ahmed Soomro
Processes 2020, 8(2), 241; https://doi.org/10.3390/pr8020241 - 20 Feb 2020
Cited by 7 | Viewed by 4157
Abstract
Soil steam disinfection (SSD) technology is an effective means of eliminating soil borne diseases. Among the soil cultivation conditions of facility agriculture in the Yangtze River Delta region of China, the clay soil particles (SPs) are fine, the soil pores are small, and [...] Read more.
Soil steam disinfection (SSD) technology is an effective means of eliminating soil borne diseases. Among the soil cultivation conditions of facility agriculture in the Yangtze River Delta region of China, the clay soil particles (SPs) are fine, the soil pores are small, and the texture is relatively viscous. When injection disinfection technology is applied in the clay soil, the diffusion of steam is hindered and the heating efficiency is substantially affected. To increase the heating efficiency of SSD, we first discretized the continuum model of Philip and De Vries into circular particle porous media of different sizes and random distribution. Then with Computational Fluid Dynamics (CFD) numerical simulation technology, a single-injection steam disinfection model for different SP size conditions was constructed. Furthermore, the diffusion pattern of the macro-porous vapor flow and matrix flow and the corresponding temperature field were simulated and analyzed. Finally, a single-pipe injection steam disinfection verification test was performed for different SP sizes. The test results show that for the clay soil in the Yangtze River Delta region of China, the test temperature filed results are consistent with the simulation results when the heat flow reaches H = 20 cm in the vertical direction, the simulation and test result of the heat flow in the maximum horizontal diffusion distance are L = 13 cm and 12 cm, respectively. At the same disinfection time, the simulated soil temperature change trend is consistent with the test results, and the test temperature is lower than the simulated temperature. The difference between the theoretical temperature and the experimental temperature may be attributed to the heat loss in the experimental device. Further, it is necessary to optimize the CFD simulation process and add the disintegration and deformation processes of soil particle size with the change of water content. Furthermore, the soil pores increase as the SP size increases and that a large amount of steam vertically diffuses along the macropores and accumulates on the soil surface, causing ineffective heat loss. Moreover, soil temperature distribution changes from oval (horizontal short radius/vertical long radius = 0.65) to irregular shape. As the SP size decreases, the soil pore flow path becomes fine; the steam primarily diffuses uniformly around the soil in the form of a matrix flow; the diffusion distance in the horizontal direction gradually increases; and the temperature distribution gradually becomes even, which is consistent with the soil temperature field simulation results. Similar to the energy consumption analysis, the maximum energy consumption for SP sizes>27mm and <2mm was 486and 477kJ, respectively. Therefore, proper pore growth was conducive to the diffusion of steam, but excessive pores cause steam to overflow, which increased energy consumption of the system. Considering that the test was carried out in an ideal soil environment, the rotary tiller must be increased for fine rotary tillage in an actual disinfection operation. Although large particles may appear during the rotary tillage process, an appropriate number of large particles contributes to the formation of a large pore flow, under the common effect of matrix flow, it will simultaneously promote greater steam diffusion and heating efficiency. The above theoretical research has practical guiding significance for improving the design and disinfection effect of soil steam sterilizers in the future. Full article
(This article belongs to the Special Issue CFD Applications in Energy Engineering Research and Simulation)
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<p>Schematic of steam heat transfer in large pores. (<b>a</b>) soil pore diagram; (<b>b</b>) heat transfer zone. Note: Point O is the heat source point of the steam, and L and H represents the horizontal and vertical diffusion distance of the steam heat flow.</p>
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<p>Soil pattern. (<b>a</b>) &lt;2 mm; (<b>b</b>) 2–7 mm; (<b>c</b>) 7–17 mm; (<b>d</b>) 17–27 mm; and (<b>e</b>) &gt;27 mm.</p>
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<p>Two-dimensional vertical profile of soil pore. (<b>a</b>) &lt;2 mm; (<b>b</b>) 2–7 mm; (<b>c</b>) 7–17 mm; (<b>d</b>) 17–27 mm; and (<b>e</b>) &gt;27 mm. Note: The black parts represent soil particles (SPs) or clods, and the white parts represent pores.</p>
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<p>Steam disinfection model. (<b>a</b>) &lt;2 mm model; (<b>b</b>)&lt;2 mm meshing; (<b>c</b>) 2–7 mm model; (<b>d</b>)2–7 mm meshing; (<b>e</b>) 7–17 mm model; (<b>f</b>) 7–17 mm meshing; (<b>g</b>) 17–27 mm model; (<b>h</b>) 17–27 mm meshing; (<b>i</b>) &gt;27 mm model; and (<b>j</b>) &gt;27 mm meshing. Note: The meshing figures are partially enlarged views.</p>
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<p>Steam disinfection model. (<b>a</b>) &lt;2 mm model; (<b>b</b>)&lt;2 mm meshing; (<b>c</b>) 2–7 mm model; (<b>d</b>)2–7 mm meshing; (<b>e</b>) 7–17 mm model; (<b>f</b>) 7–17 mm meshing; (<b>g</b>) 17–27 mm model; (<b>h</b>) 17–27 mm meshing; (<b>i</b>) &gt;27 mm model; and (<b>j</b>) &gt;27 mm meshing. Note: The meshing figures are partially enlarged views.</p>
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<p>Soil temperature simulation results: (<b>a</b>) &lt;2 mm, t = 300 s; (<b>b</b>) &lt;2 mm, t = 460s; (<b>c</b>) 2–7 mm model; (<b>d</b>) 2–7 mm meshing; (<b>e</b>) 7–17 mm model; (<b>f</b>) 7–17 mm meshing; (<b>g</b>) 17–27 mm model; (<b>h</b>) 17–27 mm meshing; (<b>i</b>) &gt;27 mm model; and (<b>j</b>) &gt;27 mm meshing. Note: The legend indicates the ST/°C.</p>
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<p>Soil temperature simulation results: (<b>a</b>) &lt;2 mm, t = 300 s; (<b>b</b>) &lt;2 mm, t = 460s; (<b>c</b>) 2–7 mm model; (<b>d</b>) 2–7 mm meshing; (<b>e</b>) 7–17 mm model; (<b>f</b>) 7–17 mm meshing; (<b>g</b>) 17–27 mm model; (<b>h</b>) 17–27 mm meshing; (<b>i</b>) &gt;27 mm model; and (<b>j</b>) &gt;27 mm meshing. Note: The legend indicates the ST/°C.</p>
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<p>Temperature variation of different soil particle sizes.</p>
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<p>Soil steam disinfection (SSD) test bench: 1. steam disinfection pipe; 2. soil trough; 3. steam transport pipe; 4. ball valve switch; 5. boiler pressure controller; 6. boiler; 7. control box screen; 8. soil temperature, water content control box; 9. Soil water content sensor; 10. Soil temperature sensor. (<b>a</b>) components of the SSD test bench; (<b>b</b>) SSD test.</p>
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<p>ST-SWC test layout. Note: The large circle represents the soil water content(SWC) sensor position, and the small circles represent the soil temperature (ST) sensor position.</p>
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<p>Mean <span class="html-italic">ST</span> with time. (<b>a</b>) Soil temperature-time change under different flow and particle size test conditions; (<b>b</b>) Soil temperature-time simulation and experimental comparison of different particle size conditions at a flow rate of 2kg/h. Note: 2 kg/h-2 mm means that the steam flow of the treatment group is 2 kg/h, and the particle size of the soil is &lt;2 mm. The same conditions apply in the following section.</p>
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<p>Variation in mean <span class="html-italic">ST</span> rise rate and disinfection time. (<b>a</b>) 2 kg/h; (<b>b</b>) 3 kg/h; and (<b>c</b>) 4 kg/h.</p>
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<p>Variation in the mean <span class="html-italic">ST</span> variation coefficient with disinfection time. (<b>a</b>) 2 kg/h; (<b>b</b>) 3 kg/h; and; (<b>c</b>)4kg/h.</p>
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<p>Variation diagram of <span class="html-italic">ST</span> and disinfection time in each layer: (<b>a</b>) &lt;2 mm; (<b>b</b>) 7–17 mm; (<b>c</b>) 17–27 mm; (<b>d</b>) &gt;27 mm. Note: A-20 indicates that A is the test point, and 20 is the test depth (−20cm). The same conditions apply in the following section.</p>
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<p>Variation diagram of <span class="html-italic">ST</span> and disinfection time in each layer: (<b>a</b>) &lt;2 mm; (<b>b</b>) 7–17 mm; (<b>c</b>) 17–27 mm; (<b>d</b>) &gt;27 mm. Note: A-20 indicates that A is the test point, and 20 is the test depth (−20cm). The same conditions apply in the following section.</p>
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<p>ST distribution: (<b>a</b>) &lt;2 mm; (<b>b</b>) 7–17 mm; (<b>c</b>) 17–27 mm; (<b>d</b>) &gt;27 mm. Note: The legend indicates the ST/°C and 60 s means that the disinfection time (<span class="html-italic">t</span>) is 60 s, 360 s means the disinfection time is 360 s, etc. The same conditions apply in the following section.</p>
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<p>ST distribution: (<b>a</b>) &lt;2 mm; (<b>b</b>) 7–17 mm; (<b>c</b>) 17–27 mm; (<b>d</b>) &gt;27 mm. Note: The legend indicates the ST/°C and 60 s means that the disinfection time (<span class="html-italic">t</span>) is 60 s, 360 s means the disinfection time is 360 s, etc. The same conditions apply in the following section.</p>
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<p>SWC distribution. (<b>a</b>) &lt;2 mm; (<b>b</b>) 7–17 mm; (<b>c</b>) 17–27 mm; (<b>d</b>) &gt;27 mm. Note: The legend is in SWC, %.</p>
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<p>SWC distribution. (<b>a</b>) &lt;2 mm; (<b>b</b>) 7–17 mm; (<b>c</b>) 17–27 mm; (<b>d</b>) &gt;27 mm. Note: The legend is in SWC, %.</p>
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<p>SWC distribution. (<b>a</b>) &lt;2 mm; (<b>b</b>) 7–17 mm; (<b>c</b>) 17–27 mm; (<b>d</b>) &gt;27 mm. Note: The legend is in SWC, %.</p>
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18 pages, 924 KiB  
Article
Hybrid Drying of Murraya koenigii Leaves: Energy Consumption, Antioxidant Capacity, Profiling of Volatile Compounds and Quality Studies
by Choong Oon Choo, Bee Lin Chua, Adam Figiel, Klaudiusz Jałoszyński, Aneta Wojdyło, Antoni Szumny, Jacek Łyczko and Chien Hwa Chong
Processes 2020, 8(2), 240; https://doi.org/10.3390/pr8020240 - 20 Feb 2020
Cited by 24 | Viewed by 4826
Abstract
This study aims to reduce the amount of specific energy consumed during the drying of fresh Murraya koenigii leaves by comparing four drying methods: (1) convective hot-air drying (CD; 40, 50 and 60 °C); (2) single-stage microwave-vacuum drying (MVD; 6, 9 and 12 [...] Read more.
