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Volume 8
Issue 3
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Processes, Volume 8, Issue 3 (March 2020) – 123 articles

Cover Story (view full-size image): Oxidative coupling of methane (OCM) is a process used to directly convert methane into ethylene. In this work, we investigated the integration of different membranes to increase the overall performance of large-scale OCM process from a techno-economic point of view. The results show that the OCM reactor yield improves when integrating membranes in packed bed reactors. These higher yields positively impact the economics and performance of the downstream separation, resulting in an ethylene cost production of €595–625/ton C2H4 depending on the type of membranes employed, which is 25%–30% lower than the benchmark technology.View this paper.
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13 pages, 1959 KiB  
Article
The Influence of Freezing and Thawing on the Yield and Energy Consumption of the Celeriac Juice Pressing Process
by Rafał Nadulski, Zbigniew Kobus and Tomasz Guz
Processes 2020, 8(3), 378; https://doi.org/10.3390/pr8030378 - 24 Mar 2020
Cited by 7 | Viewed by 4187
Abstract
The aim of this study is to indicate the influence of pretreatment, consisting of grinding vegetables and then freezing and thawing the raw material before pressing on the process efficiency and quality of obtained juice. The subject of the research was celeriac root [...] Read more.
The aim of this study is to indicate the influence of pretreatment, consisting of grinding vegetables and then freezing and thawing the raw material before pressing on the process efficiency and quality of obtained juice. The subject of the research was celeriac root (Apium graveolens L) of the Edward variety. Juice pressing was carried out in a laboratory basket press. The pressing yield and specific energy were values characterizing the pressing process, while pH and the extracted content in the juice were used to assess the juice quality. The experiment was performed according to three procedures. In each of them, the ground celeriac root (pulp or chips) was initially pretreated through freezing and thawing and then pressed twice. Among the examined methods of obtaining juice, the most beneficial method was pressing juice from the pulp, then freezing and thawing the pomace obtained in the first cycle, and finally, pressing the pomace. It is an energy-optimal method and guarantees a high pressing yield as well as obtaining juice with a higher soluble solid content than during the process of pressing chips. Full article
(This article belongs to the Collection Sustainable Food Processing Processes)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic of the press: cylinder, 2—piston, 3—pulp, 4—mesh, 5—perforated bottom, 6—base.</p> Full article ">Figure 2
<p>Experiment scheme: JA1—juice obtained in procedure A after first pressing; JA2—juice obtained in procedure A after second pressing; JA—juice obtained in procedure A; JB1—juice obtained in procedure B after first pressing; JB2—juice obtained in procedure B after second pressing; JB—juice obtained in procedure B; JC1—juice obtained in procedure C after first pressing; JC2—juice obtained in procedure C after second pressing; JC—juice obtained in procedure C.</p> Full article ">Figure 3
<p>Impact of applied procedures on the total yield <span class="html-italic">Y<sub>t</sub></span> of pressing juice from ground celeriac root. A—two-stage pressing of pulp; B—two-stage pressing, first pulp, and then pomace after freezing and thawing; C—two-stage pressing, first pulp after freezing and thawing and then pomace after freezing and thawing. a, b, c, and d—average values marked with the same letter show no statistically significant difference (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 4
<p>Impact of applied procedures on yield <span class="html-italic">Y</span><sub>1</sub> in the first pressing cycle of ground celeriac root. Abbreviations, as in <a href="#processes-08-00378-f003" class="html-fig">Figure 3</a>. a, b, and c—average values marked with the same letter show no statistically significant difference (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 5
<p>Impact of applied procedures on yield <span class="html-italic">Y</span><sub>2</sub> in the second pressing cycle of pomace. Abbreviations, as in <a href="#processes-08-00378-f003" class="html-fig">Figure 3</a>. a, b, c, d, and e—average values marked with the same letter show no statistically significant difference (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 6
<p>Impact of applied procedures on the total specific energy <span class="html-italic">E<sub>t</sub></span> of pressing juice from celeriac. Abbreviations, as in <a href="#processes-08-00378-f003" class="html-fig">Figure 3</a>. a, b, c, and d—average values marked with the same letter show no statistically significant difference (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 7
<p>Impact of applied procedures on the specific energy <span class="html-italic">E</span><sub>1</sub> in the first pressing cycle of ground celeriac root. Abbreviations, as in <a href="#processes-08-00378-f003" class="html-fig">Figure 3</a>. a, b, and c—average values marked with the same letter show no statistically significant difference (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 8
<p>Impact of applied procedures on the specific energy <span class="html-italic">E</span><sub>2</sub> in the second pressing cycle of pomace. Abbreviations, as in <a href="#processes-08-00378-f003" class="html-fig">Figure 3</a>. a, b, c, d, and e—average values marked with the same letter show no statistically significant difference (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">
46 pages, 17636 KiB  
Review
Metal–Organic Framework Thin Films: Fabrication, Modification, and Patterning
by Yujing Zhang and Chih-Hung Chang
Processes 2020, 8(3), 377; https://doi.org/10.3390/pr8030377 - 24 Mar 2020
Cited by 40 | Viewed by 14166
Abstract
Metal–organic frameworks (MOFs) have been of great interest for their outstanding properties, such as large surface area, low density, tunable pore size and functionality, excellent structural flexibility, and good chemical stability. A significant advancement in the preparation of MOF thin films according to [...] Read more.
Metal–organic frameworks (MOFs) have been of great interest for their outstanding properties, such as large surface area, low density, tunable pore size and functionality, excellent structural flexibility, and good chemical stability. A significant advancement in the preparation of MOF thin films according to the needs of a variety of applications has been achieved in the past decades. Yet there is still high demand in advancing the understanding of the processes to realize more scalable, controllable, and greener synthesis. This review provides a summary of the current progress on the manufacturing of MOF thin films, including the various thin-film deposition processes, the approaches to modify the MOF structure and pore functionality, and the means to prepare patterned MOF thin films. The suitability of different synthesis techniques under various processing environments is analyzed. Finally, we discuss opportunities for future development in the manufacturing of MOF thin films. Full article
(This article belongs to the Special Issue Materials Processing for Production of Nanostructured Thin Films)
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Figure 1

Figure 1
<p>(<b>A</b>) SEM images of HKUST-1-coated stainless steel wire at different magnifications. (Reproduced with permission from Cui et al., Analytical Chemistry; published by American Chemical Society, 2009.) (<b>B</b>) SEM images of the surface and the cross-section of Mg-MOF-74 thin films formed using different precursor solutions. <span class="html-italic">(</span>Reproduced with permission from Campbell et al., Microporous and Mesoporous Materials; published by Elsevier BV, 2017.) (<b>C</b>) Schematic of the microwave-induced solvothermal synthesis, and SEM images of MOF-5 grown on different substrates after 30 s of the microwave-induced solvothermal reaction. (Reproduced with permission from Yoo et al., Chemical Communications; published by Royal Society of Chemistry, 2008.)</p> Full article ">Figure 2
<p>(<b>A</b>) Schematic illustrations of the oriented growth of HKUST-1 nanocrystals controlled via SAMs. (<b>a</b>) XRD patterns of HKUST-1 TFs on functionalized gold surfaces, compared with a randomly oriented HKUST-1 bulk sample measurement, and SEM images of HKUST-1 crystals on OH-terminated SAMs after immersion in the mother solution for (<b>b</b>) 16, (<b>c</b>) 24, and (<b>d</b>) 45 h. All scale bars, 1 μm. (Reproduced with permission from Biemmi et al., Journal of the American Chemical Society; published by American Chemical Society, 2007.) (<b>B</b>) SEM images of HKUST-1 TFs on different surfaces, a single pyramidal crystal grown on c-plane sapphire, and a single octahedral crystal grown on COOH-terminated Si/SiO<sub>2</sub>. Optical images of HKUST-1 TFs on (<b>a</b>) a ‘‘positive’’ CF<sub>3</sub>/COOH and (<b>b</b>) a ‘‘negative’’ COOH/CF<sub>3</sub> patterned SAM surface. (Reproduced with permission from Zacher et al., Journal of Materials Chemistry; published by Royal Society of Chemistry, 2007.) (<b>C</b>) Illustration of the substrate modification process. (<b>a,c</b>) Top-view and (<b>b,d</b>) cross-section SEM of a well-intergrown and a continuous but poorly intergrown ZIF-8 TF, respectively. (Reproduced with permission from McCarthy et al., Langmuir; published by American Chemical Society, 2010.) (<b>D</b>) SEM images of (<b>a</b>) an original polypropylene (PP) fibrous membrane and (<b>b</b>) a polydopamine (PDA)-coated PP membrane; all scale bars, 3 μm. SEM images of (<b>c</b>) HKUST-1-, (<b>d</b>) MOF-5-, (<b>e</b>) MIL-100(Fe)-, and (<b>f</b>) ZIF-8-coated PDA-modified PP membranes (all scale bars, 2 μm), and the corresponding HKUST-1, MOF-5, MIL-100(Fe), and ZIF-8 nanotubes after the removal of the underlying PP fibers (all scale bars, 1 μm). Inserted are the corresponding optical photos of samples. (Reproduced with permission from Zhou et al., Chemical Communications; published by Royal Society of Chemistry, 2015.)</p> Full article ">Figure 3
<p>(<b>A</b>) Schematic of ZIF-8 growth on Cu substrates in methanol-based synthesis, and the top-view (<b>top</b>) and cross-section (<b>bottom</b>) SEM images of copper foils treated with different methanol-based protocols. All scale bars, 10 μm. (Reproduced with permission from Papporello et al., Microporous and Mesoporous Materials; published by Elsevier BV, 2015.) (<b>B</b>) Preparation schematic of the counter-diffusion method for plugging pore and the secondary growth method for ZIF-8 film on the inner surface of a ceramic tube. (Reproduced with permission from Sun et al., RSC Advances; published by Royal Society of Chemistry, 2014.) (<b>C</b>) Schematic of the templated methodology of MOF-TF fabrication on ZnO NWs, and SEM images of (<b>a</b>) IRMOF-1, (<b>b</b>) IRMOF-3, (<b>c</b>) IRMOF-8, and (<b>d</b>) IRMOF-9 films grown on ZnO NWs, respectively, and the IRMOF-1 film by (<b>e</b>) traditional solvothermal synthesis and (<b>f</b>) microwave-assisted synthesis. (Reproduced with permission from Abdollahian et al., Crystal Growth &amp; Design; published by American Chemical Society, 2014.) (<b>D</b>) Schematic of the LBL synthesis route. (<b>a</b>) Optical images of ALD-Al<sub>2</sub>O<sub>3</sub>-coated PP fibers with different LBL HKUST-1 TFs, and SEM images of an HKUST-1 TFs on (<b>b</b>) untreated and (<b>c</b>) ALD-Al<sub>2</sub>O<sub>3</sub>-coated PP fibers. (<b>d</b>) The thickness of the MOF-TFs on ALD-Al<sub>2</sub>O<sub>3</sub>-coated PP fibers measured from cross-section TEM images. (Reproduced with permission from Zhao et al., Journal of Materials Chemistry A; published by Royal Society of Chemistry, 2015.)</p> Full article ">Figure 4
<p>(<b>A</b>) Schematic of the step-by-step growth of HKUST-1 TF on a COOH-terminated SAM, and (<b>a</b>) the corresponding SPR signal as a function of time recorded in situ during sequential injections of Cu(OAc)<sub>2</sub>, ethanol, and 1,3,5-benzenetricarboxylic acid. (<b>b</b>) XRD data of an HKUST-1 TF (40 cycles) grown on COOH-terminated SAM, inserted the in-plane data. (<b>c</b>) SEM image of HKUST-1 (40 cycles) grown on an SAM laterally patterned by μCP consisting of COOH-terminated squares and CH<sub>3</sub>-terminated stripes. (Reproduced with permission from Shekhah et al., Journal of the American Chemical Society; published by American Chemical Society, 2007.) (<b>B</b>) A schematic diagram to illustrate the preparation of Cu<sub>3</sub>(HHTP)<sub>2</sub> TF via spraying. (<b>a</b>) SEM and (<b>b</b>) AFM images of a Cu<sub>3</sub>(HHTP)<sub>2</sub> TF, and the corresponding film thickness and surface roughness. (Reproduced with permission from Yao et al., Angewandte Chemie International Edition; published by Wiley-VCH, 2017.) (<b>C</b>) Schematic of the proposed model for HKUST-1 nucleation and growth on oxide surfaces (Cu-green, O-red, C-gray). (<b>a</b>) SEM and (<b>b</b>) AFM images of HKUST-1 TFs (40 cycles) on different substrate surfaces. (Reproduced with permission from Stavila et al., Chemical Science; published by Royal Society of Chemistry, 2012.)</p> Full article ">Figure 5
<p>(<b>A</b>) AFM image of a MIL-89(gel) TF <span class="html-italic">via</span> the DC method. (Reproduced with permission from Horcajada et al., Advanced Materials; published by Wiley-VCH, 2009.) (<b>B</b>) SEM images of ZIF-8 films grown on Si substrates with different cycles of dip coating, inserted is the photograph of a series of ZIF-8 films of various thicknesses grown on Si substrates. (Reproduced with permission from Lu et al., Journal of the American Chemical Society; published by American Chemical Society, 2010.) (<b>C</b>) Schematic for the fabrication of MOF-TFs using the LPE approach adapted to the SC method. (<b>a</b>) Top-view and cross-section SEM images of HKUST-1 TFs with different deposition cycles. (<b>b</b>) Height profile of the HKUST-1 TF from different deposition cycles. (<b>c</b>) SEM image of HKUST-1 TF grown by 20 cycles on a SAM laterally patterned by μCP consisting of COOH-terminated squares and CH<sub>3</sub>-terminated stripes. (Reproduced with permission from Chernikova et al., ACS applied materials &amp; interfaces; published by American Chemical Society, 2016.)</p> Full article ">Figure 6
<p>(<b>a</b>) Schematic of the asymmetric growth of MOF-TFs on 2D arrays anchored at the air–liquid interface and the fabrication of 2DOM MOF-TFs. SEM images of (<b>b</b>) ZIF-8 formed at the air–liquid interface of different sides, and (<b>c</b>) ZIF-8 TFs obtained after removal of polystyrene spheres. (<b>d</b>) Schematic and corresponding SEM images of vertically layered architectures based on transferable MOF superstructures. (Reproduced with permission from Li et al., Crystal Growth &amp; Design; published by American Chemical Society, 2016.)</p> Full article ">Figure 7
