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Processes
Volume 8
Issue 4
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Processes, Volume 8, Issue 4 (April 2020) – 119 articles

Cover Story (view full-size image): Amyloid beta peptide (Aβ)-related studies require an adequate supply of purified Aβ. Aβs are “difficult sequences” to synthesize chemically, and low yields are common due to aggregation during purification. Here, we demonstrate an easier synthesis, deprotection, reduction, cleavage, and purification process for Aβ(1-40) using standard Fmoc-protected amino acids and SPPS resin (HMBA resin) that provide higher yields of Aβ(1-40) than previous standard protocols. The method described herein is not limited to the production of Aβ(1-40) and can be used to synthesize other easily-oxidized and aggregating sequences. Our proposed methodology will contribute to various fields using “difficult sequence” peptides. View this paper.
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15 pages, 1131 KiB  
Article
Enhancing Astaxanthin Biosynthesis by Rhodosporidium toruloides Mutants and Optimization of Medium Compositions Using Response Surface Methodology
by Tuyet Nhung Tran, Dai-Hung Ngo, Quoc Tuan Tran, Hoang Chinh Nguyen, Chia-Hung Su and Dai-Nghiep Ngo
Processes 2020, 8(4), 497; https://doi.org/10.3390/pr8040497 - 24 Apr 2020
Cited by 12 | Viewed by 4611
Abstract
Astaxanthin is a valuable carotenoid, which has been approved as a food coloring by the US Food and Drug Administration and is considered as a food dye by the European Union (European Commission). This work aimed to attain Rhodosporidium toruloides mutants for enhanced [...] Read more.
Astaxanthin is a valuable carotenoid, which has been approved as a food coloring by the US Food and Drug Administration and is considered as a food dye by the European Union (European Commission). This work aimed to attain Rhodosporidium toruloides mutants for enhanced astaxanthin accumulation using ultraviolet (UV) and gamma irradiation mutagenesis. Gamma irradiation was shown to be more efficient than UV for producing astaxanthin-overproducer. Among the screened mutants, G17, a gamma-induced mutant, exhibited the highest astaxanthin production, which was significantly higher than that of the wild strain. Response surface methodology was then applied to optimize the medium compositions for maximizing astaxanthin production by the mutant G17. The optimal medium compositions for the cultivation of G17 were determined as a peptone concentration of 19.75 g/L, malt extract concentration of 13.56 g/L, and glucose concentration of 19.92 g/L, with the maximum astaxanthin yield of 3021.34 µg/L ± 16.49 µg/L. This study suggests that the R. toruloides mutant (G17) is a potential candidate for astaxanthin production. Full article
(This article belongs to the Special Issue Emerging Technologies for Biorefining, Food and Environmental Applications)
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<p>Effect of UV exposure time on the survival rate of wild-type <span class="html-italic">R. toruloides</span>. Data are mean ± SD of three replicates.</p> Full article ">Figure 2
<p>Effect of <sup>60</sup>Co-gamma irradiation dose on the survival rate of wild-type <span class="html-italic">R. toruloides</span>. Data are mean ± SD of three replicates.</p> Full article ">Figure 3
<p>Combined effect of peptone concentration and malt extract concentration on astaxanthin production by <span class="html-italic">R. toruloides</span> mutant.</p> Full article ">Figure 4
<p>Combined effect of peptone concentration and glucose concentration on astaxanthin production by <span class="html-italic">R. toruloides</span> mutant.</p> Full article ">Figure 5
<p>Combined effect of malt extract concentration and glucose concentration on astaxanthin production by <span class="html-italic">R. toruloides</span> mutant.</p> Full article ">
9 pages, 2400 KiB  
Article
Formulation and Functional Properties of Whey Protein-Based Tissue Adhesive Using Totarol as an Antimicrobial Agent
by Yifan Hou, Xiaonan Zhang, Cuina Wang and Mingruo Guo
Processes 2020, 8(4), 496; https://doi.org/10.3390/pr8040496 - 24 Apr 2020
Cited by 5 | Viewed by 3175
Abstract
Tissue adhesives have been widely used in surgical procedures. Compared to traditional surgical sutures, tissue adhesives provide fast bonding experiences and full closure of wounds. However, current tissue adhesives are mostly fossil-based synthetic products. Therefore, it is of great significance to explore the [...] Read more.
Tissue adhesives have been widely used in surgical procedures. Compared to traditional surgical sutures, tissue adhesives provide fast bonding experiences and full closure of wounds. However, current tissue adhesives are mostly fossil-based synthetic products. Therefore, it is of great significance to explore the use of natural materials in tissue adhesives. Whey is a low-end byproduct of cheese manufacturing. Whey protein, a group of small globular proteins, can exhibit adhesive properties if their structures are modified by physical or chemical means. The objectives of this study were to investigate the functional and structural properties of whey protein-based tissue adhesive, along with the antibacterial effect of totarol, a natural antimicrobial agent. Whey protein isolate (WPI) solutions (25%–33% protein) were mixed with different levels (0.1%–0.3% w/w) of totarol. The mixtures were analyzed for total plate count and yeast and mold count. The lap-shear bonding strength was tested after the WPI-totarol solutions were mixed with a crosslinking agent, glutaraldehyde (GTA). The lap-shear bonding strength of the tissue adhesive was about 20 kPa, which is comparable to that of a commercial BioGlue®. The microstructures of the mixtures were analyzed by scanning electron microscopy (SEM). Full article
(This article belongs to the Special Issue Green Synthesis Processes of Polymers & Composites)
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<p>Effect of totarol level on aerobic counts.</p> Full article ">Figure 2
<p>Effect of totarol level on yeast and mold counts.</p> Full article ">Figure 3
<p>Time needed for antimicrobial effect of totarol.</p> Full article ">Figure 4
<p>Effect of whey protein concentration on bonding strength (glutaraldehyde (GTA) at 14%).</p> Full article ">Figure 5
<p>Effect of GTA levels on bonding strength (protein at 33%).</p> Full article ">Figure 6
<p>Changes in bonding strength of whey protein adhesive during storage at 23 °C.</p> Full article ">Figure 7
<p>Scanning electron microscopy (SEM) micrographs of whey protein isolate (WPI) solution (33% protein) (<b>a</b>), WPI solution (33% protein) containing 0.2% totarol (<b>b</b>), WPI solution (33% protein) containing 0.2% totarol crosslinked by 14% glutaraldehyde (<b>c</b>) at 10,000× magnification, and porcine skins bonded by tissue adhesive at 35× magnification (<b>d</b>).</p> Full article ">
24 pages, 5078 KiB  
Article
Minimize the Route Length Using Heuristic Method Aided with Simulated Annealing to Reinforce Lean Management Sustainability
by Ahmed M. Abed and Samia Elattar
Processes 2020, 8(4), 495; https://doi.org/10.3390/pr8040495 - 23 Apr 2020
Cited by 4 | Viewed by 3548
Abstract
Cost reduction is a cornerstone of the Lean administration’s sustainability through modify its algorithms scheme to become multi-useful. This paper focuses on control “movement” waste, to minimize pipeline, cabling and sewerage network deployments time, to avoid demurrages (i.e., constructor sectors) and quickens planning [...] Read more.
Cost reduction is a cornerstone of the Lean administration’s sustainability through modify its algorithms scheme to become multi-useful. This paper focuses on control “movement” waste, to minimize pipeline, cabling and sewerage network deployments time, to avoid demurrages (i.e., constructor sectors) and quickens planning through two stages. The first belongs to the build constrained hybridization of published heuristic routing methods (e.g., S-Shape, Mid-point, Largest-Gap, Return, Ascending, FLA-5, FLA-6 [Flow Line Analysis], and Composite) to select the shortest path that serves many locations (i.e., Plan-A), while allowing for the modification of these locations during service (i.e., Plan-B). The new locations are grouped into two clusters, the first of which lay on the shortest preferred path, while the second cluster contains locations that do not lay on the preferred path and are therefore moved on the backlogs-list, then use Simulated Annealing when to serve them. Finally, the impact of the selected performance is investigated after studying its correlation with another published effective one under cost considerations. The computational results of proposed Minimize-Route-Length aided with simulated annealing (MRL-SA) significantly outperform others in terms of the performance of the routing heuristics and total costs and develop the Last Planner System, which has a good reputation in construction projects and approve the proposed algorithm to maintain its competitiveness sustainability. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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<p>Sequence of activities of plan.</p> Full article ">Figure 2
<p>The proposed Lean Planner framework.</p> Full article ">Figure 3
<p>Proposed Minimize-Route-Length aided with simulated annealing (MRL-SA) algorithm.</p> Full article ">Figure 4
<p>Illustrative examination layout.</p> Full article ">Figure 5
<p>Location distribution on the proposed layout.</p> Full article ">Figure 6
<p>Best published heuristic results.</p> Full article ">Figure 7
<p>Graphical comparison between published and proposed heuristic.</p> Full article ">Figure 8
<p>Illustrative classification for locations, as distributed in <a href="#processes-08-00495-t009" class="html-table">Table 9</a>.</p> Full article ">Figure 9
<p>Comparison under left area classification.</p> Full article ">Figure 10
<p>Comparison under right area classification.</p> Full article ">Figure 11
<p>Comparison under middle area classification.</p> Full article ">Figure 12
<p>Different implementation for reviewed heuristic methods.</p> Full article ">Figure 13
<p>Comparison under horizontal classification.</p> Full article ">Figure 13 Cont.
<p>Comparison under horizontal classification.</p> Full article ">
11 pages, 2245 KiB  
Article
The Integration of Collaborative Robot Systems and Their Environmental Impacts
by Lucian Stefanita Grigore, Iustin Priescu, Daniela Joita and Ionica Oncioiu
Processes 2020, 8(4), 494; https://doi.org/10.3390/pr8040494 - 23 Apr 2020
Cited by 22 | Viewed by 4320
Abstract
Today, industrial robots are used in dangerous environments in all sectors, including the sustainable energy sector. Sensors and processors collect and transmit information and data from users as a result of the application of robot control systems and sensory feedback. This paper proposes [...] Read more.
Today, industrial robots are used in dangerous environments in all sectors, including the sustainable energy sector. Sensors and processors collect and transmit information and data from users as a result of the application of robot control systems and sensory feedback. This paper proposes that the estimation of a collaborative robot system’s performance can be achieved by evaluating the mobility of robots. Scenarios have been determined in which an autonomous system has been used for intervention in crisis situations due to fire. The experimental model consists of three autonomous vehicles, two of which are ground vehicles and the other is an aerial vehicle. The conclusion of the research described in this paper highlights the fact that the integration of robotic systems made up of autonomous vehicles working in unstructured environments is difficult and at present there is no unitary analytical model. Full article
(This article belongs to the Special Issue Neural Computation and Applications for Sustainable Energy Systems)
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<p>Sensor systems of FFR-1 UTM (Firefighting Robot 1 from Titu Maiorescu University).</p> Full article ">Figure 2
<p>Unmanned aerial vehicle (UAV) model HEXA-01-UTM (Hexacopter Robot 01 from Titu Maiorescu University).</p> Full article ">Figure 3
<p>HIRRUS V1 payload.</p> Full article ">Figure 4
<p>Vibration modes of the HIRRUS V1 payload.</p> Full article ">Figure 5
<p>Unmanned autonomous system (UAS) 3D representation.</p> Full article ">Figure 6
<p>Unmanned ground vehicle (UGV) performing fire extinguishing testing, carried out at the Military Technical Academy yard on 26 October 2017, at the Patriot Fest contest.</p> Full article ">
14 pages, 3697 KiB  
Article
Effect of Bone Morphogenic Protein-2-Loaded Mesoporous Strontium Substitution Calcium Silicate/Recycled Fish Gelatin 3D Cell-Laden Scaffold for Bone Tissue Engineering
by Chun-Ta Yu, Fu-Ming Wang, Yen-Ting Liu, Hooi Yee Ng, Yi-Rong Jhong, Chih-Hung Hung and Yi-Wen Chen
Processes 2020, 8(4), 493; https://doi.org/10.3390/pr8040493 - 23 Apr 2020
Cited by 19 | Viewed by 3636
Abstract
Bone has a complex hierarchical structure with the capability of self-regeneration. In the case of critical-sized defects, the regeneration capabilities of normal bones are severely impaired, thus causing non-union healing of bones. Therefore, bone tissue engineering has since emerged to solve problems relating [...] Read more.