This study aims to reduce the amount of specific energy consumed during the drying of fresh Murraya koenigii leaves by comparing four drying methods: (1) convective hot-air drying (CD; 40, 50 and 60 °C); (2) single-stage microwave-vacuum drying (MVD; 6, 9 and 12 W/g); (3) two-stage convective hot-air pre-drying followed by microwave-vacuum finishing–drying (CPD-MVFD; 50 °C, 9 W/g); and (4) freeze-drying as a control in the analysis sections. The drying kinetics were also modelled using thin-layer models. The quality parameters of dried M. koenigii leaves were measured including total polyphenolic content (TPC), antioxidant capacity (ABTS and FRAP), profiling of volatile compounds, colour analysis and water activity analysis. Results showed that CPD-MVFD effectively reduced the specific energy consumption of CD at 50 °C by 67.3% in terms of kilojoules per gram of fresh weight and 48.9% in terms of kilojoules per gram of water. The modified Page model demonstrated excellent fitting to the empirical data obtained. FD showed promising antioxidant activity. The major contributor of antioxidant capacity was TPC. The volatile compounds profiled by gas chromatography-mass spectrometry, namely, β-phellandrene (31%), α-pinene (19.9%), and sabinene (16%) were identified as the major compounds of dried M. koenigii leaves. Colour analysis showed MVD’s high performance in preserving the colour parameters of M. koenigii leaves under all conditions. The colour parameters were correlated to the antioxidant capacity and TPC. Water activity analysis showed that the water activity of M. koenigii leaves for all drying methods indicating that the conditions were microbiologically and shelf-stable. Pearson correlation showed the colour parameters of the leaves had a strong correlation to TPC. Overall, MVD showed promising energy consumption reduction and recovery in TPC and volatile compounds. Full article
(This article belongs to the Special Issue Microwave Conversion Techniques Intensification)
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<p>Specific energy consumption of <span class="html-italic">M. koenigii</span> (<b>a</b>) in kJ/g fw and in (<b>b</b>) kJ/g water for convective hot air drying (CD), microwave vacuum drying (MVD) and microwave vacuum finishing-drying (MVFD) applied after CPD during CPD-MVFD.</p>
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<p>Specific energy consumption of <span class="html-italic">M. koenigii</span> (<b>a</b>) in kJ/g fw and in (<b>b</b>) kJ/g water for convective hot air drying (CD), microwave vacuum drying (MVD) and microwave vacuum finishing-drying (MVFD) applied after CPD during CPD-MVFD.</p>
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<p>Effect of the FD, CD, MVD, and CPD-MVFD on the antioxidant capacity (<b>a</b>) and total polyphenolic content (<b>b</b>) of dried <span class="html-italic">M. koenigii</span> leaves. (<b>a</b>): The same letters next to the bars indicate no significant differences in mean values at the level of 5%, according to Tukey’s significant difference test; (<b>b</b>): The same letters next to the bars indicate no significant differences in mean values at the level of 5%, according to Tukey’s significant difference test.</p>
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18 pages, 7279 KiB  
Article
A Mathematical Model Combined with Radar Data for Bell-Less Charging of a Blast Furnace
by Meng Li, Han Wei, Yao Ge, Guocai Xiao and Yaowei Yu
Processes 2020, 8(2), 239; https://doi.org/10.3390/pr8020239 - 20 Feb 2020
Cited by 11 | Viewed by 4402
Abstract
Charging directly affects the burden distribution of a blast furnace, which determines the gas distribution in the shaft of the furnace. Adjusting the charging can improve the distribution of the gas flow, increase the gas utilization efficiency of the furnace, reduce energy consumption, [...] Read more.
Charging directly affects the burden distribution of a blast furnace, which determines the gas distribution in the shaft of the furnace. Adjusting the charging can improve the distribution of the gas flow, increase the gas utilization efficiency of the furnace, reduce energy consumption, and prolong the life of the blast furnace. In this paper, a mathematical model of blast furnace charging was developed and applied on a steel plant in China, which includes the display of the burden profile, burden layers, descent speed of the layers, and ore/coke ratio. Furthermore, the mathematical model is developed to combine the radar data of the burden profile. The above model is currently used in Nanjing Steel as a reference for operators to adjust the charging. The model is being tested with a radar system on the blast furnace. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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<p>Mathematical model structure.</p>
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<p>Trajectory of the burden flow passing through the bell-less top.</p>
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<p>Schematic diagrams of the applied forces on the particle flow along the chute.</p>
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<p>The trajectory of the material in the cavity, where <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>0</mn> </msub> </mrow> </semantics></math> is the distance from the chute suspension point to the zero value stock of the line (m), <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>1</mn> </msub> </mrow> </semantics></math> is the distance from the chute suspension point to the end of the chute (m), <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>2</mn> </msub> </mrow> </semantics></math> is the vertical distance of the material after leaving the chute tip (m), and <math display="inline"><semantics> <mi>H</mi> </semantics></math> is distance between the material and zero value of the stock line (m).</p>
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<p>Schematic diagram of a new surface formation on an original one, where <math display="inline"><semantics> <mi mathvariant="sans-serif">δ</mi> </semantics></math> is the repose angle of the burden. (<b>a</b>) the location of the inner foot B is the intersection of the lower trajectory with the previous burden profile; (<b>b</b>) the material keeps the repose angle slipping on the inner surface of the profile.</p>
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<p>Burden volume partition integral.</p>
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<p>Schematic diagram of the radar installation in the top of the blast furnace.</p>
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<p>Procedure of the combination of the mathematical model and radar data in a software implementation.</p>
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<p>Radar data structure in the database.</p>
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<p>Radar data calculation of the burden profile function.</p>
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<p>Method of the burden descent velocity from radar data.</p>
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<p>Burden distribution with layer by layer.</p>
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<p>Main trajectories of coke flow.</p>
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<p>Burden descent velocity from the mathematical model.</p>
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<p>Burden distribution by a multi-ring charging program based on <a href="#processes-08-00239-t001" class="html-table">Table 1</a>, <a href="#processes-08-00239-t002" class="html-table">Table 2</a>, <a href="#processes-08-00239-t003" class="html-table">Table 3</a> and <a href="#processes-08-00239-t004" class="html-table">Table 4</a>: (<b>a</b>) 1st ring of ore; (<b>b</b>) a full burden distribution of an ore and a coke layer with multi-ring; (<b>c</b>) 2 coke and 2 ore layers. Blue line: initial material surface. Blue dash-dotted line: wall. Green line: ore layer. Red line: coke layer.</p>
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<p>(<b>a</b>–<b>h</b>) are the comparison of burden distributions corresponding to the charging matrix of (<b>a</b>–<b>h</b>) in <a href="#processes-08-00239-t005" class="html-table">Table 5</a>.</p>
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<p>Comparison of the burden distribution with different ore batches: (<b>a</b>) Ore batch of 63 t; (<b>b</b>) Ore batch of 55 t.</p>
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<p>(<b>a</b>) Burden distribution; (<b>b</b>) ratio of ore to coke from (<b>a</b>) case.</p>
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<p>Burden profile: (<b>a</b>) Burden profile from radar data; (<b>b</b>) Burden profile from the mathematical model combined with radar data.</p>
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<p>Visualization of our software by the combination of the mathematical model and radar data: (<b>a</b>) Software login interface; (<b>b</b>) Parameter setting interface; (<b>c</b>) Burden distribution display and radar data monitor; (<b>d</b>) User management interface.</p>
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9 pages, 1225 KiB  
Communication
Structural Optimization of Alkylbenzenes as Graphene Dispersants
by Shimpei Takeda and Yuta Nishina
Processes 2020, 8(2), 238; https://doi.org/10.3390/pr8020238 - 19 Feb 2020
Cited by 3 | Viewed by 3327
Abstract
Among the several methods of producing graphene, the liquid-phase exfoliation of graphite is attractive because of a simple and easy procedure, being expected for mass production. The dispersibility of graphene can be improved by adding a dispersant molecule that interacts with graphene, but [...] Read more.
