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Volume 8
Issue 9
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Processes, Volume 8, Issue 9 (September 2020) – 193 articles

Cover Story (view full-size image): Tortuosity is one of the most elusive parameters characterizing the geometrical properties of porous media. It may be calculated directly, based on the pure geometry of pore channels, or indirectly, based on transportation or diffusional properties of porous systems. Additionally, the geometry of porous media may be expressed in a vector, raster, or binary form, depending on the data origin. In the last case, the A-Star or the Patch Searching Algorithm may be used to calculate pore channel lengths and, in turn, the tortuosity value. Details are available in "Calculating the Binary Tortuosity in DEM-Generated Granular Beds". View this paper
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21 pages, 2390 KiB  
Article
A Representation of Membrane Computing with a Clustering Algorithm on the Graphical Processing Unit
by Ravie Chandren Muniyandi and Ali Maroosi
Processes 2020, 8(9), 1199; https://doi.org/10.3390/pr8091199 - 22 Sep 2020
Cited by 3 | Viewed by 2413
Abstract
Long-timescale simulations of biological processes such as photosynthesis or attempts to solve NP-hard problems such as traveling salesman, knapsack, Hamiltonian path, and satisfiability using membrane systems without appropriate parallelization can take hours or days. Graphics processing units (GPU) deliver an immensely parallel mechanism [...] Read more.
Long-timescale simulations of biological processes such as photosynthesis or attempts to solve NP-hard problems such as traveling salesman, knapsack, Hamiltonian path, and satisfiability using membrane systems without appropriate parallelization can take hours or days. Graphics processing units (GPU) deliver an immensely parallel mechanism to compute general-purpose computations. Previous studies mapped one membrane to one thread block on GPU. This is disadvantageous given that when the quantity of objects for each membrane is small, the quantity of active thread will also be small, thereby decreasing performance. While each membrane is designated to one thread block, the communication between thread blocks is needed for executing the communication between membranes. Communication between thread blocks is a time-consuming process. Previous approaches have also not addressed the issue of GPU occupancy. This study presents a classification algorithm to manage dependent objects and membranes based on the communication rate associated with the defined weighted network and assign them to sub-matrices. Thus, dependent objects and membranes are allocated to the same threads and thread blocks, thereby decreasing communication between threads and thread blocks and allowing GPUs to maintain the highest occupancy possible. The experimental results indicate that for 48 objects per membrane, the algorithm facilitates a 93-fold increase in processing speed compared to a 1.6-fold increase with previous algorithms. Full article
(This article belongs to the Special Issue Modeling, Simulation and Design of Membrane Computing System)
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Figure 1
<p>P-system structure. This system consists of membranes, objects, rules, and charges. Rules react to evolve objects and membranes. Charges respond to control processing.</p> Full article ">Figure 2
<p>A membrane is assigned to one thread block, and objects are assigned to threads within each thread block [<a href="#B31-processes-08-01199" class="html-bibr">31</a>,<a href="#B32-processes-08-01199" class="html-bibr">32</a>,<a href="#B33-processes-08-01199" class="html-bibr">33</a>].</p> Full article ">Figure 3
<p>Algorithm 1 for active membrane systems on the GPU [<a href="#B31-processes-08-01199" class="html-bibr">31</a>,<a href="#B32-processes-08-01199" class="html-bibr">32</a>,<a href="#B33-processes-08-01199" class="html-bibr">33</a>].</p> Full article ">Figure 4
<p>Representation of a membrane system as matrices.</p> Full article ">Figure 5
<p>The proposed object-weighted network and classification. Objects that have higher communication rates are organized within the same groups (dashed ellipses). The number of objects per group should satisfy Equation (3) and objects are assigned to the same entry positions within the matrices, enabling execution by the same thread.</p> Full article ">Figure 6
<p>The proposed membrane-weighted network and classification. Membranes having higher communication rates are organized within the same groups (dashed ellipses). The number of membranes per group should satisfy Equation (4), and the membranes assigned to the sub-matrices, NTx × NTy, will be executed in the same thread block.</p> Full article ">Figure 7
<p>Pseudocode of the proposed approach for active membrane systems on GPU.</p> Full article ">Figure 8
<p>Comparison of previous and new methods for GPU optimization of active membrane systems. Execution times are associated with previous methods (<a href="#processes-08-01199-f003" class="html-fig">Figure 3</a>) and the algorithm presented in this paper (<a href="#processes-08-01199-f007" class="html-fig">Figure 7</a>).</p> Full article ">
29 pages, 7994 KiB  
Article
Design and Characterization of a Fluidic Device for the Evaluation of SIS-Based Vascular Grafts
by Alejandra Riveros, Monica Cuellar, Paolo F. Sánchez, Carolina Muñoz-Camargo, Juan C. Cruz, Néstor Sandoval, Omar D. Lopez Mejia and Juan C. Briceño
Processes 2020, 8(9), 1198; https://doi.org/10.3390/pr8091198 - 22 Sep 2020
Cited by 3 | Viewed by 3409
Abstract
Currently available small diameter vascular conduits present several long-term limitations, which has prevented their full clinical implementation. Commercially available vascular grafts show no regenerative capabilities and eventually require surgical replacement; therefore, it is of great interest to develop alternative regenerative vascular grafts (RVG). [...] Read more.
Currently available small diameter vascular conduits present several long-term limitations, which has prevented their full clinical implementation. Commercially available vascular grafts show no regenerative capabilities and eventually require surgical replacement; therefore, it is of great interest to develop alternative regenerative vascular grafts (RVG). Decellularized Small Intestinal Submucosa (SIS) is an attractive material for RVG, however, the evaluation of the performance of these grafts is challenging due to the absence of devices that mimic the conditions found in vivo. Thereby, the objective of this study is to design, manufacture and validate in silico and in vitro, a novel fluidic system for the evaluation of human umbilical vein endothelial cells (HUVECs) proliferation on SIS-based RVG under dynamical conditions. Our perfusion and rotational fluidic system was designed in Autodesk Inventor 2018. In silico Computational Fluid Dynamics (CFD) validation of the system was carried out using Ansys Fluent software from ANSYS, Inc for dynamical conditions of a pulsatile pressure function measured experimentally over a rigid wall model. Mechanical and biological parameters such as flow regime, pressure gradient, wall shear stress (WSS), sterility and indirect cell viability (MTT assay) were also evaluated. Cell adhesion was confirmed by SEM imaging. The fluid flow regime within the system remains laminar. The system maintained sterility and showed low cytotoxicity levels. HUVECs were successfully cultured on SIS-based RVG under both perfusion and rotation conditions. In silico analysis agreed well with our experimental and theoretical results, and with recent in vitro and in vivo reports for WSS. The system presented is a tool for evaluating RVG and represents an alternative to develop new methods and protocols for a more comprehensive study of regenerative cardiovascular devices. Full article
(This article belongs to the Section Biological Processes and Systems)
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<p>Fluidic device for the testing of SIS-based regenerative vascular grafts. The assembly consists of a vascular graft chamber, an infusion pump, a medium reservoir, and an agitator system.</p> Full article ">Figure 2
<p>Assembly of the agitator system where the fluidic device was mounted to allow rotation in both the clockwise and counterclockwise directions.</p> Full article ">Figure 3
<p>Fluidic system setup. (<b>a</b>) Schematic of the fluidic device and the required accessories for cell seeding on the SIS-based vascular graft. (<b>b</b>) Picture of the actual implementation components where (1) is the culture medium reservoir tank, (2) is the infusion pump, (3) is the setup of the fluidic device and a SIS-based VG, and (4) is the agitator system.</p> Full article ">Figure 4
<p>Experimental setup. (<b>a</b>) Schematic of the fluidic device and the required accessories for pressure measurement. (<b>b</b>) Picture of the actual implementation.</p> Full article ">Figure 5
<p>Schematic of a cross-sectional view of the fluidic device. The blue dotted line represents the symmetry on sections and dimensions (subscript s in the <a href="#processes-08-01198-t001" class="html-table">Table 1</a>). Section 1 corresponds to the medical tubing of length L<sub>1,1s</sub> from the inlet and the outlet pressure transducers, respectively. Sections 2 and 4 represent the four inserts to connect the required medical tubing sections (L<sub>2,4,2s,4s</sub>). Section 3 represents the internal O-rings distance (L<sub>3,3s</sub>). Section 5 corresponds to the medical tubing used to connect the vascular graft to the fluidic device (L<sub>5,5s</sub>). Section 6 represents a half-length SIS-based vascular graft of length L<sub>6,6s</sub> (i.e., Total length of the SIS-based vascular graft is two-times L<sub>6</sub>).</p> Full article ">Figure 6
<p>Computational domain for the model with the asymmetrical assumption (red line). The heights of the sections correspond to the radius of the components in the experimental setup. (<b>a</b>) Schematic of the six main sections on the left half of the geometry. The right half (from the blue dotted line) is symmetrical to the left one. (<b>b</b>) Section 1 represents the first medical tubing with a height of 1.4 mm. (<b>c</b>) Section 2 corresponds to the first insert with a height of 0.9 mm. (<b>d</b>) Section 3 corresponds the O-rings with a height of 1.6 mm. (<b>e</b>) Section 4 corresponds to the second insert. (<b>f</b>) Section 5 is the second medical tubing. (<b>g</b>) Section 6 represents one half of the SIS-based vascular graft with a height of 1.5 mm. The section interfaces were smoothed out with the aid of 45° chamfers along the computational domain to approximate the transition between components of the experimental setup. The final meshes of each section are shown in panels (<b>b</b>–<b>g</b>).</p> Full article ">Figure 7
<p>In silico division for computational model analysis of phases 1 and 2 according to the upward and downward stages.</p> Full article ">Figure 8
<p>Experimental pressure measurements at the inlet and outlet of the fluidic system under a pulsatile flow of 1200 mL/h (3.33 × 10<sup>−7</sup> m<sup>3</sup>/s) using the Baxter Colleague Infusion Pump.</p> Full article ">Figure 9
<p>Mesh convergence analysis for the Mean Inlet Flow Rate for the six mesh configurations.</p> Full article ">Figure 10
<p>Comparison between the experimental Inlet pressure signal and the reconstructed DFT signal for (<b>a</b>) 10 harmonics, (<b>b</b>) 50 harmonics, (<b>c</b>) 150 harmonics, and (<b>d</b>) 200 harmonics.</p> Full article ">Figure 11
<p>Comparison between the experimental Outlet pressure signal and the reconstructed DFT signal: (<b>a</b>) 10 harmonics, (<b>b</b>) 50 harmonics, (<b>c</b>) 100 harmonics, and (<b>d</b>) 150 harmonics.</p> Full article ">Figure 12
<p>Pressure and Wall Shear Stress distributions along the global geometry of the computational domain for Phase 1. Initial and final time steps for each phase are included as a comparison reference for the chosen time steps on both stages. (<b>a</b>) Pressure and (<b>b</b>) Wall Shear Stress for t = 0.585. (<b>c</b>) Pressure and (<b>d</b>) Wall Shear Stress for t = 0.615. (<b>e</b>) Pressure and (<b>f</b>) Wall Shear Stress for t = 0.63. (<b>g</b>) Pressure and (<b>h</b>) Wall Shear Stress for t = 0.675.</p> Full article ">Figure 13
<p>Pressure and Wall Shear Stress distributions along the global geometry of the computational domain for Phase 2. Initial and final time steps for each phase are included as a comparison reference for the chosen time steps on both stages. (<b>a</b>) Pressure and (<b>b</b>) Wall Shear Stress for t = 0.81. (<b>c</b>) Pressure and (<b>d</b>) Wall Shear Stress for t = 0.855. (<b>e</b>) Pressure and (<b>f</b>) Wall Shear Stress for t = 0.9. (<b>g</b>) Pressure and (<b>h</b>) Wall Shear Stress for t = 0.99.</p> Full article ">Figure 14
<p>Time evolution of the computational Inlet pressure (<b>left y-axis</b>) and Area-Weighted Average Wall Shear Stress (<b>right y-axis</b>).</p> Full article ">Figure 15
<p>Time evolution of the computational Inlet pressure (<b>left y-axis</b>) and Inlet Flow Rate (<b>right y-axis</b>).</p> Full article ">Figure 16
<p>Local velocity profiles for selected time steps on phases I and II at the inlet (x = −15 mm), the middle (x = 0 mm) and the outlet (x = 15 mm) of the vascular graft region for (<b>a</b>) t = 0.585s, (<b>b</b>) t = 0.63s, (<b>c</b>) t = 0.675 s, (<b>d</b>) t = 0.855 s, and (<b>e</b>) t = 0.99 s.</p> Full article ">Figure 17
<p>Local velocity profiles for selected time steps on phases I and II at the middle (x = 0 mm) of the vascular graft region.</p> Full article ">Figure 18
<p>Cytotoxicity effect of circulating medium (CM) from the fluidic system on HUVEC cells. The cytotoxicity was determined via MTT colorimetric assay relative to the negative control, which was uncirculated EGM-2 medium (UM). Cells treated with circulated EGM-2 medium (for 72 h) evidenced a 93.4 ± 2.2% cell viability, while cells treated with the positive control (DMSO 10%) in EGM-2 exhibited a 5.3 ± 0.96% viability. There is no statistically significant difference between the circulated medium and the negative control according to statistical analysis determined by one-way ANOVA followed by a Tukey test (<span class="html-italic">n</span> = 3 for each experiment).</p> Full article ">Figure 19
<p>Scanning Electron Microscopy (SEM) imaging of HUVEC cells attachment on the surface of a tubular SIS-based vascular graft subjected to the initial static growing conditions during 2 h and subsequently to dynamic growing conditions of the fluidic device at a flow rate of 1200 mL/h for 3 days. Yellow arrows show endothelial cells and their cytoplasmatic projections (filopodia) on the SIS-based vascular graft lumen.</p> Full article ">
18 pages, 5481 KiB  
Article
Enhanced Adsorptive Removal of β-Estradiol from Aqueous and Wastewater Samples by Magnetic Nano-Akaganeite: Adsorption Isotherms, Kinetics, and Mechanism
by Anele Mpupa, Azile Nqombolo, Boris Mizaikoff and Philiswa Nosizo Nomngongo
Processes 2020, 8(9), 1197; https://doi.org/10.3390/pr8091197 - 22 Sep 2020
Cited by 9 | Viewed by 3020
Abstract
A surfactant-free method was used to synthesize iron oxyhydroxide (akaganeite, β-FeOOH) nanorods and characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (SEM-EDS), and transmission electron microscopy (TEM). The synthesized nanoadsorbent was applied for [...] Read more.
A surfactant-free method was used to synthesize iron oxyhydroxide (akaganeite, β-FeOOH) nanorods and characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (SEM-EDS), and transmission electron microscopy (TEM). The synthesized nanoadsorbent was applied for the adsorptive removal of β-estradiol from aqueous solutions. The parameters affecting the adsorption were optimized using a multivariate approach based on the Box–Behnken design with the desirability function. Under the optimum conditions, the equilibrium data were investigated using two and three parameter isotherms, such as the Langmuir, Freundlich, Dubinin–Radushkevich, Redlich–Peterson, and Sips models. The adsorption data were described as Langmuir and Sips isotherm models and the maximum adsorption capacities in Langmuir and Sips of the β-FeOOH nanorods were 97.0 and 103 mg g−1, respectively. The adjusted non-linear adsorption capacities were 102 and 104 mg g−1 for Langmuir and Sips, respectively. The kinetics data were analyzed by five different kinetic models, such as the pseudo-first order, pseudo-second order, intraparticle, as well as Boyd and Elovich models. The method was applied for the removal β-estradiol in spiked recoveries of wastewater, river, and tap water samples, and the removal efficiency ranged from 93–100%. The adsorbent could be reused up to six times after regeneration with acetonitrile without an obvious loss in the removal efficiency (%RE = 95.4 ± 1.9%). Based on the results obtained, it was concluded that the β-FeOOH nanorods proved to be suitable for the efficient removal of β-estradiol from environmental matrices. Full article
(This article belongs to the Special Issue Various Adsorbents for Water Purification Processes)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>FTIR spectra of akaganeite.</p> Full article ">Figure 2
<p>X-ray diffraction pattern of the akaganeite nanorods.</p> Full article ">Figure 3
<p>An exemplary (<b>A</b>) SEM image and (<b>B</b>) EDX spectrum and (<b>C</b>) section analysed using EDX of the akaganeite nanomaterial synthesized in absence of a surfactant.</p> Full article ">Figure 4
<p>Exemplary TEM images of the synthesized akaganeite nanomaterial in absence of any surfactant.</p> Full article ">Figure 5
<p>Adsorption isotherm plots for the sorption of β-estradiol using akaganeite (<b>A</b>)—Langmuir isotherm model; (<b>B</b>)—Langmuir linear model; (<b>C</b>)—Freundlich linear model; (<b>D</b>)—Dubinin–Radushkevich linear model; (<b>E</b>)—Redlich–Peterson linear model; (<b>F</b>)—Sips linear model.</p> Full article ">Figure 6
<p>Adsorption kinetics plots for the sorption of β-estradiol using akaganeite: (<b>A</b>)—adsorption kinetics model; (<b>B</b>)—pseudo-first order linear model; (<b>C</b>)—pseudo-second order linear model; (<b>D</b>)—Elovich linear model; (<b>E</b>)—intraparticle diffusion linear model; (<b>F</b>)—Boyd linear model.</p> Full article ">Figure 7
<p>Van’t Hoff plot calculation of for thermodynamic parameters.</p> Full article ">Figure 8
<p>FTIR spectrum for (<b>a</b>) akageneite before adsorption, (<b>b</b>) akageneite before adsorption, and (<b>c</b>) β-estradiol.</p> Full article ">
19 pages, 7386 KiB  
Article
Investigating the Use of Recycled Pork Fat-Based Biodiesel in Aviation Turbo Engines
by Grigore Cican, Marius Deaconu, Radu Mirea, Laurentiu Ceatra, Mihaiella Cretu and Tănase Dobre
Processes 2020, 8(9), 1196; https://doi.org/10.3390/pr8091196 - 21 Sep 2020
Cited by 15 | Viewed by 3750
Abstract
This paper presents an analysis of the possibility of using recycled pork fat-based biodiesel as fuel for aviation turbo-engines. The analysis consists of the assessment of four blends of Jet A kerosene with 10%, 30%, 50%, and 100% biodiesel and pure Jet A [...] Read more.