Bone has a complex hierarchical structure with the capability of self-regeneration. In the case of critical-sized defects, the regeneration capabilities of normal bones are severely impaired, thus causing non-union healing of bones. Therefore, bone tissue engineering has since emerged to solve problems relating to critical-sized bone defects. Amongst the many biomaterials available on the market, calcium silicate-based (CS) cements have garnered huge interest due to their versatility and good bioactivity. In the recent decade, scientists have attempted to modify or functionalize CS cement in order to enhance the bioactivity of CS. Reports have been made that the addition of mesoporous nanoparticles onto scaffolds could enhance the bone regenerative capabilities of scaffolds. For this study, the main objective was to reuse gelatin from fish wastes and use it to combine with bone morphogenetic protein (BMP)-2 and Sr-doped CS scaffolds to create a novel BMP-2-loaded, hydrogel-based mesoporous SrCS scaffold (FGSrB) and to evaluate for its composition and mechanical strength. From this study, it was shown that such a novel scaffold could be fabricated without affecting the structural properties of FGSr. In addition, it was proven that FGSrB could be used for drug delivery to allow stable localized drug release. Such modifications were found to enhance cellular proliferation, thus leading to enhanced secretion of alkaline phosphatase and calcium. The above results showed that such a modification could be used as a potential alternative for future bone tissue engineering research. Full article
(This article belongs to the Special Issue Biofabrication Scaffold in Regenerative Medicine)
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Figure 1
<p>(<b>A</b>) TEM micrographs of mesoporous SrCS nanomaterial. (<b>B</b>) The top-view photograph of 3D-printed FGSr and FGSrB scaffolds. The scale bar is 2 mm.</p> Full article ">Figure 2
<p>XRD for specimens prepared with a series of FGSr and FGSrB scaffolds.</p> Full article ">Figure 3
<p>Tensile stress–strain curves of 3D-printed FGSr and FGSrB specimens.</p> Full article ">Figure 4
<p>Microstructure images of the FGSr and FGSrB scaffolds before and after immersion in SBF. The scale bar is 10 µm.</p> Full article ">Figure 5
<p>Bone morphogenetic protein (BMP)-2 release from FGSrB scaffold after immersion in Dulbecco’s Modified Eagle’s Medium (DMEM) at 37 °C for different time points. Data presented as mean ± SEM, <span class="html-italic">n</span> = 6 for each group.</p> Full article ">Figure 6
<p>(<b>A</b>) The image of live/dead assay results of Wharton jelly-derived mesenchymal stem cells (WJMSC)-laden FGSr and FGSrB scaffolds for 1 day. Scale bar: 400 µm. (<b>B</b>) Cell viability of WJMSC-laden FGSr and FGSrB scaffolds for different time points Data presented as mean ± SEM, <span class="html-italic">n</span> = 6 for each group. “*” indicates a significant difference (<span class="html-italic">p</span> &lt; 0.05) when compared to FGSr.</p> Full article ">Figure 7
<p>(<b>A</b>) Col I, (<b>B</b>) ALP, (<b>C</b>) OPN and (<b>D</b>) OC profiles of the WJMSC-laden FGSr and FGSrB scaffolds in osteogenic differentiation medium. “*” indicates a significant difference (<span class="html-italic">p</span> &lt; 0.05) when compared to FGSr. Data presented as mean ± SEM, <span class="html-italic">n</span> = 5 for each group.</p> Full article ">Figure 8
<p>(<b>A</b>) Alizarin Red S staining and (<b>B</b>) quantification of calcium mineral by WJMSC-laden scaffolds. ‘‘*’’indicates a significant difference (<span class="html-italic">p</span> &lt; 0.05) when compared to FGSr scaffold. Data presented as mean ± SEM, <span class="html-italic">n</span> = 5 for each group.</p> Full article ">
27 pages, 4925 KiB  
Article
Optimisation and Modelling of Anaerobic Digestion of Whiskey Distillery/Brewery Wastes after Combined Chemical and Mechanical Pre-Treatment
by Burcu Gunes, Maxime Carrié, Khaled Benyounis, Joseph Stokes, Paul Davis, Cathal Connolly and Jenny Lawler
Processes 2020, 8(4), 492; https://doi.org/10.3390/pr8040492 - 23 Apr 2020
Cited by 19 | Viewed by 5536
Abstract
Whiskey distillery waste streams consisting of pot ale (liquid residue) and spent grain (solid residue) are high strength organic wastes and suitable feedstock for anaerobic digestion (AD) from both economic and environmental stand points. Anaerobic digestion of pot ale and pot ale/spent grain [...] Read more.
Whiskey distillery waste streams consisting of pot ale (liquid residue) and spent grain (solid residue) are high strength organic wastes and suitable feedstock for anaerobic digestion (AD) from both economic and environmental stand points. Anaerobic digestion of pot ale and pot ale/spent grain mixtures (with mixing ratios of 1:1, 1:3, and 1:5 by wet weight) was performed after implementation of a novel hybrid pre-treatment (combined chemical and mechanical) in order to modify lignocellulosic structure and ultimately enhance digestion yield. Lignin, hemicellulose, and cellulose fractions were determined before and after chemical pre-treatment. Effects of different inoculum rates (10–30–50% on wet basis) and beating times (0–7.5–15 min) on anaerobic digestion of pot ale alone and of pot ale/spent grain mixtures were investigated in lab scale batch mode with a major focus of optimising biogas yield by using response surface methodology (RSM) in Design Expert Software. The highest biogas yields of 629 ± 8.5 mL/g vs. (51.3% CH4) and 360 ± 10 mL/g vs. (55.0 ± 0.4) with anaerobic digestion of pot ale alone and spent grain mix after 1M NaOH and 7.5 min beating pre-treatments with 50% inoculum ratio respectively. The optimum digestion conditions to maximise the biogas quality and quantity were predicted as 10 and 13 min beating times and 32 and 38 °C digestion temperatures for anaerobic digestion of pot ale alone and spent grain mix respectively. Full article
(This article belongs to the Special Issue Current Trends in Anaerobic Digestion Processes)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Bioreactor set up.</p> Full article ">Figure 2
<p>Lignocellulosic structure of pot ale (<b>a</b>) and, spent grain (<b>b</b>) before and after alkali pre-treatment. NT: non-treated, A: alkaline pre-treated average of triplicate runs.</p> Full article ">Figure 3
<p>Biogas yields of spent grain and pot ale mixtures after alkaline and beating pre-treatments. Average of triplicate runs.</p> Full article ">Figure 4
<p>Total VFAs before and after AD of pot ale.</p> Full article ">Figure 5
<p>Total VFAs before and after AD of pot ale spent grain mix.</p> Full article ">Figure 6
<p>Normal plot of residuals on biogas generation (ml/g VS) for AD of pot ale.</p> Full article ">Figure 7
<p>Scatter diagram biogas generation (ml/g VS) for AD of pot ale.</p> Full article ">Figure 8
<p>Perturbation graphs for AD of pot ale and spent grain pot ale mix on biogas yield (<b>a</b>,<b>d</b>), CH4% (<b>b</b>,<b>e</b>), and CO<sub>2</sub>% (<b>c</b>,<b>f</b>) respectively.</p> Full article ">Figure 9
<p>Contour graphs for AD of pot ale and spent grain pot ale mix on biogas yield (<b>a</b>,<b>d</b>), CH<sub>4</sub>% (<b>b</b>,<b>e</b>), and CO<sub>2</sub>% (<b>c</b>,<b>f</b>) respectively.</p> Full article ">Figure 10
<p>Interaction graphs for AD of pot ale and spent grain and pot ale mix on CH<sub>4</sub>% (<b>a</b>,<b>d</b>) and CO<sub>2</sub>% (<b>b</b>,<b>e</b>) respectively and on biogas yield by AD of pot ale spent grain (<b>c</b>).</p> Full article ">Figure 11
<p>Graphical optimisation for AD of pot ale (<b>a</b>) and AD of spent grain pot ale mix (<b>b</b>).</p> Full article ">
3 pages, 152 KiB  
Editorial
Special Issue on “Microwave Applications in Chemical Engineering”
by Huacheng Zhu, Kama Huang and Junwu Tao
Processes 2020, 8(4), 491; https://doi.org/10.3390/pr8040491 - 23 Apr 2020
Viewed by 2139
Abstract
Microwave heating has been widely used in the chemical industry because of its advantages, such as fast heating rate, selective and controllable heating, increasing reaction rate and reducing by-products in chemical reactions. The Special Issue contains research on microwave applications in chemical engineering. [...] Read more.
Microwave heating has been widely used in the chemical industry because of its advantages, such as fast heating rate, selective and controllable heating, increasing reaction rate and reducing by-products in chemical reactions. The Special Issue contains research on microwave applications in chemical engineering. Full article
(This article belongs to the Special Issue Microwave Applications in Chemical Engineering)
15 pages, 1687 KiB  
Article
Investigations on Ozone-Based and UV/US-Assisted Synergistic Digestion Methods for the Determination of Total Dissolved Nitrogen in Waters
by Xiaofang Sun, Huixuan Chen, Zhengyu Liu, Mengfei Zhou, Yijun Cai, Haitian Pan and Luyue Xia
Processes 2020, 8(4), 490; https://doi.org/10.3390/pr8040490 - 23 Apr 2020
Cited by 6 | Viewed by 3418
Abstract
Over the past two decades, the alkaline persulfate oxidation (PO) with thermal and/or ultraviolet (UV) assisted digestion method has been widely used for digestion of nitrogen containing compounds (N-compounds) in water quality routine analysis in laboratory or on-line analysis, due to its simple [...] Read more.
Over the past two decades, the alkaline persulfate oxidation (PO) with thermal and/or ultraviolet (UV) assisted digestion method has been widely used for digestion of nitrogen containing compounds (N-compounds) in water quality routine analysis in laboratory or on-line analysis, due to its simple principle, high conversion rate, high percent recovery, low-cost. However, this digestion method still has some inevitable problems such as complex operations, high contamination potential, batch N blanks, higher reaction temperature (120–124 °C) and time-consuming (30–60 min). In this study, ozone (O3) was selected as the oxidant for digestion of N-compounds through analysis and comparison firstly. Secondly, we proposed and compared the UV and/or ultrasound (US) combined with ozone (UV/O3, US/O3 and UV/US/O3) synergistic digestion methods based on O3 with sole O3 oxidation method on digestion efficiency (digestion time and conversion rate) of standard N-compounds. Simultaneously, the influence of reaction temperature, pH of water sample, concentration of O3 and mass flow rate, UV intensity, US frequency and power on digestion efficiency were investigated, and then the optimum parameters for digestion system were obtained. Experimental results indicated that UV radiation can effectively induce and promote the decomposition and photolysis of O3 in water to generate hydroxyl radicals (•OH), while US can promote the diffusion and dissolution of O3 in water and intensify the gas-liquid mass transfer process for the reaction system. Meanwhile, results showed that the UV/US/O3 synergistic digestion method had the best digestion efficiency under the optimum conditions: water sample volume, 10 mL; pH of water sample, 11; O3 mass flow rate, 3200 mg/h; reaction temperature, 30 °C; digestion time, 25 min; UV lamp power, 18 W; distance between UV lamp and reactor, 2 cm; US frequency, 20 kHz; US power, 75 W. The conversion rate (CR) of synthetic wastewater samples varied from 99.6% to 101.4% for total dissolved nitrogen (TDN) in the range of 1.0~4.0 mg/L. The UV/US/O3 synergistic digestion method would be an effective and potential alternative for digestion of N-compounds in water quality routine analysis in laboratory or on-line analysis. Full article
(This article belongs to the Special Issue Application of Advanced Oxidation Processes)
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<p>Schematic diagram of UV/O<sub>3</sub> synergistic digestion experimental device.</p> Full article ">Figure 2
<p>Schematic diagram of US/O<sub>3</sub> synergistic digestion experimental device.</p> Full article ">Figure 3
<p>Calibration curve of TN.</p> Full article ">Figure 4
<p>Effect of ultraviolet (UV) intensity on digestion efficiency.</p> Full article ">Figure 5
<p>Effect of pH on digestion efficiency. (<b>a</b>) Digestion of carbamide solution with concentration of 1 mg/L; (<b>b</b>) Digestion of ammonia chloride solution with concentration of 1 mg/L.</p> Full article ">Figure 6
<p>Comparison of digestion efficiency between UV/O<sub>3</sub> and O<sub>3</sub>.</p> Full article ">Figure 7
<p>Comparison of digestion efficiency between US/O<sub>3</sub> and O<sub>3</sub>.</p> Full article ">Figure 8
<p>Comparison of digestion efficiency between UV/US/O<sub>3</sub> and UV/O<sub>3</sub>.</p> Full article ">
11 pages, 1973 KiB  
Article
Thermal and Torrefaction Characteristics of a Small-Scale Rotating Drum Reactor
by Nitipong Soponpongpipat, Suwat Nanetoe and Paisan Comsawang
Processes 2020, 8(4), 489; https://doi.org/10.3390/pr8040489 - 22 Apr 2020
Cited by 6 | Viewed by 5016
Abstract
The small-scale rotating drum reactor (SS-RDR) was designed and constructed without using purge gas for the purpose of household application. The thermal and torrefaction characteristics of SS-RDR were studied and compared with other reactor types. It was found that the heat loss at [...] Read more.
The small-scale rotating drum reactor (SS-RDR) was designed and constructed without using purge gas for the purpose of household application. The thermal and torrefaction characteristics of SS-RDR were studied and compared with other reactor types. It was found that the heat loss at the reactor wall and heat loss from exhaust gas of the SS-RDR were in the range of 6.3–12.4% and 27.9–42.8%, respectively. The increase of flue gas temperature resulted in the decrease of heat loss at the reactor wall and the increase of heat loss from exhaust gas. The heating rate of the SS-RDR was in the range of 7.3–21.4 °C/min. The higher heating value (HHV) ratio, mass yield, and energy yield ofthe SS-RDR were in the range of 1.2–1.6, 35.0–81.0%, and 56.2–96.5%, respectively. A comparison of torrefaction characteristics of various reactor types on HHV ratio-mass yield-iso-energy yield diagram indicated that the torrefaction characteristics of the SS-RDR were better than that of the rotating drum reactor with purge gas. Full article
(This article belongs to the Special Issue Biomass Processing and Conversion Systems)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Structure of small-scale rotary drum reactor (SS-RDR).</p> Full article ">Figure 2
<p>Detail of thermocouple installation.</p> Full article ">Figure 3
<p>Heat loss at various flue gas temperatures.</p> Full article ">Figure 4
<p>Relationship between flue gas temperature and heating rate.</p> Full article ">Figure 5
<p>Relationship between torrefied temperature and higher heating value (HHV) ratio.</p> Full article ">Figure 6
<p>Relationship between torrefied temperature and mass yield.</p> Full article ">Figure 7
<p>Relationship between torrefied temperature and energy yield.</p> Full article ">Figure 8
<p>Comparison of the SS-RDR and other reactor types.</p> Full article ">
31 pages, 9363 KiB  
Article
Mesoporous Mn-Doped Fe Nanoparticle-Modified Reduced Graphene Oxide for Ethyl Violet Elimination: Modeling and Optimization Using Artificial Intelligence
by Yu Hou, Jimei Qi, Jiwei Hu, Yiqiu Xiang, Ling Xin and Xionghui Wei
Processes 2020, 8(4), 488; https://doi.org/10.3390/pr8040488 - 22 Apr 2020
Cited by 12 | Viewed by 3367
Abstract
Mesoporous Mn-doped Fe nanoparticle-modified reduced graphene oxide (Mn-doped Fe/rGO) was prepared through a one-step co-precipitation method, which was then used to eliminate ethyl violet (EV) in wastewater. The prepared Mn-doped Fe/rGO was characterized by X-ray diffraction, X-ray photoelectron spectroscopy, Raman spectroscopy, high-resolution transmission [...] Read more.