<p>(<b>A</b>) Diffusion cell for ZIF-8 film preparation and the schematic formation of ZIF-8 films on both sides of the nylon support via the contra-diffusion method, and corresponding SEM images of a bare nylon membrane and ZIF-8 TFs formed on different sides of nylon membranes at room temperature. (Reproduced with permission from Yao et al., Chemical communications; published by Royal Society of Chemistry, 2011.) (<b>B</b>) Illustration of the gel-layer method to fabricate an oriented metal–organic framework TF on an SAM-functionalized Au substrate. SEM images of (<b>a</b>) HKUST-1 on an OH-terminated substrate and (<b>b</b>) Fe-MIL-88B-NH<sub>2</sub> TF on a COOH-terminated substrate. (<b>c</b>) A thick Fe-MIL-88B-NH<sub>2</sub> film with island formation under a higher iron(III) concentration, and (<b>d</b>) larger single crystals formed on the film surface with higher molecular weight poly(ethylene oxide) (10<sup>5</sup>). (Reproduced with permission from Schoedel et al., Angewandte Chemie International Edition; published by Wiley-VCH, 2010.)</p> Full article ">Figure 8
<p>(<b>A</b>) Schematics of the nucleation, growth, and orientation of HKUST-1 crystals in confinement during solvent evaporation, and SEM images of the patterned deposition of HKUST-1 from the positive and negative replica. Arrows indicate intergrowths caused by the second nucleation. (Reproduced with permission from Ameloot et al., Advanced Materials; published by Wiley-VCH, 2010.) (<b>B</b>) Schematics of the synthesis of bulk HKUST-1 crystals and SC fabrication of highly oriented TFs and patterns. (Reproduced with permission from Zhuang et al., Advanced Functional Materials; published by Wiley-VCH, 2011.) (<b>C</b>) Schematics for inkjet-printing SURMOFs onto flexible substrates using an HKUST-1 precursor solution as “ink”. Optical photos of (<b>a</b>) an HKUST-1 ink solution, (<b>b</b>) various patterns, letters, and a gradient wedge printed onto polyethylene terephthalate foil, (<b>c</b>) Botticelli's “Venus,” which was printed in HKUST-1 (the inset shows the original image), and (<b>d</b>) a line array (2 cycles). (Reproduced with permission from Zhuang et al., Advanced Materials; published by Wiley-VCH, 2013.)</p> Full article ">Figure 9
<p>(<b>A</b>) Schematic view of an electrochemical synthesis cell. (<b>a</b>) AFM and (<b>b</b>) SEM images of HKUST-1 TFs fabricated on copper electrodes and (<b>c</b>) on a copper mesh under different electrochemical conditions. (Reproduced with permission from Martinez et al., Crystal Growth &amp; Design; published by American Chemical Society, 2012.) (<b>B</b>) Schematic illustration of (<b>a</b>) ECD for Ln(OH)<sub>3</sub> layers on a transparent FTO glass and the microwave conversion to Ln-MOFs, and (<b>b</b>) the patterning growth of luminescent barcodes. (Reproduced with permission from Li et al., Chemical communications; published by Royal Society of Chemistry, 2016.) (<b>C</b>) Mechanism of CED. (Reproduced with permission from Li et al., Journal of the American Chemical Society; published by American Chemical Society, 2011.) (<b>D</b>) Schematic illustration of the formation of a biphasic mixed film at (cathodic) potential, <span class="html-italic">E</span><sub>i</sub>. <span class="html-italic">E</span><sub>i</sub> &lt; <span class="html-italic">E</span><sub>m</sub> &lt; <span class="html-italic">E</span><sub>h</sub>. (<b>a–c</b>) SEM images of (<b>b</b>) a film produced by sequential growth at 1.10 and 1.50 V, displaying (<b>a</b>) the characteristic feather-like morphology of (Et<sub>3</sub>NH)<sub>2</sub>Zn<sub>3</sub>(BDC)<sub>4</sub> in the top layer and (<b>c</b>) the small crystallites associated with the Zn/MOF-5 composite in the layer closer to the electrode surface. (Reproduced with permission from Li et al., Chemical Science; published by Royal Society of Chemistry, 2014.) (<b>E</b>) (<b>a</b>) Zn<b><sub>a</sub></b>/Zn<b><sub>c</sub></b>-MOF-TFs modified electrodes by the CPED and (<b>b</b>) Cu<b><sub>a</sub></b>/Zn<b><sub>c</sub></b>-MOF-TF modified electrodes by the DPED at I<span class="html-italic"><sub>app</sub></span> = 1 mA/cm<sup>2</sup>, <span class="html-italic">t</span> = 10,800 s; inserted the corresponding SEM images of the modified electrodes. (Reproduced with permission from Alizadeh et al., Scientific reports; published by Nature Research, 2019.) (<b>F</b>) Proposed mechanism of AED and (<b>a–d</b>) SEM images of the four phases. (Reproduced with permission from Campagnol et al., Journal of Materials Chemistry A; published by Royal Society of Chemistry, 2016.)</p> Full article ">Figure 10
<p>(<b>A</b>) Schematic of the two-step method for the fabrication of MOF-TFs, and SEM images of different types of MOF-TFs after sonication for 1 h. (Reproduced with permission from Abuzalat et al., Ultrasonics sonochemistry; published by Elsevier BV, 2018.) (<b>B</b>) Schematic of ZnO@ZIF-8 NRs synthesized via the self-template strategy, and (<b>a</b>–<b>d</b>) TEM images of the NRs obtained after reaction for a different time and (<b>e</b>) the thickness ratio (T<sub>ZIF-8</sub>/D<sub>ZnO</sub>) in the NRs as a function of the reaction time. (Reproduced with permission from Zhan et al., Journal of the American Chemical Society; published by American Chemical Society, 2013.) (<b>C</b>) (<b>a</b>–<b>d</b>) Top-view and cross-section SEM images and simulation models of four states of the membrane representing the reaction process as a function of time; green-anodic aluminum oxide, pink-MIL-53 MOF. (<b>e,f</b>) The cross-section SEM images of the whole freestanding membrane. (Reproduced with permission from Zhang et al., Scientific reports; published by Nature Research, 2014.)</p> Full article ">Figure 11
<p>(<b>A</b>) Schematic of the solvent-free ZIF-8 film processing and patterning approach. SEM images of (<b>a</b>) intergrown ZIF-8 TFs at different times during the transformation of 1-μm thick sputtered ZnO film on Si wafer, (<b>b</b>) a ZIF-8 pattern obtained after 20-min transformation of a ZnO pattern, and (<b>c</b>) an electrochemically deposited flake-like ZnO precursor film and resulting ZIF-8 film after a 20-min transformation. (Reproduced with permission from Stassen et al., CrystEngComm; published by Royal Society of Chemistry, 2013.) (<b>B</b>) Schematic presentation of the hot-pressing method for MOF-TFs, and the overview (all scale bars, 5 μm) and enlarged (all scale bars, 1 μm) SEM images of different MOF-TFs fabricated on carbon cloth. (Reproduced with permission from Chen et al., Angewandte Chemie International Edition; published by Wiley-VCH, 2016.)</p> Full article ">Figure 12
<p>(<b>A</b>) Experimental setup for the heat treatment in acetic acid vapor. (<b>a</b>) Cross-section SEM images of the UiO-66 TF after treatment in acetic acid vapor, and (<b>b</b>) AFM image of the same film. (Reproduced with permission from Lausund et al., Nature communications; published by Nature Research, 2016.) (<b>B</b>) Schematic of the CVD process for ZIF-8 TFs (zinc-grey, oxygen-red, nitrogen-light blue, and carbon-dark blue). Schematics of (<b>a</b>) ZIF-8 pattern-deposition by MOF-CVD and subsequent lift-off of a patterned photoresist and (<b>b</b>) the production of ZIF-8-coated PDMS pillars by soft lithography and MOF-CVD. SEM images of (<b>c,d</b>) the manufactured ZIF-8 patterns, (<b>e</b>) MOF-CVD-coated PDMS pillars, and (<b>f</b>) identical PDMS pillars after conventional solution processing of ZIF-8. The MOF-CVD processing steps are indicated with a dashed line in a and b; oxide and MOF films are represented in red and blue, respectively. (Reproduced with permission from Stassen et al., Nature materials; published by Nature Publishing Group, 2016.) (<b>C</b>) Schematic of ZIF-8 film preparation via the femto-PLD technique, and SEM images of PEG@ZIF-8 films on sapphire films with an optical image inserted. Crystals showing the typical ZIF-8 morphology are highlighted in light blue. (Reproduced with permission from Fischer et al., Chemistry of Materials; published by American Chemical Society, 2017.) (<b>D</b>) Schematic of the GVD fabrication process of ultrathin ZIF-8 film. SEM image top view of (<b>a</b>) a PVDF hollow fiber, (<b>b</b>) a Zn-based gel layer, and (<b>c</b>) a ZIF-8 TF with (<b>d</b>) a cross-section image. (Reproduced with permission from Li et al., Nature communications; published by Nature Research, 2017.)</p> Full article ">Figure 13
<p>(<b>A</b>) Schematic illustration of the fabrication method of NAFS-1 MOF-TF via the LB-LBL deposition strategy (C-grey, N-blue, O-red, Co<sup>2+</sup>-pink, and Cu<sup>2+</sup>-green). (Reproduced with permission from Makiura et al., Nature materials; published by Nature Publishing Group, 2010.) (<b>B</b>) Schematic illustration of the assembly process for the preparation of 2D-MOF-nanosheet-based TFs. SEM images of (<b>a</b>) Co-TCPP(Fe) and (<b>b</b>) Cu-TCPP(Fe) MOF-TFs on Si wafers via the LS method with different deposition cycles. (Reproduced with permission from Wang et al., Advanced Materials; published by Wiley-VCH, 2016.) (<b>C</b>) Illustration of the assembly process of this MOF-TF. (<b>a,b</b>) TEM images of the synthesized Cu−TCPP nanosheets, and (<b>c</b>) an optical photo of the MOF-TFs after 15 deposition cycles on a quartz substrate. (Reproduced with permission from Xu et al., Journal of the American Chemical Society; published by American Chemical Society, 2012.)</p> Full article ">Figure 14
<p>(<b>A</b>) Schematic presentation of in situ LBL growth of Ln(PDC)<sub>3</sub>-encapsulated HKUST-1 TF using a modified LPE pump method. Photographs of a UV-irradiated (<b>a-c</b>) Ln(PDC)<sub>3</sub>@HKUST-1 TF and (<b>d</b>) a mixed Ln(PDC)<sub>3</sub>@HKUST-1 film on quartz glasses via the modified LPE pump method, and (<b>e</b>) SEM images of the Eu(PDC)3@HKUST-1 TF. (Reproduced with permission from Gu et al., ACS applied materials &amp; interfaces; published by American Chemical Society, 2015.) (<b>B</b>) The LBL method for the hetero-epitaxial growth of MOF-on-MOF hybrid TF structure on SAMs. (Reproduced with permission from Shekhah et al., Dalton Transactions; published by Royal Society of Chemistry, 2011.) (<b>C</b>) Schematic illustration of programmed functionalization of SURMOFs via hetero-LPE growth and the PSM process. (Reproduced with permission from Tu et al., Dalton Transactions; published by Royal Society of Chemistry, 2013.) (<b>D</b>) Schematic illustration of a confined synthesis of MAPbI<sub>2</sub>X (X = Cl, Br, or I) in the interior pores of oriented MOF-TF, and (<b>a,b</b>) SEM images of MAPbI<sub>2</sub>Br@HKUST-1 TF. (Reproduced with permission from Chen et al., ACS applied materials &amp; interfaces; published by American Chemical Society, 2016.)</p> Full article ">Figure 15
<p>(<b>A</b>) Schematic showing the biomimetic replication of MOF patterns using a protein pattern. SEM images of (<b>a</b>) ZIF-8 and (<b>b</b>) Ln<sub>2</sub>(BDC)<sub>3</sub> MOF-TFs via the biomimetic replication. Photograph under UV light of (<b>c</b>) Ln<sub>2</sub>(BDC)<sub>3</sub> MOF-TFs formed with various mixing ratio of Eu, Tb, and Ce ions in the precursor solution; (<b>d</b>) Eu<sub>2</sub>(BDC)<sub>3</sub> (red), Tb<sub>2</sub>(BDC)<sub>3</sub> (green), and mixed Ln<sub>2</sub>(BDC)<sub>3</sub> (yellow) patterns; and (<b>e</b>) Tb<sub>2</sub>(BDC)<sub>3</sub> dot microarrays. (Reproduced with permission from Liang et al., Advanced Materials; published by Wiley-VCH, 2015.) (<b>B</b>) SEM images of (<b>a</b>) inlet (rough) and (<b>b</b>) outlet (smooth) sides of a laser-irradiated brass support (inserted enlarged perforations and EDX atomic compositions), and ZIF-8 TFs grown on (<b>c,d</b>) each side indicated by arrows, and on (<b>e</b>) linearly irradiated and (<b>f</b>) non-irradiated regions of the brass support. All scale bars, 100 μm. (Reproduced with permission from Navarro et al., Journal of Materials Chemistry A; published by Royal Society of Chemistry, 2014.) (<b>C</b>) The coordination replication and mesoscopic architecture concept, and SEM images of (<b>a</b>) the Al<sub>2</sub>O<sub>3</sub> pattern and (<b>b–h</b>) the same sample after replication at 140 °C for 1, 4, 6, 10, 20, 40, and 60 s, respectively. All scale bars, 1 μm. (Reproduced with permission from Reboul et al., Nature materials; published by Nature Publishing Group, 2012.)</p> Full article ">
9 pages, 2758 KiB  
Article
Eucalyptus Kraft Lignin as an Additive Strongly Enhances the Mechanical Resistance of Tree-Leaf Pellets
by Leonardo Clavijo, Slobodan Zlatanovic, Gerd Braun, Michael Bongards, Andrés Dieste and Stéphan Barbe
Processes 2020, 8(3), 376; https://doi.org/10.3390/pr8030376 - 24 Mar 2020
Cited by 3 | Viewed by 2999
Abstract
Pelleted biomass has a low, uniform moisture content and can be handled and stored cheaply and safely. Pellets can be made of industrial waste, food waste, agricultural residues, energy crops, and virgin lumber. Despite their many desirable attributes, they cannot compete with fossil [...] Read more.
Pelleted biomass has a low, uniform moisture content and can be handled and stored cheaply and safely. Pellets can be made of industrial waste, food waste, agricultural residues, energy crops, and virgin lumber. Despite their many desirable attributes, they cannot compete with fossil fuel sources because the process of densifying the biomass and the price of the raw materials make pellet production costly. Leaves collected from street sweeping are generally discarded in landfills, but they can potentially be valorized as a biofuel if they are pelleted. However, the lignin content in leaves is not high enough to ensure the physical stability of the pellets, so they break easily during storage and transportation. In this study, the use of eucalyptus kraft lignin as an additive in tree-leaf pellet production was studied. Results showed that when 2% lignin is added the abrasion resistance can be increased to an acceptable value. Pellets with added lignin fulfilled all requirements of European standards for certification except for ash content. However, as the raw material has no cost, this method can add value or contribute to financing continued sweeping and is an example of a circular economy scenario. Full article
(This article belongs to the Special Issue Production of Biofuels and Numerical Modeling of Chemical Combustion Systems)
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<p>(<b>a</b>) Yield of pellet production versus moisture content of raw material, (<b>b</b>) yield production versus lignin content.</p> Full article ">Figure 2
<p>Durability of pellets. (<b>a</b>) Influence of the moisture content of leaves, (<b>b</b>) influence of the lignin content of pellets.</p> Full article ">Figure 3
<p>SEM images of pellets without lignin (P-22) and with different lignin contents (L-1, L-2, L-3).</p> Full article ">Figure 4
<p>Net heating value (dry basis), (<b>a</b>) influence of the moisture content of leaves, (<b>b</b>) influence of the lignin content of pellets.</p> Full article ">
18 pages, 4260 KiB  
Article
Effects of Low-Temperature Drying with Intermittent Gaseous Chlorine Dioxide Treatment on Texture and Shelf-Life of Rice Cakes
by Timilehin Martins Oyinloye and Won Byong Yoon
Processes 2020, 8(3), 375; https://doi.org/10.3390/pr8030375 - 23 Mar 2020
Cited by 5 | Viewed by 5074
Abstract
We investigated the effect of chlorine dioxide (ClO2) under low temperature drying to suppress rice cake stickiness during the cutting process by initiating the onset of retrogradation until the stickiness is minimized for shelf-life extension. The intermittent ClO2 application at [...] Read more.