Among the several methods of producing graphene, the liquid-phase exfoliation of graphite is attractive because of a simple and easy procedure, being expected for mass production. The dispersibility of graphene can be improved by adding a dispersant molecule that interacts with graphene, but the appropriate molecular design has not been proposed. In this study, we focused on aromatic compounds with alkyl chains as dispersing agents. We synthesized a series of alkyl aromatic compounds and evaluated their performance as a dispersant for graphene. The results suggest that the alkyl chain length and solubility in the solvent play a vital role in graphene dispersion. Full article
(This article belongs to the Section Materials Processes)
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<p>SEM images of graphene dispersed by (<b>a</b>) octylbenzene and (<b>b</b>) 1,4-dioctylbenzene, and AFM images of graphene dispersed by (<b>c</b>) octylbenzene and (<b>d</b>) 1,4-dioctylbenzene.</p>
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<p>Raman spectra of (<b>a</b>) graphite and graphene dispersed by (<b>b</b>) octylbenzene and (<b>c</b>) 1,4-dioctylbenzene.</p>
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21 pages, 7407 KiB  
Article
Numerical Simulation of a Flow Field in a Turbo Air Classifier and Optimization of the Process Parameters
by Yun Zeng, Si Zhang, Yang Zhou and Meiqiu Li
Processes 2020, 8(2), 237; https://doi.org/10.3390/pr8020237 - 19 Feb 2020
Cited by 25 | Viewed by 5310
Abstract
Due to the rapid development of powder technology around the world, powder materials are being widely used in various fields, including metallurgy, the chemical industry, and petroleum. The turbo air classifier, as a powder production equipment, is one of the most important mechanical [...] Read more.
Due to the rapid development of powder technology around the world, powder materials are being widely used in various fields, including metallurgy, the chemical industry, and petroleum. The turbo air classifier, as a powder production equipment, is one of the most important mechanical facilities in the industry today. In order to investigate the production efficiency of ultrafine powder and improve the classification performance in a turbo air classifier, two process parameters were optimized by analyzing the influence of the rotor cage speed and air velocity on the flow field. Numerical simulations using the ANSYS-Fluent Software, as well as material classification experiments, were implemented to verify the optimal process parameters. The simulation results provide many optimal process parameters. Several sets of the optimal process parameters were selected, and the product particle size distribution was used as the inspection index to conduct a material grading experiment. The experimental results demonstrate that the process parameters of the turbo air classifier with better classification efficiency for the products of barite and iron-ore powder were an 1800 rpm rotor cage speed and 8 m/s air inlet velocity. This research study provides theoretical guidance and engineering application value for air classifiers. Full article
(This article belongs to the Special Issue Chemical Process Design, Simulation and Optimization)
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<p>Diagram of the experiment equipment (<b>a</b>) and the 3D view of the geometry (<b>b</b>) and dimensions (<b>c</b>) of the turbo air classifier.</p>
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<p>Particle-influencing forces at the inlet of the rotor cage.</p>
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<p>Contours of the tangential velocity distribution and air tangential velocity contrast under different radial distances from the axis at different process parameters (305–345 mm is the area between the rotor blades, 345–365 mm is the annular zone). (<b>a</b>) Air inlet velocity 6 m/s and 4 different rotor cgae speed. (<b>b</b>) Air inlet velocity 8 m/s and 4 different rotor cgae speed. (<b>c</b>) Air inlet velocity 10 m/s and 4 different rotor cgae speed. (<b>d</b>) Air inlet velocity 12 m/s and 4 different rotor cgae speed.</p>
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<p>Contours of the tangential velocity distribution and air tangential velocity contrast under different radial distances from the axis at different process parameters (305–345 mm is the area between the rotor blades, 345–365 mm is the annular zone). (<b>a</b>) Air inlet velocity 6 m/s and 4 different rotor cgae speed. (<b>b</b>) Air inlet velocity 8 m/s and 4 different rotor cgae speed. (<b>c</b>) Air inlet velocity 10 m/s and 4 different rotor cgae speed. (<b>d</b>) Air inlet velocity 12 m/s and 4 different rotor cgae speed.</p>
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<p>Contours of the air radial velocity distribution between rotor blades and radial velocity contrast under different radial distances from the axis at different process parameters (−345–305mm, 305–345mm is the area between the rotor blades, −305–305 mm is the central region).(<b>a</b>) Air inlet velocity 6 m/s and 4 different rotor cgae speed. (<b>b</b>) Air inlet velocity 8 m/s and 4 different rotor cgae speed. (<b>c</b>) Air inlet velocity 10 m/s and 4 different rotor cgae speed. (<b>d</b>) Air inlet velocity 12 m/s and 4 different rotor cgae speed.</p>
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<p>Contours of the air radial velocity distribution between rotor blades and radial velocity contrast under different radial distances from the axis at different process parameters (−345–305mm, 305–345mm is the area between the rotor blades, −305–305 mm is the central region).(<b>a</b>) Air inlet velocity 6 m/s and 4 different rotor cgae speed. (<b>b</b>) Air inlet velocity 8 m/s and 4 different rotor cgae speed. (<b>c</b>) Air inlet velocity 10 m/s and 4 different rotor cgae speed. (<b>d</b>) Air inlet velocity 12 m/s and 4 different rotor cgae speed.</p>
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<p>Particle tracks for 8 groups of different rotor speeds and air inlet velocity. (<b>a</b>) Rotor speed 1600 rpm, air inlet velocity 6 m/s. (<b>b</b>) Rotor speed 2200 rpm, air inlet velocity 6 m/s. (<b>c</b>) Rotor speed 1800 rpm, air inlet velocity 8 m/s. (<b>d</b>)Rotor speed 2200 rpm, air inlet velocity 8 m/s. (<b>e</b>) Rotor speed 1800 rpm, air inlet velocity 10 m/s. (<b>f</b>) Rotor speed 2000 rpm, air inlet velocity 10 m/s. (<b>g</b>) Rotor speed 2000 rpm, air inlet velocity 12 m/s. (<b>h</b>) Rotor speed 2200 rpm, air inlet velocity 12 m/s.</p>
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<p>Particle tracks for 8 groups of different rotor speeds and air inlet velocity. (<b>a</b>) Rotor speed 1600 rpm, air inlet velocity 6 m/s. (<b>b</b>) Rotor speed 2200 rpm, air inlet velocity 6 m/s. (<b>c</b>) Rotor speed 1800 rpm, air inlet velocity 8 m/s. (<b>d</b>)Rotor speed 2200 rpm, air inlet velocity 8 m/s. (<b>e</b>) Rotor speed 1800 rpm, air inlet velocity 10 m/s. (<b>f</b>) Rotor speed 2000 rpm, air inlet velocity 10 m/s. (<b>g</b>) Rotor speed 2000 rpm, air inlet velocity 12 m/s. (<b>h</b>) Rotor speed 2200 rpm, air inlet velocity 12 m/s.</p>
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<p>Particle tracks for 8 groups of different rotor speeds and air inlet velocity. (<b>a</b>) Rotor speed 1600 rpm, air inlet velocity 6 m/s. (<b>b</b>) Rotor speed 2200 rpm, air inlet velocity 6 m/s. (<b>c</b>) Rotor speed 1800 rpm, air inlet velocity 8 m/s. (<b>d</b>)Rotor speed 2200 rpm, air inlet velocity 8 m/s. (<b>e</b>) Rotor speed 1800 rpm, air inlet velocity 10 m/s. (<b>f</b>) Rotor speed 2000 rpm, air inlet velocity 10 m/s. (<b>g</b>) Rotor speed 2000 rpm, air inlet velocity 12 m/s. (<b>h</b>) Rotor speed 2200 rpm, air inlet velocity 12 m/s.</p>
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<p>The numerical partial classification efficiency curves and cut size of four different process parameters. (<b>a</b>) Iron ore powder numerical Tromp curves (<b>b</b>) Barite powder numerical Tromp curves (<b>c</b>) Iron ore powder numerical cut size (<b>d</b>) Barite powder numerical cut size</p>
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<p>The particle size distribution and the classification efficiency of two different materials. (<b>a</b>) Iron ore powder experiments distribution curves (<b>b</b>) Barite powder experiments distribution curves (<b>c</b>) Iron ore powder experiments Tromp curves (<b>d</b>) Barite powder experiments Tromp curves.</p>
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<p>Effects of four process parameters on two different materials of the coarse powder yield (<span class="html-italic">Y<sub>c</sub></span>) and Newton efficiency (<math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mi>N</mi> </msub> </mrow> </semantics></math>). (<b>a</b>) Iron ore powder (<b>b</b>) Barite powder.</p>
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<p>Effects of four process parameters on the classification sharpness index (K).</p>
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<p>Coarse- and fine-grade product size distribution curve. (<b>a</b>) <span class="html-italic">d</span><sub>10<span class="html-italic">coarse</span></sub> &lt; <span class="html-italic">d</span><sub>90fine.</sub> (<b>b</b>) <span class="html-italic">d</span><sub>10<span class="html-italic">coarse</span></sub> &gt; <span class="html-italic">d</span><sub>90<span class="html-italic">fine</span>.</sub></p>
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<p>Comparison of relative classification accuracies for different process parameters.</p>
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10 pages, 1330 KiB  
Article
Anti-Melanogenesis, Antioxidant and Anti-Tyrosinase Activities of Scabiosa columbaria L.