Mesoporous Mn-doped Fe nanoparticle-modified reduced graphene oxide (Mn-doped Fe/rGO) was prepared through a one-step co-precipitation method, which was then used to eliminate ethyl violet (EV) in wastewater. The prepared Mn-doped Fe/rGO was characterized by X-ray diffraction, X-ray photoelectron spectroscopy, Raman spectroscopy, high-resolution transmission electron microscopy, scanning electron microscopy, energy dispersive spectroscopy, N2-sorption, small angle X-ray diffraction and superconducting quantum interference device. The Brunauer–Emmett–Teller specific surface area of Mn-doped Fe/rGO composites was 104.088 m2/g. The EV elimination by Mn-doped Fe/rGO was modeled and optimized by artificial intelligence (AI) models (i.e., radial basis function network, random forest, artificial neural network genetic algorithm (ANN-GA) and particle swarm optimization). Among these AI models, ANN-GA is considered as the best model for predicting the removal efficiency of EV by Mn-doped Fe/rGO. The evaluation of variables shows that dosage gives the maximum importance to Mn-doped Fe/rGO removal of EV. The experimental data were fitted to kinetics and adsorption isotherm models. The results indicated that the process of EV removal by Mn-doped Fe/rGO obeyed the pseudo-second-order kinetics model and Langmuir isotherm, and the maximum adsorption capacity was 1000.00 mg/g. This study provides a possibility for synthesis of Mn-doped Fe/rGO by co-precipitation as an excellent material for EV removal from the aqueous phase. Full article
(This article belongs to the Special Issue Application of Advanced Oxidation Processes)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Chemical structure of cationic ethyl violet dye.</p> Full article ">Figure 2
<p>Schematic of Mn-doped Fe/rGO nanocomposite synthesis.</p> Full article ">Figure 3
<p>Back propagation artificial neural network (BP-ANN) structure.</p> Full article ">Figure 4
<p>XRD spectra of graphene oxide (GO), Fe–Mn and Mn-doped Fe/rGO.</p> Full article ">Figure 5
<p>SEM image and the corresponding particle size of Mn-doped Fe/rGO (<b>a</b>,<b>c</b>) and Fe–Mn (<b>b</b>,<b>d</b>).</p> Full article ">Figure 6
<p>EDS of Fe–Mn (<b>A</b>) and Mn-doped Fe/rGO (<b>B</b>).</p> Full article ">Figure 7
<p>Raman spectra of GO and Mn-doped Fe/rGO.</p> Full article ">Figure 8
<p>XPS survey spectra of Fe–Mn NPs and Mn-doped Fe/rGO composites.</p> Full article ">Figure 9
<p>XPS analyses of high resolution spectra of Fe2p for Fe–Mn nanoparticles (NPs) (<b>a</b>) and Mn-doped Fe/rGO composites (<b>b</b>), Mn2p for Fe–Mn NPs (<b>c</b>) and Mn-doped Fe/rGO composites (<b>d</b>).</p> Full article ">Figure 10
<p>Nitrogen adsorption–desorption isotherm of Fe–Mn and Mn-doped Fe/rGO.</p> Full article ">Figure 11
<p>Pore size distribution of Fe/Mn (<b>a</b>) and Mn-doped Fe/rGO (<b>b</b>).</p> Full article ">Figure 12
<p>Magnetization curves of Mn-doped Fe/rGO (<b>A</b>), small angle X-ray diffraction (SAXRD) patterns of Mn-doped Fe/rGO (<b>B</b>), HR-TEM (<b>C</b>–<b>E</b>) images with different magnifications of Mn-doped Fe/rGO nanocomposites.</p> Full article ">Figure 12 Cont.
<p>Magnetization curves of Mn-doped Fe/rGO (<b>A</b>), small angle X-ray diffraction (SAXRD) patterns of Mn-doped Fe/rGO (<b>B</b>), HR-TEM (<b>C</b>–<b>E</b>) images with different magnifications of Mn-doped Fe/rGO nanocomposites.</p> Full article ">Figure 13
<p>Predicted versus experimental values plot for EV removal.</p> Full article ">Figure 14
<p>Normal probability plot of residuals.</p> Full article ">Figure 15
<p>Perturbation plot of EV removal efficiency.</p> Full article ">Figure 16
<p>The three-dimensional response surface and contour plots: interactive effects of (<b>A</b>) and (<b>a</b>) X<sub>1</sub>–X<sub>2</sub>; (<b>B</b>) and (<b>b</b>) X<sub>1</sub>–X<sub>3</sub>; (<b>C</b>) and (<b>c</b>) X<sub>1</sub>–X<sub>4</sub> (<b>D</b>) and (<b>d</b>) X<sub>2</sub>–X<sub>3</sub>; (<b>E</b>) and (<b>e</b>) X<sub>2</sub>–X<sub>4</sub>; (<b>F</b>) and (<b>f</b>) X<sub>3</sub>–X<sub>4</sub> on decolorization efficiency (%) of dye.</p> Full article ">Figure 16 Cont.
<p>The three-dimensional response surface and contour plots: interactive effects of (<b>A</b>) and (<b>a</b>) X<sub>1</sub>–X<sub>2</sub>; (<b>B</b>) and (<b>b</b>) X<sub>1</sub>–X<sub>3</sub>; (<b>C</b>) and (<b>c</b>) X<sub>1</sub>–X<sub>4</sub> (<b>D</b>) and (<b>d</b>) X<sub>2</sub>–X<sub>3</sub>; (<b>E</b>) and (<b>e</b>) X<sub>2</sub>–X<sub>4</sub>; (<b>F</b>) and (<b>f</b>) X<sub>3</sub>–X<sub>4</sub> on decolorization efficiency (%) of dye.</p> Full article ">Figure 16 Cont.
<p>The three-dimensional response surface and contour plots: interactive effects of (<b>A</b>) and (<b>a</b>) X<sub>1</sub>–X<sub>2</sub>; (<b>B</b>) and (<b>b</b>) X<sub>1</sub>–X<sub>3</sub>; (<b>C</b>) and (<b>c</b>) X<sub>1</sub>–X<sub>4</sub> (<b>D</b>) and (<b>d</b>) X<sub>2</sub>–X<sub>3</sub>; (<b>E</b>) and (<b>e</b>) X<sub>2</sub>–X<sub>4</sub>; (<b>F</b>) and (<b>f</b>) X<sub>3</sub>–X<sub>4</sub> on decolorization efficiency (%) of dye.</p> Full article ">Figure 17
<p>Correlation between MSE and the number of hidden neurons.</p> Full article ">Figure 18
<p>Training process of BP-ANN.</p> Full article ">Figure 19
<p>Predicted vs. experimental values of the normalized removal of EV from the BP-ANN.</p> Full article ">Figure 20
<p>Genetic algorithm (GA) and particle swarm optimization (PSO) chart predicted optimum value of EV removal efficiency.</p> Full article ">Figure 21
<p>Comparison between experimental and predicted values of random forest (RF) and BP-ANN.</p> Full article ">Figure 22
<p>Comparison between experimental and predicted values of RBF.</p> Full article ">Figure 23
<p>Feature importance for gradient boosting regression tree (GBRT).</p> Full article ">Figure 24
<p>Feature importance for RF.</p> Full article ">Figure 25
<p>Adsorption isotherm of EV by Mn-doped Fe/rGO (sonication time = 23 min; initial pH = 5.0; dosage = 20 mg).</p> Full article ">Figure 26
<p>Time dependent study of EV removal by Mn-doped Fe/rGO (initial pH = 5.0; Mn-doped Fe/rGO dosage = 20 mg; EV concentration = 500 mg/L).</p> Full article ">
16 pages, 2478 KiB  
Article
Holographic Imaging of Insect Cell Cultures: Online Non-Invasive Monitoring of Adeno-Associated Virus Production and Cell Concentration
by Daniel A. M. Pais, Paulo R. S. Galrão, Anastasiya Kryzhanska, Jérémie Barbau, Inês A. Isidro and Paula M. Alves
Processes 2020, 8(4), 487; https://doi.org/10.3390/pr8040487 - 22 Apr 2020
Cited by 21 | Viewed by 5891
Abstract
The insect cell-baculovirus vector system has become one of the favorite platforms for the expression of viral vectors for vaccination and gene therapy purposes. As it is a lytic system, it is essential to balance maximum recombinant product expression with harvest time, minimizing [...] Read more.
The insect cell-baculovirus vector system has become one of the favorite platforms for the expression of viral vectors for vaccination and gene therapy purposes. As it is a lytic system, it is essential to balance maximum recombinant product expression with harvest time, minimizing product exposure to detrimental proteases. With this purpose, new bioprocess monitoring solutions are needed to accurately estimate culture progression. Herein, we used online digital holographic microscopy (DHM) to monitor bioreactor cultures of Sf9 insect cells. Batches of baculovirus-infected Sf9 cells producing recombinant adeno-associated virus (AAV) and non-infected cells were used to evaluate DHM prediction capabilities for viable cell concentration, culture viability and AAV titer. Over 30 cell-related optical attributes were quantified using DHM, followed by a forward stepwise regression to select the most significant (p < 0.05) parameters for each variable. We then applied multiple linear regression to obtain models which were able to predict culture variables with root mean squared errors (RMSE) of 7 × 105 cells/mL, 3% for cell viability and 2 × 103 AAV/cell for 3-fold cross-validation. Overall, this work shows that DHM can be implemented for online monitoring of Sf9 concentration and viability, also permitting to monitor product titer, namely AAV, or culture progression in lytic systems, making it a valuable tool to support the time of harvest decision and for the establishment of controlled feeding strategies. Full article
(This article belongs to the Special Issue Measurement Technologies for up- and Downstream Bioprocessing)
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<p>Viability (left) and viable cell concentration (right) predictions using leave one batch out (LOBO, top) and 3-fold cross-validation (3CV, bottom) models. Growth batch is represented in black and infected batch is colored in grey. The lines represent model-predicted values; the filled circles represent reference data; the empty circles were considered outliers and excluded from modeling. For LOBO models, the lines represent the prediction obtained with the model calibrated in the remaining batch. For 3CV models, the lines represent the model built using data from both batches. (<b>A</b>) Observed and predicted values for viability using LOBO for model validation; (<b>B</b>) Observed and predicted values for viable cell concentration using LOBO for model validation; (<b>C</b>) Observed and predicted values for viability using 3CV for model validation; (<b>D</b>) Observed and predicted values for viable cell concentration using 3CV for model validation. Model parameters and coefficients are presented in <a href="#app1-processes-08-00487" class="html-app">Table S1</a>.</p> Full article ">Figure 2
<p>AAV titer predictions for both batches. The growth batch is represented in black and the infected batch is colored in grey. The lines represent model-predicted values; the filled circles represent reference data; the empty circles represent datapoints considered outliers and excluded from modeling. Models were calibrated using the reference data for both batches (filled circles). The prediction data represented by the smooth lines were obtained by applying the model to the real-time differential digital holographic microscopy data. (<b>A</b>) Observed and predicted values for extracellular AAV titer; (<b>B</b>) observed and predicted values for intracellular specific AAV titer. Model parameters and coefficients are presented in <a href="#app1-processes-08-00487" class="html-app">Table S1</a>.</p> Full article ">Figure 3
<p>Quality characteristics overview for the models presented in <a href="#processes-08-00487-f001" class="html-fig">Figure 1</a> and <a href="#processes-08-00487-f002" class="html-fig">Figure 2</a>. R<sup>2</sup> and Q<sup>2</sup> are the correlation coefficients of calibration and validation, respectively. Also depicted are the normalized root mean squared errors (nRMSE) for calibration and validation which are scaled by the variable range. For the LOBO viable cell concentration models, the difference in the cell concentration ranges and the fact that the prediction models overfit the calibration batch result in a negative Q<sup>2</sup> (−0.69) when data from both batches are considered. As such, we chose to depict the Q<sup>2</sup> for each batch separately (0.66 for prediction of growth batch and 0.34 for prediction of infected batch). CV—3-fold cross-validation; LOBO—leave one batch out; R<sup>2</sup>—correlation coefficient of calibration; nRMSE—normalized root mean squared error; Q<sup>2</sup>—correlation coefficient of validation; VCC—viable cell concentration. Raw data are provided in <a href="#app1-processes-08-00487" class="html-app">Table S3</a>.</p> Full article ">Figure 4
<p>Time-course profiles for selected DDHM attributes. The growth batch is represented in black, while the infected batch is colored in grey. Measurements were obtained every 30 min. (<b>A</b>) Intensity Average Contrast; (<b>B</b>) Intensity Average Entropy; (<b>C</b>) Intensity Average Intensity; (<b>D</b>) Phase Skewness; (<b>E</b>) Phase Correlation; (<b>F</b>) Intensity Correlation; (<b>G</b>) Optical Height Minimum; (<b>H</b>) Optical Volume; (<b>I</b>) Peak Area Normalized; (<b>J</b>) Peak Height.</p> Full article ">Figure 5
<p>Relative contribution of each parameter to the final models. For the leave one batch out (LOBO) models, the batch used for model calibration is indicated (gr—growth; inf—infected). For the 3CV models, the coefficients presented are related to the model using both batches. Relative importance was calculated using the logworth for each parameter (<a href="#app1-processes-08-00487" class="html-app">Table S1</a>).</p> Full article ">
10 pages, 2555 KiB  
Article
Hydroxyapatite Biosynthesis Obtained from Sea Urchin Spines (Strongylocentrotus purpuratus): Effect of Synthesis Temperature
by Nayeli Sarahi Gómez Vázquez, Priscy Alfredo Luque Morales, Claudia Mariana Gomez Gutierrez, Osvaldo de Jesus Nava Olivas, Ruben Cesar Villarreal Sánchez, Alfredo Rafael Vilchis Nestor and Manuel de Jesús Chinchillas Chinchillas
Processes 2020, 8(4), 486; https://doi.org/10.3390/pr8040486 - 22 Apr 2020
Cited by 7 | Viewed by 3916
Abstract
In this investigation, hydroxyapatite (HA) was synthesized using sea urchin spines (Strongylocentrotus purpuratus) via a precipitation and heat treatment method at three different temperatures (500, 600 and 700 °C). Biosynthesized HA was characterized to determine the vibration of functional groups, morphology, [...] Read more.