This paper presents an analysis of the possibility of using recycled pork fat-based biodiesel as fuel for aviation turbo-engines. The analysis consists of the assessment of four blends of Jet A kerosene with 10%, 30%, 50%, and 100% biodiesel and pure Jet A that was used as reference in the study. The first part of the paper presents the physical-chemical properties of the blends: density, viscosity, flash point, freezing point, and calorific power. Through Fourier transform infrared spectroscopy (FTIR) analysis, a benchmark was performed on the mixtures of Jet A with 10%, 20%, 30%, 50%, and 100% biodiesel compared with Jet A. The second part of the paper presents the test results of these blends used for fuelling a Jet Cat P80 turbo engine at the Turbo Engines Laboratory of the Aerospace Engineering Faculty of Polyethnic University of Bucharest. These functional tests were performed using different operating regimes as follows: idle, cruise, intermediate, and maximum. For each regime, a testing period of around 1 min was selected and the engine parameters were monitored during the test execution. The burning efficiency was calculated for the maximum regime for all mixtures. To evaluate the functioning stability of the turbo engine using biodiesel, two accelerometers were mounted on the engine support that recorded the radial and axial vibrations. Moreover, to assess the burning stability and to identify other acoustic spectral components when biodiesel is used, two microphones were placed near the jet region. A comparative analysis between blends was made by taking the Jet A fuel as reference. Full article
(This article belongs to the Special Issue Biomass Processing and Conversion Systems)
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Figure 1

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<p>Test bench instrumentation (microphones and accelerometers location).</p> Full article ">Figure 2
<p>Fuels’ density measurement.</p> Full article ">Figure 3
<p>Automatic Flash Point Tester Cleveland.</p> Full article ">Figure 4
<p>Kinematic viscosity determination equipment.</p> Full article ">Figure 5
<p>IKA WERKE C 2000 Calorimeter.</p> Full article ">Figure 6
<p>Equipment for freezing point determination.</p> Full article ">Figure 7
<p>Fourier transform infrared (FTIR) Spectrum OilExpress Series 100, v 3.0 spectrometer.</p> Full article ">Figure 8
<p>FTIR spectra of the blends (spectra of 100% biodiesel (BD) fuel—light green, spectra of 50% BD fuel—purple, spectra of 30% BD fuel—dark green, spectra of 20% BD fuel—red, spectra of 10% BD fuel—blue, and spectra of Jet A fuel (Kerosene) with 5% Aeroshell 500 turbine engine oil added—light blue).</p> Full article ">Figure 9
<p>Variation of T<sub>3</sub>(°C) depending on regime and blends.</p> Full article ">Figure 10
<p>Variation of Qc (L/h) depending on the regime and blend.</p> Full article ">Figure 11
<p>Variation of thrust F(N) depending on the regime and blend.</p> Full article ">Figure 12
<p>Vibration levels axial (<b>a</b>) and radial (<b>b</b>).</p> Full article ">Figure 13
<p>Vibration levels variation depending on the turbo engine speed.</p> Full article ">Figure 14
<p>Noise–FFT (Fast Fourier Transformation) time domain analysis for all blends, regime 3 ((<b>a</b>) Mic 1, (<b>b</b>) Mic 2).</p> Full article ">Figure 15
<p>Vibration–FFT time domain analysis for all blends, regime 3 ((<b>a</b>) X; (<b>b</b>) Y).</p> Full article ">Figure 16
<p>Variation of specific consumption for all tested blends at the maximum regime.</p> Full article ">
20 pages, 2321 KiB  
Article
Curation and Analysis of a Saccharomyces cerevisiae Genome-Scale Metabolic Model for Predicting Production of Sensory Impact Molecules under Enological Conditions
by William T. Scott, Jr., Eddy J. Smid, Richard A. Notebaart and David E. Block
Processes 2020, 8(9), 1195; https://doi.org/10.3390/pr8091195 - 21 Sep 2020
Cited by 9 | Viewed by 5151
Abstract
One approach for elucidating strain-to-strain metabolic differences is the use of genome-scale metabolic models (GSMMs). To date GSMMs have not focused on the industrially important area of flavor production and, as such; do not cover all the pathways relevant to flavor formation in [...] Read more.
One approach for elucidating strain-to-strain metabolic differences is the use of genome-scale metabolic models (GSMMs). To date GSMMs have not focused on the industrially important area of flavor production and, as such; do not cover all the pathways relevant to flavor formation in yeast. Moreover, current models for Saccharomyces cerevisiae generally focus on carbon-limited and/or aerobic systems, which is not pertinent to enological conditions. Here, we curate a GSMM (iWS902) to expand on the existing Ehrlich pathway and ester formation pathways central to aroma formation in industrial winemaking, in addition to the existing sulfur metabolism and medium-chain fatty acid (MCFA) pathways that also contribute to production of sensory impact molecules. After validating the model using experimental data, we predict key differences in metabolism for a strain (EC 1118) in two distinct growth conditions, including differences for aroma impact molecules such as acetic acid, tryptophol, and hydrogen sulfide. Additionally, we propose novel targets for metabolic engineering for aroma profile modifications employing flux variability analysis with the expanded GSMM. The model provides mechanistic insights into the key metabolic pathways underlying aroma formation during alcoholic fermentation and provides a potential framework to contribute to new strategies to optimize the aroma of wines. Full article
(This article belongs to the Special Issue Role of Yeast in Wine Fermentation Processes)
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<p>Representation of the the components of the yeast genome-scale metabolic model (GSMM) related to aroma formation. Each colored section shows metabolism related to a respective class of aroma compounds, including the expanded pathways related to amino acid and fatty acid degradation and sulfur reduction pathways, as well as formation of fusel alcohols, esters, and sulfur compounds in yeast model metabolism. The entire metabolic network utilized is shown in the <a href="#app1-processes-08-01195" class="html-app">Supplementary Material (Figure S2)</a>.</p> Full article ">Figure 2
<p>Comparison of GSMM simulations (red bars) with experimental data [<a href="#B30-processes-08-01195" class="html-bibr">30</a>] (blue bars). Model predicted and experimental values for biomass growth rates, primary metabolite production fluxes, and secondary metabolite production fluxes represent conditions for Case I and Case II. Experimental data are shown with standard deviation error bars.</p> Full article ">Figure 2 Cont.
<p>Comparison of GSMM simulations (red bars) with experimental data [<a href="#B30-processes-08-01195" class="html-bibr">30</a>] (blue bars). Model predicted and experimental values for biomass growth rates, primary metabolite production fluxes, and secondary metabolite production fluxes represent conditions for Case I and Case II. Experimental data are shown with standard deviation error bars.</p> Full article ">Figure 3
<p>Network maps of central carbon metabolism. Network fluxes were determined for Case I (<b>left</b>) and Case II (<b>right</b>) where the network illustrates the optimal solution from flux balance analysis (FBA). Note that the scales, as indicated in the legends, are different for Case I and Case II.</p> Full article ">Figure 4
<p>Network maps of amino acid and sulfur metabolism. Network fluxes were determined for Case I (<b>left</b>) and Case II (<b>right</b>) where the network illustrates the optimal solution from FBA. Note that the scales, as indicated in the legends, are different for Case I and Case II.</p> Full article ">Figure 5
<p>Flux spans for Group I fluxes for the optimal production of aroma impact fluxes represent conditions Case I where uptake fluxes are constrained based on experimental data ((<b>a</b>), top) and unconstrained conditions ((<b>b</b>), bottom). Bars depict the possible range (minimum and maximum) fluxes (mmol gDW<sup>−1</sup> h<sup>−1</sup>) and dot indicates the average flux calculated by flux variability analysis (FVA) for reactions.</p> Full article ">
15 pages, 1646 KiB  
Article
Cu(II) and As(V) Adsorption Kinetic Characteristic of the Multifunctional Amino Groups in Chitosan
by Byungryul An
Processes 2020, 8(9), 1194; https://doi.org/10.3390/pr8091194 - 21 Sep 2020
Cited by 64 | Viewed by 3932
Abstract
Amino groups in the chitosan polymer play as a functional group for the removal of cations and anions depending on the degree of protonation, which is determined by the solution pH. A hydrogel beadlike porous adsorbent was used to investigate the functions and [...] Read more.
Amino groups in the chitosan polymer play as a functional group for the removal of cations and anions depending on the degree of protonation, which is determined by the solution pH. A hydrogel beadlike porous adsorbent was used to investigate the functions and adsorption mechanism of the amino groups by removal of Cu(II) as a cation and As(V) as an anion for a single and mixed solution. The uptakes of Cu(II) and As(V) were 5.2 and 5.6 μmol/g for the single solution and 5.9 and 3.6 μmol/g for the mixed solution, respectively. The increased total capacity in the presence of both the cation and anion indicated that the amino group (NH2 or NH3+) species was directly associated for adsorption. The application of a pseudo second-order (PSO) kinetic model was more suitable and resulted in an accurate correlation coefficient (R2) compared with the pseudo first-order (PFO) kinetic model for all experimental conditions. Due to poor linearization of the PFO reaction model, we attempted to divide it into two sections to improve the accuracy. Regardless of the model equation, the order of the rate constant was in the order of As(V)-single > Cu(II)-single > As(V)-mixed > Cu(II)-mixed. Also, the corresponding single solution and As(V) showed a higher adsorption rate. According to intraparticle and film diffusion applications displaying two linear lines and none passing through zero, the rate controlling step in the chitosan hydrogel bead was determined by both intraparticle and film diffusion. Full article
(This article belongs to the Special Issue Various Adsorbents for Water Purification Processes)
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<p>Removal uptake of Cu(II) and As(V) in a presence of single and/or mixed solution.</p> Full article ">Figure 2
<p>Degree of protonation (α) of chitosan as a function of pH and pK<sub>a</sub>.</p> Full article ">Figure 3
<p>Removal efficiency (%) of Cu(II) and As(V) in the presence of single and/or mixed solutions (dash lines indicate 90% removal efficiency (%), and the dotted line for Cu(II)-single and Cu(II)-mixed were very close).</p> Full article ">Figure 4
<p>Nonlinear pseudo first-order (dots) and second-order (solid lines) kinetic models.</p> Full article ">Figure 5
<p>Linear PFO model for (<b>a</b>) Cu(II) and (<b>b</b>) As(V) with two sections.</p> Full article ">Figure 6
<p>Linear PSO model for Cu(II) and As(V).</p> Full article ">Figure 7
<p>Weber and Morris intraparticle diffusion model with two sections.</p> Full article ">Figure 8
<p>Spahn and Schlunder film diffusion model shown for (<b>a</b>) the entire experimental time scale and (<b>b</b>) less than 2 h.</p> Full article ">
16 pages, 3441 KiB  
Article
Analysis and Anticancer Effects of Active Compounds from Spatholobi Caulis in Human Breast Cancer Cells
by Hung Manh Phung, Hesol Lee, Sullim Lee, Dongyeop Jang, Chang-Eop Kim, Ki Sung Kang, Chang-Seob Seo and You-Kyung Choi
Processes 2020, 8(9), 1193; https://doi.org/10.3390/pr8091193 - 21 Sep 2020
Cited by 7 | Viewed by 5137
Abstract
Breast cancer is the most common malignancy in both developing and developed countries. In this study, we simultaneously analyzed nine constituent compounds from Spatholobi Caulis (gallic acid, (−)-gallocatechin, 3,4-dihydroxybenzoic acid, procyanidin B1, 3,4-dihydroxybenzaldehyde, catechin, procyanidin B2, epicatechin, and (−)-epicatechin gallate) and examined their [...] Read more.
Breast cancer is the most common malignancy in both developing and developed countries. In this study, we simultaneously analyzed nine constituent compounds from Spatholobi Caulis (gallic acid, (−)-gallocatechin, 3,4-dihydroxybenzoic acid, procyanidin B1, 3,4-dihydroxybenzaldehyde, catechin, procyanidin B2, epicatechin, and (−)-epicatechin gallate) and examined their anticancer effects on MCF-7 and MDA-MB-231 human breast cancer cells. The experimental results indicated that the gallic acid showed the strongest cytotoxic effect on MCF-7 cells among tested compounds whilst most of samples did not express inhibitory effect on viability of MDA-MB-231 cells, except for 70% ethanol extract of S. Caulis. Thus, gallic acid was chosen to extend anticancer mechanism study on MCF-7 cells. Our data showed that the gallic acid induced apoptotic MCF-7 cell death through both extrinsic and intrinsic pathways, which increased the expression of cleaved caspase-7, -8, and -9, Bax and p53, but reduced the expression of Bcl-2 and poly (ADP-ribose) polymerase (PARP). In addition, the network pharmacological analysis pointed out that the p53, mitogen-activated protein kinase (MAPK), estrogen, and Wnt signaling pathways have a great correlation with the targets of gallic acid. This study suggested that gallic acid is a bioactive component of S. Caulis with potential to be used in chemotherapy for breast cancer. Full article
(This article belongs to the Special Issue Metabolic Analysis in Food Processing)
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<p>Nine marker compounds in <span class="html-italic">Spatholobi Caulis</span> (SC). Gallic acid (<b>1</b>), (−)-gallocatechin (<b>2</b>), 3,4-dihydroxybenzoic acid (<b>3</b>), procyanidin B1 (<b>4</b>), 3,4-dihydroxybenzaldehyde (<b>5</b>), catechin (<b>6</b>), procyanidin B2 (<b>7</b>), epicatechin (<b>8</b>), and (−)-epicatechin gallate (<b>9</b>).</p> Full article ">Figure 2
<p>High-performance liquid chromatography (HPLC) chromatograms of the 70% ethanol extract of <span class="html-italic">Spatholobi Caulis</span> (SC). Gallic acid (<b>1</b>), (−)-gallocatechin (<b>2</b>), 3,4-dihydroxybenzoic acid (<b>3</b>), procyanidin B1 (<b>4</b>), 3,4-dihydroxybenzaldehyde (<b>5</b>), catechin (<b>6</b>), procyanidin B2 (<b>7</b>), epicatechin (<b>8</b>), and (−)-epicatechin gallate (<b>9</b>).</p> Full article ">Figure 3
<p>The cytotoxic effects of the 70% ethanol extract of Spatholobi Caulis (<b>SC</b>), gallic acid (<b>1</b>), (−)-gallocatechin (<b>2</b>), 3,4-dihydroxybenzoic acid (<b>3</b>), procyanidin B1 (<b>4</b>), 3,4-dihydroxybenzaldehyde (<b>5</b>), catechin (<b>6</b>), procyanidin B2 (<b>7</b>), epicatechin (<b>8</b>), (−)-epicatechin gallate (<b>9</b>), and cisplatin (reference drug) in MCF-7 cells. The cells were treated with the sample for 24 h, and cell survival was detected using the Ez-Cytox cell viability assay kit (<span class="html-italic">n</span> = 5). Data are presented as the mean ± SD. * <span class="html-italic">p</span> &lt; 0.05 compared to the 0 µM group.</p> Full article ">Figure 4
<p>The cytotoxic effects of combination between gallic acid (<b>1</b>) and (−)-gallocatechin (<b>2</b>), 3,4-dihydroxybenzoic acid (<b>3</b>), procyanidin B1 (<b>4</b>), 3,4-dihydroxybenzaldehyde (<b>5</b>), catechin (<b>6</b>), procyanidin B2 (<b>7</b>), epicatechin (<b>8</b>), and (−)-epicatechin gallate (<b>9</b>) at 50 µM in MCF-7 cells. The cells were treated with the sample for 24 h, and cell survival was detected using the Ez-Cytox cell viability assay kit (<span class="html-italic">n</span> = 3). Data are presented as the mean ± SD. * <span class="html-italic">p</span> &lt; 0.05 compared with the non-treated group. White bar: non-treated group; black bar: group was treated with 50 µM gallic acid (<b>1</b>); dark gray bars: groups were single treated with 8 phenolic compounds (<b>2–9</b>) at 50 µM; and light gray bars: groups were co-treated gallic acid (<b>1</b>) with each phenolic compound (<b>2–9</b>) at 50 µM.</p> Full article ">Figure 5
<p>The cytotoxic effects of the 70% ethanol extract of Spatholobi Caulis (<b>SC</b>), gallic acid (<b>1</b>), (−)-gallocatechin (<b>2</b>), 3,4-dihydroxybenzoic acid (<b>3</b>), procyanidin B1 (<b>4</b>), 3,4-dihydroxybenzaldehyde (<b>5</b>), catechin (<b>6</b>), procyanidin B2 (<b>7</b>), epicatechin (<b>8</b>), (−)-epicatechin gallate (<b>9</b>), and cisplatin (reference drug) in MDA-MB-231 cells. The cells were treated with the sample for 24 h, and cell survival was detected using the Ez-Cytox cell viability assay kit (<span class="html-italic">n</span> = 5). Data are presented as the mean ± SD. * <span class="html-italic">p</span> &lt; 0.05 compared to control group.</p> Full article ">Figure 5 Cont.