by Wilfred Otang-Mbeng and Idowu Jonas Sagbo
Processes 2020, 8(2), 236; https://doi.org/10.3390/pr8020236 - 19 Feb 2020
Cited by 24 | Viewed by 6870
Abstract
Scabiosa columbaria is a plant traditionally used to treat skin ailments, such as scabies, wound bruises, sores and hyperpigmentation. To find a novel skin depigmenting agent, the present study was investigated to determine the possible anti-melanogenesis, antioxidant and anti-tyrosinase effects of methanol extract [...] Read more.
Scabiosa columbaria is a plant traditionally used to treat skin ailments, such as scabies, wound bruises, sores and hyperpigmentation. To find a novel skin depigmenting agent, the present study was investigated to determine the possible anti-melanogenesis, antioxidant and anti-tyrosinase effects of methanol extract of S. columbaria leaves. Cytotoxicity towards human dermal fibroblast (MRHF) cells was assessed using the live-cell fluorescence imaging microscopy. The inhibitory effects of the extract on tyrosinase, collagenase and melanin synthesis were also investigated using standard in vitro method, while ferric reducing power (FRAP) was used to determine the antioxidant potential of the plant extract. The effect of the extract on collagen content in MRHF cells was also investigated. The plant extract displayed no meaningful cytotoxicity towards MRHF cells and no significant cell death was recorded at all the tested concentrations. The extract (25–100 µg/mL) effectively decreased melanin content in B16F10 (mouse melanoma) cells with moderate inhibition of tyrosinase enzyme in a dose-dependent manner. However, the extract also demonstrated no significant effect on collagenase and collagen content in MRHF cells, but showed strong antioxidant activity at the concentrations tested. The results suggest that S. columbaria could be a promising candidate in the treatment of skin hyperpigmentation disorders Full article
(This article belongs to the Special Issue Extraction Optimization Processes of Antioxidants)
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Figure 1
<p>Effect of methanol extract of <span class="html-italic">S. columbaria</span> on tyrosinase activity using L-DOPA as substrate. Values represent the mean ± SD, n = 3. * <span class="html-italic">p</span> &lt; 0.05 compared to untreated (UT) control.</p>
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<p>Effect of methanol extract of <span class="html-italic">S. columbaria</span> on collagenase activity. Values represent the mean ± SD, n = 3. * <span class="html-italic">p</span> &lt; 0.05 compared to untreated (UT) control.</p>
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<p>Antioxidant activity of leaf extract of <span class="html-italic">S. columbaria</span> using ferric reducing power (FRAP) Method. Values represent the mean ± SD, n = 3. * <span class="html-italic">p</span> &lt; 0.05 compared to untreated (UT) control.</p>
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<p>Cytotoxic effect in human dermal fibroblast (MRHF) cells when treated with extract of <span class="html-italic">S. columbaria</span> using the staining method. % relative cell count (%RCC) represents the mean number of cells per site expressed as a percentage of the untreated control. Values represent the mean ± SD, n = 3. * <span class="html-italic">p</span> &lt; 0.05 compared to untreated (UT) control.</p>
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<p>Collagen content of MRHF cells when treated with methanol extract <span class="html-italic">of S. columbaria</span>. Collagen content was measured using Sirius Red and normalised for cell density based on crystal violet staining after removal of the bound Sirius Red. Values represent the mean ± SD, n = 3. * <span class="html-italic">p</span> &lt; 0.05 compared to untreated (UT) control.</p>
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<p>Effect of methanol extract of <span class="html-italic">S. columbaria</span> on melanin production in B6F10 cells. Melanin content, protein content and melanin normalised relative to the total cellular protein. Values represent the mean ± SD, n = 3. * <span class="html-italic">p</span> &lt; 0.05 compared to untreated (UT) control. KA: Kojic acid. (<b>a</b>): Melanin content. (<b>b</b>): Protein content. (<b>c</b>): Melanin normalised relative to the total cellular protein</p>
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19 pages, 4351 KiB  
Article
An Improved Mathematical Model for Accurate Prediction of the Heavy Oil Production Rate during the SAGD Process
by Aria Rahimbakhsh, Morteza Sabeti and Farshid Torabi
Processes 2020, 8(2), 235; https://doi.org/10.3390/pr8020235 - 19 Feb 2020
Cited by 6 | Viewed by 3976
Abstract
Steam-assisted gravity drainage (SAGD) is one of the most successful thermal enhanced oil recovery (EOR) methods for cold viscose oils. Several analytical and semi-analytical models have been theorized, yet the process needs more studies to be conducted to improve quick production rate predictions. [...] Read more.
Steam-assisted gravity drainage (SAGD) is one of the most successful thermal enhanced oil recovery (EOR) methods for cold viscose oils. Several analytical and semi-analytical models have been theorized, yet the process needs more studies to be conducted to improve quick production rate predictions. Following the exponential geometry theory developed for finding the oil production rate, an upgraded predictive model is presented in this study. Unlike the exponential model, the current model divides the steam-oil interface into several segments, and then the heat and mass balances are applied to each of the segments. By manipulating the basic equations, the required formulas for estimating the oil drainage rate, location of interface, heat penetration depth of steam ahead of the interface, and the steam required for the operation are obtained theoretically. The output of the proposed theory, afterwards, is validated with experimental data, and then finalized with data from the real SAGD process in phase B of the underground test facility (UTF) project. According to the results, the model with a suitable heat penetration depth correlation can produce fairly accurate outputs, so the idea of using this model in field operations is convincing. Full article
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Figure 1
<p>A simple schematic of the Steam-assisted gravity drainage (SAGD) process [<a href="#B5-processes-08-00235" class="html-bibr">5</a>]. Reproduced with permission from (Sabeti et al.), (Journal of Petroleum Science and Engineering); published by (Elsevier), (2016).</p>
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<p>An element ahead of the steam-oil interface where the heated oil flows downward [<a href="#B19-processes-08-00235" class="html-bibr">19</a>]. Reproduced with permission from (Sabeti et al.), (Handbook On Oil Production Research); published by (Nova Science Publishers, Inc.), (2014).</p>
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<p>A cross section area inside the reservoir during the SAGD operation. The figure shows a1 as the area below the interface and a2 as the area below <span class="html-italic">Ws</span>.</p>
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<p>Division of the interface into even subdivisions. The black line is the steam-oil interface and each segment on the line is separated by the red bubbles.</p>
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<p>The output of the presented model against the experimental data and the exponential model in the SAGD process. Heat penetration depth has been initiated with <math display="inline"><semantics> <mrow> <mfrac> <mi>γ</mi> <mi>H</mi> </mfrac> <mo>=</mo> <mn>0.15</mn> </mrow> </semantics></math>.</p>
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<p>Trend of the maximum horizontal velocity of the interface Um and the last segment’s heat penetration depth <math display="inline"><semantics> <mi>γ</mi> </semantics></math> with respect to time. The magnitude of the heat penetration depth rises gradually during the steam injection.</p>
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<p>Variations in heat penetration depth, degree coefficient <math display="inline"><semantics> <mrow> <mi>sin</mi> <mi>θ</mi> <mo>=</mo> <msqrt> <mrow> <mfrac> <mn>1</mn> <mrow> <mn>1</mn> <mo>+</mo> <mfrac> <mn>1</mn> <mrow> <msup> <mrow> <mrow> <mo>(</mo> <mrow> <mi>C</mi> <mi>b</mi> </mrow> <mo>)</mo> </mrow> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> <msup> <mi>e</mi> <mrow> <mn>2</mn> <mi>b</mi> <mi>x</mi> </mrow> </msup> </mrow> </mfrac> </mrow> </msqrt> </mrow> </semantics></math> and height of the reservoir with time</p>
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<p>The temperature distribution inside the reservoir at three times: 60 min, 90 min, and 120 min after steam injection in centigrade degrees. The figure compares the computer outputs with experimental screenshots.</p>
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<p>The temperature distribution inside the reservoir at three times: 60 min, 90 min, and 120 min after steam injection in centigrade degrees. The figure compares the computer outputs with experimental screenshots.</p>
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<p>Effect of initial estimate values of the heat penetration depth on the value and trend of the depth itself.</p>
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<p>Comparison of the results from the new model with various heat penetration depth estimates against the experimental data.</p>
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<p>Study on the effects on thermal diffusivity variation on oil production rate in the present SAGD model.</p>
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<p>The oil production and steam injection rates from the proposed model, as well as field data, and phase B of the UTF project.</p>
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13 pages, 1705 KiB  
Article
Effect of Dehydration on the Rheological Measurement of Surimi Paste in Cone-Plate Rheometry: Heat and Mass Transfer Simulation
by Hyeon Woo Park, Jae Won Park and Won Byong Yoon
Processes 2020, 8(2), 234; https://doi.org/10.3390/pr8020234 - 19 Feb 2020
Cited by 3 | Viewed by 3034
Abstract
Moisture transfer characteristics of Alaska pollock (AP) surimi were investigated at various temperatures. The effective moisture diffusivity increased from 5.50 × 10−11 to 2.07 × 10−9 m2/s as the temperature increased from 30 °C to 90 °C. In order [...] Read more.