In this investigation, hydroxyapatite (HA) was synthesized using sea urchin spines (Strongylocentrotus purpuratus) via a precipitation and heat treatment method at three different temperatures (500, 600 and 700 °C). Biosynthesized HA was characterized to determine the vibration of functional groups, morphology, particle size, crystalline structure and chemical composition. For this, Fourier-Transform Infrared Spectroscopy with Attenuated Total Reflectance (FTIR-ATR), Scanning Electron Microscopy (SEM) coupled with Energy Dispersive X-ray Spectroscopy (EDS), X-ray Diffraction (XRD) and X-ray Photoelectron Spectroscopy (XPS) were used, respectively. The FTIR-ATR results reveal that the most defined characteristic HA bonds (O-H, P-O and C-O bonds) were better defined at higher synthesis temperatures. SEM also presented evidence that temperature has a significant effect on morphology. EDS results showed that the Ca/P ratio increased in the samples at higher temperatures. XRD analysis presented the characteristic peaks of HA, showing a lower crystallinity when the synthesis temperature increased. Finally, the XPS confirmed that the material resulting from biosynthesis was HA. Hence, according to these results, the synthesis temperature of HA has a significant effect on the characteristics of the resulting material. Full article
(This article belongs to the Special Issue Screening of Bioactive Compounds from Food Processing Waste)
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<p>Biosynthesized hydroxyapatite (HA) from sea urchin spines.</p> Full article ">Figure 2
<p>FTIR-ATR spectrum of the three HA samples synthesized at different temperatures.</p> Full article ">Figure 3
<p>Elemental morphology and composition for HA samples: (<b>a</b>,<b>b</b>) SEM HA700; (<b>c</b>,<b>d</b>) SEM HA600; (<b>e</b>,<b>f</b>) SEM HA500.</p> Full article ">Figure 4
<p>XRD spectra of the three HA samples synthesized at different temperatures.</p> Full article ">Figure 5
<p>XRD spectrum: (<b>a</b>) general spectrum. High-resolution spectra of (<b>b</b>) Ca 2p and (<b>c</b>) O 1s.</p> Full article ">
17 pages, 5505 KiB  
Article
Computational Approaches for Studying Slag–Matte Interactions in the Flash Smelting Furnace (FSF) Settler
by Jani-Petteri Jylhä, Nadir Ali Khan and Ari Jokilaakso
Processes 2020, 8(4), 485; https://doi.org/10.3390/pr8040485 - 22 Apr 2020
Cited by 14 | Viewed by 7375
Abstract
Computational methods have become reliable tools in many disciplines for research and industrial design. There are, however, an ever-increasing number of details waiting to be included in the models and software, including, e.g., chemical reactions and many physical phenomena, such as particle and [...] Read more.
Computational methods have become reliable tools in many disciplines for research and industrial design. There are, however, an ever-increasing number of details waiting to be included in the models and software, including, e.g., chemical reactions and many physical phenomena, such as particle and droplet behavior and their interactions. The dominant method for copper production, flash smelting, has been extensively investigated, but the settler part of the furnace containing molten high temperature melts termed slag and matte, still lacks a computational modeling tool. In this paper, two commercial modeling software programs have been used for simulating slag–matte interactions in the settler, the target being first to develop a robust computational fluid dynamics (CFD) model and, second, to apply a new approach for molten droplet behavior in a continuum. The latter is based on CFD coupled with the discrete element method (DEM), which was originally developed for modeling solid particle–particle interactions and movement, and is applied here for individual droplets for the first time. The results suggest distinct settling flow phenomena and the significance of droplet coalescence for settling velocity and efficiency. The computing capacity requirement for both approaches is the main limiting factor preventing full-scale geometry modeling with detailed droplet interactions. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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<p>Illustration of the Outotec flash smelting furnace.</p> Full article ">Figure 2
<p>Illustration of the computational fluid dynamics–discrete element method (CFD–DEM) calculation process.</p> Full article ">Figure 3
<p>Geometry and mesh of the CFD model for CFD–DEM simulation. Inlet (blue) on the top and tapping hole (red) on the right side.</p> Full article ">Figure 4
<p>Number fraction distribution of different-sized droplets inside the settler.</p> Full article ">Figure 5
<p>Volume fraction distribution curves.</p> Full article ">Figure 6
<p>Formation of the funneling effect in the slag at 5 s, 10 s, 20 s, and 40 s.</p> Full article ">Figure 7
<p>Average velocity of droplets and calculated velocity.</p> Full article ">Figure 8
<p>Droplet sizes in the slag layer at 60 s. The 50 mm settling distance is marked with a line.</p> Full article ">Figure 9
<p>Droplet count and diameter development during the simulation.</p> Full article ">Figure 10
<p>A comparison of the CFD and CFD–DEM results for matte settling in the small-scale settler model, revealing a similar funneling flow pattern at 15, 17, 18, and 20 s. Upper row: CFD, lower row: CFD–DEM.</p> Full article ">
22 pages, 5133 KiB  
Article
Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis
by Aida Mehdipour Pirbazari, Mina Farmanbar, Antorweep Chakravorty and Chunming Rong
Processes 2020, 8(4), 484; https://doi.org/10.3390/pr8040484 - 21 Apr 2020
Cited by 44 | Viewed by 6325
Abstract
Short-term load forecasting ensures the efficient operation of power systems besides affording continuous power supply for energy consumers. Smart meters that are capable of providing detailed information on buildings energy consumption, open several doors of opportunity to short-term load forecasting at the individual [...] Read more.
Short-term load forecasting ensures the efficient operation of power systems besides affording continuous power supply for energy consumers. Smart meters that are capable of providing detailed information on buildings energy consumption, open several doors of opportunity to short-term load forecasting at the individual building level. In the current paper, four machine learning methods have been employed to forecast the daily peak and hourly energy consumption of domestic buildings. The utilized models depend merely on buildings historical energy consumption and are evaluated on the profiles that were not previously trained on. It is evident that developing data-driven models lacking external information such as weather and building data are of great importance under the situations that the access to such information is limited or the computational procedures are costly. Moreover, the performance evaluation of the models on separated house profiles determines their generalization ability for unseen consumption profiles. The conducted experiments on the smart meter data of several UK houses demonstrated that if the models are fed with sufficient historical data, they can be generalized to a satisfactory level and produce quite accurate results even if they only use past consumption values as the predictor variables. Furthermore, among the four applied models, the ones based on deep learning and ensemble techniques, display better performance in predicting daily peak load consumption than those of others. Full article
(This article belongs to the Special Issue Clean Energy Conversion Processes)
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<p>Architecture of applied Artificial Neural Network (ANN).</p> Full article ">Figure 2
<p>Architecture of applied Long Short-Term Memory (LSTM).</p> Full article ">Figure 3
<p>Architecture of an LSTM cell.</p> Full article ">Figure 4
<p>Hourly energy consumption of sample houses in different groups over one year (2013).</p> Full article ">Figure 5
<p>Boxplot statistics for House 33 over one week. (4 March to 11 March 2013).</p> Full article ">Figure 6
<p>Boxplot statistics for 75 houses in a working day 2013 March 25.</p> Full article ">Figure 7
<p>Error analysis with respect to the number of input variables and the size of the training set.</p> Full article ">Figure 8
<p>Real consumption of test houses versus predictions over one week.</p> Full article ">Figure 9
<p>Comparison of models based on boxplot of forecasting error statistics.</p> Full article ">Figure 10
<p>Comparison of models based on average Mean Absolute Error (MAE) per customer group.</p> Full article ">Figure 11
<p>Comparison of models based on average MAE per season.</p> Full article ">
23 pages, 2894 KiB  
Article
Dynamic Modeling and Simulation of Basic Oxygen Furnace (BOF) Operation
by Daniela Dering, Christopher Swartz and Neslihan Dogan
Processes 2020, 8(4), 483; https://doi.org/10.3390/pr8040483 - 21 Apr 2020
Cited by 21 | Viewed by 11544
Abstract
Basic oxygen furnaces (BOFs) are widely used to produce steel from hot metal. The process typically has limited automation which leads to sub-optimal operation. Economically optimal operation can be potentially achieved by using a dynamic optimization framework to provide operators the best combination [...] Read more.
Basic oxygen furnaces (BOFs) are widely used to produce steel from hot metal. The process typically has limited automation which leads to sub-optimal operation. Economically optimal operation can be potentially achieved by using a dynamic optimization framework to provide operators the best combination of input trajectories. In this paper, a first-principles based dynamic model for the BOF that can be used within the dynamic optimization routine is described. The model extends a previous work by incorporating a model for slag formation and energy balances. In this new version of the mathematical model, the submodel for the decarburization in the emulsion zone is also modified to account for recent findings, and an algebraic equation for the calculation of the calcium oxide saturation in slag is developed. The dynamic model is then used to simulate the operation of two distinct furnaces. It was found that the prediction accuracy of the developed model is significantly superior to its predecessor and the number of process variables that it is able to predict is also higher. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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Graphical abstract
Full article ">Figure 1
<p>Schematic representation of the Basic Oxygen Furnace (BOF).</p> Full article ">Figure 2
<p>Material flow in the BOF assumed for the current study.</p> Full article ">Figure 3
<p>Schematic representation of the melting of scrap. (<b>a</b>) Schematic representation of a plate. (<b>b</b>) Schematic representation of temperature gradient between hot metal and a cold metal plate.</p> Full article ">Figure 4
<p>(<math display="inline"><semantics> <mrow> <mo>%</mo> <mi>C</mi> <mi>a</mi> <msub> <mi>O</mi> <mrow> <mi>s</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>) obtained using the Cell Model and Equation (<a href="#FD43-processes-08-00483" class="html-disp-formula">43</a>) for the Cicutti et al. [<a href="#B13-processes-08-00483" class="html-bibr">13</a>] slag data.</p> Full article ">Figure 5
<p>Droplet decarburization.</p> Full article ">Figure 6
<p>Comparison of final carbon content of metal droplets in the emulsion reported by Cicutti et al. [<a href="#B13-processes-08-00483" class="html-bibr">13</a>], and the values predicted using Kadrolkar and Dogan [<a href="#B10-processes-08-00483" class="html-bibr">10</a>]’s first-principles model and Equation (<a href="#FD47-processes-08-00483" class="html-disp-formula">47</a>) as a function of blow time.</p> Full article ">Figure 7
<p>(<b>a</b>) Comparison between measured [<a href="#B13-processes-08-00483" class="html-bibr">13</a>] and predicted values for the temperature of liquid metal and slag. (<b>b</b>) Comparison between measured [<a href="#B13-processes-08-00483" class="html-bibr">13</a>] and predicted values for the carbon content of liquid metal and the returning metal droplets.</p> Full article ">Figure 8
<p>(<b>a</b>) Total decarburization rate and decarburization rate at the emulsion zone and (<b>b</b>) composition of the off-gas stream exiting the BOF.</p> Full article ">Figure 9
<p>(<b>a</b>) Control profile for Cicutti et al. [<a href="#B13-processes-08-00483" class="html-bibr">13</a>]’s data and (<b>b</b>) scaled control profiles for a heat from Plant A.</p> Full article ">Figure 10
<p>Evolution of slag composition for the Cicutti data [<a href="#B13-processes-08-00483" class="html-bibr">13</a>] and model prediction: (<b>a</b>) FeO, (<b>b</b>) SiO<sub>2</sub>, (<b>c</b>) CaO, (<b>d</b>) MgO.</p> Full article ">Figure 11
<p>Comparison of the carbon content prediction by different models [<a href="#B6-processes-08-00483" class="html-bibr">6</a>,<a href="#B8-processes-08-00483" class="html-bibr">8</a>,<a href="#B9-processes-08-00483" class="html-bibr">9</a>] and the measured values [<a href="#B13-processes-08-00483" class="html-bibr">13</a>] for a 200-ton furnace.</p> Full article ">Figure 12
<p>Average process and predicted values for the end-point slag composition in weight percentage.</p> Full article ">Figure 13
<p>Average and standard deviation of the model predictions and process data for the end-point (<b>a</b>) carbon content of the liquid metal and (<b>b</b>) temperature of the liquid metal.</p> Full article ">Figure 14
<p>Evolution of: (<b>a</b>) Carbon content of liquid metal and final carbon content <span class="html-italic">C<sub>C,f</sub></span> of the metal droplets, (<b>b</b>) silicon content of liquid metal, (<b>c</b>) slag and metal bath temperature and (<b>d</b>) slag composition. The values have been scaled for proprietary reasons.</p> Full article ">
16 pages, 652 KiB  
Article
Bioactivity of Selected Phenolic Acids and Hexane Extracts from Bougainvilla spectabilis and Citharexylum spinosum on the Growth of Pectobacterium carotovorum and Dickeya solani Bacteria: An Opportunity to Save the Environment
by Nader A. Ashmawy, Said I. Behiry, Asma A. Al-Huqail, Hayssam M. Ali and Mohamed Z. M. Salem
Processes 2020, 8(4), 482; https://doi.org/10.3390/pr8040482 - 21 Apr 2020
Cited by 26 | Viewed by 4609
Abstract
Phenolic acids and natural extracts, as ecofriendly environmental agents, can be used as bio bactericides against the growth of plant pathogenic bacteria. In this study, isolation trails from infected potato tubers and stems that showed soft rot symptoms in fields revealed two soft [...] Read more.