We investigated the effect of chlorine dioxide (ClO2) under low temperature drying to suppress rice cake stickiness during the cutting process by initiating the onset of retrogradation until the stickiness is minimized for shelf-life extension. The intermittent ClO2 application at low-temperature drying was conducted at 10 °C for different drying periods (0, 6, 12, 18, and 24 h). Texture analysis showed significant differences with increasing values of hardness (901.39 ± 53.87 to 12,653 ± 1689.35 g) and reduced values of modified adhesiveness (3614.37 ±578.23 to 534.81 ± 89.37 g). The evaluation of rice cake stickiness during the cutting process revealed an optimum drying period of 18 h with no significant difference (p ≤ 0.05) compared to the 24 h drying process. Microbial contamination during the drying process increased, with microbial load from 6.39 ± 0.37 to 7.94 ± 0.29 CFU/g. Intermittent ClO2 application at 22 ppm successfully reduced the microbial load by 63% during drying process. The inhibitory property of ClO2 was further analyzed on a sample with high initial microbial load (3.01 ± 0.14 CFU/g) using primary and modified secondary growth models fitted to all experimental storage temperatures (5–25 °C) with R2 values > 0.99. The model demonstrated a strong inhibition by ClO2 with microbial growth not exceeding the accepted population threshold (106 CFU/g) for toxin production. The shelf-life of rice cake was increased by 86 h and 432 h at room temperature (25 °C) and 5 °C respectively. Microbial inactivation via ClO2 treatment is a novel method for improved food storage without additional thermal sterilization or the use of an additional processing unit. Full article
(This article belongs to the Special Issue Drying Kinetics and Quality Control in Food Processing)
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<p>A schematic diagram of batch process showing low-temperature drying of rice cake. 1. Low-temperature inlet (10 °C), 2. ClO<sub>2</sub> generator, 3. Pre-cooked rice cake, 4. Perforated drying stand, 5. Chlorine dioxide gas detector, 6. Enclosed drying chamber.</p> Full article ">Figure 2
<p>Texture analysis of intermittent ClO<sub>2</sub>-low temperature-dried rice cake.</p> Full article ">Figure 3
<p>Overall procedure of the image analysis of treated dried rice cake during cutting test, (<b>a</b>) control sample with bounding box function, (<b>b</b>) Treated rice cake by drying period.</p> Full article ">Figure 4
<p>Changes in ClO<sub>2</sub> concentration at various intermittencies.</p> Full article ">Figure 5
<p>ClO<sub>2</sub> concentration during low-temperature (10 °C) drying, (<b>A</b>) concentration of ClO<sub>2</sub> gas in the drying chamber during intermittent drying, (<b>B</b>) quantity of ClO<sub>2</sub> absorbed by rice cake sample during the rest period.</p> Full article ">Figure 6
<p>Moisture loss in rice cake sample during drying process.</p> Full article ">Figure 7
<p>Changes in force (g) during the cutting test of retrograded rice cake after various drying times.</p> Full article ">Figure 8
<p>Inactivation by ClO<sub>2</sub> gaseous treatment of steamed rice cake.</p> Full article ">Figure 9
<p>Microbial growth curve of rice cake treated with gaseous ClO<sub>2</sub> during 21 days storage at 4 °C.</p> Full article ">Figure 10
<p><span class="html-italic">B. cereus</span> growth during storage at 5–25 °C fitted with Baranyi growth model.</p> Full article ">
6 pages, 192 KiB  
Editorial
Green Separation and Extraction Processes: Part I
by George Z. Kyzas and Kostas A. Matis
Processes 2020, 8(3), 374; https://doi.org/10.3390/pr8030374 - 23 Mar 2020
Cited by 1 | Viewed by 2813
Abstract
Supercritical fluid extraction comprises a known technology applied to obtain volatile compounds from flowers, i [...] Full article
(This article belongs to the Special Issue Green Separation and Extraction Processes)
13 pages, 5169 KiB  
Article
Journey Making: Applying PSE Principles to Complex Curriculum Designs
by Ian Cameron and Greg Birkett
Processes 2020, 8(3), 373; https://doi.org/10.3390/pr8030373 - 23 Mar 2020
Cited by 2 | Viewed by 3169
Abstract
Since the 1950s, Process Systems Engineering (PSE) concepts have traditionally been applied to the process industries, with great effect and with significant benefit. However, the same general approaches and principles in designing complex process designs can be applied to the design of higher [...] Read more.
Since the 1950s, Process Systems Engineering (PSE) concepts have traditionally been applied to the process industries, with great effect and with significant benefit. However, the same general approaches and principles in designing complex process designs can be applied to the design of higher education (HE) curricula. Curricula represent intended learning journeys, these being similar to the design of process flowsheets. In this paper, we set out the formal framework and concepts that underlie the challenges in design of curricula. The approaches use generic and fundamental concepts that can be applied by any discipline to curriculum design. We show how integration of discipline-specific concepts, across time and space, can be combined through design choices, to create learning journeys for students. These concepts are captured within a web-based design tool that permits wide choices for designers to build innovative curricula. The importance of visualization of curricula is discussed and illustrated, using a range of tools that permit insight into the nature of the designs. The framework and tool presented in this paper have been widely used across many disciplines, such as science, engineering, nursing, philosophy and pharmacy. As a special issue in memory of Professor Roger W.H. Sargent; we show these new developments in curriculum design are similar to the development of process flowsheets. Professor Sargent was not only an eminent research leader and pioneer, but an influential educator who gave rise to a new area in Chemical Engineering, influencing its many directions for more than 50 years. Full article
(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)
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<p>Educational schema as the major focus of educational designs.</p> Full article ">Figure 2
<p>Engineers Australia, Stage 1 competency schema.</p> Full article ">Figure 3
<p>Curriculum design building blocks and information.</p> Full article ">Figure 4
<p>Part of an “attainment” taxonomy for building ILOs.</p> Full article ">Figure 5
<p>Knowledge taxonomies for Chemical Engineering (domain/sub-domain/topic).</p> Full article ">Figure 6
<p>Example of intended learning outcomes (ILOs) (left) and the structured text ILO builder (right).</p> Full article ">Figure 7
<p>Example of knowledge domain outcome linkages for process system courses. (By permission of Engineers Australia [<a href="#B17-processes-08-00373" class="html-bibr">17</a>]).</p> Full article ">Figure 8
<p>Overview of <span class="html-italic">The Journey Maker</span> design-environment functionality.</p> Full article ">Figure 9
<p>Compulsory course requirements for five-year BE/ME program.</p> Full article ">Figure 10
<p>ILO frequencies for major knowledge domains across whole curriculum.</p> Full article ">Figure 11
<p>Lower-level domain knowledge visualization and location within the curriculum.</p> Full article ">Figure 12
<p>Attainment levels and distribution across the curriculum.</p> Full article ">Figure 13
<p>Tracking the linkages of a specific learning outcome through courses in the curriculum.</p> Full article ">Figure 14
<p>Tracking linkages between courses that have specified required prior learning.</p> Full article ">
16 pages, 3681 KiB  
Article
Preparation of Nano-Porous Carbon-Silica Composites and Its Adsorption Capacity to Volatile Organic Compounds
by Lipei Fu, Jiahui Zhu, Weiqiu Huang, Jie Fang, Xianhang Sun, Xinya Wang and Kaili Liao
Processes 2020, 8(3), 372; https://doi.org/10.3390/pr8030372 - 23 Mar 2020
Cited by 28 | Viewed by 5139
Abstract
Carbon-silica composites with nanoporous structures were synthesized for the adsorption of volatile organic compounds (VOCs), taking tetraethyl orthosilicate (TEOS) as the silicon source and activated carbon powder as the carbon source. The preparation conditions were as follows: the pH of the reaction system [...] Read more.
Carbon-silica composites with nanoporous structures were synthesized for the adsorption of volatile organic compounds (VOCs), taking tetraethyl orthosilicate (TEOS) as the silicon source and activated carbon powder as the carbon source. The preparation conditions were as follows: the pH of the reaction system was 5.5, the hydrophobic modification time was 50 h, and the dosage of activated carbon was 2 wt%. Infrared spectrum analysis showed that the activated carbon was dispersed in the pores of aerogel to form the carbon-silica composites material. The static adsorption experiments, dynamic adsorption-desorption experiments, and regeneration experiments show that the prepared carbon-silica composites have microporous and mesoporous structures, the adsorption capacity for n-hexane is better than that of conventional hydrophobic silica gel, and the desorption performance is better than that of activated carbon. It still has a high retention rate of adsorption capacity after multiple adsorption-desorption cycles. The prepared carbon-silica composites material has good industrial application prospects in oil vapor recovery, providing a new alternative for solving organic waste gas pollution. Full article
(This article belongs to the Special Issue Advances in Nanomaterials for Selective Adsorption)
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<p>(Schematic diagram of the experimental equipment for oil vapor static adsorption: 1: stirrer; 2: vacuum retainer; 3: thermometer; 4: thermostatic water bath; 5: weighting bottle; 6: adsorbent; 7 and 8: adsorbates; 9: vacuum pump; 10, 12, and 13: valves; and 11: triple valves).</p> Full article ">Figure 2
<p><a href="#processes-08-00372-f001" class="html-fig">Figure 1</a> schematic diagram of the experimental equipment for oil vapor dynamic adsorption-desorption: 1: nitrogen cylinder; 2, 4, 7, and 8: valves; 3: <span class="html-italic">n</span>-hexane vapor generator; 5: gas mixing bottle; 6: rotor flow meter; 9: adsorption bed; and 10: gas chromatograph.</p> Full article ">Figure 3
<p>N<sub>2</sub> adsorption-desorption isotherms of the aerogels prepared at different pH values.</p> Full article ">Figure 4
<p>Pore size distributions of the aerogels prepared at different pH values.</p> Full article ">Figure 5
<p>N<sub>2</sub> adsorption-desorption isotherms of the aerogels prepared at different treating times.</p> Full article ">Figure 6
<p>Pore size distributions of the aerogels prepared at different treating times.</p> Full article ">Figure 7
<p>N<sub>2</sub> adsorption-desorption isotherms of the silica aerogels prepared under the optimum condition.</p> Full article ">Figure 8
<p>Pore size distributions of hydrophobic silica aerogel prepared under the optimum condition.</p> Full article ">Figure 9
<p>Fourier transform infra-red (FTIR) spectra of samples of unmodified silica aerogel and hydrophobic silica aerogel.</p> Full article ">Figure 10
<p>N<sub>2</sub> adsorption-desorption isotherms of the carbon-silica composites, hydrophobic silica aerogel, and active carbon.</p> Full article ">Figure 11
<p>Pore size distributions of carbon-silica composites, hydrophobic silica aerogel, and active carbon.</p> Full article ">Figure 12
<p>FTIR spectra of samples of carbon-silica composites, hydrophobic silica aerogel.</p> Full article ">Figure 13
<p>Static adsorption-desorption isotherms of <span class="html-italic">n</span>-hexane on SG/AC-0, SG/AC-2, and AC at different temperatures.</p> Full article ">Figure 14
<p>Equilibrium adsorption capacities of <span class="html-italic">n</span>-hexane on carbon-silica composites, hydrophobic silica aerogel, and active carbon at different temperatures.</p> Full article ">Figure 15
<p>Breakthrough curves and desorption curves of <span class="html-italic">n</span>-hexane adsorption (0–300 min)/desorption (300–700 min) on carbon-silica composites, hydrophobic silica aerogel, and active carbon at ambient temperatures and pressures.</p> Full article ">Figure 16
<p>Dynamic adsorption capacity and adsorption retention rates of hydrophobic silica aerogel after different regeneration cycles.</p> Full article ">Figure 17
<p>Dynamic adsorption capacity and adsorption retention rates of carbon-silica composites after different regeneration cycles.</p> Full article ">
15 pages, 3012 KiB  
Article
Machine Learning-Based Prediction of a BOS Reactor Performance from Operating Parameters
by Alireza Rahnama, Zushu Li and Seetharaman Sridhar
Processes 2020, 8(3), 371; https://doi.org/10.3390/pr8030371 - 23 Mar 2020
Cited by 12 | Viewed by 3978
Abstract
A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of decarburization (dc/dt). Correlation analysis showed, as expected a [...] Read more.
A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of decarburization (dc/dt). Correlation analysis showed, as expected a strong positive correlation between the rate of decarburization (dc/dt) and total oxygen flow. On the other hand, the decarburization rate exhibited a negative correlation with lance height. Less obviously, the decarburization rate, also showed a positive correlation with temperature of the waste gas and CO2 content in the waste gas. The second purpose was to train the pilot-plant dataset and develop a neural network based regression to predict the decarburization rate. This was used to predict the decarburization rate in a BOS furnace in an actual manufacturing plant based on lance height and total oxygen flow. The performance was satisfactory with a coefficient of determination of 0.98, confirming that the trained model can adequately predict the variation in the decarburization rate (dc/dt) within BOS reactors. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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<p>A schematic of the pilot plant BOF converter used in the Improved Phosphorus-refining (IMPHOS) trials with dimensions and indicated location heights of the sampling levels [<a href="#B2-processes-08-00371" class="html-bibr">2</a>].</p> Full article ">Figure 2
<p>The correlations between different features within the dataset from the six-ton (6t) pilot plant trial. The figure has been vertically split into two parts, and the top and the bottom shown above are actually the left and right parts of the figure, respectively.</p> Full article ">Figure 3
<p>Histogram (blue), cumulative distribution function (green) and probability density function (purple) for (<b>a</b>) the actual values of the dc/dt and (<b>b</b>) the predicted values of the dc/dt for the pilot dataset with all the features included.</p> Full article ">Figure 4
<p>Scatter plot comparing the predicted values of the dc/dt using the neural network method with the actual values of the dc/dt for the pilot dataset with all the features included.</p> Full article ">Figure 5
<p>Error histogram of the predicted values of the dc/dt by using all the features included in the pilot plant dataset.</p> Full article ">Figure 6
<p>Scatter plot comparing the predicted values of the dc/dt using the neural network method with the actual values of dc/dt by using the two features of total oxygen flow and lance height in the pilot dataset.</p> Full article ">Figure 7
<p>Error histogram of the predicted values of the dc/dt by using the two operating parameters of total oxygen flow and lance height in the pilot plant dataset.</p> Full article ">Figure 8
<p>Scatter plot comparing the predicted values of the dc/dt using neural network method with the actual values of the dc/dt for an industrial dataset with the two operating parameters of total oxygen flow and lance height only.</p> Full article ">Figure 9
<p>Error histogram of the predicted values of the dc/dt for the industrial dataset with the two operating parameters of total oxygen flow and lance height only.</p> Full article ">
21 pages, 2331 KiB  
Article
Distributed Secondary Control for Islanded Microgrids Cluster Based on Hybrid-Triggered Mechanisms
by Shengxuan Weng, Yusheng Xue, Jianbo Luo and Yanman Li
Processes 2020, 8(3), 370; https://doi.org/10.3390/pr8030370 - 23 Mar 2020
Cited by 9 | Viewed by 2692
Abstract
Considering the communication resources limitation, the hybrid-triggered mechanism based distributed control of islanded microgrids cluster is proposed, which can restore the frequency to the rated value and realize the active power sharing when the disturbance occurs. The hybrid-triggered mechanism consists of the self- [...] Read more.