<p>The cytotoxic effects of the 70% ethanol extract of Spatholobi Caulis (<b>SC</b>), gallic acid (<b>1</b>), (−)-gallocatechin (<b>2</b>), 3,4-dihydroxybenzoic acid (<b>3</b>), procyanidin B1 (<b>4</b>), 3,4-dihydroxybenzaldehyde (<b>5</b>), catechin (<b>6</b>), procyanidin B2 (<b>7</b>), epicatechin (<b>8</b>), (−)-epicatechin gallate (<b>9</b>), and cisplatin (reference drug) in MDA-MB-231 cells. The cells were treated with the sample for 24 h, and cell survival was detected using the Ez-Cytox cell viability assay kit (<span class="html-italic">n</span> = 5). Data are presented as the mean ± SD. * <span class="html-italic">p</span> &lt; 0.05 compared to control group.</p> Full article ">Figure 6
<p>Apoptosis-inducing effect of gallic acid (GA) on MCF-7 cells. (<b>A</b>) The cells were exposed to the indicated doses of GA for 12 h and stained with Hoechst 33258. Fluorescent images were captured using a fluorescent microscope. (<b>B</b>) The bat chart depicts the proportion of nuclear abnormalities obtained from the fluorescence images (<span class="html-italic">n</span> = 2). (<b>C</b>) MCF-7 cells were treated with specific doses of GA for 12 h and dual stained with PI and annexin V to detect dead and apoptotic cells using the Tali<sup>®</sup> Image-Based Cytometer. (<b>D</b>) The bar graph describes the portion of annexin V stained cells indicating for cells undergoing apoptosis (<span class="html-italic">n</span> = 2). Data are presented as the mean ± SD. * <span class="html-italic">p</span> &lt; 0.05 compared to the non-treated group.</p> Full article ">Figure 7
<p>Effect of gallic acid (GA) on expression of protein-mediated apoptosis in MCF-7 cells. The cells were treated with specific doses of GA for 24 h, and the protein expression was determined by western blotting (<span class="html-italic">n</span> = 2).</p> Full article ">Figure 8
<p>p53 signaling pathway (hsa04115) and gallic acid (GA)-related genes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) mapper was applied to build pathway maps. White round squares and green boxes depict pathways and genes, respectively. Pink-colored boxes and red-rimmed boxes represent predicted targets of GA and validated genes in western blotting analysis, respectively.</p> Full article ">Figure 9
<p>Compound-target network of gallic acid (GA). An edge between a compound and a target indicates the compound interacts with the target. The colors of target nodes represent which pathways the targets involve in. MAPK, mitogen-activated protein kinase.</p> Full article ">
15 pages, 8188 KiB  
Article
Numerical Simulation of Axial Vortex in a Centrifugal Pump as Turbine with S-Blade Impeller
by Xiaohui Wang, Kailin Kuang, Zanxiu Wu and Junhu Yang
Processes 2020, 8(9), 1192; https://doi.org/10.3390/pr8091192 - 20 Sep 2020
Cited by 21 | Viewed by 4316
Abstract
Pump as turbines (PATs) are widely applied for recovering the dissipated energy of high-pressure fluids in several hydraulic energy resources. When a centrifugal pump operates as turbine, the large axial vortex occurs usually within the impeller flow passages. In view of the structure [...] Read more.
Pump as turbines (PATs) are widely applied for recovering the dissipated energy of high-pressure fluids in several hydraulic energy resources. When a centrifugal pump operates as turbine, the large axial vortex occurs usually within the impeller flow passages. In view of the structure and evolution of the vortex, and its effect on pressure fluctuation and energy conversion of the machine, a PAT with specific-speed 9.1 was analyzed based on detached eddy simulation (DES), and the results showed that vortices generated at the impeller inlet region, and the size and position of detected vortices, were fixed as the impeller rotated. However, the swirling strength of vortex cores changed periodically with double rotational frequency. The influence of vortices on pressure fluctuation of PAT was relatively obvious, deteriorating the operating stability of the machine evidently. In addition, the power loss near impeller inlet region was obviously heavy as the impact of large axial vortices, which was much more serious in low flow rate conditions. The results are helpful to realize the flow field of PAT and are instructive for blade optimization design. Full article
(This article belongs to the Special Issue CFD Based Researches and Applications for Fluid Machinery and Fluid Device)
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<p>Directions of flow and rotation in pump and pump as turbine (PAT). (<b>a</b>) Pump; (<b>b</b>) PAT.</p> Full article ">Figure 2
<p>The model of the large axial vortex in PAT.</p> Full article ">Figure 3
<p>Structure of modified PAT. (<b>a</b>) Structure of selected PAT; (<b>b</b>) modified impeller of PAT.</p> Full article ">Figure 4
<p>Computational domain and mesh. (<b>a</b>) Computational domain; (<b>b</b>) mesh.</p> Full article ">Figure 5
<p>Grid independent test.</p> Full article ">Figure 6
<p>Monitoring sites of PAT.</p> Full article ">Figure 7
<p>Experimental equipment of PAT. (<b>a</b>) Schematic diagram of experiment; (<b>b</b>) test rig.</p> Full article ">Figure 8
<p>Comparison between experimental and numerical results.</p> Full article ">Figure 9
<p>Streamline and swirling strength contour of impeller.</p> Full article ">Figure 10
<p>Pressure fluctuation coefficient with time. (<b>a</b>) Points 1, 2, 3; (<b>b</b>)Points 5, 7, 9; (<b>c</b>)Points 4, 6, 8; (<b>d</b>)Points 11, 13, 15; (<b>e</b>)Points 12, 14, 16; (<b>f</b>)Points 4, 17.</p> Full article ">Figure 11
<p>Pressure fluctuation coefficient with frequency. (<b>a</b>) Points 1, 2, 3; (<b>b</b>)Points 5, 7, 9; (<b>c</b>)Points 4, 6, 8; (<b>d</b>)Points 11, 13, 15; (<b>e</b>)Points 12, 14, 16; (<b>f</b>)Points 4, 17.</p> Full article ">Figure 11 Cont.
<p>Pressure fluctuation coefficient with frequency. (<b>a</b>) Points 1, 2, 3; (<b>b</b>)Points 5, 7, 9; (<b>c</b>)Points 4, 6, 8; (<b>d</b>)Points 11, 13, 15; (<b>e</b>)Points 12, 14, 16; (<b>f</b>)Points 4, 17.</p> Full article ">Figure 12
<p>Streamline at radial plane of impeller. (<b>a</b>) 0.6 <span class="html-italic">Q<sub>d</sub></span>; (<b>b</b>) 1.0 <span class="html-italic">Q<sub>d</sub></span>; (<b>c</b>) 1.6 <span class="html-italic">Q<sub>d</sub></span>.</p> Full article ">Figure 13
<p>Monitoring surfaces of the impeller.</p> Full article ">Figure 14
<p>Average relative velocity of monitoring surfaces.</p> Full article ">Figure 15
<p>Average pressure of monitoring surfaces.</p> Full article ">Figure 16
<p>Zones of the impeller.</p> Full article ">Figure 17
<p>Hydraulic losses of different zones.</p> Full article ">
10 pages, 2904 KiB  
Article
Inhibitory Effects of Thymol Isolated from Curcuma longa L. on Adipogenesis in HepG2 Cells
by Dam-Hee Kang, Young-Seob Lee, Seon Min Oh, Dahye Yoon, Doo Jin Choi, Dong-Yeul Kwon, Ok-Hwa Kang and Dae Young Lee
Processes 2020, 8(9), 1191; https://doi.org/10.3390/pr8091191 - 20 Sep 2020
Cited by 4 | Viewed by 3442
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a disease associated with metabolic syndromes such as diabetes and obesity, regardless of alcohol consumption, and refers to the accumulation of triacylglycerols in the liver. Thymol (THY) is a vegetable essential oil that is naturally contained in [...] Read more.
Non-alcoholic fatty liver disease (NAFLD) is a disease associated with metabolic syndromes such as diabetes and obesity, regardless of alcohol consumption, and refers to the accumulation of triacylglycerols in the liver. Thymol (THY) is a vegetable essential oil that is naturally contained in the Zingiberaceae and Lamiaceae families. THY was isolated from Curcuma longa L. The rhizomes of Curcuma longa L. were dried, sliced and extracted with 50% ethanol and then isolated through repeated column chromatography. This study was conducted to investigate the inhibitory effect of THY, even in non-alcoholic fatty liver disease, in relation to the inhibiting hyperlipidemia effect of THY, which was demonstrated in previous studies. Hepatocytes were treated with oleate (OA) containing THY to observe lipid accumulation by Oil Red O staining (ORO). We also tested the effect of THY on triacylglycerols (TG) and total cholesterol (TC) in HepG2 cells. Western blot and real-time RT-PCR using sterol regulatory element-binding protein-1c (SREBP-1c), fatty acid synthase (FAS), acetyl-CoA carboxylase (ACC), CCAAT-enhancer-binding protein (C/EBP), proliferator-activated receptor γ (PPARγ), and adenosine monophosphate (AMP)-activated protein kinase (AMPK) expressions were carried out. Consequently, inhibition of lipogenesis by THY (100 μM or 200 μM) in NAFLD treated with OA in HepG2 cells was confirmed. The results of TG and TC experiments confirmed a decrease in the degree of fat accumulation in the liver. Furthermore, inhibition of the SREBP-1c, FAS, ACC, C/EBP and PPARγ expressions that mediated fat accumulation and increased AMPK phosphorylation was observed. Taken together, THY is proposed as a potential natural constituent for the treatment of NAFLD. Full article
(This article belongs to the Special Issue Metabolic Analysis in Food Processing)
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<p>(<b>a</b>) Chemical structure and quantitation range of thymol. (<b>b</b>) HPLC chromatogram of <span class="html-italic">C. longa</span> extract at 280 nm.</p> Full article ">Figure 2
<p>Effects of silymarin (SM) and thymol (THY) concentrations on cell viability in HepG2 cells. Cell viability was measured in HepG2 cells after treatment with EtOH (CE) (200 μg), SM (20 μg), and THY (50, 100, 200, and 400 μM). Cell viability was evaluated using the MTS assay. N: normal; OA: oleate; CE: <span class="html-italic">C. longa</span> 50% EtOH extract; SM: silymarin; THY: thymol.</p> Full article ">Figure 3
<p>Effects of SM and THY on intracellular lipid accumulation in HepG2 cells. (<b>a</b>) Normal, (<b>b</b>) OA (500 μM), (<b>c</b>) CE (200 μg), (<b>d</b>) SM (20 μg), (<b>e</b>) THY 100 (100 μM), and (<b>f</b>) THY 200 (200 μM) were treated with OA (500 μM) for 24 h. Cells were stained with Oil Red O and analyzed by a spectraphotometer. OA: oleate; CE: <span class="html-italic">C. longa</span> 50% EtOH extract; SM: silymarin; THY: thymol.</p> Full article ">Figure 4
<p>Effects on TG and TC levels in HepG2 cells. (<b>a</b>) Cellular production of TG and (<b>b</b>) cellular production of TC were induced by 500 μM of OA, and cells were treated with indicated concentrations of CE (200 μg), SM (20 μg), THY 100 (100 μM), and THY 200 (200 μM) for 24 h. Total intracellular triglyceride and total cholesterol were analyzed using the enzymatic colorimetric method. ## <span class="html-italic">p</span> &lt; 0.01, relative to normal; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, relative to OA. N: normal; OA: oleate; CE: <span class="html-italic">C. longa</span> 50% EtOH extract; SM: silymarin; THY: thymol.</p> Full article ">Figure 5
<p>Effects of SM and THY on hepatic lipogenesis-related protein (sterol regulatory element-binding protein-1c (SREBP-1c), acetyl-CoA carboxylase (ACC), fatty acid synthase (FAS), CCAAT-enhancer-binding protein (C/EBP), and proliferator-activated receptor γ (PPARγ)) expression levels in OA-treated HepG2 cells. Data are representative of three independent experiments. N: normal; OA: oleate; CE: <span class="html-italic">C. longa</span> 50% EtOH extract; SM: silymarin; THY: thymol.</p> Full article ">Figure 6
<p>Effects on hepatic lipogenesis. (<b>a</b>) Gene mRNA expression level of SREBP-1c, (<b>b</b>) gene mRNA expression level of ACC, (<b>c</b>) gene mRNA expression level of FAS, (<b>d</b>) gene mRNA expression level of C/EBP, and (<b>e</b>) gene mRNA expression level of PPARγ. Data are representative of three independent experiments. Expression levels were normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA levels. ## <span class="html-italic">p</span> &lt; 0.01 relative to normal; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, relative to the OA. N: normal; OA: oleate; CE: <span class="html-italic">C. longa</span> 50% EtOH extract; SM: silymarin; THY: thymol.</p> Full article ">Figure 7
<p>Effects on AMP-activated protein kinase (AMPK) phosphorylation in HepG2 Cells. Protein expression of AMPK was detected by western blot analysis. N: normal; OA: oleate; CE: <span class="html-italic">C. longa</span> 50% EtOH extract; SM: silymarin; THY: thymol.</p> Full article ">
17 pages, 1374 KiB  
Review
Microwave-Assisted Pyrolysis of Biomass Waste: A Mini Review
by Saleem Ethaib, Rozita Omar, Siti Mazlina Mustapa Kamal, Dayang Radiah Awang Biak and Salah L. Zubaidi
Processes 2020, 8(9), 1190; https://doi.org/10.3390/pr8091190 - 19 Sep 2020
Cited by 94 | Viewed by 14413
Abstract
The utilization of biomass waste as a raw material for renewable energy is a global concern. Pyrolysis is one of the thermal treatments for biomass wastes that results in the production of liquid, solid and gaseous products. Unfortunately, the complex structure of the [...] Read more.