Phenolic acids and natural extracts, as ecofriendly environmental agents, can be used as bio bactericides against the growth of plant pathogenic bacteria. In this study, isolation trails from infected potato tubers and stems that showed soft rot symptoms in fields revealed two soft rot bacterial isolates and were initially identified through morphological, physiological, and pathogenicity tests. The molecular characterization of these isolates via PCR, based on the 16S rRNA region, was carried out by an analysis of the DNA sequence via BLAST and Genbank, and showed that the soft rot bacterial isolates belong to Pectobacterium carotovorum subsp. carotovorum (PCC1) and Dickeya solani (Ds1). The in vitro results of the tested phenolic acids against the cultured bacterial isolates proved that concentrations of 800, 1600, and 3200 μg/mL were the most effective. Ferulic acid was the potent suppressive phenolic acid tested against the Ds1 isolate, with an inhibition zone ranging from 6.00 to 25.75 mm at different concentrations (25–3200 μg/mL), but had no effect until reaching a concentration of 100 μg/mL in the PCC1 isolate, followed by tannic acid, which ranged from 7.00 to 25.50 mm. On the other hand, tannic acid resulted in a significant decrease in the growth rate of the PCC1 isolate with a mean of 9.11 mm. Chlorogenic acid was not as effective as the rest of the phenolic acids compared with the control. The n-hexane oily extract (HeOE) from Bougainvillea spectabilis bark showed the highest activity against PCC1 and Ds1, with inhibition zone values of 12 and 12.33 mm, respectively, at a concentration of 4000 μg/mL; while the HeOE from Citharexylum spinosum wood showed less activity. In the GC/MS analysis, nonanal, an oily liquid compound, was found ata percentage of 38.28%, followed by cis-2-nonenal (9.75%), which are the main compounds in B. spectabilis bark HeOE, and 2-undecenal (22.39%), trans-2-decenal (18.74%), and oleic acid (10.85%) were found, which are the main compounds in C. spinosum wood HeOE. In conclusion, the phenolic acids and plant HeOEs seem to raise the resistance of potato plants, improving their defense mechanisms against soft rot bacterial pathogens. Full article
(This article belongs to the Special Issue Green Separation and Extraction Processes)
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<p>Natural infection of potato tubers with soft rot and blackleg symptoms: (<b>left</b>) <span class="html-italic">Pectobacterium carotovorum</span> subsp. <span class="html-italic">carotovorum</span> (PCC1) and (<b>right</b>) <span class="html-italic">Dickeya solani</span> (Ds1).</p> Full article ">
12 pages, 8089 KiB  
Article
Oxidation and Characterization of Low-Concentration Gas in a High-Temperature Reactor
by Jinhua Chen, Guangcai Wen, Song Yan, Xiangyun Lan and Lu Xiao
Processes 2020, 8(4), 481; https://doi.org/10.3390/pr8040481 - 21 Apr 2020
Cited by 5 | Viewed by 3014
Abstract
To achieve the efficient utilization of low-concentration mine gas, reduce resource waste and alleviate environmental pollution, the high-temperature oxidation of low-concentration gas at a concentration range of 1.00% to 1.50%, which is directly discharged into the atmosphere during coal mine production, was carried [...] Read more.
To achieve the efficient utilization of low-concentration mine gas, reduce resource waste and alleviate environmental pollution, the high-temperature oxidation of low-concentration gas at a concentration range of 1.00% to 1.50%, which is directly discharged into the atmosphere during coal mine production, was carried out to recover heat for reuse. The gas oxidation equipment was improved for the heating process and the safety of low-concentration gas oxidation under a high-temperature environment was evaluated. The experimental results showed that the reactor could provide a 1000 °C high-temperature oxidation environment for gas oxidation after installing high-temperature resistant ceramics. The pressure variation curves of the reactor with air and different concentrations of gas were similar. Due to the thermal expansion, the air pressure slightly increased and then returned to normal pressure. In contrast, the low-concentration gas exhibited a stable pressure response in the high-temperature environment of 1000 °C. The outlet pressure was significantly greater than the inlet pressure, and the pressure difference between the inlet and outlet exhibited a trend to increase with the gas concentration. The minimum pressure difference was 4 kPa (air) and the maximum was 11 kPa (1.50% gas). The explosion limit varied with the temperature and the blend of oxidation products. The ratio of measured gas pressure to air pressure after oxidation was below the explosion criterion, indicating that the measured concentration of gas is still safe after the shift of the explosion limit, which provides a safe concentration range for the efficient use of low-concentration gas in the future. Full article
(This article belongs to the Special Issue Progress in Energy Conversion Systems and Emission Control)
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<p>Exothermic oxidation of the gas.</p> Full article ">Figure 2
<p>Reactor ceramics.</p> Full article ">Figure 3
<p>Experimental equipment and process.</p> Full article ">Figure 4
<p>Empty chamber temperature.</p> Full article ">Figure 5
<p>Temperature after installing the ceramic.</p> Full article ">Figure 6
<p>Measured flashover air pressure.</p> Full article ">Figure 7
<p>Experimental pressure detection chart (<b>a</b>, <b>b</b>, <b>c</b>, <b>d</b>, <b>e</b>, <b>f</b> and <b>g</b> show the pressures of air (<b>a</b>) and gas concentrations of 1.00%, 1.10%, 1.20%, 1.30%, 1.40% and 1.50%).</p> Full article ">Figure 7 Cont.
<p>Experimental pressure detection chart (<b>a</b>, <b>b</b>, <b>c</b>, <b>d</b>, <b>e</b>, <b>f</b> and <b>g</b> show the pressures of air (<b>a</b>) and gas concentrations of 1.00%, 1.10%, 1.20%, 1.30%, 1.40% and 1.50%).</p> Full article ">Figure 8
<p>Inlet and outlet pressure.</p> Full article ">Figure 9
<p>Pressure difference between inlet and outlet.</p> Full article ">Figure 10
<p>Explosion criteria.</p> Full article ">Figure 11
<p>Low-concentration gas utilization system.</p> Full article ">
13 pages, 3086 KiB  
Article
A Geometric Observer-Assisted Approach to Tailor State Estimation in a Bioreactor for Ethanol Production
by Silvia Lisci, Massimiliano Grosso and Stefania Tronci
Processes 2020, 8(4), 480; https://doi.org/10.3390/pr8040480 - 20 Apr 2020
Cited by 16 | Viewed by 2836
Abstract
In this work, a systematic approach based on the geometric observer is proposed to design a model-based soft sensor, which allows the estimation of quality indexes in a bioreactor. The study is focused on the structure design problem where the set of innovated [...] Read more.
In this work, a systematic approach based on the geometric observer is proposed to design a model-based soft sensor, which allows the estimation of quality indexes in a bioreactor. The study is focused on the structure design problem where the set of innovated states has to be chosen. On the basis of robust exponential estimability arguments, it is found that it is possible to distinguish all the unmeasured states if temperature and dissolved oxygen concentration measurements are combined with substrate concentrations. The proposed estimator structure is then validated through numerical simulation considering two different measurement processor algorithms: the geometric observer and the extended Kalman filter. Full article
(This article belongs to the Special Issue Bioprocess Monitoring and Control)
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<p>Dynamic response of biomass concentration calculated with the virtual plant (continuous line), open-loop model (dashed line) and geometric observer (GO) (dotted grey line) for structure <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-sans-serif">ϕ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> along trajectory T1 (<b>left</b> panel) and T2 (<b>right</b> panel).</p> Full article ">Figure 2
<p>Dynamic response of ethanol concentration calculated with the virtual plant (continuous line), open-loop model (dashed line) and GO (dotted grey line) for structure <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-sans-serif">ϕ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> along trajectory T1 (<b>left</b> panel) and T2 (<b>right</b> panel).</p> Full article ">Figure 3
<p>Dynamic response of biomass concentration calculated with the virtual plant (continuous line), open-loop model (dashed line) and GO (dotted grey line) for structure <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-sans-serif">ϕ</mi> <mn>2</mn> </msub> </mrow> </semantics></math> along trajectory T1 (<b>left</b> panel) and T2 (<b>right</b> panel).</p> Full article ">Figure 4
<p>Dynamic response of ethanol concentration calculated with the virtual plant (continuous line), open-loop model (dashed line) and GO (dotted grey line) for structure <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-sans-serif">ϕ</mi> <mn>2</mn> </msub> </mrow> </semantics></math> along trajectory T1 (<b>left</b> panel) and T2 (<b>right</b> panel).</p> Full article ">Figure 5
<p>Dynamic response of the biomass concentration calculated with the virtual plant (continuous black line), GO with map <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-sans-serif">ϕ</mi> <mrow> <mi mathvariant="normal">p</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> (dashed dark grey line), GO <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-sans-serif">ϕ</mi> <mrow> <mi mathvariant="normal">p</mi> <mn>4</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> (dotted black line), GO with map <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-sans-serif">ϕ</mi> <mrow> <mi mathvariant="normal">p</mi> <mn>4</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> (dashed-dotted grey line) along the trajectory T1 (<b>left</b> panel) and T2 (<b>right</b> panel).</p> Full article ">Figure 6
<p>Dynamic response of the product concentration calculated with the virtual plant (continuous line), GO with map <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-sans-serif">ϕ</mi> <mrow> <mi mathvariant="normal">p</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> (dashed black line), GO with map <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-sans-serif">ϕ</mi> <mrow> <mi mathvariant="normal">p</mi> <mn>4</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> (dotted black line), GO with map <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-sans-serif">ϕ</mi> <mrow> <mi mathvariant="normal">p</mi> <mn>4</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> (dashed-dotted grey line) along trajectory T1 (<b>left</b> panel) and T2 (<b>right</b> panel).</p> Full article ">Figure 7
<p>Dynamic response of biomass concentration (<b>left</b> panel) and ethanol concentration (<b>right</b> panel) calculated with the virtual plant (continuous line), open-loop model (dashed line) and GO (dotted grey line) for structure <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-sans-serif">ϕ</mi> <mrow> <mi mathvariant="normal">p</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> along the trajectory T3.</p> Full article ">Figure 8
<p>Dynamic response of biomass concentration and ethanol concentration calculated with the virtual plant (continuous line), open-loop model (dashed line) and extended Kalman filter (EKF) (dotted line) for structure <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-sans-serif">ϕ</mi> <mrow> <mi mathvariant="normal">p</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> along the trajectory T3.</p> Full article ">
17 pages, 1839 KiB  
Article
Polyphenol Profile and Antimicrobial and Cytotoxic Activities of Natural Mentha × piperita and Mentha longifolia Populations in Northern Saudi Arabia
by Hosam O. Elansary, Agnieszka Szopa, Paweł Kubica, Halina Ekiert, Marta Klimek-Szczykutowicz, Diaa O. El-Ansary and Eman A. Mahmoud
Processes 2020, 8(4), 479; https://doi.org/10.3390/pr8040479 - 20 Apr 2020
Cited by 45 | Viewed by 5182
Abstract
New sources of polyphenols with anticancer, antioxidant, and antimicrobial properties in arid environments are critical for the development of alternative medicines and natural remedies. This study explored the polyphenol profiles and biological activities of methanolic leaf extracts from natural Mentha × piperita and [...] Read more.
New sources of polyphenols with anticancer, antioxidant, and antimicrobial properties in arid environments are critical for the development of alternative medicines and natural remedies. This study explored the polyphenol profiles and biological activities of methanolic leaf extracts from natural Mentha × piperita and Mentha longifolia populations in northern Saudi Arabia. Chromatographic analyses identified several polyphenols in M. × piperita including phenolic acids: rosmarinic acid (1547.6 mg/100 g DW (dry weight)), cryptochlorogenic acid (91.7 mg/100 g DW), and chlorogenic acid (69.4 mg/100 g DW), as well as flavonoids: naringin (328.8 mg/100 g DW) and cynaroside (162.8 mg/100 g DW). The major polyphenols in M. longifolia were: rosmarinic acid (781.6 mg/100 g DW), cryptochlorogenic acid (191.1 mg/100 g DW), p-coumaric acid (113.0 mg/100 g DW), m-coumaric acid (112.2 mg/100 g DW), and chlorogenic acid (63.8 mg/100 g DW). M. × piperita and M. longifolia leaf extracts had high antioxidant activities due to the major polyphenols (cynaroside, rosmarinic and cryptochlorogenic acids). M. × piperita had higher activities against different cancer cells than M. longifolia. Naringin, cryptochlorogenic acid, and rosmarinic acid had the highest activities against cancer cells. The leaf extracts had antibacterial effects against most bacteria species (Pseudomonas aeruginosa was most sensitive), which was attributed to the polyphenols. Antifungal activities were similarly broad (Aspergillus flavus was most sensitive) and attributed to naringin, cryptochlorogenic acid, and caffeic acid. Populations of M. × piperita and M. longifolia in Northern Riyadh may be a valuable source of natural biologically active compounds. Full article
(This article belongs to the Section Biological Processes and Systems)
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<p>Examples of high-performance liquid chromatography with diode-array detection (HPLC-DAD) chromatographic separation (λ = 254 nm) for the leaf extracts of (<b>A</b>) <span class="html-italic">Mentha × piperita</span> (1—protocatechuic acid; 2—cryptochlorogenic acid; 3—chlorogenic acid; 4—caffeic acid; 5—isochlorogenic acid; 6—cynaroside; 7—naringin; 8—rosmarinic acid) and (<b>B</b>) <span class="html-italic">Mentha longifolia</span> (1—caftaric acid; 2—neochlorogenic acid; 3—cryptochlorogenic acid; 4—chlorogenic acid; 5—caffeic acid; 6—p-coumaric acid; 7—m-coumaric acid; 8—o-coumaric acid; 9—rosmarinic acid).</p> Full article ">Figure 1 Cont.