Moisture transfer characteristics of Alaska pollock (AP) surimi were investigated at various temperatures. The effective moisture diffusivity increased from 5.50 × 10−11 to 2.07 × 10−9 m2/s as the temperature increased from 30 °C to 90 °C. In order to investigate the mass and heat transfer characteristics of AP surimi, the simulation model was developed and evaluated by root-mean-square error (RMSE) (<2.95%). Rheological properties of AP surimi were investigated at different heating rates (1 °C/min, 5 °C/min, 10 °C/min, 20 °C/min and 30 °C/min). As heating rate increased to 20 °C/min and 30 °C/min, elastic modulus (G’) significantly diminished. The diminished G’ could be explained by impaired gel during temperature sweep supported by the predicted temperature distribution in the simulation model. Changes in moisture content of AP surimi during temperature sweep were also measured and predicted by the simulation model. The results showed the decreased amount of moisture content significantly increased as heating rate increased. Full article
(This article belongs to the Special Issue Drying Kinetics and Quality Control in Food Processing)
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<p>Changes in moisture content of Alaska pollock surimi paste during heating in the dynamic rheometer system under flat-plate geometry.</p>
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<p>Effective moisture diffusivities of Alaska pollock surimi paste at different temperatures.</p>
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<p>Changes in moisture content of Alaska pollock surimi paste during temperature sweep in the dynamic rheometer system under cone-plate geometry. Symbols and lines represent experimental data and simulation results, respectively.</p>
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<p>Elastic modulus of Alaska pollock surimi paste measured under different heating rates.</p>
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<p>(<b>a</b>) Average temperature and (<b>b</b>) temperature distributions of Alaska pollock surimi during temperature sweep at different heating rates.</p>
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<p>Moisture distribution of Alaska pollock surimi during temperature sweep at different heating rates.</p>
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17 pages, 4862 KiB  
Article
Optimal Speed Control for a Semi-Autogenous Mill Based on Discrete Element Method
by Xiaoli Wang, Jie Yi, Ziyu Zhou and Chunhua Yang
Processes 2020, 8(2), 233; https://doi.org/10.3390/pr8020233 - 18 Feb 2020
Cited by 7 | Viewed by 7175
Abstract
The rotation speed of a mill is an important factor related to its operation and grinding efficiency. Analysis and regulation of the optimal speed under different working conditions can effectively reduce energy loss, improve productivity, and extend the service life of the equipment. [...] Read more.
The rotation speed of a mill is an important factor related to its operation and grinding efficiency. Analysis and regulation of the optimal speed under different working conditions can effectively reduce energy loss, improve productivity, and extend the service life of the equipment. However, the relationship between the optimal speed and different operating parameters has not received much attention. In this study, the relationship between the optimal speed and particle size and number was investigated using discrete element method (DEM). An improved exponential approaching law sliding mode control method is proposed to track the optimal speed of the mill. Firstly, a simulation was carried out to investigate the relationship between the optimal speed and different operating parameters under cross-over testing. The model of the relationships between the optimal rotation speed and the size and number of particles was established based on the response surface method. An improved sliding mode control using exponential approaching law is proposed to track the optimal speed, and simulation results show it can improve the stability and speed of sliding mode control near the sliding surface. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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<p>The schematic diagram of the working principle of a semi-autogenous (SAG) mill.</p>
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<p>Three types of grinding balls: (<b>a</b>) cascading type; (<b>b</b>) throwing type; (<b>c</b>) centrifugal type.</p>
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<p>Force equilibrium of the grinding medium.</p>
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<p>Trajectory of the grinding medium.</p>
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<p>Model of the roller.</p>
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<p>NZ value recorded: (<b>a</b>) number of particles in each small area of the dropping area; (<b>b</b>) the number of particles dropped in the whole dropping area.</p>
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<p>Relationship between the optimal speed and different parameters: (<b>a</b>) the optimal speed and the number of particles; (<b>b</b>) the optimal speed and particle size.</p>
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<p>Three-dimensional surface diagram of the response surface.</p>
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<p>The contour map of the response surface.</p>
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<p>Motor model of the SAG mill and sliding mode control.</p>
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<p>Results of sliding mode control under low speed.</p>
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<p>Results of sliding mode control under high speed.</p>
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<p>Results of sliding mode control under starting process.</p>
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<p>Results of speed response in the case of sudden disturbance.</p>
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<p>Stability of motor speed under sliding mode control.</p>
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<p>Comparison of the response of improved and traditional exponential approaching law.</p>
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16 pages, 3589 KiB  
Article
Optimization of a Confined Jet Geometry to Improve Film Cooling Performance Using Response Surface Methodology (RSM)
by Mohammed Al-Hemyari, Mohammad O. Hamdan and Mehmet F. Orhan
Processes 2020, 8(2), 232; https://doi.org/10.3390/pr8020232 - 18 Feb 2020
Cited by 6 | Viewed by 2891
Abstract
This study investigates the interrelated parameters affecting heat transfer from a hot gas flowing on a flat plate while cool air is injected adjacent to the flat plate. The cool air forms an air blanket that shield the flat plate from the hot [...] Read more.
This study investigates the interrelated parameters affecting heat transfer from a hot gas flowing on a flat plate while cool air is injected adjacent to the flat plate. The cool air forms an air blanket that shield the flat plate from the hot gas flow. The cool air is blown from a confined jet and is simulated using a two-dimensional numerical model under three variable parameters; namely, blowing ratio, jet angle and density ratio. The interrelations between these parameters are evaluated to properly understand their effects on heat transfer. The analyses are conducted using ANSYS-Fluent, and the performance of the air blanket is reported using local and average adiabatic film cooling effectiveness (AFCE). The interrelation between these parameters and the AFCE is established through a statistical method known as response surface methodology (RSM). The RSM model shows that the AFCE has a second order relation with the blowing ratio and a first order relation with both jet angle and density ratio. Also, it is found that the highest average AFCE is reached at an injection angle of 30 degree, a density ratio of 1.2 and a blowing ratio of 1.8. Full article
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<p>A schematic of the computational domain for 90 degree jet angle with hot inlet air from left-side, cool jet from bottom side and one outlet for the mixed flow.</p>
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<p>Mesh used in the numerical analysis for 90-degree jet angle with close-up mesh image showing fine mesh near the wall.</p>
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<p>Mesh independence study with three different mesh sizes.</p>
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<p>Turbulence model selection based on the validation case in <a href="#processes-08-00232-t001" class="html-table">Table 1</a> versus against numerical study of Bayraktar and Yilmaz [<a href="#B31-processes-08-00232" class="html-bibr">31</a>].</p>
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<p>Experimental validation of velocity profile against O’Malley [<a href="#B37-processes-08-00232" class="html-bibr">37</a>] experimental results.</p>
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<p>Adiabatic film cooling effectiveness (AFCE) variation with x direction for <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>R</mi> <mo>=</mo> <mn>1.2</mn> </mrow> </semantics></math> and injection angle of (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>30</mn> <mo>°</mo> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>60</mn> <mo>°</mo> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>90</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
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<p>AFCE for <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>R</mi> <mo>=</mo> <mn>1.2</mn> </mrow> </semantics></math> at various injection angles and blowing ratio of (a) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, (b) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>, (c) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math> and (d) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p>
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<p>AFCE at injection angle of <math display="inline"><semantics> <mrow> <mn>30</mn> </mrow> </semantics></math> degree and blowing ratio of (a) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, (b) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>, (c) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math> and (d) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p>
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<p>Variation of average AFCE with blowing ratio at three different values of <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>R</mi> </mrow> </semantics></math>.</p>
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<p>Velocity streamlines from the coolant jet for (a) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, (b) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>90</mn> </mrow> </semantics></math>, (c) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, and (d) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>90</mn> </mrow> </semantics></math>.</p>
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<p>Contour plots of the averaged AFCE for all the parameters. (<b>a</b>) Blowing ratio for different injection angles at <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>R</mi> <mo>=</mo> <mn>1.2</mn> </mrow> </semantics></math>, (<b>b</b>) Injection angle for different density ratios at <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math> and (<b>c</b>) Blowing ratio for different density ratios at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>.</p>
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25 pages, 5677 KiB  
Article
Thermal–Hydraulic Performance in a Microchannel Heat Sink Equipped with Longitudinal Vortex Generators (LVGs) and Nanofluid
by Basel AL Muallim, Mazlan A. Wahid, Hussein A. Mohammed, Mohammed Kamil and Daryoush Habibi
Processes 2020, 8(2), 231; https://doi.org/10.3390/pr8020231 - 17 Feb 2020
Cited by 7 | Viewed by 4128
Abstract
In this study, the numerical conjugate heat transfer and hydraulic performance of nanofluids flow in a rectangular microchannel heat sink (RMCHS) with longitudinal vortex generators (LVGs) was investigated at different Reynolds numbers (200–1200). Three-dimensional simulations are performed on a microchannel heated by a [...] Read more.