The utilization of biomass waste as a raw material for renewable energy is a global concern. Pyrolysis is one of the thermal treatments for biomass wastes that results in the production of liquid, solid and gaseous products. Unfortunately, the complex structure of the biomass materials matrix needs elevated heating to convert these materials into useful products. Microwave heating is a promising alternative to conventional heating approaches. Recently, it has been widely used in pyrolysis due to easy operation and its high heating rate. This review tries to identify the microwave-assisted pyrolysis treatment process fundamentals and discusses various key operating parameters which have an effect on product yield. It was found that several operating parameters govern this process such as microwave power and the degree of temperature, microwave absorber addition and its concentration, initial moisture content, initial sweep gas flow rate/residence time. Moreover, this study highlighted the most attractive products of the microwave pyrolysis process. These products include synthesis gas, bio-char, and bio-oil. The benefits and challenges of microwave heating are discussed. Full article
(This article belongs to the Section Environmental and Green Processes)
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<p>Graphic representation for the pretreatment process of lignocellulosic biomass and its role in biomass conversion into useful components.</p> Full article ">Figure 2
<p>Schematic drawing of the modified microwave oven and the position of the quartz reactor: (<b>1</b>) magnetron; (<b>2</b>) quartz reactor; (<b>3</b>) thermocouple; (<b>4</b>) oven casing; (<b>5</b>) quartz distributor plate; (<b>6</b>) spring for quartz holder fitting; (<b>7</b>) N<sub>2</sub> gas inlet; (<b>8</b>) quartz holder fitting; (<b>9</b>) cooler system for magnetron; (<b>10</b>) hole for wave into cavity; (<b>11</b>) gas outlet; (<b>12</b>) electrical connection [<a href="#B28-processes-08-01190" class="html-bibr">28</a>].</p> Full article ">Figure 3
<p>Heat transfer in the conventional and microwave heating of wood [<a href="#B70-processes-08-01190" class="html-bibr">70</a>]. (Reproduced with permission from Elsevier)<b>.</b></p> Full article ">
18 pages, 2259 KiB  
Article
Carbon-Efficient Production Scheduling of a Bioethanol Plant Considering Diversified Feedstock Pelletization Density: A Case Study
by Xinchao Li, Xin Jin, Shan Lu, Zhe Li, Yue Wang and Jiangtao Cao
Processes 2020, 8(9), 1189; https://doi.org/10.3390/pr8091189 - 18 Sep 2020
Cited by 1 | Viewed by 2784
Abstract
This paper presents a dual-objective optimization model for production scheduling of bioethanol plant with carbon-efficient strategies. The model is developed throughout the bioethanol production process. Firstly, the production planning and scheduling of the bioethanol plant’s transportation, storage, pretreatment, and ethanol manufacturing are fully [...] Read more.
This paper presents a dual-objective optimization model for production scheduling of bioethanol plant with carbon-efficient strategies. The model is developed throughout the bioethanol production process. Firstly, the production planning and scheduling of the bioethanol plant’s transportation, storage, pretreatment, and ethanol manufacturing are fully considered. Secondly, the carbon emissions in the ethanol manufacturing process are integrated into the model to form a dual-objective optimization model that simultaneously optimizes the production plan and carbon emissions. The effects of different biomass raw materials with optional pelletization density and pretreatment methods on production scheduling are analyzed. The influence of demand and pretreatment cost on selecting a pretreatment method and total profit is considered. A membership weighted method is developed to solve the dual-objective model. The carbon emission model and economic model are integrated into one model for analysis. An example is given to verify the effectiveness of the optimization model. At the end of the paper, the limitation of this study is discussed to provide directions for future research. Full article
(This article belongs to the Section Environmental and Green Processes)
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<p>Flow chart of the bioethanol production process.</p> Full article ">Figure 2
<p>The biomass raw material acquisition.</p> Full article ">Figure 3
<p>Curves of normalized <math display="inline"><semantics> <mrow> <msub> <mi>F</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>F</mi> <mn>2</mn> </msub> </mrow> </semantics></math> varying with <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mn>1</mn> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Total profit and carbon emissions under different <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mn>1</mn> </msub> </mrow> </semantics></math><sub>.</sub></p> Full article ">Figure 5
<p>The result for each part of the cost function.</p> Full article ">Figure 6
<p>The biomass processed at the bioethanol plant per supply source for each period.</p> Full article ">Figure 7
<p>The biomass processed at the bioethanol plant per baling form for each period.</p> Full article ">
22 pages, 9563 KiB  
Article
Potential Dynamics of CO2 Stream Composition and Mass Flow Rates in CCS Clusters
by Sven-Lasse Kahlke, Martin Pumpa, Stefan Schütz, Alfons Kather and Heike Rütters
Processes 2020, 8(9), 1188; https://doi.org/10.3390/pr8091188 - 18 Sep 2020
Cited by 6 | Viewed by 3275
Abstract
Temporal variations in CO2 stream composition and mass flow rates may occur in a CO2 transport network, as well as further downstream when CO2 streams of different compositions and temporally variable mass flow rates are fed in. To assess the [...] Read more.
Temporal variations in CO2 stream composition and mass flow rates may occur in a CO2 transport network, as well as further downstream when CO2 streams of different compositions and temporally variable mass flow rates are fed in. To assess the potential impacts of such variations on CO2 transport, injection, and storage, their characteristics must be known. We investigated variation characteristics in a scenario of a regional CO2 emitter cluster of seven fossil-fired power plants and four industrial plants that feed captured CO2 streams into a pipeline network. Variations of CO2 stream composition and mass flow rates in the pipelines were simulated using a network analysis tool. In addition, the potential effects of changes in the energy mix on resulting mass flow rates and CO2 stream compositions were investigated for two energy mix scenarios that consider higher shares of renewable energy sources or a replacement of lignite by hard coal and natural gas. While resulting maximum mass flow rates in the trunk line were similar in all considered scenarios, minimum flow rates and pipeline capacity utilisation differed substantially between them. Variations in CO2 stream composition followed the power plants’ operational load patterns resulting e.g., in stronger composition variations in case of higher renewable energy production. Full article
(This article belongs to the Special Issue Carbon Capture, Utilization and Storage Technology)
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<p>Schematic representation of the transport network (dimensions not to scale). The regional cluster of the 11 CO<sub>2</sub> emitters is 75 km in diameter, while the trunk pipeline has a length of 300 km in its onshore part. Numbers on the pipeline segments are identifiers for further considerations.</p> Full article ">Figure 2
<p>Net efficiency over the rated thermal input of the considered power plants equipped with CO<sub>2</sub> capture.</p> Full article ">Figure 3
<p>CO<sub>2</sub> emissions and resulting pipeline mass flow rates of industrial plants with additional CO<sub>2</sub> emissions due to CO<sub>2</sub> capture.</p> Full article ">Figure 4
<p>Stacked pipeline mass flow rates in the three examined energy mix scenarios during one year: (<b>A</b>) “RE 27%”, (<b>B</b>) “RE 45%” and (<b>C</b>) “No lignite”. (Note that the plants NGCC PCC3, Hard Coal PCC2, and Hard Coal Oxy2 occur only in scenario “No lignite”).</p> Full article ">Figure 5
<p>Relative full load hours (<b>A</b>) and pipeline mass flow rates (<b>B</b>) in the energy mix scenarios (the baseline scenario is scenario “RE 27%”).</p> Full article ">Figure 6
<p>Simulated variation (hourly averages) of individual impurity concentrations within the CO<sub>2</sub> stream at the entry of the trunk pipeline throughout one year (= 8760 h) for the baseline scenario “RE 27%” (T = 288 K, p<sub>inlet</sub> ≤ 12.33 MPa; note that COS concentration data are hidden behind the H<sub>2</sub>S data as its concentration values are identical to those of H<sub>2</sub>S throughout the year).</p> Full article ">Figure 7
<p>Simulated variation (hourly averages) of individual impurity concentrations within the CO<sub>2</sub> stream at the entry of the trunk pipeline throughout one year (= 8760 h) for the scenario “RE 45%” (T = 288 K, p<sub>inlet</sub> <tt>≤</tt> 12.41 MPa; note that COS concentration data are hidden behind the H<sub>2</sub>S data as its concentration values are identical to those of H<sub>2</sub>S throughout the year).</p> Full article ">Figure 8
<p>Simulated variation (hourly averages) of individual impurity concentrations at the entry of the trunk pipeline throughout one year (= 8760 h) for the scenario “No lignite” (T = 288 K, p<sub>inlet</sub> ≤ 12.62 MPa; note that COS concentration data are hidden behind the H<sub>2</sub>S data as its concentration values are identical to those of H<sub>2</sub>S throughout the year).</p> Full article ">Figure 9
<p>Viscosity variations at the trunk line entry calculated from the fed-in variable pipeline mass flow rates and CO<sub>2</sub> stream compositions (hourly averages) throughout one year (= 8760 h) for the scenarios “RE 27%”, “RE 45%”, and “No lignite” (note that the figure is zoomed in on the y-axis).</p> Full article ">Figure 10
<p>Modelled occurrence of different impurity concentrations at the trunk line entry per year—a comparison between the energy mix scenarios. (Occurrences of H<sub>2</sub>, CO, O<sub>2</sub>, SO<sub>2</sub>, and SO<sub>3</sub> concentrations are given in (<b>A</b>–<b>E</b>), respectively.)</p> Full article ">Figure 11
<p>Modelled occurrence of different pipeline mass flow rates at the trunk line entry per year—a comparison between the energy mix scenarios.</p> Full article ">
11 pages, 1110 KiB  
Article
Experimental Investigation of Stability of Vegetable Oils Used as Dielectric Fluids for Electrical Discharge Machining
by Maria-Crina Radu, Raluca Tampu, Valentin Nedeff, Oana-Irina Patriciu, Carol Schnakovszky and Eugen Herghelegiu
Processes 2020, 8(9), 1187; https://doi.org/10.3390/pr8091187 - 18 Sep 2020
Cited by 17 | Viewed by 3517
Abstract
One main drawback of electrical discharge machining (EDM) is related to the dielectric fluid, since it impacts both the environment and operator health and safety. To resolve these issues, recent research has demonstrated the technical feasibility and qualitative performance of vegetable oils as [...] Read more.
One main drawback of electrical discharge machining (EDM) is related to the dielectric fluid, since it impacts both the environment and operator health and safety. To resolve these issues, recent research has demonstrated the technical feasibility and qualitative performance of vegetable oils as substitutes for hydrocarbon-based dielectric and synthetic oils in EDM. However, due to the higher content of unsaturated fatty acids, vegetable oils lose their stability, due to several factors such as heating or exposure to light or oxygen. The present study is a first attempt to analyze the extent to which the physic-chemical properties of vegetable oils change during EDM processing. Refractive index, dynamic viscosity and spectra analyses were conducted for sunflower and soybean oils. The results revealed that, under the applied processing conditions, no structural changes occurred. These findings are very promising from the perspective of EDM sustainability. Full article
(This article belongs to the Section Environmental and Green Processes)
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<p>EDM set-up: (<b>a</b>) Copper pot incorporated into the machine tank; (<b>b</b>) Copper electrode.</p> Full article ">Figure 2
<p>UV/Vis spectra of dielectric fluids: (<b>a</b>) Before machining; (<b>b</b>) Sunflower oil, before and after machining the 17–4 PH stainless steel; (<b>c</b>) Sunflower oil, before and after machining the AZ31B magnesium alloy; (<b>d</b>) Sunflower oil, before and after machining the AA 7075-T7351 aluminum alloy.</p> Full article ">Figure 3
<p>FTIR spectra of the sunflower oil.</p> Full article ">
11 pages, 1405 KiB  
Article
Maintaining Physicochemical, Microbiological, and Sensory Quality of Pineapple Juice (Ananas comosus, Var. ‘Queen Victoria’) through Mild Heat Treatment
by Charlène Leneveu-Jenvrin, Baptiste Quentin, Sophie Assemat and Fabienne Remize
Processes 2020, 8(9), 1186; https://doi.org/10.3390/pr8091186 - 18 Sep 2020
Cited by 16 | Viewed by 6609
Abstract
Shelf life of freshly prepared pineapple juice is short and requires refrigerated conditions of storage. Mild heat treatment remains the easiest way to prolong juice shelf life for small companies. This study was constructed to assess pineapple cv. Queen Victoria juice shelf life [...] Read more.
Shelf life of freshly prepared pineapple juice is short and requires refrigerated conditions of storage. Mild heat treatment remains the easiest way to prolong juice shelf life for small companies. This study was constructed to assess pineapple cv. Queen Victoria juice shelf life from a broad examination of its quality and to propose the most appropriate thermal treatment to increase shelf life without any perceptible decrease in quality. From 25 independent batches of pineapple, collected in different areas and seasons from Reunion Island, the variability of juice physicochemical and microbiological quality was determined. Juice pH values were the highest for fruit harvested in summer, but the juice acidity remained low enough to prevent pathogen spore-forming bacteria growth. During storage at 4 °C, color was modified, and yeasts and molds were the main microbial group exhibiting growth. Assessment of sensory quality resulted in the proposal of a shelf life comprising between three and seven days. Compared to higher temperatures, heat treatment at 60 °C was enough to ensure a good microbiological quality for 30 days, but sensory characteristics and color changes led to the proposal of a shelf life of seven days for pineapple juice treated at 60 °C. Full article
(This article belongs to the Special Issue Processing Foods: Process Optimization and Quality Assessment)
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<p>Microbial population (log CFU/g) modifications during refrigerated storage of untreated pineapple juice; (<b>a</b>) psychrotrophic bacteria, (<b>b</b>) enterobacteria, and (<b>c</b>) yeasts and molds. Black circles: mean; open triangle: extreme values; boxes: 1st and 3rd quartiles.</p> Full article ">Figure 2
<p>Variation of color parameters for different pasteurization temperatures. (<b>a</b>) L*, (<b>b</b>) a*, (<b>c</b>) b* and (<b>d</b>) color difference. Each bar represents the difference with the unpasteurized juice. For 86 °C, <span class="html-italic">n</span> = 1; for 80 °C, <span class="html-italic">n</span> = 2; for 70 °C, <span class="html-italic">n</span> = 1; and for 60 °C, <span class="html-italic">n</span> = 4.</p> Full article ">Figure 3
<p>Proportion of panelist answers in triangle test comparison of (<b>a</b>) untreated juice at day 0 vs. pasteurized juice at day 0, (<b>b</b>) untreated juice at day 0 vs. pasteurized juice after 7 days of storage at 4 °C, (<b>c</b>) untreated juice after 7 days of storage at 4 °C vs. pasteurized juice after 7 days of storage at 4 °C.</p> Full article ">
18 pages, 3405 KiB  
Article
Finite Element Study of Magnetohydrodynamics (MHD) and Activation Energy in Darcy–Forchheimer Rotating Flow of Casson Carreau Nanofluid
by Bagh Ali, Ghulam Rasool, Sajjad Hussain, Dumitru Baleanu and Sehrish Bano
Processes 2020, 8(9), 1185; https://doi.org/10.3390/pr8091185 - 18 Sep 2020
Cited by 54 | Viewed by 3113
Abstract
Here, a study for MHD (magnetohydrodynamic) impacts on the rotating flow of Casson Carreau nanofluids is considered. The temperature distribution is associated with thermophoresis, Brownian motion, and heat source. The diffusion of chemically reactive specie is investigated with Arrhenius activation energy. The governing [...] Read more.