<p>Examples of high-performance liquid chromatography with diode-array detection (HPLC-DAD) chromatographic separation (λ = 254 nm) for the leaf extracts of (<b>A</b>) <span class="html-italic">Mentha × piperita</span> (1—protocatechuic acid; 2—cryptochlorogenic acid; 3—chlorogenic acid; 4—caffeic acid; 5—isochlorogenic acid; 6—cynaroside; 7—naringin; 8—rosmarinic acid) and (<b>B</b>) <span class="html-italic">Mentha longifolia</span> (1—caftaric acid; 2—neochlorogenic acid; 3—cryptochlorogenic acid; 4—chlorogenic acid; 5—caffeic acid; 6—p-coumaric acid; 7—m-coumaric acid; 8—o-coumaric acid; 9—rosmarinic acid).</p> Full article ">Figure 2
<p>Cytotoxicity of the <span class="html-italic">M. × piperita</span> and <span class="html-italic">M. longifolia</span> leaf extracts and naringin, rosmarinic acid, and cryptochlorogenic acid, estimated with flow cytometry. There was accumulation of apoptotic cells in the early (lower right quadrant) and late apoptotic (upper right quadrant).</p> Full article ">
26 pages, 2684 KiB  
Article
Quadratic Interpolation Based Simultaneous Heat Transfer Search Algorithm and Its Application to Chemical Dynamic System Optimization
by Ebrahim Alnahari, Hongbo Shi and Khalil Alkebsi
Processes 2020, 8(4), 478; https://doi.org/10.3390/pr8040478 - 19 Apr 2020
Cited by 5 | Viewed by 3851
Abstract
Dynamic optimization problems (DOPs) are widely encountered in complex chemical engineering processes. However, due to the existence of highly constrained, nonlinear, and nonsmooth environment in chemical processes, which usually causes nonconvexity, multimodality and discontinuity, handling DOPs is not a straightforward task. Heat transfer [...] Read more.
Dynamic optimization problems (DOPs) are widely encountered in complex chemical engineering processes. However, due to the existence of highly constrained, nonlinear, and nonsmooth environment in chemical processes, which usually causes nonconvexity, multimodality and discontinuity, handling DOPs is not a straightforward task. Heat transfer search (HTS) algorithm is a relative novel metaheuristic approach inspired by the natural law of thermodynamics and heat transfer. In order to solve DOPs efficiently, a new variant of HTS algorithm named quadratic interpolation based simultaneous heat transfer search (QISHTS) algorithm is proposed in this paper. The QISHTS algorithm introduces three modifications into the original HTS algorithm, namely the effect of simultaneous heat transfer search, quadratic interpolation method, and population regeneration mechanism. These three modifications are employed to provide lower computational complexity, as well as to enhance the exploration and exploitation capabilities. Therefore, the ensemble of these modifications can provide a more efficient optimization algorithm with well-balanced exploration and exploitation capabilities. The proposed variant is firstly investigated by well-defined benchmark problems and then applied to solve four chemical DOPs. Moreover, it is compared with different well-established methods existing in the literature. The results demonstrate that QISHTS algorithm has the greatest robustness and precision than other competitors. Full article
(This article belongs to the Section Process Control and Monitoring)
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<p>Flowchart of the heat transfer search (HTS) algorithm. CDF; conduction factor, COF: convection factor, RDF: radiation factor, and R<sub>n</sub>: random variable.</p> Full article ">Figure 2
<p>Flowchart of the quadratic interpolation based simultaneous heat transfer search (QISHTS) algorithm.</p> Full article ">Figure 3
<p>(<b>a</b>) Convergence graph of the QISHTS algorithm for the first-order reversible series reaction model. (<b>b</b>) Fitting state trajectories and experimental points for the first-order reversible series reaction model.</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>) Convergence graph of the QISHTS algorithm for the first-order reversible series reaction model. (<b>b</b>) Fitting state trajectories and experimental points for the first-order reversible series reaction model.</p> Full article ">Figure 4
<p>(<b>a</b>) Convergence graph of QISHTS algorithm for the catalytic cracking of gas oil model. (<b>b</b>) Fitting state trajectories and experimental points for the catalytic cracking of gas oil model.</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>) Convergence graph of QISHTS algorithm for the catalytic cracking of gas oil model. (<b>b</b>) Fitting state trajectories and experimental points for the catalytic cracking of gas oil model.</p> Full article ">Figure 5
<p>(<b>a</b>) Convergence graph of the QISHTS algorithm for the multi-modal CSTR model. (<b>b</b>) Optimal control trajectory for the multi-modal CSTR model. <span class="html-italic">u</span> portrays the optimal control variable of this problem.</p> Full article ">Figure 5 Cont.
<p>(<b>a</b>) Convergence graph of the QISHTS algorithm for the multi-modal CSTR model. (<b>b</b>) Optimal control trajectory for the multi-modal CSTR model. <span class="html-italic">u</span> portrays the optimal control variable of this problem.</p> Full article ">Figure 6
<p>(<b>a</b>) Convergence graph of the QISHTS algorithm for the six-plate gas absorption tower model. (<b>b</b>) Optimal control trajectories for the six-plate gas absorption tower model. <span class="html-italic">u</span><sub>1</sub> and <span class="html-italic">u</span><sub>2</sub> portray the optimal control variables of this problem.</p> Full article ">
18 pages, 7002 KiB  
Case Report
Investigation and Analysis of a Hazardous Chemical Accident in the Process Industry: Triggers, Roots, and Lessons Learned
by Jianhao Wang, Gui Fu and Mingwei Yan
Processes 2020, 8(4), 477; https://doi.org/10.3390/pr8040477 - 18 Apr 2020
Cited by 16 | Viewed by 14751
Abstract
This paper performs an in-depth investigation and analysis on a catastrophic hazardous chemical accident involving domino effects in China based on an emerging accident causation model—the 24Model. The triggers and roots of the incident from the individual and organizational levels have been identified [...] Read more.
This paper performs an in-depth investigation and analysis on a catastrophic hazardous chemical accident involving domino effects in China based on an emerging accident causation model—the 24Model. The triggers and roots of the incident from the individual and organizational levels have been identified and several useful lessons have been summarized to avoid similar mistakes. This accident began with a leak of vinyl chloride caused by the failure of the gas holder’s bell housing and the operators’ mishandling. Leaked vinyl chloride was ignited by a high-temperature device in the process of diffusion and the fire quickly spread to the illegally parked vehicles. Several organizations were involved in this accident, and the chemical company should bear the main responsibility for it, and shall establish and implement an effective safety management system in its organizational structure and staffing, facilities management, hazards identification, emergency disposal, etc., to improve safety performance in a systematic way. Enterprises in the chemical industry park shall enhance the communication to clarify major hazard installations in their domains, and conduct regular safety evaluation for the plant as the external environment changed. Government agencies shall plan the layout of the chemical industry park scientifically and ensure safety starts with the design stage. The case study provides a practical procedure for accident investigation and analysis, and thus, preventive measures can be made according to the various causations at different levels. Full article
(This article belongs to the Special Issue Thermal Safety of Chemical Processes)
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<p>The framework of 24Model [<a href="#B25-processes-08-00477" class="html-bibr">25</a>,<a href="#B26-processes-08-00477" class="html-bibr">26</a>,<a href="#B27-processes-08-00477" class="html-bibr">27</a>].</p> Full article ">Figure 2
<p>The accident analysis procedure.</p> Full article ">Figure 3
<p>The process flow diagram related to this incident.</p> Full article ">Figure 4
<p>The map of the accident scene.</p> Full article ">Figure 5
<p>The structure diagram of 1# vinyl chloride gas holder.</p> Full article ">Figure 6
<p>Changes of the inlet pressure of the compressor under different opening degrees of the return valve.</p> Full article ">Figure 7
<p>Changes of the height of 1# gas holder after vinyl chloride leaked.</p> Full article ">Figure 8
<p>Damaged vehicles on the Provincial Highway 310.</p> Full article ">Figure 9
<p>Event sequence diagram (ESD) of the accident.</p> Full article ">Figure 10
<p>The schematic diagram of the tilting state of the bell housing.</p> Full article ">Figure 11
<p>Organizations involved in the accident and the interrelationship among them.</p> Full article ">Figure 12
<p>Fishbone diagram of the causes of the hazardous chemical accident.</p> Full article ">
38 pages, 7074 KiB  
Article
Optimization of the Algal Biomass to Biodiesel Supply Chain: Case Studies of the State of Oklahoma and the United States
by Soumya Yadala, Justin D. Smith, David Young, Daniel W. Crunkleton and Selen Cremaschi
Processes 2020, 8(4), 476; https://doi.org/10.3390/pr8040476 - 18 Apr 2020
Cited by 12 | Viewed by 5147
Abstract
The goal of this work is to design a supply chain network that distributes algae biomass from supply locations to meet biodiesel demand at specified demand locations, given a specified algae species, cultivation (i.e., supply) locations, demand locations, and demand requirements. The final [...] Read more.
The goal of this work is to design a supply chain network that distributes algae biomass from supply locations to meet biodiesel demand at specified demand locations, given a specified algae species, cultivation (i.e., supply) locations, demand locations, and demand requirements. The final supply chain topology includes the optimum sites to grow biomass, to extract algal oil from the biomass, and to convert the algae oil into biodiesel. The objective is to minimize the overall cost of the supply chain, which includes production, operation, and transportation costs over a planning horizon of ten years. Algae production was modeled both within the U.S. State of Oklahoma, as well as the entire contiguous United States. The biodiesel production cost was estimated at $7.07 per U.S. gallon ($1.87 per liter) for the State of Oklahoma case. For the contiguous United States case, a lower bound on costs of $13.68 per U.S. gallon ($3.62 per liter) and an upper bound of $61.69 ($16.32 per liter) were calculated, depending on the transportation distance of algal biomass from production locations. Full article
(This article belongs to the Special Issue Bioenergy Systems, Material Management, and Sustainability)
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<p>Algae-to-biodiesel supply chain.</p> Full article ">Figure 2
<p>Schematic of the data sets included in the supply chain model. <span class="html-italic">J<sup>S</sup></span> = Supply locations; <span class="html-italic">J<sup>Ex</sup></span> = Extraction locations; <span class="html-italic">J<sup>Es</sup></span> = Transesterification locations; and <span class="html-italic">J<sup>D</sup></span> = Demand locations. The possible modes of transportation between the different locations that are considered in this work are shown and include trucks, rail, barges, and pipelines.</p> Full article ">Figure 3
<p>Schematic of a raceway pond, indicating the physical dimension used in the pond model [<a href="#B8-processes-08-00476" class="html-bibr">8</a>].</p> Full article ">Figure 4
<p>Network flow topology.</p> Full article ">Figure 5
<p>Variables for transportation between each production layer.</p> Full article ">Figure 6
<p>Counties of the State of Oklahoma [<a href="#B26-processes-08-00476" class="html-bibr">26</a>].</p> Full article ">Figure 7
<p>Demand counties, demand regions, and currently existing fuel terminals for the state of Oklahoma. Base map from U.S. Census Bureau [<a href="#B26-processes-08-00476" class="html-bibr">26</a>]. Counties outlines in pink indicate those with a population density greater than 100 mi<sup>−1</sup> and are taken to be the demand centers. Blue squares indicate existing fuel terminals.</p> Full article ">Figure 8
<p>Counties of Oklahoma, with green outlines indicating a county is considered as a potential supplier of algal biomass based on having the top 25% of well depth. Base map from U.S. Census Bureau [<a href="#B26-processes-08-00476" class="html-bibr">26</a>].</p> Full article ">Figure 9
<p>Network flow diagram of the algal biomass to biodiesel supply chain network for the state of Oklahoma. Supply locations (<math display="inline"><semantics> <mrow> <msup> <mi>J</mi> <mi>S</mi> </msup> </mrow> </semantics></math>) are those in <a href="#processes-08-00476-t002" class="html-table">Table 2</a>, and demand regions (<math display="inline"><semantics> <mrow> <msup> <mi>J</mi> <mi>D</mi> </msup> </mrow> </semantics></math>) are those in <a href="#processes-08-00476-t001" class="html-table">Table 1</a>. Both sets of locations are used as possible sites for extraction (<math display="inline"><semantics> <mrow> <msup> <mi>J</mi> <mrow> <mi>E</mi> <mi>x</mi> </mrow> </msup> </mrow> </semantics></math>) and transesterification (<math display="inline"><semantics> <mrow> <msup> <mi>J</mi> <mrow> <mi>E</mi> <mi>s</mi> </mrow> </msup> </mrow> </semantics></math>).</p> Full article ">Figure 10
<p>Supply states (outlined in green) and demand states (filled in red) as considered for the United States case of the algal biomass to biodiesel problem.</p> Full article ">Figure 11
<p>Condensed network flow diagram of the algal biomass to biodiesel problem for the case of the contiguous United States.</p> Full article ">Figure 12
<p>Supply chain optimization for the State of Oklahoma. To replace 25% of the diesel in Oklahoma, the model shows 118,843 ponds are needed in Kay County, with trucking to the demand locations indicated.</p> Full article ">Figure 13
<p>Change in biomass concentration with time inside the Raceway Pond for Kay County.</p> Full article ">Figure 14
<p>The cost associated with the Oklahoma algae biomass to biodiesel supply chain network.</p> Full article ">Figure 15
<p>Supply chain optimization for the case of United States. These results indicate that 65% of the marginal farmland of the State of Mississippi is needed to supply the U.S. demand for biodiesel in the states in which the demand locations are located.</p> Full article ">Figure 16
<p>Change in biomass concentration with time inside the Raceway Pond.</p> Full article ">Figure 17
<p>Amount of algae biomass, algae oil, biodiesel being transported between the layers.</p> Full article ">Figure 18
<p>Cost associated with the United States biomass to biodiesel supply chain network problem.</p> Full article ">Figure 19
<p>Cost associated with the United States biomass to biodiesel supply chain network problem when there is no transportation cost for the first layer.</p> Full article ">
16 pages, 3670 KiB  
Article
Combustion Kinetics Characteristics of Solid Fuel in the Sintering Process
by Jihui Liu, Yaqiang Yuan, Junhong Zhang, Zhijun He and Yaowei Yu
Processes 2020, 8(4), 475; https://doi.org/10.3390/pr8040475 - 17 Apr 2020
Cited by 5 | Viewed by 3478
Abstract
In order to systematically elucidate the combustion performance of fuel during sintering, this paper explores the influence of three factors, namely coal substitution for coke, quasi-particle structure and the coupling effect with reduction and oxidation of iron oxide, on fuel combustion characteristics, and [...] Read more.