Considering the communication resources limitation, the hybrid-triggered mechanism based distributed control of islanded microgrids cluster is proposed, which can restore the frequency to the rated value and realize the active power sharing when the disturbance occurs. The hybrid-triggered mechanism consists of the self- and event-triggered mechanisms, which are configured at each leader and follower distributed generation to determine the inter-microgrids and intra-microgrid information transmission, respectively. The communication burdens can be sharply reduced since the information is transmitted aperiodically only when the proposed triggering conditions are satisfied under the hybrid-triggered mechanism. Moreover, Zeno behavior is analyzed to be avoided to make the hybrid-triggered mechanism reasonable and practicable for practical islanded microgrids cluster. The simulation verifies the effectiveness of theoretical results. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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<p>An example of double-layer communication network configuration in islanded MGs cluster.</p> Full article ">Figure 2
<p>Test islanded MGs cluster.</p> Full article ">Figure 3
<p>Control performance comparison in Case A. Frequency response under: (<b>a</b>): hybrid-triggered mechanism; (<b>b</b>): periodic sampling mechanism.</p> Full article ">Figure 4
<p>Control performance comparison in Case A. Active power output ratio response under: (<b>a</b>): hybrid-triggered mechanism; (<b>b</b>): periodic sampling mechanism.</p> Full article ">Figure 5
<p>Triggering time instants of <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>G</mi> <mrow> <mn>3</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> under hybrid-triggered mechanism in Case A about: (<b>a</b>): frequency; (<b>b</b>): active power output ratio.</p> Full article ">Figure 6
<p>Communication number comparison between hybrid-triggered and periodic sampling mechanisms in Case A about: (<b>a</b>): frequency; (<b>b</b>): active power output ratio.</p> Full article ">Figure 7
<p>Control performance comparison in Case B. Frequency response under: (<b>a</b>): hybrid-triggered mechanism; (<b>b</b>): periodic sampling mechanism.</p> Full article ">Figure 8
<p>Control performance comparison in Case B. Active power output ratio response under: (<b>a</b>): hybrid-triggered mechanism; (<b>b</b>): periodic sampling mechanism.</p> Full article ">Figure 9
<p>Triggering time instants of <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>G</mi> <mrow> <mn>3</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> under hybrid-triggered mechanism in Case B about: (<b>a</b>): frequency; (<b>b</b>): active power output ratio.</p> Full article ">Figure 10
<p>Communication number comparison between hybrid-triggered and periodic sampling mechanisms in Case B about: (<b>a</b>): frequency; (<b>b</b>): active power output ratio.</p> Full article ">
21 pages, 3042 KiB  
Article
A Molecular Force Field-Based Optimal Deployment Algorithm for UAV Swarm Coverage Maximization in Mobile Wireless Sensor Network
by Xi Wang, Guan-zheng Tan, Fan-Lei Lu, Jian Zhao and Yu-si Dai
Processes 2020, 8(3), 369; https://doi.org/10.3390/pr8030369 - 22 Mar 2020
Cited by 10 | Viewed by 3381
Abstract
In the mobile wireless sensor network (MWSN) field, there exists an important problem—how can we quickly form an MWSN to cover a designated working area on the ground using an unmanned aerial vehicle (UAV) swarm? This problem is of significance in many military [...] Read more.
In the mobile wireless sensor network (MWSN) field, there exists an important problem—how can we quickly form an MWSN to cover a designated working area on the ground using an unmanned aerial vehicle (UAV) swarm? This problem is of significance in many military and civilian applications. In this paper, inspired by intermolecular forces, a novel molecular force field-based optimal deployment algorithm for a UAV swarm is proposed to solve this problem. A multi-rotor UAV swarm is used to carry sensors and quickly build an MWSN in a designated working area. The necessary minimum number of UAVs is determined according to the principle that the coverage area of any three UAVs has the smallest overlap. Based on the geometric properties of a convex polygon, two initialization methods are proposed to make the initial deployment more uniform, following which, the positions of all UAVs are subsequently optimized by the proposed molecular force field-based deployment algorithm. Simulation experiment results show that the proposed algorithm, when compared with three existing algorithms, can obtain the maximum coverage ratio for the designated working area thanks to the proposed initialization methods. The probability of falling into a local optimum and the computational complexity are reduced, while the convergence rate is improved. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
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<p>The system model of the communication coverage problem of the mobile wireless sensor network (MWSN). The black points represent the unmanned aerial vehicles (UAVs), and their communication coverage areas on the ground are the circles.</p> Full article ">Figure 2
<p>The schematic of the communication coverage area of a UAV on the ground.</p> Full article ">Figure 3
<p>The ideal deployment scheme when using equal circles to cover, where the center distance of each two circles is <math display="inline"><semantics> <mrow> <msqrt> <mn>3</mn> </msqrt> <mi>R</mi> </mrow> </semantics></math>, and the definition of <span class="html-italic">S<sub>s</sub></span> is shown.</p> Full article ">Figure 4
<p>This sample shows the meaning of each symbol in this triangle, including sides, vertices, incenter, equal division points, and perpendiculars.</p> Full article ">Figure 5
<p>Steps of dividing a triangle into several polygons of equal area; different line types are used to distinguish different sides.</p> Full article ">Figure 6
<p>A collection of these examples, which shows the allocation method and the allocated number of the UAVs in each triangle.</p> Full article ">Figure 7
<p>The number of convex polygons with each difference value.</p> Full article ">Figure 8
<p>The initial position of UAVs obtained by three initialization methods: (<b>a</b>) is the result of random initialization; (<b>b</b>,<b>c</b>) are the results of the proposed initialization methods 1 and 2, respectively. The black “*” points represent the positions of the UAVs in the working area. The lines represent the dividing lines and boundaries.</p> Full article ">Figure 9
<p>For ease of observation, two figures are used to show the comparison of the three experiments in the relationship between coverage ratio and number of iterations. The comparison between Tests 1 and 3 is shown in (<b>a</b>), and the comparison between Tests 2 and 3 is shown in (<b>b</b>).</p> Full article ">Figure 10
<p>The proportion of the maximum coverage ratio in each numerical interval, in which the distribution proportion of the maximum coverage ratio in the three tests is shown, including 20 rounds of Tests 1 and 2, respectively, and a round of Test 3.</p> Full article ">Figure 11
<p>The optimal deployment scheme obtained by the proposed deployment algorithm, where method 2 (Test 3) was used to initialize the positions of the UAVs: (<b>a</b>) represents the position of the UAVs in iteration #1; <b>(b</b>) represents the position of the UAVs at iteration #17; (<b>c</b>) represents the position of the UAVs at iteration #34; and (<b>d</b>) represents the position of the UAVs at iteration #50.</p> Full article ">Figure 12
<p>The relationship between the coverage ratio and the number of iterations of the proposed algorithm in the six tests. Figures (<b>a</b>–<b>f</b>) represent Tests 1–6, respectively.</p> Full article ">
13 pages, 340 KiB  
Article
Improved Q-Learning Method for Linear Discrete-Time Systems
by Jian Chen, Jinhua Wang and Jie Huang
Processes 2020, 8(3), 368; https://doi.org/10.3390/pr8030368 - 22 Mar 2020
Cited by 1 | Viewed by 3016
Abstract
In this paper, the Q-learning method for quadratic optimal control problem of discrete-time linear systems is reconsidered. The theoretical results prove that the quadratic optimal controller cannot be solved directly due to the linear correlation of the data sets. The following corollaries have [...] Read more.
In this paper, the Q-learning method for quadratic optimal control problem of discrete-time linear systems is reconsidered. The theoretical results prove that the quadratic optimal controller cannot be solved directly due to the linear correlation of the data sets. The following corollaries have been made: (1) The correlation of data is the key factor in the success for the calculation of quadratic optimal control laws by Q-learning method; (2) The control laws for linear systems cannot be derived directly by the existing Q-learning method; (3) For nonlinear systems, there are some doubts about the data independence of current method. Therefore, it is necessary to discuss the probability of the controllers established by the existing Q-learning method. To solve this problem, based on the ridge regression, an improved model-free Q-learning quadratic optimal control method for discrete-time linear systems is proposed in this paper. Therefore, the computation process can be implemented correctly, and the effective controller can be solved. The simulation results show that the proposed method can not only overcome the problem caused by the data correlation, but also derive proper control laws for discrete-time linear systems. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
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<p>Closed-loop response and the control effort of system 2. (<b>a</b>) Trajectories of state values. (<b>b</b>) Trajectory of control value.</p> Full article ">Figure 2
<p>Closed-loop response and the control effort of system 1. (<b>a</b>) Trajectories of state values. (<b>b</b>) Trajectory of control value.</p> Full article ">Figure 3
<p>Closed-loop response and the control effort of system 2 with <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mo>[</mo> <mn>0.91710.4143</mn> <mo>]</mo> </mrow> </semantics></math>. (<b>a</b>) Trajectories of state values. (<b>b</b>) Trajectory of control value.</p> Full article ">
30 pages, 5648 KiB  
Article
Optimum Design of a Standalone Solar Photovoltaic System Based on Novel Integration of Iterative-PESA-II and AHP-VIKOR Methods
by Hussein Mohammed Ridha, Chandima Gomes, Hashim Hizam, Masoud Ahmadipour, Dhiaa Halboot Muhsen and Saleem Ethaib
Processes 2020, 8(3), 367; https://doi.org/10.3390/pr8030367 - 22 Mar 2020
Cited by 22 | Viewed by 5119
Abstract
Solar energy is considered one of the most important renewable energy resources, and can be used to power a stand-alone photovoltaic (SAPV) system for supplying electricity in a remote area. However, inconstancy and unpredictable amounts of solar radiation are considered major obstacles in [...] Read more.
Solar energy is considered one of the most important renewable energy resources, and can be used to power a stand-alone photovoltaic (SAPV) system for supplying electricity in a remote area. However, inconstancy and unpredictable amounts of solar radiation are considered major obstacles in designing SAPV systems. Therefore, an accurate sizing method is necessary to apply in order to find an optimal configuration and fulfil the required load demand. In this study, a novel hybrid sizing approach was developed on the basis of techno-economic objectives to optimally size the SAPV system. The proposed hybrid method consisted of an intuitive method to estimate initial numbers of PV modules and storage battery, an iterative approach to accurately generate a set of wide ranges of optimal configurations, and a Pareto envelope-based selection algorithm (PESA-II) to reduce large configuration by efficacy obtaining a set of Pareto front (PF) solutions. Subsequently, the optimal configurations were ranked by using an integrated analytic hierarchy process (AHP) and vlsekeriterijumskaoptimizacija i kompromisonoresenje (VIKOR). The techno-economic objectives were loss of load probability, life cycle cost, and levelized cost of energy. The performance analysis results demonstrated that the lead–acid battery was reliable and more cost-effective than the other types of storage battery. Full article
(This article belongs to the Special Issue Control and Optimization of Multi-Agent Systems and Complex Networks for Systems Engineering)
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<p>The block diagram of a generalized stand-alone photovoltaic (SAPV) system.</p> Full article ">Figure 2
<p>Electrical circuit of the single-diode solar cell.</p> Full article ">Figure 3
<p>PV curves at different weather conditions of the improved electromagnetism-like (IEM) algorithm: (<b>a</b>) I–V curves and (<b>b</b>) P–V curves.</p> Full article ">Figure 4
<p>The proposed hybrid methodology for optimal sizing of SAPV system.</p> Full article ">Figure 5
<p>Flow chart of the proposed sizing numerical method of the SAPV system.</p> Full article ">Figure 6
<p>Region-based selection method of Pareto envelope-based selection algorithm (PESA-II).</p> Full article ">Figure 7
<p>Integrated analytic hierarchy process–vlsekeriterijumskaoptimizacija i kompromisonoresenje (AHP-VIKOR) method for ranking optimal solutions.</p> Full article ">Figure 8
<p>The mathematical steps of the VIKOR method.</p> Full article ">Figure 9
<p>Observation data of wide ranges of configurations based on the numerical approach for three types of batteries.</p> Full article ">Figure 10
<p>PF solutions for three lead–acid, AGM, and lithium-ion batteries using the PESA-II method.</p> Full article ">Figure 11
<p>Scattering of N and Bat at predefined levels of LLP using the PESA-II method for three types of batteries.</p> Full article ">Figure 12
<p>Daily performance of the SAPV system under optimal configuration for one year.</p> Full article ">Figure 13
<p>Monthly state of charge (SOC), PV modules (P_PV), load demand (E_L), solar irradiation (G), E_excess, E_deficit, and ambient temperature (T) through one year.</p> Full article ">
13 pages, 664 KiB  
Review
Available Technologies and Materials for Waste Cooking Oil Recycling
by Alberto Mannu, Sebastiano Garroni, Jesus Ibanez Porras and Andrea Mele
Processes 2020, 8(3), 366; https://doi.org/10.3390/pr8030366 - 22 Mar 2020
Cited by 95 | Viewed by 22133
Abstract
Recently, the interest in converting waste cooking oils (WCOs) to raw materials has grown exponentially. The driving force of such a trend is mainly represented by the increasing number of WCO applications, combined with the definition, in many countries, of new regulations on [...] Read more.