Here, a study for MHD (magnetohydrodynamic) impacts on the rotating flow of Casson Carreau nanofluids is considered. The temperature distribution is associated with thermophoresis, Brownian motion, and heat source. The diffusion of chemically reactive specie is investigated with Arrhenius activation energy. The governing equations in the 3D form are changed into dimensionless two-dimensional form with the implementation of suitable scaling transformations. The Variational finite element procedure is harnessed and coded in Matlab script to obtain the numerical solution of the coupled non-linear partial differential problem. The variation patterns of Sherwood number, Nusselt number, skin friction coefficients, velocities, concentration, and temperature functions are computed to reveal the physical nature of this examination. It is seen that higher contributions of the magnetic force, Casson fluid, and rotational fluid parameters cause a raise in the temperature like thermophoresis and Brownian motion does but also causes a slowing down in the primary as well as secondary velocities. The FEM solutions show an excellent correlation with published results. The current study has significant applications in the biomedical, modern technologies of aerospace systems, and relevance to energy systems. Full article
(This article belongs to the Section Advanced Digital and Other Processes)
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<p>Schematic configuration with coordinate system.</p> Full article ">Figure 2
<p>Finite element grid and finite element mesh of rectangular element.</p> Full article ">Figure 3
<p>Fluctuation of <math display="inline"><semantics> <mrow> <msup> <mover accent="true"> <mi>f</mi> <mo stretchy="false">˜</mo> </mover> <mo>′</mo> </msup> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>h</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> along with kp and Fr.</p> Full article ">Figure 4
<p>Fluctuation of <math display="inline"><semantics> <mrow> <msup> <mover accent="true"> <mi>f</mi> <mo stretchy="false">˜</mo> </mover> <mo>′</mo> </msup> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>h</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> along with M and <math display="inline"><semantics> <mi>β</mi> </semantics></math>.</p> Full article ">Figure 5
<p>Fluctuation of <math display="inline"><semantics> <mrow> <msup> <mover accent="true"> <mi>f</mi> <mo stretchy="false">˜</mo> </mover> <mo>′</mo> </msup> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>h</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> along with <math display="inline"><semantics> <mi>λ</mi> </semantics></math> and <math display="inline"><semantics> <mi>τ</mi> </semantics></math>.</p> Full article ">Figure 6
<p>Fluctuation of <math display="inline"><semantics> <mrow> <msup> <mover accent="true"> <mi>f</mi> <mo stretchy="false">˜</mo> </mover> <mo>′</mo> </msup> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>h</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> along with We and <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math>.</p> Full article ">Figure 7
<p>Fluctuation of <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> along with Nb, Nt, <math display="inline"><semantics> <msub> <mi>γ</mi> <mi>T</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>Q</mi> <mi>s</mi> </msub> </semantics></math>.</p> Full article ">Figure 8
<p>Fluctuation of <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>,</mo> <mi>η</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> along with <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>E</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mi mathvariant="sans-serif">Ω</mi> </semantics></math>, Nt, and Nb.</p> Full article ">Figure 9
<p>Fluctuation of <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mi>f</mi> <mi>x</mi> </msub> <mi>R</mi> <msubsup> <mi>e</mi> <mi>x</mi> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mi>f</mi> <mi>y</mi> </msub> <mi>R</mi> <msubsup> <mi>e</mi> <mi>y</mi> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </msubsup> </mrow> </semantics></math> along with <math display="inline"><semantics> <mrow> <mi>K</mi> <mi>p</mi> </mrow> </semantics></math> and Fr.</p> Full article ">Figure 10
<p>Fluctuation of <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mi>f</mi> <mi>x</mi> </msub> <mi>R</mi> <msubsup> <mi>e</mi> <mi>x</mi> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mi>f</mi> <mi>y</mi> </msub> <mi>R</mi> <msubsup> <mi>e</mi> <mi>y</mi> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </msubsup> </mrow> </semantics></math> along with M and <math display="inline"><semantics> <mi>β</mi> </semantics></math>.</p> Full article ">Figure 11
<p>Fluctuation of <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>u</mi> <mi>x</mi> </msub> <mi>R</mi> <msubsup> <mi>e</mi> <mi>x</mi> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>S</mi> <msub> <mi>h</mi> <mi>x</mi> </msub> <mi>R</mi> <msubsup> <mi>e</mi> <mi>x</mi> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </msubsup> </mrow> </semantics></math> along with Nb, Nt, M, and <math display="inline"><semantics> <mi>β</mi> </semantics></math>.</p> Full article ">Figure 12
<p>Fluctuation of <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>u</mi> <mi>x</mi> </msub> <mi>R</mi> <msubsup> <mi>e</mi> <mi>x</mi> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>S</mi> <msub> <mi>h</mi> <mi>x</mi> </msub> <mi>R</mi> <msubsup> <mi>e</mi> <mi>x</mi> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </msubsup> </mrow> </semantics></math> along with Nb, Nt, <math display="inline"><semantics> <mi>λ</mi> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>Q</mi> <mi>s</mi> </msub> </semantics></math>.</p> Full article ">Figure 13
<p>Fluctuation of <math display="inline"><semantics> <mrow> <mi>S</mi> <msub> <mi>h</mi> <mi>x</mi> </msub> <mi>R</mi> <msubsup> <mi>e</mi> <mi>x</mi> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </msubsup> </mrow> </semantics></math> along with Kp, Fr, EE, Le, and <math display="inline"><semantics> <mi mathvariant="sans-serif">Ω</mi> </semantics></math>.</p> Full article ">
23 pages, 3947 KiB  
Article
Methodology to Solve the Multi-Objective Optimization of Acrylic Acid Production Using Neural Networks as Meta-Models
by Geraldine Cáceres Sepulveda, Silvia Ochoa and Jules Thibault
Processes 2020, 8(9), 1184; https://doi.org/10.3390/pr8091184 - 18 Sep 2020
Cited by 5 | Viewed by 3318
Abstract
It is paramount to optimize the performance of a chemical process in order to maximize its yield and productivity and to minimize the production cost and the environmental impact. The various objectives in optimization are often in conflict, and one must determine the [...] Read more.
It is paramount to optimize the performance of a chemical process in order to maximize its yield and productivity and to minimize the production cost and the environmental impact. The various objectives in optimization are often in conflict, and one must determine the best compromise solution usually using a representative model of the process. However, solving first-principle models can be a computationally intensive problem, thus making model-based multi-objective optimization (MOO) a time-consuming task. In this work, a methodology to perform the multi-objective optimization for a two-reactor system for the production of acrylic acid, using artificial neural networks (ANNs) as meta-models, is proposed in an effort to reduce the computational time required to circumscribe the Pareto domain. The performance of the meta-model confirmed good agreement between the experimental data and the model-predicted values of the existent relationships between the eight decision variables and the nine performance criteria of the process. Once the meta-model was built, the Pareto domain was circumscribed based on a genetic algorithm (GA) and ranked with the net flow method (NFM). Using the ANN surrogate model, the optimization time decreased by a factor of 15.5. Full article
(This article belongs to the Collection Multi-Objective Optimization of Processes)
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<p>Process flow diagram of the two-reactor section of the production of acrylic acid.</p> Full article ">Figure 2
<p>Reaction scheme for the propylene oxidation reactor (adapted from [<a href="#B18-processes-08-01184" class="html-bibr">18</a>]).</p> Full article ">Figure 3
<p>Flowchart of the proposed methodology for solving multi-objective optimization using a three-layer artificial neural network (ANN) as meta-model.</p> Full article ">Figure 4
<p>Three layer feedforward artificial neural network for the acrylic acid MOO problem.</p> Full article ">Figure 5
<p>R<sup>2</sup> values for all the objective functions using 50 learning and 20 validation uniform design (UD) design points vs. Number of neurons in the hidden layer.</p> Full article ">Figure 6
<p>R<sup>2</sup> values for different number of random data points used for training and validation vs. Number of neurons in the hidden layer for (<b>a</b>) compression power in C-100 and (<b>b</b>) heat recovery in R-101.</p> Full article ">Figure 7
<p>Predictions of (<b>a</b>) conversion in R-101 and (<b>b</b>) compression power in C-100.</p> Full article ">Figure 8
<p>Ranked Pareto domain with net flow method (NFM) obtained with: (<b>a</b>) and (<b>c</b>) the phenomenological model and (<b>b</b>) and (<b>d</b>) the ANNs.</p> Full article ">Figure 9
<p>Best ranked solution for (<b>a</b>) decision variables and (<b>b</b>) objective functions using NFM with ANN, normalized with respect to the best solution of the phenomenological model.</p> Full article ">
24 pages, 1582 KiB  
Article
PID Tuning Method Based on IMC for Inverse-Response Second-Order Plus Dead Time Processes
by Duby Castellanos-Cárdenas, Fabio Castrillón, Rafael E. Vásquez and Carlos Smith
Processes 2020, 8(9), 1183; https://doi.org/10.3390/pr8091183 - 18 Sep 2020
Cited by 9 | Viewed by 5989
Abstract
This work addresses a set of tuning rules for PID controllers based on Internal Model Control (IMC) for inverse-response second-order systems with dead time. The transfer function, and some time-response characteristics for such systems are first described. Then, the IMC-based methodology is developed [...] Read more.
This work addresses a set of tuning rules for PID controllers based on Internal Model Control (IMC) for inverse-response second-order systems with dead time. The transfer function, and some time-response characteristics for such systems are first described. Then, the IMC-based methodology is developed by using an optimization objective function that mixes performance and robustness. A correlation that minimizes the objective function and that allows the user to compute the controller’s tuning parameter is found. The obtained expressions are mathematically simple, which facilitate their application in a ten-step systematic methodology. Finally, the proposed tuning method is compared to other well-known tuning rules that have been reported in literature, for a wide range of parameters of the process. The performance achieved with the proposed method is very good not only for disturbance rejection but for set-point tracking, when considering a wide-range of parameters of the process’ transfer function. Full article
(This article belongs to the Section Process Control and Monitoring)
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<p>Internal Model Control.</p> Full article ">Figure 2
<p>Behavior of <math display="inline"><semantics> <msub> <mover accent="true"> <mi>τ</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>c</mi> <mi>O</mi> <mi>F</mi> </mrow> </msub> </semantics></math> for different values of <math display="inline"><semantics> <msub> <mover accent="true"> <mi>τ</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>c</mi> <mi>u</mi> <mi>l</mi> <mi>t</mi> </mrow> </msub> </semantics></math>, using different values of <math display="inline"><semantics> <mi>γ</mi> </semantics></math>. The range of <math display="inline"><semantics> <msub> <mover accent="true"> <mi>τ</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>c</mi> <mi>u</mi> <mi>l</mi> <mi>t</mi> </mrow> </msub> </semantics></math> was selected arbitrarily for illustration purposes.</p> Full article ">Figure 3
<p>Behavior of <math display="inline"><semantics> <msub> <mi>m</mi> <mrow> <mi>O</mi> <mi>F</mi> </mrow> </msub> </semantics></math> with respect to variations in <math display="inline"><semantics> <mi>γ</mi> </semantics></math>. %TO: percentage of transmitter output, %CO: percentage of controller output, UT: units of time.</p> Full article ">Figure 4
<p>CCV PID tuning methodology.</p> Full article ">Figure 5
<p>Time response disturbance rejection for the set <math display="inline"><semantics> <msub> <mi>P</mi> <mn>4</mn> </msub> </semantics></math>. %TO: percentage of transmitter output, UT: units of time.</p> Full article ">Figure 6
<p>Time response set-point tracking for the set <math display="inline"><semantics> <msub> <mi>P</mi> <mn>4</mn> </msub> </semantics></math>. %TO: percentage of transmitter output, UT: units of time.</p> Full article ">Figure A1
<p>Time response disturbance rejection for the set <math display="inline"><semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics></math>. %TO: percentage of transmitter output, UT: units of time.</p> Full article ">Figure A2
<p>Time response set-point tracking for the set <math display="inline"><semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics></math>. %TO: percentage of transmitter output, UT: units of time.</p> Full article ">Figure A3
<p>Time response disturbance rejection for the set <math display="inline"><semantics> <msub> <mi>P</mi> <mn>2</mn> </msub> </semantics></math>. %TO: percentage of transmitter output, UT: units of time.</p> Full article ">Figure A4
<p>Time response set-point tracking for the set <math display="inline"><semantics> <msub> <mi>P</mi> <mn>2</mn> </msub> </semantics></math>. %TO: percentage of transmitter output, UT: units of time.</p> Full article ">Figure A5
<p>Time response disturbance rejection for the set <math display="inline"><semantics> <msub> <mi>P</mi> <mn>3</mn> </msub> </semantics></math>. %TO: percentage of transmitter output, UT: units of time.</p> Full article ">Figure A6
<p>Time response set-point tracking for the set <math display="inline"><semantics> <msub> <mi>P</mi> <mn>3</mn> </msub> </semantics></math>. %TO: percentage of transmitter output, UT: units of time.</p> Full article ">Figure A7
<p>Time response disturbance rejection for the set <math display="inline"><semantics> <msub> <mi>P</mi> <mn>5</mn> </msub> </semantics></math>. %TO: percentage of transmitter output, UT: units of time.</p> Full article ">Figure A8
<p>Time response set-point tracking for the set <math display="inline"><semantics> <msub> <mi>P</mi> <mn>5</mn> </msub> </semantics></math>. %TO: percentage of transmitter output, UT: units of time.</p> Full article ">Figure A9
<p>Time response disturbance rejection for the set <math display="inline"><semantics> <msub> <mi>P</mi> <mn>6</mn> </msub> </semantics></math>. %TO: percentage of transmitter output, UT: units of time.</p> Full article ">Figure A10
<p>Time response set-point tracking for the set <math display="inline"><semantics> <msub> <mi>P</mi> <mn>6</mn> </msub> </semantics></math>. %TO: percentage of transmitter output, UT: units of time.</p> Full article ">
20 pages, 3207 KiB  
Article
Life-Cycle Assessment of Dairy Products—Case Study of Regional Cheese Produced in Portugal
by Óscar Soares Nunes, Pedro Dinis Gaspar, José Nunes, Paula Quinteiro, Ana Cláudia Dias and Radu Godina
Processes 2020, 8(9), 1182; https://doi.org/10.3390/pr8091182 - 18 Sep 2020
Cited by 8 | Viewed by 6604
Abstract
Nowadays, there is a growing promotion to label products ecologically in European markets. Knowing that daily products have relevant environmental impact associated with their production, it is of utmost importance to analyse all the related production processes for a better understanding of each [...] Read more.
Nowadays, there is a growing promotion to label products ecologically in European markets. Knowing that daily products have relevant environmental impact associated with their production, it is of utmost importance to analyse all the related production processes for a better understanding of each process impact. The present study analysed the potential environmental impacts of a Portuguese regional product, the Beira Baixa cheese, coming from the largest national sheep milk region. So, a life cycle assessment (LCA) methodology is used from -cradle-to -gate, including the supplying of the animal feedstock. Impact calculations are performed using the ReCiPe midpoint 2008 method, allowing an analysis of the environmental impacts contributing to climate change, terrestrial acidification, freshwater and marine eutrophication of all productive processes. The results have shown that the greatest impacts occur within the milk production process for all four selected impact categories. This happens mainly due to the fodder cultivation process, also necessary to produce animal feed, which contain processes of fertilization and land preparation. The enteric fermentation and manure management processes have also shown relevant contributions. The impact assessment also showed that the cheesemaking industry has practically insignificant impacts. Nonetheless, the cheesemaking industry can promote their business with these results, by advertising and marketing their product as environmentally friendly, with production processes causing reduced impacts, and therefore also their products. Full article
(This article belongs to the Special Issue Sustainable Development of Waste towards Green Growth)
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<p>Geographical location of the Beira Baixa PDO cheese production area.</p> Full article ">Figure 2
<p>System boundaries.</p> Full article ">Figure 3
<p>Inventory from both main systems, dairy farm and dairy factory.</p> Full article ">Figure 4
<p>Relative contribution of involved system processes for cheese production by impact category.</p> Full article ">Figure 5
<p>Climate change results associated with each sub-system of the production of 1 kg of cheese.</p> Full article ">Figure 6
<p>Terrestrial acidification results associated with each sub-system of the production of 1 kg of cheese.</p> Full article ">Figure 7
<p>Freshwater eutrophication results associated with each sub-system of the production of 1 kg of cheese.</p> Full article ">Figure 8
<p>Marine eutrophication results associated with each sub-system of the production of 1 kg of cheese.</p> Full article ">Figure 9
<p>Climate change results comparison with similar studies.</p> Full article ">Figure 10
<p>Terrestrial acidification results comparison with similar studies.</p> Full article ">Figure 11
<p>Freshwater eutrophication result comparison with similar studies.</p> Full article ">Figure 12
<p>Marine eutrophication result comparison with similar studies.</p> Full article ">
18 pages, 7796 KiB  
Article
Degradation of Direct Blue 1 through Heterogeneous Photocatalysis with TiO2 Irradiated with E-Beam
by Elvia Gallegos, Florinella Muñoz Bisesti, Katherine Vaca-Escobar, Cristian Santacruz, Lenys Fernández, Alexis Debut and Patricio J. Espinoza-Montero
Processes 2020, 8(9), 1181; https://doi.org/10.3390/pr8091181 - 18 Sep 2020
Cited by 5 | Viewed by 3611
Abstract
Most dyes used in the textile industry are chemically stable and poorly biodegradable, therefore, they are persistent in the environment and difficult to degrade by conventional methods. An alternative treatment for this kind of substance is heterogeneous photocatalysis using TiO2, so, [...] Read more.
Most dyes used in the textile industry are chemically stable and poorly biodegradable, therefore, they are persistent in the environment and difficult to degrade by conventional methods. An alternative treatment for this kind of substance is heterogeneous photocatalysis using TiO2, so, in this work, it is proposed to degrade Direct Blue 1 (DB1) using microparticulate TiO2 irradiated with e-beam at three different doses: 5, 10 and 20 kGy (J/kg). The DB1 degradation was implemented in a batch reactor (DB1 initial concentration = 50 mg L−1, pH 2.5, TiO2 concentration = 200 mg L−1). We have demonstrated that the photocatalytic power of TiO2, when irradiated with e-beam (5, 10, 20 kGy), varies slightly, with minor effects on photodegradation performance. However, the dose of 10 kGy showed a slightly better result, according to the DB1 photodegradation rate constant. Adsorption process was not affected by irradiation; its isotherm was fitted to Freundlich’s mathematical model. The DB1 photodegradation rate constants, after one hour of treatment, were: 0.0661 and 0.0742 min−1 for irradiated (10 kGy) and nonirradiated TiO2, respectively. The degradation rate constant has an increase of 12.3% for irradiated TiO2. Finally, there was no evidence of mineralization in the degradation process after 60 min of treatment. According to the results, the irradiation of microparticulate TiO2 with e-beam (10 kGy) slightly improves the photodegradation rate constant of DB1. Full article
(This article belongs to the Special Issue Photocatalytic Processes for Environmental Applications)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Direct Blue 1 (DB1) structure [<a href="#B8-processes-08-01181" class="html-bibr">8</a>].</p> Full article ">Figure 2
<p>Diagram of the photoreactor used in the degradation of the DB1 dye. (1) Water inlet, (2) UV lamps, (3) Samples, (4) Magnetic stirrers, (5) Water outlet.</p> Full article ">Figure 3
<p>TiO<sub>2</sub> X-ray diffraction with and without irradiation.</p> Full article ">Figure 4
<p>FT-IR spectra of TiO<sub>2</sub> with and without e-beam treatment measured under ambient conditions. Inset, a magnified view of the spectrum of the samples.</p> Full article ">Figure 5
<p>Diffuse Reflectance Spectroscopy (DRS) spectra of TiO<sub>2</sub> with and without e-beam treatment are displayed. Inset, the band gaps estimated from the DRS spectra are summarized.</p> Full article ">Figure 6
<p>Energy-Dispersive X-ray Spectroscopy (EDS) mapping of composition of sample of TiO<sub>2</sub> without irradiation, and EDS spectrum of irradiated and unirradiated TiO<sub>2</sub>.</p> Full article ">Figure 6 Cont.