In order to systematically elucidate the combustion performance of fuel during sintering, this paper explores the influence of three factors, namely coal substitution for coke, quasi-particle structure and the coupling effect with reduction and oxidation of iron oxide, on fuel combustion characteristics, and carries out the kinetic calculation of monomer blended fuel (MBF) and quasi-granular fuel (QPF). The results show that replacing coke powder with anthracite can accelerate the whole combustion process. MBF and QPF are more consistent with the combustion law of the double-parallel random pore model. Although the quasi-particle structure increases the apparent activation energy of fuel combustion, it can also produce a heat storage effect on fuel particles, improve their combustion performance, and reduce the adverse effect of diffusion on the reaction process. In the early stage of reaction, the coupling between combustion of volatiles and reduction of iron oxide is obvious. The oxidation of iron oxide will occur again when the combustion reaction of fuel is weakened. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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<p>Schematic diagram of monomer blended fuel (MBF), quasi-granular fuel (QPF) and sintered mixture (SDM).</p> Full article ">Figure 2
<p>Schematic diagram of characteristic parameter determination method of the thermogravimetric curve.</p> Full article ">Figure 3
<p>Fourier infrared spectra of anthracite and coke.</p> Full article ">Figure 4
<p>Thermogravimetric curves of anthracite and coke.</p> Full article ">Figure 5
<p>Fractional conversion and reaction rate-conversion curves of MBF and QPF at different heating rates.</p> Full article ">Figure 6
<p>Thermal storage of quasi-particle sintering.</p> Full article ">Figure 7
<p>Combustion rates of MBF, QPF and fitting curves of double parallel reaction volume model (DVM) and double parallel random pore model (DRPM).</p> Full article ">Figure 8
<p>Comparison the correlation between experimental data and calculation results of MBF and QPF at different heating rates.</p> Full article ">Figure 9
<p>Comparison the apparent activation energy of MBF and QPF calculated by DRPM.</p> Full article ">Figure 10
<p>Fractional conversion and reaction rate-conversion curves of the sintered mixture at different heating rates.</p> Full article ">Figure 11
<p>Differential thermal analysis curves of the sintered mixture at different heating rates. (<b>a</b>) 5 °C/min, (<b>b</b>) 10 °C/min, (<b>c</b>) 15 °C/min, (<b>d</b>) 20 °C/min.</p> Full article ">
21 pages, 6362 KiB  
Article
Leak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholds
by Syed Muhammad Mujtaba, Tamiru Alemu Lemma, Syed Ali Ammar Taqvi, Titus Ntow Ofei and Seshu Kumar Vandrangi
Processes 2020, 8(4), 474; https://doi.org/10.3390/pr8040474 - 17 Apr 2020
Cited by 21 | Viewed by 5112
Abstract
Conventional leak detection techniques require improvements to detect small leakage (<10%) in gas mixture pipelines under transient conditions. The current study is aimed to detect leakage in gas mixture pipelines under pseudo-random boundary conditions with a zero percent false alarm rate (FAR). Pressure [...] Read more.
Conventional leak detection techniques require improvements to detect small leakage (<10%) in gas mixture pipelines under transient conditions. The current study is aimed to detect leakage in gas mixture pipelines under pseudo-random boundary conditions with a zero percent false alarm rate (FAR). Pressure and mass flow rate signals at the pipeline inlet were used to estimate mass flow rate at the outlet under leak free conditions using Hammerstein model. These signals were further used to define adaptive thresholds to separate leakage from normal conditions. Unlike past studies, this work successfully detected leakage under transient conditions in an 80-km pipeline. The leakage detection performance of the proposed methodology was evaluated for several leak locations, varying leak sizes and, various signal to noise ratios (SNR). Leakage of 0.15 kg/s—3% of the nominal flow—was successfully detected under transient boundary conditions with a F-score of 99.7%. Hence, it can be concluded that the proposed methodology possesses a high potential to avoid false alarms and detect small leaks under transient conditions. In the future, the current methodology may be extended to locate and estimate the leakage point and size. Full article
(This article belongs to the Special Issue Optimization-Driven Methods for Optimal Operation and Control Strategies)
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<p>Updated classification of fault detection and diagnostics techniques (FDD).</p> Full article ">Figure 2
<p>Novel methodology of adaptive threshold-based leak detection (ATBLD).</p> Full article ">Figure 3
<p>Block diagram representation of SISO Hammerstein model.</p> Full article ">Figure 4
<p>Mass flow rate changes at the outlet of the pipeline for validation test.</p> Full article ">Figure 5
<p>Pipeline outlet pressure comparison with the experimental data and other studies.</p> Full article ">Figure 6
<p>Hammerstein model identification results (training), (<b>a</b>) pressure and mass flow rate signals at inlet, used as a model input, (<b>b</b>) actual mass flow rate signals at outlet, used as a model output. Estimated mass flow rate sign for given input and output signals, (<b>c</b>) modeling errors between actual and estimated mass flow rate.</p> Full article ">Figure 7
<p>Validation of Hammerstein model with 1201 parameters and noise ratio of 0.2%, (<b>a</b>) Pressure and mass flow rate signals at inlet, used as a model input, (<b>b</b>) actual mass flow rate signals at outlet, used as a model output. Estimated mass flow rate sign for given input and output signals, (<b>c</b>) Modeling errors between actual and estimated mass flow rate.</p> Full article ">Figure 8
<p>Testing of Hammerstein model with 0% leak at 30 h, 1201 parameters and 0.2% noise ratio.</p> Full article ">Figure 9
<p>Testing of Hammerstein model with 5% leak at 30 h, 1201 parameters and 0.2% noise ratio.</p> Full article ">Figure 10
<p>Binary signals indicating normal and leakage conditions.</p> Full article ">Figure 11
<p>F-Score of 5% leak detection using various parameters at different locations.</p> Full article ">
11 pages, 1870 KiB  
Article
Response Surface Methodology as a Useful Tool for Evaluation of the Recovery of the Fluoroquinolones from Plasma—The Study on Applicability of Box-Behnken Design, Central Composite Design and Doehlert Design
by Andrzej Czyrski and Hubert Jarzębski
Processes 2020, 8(4), 473; https://doi.org/10.3390/pr8040473 - 17 Apr 2020
Cited by 31 | Viewed by 6691
Abstract
The aim of this study was to find the best design that is suitable for optimizing the recovery of the representatives of the 2nd, 3rd and 4th generation of fluoroquinolones. The following designs were applied: Central Composite Design, Box–Behnken Design and Doehlert Design. [...] Read more.
The aim of this study was to find the best design that is suitable for optimizing the recovery of the representatives of the 2nd, 3rd and 4th generation of fluoroquinolones. The following designs were applied: Central Composite Design, Box–Behnken Design and Doehlert Design. The recovery, which was a dependent variable, was estimated for liquid–liquid extraction. The time of shaking, pH, and the volume of the extracting agent (dichloromethane) were the independent variables. All results underwent the statistical analysis (ANOVA), which indicated Central Composite Design as the best model for evaluation of the recovery. For each analyte, an equation was generated that enabled to estimate the theoretical value for the applied conditions. The graphs for these equations were provided by the Response Surface Methodology. The statistical analysis also estimated the most significant factors that have an impact on the liquid–liquid extraction, which occurred to be pH for ciprofloxacin and moxifloxacin and the volume of an extracting solvent for levofloxacin. Full article
(This article belongs to the Special Issue Applications of Chromatographic Separation Techniques in Food and Chemistry)
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<p>Standardized Pareto charts for recovery of CIPRO, LEVO and MOXI.</p> Full article ">Figure 2
<p>The RSM diagrams illustrating the change of recovery of CIPRO when analyzed: (<b>a</b>) VDCM and pH; (<b>b</b>) time and pH; (<b>c</b>) time and VDCM for CCD.</p> Full article ">Figure 3
<p>The RSM diagrams illustrating the change of recovery of LEVO when analyzed: (<b>a</b>) VDCM and pH; (<b>b</b>) time and pH; (<b>c</b>) time and VDCM for CCD.</p> Full article ">Figure 4
<p>The RSM diagrams illustrating the change of recovery of MOXI when analyzed: (<b>a</b>) VDCM and pH; (<b>b</b>) time and pH; (<b>c</b>) time and VDCM for CCD.</p> Full article ">
15 pages, 1568 KiB  
Article
Application of Evolutionary Game Theory in Safety Management of Chemical Production
by Qiusheng Song, Peng Jiang and Song Zheng
Processes 2020, 8(4), 472; https://doi.org/10.3390/pr8040472 - 16 Apr 2020
Cited by 11 | Viewed by 3403
Abstract
The chemical industry is essential in the social economy, and the issue of production safety has aroused widespread concern. Chemical safety incidents occupy the headlines from time to time, and chemical production safety management is particularly important. This paper presents an application model [...] Read more.
The chemical industry is essential in the social economy, and the issue of production safety has aroused widespread concern. Chemical safety incidents occupy the headlines from time to time, and chemical production safety management is particularly important. This paper presents an application model based on evolutionary game theory in the assessment and analysis of chemical production safety management. The model uses evolutionary game theory to construct a strategic interactive payoff matrix between the management department of a chemical plant and the chemical plant using a replicated dynamic equation to analyze their strategic interaction and to reveal the evolution of behavioral strategy selection. The evolution results were verified and simulated. The application of this model provides an effective safety management basis and recommendations for the management of chemical plants, providing a foundation for the safe production and healthy development of chemical plants. Full article
(This article belongs to the Special Issue Thermal Safety of Chemical Processes)
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<p>Phase diagram of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>&lt;</mo> <mi mathvariant="normal">P</mi> <mo>-</mo> <mi>β</mi> <msub> <mi>f</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>&lt;</mo> <mfrac> <mrow> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> <mi>β</mi> </mfrac> </mrow> </semantics></math>.</p> Full article ">Figure 2
<p>Phase diagram of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>&lt;</mo> <mi mathvariant="normal">P</mi> <mo>-</mo> <mi>β</mi> <msub> <mi>f</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>&gt;</mo> <mfrac> <mrow> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> <mi>β</mi> </mfrac> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi mathvariant="normal">P</mi> <mo>&gt;</mo> <mi>Q</mi> <mo>&gt;</mo> <mi mathvariant="normal">P</mi> <mo>-</mo> <mi>β</mi> <msub> <mi>f</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>&gt;</mo> <mfrac> <mrow> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> <mi>β</mi> </mfrac> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Phase diagram of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>&gt;</mo> <mi mathvariant="normal">P</mi> <mo>,</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>&gt;</mo> <mfrac> <mrow> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> <mi>β</mi> </mfrac> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>&gt;</mo> <mi mathvariant="normal">P</mi> <mo>,</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>&lt;</mo> <mfrac> <mrow> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> <mi>β</mi> </mfrac> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Phase diagram of <math display="inline"><semantics> <mrow> <mi mathvariant="normal">P</mi> <mo>&gt;</mo> <mi>Q</mi> <mo>&gt;</mo> <mi mathvariant="normal">P</mi> <mo>-</mo> <mi>β</mi> <msub> <mi>f</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>&lt;</mo> <mfrac> <mrow> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> <mi>β</mi> </mfrac> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Current game simulation analysis evolution processes.</p> Full article ">Figure 6
<p>Optimal game simulation analysis evolution processes.</p> Full article ">
15 pages, 4545 KiB  
Article
Catalytic Oxidation of Methylene Blue by Use of Natural Zeolite-Based Silver and Magnetite Nanocomposites
by Aldiyar Kuntubek, Nurassyl Kinayat, Kulyash Meiramkulova, Stavros G. Poulopoulos, Joseph C. Bear and Vassilis J. Inglezakis
Processes 2020, 8(4), 471; https://doi.org/10.3390/pr8040471 - 16 Apr 2020
Cited by 14 | Viewed by 4677
Abstract
This work reports the synthesis of natural zeolite-based silver and magnetite nanocomposites and their application for the catalytic oxidation of methylene blue in water. The zeolite was impregnated with 5.5 wt.% Fe in the form of magnetite nanoparticles with size of 32 nm, [...] Read more.
This work reports the synthesis of natural zeolite-based silver and magnetite nanocomposites and their application for the catalytic oxidation of methylene blue in water. The zeolite was impregnated with 5.5 wt.% Fe in the form of magnetite nanoparticles with size of 32 nm, and with 6.4 wt.% Ag in the form of silver oxide and metallic silver nanoparticles with sizes of 42 and 20 nm, respectively. The results showed that physical adsorption contributed to the removal of methylene blue by 25–36% and that Fe3O4@NZU is superior to Ag2O@NZU and Ag0@NZU, leading to 55% removal without oxidant and 97% in the presence of H2O2. However, there is no evidence of significant mineralization of methylene blue. The application of reaction rate models showed that the reaction order changes from zero to first and second order depending on the H2O2 concentration. Full article
(This article belongs to the Special Issue Sustainable Remediation Processes Based on Zeolites)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The molecular structure of MB.</p> Full article ">Figure 2
<p>Adsorption-desorption isotherms of NZU.</p> Full article ">Figure 3
<p>Cumulative pore volume (<b>left</b>) and pore size distribution of NZU (<b>right</b>).</p> Full article ">Figure 4
<p>XRD spectra of NZU, Fe<sub>3</sub>O<sub>4</sub>@NZU, Ag<sub>2</sub>O@NZU and Ag<sup>0</sup>@NZU samples.</p> Full article ">Figure 5
<p>X-ray photoelectron spectroscopy of Na-NZU. Doublet separation was 13.50 eV.</p> Full article ">Figure 6
<p>XPS survey scan of the Na-NZU zeolite over the range 0–1350 eV.</p> Full article ">Figure 7
<p>Scanning electron microscopy images of (<b>A</b>) Fe<sub>3</sub>O<sub>4</sub>@NZU, (<b>B</b>) Ag<sub>2</sub>O@NZU, (<b>C</b>) Ag<sup>0</sup>@NZU and energy dispersive X-ray spectroscopy of (<b>D</b>) iron on Fe<sub>3</sub>O<sub>4</sub>@NZU and silver on (<b>E</b>) Ag<sub>2</sub>O@NZU and (<b>F</b>) Ag<sup>0</sup>@NZU.</p> Full article ">Figure 7 Cont.