In this study, the numerical conjugate heat transfer and hydraulic performance of nanofluids flow in a rectangular microchannel heat sink (RMCHS) with longitudinal vortex generators (LVGs) was investigated at different Reynolds numbers (200–1200). Three-dimensional simulations are performed on a microchannel heated by a constant temperature with five different configurations with different angles of attack for the LVGs under laminar flow conditions. The study uses five different nanofluid combinations of Al2O3 or CuO, containing low volume fractions in the range of 0.5% to 3.0% with various nanoparticle sizes that are dispersed in pure water, PAO (Polyalphaolefin) or ethylene glycol. The results show that for Reynolds number ranging from 100 to 1100, Al2O3–water has the best performance compared with CuO nanofluid with Nusselt number values between 7.67 and 14.7, with an associated increase in Fanning friction factor by values of 0.0219–0.095. For the case of different base fluids, the results show that CuO–PAO has the best performance with Nusselt number values between 9.57 and 15.88, with an associated increase in Fanning friction factor by 0.022–0.096. The overall performance of all configurations of microchannels equipped with LVGs and nanofluid showed higher values than the ones without LVG and water as a working fluid. Full article
(This article belongs to the Special Issue Fluid Flow and Heat Transfer of Nanofluids)
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Graphical abstract

Graphical abstract
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<p>The physical model and the computational domain.</p>
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<p>The grid generation of the computational model.</p>
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<p>Close-up look for the grid near to the LVG refinement.</p>
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<p>Comparison of Nu number values from the present numerical simulation and the experimental results of Liu et al. [<a href="#B25-processes-08-00231" class="html-bibr">25</a>].</p>
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<p>Comparison of friction factor values from the present numerical simulation and the experimental results of Liu et al. [<a href="#B25-processes-08-00231" class="html-bibr">25</a>].</p>
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<p>(<b>a</b>) Temperature distribution on different cross-sections along the stream-wise direction for <b>A<sub>1</sub></b> configurations at Re = 800; (<b>b</b>) cross-sections positions along the microchannel.</p>
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<p>Flow pattern behind the VG at Re =1100.</p>
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<p>Isotherms for A<sub>1</sub> configurations at (<b>a</b>) Re = 200, (<b>b</b>) Re=600 and (<b>c</b>) Re= 1200.</p>
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<p>Streamlines for various configurations of a microchannel at Re =700 for (<b>a</b>) A<sub>1</sub>, (<b>b</b>) A<sub>2</sub>, (<b>c</b>) A<sub>3</sub> and (<b>d</b>) A<sub>4</sub>.</p>
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<p>Nusselt number and Fanning friction factor versus Reynolds number for various microchannel configurations.</p>
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<p>JF factor versus Reynolds number for various microchannel configurations.</p>
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<p>Variation of Nusselt number and Fanning friction with Reynolds number for various nanoparticle material and Reynolds numbers.</p>
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<p>The effect of base fluid material and Reynolds number on Nusselt number and Fanning friction factor.</p>
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<p>The effect of different nanoparticles and Reynolds number on the JF factor.</p>
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<p>The effect of various base fluids and Reynolds number on the JF factor.</p>
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<p>Thermal performance of the A1 configuration microchannels with various nanofluids for different Reynolds numbers.</p>
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22 pages, 9684 KiB  
Article
Sustainable Jatropha Oil-Based Membrane with Graphene Oxide for Potential Application in Cu(II) Ion Removal from Aqueous Solution
by Nur Haninah Harun, Zurina Zainal Abidin, Abdul Halim Abdullah and Rizafizah Othaman
Processes 2020, 8(2), 230; https://doi.org/10.3390/pr8020230 - 17 Feb 2020
Cited by 5 | Viewed by 4270
Abstract
More recent attention has been focused on the utilization of Jatropha curcas in the field of water treatment. The potential of Jatropha oil in the synthesis of membrane for water filtration had been explored, its performance compared to the addition of graphene oxide [...] Read more.
More recent attention has been focused on the utilization of Jatropha curcas in the field of water treatment. The potential of Jatropha oil in the synthesis of membrane for water filtration had been explored, its performance compared to the addition of graphene oxide (GO) in the polymer matrix. Jatropha oil was modified in a two-step method to produce Jatropha oil-based polyol (JOL) and was blended with hexamethylene diisocyanate (HDI) to produce Jatropha polyurethane membrane (JPU). JPU was synthesized in different conditions to obtain the optimized membrane and was blended with different GO loading to form Jatropha/graphene oxide composite membrane (JPU/GO) for performance improvement. The synthesized pristine JPU and JPU/GO were evaluated and the materials were analyzed using fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), contact angle, water flux, and field emission scanning electron microscopy (FESEM). Results showed that the ratio of HDI to JOL for optimized JPU was obtained at 5:5 (v/v) with the cross-linking temperature at 90 °C and curing temperature at 150 °C. As GO was added into JPU, several changes were observed. The glass transition temperature (Tg) and onset temperature (To) increased from 58 °C to 69 °C and from 170 °C to 202 °C, respectively. The contact angle, however, decreased from 88.8° to 52.1° while the water flux improved from 223.33 L/m2·h to 523.33 L/m2·h, and the pore distribution in JPU/GO became more orderly. Filtration of copper ions using the synthesized membrane was performed to give rejection percentages between 33.51% and 71.60%. The results indicated that GO had a significant impact on JPU. Taken together, these results have suggested that JPU/GO has the potential for use in water filtration. Full article
(This article belongs to the Special Issue Synthesis and Applications of Eco-Friendly Polymers)
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<p>Schematic representation of the experimental setup with (1) feed tank, (2) peristaltic pump, (3) pressure gauge, (4) cross-flow module, (5) membrane, (6) permeate, (7) control valve, and (8) concentrate.</p>
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<p>FTIR spectra of (<span class="html-italic">a</span>) EJO, (<span class="html-italic">b</span>) JOL, (<span class="html-italic">c</span>) JPU, (<span class="html-italic">d</span>) JPU/GO 0.35 wt%, (<span class="html-italic">e</span>) JPU/GO 0.50 wt%, and (<span class="html-italic">f</span>) JPU/GO 0.65 wt%.</p>
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<p>DSC thermograms of JPU, JPU/GO 0.35 wt%, JPU/GO 0.50 wt%, and JPU/GO 0.65 wt%.</p>
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<p>TGA curves for JPU, JPU/GO 0.35 wt%, JPU/GO 0.50 wt%, and JPU/GO 0.65 wt%.</p>
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<p>DTG curves for JPU, JPU/GO 0.35 wt%, JPU/GO 0.50 wt%, and JPU/GO 0.65 wt%.</p>
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<p>FESEM micrograph image of different JPU samples with different loading of GO for (<b>a</b>) JPU, (<b>b</b>) JPU/GO 0.35 wt%, (<b>c</b>) JPU/GO 0.50 wt%, and (<b>d</b>) JPU/GO 0.65 wt%.</p>
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<p>Image wetting test for the contact angle at 8 min for sample (<b>a</b>) JPU, (<b>b</b>) JPU/GO 0.35 wt%, (<b>c</b>) JPU/GO 0.50 wt%, and (<b>d</b>) JPU/GO 0.65 wt%.</p>
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<p>Outline porosity of the selected area from ImageJ analysis for (<b>a</b>) JPU, (<b>b</b>) JPU/GO 0.35 wt%, (<b>c</b>) JPU/GO 0.50 wt%, (<b>d</b>) and JPU/GO 65 wt%.</p>
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<p>Histogram porosity Feret diameter (µm) from ImageJ analysis for (<b>a</b>) JPU, (<b>b</b>) JPU/GO 0.35 wt%, (<b>c</b>) JPU/GO 0.50 wt%, (<b>d</b>) and JPU/GO 65 wt%.</p>
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<p>Membrane (<b>a</b>) water content and (<b>b</b>) porosity in JPU, JPU/GO 0.35 wt%, JPU/GO 0.50 wt%, and JPU/GO 0.65 wt%.</p>
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<p>Water flux JPU, JPU/GO 0.35 wt%, JPU/GO 0.50 wt%, and JPU/GO 0.65 wt%.</p>
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<p>Copper ion rejection on JPU, JPU/GO 0.35 wt%, JPU/GO 0.50 wt%, and JPU/GO 0.65 wt% membranes.</p>
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17 pages, 1575 KiB  
Article
A Technoeconomic Platform for Early-Stage Process Design and Cost Estimation of Joint Fermentative‒Catalytic Bioprocessing
by Mothi Bharath Viswanathan, D. Raj Raman, Kurt A. Rosentrater and Brent H. Shanks
Processes 2020, 8(2), 229; https://doi.org/10.3390/pr8020229 - 16 Feb 2020
Cited by 22 | Viewed by 7464
Abstract
Technoeconomic analyses using established tools such as SuperPro Designer® require a level of detail that is typically unavailable at the early stage of process evaluation. To facilitate this, members of our group previously created a spreadsheet-based process modeling and technoeconomic platform explicitly [...] Read more.