Recently, the interest in converting waste cooking oils (WCOs) to raw materials has grown exponentially. The driving force of such a trend is mainly represented by the increasing number of WCO applications, combined with the definition, in many countries, of new regulations on waste management. From an industrial perspective, the simple chemical composition of WCOs make them suitable as valuable chemical building blocks, in fuel, materials, and lubricant productions. The sustainability of such applications is sprightly related to proper recycling procedures. In this context, the development of new recycling processes, as well as the optimization of the existing ones, represents a priority for applied chemistry, chemical engineering, and material science. With the aim of providing useful updates to the scientific community involved in vegetable oil processing, the current available technologies for WCO recycling are herein reported, described, and discussed. In detail, two main types of WCO treatments will be considered: chemical transformations, to exploit the chemical functional groups present in the waste for the synthesis of added value products, and physical treatments as extraction, filtration, and distillation procedures. The first part, regarding chemical synthesis, will be connected mostly to the production of fuels. The second part, concerning physical treatments, will focus on bio-lubricant production. Moreover, during the description of filtering procedures, a special focus will be given to the development and applicability of new materials and technologies for WCO treatments. Full article
(This article belongs to the Special Issue Recycling of Waste Oils: Technology and Application)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Process for WCO recycling, Mannu et al. [<a href="#B29-processes-08-00366" class="html-bibr">29</a>].</p> Full article ">Scheme 1
<p>Esterification of waste cooking oils (WCOs) and related processes.</p> Full article ">
15 pages, 3720 KiB  
Article
Recovering Scandium from Scandium Rough Concentrate Using Roasting-Hydrolysis-Leaching Process
by Junhui Xiao, Yang Peng, Wei Ding, Tao Chen, Kai Zou and Zhen Wang
Processes 2020, 8(3), 365; https://doi.org/10.3390/pr8030365 - 22 Mar 2020
Cited by 18 | Viewed by 5235
Abstract
In this study, a roasting-hydrolysis-acid leaching process is used to extract scandium from the scandium rough concentrate. The scandium rough concentrate containing Sc2O3 of 76.98 g/t was obtained by magnetic separation, gravity separation, and electric separation from Sc-bearing Vi-Ti magnetite [...] Read more.
In this study, a roasting-hydrolysis-acid leaching process is used to extract scandium from the scandium rough concentrate. The scandium rough concentrate containing Sc2O3 of 76.98 g/t was obtained by magnetic separation, gravity separation, and electric separation from Sc-bearing Vi-Ti magnetite tailings in the Panxi area of China. The majority of scandium in scandium rough concentrate mainly occurs in diopside, titanopyroxene, montmorillonite, chlorite, talc, aluminosilicate minerals, and isomorphism. Sodium salt and scandium coarse concentrate are added into the roasting furnace for roasting, which makes the fusion reaction of silicon, aluminum and sodium salt to produce soluble salts such as sodium silicate and sodium metaaluminate. Scandium is further recovered from the hydrolysis residue by acid leaching. Test results show scandium leaching recovery of 95.12% and the acid leaching residue with Sc2O3 content of 8.12 g/t are obtained, while the extraction of scandium is obvious. There is no obvious peak value of Scandium spectrum in hydrochloric acid leach residue. Most of scandium in hydrolytic residue is dissolved into Sc3+ and enters into the liquid phase. The main minerals in leach residue are perovskite, ferric silicate, and olivine. Full article
(This article belongs to the Special Issue Green Separation and Extraction Processes)
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<p>X-ray diffraction (XRD) diffractogram of scandium rough concentrate.</p> Full article ">Figure 2
<p>Scheme of the sandium separation process from scandium rough concentrate.</p> Full article ">Figure 3
<p>Effect results of fusion agent dosage (<b>a</b>) and alkali fusion roasting temperature (<b>b</b>).</p> Full article ">Figure 4
<p>Effect results of alkali fusion roasting time (<b>a</b>) and hydrolysis temperature (<b>b</b>).</p> Full article ">Figure 5
<p>Effect results of hydrolysis leaching temperature (<b>a</b>) and hydrolysis solid to liquid ratio (<b>b</b>).</p> Full article ">Figure 6
<p>Effect results of hydrochloric acid dosage (<b>a</b>) and leaching time (<b>b</b>).</p> Full article ">Figure 7
<p>Effect results of leaching temperature (<b>a</b>) and leaching solid to liquid ratio (<b>b</b>).</p> Full article ">Figure 8
<p>SEM-EDS images analysis results (<b>a</b>) scandium rough concentrate, (<b>b</b>) roasting ores, (<b>c</b>) hydrolysis residue, (<b>d</b>) leaching residue.</p> Full article ">Figure 8 Cont.
<p>SEM-EDS images analysis results (<b>a</b>) scandium rough concentrate, (<b>b</b>) roasting ores, (<b>c</b>) hydrolysis residue, (<b>d</b>) leaching residue.</p> Full article ">Figure 9
<p>X-ray diffraction (XRD) diffractogram of hydrochloric acid leaching residue.</p> Full article ">
15 pages, 232 KiB  
Review
A Review of Exergy Based Optimization and Control
by Corey James, Tae Young Kim and Robert Jane
Processes 2020, 8(3), 364; https://doi.org/10.3390/pr8030364 - 21 Mar 2020
Cited by 20 | Viewed by 5447
Abstract
This work presents a critical review of the use of exergy based control and optimization for efficiency improvements in energy networks, with a background of exergy based analysis given for context. Over the past three decades, a number of studies using exergy were [...] Read more.
This work presents a critical review of the use of exergy based control and optimization for efficiency improvements in energy networks, with a background of exergy based analysis given for context. Over the past three decades, a number of studies using exergy were conducted to gain a performance advantage for high energy consumption systems and networks. Due to their complexity and the increased scale of the systems, the opportunity to misuse energy inevitability leads to inefficient operations. The studies accomplished in this area are grouped into either control or optimization to highlight each method’s ability to minimize system irreversibilities that lead to exergy destruction. The exergy based optimization and control studies featured demonstrate substantial improvements (as high as 40%) over traditional methods based on the first law of thermodynamics. This paper reviews the work completed in the area of exergy based optimization and control as of the end of September 2019, outlines the progress made, and identifies specific areas where future work can advance this area of study. A relatively small amount of publications are available compared to other fields, with most work occurring in the area of exergy based multi-objective optimization. Full article
(This article belongs to the Special Issue Feature Review Papers)
12 pages, 5721 KiB  
Article
Finite Element Analysis in Setting of Fillings of V-Shaped Tooth Defects Made with Glass-Ionomer Cement and Flowable Composite
by Tsanka Dikova, Tihomir Vasilev, Vesela Hristova and Vladimir Panov
Processes 2020, 8(3), 363; https://doi.org/10.3390/pr8030363 - 21 Mar 2020
Cited by 6 | Viewed by 3466
Abstract
The aim of the present paper is to investigate the deformation–stress state of fillings of V-shaped tooth defects by finite element analysis (FEA). Two different materials are used—auto-cured resin-reinforced glass-ionomer cement (GIC) and flowable photo-cured composite (FPC). Two materials are placed into the [...] Read more.
The aim of the present paper is to investigate the deformation–stress state of fillings of V-shaped tooth defects by finite element analysis (FEA). Two different materials are used—auto-cured resin-reinforced glass-ionomer cement (GIC) and flowable photo-cured composite (FPC). Two materials are placed into the cavity in one portion, as before the application of the composite the cavity walls are covered with a thin adhesive layer. Deformations and equivalent von Mises stresses are evaluated by FEA. Experimental study of micro-leakage is performed. It is established that there is an analogous non-homogeneous distribution of equivalent Von Mises stresses at fillings of V-shaped defects, made with GIC and FPC. Maximum stresses are generated along the boundaries of the filling on the vestibular surface of the tooth and at the bottom of the filling itself. Values of equivalent Von Mises stresses of GIC fillings are higher than that of FPC. Magnitude and character of deformation distribution at GIC and FPC fillings are similar—deformation is maximum along the vestibular surface of the filling and is 0.056 and 0.053 mm, respectively. In FPC fillings, the adhesive layer, located along the cavity/filling boundary, is characterized with greatest strain. The experimental study of micro-leakage has confirmed the adequacy of models used in FEA. Full article
(This article belongs to the Special Issue Synthesis and Characterization of Biomedical Materials)
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<p>Virtual model of tooth 11: (<b>a</b>) fixed tooth with filling of glass-ionomer cement (GIC); (<b>b</b>) simulation mesh used.</p> Full article ">Figure 2
<p>Virtual model of tooth 15: (<b>a</b>) fixed tooth with filling of flowable composite; (<b>b</b>) mesh used in simulation.</p> Full article ">Figure 3
<p>Distribution of equivalent Von Mises stresses during setting process of fillings made with: (<b>a</b>–<b>c</b>) glass-ionomer cement; (<b>d</b>–<b>f</b>) flowable photo-cured composite (FPC). (1% shrinkage—(<b>a</b>,<b>d</b>); 2% shrinkage—(<b>b</b>,<b>e</b>); 3% shrinkage—(<b>c</b>,<b>f</b>)).</p> Full article ">Figure 4
<p>Displacement during setting process of fillings made with: (<b>a</b>–<b>c</b>) glass-ionomer cement; (<b>d</b>–<b>f</b>) flowable photo-cured composite. (1% shrinkage—(<b>a</b>,<b>d</b>); 2% shrinkage—(<b>b</b>,<b>e</b>); 3% shrinkage—(<b>c</b>,<b>f</b>)).</p> Full article ">Figure 5
<p>Strain during setting process of fillings made with: (<b>a</b>–<b>c</b>) glass-ionomer cement; (<b>d</b>–<b>f</b>) flowable photo-cured composite. (1% shrinkage—(<b>a</b>,<b>d</b>); 2% shrinkage—(<b>b</b>,<b>e</b>); 3% shrinkage—(<b>c</b>,<b>f</b>)).</p> Full article ">Figure 6
<p>GIC filling of tooth cavity: (<b>a</b>) Displacement in the zone of filling; (<b>b</b>) Von Mises equivalent stresses; (<b>c</b>) Micro-leakage. (3% shrinkage).</p> Full article ">Figure 7
<p>Filling of tooth cavity made with FPC: (<b>a</b>) Strain in the zone of filling; (<b>b</b>) Von Mises equivalent stresses; (<b>c</b>) Micro-leakage. (3% shrinkage).</p> Full article ">
17 pages, 4487 KiB  
Article
Steady-State Water Drainage by Oxygen in Anodic Porous Transport Layer of Electrolyzers: A 2D Pore Network Study
by Haashir Altaf, Nicole Vorhauer, Evangelos Tsotsas and Tanja Vidaković-Koch
Processes 2020, 8(3), 362; https://doi.org/10.3390/pr8030362 - 21 Mar 2020
Cited by 25 | Viewed by 4580
Abstract
Recently, pore network modelling has been attracting attention in the investigation of electrolysis. This study focuses on a 2D pore network model with the purpose to study the drainage of water by oxygen in anodic porous transport layers (PTL). The oxygen gas produced [...] Read more.
Recently, pore network modelling has been attracting attention in the investigation of electrolysis. This study focuses on a 2D pore network model with the purpose to study the drainage of water by oxygen in anodic porous transport layers (PTL). The oxygen gas produced at the anode catalyst layer by the oxidation of water flows counter currently to the educt through the PTL. When it invades the water-filled pores of the PTL, the liquid is drained from the porous medium. For the pore network model presented here, we assume that this process occurs in distinct steps and applies classical rules of invasion percolation with quasi-static drainage. As the invasion occurs in the capillary-dominated regime, it is dictated by the pore structure and the pore size distribution. Viscous and liquid film flows are neglected and gravity forces are disregarded. The curvature of the two-phase interface within the pores, which essentially dictates the invasion process, is computed from the Young Laplace equation. We show and discuss results from Monte Carlo pore network simulations and compare them qualitatively to microfluidic experiments from literature. The invasion patterns of different types of PTLs, i.e., felt, foam, sintered, are compared with pore network simulations. In addition to this, we study the impact of pore size distribution on the phase patterns of oxygen and water inside the pore network. Based on these results, it can be recommended that pore network modeling is a valuable tool to study the correlation between kinetic losses of water electrolysis processes and current density. Full article
(This article belongs to the Special Issue Electrolysis Processes)
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<p>(<b>a</b>) Schematic representation of different porosity regions in PTL as studied in [<a href="#B33-processes-08-00362" class="html-bibr">33</a>]. (<b>b</b>) Pore size gradient within PTL as in the study from [<a href="#B39-processes-08-00362" class="html-bibr">39</a>].</p> Full article ">Figure 2
<p>Different throat size distributions with same porosity but different mean throat diameter and standard deviation of the throat size. Solid in white and liquid saturated void space (i.e., pores and throats) in blue.</p> Full article ">Figure 3
<p>Different saturations for constant porosity and constant mean throat diameter but different organization of the pore network (PN). Solid and empty pores and throats in white and liquid saturated pores and throats in blue.</p> Full article ">Figure 4
<p>Scheme of the algorithm.</p> Full article ">Figure 5
<p>Pore and throat numbering in a 2D PN. The dashed lines illustrate the periodicity at the lateral boundaries.</p> Full article ">Figure 6
<p>Geometric information about pores and throats.</p> Full article ">Figure 7
<p>Microfluidic drainage experiment from Arbabi et al. [<a href="#B20-processes-08-00362" class="html-bibr">20</a>] (Reprinted with the permission from Elsevier, 2014). Solids and gas invaded area in black, liquid in white. The PN is identified by the circles and throats. The image shows the steady-state invasion pattern after breakthrough of the gas phase from inlet (<b>at the bottom</b>) to water channel (<b>at the top</b>).</p> Full article ">Figure 8
<p>(<b>a</b>) PNM simulation result, (<b>b</b>) invasion pattern comparison of simulation and experiment result. Liquid-filled throats are shown in black, invaded pores in red, and invaded throats in blue. Liquid-filled pores are not shown.</p> Full article ">Figure 9
<p>Histograms of pore size distribution (PSD) with varying standard deviation in µm as indicated in the legend.</p> Full article ">Figure 10
<p>Gas saturation at breakthrough of the gas phase for different PSDs. The mean value of throat sizes is 17 µm.</p> Full article ">Figure 11
<p>Bimodal throat size distribution with standard deviation 2.0 µm for smaller (the first peak) and 2.5 µm for larger (the second peak) throats.</p> Full article ">Figure 12
<p>(<b>a</b>) Exemplary invasion patterns of the monomodal PN with porosity 71%. Liquid in blue, gas in white and solid in gray. The arrow indicates the direction of gas invasion. (<b>b</b>) Saturation profiles for different overall number of invaded throats and pores achieved during one drainage simulation of the PN with randomly distributed pore and throat sizes.</p> Full article ">Figure 12 Cont.