<p>Scanning electron microscopy images of (<b>A</b>) Fe<sub>3</sub>O<sub>4</sub>@NZU, (<b>B</b>) Ag<sub>2</sub>O@NZU, (<b>C</b>) Ag<sup>0</sup>@NZU and energy dispersive X-ray spectroscopy of (<b>D</b>) iron on Fe<sub>3</sub>O<sub>4</sub>@NZU and silver on (<b>E</b>) Ag<sub>2</sub>O@NZU and (<b>F</b>) Ag<sup>0</sup>@NZU.</p> Full article ">Figure 8
<p>Transmission electron microscopy images of (<b>A</b>) Fe<sub>3</sub>O<sub>4</sub>@NZU, (<b>B</b>) Ag<sub>2</sub>O@NZU and (<b>C</b>) Ag<sup>0</sup>@NZU.</p> Full article ">Figure 9
<p>Discoloration of MB solutions by the modified zeolites (H<sub>2</sub>O<sub>2</sub> concentration: 42.4 mM).</p> Full article ">Figure 10
<p>Discoloration of MB solutions and and TC removal by the modified zeolites (H<sub>2</sub>O<sub>2</sub> concentration: 42.4 mM).</p> Full article ">Figure 11
<p>Kinetics of MB solutions discoloration by the modified zeolites.</p> Full article ">Figure 12
<p>Models results.</p> Full article ">
19 pages, 2422 KiB  
Review
Microbial Natural Products in Drug Discovery
by Ahmed S. Abdel-Razek, Mehrez E. El-Naggar, Ahmed Allam, Osama M. Morsy and Sarah I. Othman
Processes 2020, 8(4), 470; https://doi.org/10.3390/pr8040470 - 16 Apr 2020
Cited by 119 | Viewed by 19037
Abstract
Over a long period of time, humans have explored many natural resources looking for remedies of various ailments. Traditional medicines have played an intrinsic role in human life for thousands of years, with people depending on medicinal plants and their products as dietary [...] Read more.
Over a long period of time, humans have explored many natural resources looking for remedies of various ailments. Traditional medicines have played an intrinsic role in human life for thousands of years, with people depending on medicinal plants and their products as dietary supplements as well as using them therapeutically for treatment of chronic disorders, such as cancer, malaria, diabetes, arthritis, inflammation, and liver and cardiac disorders. However, plant resources are not sufficient for treatment of recently emerging diseases. In addition, the seasonal availability and other political factors put constrains on some rare plant species. The actual breakthrough in drug discovery came concurrently with the discovery of penicillin from Penicillium notatum in 1929. This discovery dramatically changed the research of natural products and positioned microbial natural products as one of the most important clues in drug discovery due to availability, variability, great biodiversity, unique structures, and the bioactivities produced. The number of commercially available therapeutically active compounds from microbial sources to date exceeds those discovered from other sources. In this review, we introduce a short history of microbial drug discovery as well as certain features and recent research approaches, specifying the microbial origin, their featured molecules, and the diversity of the producing species. Moreover, we discuss some bioactivities as well as new approaches and trends in research in this field. Full article
(This article belongs to the Special Issue Cancer Systems Biology and Natural Products)
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<p>(<b>a</b>) Core structure of penicillin, the first antibiotic from fungus; (<b>b</b>) <span class="html-italic">Penicillium notatum.</span></p> Full article ">Figure 2
<p>Some medically important natural compounds from fungi.</p> Full article ">Figure 3
<p>Some important bioactive compounds from endophytic fungi.</p> Full article ">Figure 4
<p>Some novel natural compounds produced by marine actinomycetes.</p> Full article ">Figure 5
<p>Some novel bioactive compounds from marine algae.</p> Full article ">
20 pages, 3616 KiB  
Article
Mathematical Model of a Heating Furnace Implemented with Volumetric Fuel Combustion
by Miroslav Rimar, Andrii Kulikov, Marcel Fedak, Oleksandr Yeromin, Kostyantyn Sukhyy, Olena Gupalo, Elena Belyanovskaya, Rene Berta, Michal Smajda and Madhawa Rasuwan Ratnayake
Processes 2020, 8(4), 469; https://doi.org/10.3390/pr8040469 - 16 Apr 2020
Cited by 9 | Viewed by 5656
Abstract
Heating flame furnaces are the main type of furnaces used for heating and heat treatment of metal products in metallurgy and mechanical engineering. In the working chamber of a modern heating furnace, there should be neither high-temperature nor stagnation zones. One of the [...] Read more.
Heating flame furnaces are the main type of furnaces used for heating and heat treatment of metal products in metallurgy and mechanical engineering. In the working chamber of a modern heating furnace, there should be neither high-temperature nor stagnation zones. One of the methods used to provide such combustion conditions is the application of distributed (volumetric) combustion. Owing to this method, heating quality is ensured by creating a uniform temperature field and equivalent heat exchange conditions, regardless of the placement of the charge in the working chamber of the furnace. In this work, we numerically study the volumetric combustion and influences of small- and large-scale recirculation ratios of furnace gases, the influence of temperature fluctuation on the regenerator nozzle, and the working parameters at the starting phase and reverse. Full article
(This article belongs to the Special Issue Progress in Energy Conversion Systems and Emission Control)
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<p>Scheme of a reheating furnace with regenerators and with distributed volumetric combustion: 1—regenerator; 2—working chamber; 3—heated material; 4—gas nozzle; 5—flue gas ducts.</p> Full article ">Figure 2
<p>Scheme of gas circulation inside the furnace. Red—2nd-order boundary conditions, blue—3rd-order boundary conditions.</p> Full article ">Figure 3
<p>Calculation scheme of the furnace and the scheme of movement of furnace gases.</p> Full article ">Figure 4
<p>Reheating furnace and locations of installed thermocouples. 1—burners, 2—regenerators, 3—working chamber, 4—calculation zones.</p> Full article ">Figure 5
<p>Results of the simulation and measurements.</p> Full article ">Figure 6
<p>The change of the temperature of the flue gases during their movement in the heating furnace at various values of the dimensionless length of the heat-release volume zone.</p> Full article ">Figure 7
<p>Temperature distribution over the zones of the furnace with variable rate of recirculation of furnace gases with minimum thermal power.</p> Full article ">Figure 8
<p>Temperature distribution over the zones of the furnace with variable rate of recirculation of furnace gases with maximum heat output and minimum resulting heat flux to metal.</p> Full article ">Figure 9
<p>Temperature distribution over the zones of the furnace with variable rate of recirculation of furnace gases with minimum thermal power.</p> Full article ">Figure 10
<p>The temperature distribution in the zones of the furnace at <span class="html-italic">M<sub>Total</sub></span> = max (<span class="html-italic">q<sub>total</sub></span> = max) with a variable rate of recirculation of furnace gases with minimum thermal power.</p> Full article ">Figure 11
<p>The temperature distribution in the zones of the furnace at <span class="html-italic">M<sub>Total</sub></span> = max (<span class="html-italic">q<sub>total</sub></span> = min) with a variable rate of recirculation of furnace gases with minimum thermal power.</p> Full article ">Figure 12
<p>The temperature distribution in the zones of the furnace at <span class="html-italic">M<sub>Total</sub></span> = min with a variable rate of recirculation of furnace gases with minimum thermal power.</p> Full article ">Figure 13
<p>Change in the temperature differential across the zones of the furnace with a change in thermal power (for the case with <span class="html-italic">K<sub>rec</sub></span> = 2). The time of changing the heating parameters from the red to the yellow line is the first heating period; from yellow to green is the second.</p> Full article ">
20 pages, 2466 KiB  
Article
Protective Effects of Ginger Extract against Glycation and Oxidative Stress-Induced Health Complications: An In Vitro Study
by Shehwaz Anwar, Ahmad Almatroudi, Khaled S. Allemailem, Rejo Jacob Joseph, Amjad Ali Khan and Arshad Husain Rahmani
Processes 2020, 8(4), 468; https://doi.org/10.3390/pr8040468 - 16 Apr 2020
Cited by 45 | Viewed by 7295
Abstract
Protein glycation and oxidative stress lead to severe health complications in various diseases including diabetes mellitus. The intake of flavonoid-rich foods has been confirmed previously to have a positive effect on human health. Ginger is an important source of flavonoids and is one [...] Read more.
Protein glycation and oxidative stress lead to severe health complications in various diseases including diabetes mellitus. The intake of flavonoid-rich foods has been confirmed previously to have a positive effect on human health. Ginger is an important source of flavonoids and is one of the most widely used traditional medicines in many Asian countries. The aim of this study was to verify the therapeutic potential of methanolic extract from ginger against glycation and other oxidative stress-induced complications using in vitro study. In this study, quantitative estimations of antioxidant components such as total phenolic and flavonoids were determined by UV–visible spectrophotometry. The anti-inflammatory action of the ginger extract was checked by determining its protective action against the denaturation of proteins, anti-proteinase activity and its membrane stabilization effect. The anti-inflammatory action of ginger extract was found to be comparable with reference standard drugs. The antiglycating effect of ginger extract was investigated by placing bovine serum albumin (BSA) with glucose in the presence and absence of ginger extract for two weeks at 37 °C. The incubated samples were analyzed for the number of glycation products, secondary structural changes, aggregation and advanced glycation end products (AGEs) formation by checking browning intensity, determination of aggregation index and Congo red assays. Our findings demonstrated that ginger extract (600 µg/mL) significantly reduced the browning, secondary structural changes, aggregation and AGEs formation. Thus, it can be concluded from these results that ginger extract is a wealthy source of antioxidants and can be used to prevent the glycation and oxidative stress-induced damage of biomolecules in various health complications including inflammation. Full article
(This article belongs to the Special Issue Development of In Vitro Disease Modelling)
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<p>Reducing power of ginger extract in terms of absorbance at 700 nm. The <span class="html-italic">X</span>-axis indicates the various concentrations of the ginger methanolic extract. The <span class="html-italic">Y</span>-axis shows the corresponding absorbance at 700 nm.</p> Full article ">Figure 2
<p>H<sub>2</sub>O<sub>2</sub> scavenging activity of methanolic ginger extract. Samples 1 to 7 correspond to various concentrations of ginger extract (50–600 µg/mL). Sample 8 contained 100 µg/mL of standard antioxidant “ascorbic acid”. The results are presented as means ± SEM (n = 3, <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3
<p>Blue plot represents the curve having % of free radical reduced vs. concentration (µg/mL) of ascorbic acid while orange curve shows the % free radical scavenging activity vs. concentration (µg/mL) of methanolic ginger extract. The <span class="html-italic">p</span>-value significance was found to be less than 0.03 for this figure (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Protection from heat-induced protein denaturation. Samples from 1 to 6 at <span class="html-italic">X</span>-axis represent concentration 100–600 µg/mL of ginger methanolic extract. Samples 7 and 8 denote a sample having reference drug ibuprofen with concentrations of 100 and 200 µg/mL, respectively. The results are presented as means ± SEM (n = 3, * <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 5
<p>Anti-proteinase activity of the ginger extract. Samples 1–6 represent the various concentrations (100–600 µg/mL) of ginger methanolic extract. Samples 7 and 8, the last two samples, contained 100 and 200 µg/mL of diclofenac sodium, respectively. The results are presented as means ± SEM (n = 3, <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 6
<p>Protection from heat-induced hemolysis. Samples 1–6 contained 100–600 µg/mL ginger methanolic extract, respectively. Samples 7 and 8 contained aspirin 100 and 200 µg/mL, respectively. The results are presented as means ± SEM (n = 3, <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 7
<p>Protection from hypotonicity-induced hemolysis. The figure shows that the ginger methanolic extract provides protection against hypotonicity-induced hemolysis in a concentration-dependent manner. Samples 1–6 contained 100–600 µg/mL of the ginger methanolic extract, respectively. Samples 7 and 8, the last two samples, contained 100 and 200 µg/mL diclofenac sodium as the reference drug. The results are presented as means ± SEM (n = 3, <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 8
<p>Microscopic study of <span class="html-italic">A. fumigatus</span> grown in media with high glucose content and in the presence or absence of ginger extract. A1 and A2 represent the 10× image and 40× image of <span class="html-italic">A. fumigatus</span> grown in media with high glucose content in the presence of ginger extract, respectively. B1 and B2 represent the 10× image and 40× image of <span class="html-italic">A. fumigatus</span> grown in media with high glucose content in absence of ginger extract, respectively.</p> Full article ">Figure 9
<p>Decrease in browning in the presence of the ginger methanolic extract. Sample 1 corresponds to bovine serum albumin (BSA) incubated with glucose for 15 days and considered to show 100% glycation (browning). Samples 2–7 contained BSA and glucose with varying concentrations (100–600 µg/mL) of ginger methanolic extract. The browning intensity (the degree of glycation) is found to decrease with an increase in the concentration of the ginger methanolic extract. Sample 8 contained BSA incubated in the absence of glucose and ginger methanolic extract. It showed the least glycation. The results are presented as means ± SEM (n = 3, <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 10
<p>The decrease in aggregation index in the presence of ginger methanolic extract. Sample 1 corresponds to BSA incubated with glucose for 15 days and considered to show maximum aggregation index and, hence, glycation aggregation index. Samples 2–7 contained BSA and glucose with varying concentrations (100–600 µg/mL) of ginger methanolic extract and aggregation index is shown to decrease with an increase in the concentration of ginger methanolic extract. Sample 8 contained BSA incubated in the absence of glucose and ginger methanolic extract and showed the least aggregation index. The results are presented as means ± SEM (n = 3, <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 11
<p>Decrease in cross amyloid structures in presence of ginger methanolic extract. Sample 1 corresponds to BSA incubated with glucose for 15 days and considered to show maximum structural maximum modifications and hence considered to be 100%. Samples 2–7 contained BSA and glucose with varying concentration (100–600 µg/mL) of ginger methanolic extract and cross amyloid structures are shown to be decreased with increase in the concentration of ginger methanolic extract. Sample 8 contained BSA incubated in the absence of glucose and ginger methanolic extract and showed the least cross amyloid structures. The results are presented as means ± SEM (n = 3, <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
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