Technoeconomic analyses using established tools such as SuperPro Designer® require a level of detail that is typically unavailable at the early stage of process evaluation. To facilitate this, members of our group previously created a spreadsheet-based process modeling and technoeconomic platform explicitly aimed at joint fermentative‒catalytic biorefinery processes. In this work, we detail the reorganization and expansion of this model—ESTEA2 (Early State Technoeconomic Analysis, version 2), including detailed design and cost calculations for new unit operations. Furthermore, we describe ESTEA2 validation using ethanol and sorbic acid process. The results were compared with estimates from the literature, SuperPro Designer® (Version 8.5, Intelligen Inc., Scotch Plains, NJ, 2013), and other third-party process models. ESTEA2 can perform a technoeconomic analysis for a joint fermentative‒catalytic process with just 12 user-supplied inputs, which, when modeled in SuperPro Designer®, required approximately eight additional inputs such as equipment design configurations. With a reduced amount of user information, ESTEA2 provides results similar to those in the literature, and more sophisticated models (ca. 7%–11% different). Full article
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<p>ESTEA2 Structure—Explaining Input/Output, Design, Support, Upstream, Downstream Process Data, and Cost Data brackets, their respective tabs, and flow of data across the model.</p>
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<p>Product cost distribution elaborating direct, indirect and operating cost factors as calculated by ESTEA2.</p>
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<p>Ethanol process flow diagram as modeled in ESTEA2 (based on Kwiatkowski’s SuperPro model).</p>
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<p>Sorbic acid process flow diagram as designed in ESTEA2 based on Chia et al., 2012 and CBiRC internal reports.</p>
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<p>Ethanol—Capital cost distribution (based on data Kwiatkowski’s SuperPro results).</p>
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<p>(<b>A</b>) Percent variation of ESTEA2’s line-item cost results from that of EV, with our unit cost data; (<b>B</b>) Percent variation of ESTEA2’s line-item cost results from that of EV, with EV’s unit cost data.</p>
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<p>(<b>A</b>) Percent variation of ESTEA2’s line-item cost results from that of EV, with our unit cost data; (<b>B</b>) Percent variation of ESTEA2’s line-item cost results from that of EV, with EV’s unit cost data.</p>
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26 pages, 7667 KiB  
Review
CFD Modeling of Gas–Solid Cyclone Separators at Ambient and Elevated Temperatures
by Mohammadhadi Nakhaei, Bona Lu, Yujie Tian, Wei Wang, Kim Dam-Johansen and Hao Wu
Processes 2020, 8(2), 228; https://doi.org/10.3390/pr8020228 - 15 Feb 2020
Cited by 44 | Viewed by 19777
Abstract
Gas–solid cyclone separators are widely utilized in many industrial applications and usually involve complex multi-physics of gas–solid flow and heat transfer. In recent years, there has been a progressive interest in the application of computational fluid dynamics (CFD) to understand the gas–solid flow [...] Read more.
Gas–solid cyclone separators are widely utilized in many industrial applications and usually involve complex multi-physics of gas–solid flow and heat transfer. In recent years, there has been a progressive interest in the application of computational fluid dynamics (CFD) to understand the gas–solid flow behavior of cyclones and predict their performance. In this paper, a review of the existing CFD studies of cyclone separators, operating in a wide range of solids loadings and at ambient and elevated temperatures, is presented. In the first part, a brief background on the important performance parameters of cyclones, namely pressure drop and separation efficiency, as well as how they are affected by the solids loading and operating temperature, is described. This is followed by a summary of the existing CFD simulation studies of cyclones at ambient temperature, with an emphasis on the high mass loading of particles, and at elevated temperatures. The capabilities as well as the challenges and limitations of the existing CFD approaches in predicting the performance of cyclones operating in such conditions are evaluated. Finally, an outlook on the prospects of CFD simulation of cyclone separators is provided. Full article
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<p>(<b>a</b>) Schematic drawing of a conical reverse-flow cyclone separator illustrating the basic operating principle and the presence of a double vortex inside the cyclone [<a href="#B21-processes-08-00228" class="html-bibr">21</a>], reproduced with permission from G. Towler and R. Sinnott, Specification and design of solids-handling equipment, published by Elsevier, 2013. (<b>b</b>) Qualitative patterns of axial, tangential, and radial velocity components of the gas-flow field in cyclones (right) [<a href="#B22-processes-08-00228" class="html-bibr">22</a>], reproduced with permission from M. Trefz and E. Muschelknautz, Extended cyclone theory for gas flows with high solids concentrations, published by John Wiley and Sons, 1993. (<b>c</b>) The secondary flow pattern caused by the swirl and pressure gradients in the cyclone [<a href="#B19-processes-08-00228" class="html-bibr">19</a>], reproduced with permission from A. Hoffmann and L. Stein, Gas Cyclones and Swirl Tubes: Principles, Design and Operation; published by Springer Nature, 2007.</p>
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<p>Overall cyclone separation efficiency versus mass loading ratio at the cyclone inlet for selected experimental data in the literature [<a href="#B7-processes-08-00228" class="html-bibr">7</a>,<a href="#B8-processes-08-00228" class="html-bibr">8</a>,<a href="#B19-processes-08-00228" class="html-bibr">19</a>,<a href="#B29-processes-08-00228" class="html-bibr">29</a>,<a href="#B31-processes-08-00228" class="html-bibr">31</a>]. The Reynolds number is calculated based on the inlet velocity and the cyclone body diameter.</p>
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<p>Experimental data of pressure drop (normalized with the pressure drop of a particle-free cyclone, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>P</mi> <mn>0</mn> </msub> </mrow> </semantics></math>) versus mass loading of pilot-scale cyclones from selected studies [<a href="#B7-processes-08-00228" class="html-bibr">7</a>,<a href="#B8-processes-08-00228" class="html-bibr">8</a>,<a href="#B9-processes-08-00228" class="html-bibr">9</a>,<a href="#B29-processes-08-00228" class="html-bibr">29</a>,<a href="#B31-processes-08-00228" class="html-bibr">31</a>,<a href="#B32-processes-08-00228" class="html-bibr">32</a>,<a href="#B37-processes-08-00228" class="html-bibr">37</a>]. The lines are numerical fits to each set of experimental data. The Reynolds number is calculated based on the inlet velocity and the cyclone body diameter.</p>
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<p>Map of gas–solid interaction regimes of particle-laden turbulent flows. <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>p</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>e</mi> </msub> </semantics></math> are the particle kinetic response time and time-scale of large eddies in a turbulent flow, respectively. Reproduced with permission from S. Elghobashi, On predicting particle-laden turbulent flows; published by Springer Nature, 1994 [<a href="#B61-processes-08-00228" class="html-bibr">61</a>].</p>
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<p>Comparison of predicted mean velocity profiles (<b>a</b>,<b>b</b>), rms velocities (<b>c</b>,<b>d</b>), and grade efficiencies (<b>e</b>) using the Reynolds stress transport model (RSTM) and large eddy simulation (LES) for a cyclone with a body diameter of 0.29 m and operating at ambient temperature and pressure [<a href="#B96-processes-08-00228" class="html-bibr">96</a>]. The experimental data on separation efficiency are from [<a href="#B97-processes-08-00228" class="html-bibr">97</a>]. Reproduced with permission from S. Shukla et al., The effect of modeling of velocity fluctuations on prediction of collection efficiency of cyclone separators; published by Elsevier, 2013.</p>
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<p>The predicted time-averaged values of axial and tangential velocities as well as the resolved turbulent kinetic energy of the gas for a pilot-scale cyclone with a body diameter of 0.29 m and operating with different mass loadings of particles using two-way coupled LES at 0.75D (top) and 2D (bottom) below the cyclone roof [<a href="#B116-processes-08-00228" class="html-bibr">116</a>]. Reproduced with permission from J.J. Derksen, Simulation of mass-loading effects in gas-solid cyclone separators; published by Elsevier, 2006.</p>
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<p>Grade efficiency predicted by the CFD simulation of Sgrott and Sommerfeld [<a href="#B108-processes-08-00228" class="html-bibr">108</a>] for a pilot-scale cyclone loaded with particles with a diameter of 0.5–60 microns and a mass loading of 0.1 kg<math display="inline"><semantics> <msub> <mrow/> <mi>s</mi> </msub> </semantics></math>/kg<math display="inline"><semantics> <msub> <mrow/> <mi>g</mi> </msub> </semantics></math>. 1 W-C, 2 W-C, and 4 W-C refer to one-way coupling, two-way coupling, and four-way coupling methods (using CFD–DEM without agglomeration), respectively. Sphere and history models are volume-equivalent and inertia-equivalent approaches for agglomeration, respectively. Reproduced with permission from O.L. Sgrott and M. Sommerfeld, Influence of inter-particle collisions and agglomeration on cyclone performance and collection efficiency; published by John Wiley and Sons, 2018.</p>
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<p>Predicted trajectories of particles of different diameters in an industrial-scale cyclone with a body diameter of 3.45 m. The trajectories are colored with particle temperature. The inlet gas and particle temperatures are 634 <span class="html-italic">K</span> and 611 <span class="html-italic">K</span>, respectively, and inlet solids mass loading is around 0.8 kg<math display="inline"><semantics> <msub> <mrow/> <mi>s</mi> </msub> </semantics></math>/kg<math display="inline"><semantics> <msub> <mrow/> <mi>g</mi> </msub> </semantics></math> [<a href="#B145-processes-08-00228" class="html-bibr">145</a>]. Reproduced with permission from M. Wasilewski, Analysis of the effects of temperature and the share of solid and gas phases on the process of separation in a cyclone suspension preheater; published by Elsevier, 2016.</p>
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<p>Summary of the available approaches for computational fluid dynamics (CFD) simulation of gas–solid flows with the inclusion of closure models to be considered in each approach. The table is inspired from [<xref ref-type="bibr" rid="B60-processes-08-00228">60</xref>].</p>
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14 pages, 4454 KiB  
Article
Research on Rotordynamic Characteristics of Pump Annular Seals Based on a New Transient CFD Method
by Fengqin Li, Baoling Cui and Lulu Zhai
Processes 2020, 8(2), 227; https://doi.org/10.3390/pr8020227 - 15 Feb 2020
Cited by 9 | Viewed by 3817
Abstract
Pump annular seals can cause fluid reaction forces that have great effects on the vibration characteristic and stability of a pump system. For this reason, it is important to study rotordynamic characteristics of annular seals. In this paper, a new transient computational fluid [...] Read more.