<p>Energy-Dispersive X-ray Spectroscopy (EDS) mapping of composition of sample of TiO<sub>2</sub> without irradiation, and EDS spectrum of irradiated and unirradiated TiO<sub>2</sub>.</p> Full article ">Figure 7
<p>DB1 photodegradation on TiO<sub>2</sub> with and without e-beam treatment is displayed. Conditions: C<sub>o, DB1</sub> = 50 mg L<sup>−1</sup>; catalyst dose = 200 mg L<sup>−1</sup>; pH<sub>o</sub> = 2,5; T<sub>amb</sub> ≈ 20 °C.</p> Full article ">Figure 8
<p>DB1 linear adjustment according to pseudo first order kinetics, data from <a href="#processes-08-01181-f007" class="html-fig">Figure 7</a>.</p> Full article ">Figure 9
<p>Statistical analysis of irradiation dose effect on the photodegradation of DB1. Conditions: C<sub>o, DB1</sub> = 50 mg L<sup>−1</sup>; catalyst dose = 200 mg L<sup>−1</sup>; pH<sub>o</sub> = 2.5; T<sub>amb</sub> ≈ 20 °C.</p> Full article ">Figure 10
<p>Adsorption DB1 dye isotherms of unirradiated and irradiated TiO<sub>2</sub> at different doses (5, 10 y 20 kGy).</p> Full article ">Figure 11
<p>Statistical analysis of irradiation dose effect on DB1 adsorption. (C<sub>O, DB1</sub> = 50 mg L<sup>−1</sup>; catalyst dose = 200 mg L<sup>−1</sup>; pH<sub>o</sub> = 2.5; T<sub>amb</sub>).</p> Full article ">Figure 12
<p>Contact angle by sessile drop without drying the sample (ambient), of the nonirradiated and irradiated TiO<sub>2</sub>. Insert, contact angle.</p> Full article ">
16 pages, 4599 KiB  
Article
SuperPro Designer®, User-Oriented Software Used for Analyzing the Techno-Economic Feasibility of Electrical Energy Generation from Sugarcane Vinasse in Colombia
by Licelly Canizales, Fredy Rojas, Carlos A. Pizarro, Nelson. H. Caicedo-Ortega and M. F. Villegas-Torres
Processes 2020, 8(9), 1180; https://doi.org/10.3390/pr8091180 - 18 Sep 2020
Cited by 28 | Viewed by 11447
Abstract
SuperPro Designer® is a process simulator software used for analyzing the techno-economic feasibility of large-scale bioprocesses. Its predetermined built-in features allow for easy implementation by non-experts, but a lack of examples limits its appropriation. This study aims to validate the implementation of [...] Read more.
SuperPro Designer® is a process simulator software used for analyzing the techno-economic feasibility of large-scale bioprocesses. Its predetermined built-in features allow for easy implementation by non-experts, but a lack of examples limits its appropriation. This study aims to validate the implementation of SuperPro Designer® by non-experts for the techno-economic analysis of anaerobic digestion in Colombia, using vinasse as feedstock. These results demonstrate the financial feasibility of such a process when a processing flow rate of 25 m3/h is ensured. Additionally, this study validates the manageability of the tool for assessing the economic feasibility of a technology, a key practice during technology development regardless of the area of expertise. Full article
(This article belongs to the Special Issue Biomass to Renewable Energy Processes)
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<p>Process flow for electrical energy generation from biogas generated by a sugarcane-based vinasse-fed anaerobic digestion (AD) plant.</p> Full article ">Figure 2
<p>Variation in the biogas production yield as a function of the % VS of the annexed distilleries in Colombia. Triplicates from the same sample are represented by circles. Circles with the same color indicate samples from the same plant. The blue line represents a confidence level of 95%.</p> Full article ">Figure 3
<p>Effect of the vinasse processing flow rate on biogas flow and electricity generation. Red circles: power generation; blue triangles: biogas flow.</p> Full article ">Figure 4
<p>Effect of the vinasse processing flow rate on the electrical energy yield of an AD plant.</p> Full article ">Figure 5
<p>Effect of vinasse processing flow rate on electrical generation capacity (capital expenditure (CAPEX) and operational expenditure (OPEX)). Blue circles: CAPEX; red triangles: electrical generation capacity; and green squares: OPEX.</p> Full article ">Figure 6
<p>OPEX breakdown as a function of the vinasse processing flow rate. Orange squares: utilities; purple inverted triangles: other facility-dependent; red diamonds: labor-dependent; yellow circles: depreciation; green crosses: raw materials; and pink triangles: laboratory/QC/QA.</p> Full article ">Figure 7
<p>Economic assessment for the implementation of a vinasse-fed AD plant. (<b>A</b>) Gross margin, (<b>B</b>) return on investment (ROI), (<b>C</b>) payback time, and (<b>D</b>) net present value (NPV).</p> Full article ">Figure 8
<p>Internal rate of return as a function of the vinasse processing flow rate and cost/return of the digestate for a vinasse-fed AD plant.</p> Full article ">Figure 9
<p>Geographical distribution of annexed distilleries in the valley of the Cauca River.</p> Full article ">
31 pages, 4801 KiB  
Article
High-Throughput Raman Spectroscopy Combined with Innovate Data Analysis Workflow to Enhance Biopharmaceutical Process Development
by Stephen Goldrick, Alexandra Umprecht, Alison Tang, Roman Zakrzewski, Matthew Cheeks, Richard Turner, Aled Charles, Karolina Les, Martyn Hulley, Chris Spencer and Suzanne S. Farid
Processes 2020, 8(9), 1179; https://doi.org/10.3390/pr8091179 - 17 Sep 2020
Cited by 27 | Viewed by 9954
Abstract
Raman spectroscopy has the potential to revolutionise many aspects of biopharmaceutical process development. The widespread adoption of this promising technology has been hindered by the high cost associated with individual probes and the challenge of measuring low sample volumes. To address these issues, [...] Read more.
Raman spectroscopy has the potential to revolutionise many aspects of biopharmaceutical process development. The widespread adoption of this promising technology has been hindered by the high cost associated with individual probes and the challenge of measuring low sample volumes. To address these issues, this paper investigates the potential of an emerging new high-throughput (HT) Raman spectroscopy microscope combined with a novel data analysis workflow to replace off-line analytics for upstream and downstream operations. On the upstream front, the case study involved the at-line monitoring of an HT micro-bioreactor system cultivating two mammalian cell cultures expressing two different therapeutic proteins. The spectra generated were analysed using a partial least squares (PLS) model. This enabled the successful prediction of the glucose, lactate, antibody, and viable cell density concentrations directly from the Raman spectra without reliance on multiple off-line analytical devices and using only a single low-volume sample (50–300 μL). However, upon the subsequent investigation of these models, only the glucose and lactate models appeared to be robust based upon their model coefficients containing the expected Raman vibrational signatures. On the downstream front, the HT Raman device was incorporated into the development of a cation exchange chromatography step for an Fc-fusion protein to compare different elution conditions. PLS models were derived from the spectra and were found to predict accurately monomer purity and concentration. The low molecular weight (LMW) and high molecular weight (HMW) species concentrations were found to be too low to be predicted accurately by the Raman device. However, the method enabled the classification of samples based on protein concentration and monomer purity, allowing a prioritisation and reduction in samples analysed using A280 UV absorbance and high-performance liquid chromatography (HPLC). The flexibility and highly configurable nature of this HT Raman spectroscopy microscope makes it an ideal tool for bioprocess research and development, and is a cost-effective solution based on its ability to support a large range of unit operations in both upstream and downstream process operations. Full article
(This article belongs to the Special Issue Measurement Technologies for up- and Downstream Bioprocessing)
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<p>Raman spectroscopy model generation workflow defining the primary steps to ensure that a robust MVDA model is generated. SNV = standard normal variate, RMSE = root mean square, and RMSEP = root mean square error of prediction, MVDA = multivariate data analysis.</p> Full article ">Figure 2
<p>Time series plots of off-line analytics recorded on a micro-bioreactor system for (<b>A</b>) glucose concentration, (<b>B</b>) viable cell density (VCD) concentration, (<b>C</b>) lactate concentration, and (<b>D</b>) antibody concentration. Cell line A is indicated by the solid lines with the red lines and squares representing the calibration cell cultures: 1–5 and 7–11. The validation runs are indicated by the blue lines with diamonds, and represent cell cultures 6 and 12. Cell line B is indicated by the dashed lines with the green lines and squares representing the calibration cell cultures: 13–17 and 19–23. The validation runs are indicated by the yellow lines with diamonds and represent cell cultures 18 and 24. The micro-bioreactor system had 24 parallel bioreactors with an initial working volume of 13.5 mL, operated using a pH set-point of 7, DO<sub>2</sub> concentrations of between 10–40%, and a temperature maintained at 35.5 °C.</p> Full article ">Figure 3
<p>Raman spectra recorded by the high-throughput Raman spectroscopy microscope for each of the 24 micro-bioreactor cell culture runs on day 0–10 shown in the form of (<b>A</b>) the raw spectral data and (<b>B</b>) the baseline-corrected spectra, using a 1st order polynomial function followed by the application of a standard normal variate (SNV) scattering scatter algorithm and a Savitzky–Golay smoothing filter. Each spectrum was generated using 5 accumulations each with a 30 s acquisition time, recorded using 10% laser power (30 mW).</p> Full article ">Figure 4
<p>Parity plots demonstrating the prediction performance of four separate PLS models, highlighting the calibration and validation data sets of cell line A and B for (<b>A</b>) glucose concentration, (<b>B</b>) viable cell density (VCD), (<b>C</b>) lactate concentration, and (<b>D</b>) antibody concentration. Four separate PLS models were built utilising 7 latent variables for each variable investigated, and calibrated using cell culture runs 1–5 and 7–11 from cell line A (indicated by the red squares) and runs 13–17 and 19–23 from cell line B (indicated by the green squares). The model was validated using cell culture runs 6 and 12 from cell line A (indicated by the blue diamonds) and runs 18 and 24 from cell line B (indicated by the yellow diamonds). The spectral data utilised in each PLS model were baseline-corrected spectra using a 1st order polynomial function, followed by the application of an SNV scattering scatter algorithm and a Savitzky–Golay smoothing filter.</p> Full article ">Figure 5
<p>PLS regression coefficients (β) of each PLS model generated for (<b>A</b>) glucose, (<b>B</b>) viable cell density, (<b>C</b>) lactate, and (<b>D</b>) antibodies, with the wavenumbers corresponding to the Raman molecular signature of each variable highlighted by the shaded areas. The cut-off points for these PLS regression coefficients are glucose: <span class="html-italic">β</span> &gt; 0.02; VCD: <span class="html-italic">β</span> &gt; 0.2; lactate: <span class="html-italic">β</span> &gt; 0.05; and antibody: <span class="html-italic">β</span> &gt; 0.015. The spectral data utilised in each PLS model were baseline-corrected spectra using a 1st order polynomial function, followed by the application of an SNV scattering scatter algorithm and a Savitzky–Golay smoothing filter.</p> Full article ">Figure 6
<p>Prediction performance of the PLS model predicting product concentration with (<b>A</b>) displaying a parity plot for validation set T1, (<b>B</b>) parity plot for calibration set P1, (<b>C</b>) parity plot for calibration set P2, and (<b>D</b>) confusion matrix that classifies the product concentration predictions above or below 1.5 g L<sup>−1</sup> in comparison to the experimental product concentrations. In the confusion matrix (<b>D</b>), the top left cell indicates true positives, the top middle cell indicates false positives, the middle left cell indicates false negatives, and the middle cell indicates true negatives. In each of these matrix cells, the value indicates the number and percentage of samples in each category. In the total matrix cell columns and rows, the total number of samples is given, with the percentage indicating the number of true positives or true negatives.</p> Full article ">Figure 7
<p>Prediction performance of the PLS model predicting the monomer purity of the fusion protein with (<b>A</b>) displaying a parity plot for validation set T1, (<b>B</b>) parity plot for calibration set P1, (<b>C</b>) parity plot for calibration set P2, and (<b>D</b>) confusion matrix that classifies the monomer purity predictions above or below 90% in comparison to the experimental monomer purity. In the confusion matrix (<b>D</b>), the top left cell indicates true positives, the top middle cell indicates false positives, the middle left cell indicates false negatives, and the middle cell indicates true negatives. In each of these matrix cells, the value indicates the number and percentage of samples in each category. In the total matrix cell columns and rows, the total number of samples is given, with the percentage indicating the number of true positives or true negatives.</p> Full article ">Figure 8
<p>PLS regression coefficient (<math display="inline"><semantics> <mi>β</mi> </semantics></math>) plots for each PLS model generated for (<b>A</b>) the monomer purity model and (<b>B</b>) product concentration, with the wavenumbers corresponding to the Raman molecular signature of each variable highlighted by the shaded areas. The cut-off points for these PLS regression coefficients are product: <span class="html-italic">β</span> &gt; 0.005; monomer <span class="html-italic">β</span> &gt; 0.075. The PLS model for monomer purity was developed using the raw spectra. The PLS model for the product concentration was developed using spectra that was baseline-corrected using a 1st order polynomial function, followed by the application of an SNV scattering scatter algorithm and a Savitzky–Golay smoothing filter. The PLS model for product concentration utilised 7 latent variables and the model for monomer purity utilised 8 latent variables.</p> Full article ">Figure 9
<p>Comparison of acquisition settings of the spectra recorded by HT Raman spectroscopy microscope using polypropylene plates (in black) compared to the improved acquisition settings using stainless steel (SS) plates, a higher laser power, and higher magnification objective (in red).</p> Full article ">
16 pages, 3911 KiB  
Article
A Numerical Investigation on De-NOx Technology and Abnormal Combustion Control for a Hydrogen Engine with EGR System
by Hao Guo, Song Zhou, Jiaxuan Zou and Majed Shreka
Processes 2020, 8(9), 1178; https://doi.org/10.3390/pr8091178 - 17 Sep 2020
Cited by 23 | Viewed by 4578
Abstract
The combustion emissions of the hydrogen-fueled engines are very clean, but the problems of abnormal combustion and high NOx emissions limit their applications. Nowadays hydrogen engines use exhaust gas recirculation (EGR) technology to control the intensity of premixed combustion and reduce the NOx [...] Read more.