<p>(<b>a</b>) Exemplary invasion patterns of the monomodal PN with porosity 71%. Liquid in blue, gas in white and solid in gray. The arrow indicates the direction of gas invasion. (<b>b</b>) Saturation profiles for different overall number of invaded throats and pores achieved during one drainage simulation of the PN with randomly distributed pore and throat sizes.</p> Full article ">Figure 13
<p>(<b>a</b>) Exemplary invasion patterns of the bimodal PN with porosity 71%. Liquid in blue, gas in white and solid in gray. Macro-pores are represented by thicker lines. The arrow indicates the direction of gas invasion. (<b>b</b>) Saturation profiles for different overall number of invaded throats and pores from one drainage simulation.</p> Full article ">Figure 14
<p>PSDs used for different PTL types with standard deviations: 1.0 µm for foam, 1.7 µm for felt and 2.5 µm for sintered.</p> Full article ">Figure 15
<p>Invasion pattern comparison: (<b>a</b>) foam PTL, (<b>b</b>) felt PTL, (<b>c</b>) sintered PTL (Experimental images from [<a href="#B20-processes-08-00362" class="html-bibr">20</a>] are reprinted with the permission from Elsevier, 2014).</p> Full article ">
45 pages, 2335 KiB  
Review
Evaluation of Polymeric Materials for Chemical Enhanced Oil Recovery
by Alison J. Scott, Laura Romero-Zerón and Alexander Penlidis
Processes 2020, 8(3), 361; https://doi.org/10.3390/pr8030361 - 21 Mar 2020
Cited by 70 | Viewed by 9156
Abstract
Polymer flooding is a promising enhanced oil recovery (EOR) technique; sweeping a reservoir with a dilute polymer solution can significantly improve the overall oil recovery. In this overview, polymeric materials for enhanced oil recovery are described in general terms, with specific emphasis on [...] Read more.
Polymer flooding is a promising enhanced oil recovery (EOR) technique; sweeping a reservoir with a dilute polymer solution can significantly improve the overall oil recovery. In this overview, polymeric materials for enhanced oil recovery are described in general terms, with specific emphasis on desirable characteristics for the application. Application-specific properties should be considered when selecting or developing polymers for enhanced oil recovery and should be carefully evaluated. Characterization techniques should be informed by current best practices; several are described herein. Evaluation of fundamental polymer properties (including polymer composition, microstructure, and molecular weight averages); resistance to shear/thermal/chemical degradation; and salinity/hardness compatibility are discussed. Finally, evaluation techniques to establish the polymer flooding performance of candidate EOR materials are described. Full article
(This article belongs to the Special Issue Feature Review Papers)
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<p>Simplified schematic of the polymer flooding process for enhanced oil recovery.</p> Full article ">Figure 2
<p>Effect of ionic polymer backbone on hydrodynamic volume; comparison of (<b>a</b>) polyacrylamide and (<b>b</b>) partially hydrolyzed polyacrylamide (HPAM).</p> Full article ">Figure 3
<p>Structure of (<b>a</b>) gradient (branched) copolymer, (<b>b</b>) random copolymer, and (<b>c</b>) block copolymer chains with comonomers A and B.</p> Full article ">Figure 4
<p>Capillary tube set-up for the determination of mechanical degradation (adapted from [<a href="#B12-processes-08-00361" class="html-bibr">12</a>]).</p> Full article ">Figure 5
<p>Temperature distribution of global oil fields (as of 1998) (reproduced from Bjorkum and Nadeau [<a href="#B100-processes-08-00361" class="html-bibr">100</a>] with permission from CSIRO Publishing).</p> Full article ">Figure 6
<p>Hypothetical conformation of an adsorbed polymer molecule on a rock surface (adapted from [<a href="#B129-processes-08-00361" class="html-bibr">129</a>]).</p> Full article ">Figure 7
<p>Wall-effect model: water-based polymer with “water-wet” rock (adapted from [<a href="#B128-processes-08-00361" class="html-bibr">128</a>]).</p> Full article ">Figure 8
<p>Experimental sand-pack system set-up for the evaluation of polymer flooding.</p> Full article ">
12 pages, 3941 KiB  
Article
Ketone Solvent to Reduce the Minimum Miscibility Pressure for CO2 Flooding at the South Sumatra Basin, Indonesia
by Adi Novriansyah, Wisup Bae, Changhyup Park, Asep K. Permadi and Shabrina Sri Riswati
Processes 2020, 8(3), 360; https://doi.org/10.3390/pr8030360 - 21 Mar 2020
Cited by 8 | Viewed by 3861
Abstract
This paper experimentally analyzes the chemical additives, i.e., methanol and ethanol, as alcohol solvents, and acetone as a ketone solvent, and the temperature influencing the minimum miscibility pressure (MMP) that is essential to design miscible CO2 flooding at an oil field, the [...] Read more.
This paper experimentally analyzes the chemical additives, i.e., methanol and ethanol, as alcohol solvents, and acetone as a ketone solvent, and the temperature influencing the minimum miscibility pressure (MMP) that is essential to design miscible CO2 flooding at an oil field, the South Sumatra basin, Indonesia. The experiments were designed to measure CO2-oil interfacial tension with the vanishing interfacial tension (VIT) method in the ranges up to 3000 psi (208.6 bar) and 300 degrees Celsius. The experiment results show that lower temperatures, larger solvent volumes, and the acetone were effective in reducing MMP. The acetone, an aprotic ketone solvent, reduced MMP more than the methanol and the ethanol in the CO2-oil system. The high temperature was negative to obtain the high CO2 solubility into the oil as well as the lower MMP. The experimental results confirm that the aprotic ketone solvent could be effective in decreasing the MMP for the design of miscible CO2 flooding at the shallow mature oilfields with a low reservoir temperature. Full article
(This article belongs to the Special Issue Research on the Polymer-Coated Nanoparticles for Enhanced Oil Recovery)
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<p>A target reservoir planned CO<sub>2</sub> flooding at the South Sumatra basin, Indonesia.</p> Full article ">Figure 2
<p>Vanishing interfacial tension (VIT) test apparatus measuring the interfacial tension (IFT): (<b>a</b>) a schematic diagram of the VIT test system and (<b>b</b>) a photograph of the actual experiment equipment.</p> Full article ">Figure 3
<p>HPHT optical cell: (<b>a</b>) a schematic diagram with part details and (<b>b</b>) an actual equipment.</p> Full article ">Figure 4
<p>Variables to estimate the IFT with an oil-drop image.</p> Full article ">Figure 5
<p>Oil-drop images at CO<sub>2</sub> injection pressures and crude oil samples at 60 °C.</p> Full article ">Figure 6
<p>IFT versus CO<sub>2</sub> injection pressure for samples A and B at (<b>a</b>) 60 and (<b>b</b>) 80 °C without additives, i.e., pure CO<sub>2</sub> and crude oils.</p> Full article ">Figure 7
<p>IFT versus CO<sub>2</sub> injection pressure as different temperatures: (<b>a</b>) crude oil A and (<b>b</b>) crude oil B without additives, i.e., pure CO<sub>2</sub> and the specific oil.</p> Full article ">Figure 8
<p>Oil-drop images of the mixture of CO<sub>2</sub> and 15 cm<sup>3</sup> acetone at 60 °C.</p> Full article ">Figure 9
<p>Effects of additive volumes and types on decreasing minimum miscibility pressure (MMP): (<b>a</b>) crude oil A and (<b>b</b>) crude oil B. The larger acetone volume results in more decrement of MMP values.</p> Full article ">
17 pages, 2595 KiB  
Article
Robust Mixed H2/H∞ State Feedback Controller Development for Uncertain Automobile Suspensions with Input Delay
by Nan Liu, Hui Pang and Rui Yao
Processes 2020, 8(3), 359; https://doi.org/10.3390/pr8030359 - 20 Mar 2020
Cited by 2 | Viewed by 2824
Abstract
In order to achieve better dynamics performances of a class of automobile active suspensions with the model uncertainties and input delays, this paper proposes a generalized robust linear H2/H∞ state feedback control approach. First, the mathematical model of a [...] Read more.
In order to achieve better dynamics performances of a class of automobile active suspensions with the model uncertainties and input delays, this paper proposes a generalized robust linear H2/H∞ state feedback control approach. First, the mathematical model of a half-automobile active suspension is established. In this model, the H∞ norm of body acceleration is determined as the performance index of the designed controller, and the hard constraints of suspension dynamic deflection, tire dynamic load and actuator saturation are selected as the generalized H2 performance output index of the designed controller to satisfy the suspension safety requirements. Second, a generalized H2/H∞ guaranteed cost state-feedback controller is developed in terms of Lyapunov stability theory. In addition, the Cone Complementarity Linearization (CCL) algorithm is employed to convert the generalized H2/H∞ output-feedback control problem into a finite convex optimization problem (COP) in a linear matrix inequality framework. Finally, a numerical simulation case of this half-automobile active suspension is presented to illustrate the effectiveness of the proposed controller in frequency-domain and time-domain. Full article
(This article belongs to the Section Process Control and Monitoring)
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<p>Dynamics model of half-automobile active suspension system (ASS).</p> Full article ">Figure 2
<p>Response comparisons of (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>z</mi> <mo>¨</mo> </mover> <mi>c</mi> </msub> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mover accent="true"> <mi>φ</mi> <mo>¨</mo> </mover> </semantics></math> in the frequency domain.</p> Full article ">Figure 3
<p>Response comparisons of (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>z</mi> <mo>¨</mo> </mover> <mi>c</mi> </msub> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mover accent="true"> <mi>φ</mi> <mo>¨</mo> </mover> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>y</mi> <mi>f</mi> </msub> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>y</mi> <mi>r</mi> </msub> </mrow> </semantics></math>, (<b>e</b>) <math display="inline"><semantics> <mrow> <msubsup> <mi>F</mi> <mrow> <mi>radio</mi> </mrow> <mi>f</mi> </msubsup> </mrow> </semantics></math>, (<b>f</b>) <math display="inline"><semantics> <mrow> <msubsup> <mi>F</mi> <mrow> <mi>r</mi> <mi>a</mi> <mi>d</mi> <mi>i</mi> <mi>o</mi> </mrow> <mi>r</mi> </msubsup> </mrow> </semantics></math> under bump road disturbances.</p> Full article ">Figure 4
<p>Response comparisons of (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mi>f</mi> </msub> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mi>r</mi> </msub> </mrow> </semantics></math> under bump road disturbances.</p> Full article ">Figure 5
<p>The RMS ratio of <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>z</mi> <mo>¨</mo> </mover> <mi>c</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mover accent="true"> <mi>φ</mi> <mo>¨</mo> </mover> </semantics></math> under different input time delay and B-class road surface.</p> Full article ">Figure 6
<p>The RMS ratio of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>y</mi> <mi>f</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>y</mi> <mi>r</mi> </msub> </mrow> </semantics></math> under different input time delay and B-class road surface.</p> Full article ">Figure 7
<p>The RMS ratio of <math display="inline"><semantics> <mrow> <msubsup> <mi>F</mi> <mrow> <mi>radio</mi> </mrow> <mi>f</mi> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>F</mi> <mrow> <mi>r</mi> <mi>a</mi> <mi>d</mi> <mi>i</mi> <mi>o</mi> </mrow> <mi>r</mi> </msubsup> </mrow> </semantics></math> under different input time delay and B-class road surface.</p> Full article ">Figure 8
<p>Response comparisons of <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mi>f</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mi>r</mi> </msub> </mrow> </semantics></math> under B-class random road.</p> Full article ">
21 pages, 5625 KiB  
Article
Intelligent Colored Token Petri Nets for Modeling, Control, and Validation of Dynamic Changes in Reconfigurable Manufacturing Systems
by Husam Kaid, Abdulrahman Al-Ahmari, Zhiwu Li and Reggie Davidrajuh
Processes 2020, 8(3), 358; https://doi.org/10.3390/pr8030358 - 20 Mar 2020
Cited by 23 | Viewed by 5028
Abstract
The invention of reconfigurable manufacturing systems (RMSs) has created a challenging problem: how to quickly and effectively modify an RMS to address dynamic changes in a manufacturing system, such as processing failures and rework, machine breakdowns, addition of new machines, addition of new [...] Read more.
The invention of reconfigurable manufacturing systems (RMSs) has created a challenging problem: how to quickly and effectively modify an RMS to address dynamic changes in a manufacturing system, such as processing failures and rework, machine breakdowns, addition of new machines, addition of new products, removal of old machines, and changes in processing routes induced by the competitive global market. This paper proposes a new model, the intelligent colored token Petri net (ICTPN), to simulate dynamic changes or reconfigurations of a system. The main idea is that intelligent colored tokens denote part types that represent real-time knowledge about changes and status of a system. Thus, dynamic configurations of a system can be effectively modeled. The developed ICTPN can model dynamic changes of a system in a modular manner, resulting in the development of a very compact model. In addition, when configurations appear, only the changed colored token of the part type from the current model has to be modified. Based on the resultant ICTPN model, deadlock-free, conservative, and reversible behavioral properties, among others, are guaranteed. The developed ICTPN model was tested and validated using the GPenSIM tool and compared with existing methods from the literature. Full article
(This article belongs to the Special Issue Active Flow Control Processes with Machine Learning and the Internet of Things)
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<p>(<b>a</b>) An AMS example, (<b>b</b>) Production sequence.</p> Full article ">Figure 2
<p>Intelligent colored token Petri net (ICTPN) of an AMS example, as shown in <a href="#processes-08-00358-f001" class="html-fig">Figure 1</a>a.</p> Full article ">Figure 3
<p>An ICTPN for the reconfigured system by machine breakdowns.</p> Full article ">Figure 4
<p>An ICTPN for the reconfigured system by addition of a new product.</p> Full article ">Figure 5
<p>An ICTPN for the reconfigured system by addition of a new machine.</p> Full article ">Figure 6
<p>An ICTPN for the reconfigured system by removal of an old machine.</p> Full article ">Figure 7
<p>An ICTPN for the reconfigured system by removal of an old machine.</p> Full article ">Figure 8
<p>An ICTPN for the reconfigured system by rework.</p> Full article ">Figure 9
<p>Reachable marking of an ICTPN example, as shown in <a href="#processes-08-00358-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 10
<p>Comparison of utilization for the Petri net model in <a href="#processes-08-00358-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 11
<p>Comparison of throughput for the Petri net model in <a href="#processes-08-00358-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 12
<p>Comparison of the throughput time per part for the Petri net model in <a href="#processes-08-00358-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 13
<p>Comparison of WIP for the Petri net model in <a href="#processes-08-00358-f002" class="html-fig">Figure 2</a>.</p> Full article ">
13 pages, 1777 KiB  
Article
Power Plant Optimisation—Effective Use of the Nelder-Mead Approach
by Paweł Niegodajew, Maciej Marek, Witold Elsner and Łukasz Kowalczyk
Processes 2020, 8(3), 357; https://doi.org/10.3390/pr8030357 - 20 Mar 2020
Cited by 15 | Viewed by 4922
Abstract
This paper demonstrates the use of a combined software package including IPSEpro and MATLAB in the optimisation of a modern thermal cycle. A 900 MW power plant unit (operating at ultra-supercritical conditions) was considered as the study object. The Nelder-Mead simplex-based, direct search [...] Read more.