Pump annular seals can cause fluid reaction forces that have great effects on the vibration characteristic and stability of a pump system. For this reason, it is important to study rotordynamic characteristics of annular seals. In this paper, a new transient computational fluid dynamics (CFD) method with dynamic mesh is proposed to investigate rotordynamic characteristics of the pump annular seal. The reliability of the transient CFD method is validated by comparison with the results from the experiment and the bulk-flow method, and the relationship between the seal length and rotordynamic characteristics is investigated by the transient CFD method. The results indicate that direct stiffness decreases sharply even turns to negative as the seal length increases, this phenomenon may change the direction of fluid force on the rotor surface and affect supporting condition of the pump rotor. With the increasing seal length, the whirl frequency ratio gradually increases, which would weaken the stability of the pump rotor system. Full article
(This article belongs to the Special Issue Advancement in Computational Fluid Mechanics and Optimization Methods)
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<p>The schematic diagram of the position between the rotor and the stator: (<b>a</b>) initial position of the rotor, (<b>b</b>) eccentric position of the rotor.</p>
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<p>(<b>a</b>) Calculation domain and (<b>b</b>) grid.</p>
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<p>Grid independency analysis.</p>
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<p>Diagram of the grid nodes moving.</p>
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<p>Front view of the meshed rotor: (<b>a</b>) initial grids, (<b>b</b>) moved grids.</p>
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<p>Calculation flow chart of the proposed transient CFD method.</p>
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<p>Reaction forces acting on the rotor as a function of time: (<b>a</b>) forward whirling, (<b>b</b>) backward whirling.</p>
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<p>Rotordynamics coefficients as a function of pressure difference: (<b>a</b>) direct stiffness, (<b>b</b>) cross coupled stiffness, (<b>c</b>) direct damping.</p>
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<p>Leakage as a function of pressure difference.</p>
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<p>Direct stiffness for different seal lengths.</p>
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<p>Z-direction velocity profile of the short seal (PD = 1.38 MPa): (<b>a</b>) the maximum clearance cross section (<b>b</b>) the minimum clearance cross section, [L = 13.13 mm], (<b>c</b>) the maximum clearance cross section (<b>d</b>) the minimum clearance cross section, [L = 38.1 mm].</p>
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<p>Z-direction velocity profile of the long seal (PD = 1.38 MPa): (<b>a</b>) the maximum clearance cross section, (<b>b</b>) the minimum clearance cross section, [L = 76.2 mm], (<b>c</b>) the maximum clearance cross section, (<b>d</b>) the minimum clearance cross section, [L = 91.44 mm].</p>
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<p>Z-direction velocity profile of the long seal (PD = 1.38 MPa): (<b>a</b>) the maximum clearance cross section, (<b>b</b>) the minimum clearance cross section, [L = 76.2 mm], (<b>c</b>) the maximum clearance cross section, (<b>d</b>) the minimum clearance cross section, [L = 91.44 mm].</p>
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<p>Static pressure distribution on the maximum clearance and minimum clearance: (<b>a</b>) 13.13 mm (L/D = 0.17), (<b>b</b>) 38.1 mm (L/D = 0.5), (<b>c</b>) 76.2 mm (L/D = 1.0), (<b>d</b>) 91.44 mm (L/D = 1.2).</p>
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<p>Whirl frequency ratio as function of the seal length.</p>
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24 pages, 13819 KiB  
Article
On Fluid Flow Field Visualization in a Staggered Cavity: A Numerical Result
by Khalil Ur Rehman, Nabeela Kousar, Waqar A. Khan and Nosheen Fatima
Processes 2020, 8(2), 226; https://doi.org/10.3390/pr8020226 - 15 Feb 2020
Cited by 5 | Viewed by 3443
Abstract
In this paper we have considered a staggered cavity. It is equipped with purely viscous fluid. The physical design is controlled through mathematical formulation in terms of both the equation of continuity and equation of momentum along with boundary constraints. To be more [...] Read more.
In this paper we have considered a staggered cavity. It is equipped with purely viscous fluid. The physical design is controlled through mathematical formulation in terms of both the equation of continuity and equation of momentum along with boundary constraints. To be more specific, the Navier-Stokes equations for two dimensional Newtonian fluid flow in staggered enclosure is formulated and solved by well trusted method named finite element method. The novelty is increased by considering the motion of upper and lower walls of staggered cavity case-wise namely, in first case we consider that the upper wall of staggered cavity is moving and rest of walls are kept at zero velocity. In second case we consider that the upper and bottom walls are moving in a parallel way. Lastly, the upper and bottom walls are considered in an antiparallel direction. In all cases the deep analysis is performed and results are proposed by means of contour plots. The velocity components are explained by line graphs as well. The kinetic energy examination is reported for all cases. It is trusted that the findings reported in present pagination well serve as a helping source for the upcoming studies towards fluid flow in an enclosure domains being involved in an industrial areas. Full article
(This article belongs to the Special Issue Fluid Flow and Heat Transfer of Nanofluids)
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<p>Geometry of problem.</p>
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<p>Refinement level-1.</p>
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<p>Refinement level-2.</p>
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<p>Refinement level-3.</p>
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<p>Refinement level-4.</p>
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<p>Refinement level-5.</p>
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<p>Refinement level-6.</p>
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<p>Refinement level-7.</p>
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<p>Refinement level-8.</p>
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<p>Refinement level-9.</p>
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<p>Velocity distribution at Re = 50 for case-I.</p>
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<p>Velocity distribution at Re = 100 for case-I.</p>
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<p>Velocity distribution at Re = 400 for case-I.</p>
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<p>Velocity distribution at Re = 1000 for case-I.</p>
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<p>Pressure distribution at Re = 50 for case-I.</p>
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<p>Pressure distribution at Re = 100 for case-I.</p>
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<p>Pressure distribution at Re = 400 for case-I.</p>
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<p>Pressure distribution at Re = 1000 for case-I.</p>
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<p>The <b><span class="html-italic">u</span></b>-velocity profile for case-I.</p>
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<p>The <b><span class="html-italic">v</span></b>-velocity profile for case-I.</p>
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<p>Velocity distribution at Re = 50 for case-II.</p>
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<p>Velocity distribution at Re = 100 for case-II.</p>
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<p>Velocity distribution at Re = 400 for case-II.</p>
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<p>Velocity distribution at Re = 1000 for case-II.</p>
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<p>Pressure distribution at Re = 50 for case-II.</p>
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<p>Pressure distribution at Re = 100 for case-II.</p>
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<p>Pressure distribution at Re = 400 for case-II.</p>
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<p>Pressure distribution at Re = 1000 for case-II.</p>
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<p>The <b><span class="html-italic">u</span></b>-velocity profile for case-II.</p>
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<p>The <b><span class="html-italic">v</span></b>-velocity profile for case-I.</p>
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<p>Velocity distribution at Re = 50 for case-III.</p>
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<p>Velocity distribution at Re = 100 for case-III.</p>
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<p>Velocity distribution at Re = 400 for case-III.</p>
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<p>Velocity distribution at Re = 1000 for case-III.</p>
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<p>Pressure distribution at Re = 50 for case-III.</p>
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<p>Pressure distribution at Re = 100 for case-III.</p>
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<p>Pressure distribution at Re = 400 for case-III.</p>
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<p>Pressure distribution at Re = 1000 for case-III.</p>
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<p>The <b><span class="html-italic">u</span></b>-velocity profile for case-III.</p>
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<p>The <b><span class="html-italic">v</span></b>-velocity profile for case-III.</p>
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