The combustion emissions of the hydrogen-fueled engines are very clean, but the problems of abnormal combustion and high NOx emissions limit their applications. Nowadays hydrogen engines use exhaust gas recirculation (EGR) technology to control the intensity of premixed combustion and reduce the NOx emissions. This study aims at improving the abnormal combustion and decreasing the NOx emissions of the hydrogen engine by applying a three-dimensional (3D) computational fluid dynamics (CFD) model of a single-cylinder hydrogen-fueled engine equipped with an EGR system. The results indicated that peak in-cylinder pressure continuously increased with the increase of the ignition advance angle and was closer to the top dead center (TDC). In addition, the mixture was burned violently near the theoretical air–fuel ratio, and the combustion duration was shortened. Moreover, the NOx emissions, the average pressure, and the in-cylinder temperature decreased as the EGR ratio increased. Furthermore, increasing the EGR ratio led to an increase in the combustion duration and a decrease in the peak heat release rate. EGR system could delay the spontaneous combustion reaction of the end-gas and reduce the probability of knocking. The pressure rise rate was controlled and the in-cylinder hot spots were reduced by the EGR system, which could suppress the occurrence of the pre-ignition in the hydrogen engine. Full article
(This article belongs to the Special Issue Modelling, Simulation and Control in Combustion Processes of Renewable Fuels)
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<p>Computational fluid dynamics (CFD) model of a hydrogen engine.</p> Full article ">Figure 2
<p>Data comparisons to a hydrogen engine</p> Full article ">Figure 3
<p>Pressure and heat release rate at different spark timings.</p> Full article ">Figure 4
<p>Average temperature and NO production rate at different spark timings: (<b>a</b>) average temperature; (<b>b</b>) NO generation rate.</p> Full article ">Figure 5
<p>Different equivalent ratios of pressure and heat release rate.</p> Full article ">Figure 6
<p>Different equivalent ratios of the mass fraction and NO reaction rate.</p> Full article ">Figure 7
<p>Mean in-cylinder pressure and temperature at different exhaust gas recirculation (EGR) ratios: (<b>a</b>) Mean in-cylinder pressure; (<b>b</b>) Mean in-cylinder temperature.</p> Full article ">Figure 8
<p>Rate of heat release (ROHR) and NOx emissions at different EGR ratios: (<b>a</b>) ROHR; (<b>b</b>) NOx emissions.</p> Full article ">Figure 9
<p>Influence of the EGR ratio on pressure increase rate.</p> Full article ">Figure 10
<p>Combustion reaction progress variable at 10°CA after the top dead center (aTDC): (<b>a</b>) 20% EGR; (<b>b</b>) 10% EGR.</p> Full article ">Figure 11
<p>Comparison of reaction rates (kg/m<sup>3</sup>/s) when flames travel at the same distance: (<b>a</b>) 1% EGR (TDC); (<b>b</b>) 10% EGR (10°CA aTDC).</p> Full article ">Figure 12
<p>The pressure comparison for the normal combustion and the pre-ignition.</p> Full article ">
18 pages, 2189 KiB  
Review
Olive Tree Leaves—A Source of Valuable Active Compounds
by Fereshteh Safarzadeh Markhali, José A. Teixeira and Cristina M. R. Rocha
Processes 2020, 8(9), 1177; https://doi.org/10.3390/pr8091177 - 17 Sep 2020
Cited by 79 | Viewed by 12763
Abstract
The agricultural and processing activities of olive crops generate a substantial amount of food by-products, particularly olive leaves, which are mostly underexploited, representing a significant threat to the environment. Olive leaves are endowed with endogenous bioactive compounds. Their beneficial/health-promoting potential, together with environmental [...] Read more.
The agricultural and processing activities of olive crops generate a substantial amount of food by-products, particularly olive leaves, which are mostly underexploited, representing a significant threat to the environment. Olive leaves are endowed with endogenous bioactive compounds. Their beneficial/health-promoting potential, together with environmental protection and circular economy, merit their exploitation to recover and reuse natural components that are potentially safer alternatives to synthetic counterparts. These biomass residues have great potential for extended industrial applications in food/dietary systems but have had limited commercial uses so far. In this regard, many researchers have endeavoured to determine a green/sustainable means to replace the conventional/inefficient methods currently used. This is not an easy task as a sustainable bio-processing approach entails careful designing to maximise the liberation of compounds with minimum use of (i) processing time, (ii) toxic solvent (iii) fossil fuel energy, and (iv) overall cost. Thus, it is necessary to device viable strategies to (i) optimise the extraction of valuable biomolecules from olive leaves and enable their conversion into high added-value products, and (ii) minimise generation of agro-industrial waste streams. This review provides an insight to the principal bioactive components naturally present in olive leaves, and an overview of the existing/proposed methods associated with their analysis, extraction, applications, and stability. Full article
(This article belongs to the Special Issue Sustainable Development of Waste towards Green Growth)
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<p>Factors involved in the sustainable extraction of active compounds from olive leaves.</p> Full article ">
13 pages, 2158 KiB  
Article
Preparation and Characterization of New Electrically Conductive Composites Based on Expanded Graphite with Potential Use as Remote Environmental Detectors
by Martin Prostredný, Igor Krupa and Zdenko Špitalský
Processes 2020, 8(9), 1176; https://doi.org/10.3390/pr8091176 - 17 Sep 2020
Viewed by 2216
Abstract
The presented paper is focused on studying electrically conductive composites based on an elastomeric matrix and expanded graphite as the filler. A potential application as an environmental remote detector was studied. The influence of filler particle size, film thickness, detector length, temperature, and [...] Read more.
The presented paper is focused on studying electrically conductive composites based on an elastomeric matrix and expanded graphite as the filler. A potential application as an environmental remote detector was studied. The influence of filler particle size, film thickness, detector length, temperature, and the amount of oil on the detector response rate were explored. Peel tests were performed in order to investigate the adhesion of prepared detector films to different materials. Expanded graphite with average particle size 5 µm was chosen for the experiments due to its fastest response. Decreasing the detector film thickness has caused an increase in the response rate but also a decrease in the signal measured. The response rate of the detector system was in a practical range even for lower temperatures. From the obtained data, the proposed detector seems to be suitable for a practical application. Full article
(This article belongs to the Section Materials Processes)
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Graphical abstract
Full article ">Figure 1
<p>The film applicator used for making thin composite films.</p> Full article ">Figure 2
<p>A schematic of the detector response rate measurement set up, (<b>a</b>) deionized water, (<b>b</b>) sample strip on polyethylene terephthalate (PET) foil, (<b>c</b>) DC power supply and multimeter connected to the sample with copper wires, (<b>d</b>) oil droplet on water surface.</p> Full article ">Figure 3
<p>Schematic of the peel test set-up: (<b>a</b>) top clamp, (<b>b</b>) polymer composite sample, (<b>c</b>) polyvinyl chloride (PVC) veneer, (<b>d</b>) bottom wheel fixture.</p> Full article ">Figure 4
<p>Change of relative current I/I<sub>0</sub> with time t for different filler particle sizes with data points corresponding to mean values and their standard deviation values (error bars) from three experiments.</p> Full article ">Figure 5
<p>Dependence of the parameter t<sub>1/2</sub> and the initial current value I<sub>0</sub> on the filler particle size (axis in logarithmic scale) with data points corresponding to mean values and their standard deviation values (error bars) from three experiments.</p> Full article ">Figure 6
<p>Dependence of the parameter t<sub>1/2</sub> and the initial value of current I<sub>0</sub> on film thickness with data points corresponding to mean values and their standard deviation values (error bars) from three experiments.</p> Full article ">Figure 7
<p>Dependence of the parameter t<sub>1/2</sub> and the value I<sub>0</sub> on the contact distance along with a power function fit for I<sub>0</sub> data for contact distances 3, 9, 30, and 90 cm with data points corresponding to mean values and their standard deviation values (error bars) from three experiments.</p> Full article ">Figure 8
<p>Dependence of the parameter t<sub>1/2</sub> on temperature with data points corresponding to mean values and their standard deviation values (error bars) from three experiments.</p> Full article ">Figure 9
<p>Dependence of the parameter t<sub>1/2</sub> on the amount of oil present with data points corresponding to mean values and their standard deviation values (error bars) from three experiments.</p> Full article ">Figure 10
<p>Peel test results for measured surfaces with peel force/width as the studied quantity with standard deviation values (error bars) from three experiments.</p> Full article ">
16 pages, 2539 KiB  
Article
Phytotoxicity and Effect of Ionic Liquids on Antioxidant Parameters in Spring Barley Seedlings: The Impact of Exposure Time
by Robert Biczak, Barbara Pawłowska, Wiesław Pilis, Jan Szczegielniak, Jacek Wróbel and Arkadiusz Telesiński
Processes 2020, 8(9), 1175; https://doi.org/10.3390/pr8091175 - 17 Sep 2020
Cited by 6 | Viewed by 2532
Abstract
The influence of the ionic liquids (ILs) tetrabutylammonium bromide [TBA][Br], 1-butyl-3-methylimidazole bromide [BMIM][Br], and tetrabutylphosphonium bromide [TBP][Br] added at different concentrations to the soil were studied for the growth and development of spring barley seedlings. Samples were harvested at three different time points: [...] Read more.
The influence of the ionic liquids (ILs) tetrabutylammonium bromide [TBA][Br], 1-butyl-3-methylimidazole bromide [BMIM][Br], and tetrabutylphosphonium bromide [TBP][Br] added at different concentrations to the soil were studied for the growth and development of spring barley seedlings. Samples were harvested at three different time points: day 7, 14, and 21 after addition of ILs. The results show that [TBP][Br] was the most toxic. The introduction of this IL at the dose of 100 mg kg−1 of soil DM decreased the growth of seedlings at all test dates. The addition of the studied ILs to the soil in higher doses resulted in an increase in peroxidase and catalase activity, which may indicate the occurrence of oxidative stress in plants. An increase in the content of plant dry matter weight, contents of H2O2 and proline and a decrease in the content of photosynthetic pigments in barley seedlings were also observed. The malondialdehyde content and superoxide dismutase activity fluctuated randomly during the experiment. As a result, it was found that the phytotoxicity of ILs and the magnitude of oxidative stress in seedlings depended more on the added doses of these compounds than on the measurement date. Full article
(This article belongs to the Special Issue Applications of Bioprocess Processes in Environmental Protection and Purification)
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<p>Effect of [TBA][Br], [BMIM][Br] and [TBP][Br] on the total chlorophyll (Chl <span class="html-italic">a + b</span>) and carotenoids (Car) in seedlings of spring barley; data are means ± SD (<span class="html-italic">n</span> = 3); values denoted with the same letters form homogeneous groups at the level of <span class="html-italic">p</span> &lt; 0.05 (post hoc Tukey’s HSD test).</p> Full article ">Figure 2
<p>Changes in enzymatic activities of peroxidase (POD) and catalase (CAT) in seedlings of spring barley treated with ILs; data are means ± SD (<span class="html-italic">n</span> = 3); values denoted with the same letters form homogeneous groups at the level of <span class="html-italic">p</span> &lt; 0.05 (post hoc Tukey’s HSD test).</p> Full article ">
32 pages, 636 KiB  
Review
Scope and Limitations of the Mathematical Models Developed for the Forward Feed Multi-Effect Distillation Process—A Review
by Omer Mohamed Abubaker Al-Hotmani, Mudhar Abdul Alwahab Al-Obaidi, Yakubu Mandafiya John, Raj Patel and Iqbal M Mujtaba
Processes 2020, 8(9), 1174; https://doi.org/10.3390/pr8091174 - 17 Sep 2020
Cited by 7 | Viewed by 4654
Abstract
Desalination has become one of the obvious solutions for the global water crisis due to affording high-quality water from seawater and brackish water resources. As a result, there are continuing efforts being made to improve desalination technologies, especially the one producing high-quantity freshwater, [...] Read more.
Desalination has become one of the obvious solutions for the global water crisis due to affording high-quality water from seawater and brackish water resources. As a result, there are continuing efforts being made to improve desalination technologies, especially the one producing high-quantity freshwater, i.e., thermal desalination. This improvement must be accomplished via enhancement of process design through optimization which is implicitly dependent on providing a generic process model. Due to the scarcity of a comprehensive review paper for modeling multi-effect distillation (MED) process, this topic is becoming more important. Therefore, this paper intends to capture the evolution of modeling the forward feed MED (most common type) and shed a light on its branches of steady-state and dynamic modeling. The maturity of the models developed for MED will be thoroughly reviewed to clarify the general efforts made highlighting the advantages and disadvantages. Depending on the outputs of this review, the requirements of process development and emerging challengeable matters of modeling will be specified. This, in turn, would afford a possible improvement strategy to gain a reliable and sustainable thermal desalination process. Full article
(This article belongs to the Special Issue Feature Review Papers)
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<p>Schematic diagram of forward feed multi-effect distillation-thermal vapor compression (MED-TVC) seawater desalination system (adapted from Druetta et al. [<a href="#B7-processes-08-01174" class="html-bibr">7</a>]).</p> Full article ">
21 pages, 1337 KiB  
Review
Antibacterial Activity of Chitosan Nanoparticles: A Review
by Murugesan Chandrasekaran, Ki Deok Kim and Se Chul Chun
Processes 2020, 8(9), 1173; https://doi.org/10.3390/pr8091173 - 17 Sep 2020
Cited by 216 | Viewed by 17871
Abstract
In recent years, nanotechnology has attracted attention in many fields because it has several up-and-coming novel uses. Many researchers have suggested that chitosan nanoparticles (CS-NPs) and their derivatives are one of the best nanomaterials for delivering antibacterial activity. CS-NPs have a broad spectrum [...] Read more.
In recent years, nanotechnology has attracted attention in many fields because it has several up-and-coming novel uses. Many researchers have suggested that chitosan nanoparticles (CS-NPs) and their derivatives are one of the best nanomaterials for delivering antibacterial activity. CS-NPs have a broad spectrum of antibacterial activity, but they manifest different inhibitory efficacy against gram-negative (G−) and gram-positive (G+) bacterial species. The mechanism of antibacterial action is an intricate process that varies between G− and G+ bacteria as a result of the differences in cell wall and cell membrane chemistry. In previous studies, greater antibacterial activity was more evident against G− bacteria than G+ bacteria, whereas in some studies G+ bacteria were more sensitive. Researchers predicted that the varied responses of bacteria are caused by the mixed hydrophilicity and negative charge distribution on the bacterial surface. Moreover, its activity depends on a number of variables including bacterial target (i.e., G− or G+ bacteria) and bacterial growth, as well as its concentration, pH, zeta-potential, molecular weight, and degree of acetylation. Therefore, this review examines current research on the mechanisms and factors affecting antibacterial activity, and application of CS-NPs specifically against animal and plant pathogenic bacteria. Full article
(This article belongs to the Section Biological Processes and Systems)
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<p>Schematic representation of chitosan.</p> Full article ">Figure 2
<p>Applications of chitosan nanoparticles.</p> Full article ">Figure 3
<p>Antibacterial mechanisms of chitosan nanoparticles.</p> Full article ">Figure 4
<p>Factors affecting antibacterial activity of chitosan nanoparticles.</p> Full article ">
41 pages, 16462 KiB  
Review
Supported Palladium Nanocatalysts: Recent Findings in Hydrogenation Reactions
by Marta A. Andrade and Luísa M. D. R. S. Martins
Processes 2020, 8(9), 1172; https://doi.org/10.3390/pr8091172 - 17 Sep 2020
Cited by 8 | Viewed by 5151
Abstract
Catalysis has witnessed a dramatic increase on the use of metallic nanoparticles in the last decade, opening endless opportunities in a wide range of research areas. As one of the most investigated catalysts in organic synthesis, palladium finds numerous applications being of significant [...] Read more.