This paper demonstrates the use of a combined software package including IPSEpro and MATLAB in the optimisation of a modern thermal cycle. A 900 MW power plant unit (operating at ultra-supercritical conditions) was considered as the study object. The Nelder-Mead simplex-based, direct search method was used to increase power plant efficiency and to find the optimal thermal cycle configuration. As the literature reveals, the Nelder-Mead approach is very sensitive to the simplex size and to the choice of method coefficients, i.e., reflection, expansion and contraction. When these coefficients are improperly chosen, the finding of the optimal solution cannot be guaranteed, particularly in such complex systems as thermal cycles. Hence, the main goal of the present work was to demonstrate the capability of an integrated software package including IPSEpro, MATLAB and MS Excel in the optimisation process of a complex thermal cycle, as well as to examine the effectiveness of the most popular sets of Nelder-Mead coefficients previously proposed by other researchers. For the investigation purposes, the bleed and outlet pressures from the turbines were considered as decision variables, and the power plant efficiency was used as an objective function. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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<p>A scheme of the communication between IPSEpro, PSExcel and MATLAB.</p> Full article ">Figure 2
<p>The general scheme of the power plant (PP) thermal cycle with the set of parameters used in optimisation.</p> Full article ">Figure 3
<p>The location of each simplex centre for each run, together with the position of the local optimum.</p> Full article ">Figure 4
<p>The evolution of the iteration procedure for three different NM operating conditions, for run 1 (<b>a</b>), run 2 (<b>b</b>), run 3 (<b>c</b>) and run 4 (<b>d</b>).</p> Full article ">Figure 4 Cont.
<p>The evolution of the iteration procedure for three different NM operating conditions, for run 1 (<b>a</b>), run 2 (<b>b</b>), run 3 (<b>c</b>) and run 4 (<b>d</b>).</p> Full article ">Figure 5
<p>The optimised PP efficiency (<b>a</b>) and computing time (<b>b</b>) for each run and NM setup.</p> Full article ">
23 pages, 6057 KiB  
Article
A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems
by Ai-Qing Tian, Shu-Chuan Chu, Jeng-Shyang Pan and Yongquan Liang
Processes 2020, 8(3), 356; https://doi.org/10.3390/pr8030356 - 20 Mar 2020
Cited by 30 | Viewed by 4547
Abstract
The conventional maximum power point tracking (MPPT) method fails in partially shaded conditions, because multiple peaks may appear on the power–voltage characteristic curve. The Pigeon-Inspired Optimization (PIO) algorithm is a new type of meta-heuristic algorithm. Aiming at this situation, this paper proposes a [...] Read more.
The conventional maximum power point tracking (MPPT) method fails in partially shaded conditions, because multiple peaks may appear on the power–voltage characteristic curve. The Pigeon-Inspired Optimization (PIO) algorithm is a new type of meta-heuristic algorithm. Aiming at this situation, this paper proposes a new type of algorithm that combines a new pigeon population algorithm named Parallel and Compact Pigeon-Inspired Optimization (PCPIO) with MPPT, which can solve the problem that MPPT cannot reach the near global maximum power point. This hybrid algorithm is fast, stable, and capable of globally optimizing the maximum power point tracking algorithm. Therefore, the purpose of this article is to study the performance of two optimization techniques. The two algorithms are Particle Swarm Algorithm (PSO) and improved pigeon algorithm. This paper first studies the mechanism of multi-peak output characteristics of photovoltaic arrays in complex environments, and then proposes a multi-peak MPPT algorithm based on a combination of an improved pigeon population algorithm and an incremental conductivity method. The improved pigeon algorithm is used to quickly locate near the maximum power point, and then the variable step size incremental method INC (incremental conductance) is used to accurately locate the maximum power point. A simulation was performed on Matlab/Simulink platform. The results prove that the method can achieve fast and accurate optimization under complex environmental conditions, effectively reduce power oscillations, enhance system stability, and achieve better control results. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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<p>The principle diagram of the photovoltaic effect.</p> Full article ">Figure 2
<p>The physical approximate equivalent model of photovoltaic cells.</p> Full article ">Figure 3
<p>Flowchart of perturbation and observation algorithm.</p> Full article ">Figure 4
<p>Comparison of running times of the PCPIO, with the CPIO, PIO, and PSO algorithms in the test functions. (<b>a</b>): f1 function, (<b>b</b>): f2 function, (<b>c</b>): f3 function, (<b>d</b>): f4 function.</p> Full article ">Figure 5
<p>Comparison of running times of the PCPIO, with the CPIO, PIO, and PSO algorithms in the test functions. (<b>a</b>): f5 function, (<b>b</b>): f6 function, (<b>c</b>): f7 function, (<b>d</b>): f8 function, (<b>e</b>): f10 function, (<b>f</b>): f11 function, (<b>g</b>): f12 function, (<b>h</b>): f13 function.</p> Full article ">Figure 6
<p>Comparison of running times of the PCPIO, with the CPIO, PIO, and PSO algorithms in the test functions. (<b>a</b>): f14 function, (<b>b</b>): f15 function, (<b>c</b>): f20 function, (<b>d</b>): f21 function, (<b>e</b>): f22 function, (<b>f</b>): f23 function.</p> Full article ">Figure 7
<p>P–V curve of PV array under partial shadow and standard environment. (<b>a</b>): PV array under standard environment (<b>b</b>): PV array under shadow environment</p> Full article ">Figure 8
<p>I–V curve of PV array under partial shadow and standard environment. (<b>a</b>): PV array under standard environment (<b>b</b>): PV array under shadow environment</p> Full article ">Figure 9
<p>The part of the simulation model of MPPT algorithm.</p> Full article ">Figure 10
<p>The part of the simulation model of PCPIO-based MPPT algorithm.</p> Full article ">Figure 11
<p>Maximum power curve of photovoltaic power generation under the control of MPPT algorithm and PCPIO and MPPT hybrid algorithm.</p> Full article ">
16 pages, 3115 KiB  
Review
Current Use of Carbon-Based Materials for Biomedical Applications—A Prospective and Review
by Govindasamy Rajakumar, Xiu-Hua Zhang, Thandapani Gomathi, Sheng-Fu Wang, Mohammad Azam Ansari, Govindarasu Mydhili, Gnanasundaram Nirmala, Mohammad A. Alzohairy and Ill-Min Chung
Processes 2020, 8(3), 355; https://doi.org/10.3390/pr8030355 - 20 Mar 2020
Cited by 58 | Viewed by 7317
Abstract
Among a large number of current biomedical applications in the use of medical devices, carbon-based nanomaterials such as graphene (G), graphene oxides (GO), reduced graphene oxide (rGO), and carbon nanotube (CNT) are frontline materials that are suitable for developing medical devices. Carbon Based [...] Read more.
Among a large number of current biomedical applications in the use of medical devices, carbon-based nanomaterials such as graphene (G), graphene oxides (GO), reduced graphene oxide (rGO), and carbon nanotube (CNT) are frontline materials that are suitable for developing medical devices. Carbon Based Nanomaterials (CBNs) are becoming promising materials due to the existence of both inorganic semiconducting properties and organic π-π stacking characteristics. Hence, it could effectively simultaneously interact with biomolecules and response to the light. By taking advantage of such aspects in a single entity, CBNs could be used for developing biomedical applications in the future. The recent studies in developing carbon-based nanomaterials and its applications in targeting drug delivery, cancer therapy, and biosensors. The development of conjugated and modified carbon-based nanomaterials contributes to positive outcomes in various therapies and achieved emerging challenges in preclinical biomedical applications. Subsequently, diverse biomedical applications of carbon nanotube were also deliberately discussed in the light of various therapeutic advantages. Full article
(This article belongs to the Special Issue Production and Biomedical Applications of Bioactive Compounds)
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<p>Biomedical applications of nano based materials.</p> Full article ">Figure 2
<p>Transferrin-poly (allamine hydrochloride) (Tf-PAH) modified Graphene Oxide (GO) for docetaxel (DTX) delivery. [<a href="#B50-processes-08-00355" class="html-bibr">50</a>,<a href="#B51-processes-08-00355" class="html-bibr">51</a>] Reprinted with permission from Elsevier.</p> Full article ">Figure 3
<p>The strategy of the utilization of carbon nanotubes (CNTs) to cross the blood brain barrier (BBB). CNTs could be functionalized to increase the stability, biocompatibility, and active targeting of CNTs to BBB and nerve cells. Functionalized CNTs could effectively penetrate BBB and enter CNS for bio-imaging and drug delivery [<a href="#B59-processes-08-00355" class="html-bibr">59</a>]. Reprinted with permission from Elsevier.</p> Full article ">Figure 4
<p>Schematic illustration of NGO-808 preparation and combined A549 tumor xenografts-targeted NIR imaging and synergistic phototherapy (PDT and PTT [<a href="#B74-processes-08-00355" class="html-bibr">74</a>,<a href="#B75-processes-08-00355" class="html-bibr">75</a>] Reprinted with permission from Elsevier.</p> Full article ">
23 pages, 1960 KiB  
Review
Effect of Freeze-Drying on Quality and Grinding Process of Food Produce: A Review
by Timilehin Martins Oyinloye and Won Byong Yoon
Processes 2020, 8(3), 354; https://doi.org/10.3390/pr8030354 - 20 Mar 2020
Cited by 130 | Viewed by 21545
Abstract
Freeze-drying is an important processing unit operation in food powder production. It offers dehydrated products with extended shelf life and high quality. Unfortunately, food quality attributes and grinding characteristics are affected significantly during the drying process due to the glass transition temperature (during [...] Read more.
Freeze-drying is an important processing unit operation in food powder production. It offers dehydrated products with extended shelf life and high quality. Unfortunately, food quality attributes and grinding characteristics are affected significantly during the drying process due to the glass transition temperature (during drying operation) and stress generated (during grinding operation) in the food structure. However, it has been successfully applied to several biological materials ranging from animal products to plants products owning to its specific advantages. Recently, the market demands for freeze-dried and ground food products such as spices, vegetables, and fruits are on the increase. In this study, the effect of the freeze-drying process on quality attributes, such as structural changes, the influence of glass transition during grinding, together with the effect on grinding efficiency in terms of energy requirement, grinding yield, and morphological changes in the powder as a result of temperature, drying time were discussed. An overview of models for drying kinetics for freeze-dried food sample, and grinding characteristics developed to optimize the drying processes, and a prediction of the grinding characteristics are also provided. Some limitations of the drying process during grinding are also discussed together with innovative methods to improve the drying and grinding processes. Full article
(This article belongs to the Special Issue Drying Kinetics and Quality Control in Food Processing)
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<p>Effects of water activity (<b>a</b>) and water content (g/100 g of SNF) (<b>b</b>) on the onset <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>g</mi> </msub> </mrow> </semantics></math> in anhydrous and humidified trehalose-WPI-SO systems 1, 2, 3 (stages of emulsion) stored for 360 h at 25 ± 2 °C. Source: Maidannyk et al. [<a href="#B56-processes-08-00354" class="html-bibr">56</a>].</p> Full article ">Figure 2
<p>Micrographs of quince fruits during freeze and convective drying. Source; Izli and Polat [<a href="#B60-processes-08-00354" class="html-bibr">60</a>].</p> Full article ">Figure 3
<p>Scanning electron micrograph of potato starch granules (<b>a</b>) and, Freeze-dried potato starch granules (<b>b</b>), Source: Apinan et al. [<a href="#B63-processes-08-00354" class="html-bibr">63</a>].</p> Full article ">Figure 4
<p>Fraction of initial moisture left vs time relationship from equation <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <msup> <mrow> <mi>Ae</mi> </mrow> <mrow> <mo>−</mo> <mi>Bt</mi> </mrow> </msup> </mrow> </semantics></math>. Source: George and Datta [<a href="#B70-processes-08-00354" class="html-bibr">70</a>].</p> Full article ">Figure 5
<p>SEM image of carrot freeze-dried at −28 °C and −196 °C. Source: Voda et al. [<a href="#B100-processes-08-00354" class="html-bibr">100</a>].</p> Full article ">Figure 6
<p>Effect of temperature, relative humidity (RH) and exposure time on the cake strength of skim milk powder. Source: Fitzpatrick et al. [<a href="#B101-processes-08-00354" class="html-bibr">101</a>].</p> Full article ">
10 pages, 3041 KiB  
Article
Studies on Influence of Cell Temperature in Direct Methanol Fuel Cell Operation
by R. Govindarasu and S. Somasundaram
Processes 2020, 8(3), 353; https://doi.org/10.3390/pr8030353 - 19 Mar 2020
Cited by 22 | Viewed by 4535
Abstract
Directmethanol fuel cells (DMFCs) offer one of the most promising alternatives for the replacement of fossil fuels. A DMFC that had an active Membrane Electrode Assembly (MEA) area of 45 cm2, a squoval-shaped manifold hole design, and a Pt-Ru/C catalyst combination [...] Read more.
Directmethanol fuel cells (DMFCs) offer one of the most promising alternatives for the replacement of fossil fuels. A DMFC that had an active Membrane Electrode Assembly (MEA) area of 45 cm2, a squoval-shaped manifold hole design, and a Pt-Ru/C catalyst combination at the anode was taken for analysis in simulation and real-time experimentation. A mathematical model was developed using dynamic equations of a DMFC. Simulation of a DMFC model using MATLAB software was carried out to identify the most influencing process variables, namely cell temperature, methanol flow rate and methanol concentration during a DMFC operation. Simulation results were recorded and analyzed. It was observed from the results that the cell temperature was the most influencing process variable in the DMFC operation, more so than the methanol flow rate and the methanol concentration. In the DMFC, real-time experimentation was carried out at different cell temperatures to find out the optimum temperature at which maximum power density was obtained. The results obtained in simulation and the experiment were compared and it was concluded that the temperature was the most influencing process variable and 333K was the optimum operating temperature required to achieve the most productive performance in power density of the DMFC. Full article
(This article belongs to the Special Issue Modelling and Process Control of Fuel Cell Systems)
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<p>Real-time experimental setup of the direct-methanol fuel cell (DMFC) system.</p> Full article ">Figure 2
<p>Schematic diagram of the direct methanol fuel cell system.</p> Full article ">Figure 3
<p>Effect of methanol flowrate.</p> Full article ">Figure 4
<p>Effect of methanol concentration.</p> Full article ">Figure 5
<p>Effect of cell temperature.</p> Full article ">Figure 6
<p>Step response of DMFC at 25% ofthe operating temperature.</p> Full article ">Figure 7
<p>Step response of DMFC at 50% of the operating temperature.</p> Full article ">Figure 8
<p>Step response of DMFC at 75% of the operating temperature.</p> Full article ">Figure 9
<p>C<sub>CH3OM</sub>: Steady-state current density–voltage curve.</p> Full article ">Figure 10
<p>C<sub>CH3OM</sub>: Steady-state current density–power density curve.</p> Full article ">Figure 11
<p>Durability test on a squoval-shaped manifold hole design-based DMFC.</p> Full article ">
15 pages, 5595 KiB  
Article
Impact of Varying Load Conditions and Cooling Energy Comparison of a Double-Inlet Pulse Tube Refrigerator
by Muhammad Arslan, Muhammad Farooq, Muhamamd Naqvi, Umair Sultan, Zia-ur-Rehman Tahir, Saad Nawaz, Nazim Waheed, Salman Raza Naqvi, Qasim Ali, M. Suleman Tariq, Ijaz Ahmad Chaudhry, John M. Anderson and Anthony Anukam
Processes 2020, 8(3), 352; https://doi.org/10.3390/pr8030352 - 19 Mar 2020
Cited by 5 | Viewed by 5544
Abstract
Modeling and optimization of a double-inlet pulse tube refrigerator (DIPTR) is very difficult due to its geometry and nature. The objective of this paper was to optimize-DIPTR through experiments with the cold heat exchanger (CHX) along the comparison of cooling load with experimental [...] Read more.
Modeling and optimization of a double-inlet pulse tube refrigerator (DIPTR) is very difficult due to its geometry and nature. The objective of this paper was to optimize-DIPTR through experiments with the cold heat exchanger (CHX) along the comparison of cooling load with experimental data using different boundary conditions. To predict its performance, a detailed two-dimensional DIPTR model was developed. A double-drop pulse pipe cooler was used for solving continuity, dynamic and power calculations. External conditions for applicable boundaries include sinusoidal pressure from an end of the tube from a user-defined function and constant temperature or limitations of thermal flux within the outer walls of exchanger walls under colder conditions. The results of the system’s cooling behavior were reported, along with the connection between the mass flow rates, heat distribution along pulse tube and cold-end pressure, the cooler load’s wall temp profile and cooler loads with varied boundary conditions i.e. opening of 20% double-inlet and 40-60% orifice valves, respectively. Different loading conditions of 1 and 5 W were applied on the CHX. At 150 K temperature of the cold-end heat exchanger, a maximum load of 3.7 W was achieved. The results also reveal a strong correlation between computational fluid dynamics modeling results and experimental results of the DIPTR. Full article
(This article belongs to the Special Issue Progress in Energy Conversion Systems and Emission Control)
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<p>Geometry of GM-type double-inlet pulse tube refrigerator (DIPTR) with meshing.</p> Full article ">Figure 2
<p>Grid independence test of DIPTR.</p> Full article ">Figure 3
<p>Convergence criteria of DIPTR.</p> Full article ">Figure 4
<p>Phase relation effect between pressure and mass flow rate: (<b>a</b>) Case 1; (<b>b</b>) case 2; (<b>c</b>) case 3.</p> Full article ">Figure 4 Cont.
<p>Phase relation effect between pressure and mass flow rate: (<b>a</b>) Case 1; (<b>b</b>) case 2; (<b>c</b>) case 3.</p> Full article ">Figure 5
<p>Between cold load temperature and cases 1, 2 and 3.</p> Full article ">Figure 6
<p>Effect of mass flow rates through pulse tube cross section for cases 1–3.</p> Full article ">Figure 7
<p>Distribution of temperature along the length of pulse tube for case 1.</p> Full article ">Figure 8
<p>(<b>a</b>) Temperature contour; (<b>b</b>) density contour.</p> Full article ">Figure 9
<p>Velocity vector for the entire system.</p> Full article ">Figure 10
<p>Rate of heat transfer over a cycle at CHX for case #5.</p> Full article ">Figure 11
<p>Comparison of cold-end temperature of DIPTR with time step for case 1.</p> Full article ">Figure 12
<p>Comparison of heat load and cooling energy of DIPTR for case 1.</p> Full article ">Figure 13
<p>Comparison of variation of temperature along the pulse tube length.</p> Full article ">Figure 14
<p>Comparison of pressure at the regenerator inlet of DIPTR.</p> Full article ">
17 pages, 4795 KiB  
Article
Multi-Objective Optimal Configuration of the CCHP System
by Liukang Zheng, Xiaoli Wang and Baochen Jiang
Processes 2020, 8(3), 351; https://doi.org/10.3390/pr8030351 - 19 Mar 2020
Cited by 9 | Viewed by 2671
Abstract
The combined cooling, heating and power (CCHP) system not only has high energy efficiency but also has different load structures. Traditional separate production (SP) system and power supply system do not consider the land cost in terms of the environmental benefits, and in [...] Read more.
The combined cooling, heating and power (CCHP) system not only has high energy efficiency but also has different load structures. Traditional separate production (SP) system and power supply system do not consider the land cost in terms of the environmental benefits, and in the aspect of the power supply reliability, the grid-connected inverter cost is also ignored. Considering the deficiency of the traditional energy supply system, this paper builds the CCHP system construction cost model. The particle swarm optimization (PSO) is adopted to find out the minimum value of the construction cost, and the optimal system construction scheme is constructed from three aspects which are system reliability, economic benefits and environmental benefits. In this paper, the typical daily data, as well as the meteorological data and the load data, in the last four years are taken as experimental dataset. The experimental results show that compared with the traditional SP system and power supply system, the CCHP system established in this paper not only achieves lower cumulative investment cost, but also has a good power supply reliability and environmental benefits. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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<p>Energy transfer diagram of the proposed combined cooling heating and power (CCHP) system.</p> Full article ">Figure 2
<p>Power cost laws based on different methods. (<b>a</b>) The energy cost changing with the number of photovoltaics (PVs); (<b>b</b>) The energy cost changing with the number of wind turbines (WTs); (<b>c</b>) The power cost generated by gas turbines (GTs) changing with the natural gas price; (<b>d</b>) The power price at Yantai changing with different points in time.</p> Full article ">Figure 3
<p>Heating and cooling energy cost per 1 kW·h. (<b>a</b>) Cost of direct-fired lithium bromide absorption cold/warm water units (DLB) absorbing flue gas to produce cooling energy; (<b>b</b>) Cost of DLB consuming natural gas to produce cooling energy; (<b>c</b>) Cost of electric refrigerator (ER) consuming power to produce cooling energy; (<b>d</b>) Cost of DLB absorbing flue gas to produce heating energy; (<b>e</b>) Cost of DLB consuming natural gas to produce heating energy; (<b>f</b>) Cost of electric boiler (EB) consuming power to produce heating energy.</p> Full article ">Figure 4
<p>Power load, cooling load, and heating load in a typical day.</p> Full article ">Figure 5
<p>Different output energy of the separate production (SP) and WT in a typical day. (<b>a</b>) The power generated by a WT on a typical day in heating season; (<b>b</b>) The power generated by a WT on a typical day in cooling season; (<b>c</b>) The power generated by a WT on a typical day in transition season; (<b>d</b>) The power generated by a PV on a typical day in cooling season; (<b>e</b>) The power generated by a PV on a typical day in heating season; (<b>f</b>) The power generated by a PV on a typical day in transition season.</p> Full article ">Figure 6
<p>System construction cost model.</p> Full article ">Figure 7
<p>The loss of daily load. (<b>a</b>) The daily missing load in 2016; (<b>b</b>) The daily missing load in 2017; (<b>c</b>) The daily missing load in 2018; (<b>d</b>) The daily missing load in 2019.</p> Full article ">Figure 8
<p>The missing cooling load in each year.</p> Full article ">Figure 9
<p>The cumulative investment cost of the proposed CCHP system, the SP and the traditional grid power system.</p> Full article ">Figure 10
<p>Environmental cost of the system among different energy supply modes.</p> Full article ">Figure A1
<p>Energy scheduling flowchart of the proposed CCHP system.</p> Full article ">
22 pages, 2509 KiB  
Article
Techno-Economic Analysis of CO2 Capture Technologies in Offshore Natural Gas Field: Implications to Carbon Capture and Storage in Malaysia
by Norhasyima Rahmad Sukor, Abd Halim Shamsuddin, Teuku Meurah Indra Mahlia and Md Faudzi Mat Isa
Processes 2020, 8(3), 350; https://doi.org/10.3390/pr8030350 - 19 Mar 2020
Cited by 33 | Viewed by 9181
Abstract
Growing concern on global warming directly related to CO2 emissions is steering the implementation of carbon capture and storage (CCS). With Malaysia having an estimated 37 Tscfd (Trillion standard cubic feet) of natural gas remains undeveloped in CO2 containing natural gas [...] Read more.
Growing concern on global warming directly related to CO2 emissions is steering the implementation of carbon capture and storage (CCS). With Malaysia having an estimated 37 Tscfd (Trillion standard cubic feet) of natural gas remains undeveloped in CO2 containing natural gas fields, there is a need to assess the viability of CCS implementation. This study performs a techno-economic analysis for CCS at an offshore natural gas field in Malaysia. The framework includes a gas field model, revenue model, and cost model. A techno-economic spreadsheet consisting of Net Present Value (NPV), Payback Period (PBP), and Internal Rate of Return (IRR) is developed over the gas field’s production life of 15 years for four distinctive CO2 capture technologies, which are membrane, chemical absorption, physical absorption, and cryogenics. Results predict that physical absorption solvent (Selexol) as CO2 capture technology is most feasible with IRR of 15% and PBP of 7.94 years. The output from the techno-economic model and associated risks of the CCS project are quantified by employing sensitivity analysis (SA), which indicated that the project NPV is exceptionally sensitive to gas price. On this basis, the economic performance of the project is reliant on revenues from gas sales, which is dictated by gas market price uncertainties. Full article
(This article belongs to the Special Issue Gas, Water and Solid Waste Treatment Technology)
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Figure 1

Figure 1
<p>Schematic of the techno-economic analysis framework.</p> Full article ">Figure 2
<p>Tangga Barat Development Project.</p> Full article ">Figure 3
<p>(<b>a</b>–<b>c</b>) Basic methods for CO<sub>2</sub> capture from natural gas.</p> Full article ">Figure 4
<p>Carbon capture and storage (CCS) concept at Tangga Barat gas field.</p> Full article ">Figure 5
<p>(<b>a</b>) Project cash flow base case using polymeric membrane. (<b>b</b>) Project cash flow using chemical absorption (Amine). (<b>c</b>) Project cash flow using physical absorption solvent (Selexol). (<b>d</b>) Project cash flow using cryogenic distillation.</p> Full article ">Figure 5 Cont.
<p>(<b>a</b>) Project cash flow base case using polymeric membrane. (<b>b</b>) Project cash flow using chemical absorption (Amine). (<b>c</b>) Project cash flow using physical absorption solvent (Selexol). (<b>d</b>) Project cash flow using cryogenic distillation.</p> Full article ">Figure 6
<p>(<b>a</b>) Sensitivity analysis (SA) of Net Present Value (NPV) for CO<sub>2</sub> capture using membrane. (<b>b</b>) SA of NPV for CO<sub>2</sub> capture using chemical absorption (Amine). (<b>c</b>) SA of NPV for CO<sub>2</sub> capture using physical absorption solvent (Selexol). (<b>d</b>) SA of NPV for CO<sub>2</sub> capture using cryogenic distillation.</p> Full article ">Figure 6 Cont.
<p>(<b>a</b>) Sensitivity analysis (SA) of Net Present Value (NPV) for CO<sub>2</sub> capture using membrane. (<b>b</b>) SA of NPV for CO<sub>2</sub> capture using chemical absorption (Amine). (<b>c</b>) SA of NPV for CO<sub>2</sub> capture using physical absorption solvent (Selexol). (<b>d</b>) SA of NPV for CO<sub>2</sub> capture using cryogenic distillation.</p> Full article ">Figure 6 Cont.
<p>(<b>a</b>) Sensitivity analysis (SA) of Net Present Value (NPV) for CO<sub>2</sub> capture using membrane. (<b>b</b>) SA of NPV for CO<sub>2</sub> capture using chemical absorption (Amine). (<b>c</b>) SA of NPV for CO<sub>2</sub> capture using physical absorption solvent (Selexol). (<b>d</b>) SA of NPV for CO<sub>2</sub> capture using cryogenic distillation.</p> Full article ">
17 pages, 3711 KiB  
Article
Data-Driven Modelling of the Complex Interaction between Flocculant Properties and Floc Size and Structure
by Anita Lourenço, Marco S. Reis, Julien Arnold and Maria Graca Rasteiro
Processes 2020, 8(3), 349; https://doi.org/10.3390/pr8030349 - 19 Mar 2020
Cited by 6 | Viewed by 2937
Abstract
Polymeric flocculants are widely used due to their ability to efficiently promote flocculation at low dosages. However, fundamental background knowledge about how they act and interact with the substrates is often scarce, or insufficient to infer the best chemical configuration for treating a [...] Read more.
Polymeric flocculants are widely used due to their ability to efficiently promote flocculation at low dosages. However, fundamental background knowledge about how they act and interact with the substrates is often scarce, or insufficient to infer the best chemical configuration for treating a specific effluent. Inductive, data-driven approaches offer a viable solution, enabling the development of effective solutions for each type of effluent, overcoming the knowledge gap. In this work, we present such an inductive workflow that combines the statistical design of experiments and predictive modelling, and demonstrates its effectiveness in the development of anionic polymeric flocculants for the treatment of a real effluent from the potato crisps manufacturing industry. Based on the results presented, it is possible to conclude that the hydrodynamic diameter, charged fraction and concentration are the parameters with a stronger influence on the characteristics of flocs obtained when using copolymers, while the charged fraction, concentration and hydrophobic content present a stronger influence on the characteristics of flocs obtained using terpolymers containing a hydrophobic monomer. Full article
(This article belongs to the Special Issue Design and Applications of Polymeric Flocculants)
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Figure 1

Figure 1
<p>An effect summary table for case 1.</p> Full article ">Figure 2
<p>A summary of fit report for case 1.</p> Full article ">Figure 3
<p>The parameter estimates report for case 1 (JMP displays an asterisk next to the Prob&gt;|t| values that are less than 0.05).</p> Full article ">Figure 4
<p>An actual versus predicted plot for case 1.</p> Full article ">Figure 5
<p>An effect summary table for case 2.</p> Full article ">Figure 6
<p>A summary of fit report for case 2.</p> Full article ">Figure 7
<p>The parameter estimates for case 2.</p> Full article ">Figure 8
<p>An actual versus predicted plot for case 2.</p> Full article ">Figure 9
<p>An effect summary for case 3.</p> Full article ">Figure 10
<p>A summary of fit for case 3.</p> Full article ">Figure 11
<p>The parameter estimates for case 3.</p> Full article ">Figure 12
<p>An actual versus predicted plot for case 3.</p> Full article ">Figure 13
<p>An effect summary for case 4.</p> Full article ">Figure 14
<p>A summary of fit for case 4.</p> Full article ">Figure 15
<p>The parameter estimates for case 4.</p> Full article ">Figure 16
<p>An actual versus predicted plot for case 4.</p> Full article ">Figure 17
<p>A variables importance plot for case 5.</p> Full article ">Figure 18
<p>A prediction profiler for no hydrophobic content for case 5.</p> Full article ">Figure 19
<p>A prediction profiler for maximum hydrophobic content (3 mol%), for case 5.</p> Full article ">Figure 20
<p>A variables importance plot for case 6.</p> Full article ">Figure 21
<p>A prediction profiler for no hydrophobic content for case 6.</p> Full article ">Figure 22
<p>A prediction profiler for maximum hydrophobic content (3 mol%), for case 6.</p> Full article ">
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