Catalysis has witnessed a dramatic increase on the use of metallic nanoparticles in the last decade, opening endless opportunities in a wide range of research areas. As one of the most investigated catalysts in organic synthesis, palladium finds numerous applications being of significant relevance in industrial hydrogenation reactions. The immobilization of Pd nanoparticles in porous solid supports offers great advantages in heterogeneous catalysis, allowing control of the major factors that influence activity and selectivity. The present review deals with recent developments in the preparation and applications of immobilized Pd nanoparticles on solid supports as catalysts for hydrogenation reactions, aiming to give an insight on the key factors that contribute to enhanced activity and selectivity. The application of mesoporous silicas, carbonaceous materials, zeolites, and metal organic frameworks (MOFs) as supports for palladium nanoparticles is addressed. Full article
(This article belongs to the Special Issue Advances in Supported Nanoparticle Catalysts)
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<p>Rising interest on the development of supported palladium nanoparticles (PdNPs) and metallic NPs (MNPs). Topics: (metal nanoparticles and supported and catalysis. Analysis: publication years: (2010 to 2020) and research areas: (chemistry, materials science, science technology other topics, engineering, physics, energy fuels, electrochemistry, environmental sciences, ecology, polymer science). Source: ISI Web of Knowledge, accessed on 25, July 2020.</p> Full article ">Figure 2
<p>Immobilization of MNPs to various high surface area materials. Adapted with permission from Zhu et al. [<a href="#B30-processes-08-01172" class="html-bibr">30</a>], <span class="html-italic">Chem</span>; published by Elsevier Inc., 2016.</p> Full article ">Figure 3
<p>Preparation of Pd-mesoporous silicas (MS) catalysts and major catalytic outcomes for the solvent-free application to hydrogenation of <span class="html-italic">p</span>-chloronitrobenzene. Reproduced with permission from Yu et al., <span class="html-italic">Chemical Engineering Journal</span>; published by Elsevier B.V., 2017.</p> Full article ">Figure 4
<p>Top: Illustrated scheme of Pd supported ethylene, phenylene bridged or carbon doped organosilica nanotubes and reaction process. Bottom: TEM images of (<b>a</b>) E-silica nanotubes (SNT), (<b>b</b>) B-SNT, (<b>c</b>) E-CS-NT, (<b>d</b>) B-CS-NT, (<b>e</b>) SNT, and (<b>f</b>) SBA-15. Reproduced with permission from Sun et al., <span class="html-italic">Microporous and Mesoporous Materials</span>; published by Elsevier B.V., 2017.</p> Full article ">Figure 5
<p>Scheme for the enantioselective hydrogenation reaction of α,β-unsaturated carboxylic acids.</p> Full article ">Figure 6
<p>Synthetic procedure of APPd(0)@Si and BPPd(0)@Si. Reproduced with permission from Shabbir et al., <span class="html-italic">Journal of Organometallic Chemistry</span>; published by Elsevier B.V., 2017.</p> Full article ">Figure 7
<p>Schematic procedure of the preparation of Pd/MCM-41 catalysts and hydrogenation results. Reproduced with permission from Rungsi et al., Applied Catalysis A: General; published by Elsevier B.V., 2018.</p> Full article ">Figure 8
<p>Influence of the solvent polarity on the catalytic performance for nitrobenzene hydrogenation over Pd/SBA-COOH catalyst. Reproduced with permission of Ganji et al., <span class="html-italic">New Journal of Chemistry</span>; published by The Royal Society of Chemistry and the Centre National de la Recherche Scientifique, 2019.</p> Full article ">Figure 9
<p>Synthetic approach for the preparation of Pd/ reduced graphene oxide (RGO), TEM image of the catalyst and application in nitrobenzene hydrogenation. Reproduced with permission from El-Hout et al., Applied Catalysis A: General; published by Elsevier B.V., 2015.</p> Full article ">Figure 10
<p>Left: recycling ability of Pd/RGO-H.H and Pd/RGO-salicylic acid catalysts in reduction of nitrobenzene. Right: TEM image of Pd/RGO-salicylic acid catalyst after the 6th run. Reproduced with permission from El-Hout et al., Applied Catalysis A: General; published by Elsevier B.V., 2015.</p> Full article ">Figure 11
<p>Low- and high-magnification SEM images of the PdNPs/RGO. Reproduced with permission from Nasrollahzadeh et al., <span class="html-italic">Journal of Colloid and Interface Science</span>; published by Elsevier Inc., 2015.</p> Full article ">Figure 12
<p>Catalytic reduction of aromatic nitroarenes over PdNPs/RGO.</p> Full article ">Figure 13
<p>(<b>a</b>) Acetylene conversion and ethylene selectivity as a function of reaction temperature for Pd@C/carbon nanotubes (CNTs) and Pd/CNTs (the inset in (<b>a</b>)), (<b>b</b>) ethylene selectivity (red circles) and acetylene conversion (black squares) with time on stream for selective hydrogenation of acetylene to ethylene over Pd@C/CNTs. Reproduced with permission from Zhang et al., <span class="html-italic">Nanoscale</span>; published by The Royal Society of Chemistry, 2017.</p> Full article ">Figure 14
<p>TEM, High Resolution Transmission Electron Microscopy (HRTEM) image, and particle size histogram of CPG. Reproduced with permission from Bilgicli et al., <span class="html-italic">Microporous and Mesoporous Materials</span>; published by Elsevier Inc., 2020.</p> Full article ">Figure 15
<p>Followed approach for the synthesis of the catalysts Pd@Hal-Char. Reproduced with permission from Sadjadi et al., <span class="html-italic">ACS Sustainable Chemistry &amp; Engineering</span>; published by American Chemical Society, 2019.</p> Full article ">Figure 16
<p>Recyclability of Pd@Hal-Char for the hydrogenation reaction of nitrobenzene. Reproduced with permission from Sadjadi et al., <span class="html-italic">ACS Sustainable Chemistry &amp; Engineering</span>; published by American Chemical Society, 2019.</p> Full article ">Figure 17
<p>Scheme for the synthesis of functionalized hydrochars and PdNPs@hydrochars. Reproduced with permission from Duarte et al., <span class="html-italic">ChemCatChem</span>; published by Wiley-VCH Verlag GmbH &amp; Co. KGaA, Weinheim, Germany, 2020.</p> Full article ">Figure 18
<p>Left: SEM micrographs of thermally treated hydrochars (HCh-C and HCh-D) and their functionalization via ether linkages (HCh-E and HCh-G from HCh-C; HCh-F and HCh-H from HCh-D). Right: TEM images of hydrochar-supported PdNPs (Pd@HCh-C to Pd@HCh-H). Reproduced with permission from Duarte et al., <span class="html-italic">ChemCatChem</span>; published by Wiley-VCH Verlag GmbH &amp; Co. KGaA, Weinheim, Germany, 2020.</p> Full article ">Figure 19
<p>Procedure of the preparation of Pd/ZSM-5-IS catalyst. Reproduced with permission from Ma et al., <span class="html-italic">Chemical Engineering Journal</span>; published by Elsevier B.V., 2015.</p> Full article ">Figure 20
<p>(<b>a</b>) Influence of the reaction temperature and (<b>b</b>) Influence of the reduction temperature on the conversion of <span class="html-italic">p</span>-nitrophenol catalyzed by Pd/ZSM-5-IS and Pd/ZSM-5-IM. Reproduced with permission from Ma et al., <span class="html-italic">Chemical Engineering Journal</span>; published by Elsevier B.V., 2015.</p> Full article ">Figure 21
<p>Schematic procedure for Pd@mnc-S1. Reproduced with permission from Cui et al., <span class="html-italic">Angewandte Chemie</span>—International Edition; published by Wiley-VCH Verlag GmbH &amp; Co. KGaA, Weinheim, Germany, 2016.</p> Full article ">Figure 22
<p>Hydrogenation reactions over Pd@mnc-S1 or Pd/C. Reproduced with permission from Cui et al., <span class="html-italic">Angewandte Chemie</span>—International Edition; published by Wiley-VCH Verlag GmbH &amp; Co. KGaA, Weinheim, Germany, 2016.</p> Full article ">Figure 23
<p>Left: (<b>A</b>) SEM, (<b>B</b>) STEM, and (<b>C</b>–<b>E</b>) HRTEM images of Pd@Beta. PdNPs are highlighted in yellow. (<b>F</b>) Size distribution of the PdNPs. Right: Substrate conversions (N) and product selectivities (colored columns) for the hydrogenation of (<b>G</b>) 4-nitrochlorobenzene and (<b>H</b>) 4-nitrobenzaldehyde on various catalysts. Proposed models for the 4-nitrochlorobenzene adsorption on (<b>I</b>) Pd/C and (<b>J</b>) Pd@Beta. Color code: C gray, Cl light green, H white, N blue, O red, Pd dark green. Reproduced with permission from Zhang et al., <span class="html-italic">Angewandte Chemie</span>—International Edition; published by Elsevier B.V., 2017.</p> Full article ">Figure 24
<p>Experimental approach for the preparation of Pd@S-1-OH. Reproduced with permission from Wang et al., <span class="html-italic">ACS Catalysis</span>; published by American Chemical Society, 2017.</p> Full article ">Figure 25
<p>Procedure for the encapsulation of PdNPs in FER zeolite. Reproduced with permission from Zhao et al., <span class="html-italic">ChemCatChem</span>; published by Wiley-VCH Verlag GmbH &amp; Co. KGaA, Weinheim, Germany, 2018.</p> Full article ">Figure 26
<p>(<b>a</b>) Hydrogenation activities of 1-hexene and 1-phenyl-1-cyclohexene; (<b>b</b>) product distributions for 1-hexene hydrogenation over Pd@FER and Pd/RUB-37 catalysts. Reproduced with permission from Zhao et al., <span class="html-italic">ChemCatChem</span>; published by Wiley-VCH Verlag GmbH &amp; Co. KGaA, Weinheim, Germany, 2018.</p> Full article ">Figure 27
<p>Proposed approach towards preparation and application of Pd@Beta and Pd@MWW. Reproduced with permission from Zhang et al., <span class="html-italic">Catalysis Today</span>; published by Elsevier B.V., 2019.</p> Full article ">Figure 28
<p>Initial reaction rates for the hydrogenation of nitroarenes over Pd@Beta and Pd@MWW. Reproduced with permission from Zhang et al., <span class="html-italic">Catalysis Today</span>; published by Elsevier B.V., 2019.</p> Full article ">Figure 29
<p>TEM images of the Pd@MOFs applied towards phenol hydrogenation. Reproduced with permission from Zhang et al., <span class="html-italic">Catalysis Communications</span>; published by Elsevier B.V., 2013.</p> Full article ">Figure 30
<p>Left: (<b>a</b>) HRTEM image of Pd@MIL-100(Fe), (<b>b</b>) lattice fringe of the (111) plane of face-centered cubic Pd. Right: proposed mechanisms of nitrophenol reduction by Pd@MIL-100(Fe). Reproduced with permission from Xu et al., <span class="html-italic">Microporous and Mesoporous Materials</span>; published by Elsevier Inc., 2017.</p> Full article ">Figure 31
<p>Scheme of the synthesis of Pd0-in-UiO-67. Color code: cyan, secondary building unit of MOFs; grey, organic linkers; orange, metal precursors or NPs; red, stabilizing agents. Reproduced with permission from Chen et al. [<a href="#B99-processes-08-01172" class="html-bibr">99</a>], <span class="html-italic">Chemical Science</span>; published by Royal Society of Chemistry, 2014.</p> Full article ">Figure 32
<p>(<b>a</b>) and (<b>b</b>) TEM images of 1.0% Pd0-in-UiO-67. Reproduced with permission from Chen et al., <span class="html-italic">Chemical Science</span>; published by Royal Society of Chemistry, 2014.</p> Full article ">Figure 33
<p>Schematic illustration of the preparation of Pd@UiO-66-X. Reproduced with permission Reproduced with permission from Chen et al., Advanced Materials; published by Wiley-VCH Verlag &amp; Co. KGaA, Weinheim, Germany, 2020.</p> Full article ">Figure 34
<p>Left: Conversion and selectivity performance of Pd@UiO-66-X towards hydrogenation of benzoic acid. Right: Electron transfer from Pd clusters to the MOF. Reproduced with permission from Chen et al., <span class="html-italic">Advanced Materials</span>; published by Wiley-VCH Verlag &amp; Co. KGaA, Weinheim, Germany, 2020.</p> Full article ">Figure 35
<p>Left: Synthetic procedure for C@Pd/ZIF-8 catalysts; Right: SEM images and particle size distribution of ZIF-8 (<b>a</b>,<b>b</b>), Pd/ZIF-8 (<b>c</b>,<b>d</b>), and C@Pd/ZIF-8 (<b>e</b>,<b>f</b>) (right). Adapted with permission from Zhou et al., <span class="html-italic">Nanoscale</span>; published by Royal Society of Chemistry, 2015.</p> Full article ">Figure 36
<p>Synthetic procedure of Pd@ZIF-8 hollow microspheres. Reproduced with permission from Zhao et al., <span class="html-italic">Langmuir</span>; published by The American Chemical Society, 2020.</p> Full article ">Figure 37
<p>SEM image (<b>a</b>) and TEM images (<b>b</b>,<b>c</b>) of the Pd@ZIF-8 hollow microspheres with the diameter of PdNPs being ca. 7.0 nm. Reproduced with permission from Zhao et al., <span class="html-italic">Langmuir</span>; published by The American Chemical Society, 2020.</p> Full article ">Figure 38
<p>(<b>a</b>) Catalytic performance of PS/Pd composite particles, PS/Pd@ZIF-8 composite particles, and Pd@ZIF-8 hollow microspheres for the hydrogenation of 1-hexene, cyclohexene, and cyclooctene in the liquid phase. (<b>b</b>) Recycling stability of Pd@ZIF-8 hollow microspheres in the hydrogenation of 1-hexene. Reproduced with permission from Zhao et al., <span class="html-italic">Langmuir</span>; published by The American Chemical Society, 2020.</p> Full article ">
18 pages, 944 KiB  
Article
Drag Effect of Carbon Emissions on the Urbanisation Process: Evidence from China’s Province Panel Data
by Jiajia Li, Jiangang Shi, Heng Li, Kaifeng Duan, Rui Zhang and Quanwei Xu
Processes 2020, 8(9), 1171; https://doi.org/10.3390/pr8091171 - 17 Sep 2020
Cited by 1 | Viewed by 2613
Abstract
This study attempts to measure the drag effect of carbon emissions on China’s economic growth by incorporating carbon emissions as an endogenous variable into an economic growth model and by relaxing the assumption that the size of the economy will remain unchanged. The [...] Read more.
This study attempts to measure the drag effect of carbon emissions on China’s economic growth by incorporating carbon emissions as an endogenous variable into an economic growth model and by relaxing the assumption that the size of the economy will remain unchanged. The drag effect of carbon emissions on the process of urbanisation is derived based on the intrinsic relationship between economic growth and urban development. Then, unit root and cointegration tests are performed using panel data from 30 provincial regions in Mainland China from 2003 to 2016 to prove and estimate the resistance caused by carbon emission in the process of urbanisation. Results show that the drag effect of carbon emission between 2003 and 2016 has a certain negative impact on the process of urbanisation in China. Due to the constraints of carbon emissions, the growth rate of China’s economic growth and urbanization level is 0.74% and 4.96% lower than that without constraints, respectively. Therefore, in the process of rapid urbanisation, formulating a reasonable carbon emission reduction strategy by the provincial government is conducive to the healthy and sustainable development of urbanisation. Full article
(This article belongs to the Special Issue Carbon Capture and Utilisation)
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<p>Research content structure.</p> Full article ">Figure 2
<p>Map of China’s regional division. <span class="html-italic">Note</span>: The study area separates the mainland region of Tibet Autonomous Region; the image above is shown in a coloured region.</p> Full article ">Figure 3
<p>Theoretical framework of the drag effect relationship between urbanisation development and carbon emissions.</p> Full article ">
18 pages, 7582 KiB  
Article
Optimization Design of a Two-Vane Pump for Wastewater Treatment Using Machine-Learning-Based Surrogate Modeling
by Sang-Bum Ma, Sung Kim and Jin-Hyuk Kim
Processes 2020, 8(9), 1170; https://doi.org/10.3390/pr8091170 - 17 Sep 2020
Cited by 16 | Viewed by 4461
Abstract
This paper deals with three-objective optimization, using machine-learning-based surrogate modeling to improve the hydraulic performances of a two-vane pump for wastewater treatment. For analyzing the internal flow field in the pump, steady Reynolds-averaged Navier-Stokes equations were solved with the shear stress transport turbulence [...] Read more.
This paper deals with three-objective optimization, using machine-learning-based surrogate modeling to improve the hydraulic performances of a two-vane pump for wastewater treatment. For analyzing the internal flow field in the pump, steady Reynolds-averaged Navier-Stokes equations were solved with the shear stress transport turbulence model as a turbulence closure model. The radial basis neural network model, which is an artificial neural network, was used as the surrogate model and trained to improve prediction accuracy. Three design variables related to the geometry of blade and volute were selected to optimize concurrently the objective functions with the total head and efficiency of the pump and size of the waste solids. The optimization results obtained by using the model showed highly accurate prediction values, and compared with the reference design, the optimum design provided improved hydraulic performances. Full article
(This article belongs to the Special Issue CFD Based Researches and Applications for Fluid Machinery and Fluid Device)
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<p>Geometry of the two-vane pump.</p> Full article ">Figure 2
<p>Efficiency according to the specific speed for the pump.</p> Full article ">Figure 3
<p>Grid systems of the two-vane pump.</p> Full article ">Figure 4
<p>Procedure of the optimization design based on machine learning.</p> Full article ">Figure 5
<p>Genetic algorithm.</p> Full article ">Figure 6
<p>Definition of the waste solid volume.</p> Full article ">Figure 7
<p>Definition of the design variables.</p> Full article ">Figure 8
<p>Schematic of the radial basis neural network.</p> Full article ">Figure 9
<p>Cross-validation errors vs. spread constant (SC) values.</p> Full article ">Figure 10
<p>K-fold cross-validation.</p> Full article ">Figure 11
<p>Grid dependency test.</p> Full article ">Figure 12
<p>Validation data of previous study [<a href="#B29-processes-08-01170" class="html-bibr">29</a>].</p> Full article ">Figure 13
<p>Pareto-optimal front surface with arbitrary optimal designs (AODs).</p> Full article ">Figure 14
<p>Flow passage area distribution in the meridional direction.</p> Full article ">Figure 15
<p>Comparison of three-dimensional (3D) geometries.</p> Full article ">Figure 16
<p>Cross-sectional area distribution of the volute.</p> Full article ">Figure 17
<p>Velocity distributions at 50% span.</p> Full article ">Figure 18
<p>Streamlines at 50% span.</p> Full article ">Figure 19
<p>Incidence angle distribution at the leading edge (LE) along the span.</p> Full article ">Figure 20
<p>Three-dimensional (3D) streamlines with vortices distribution on the iso-surface of the velocity invariant (&gt;5 × 10<sup>5</sup> s<sup>−2</sup>).</p> Full article ">Figure 21
<p>Pressure distributions at 50% span.</p> Full article ">Figure 22
<p>Velocity distributions inside the volute.</p> Full article ">Figure 23
<p>Iso-surface for the low-velocity region (&lt;1 m/s).</p> Full article ">
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