Nothing Special   »   [go: up one dir, main page]
Next Issue
Volume 8, June
Previous Issue
Volume 8, April
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
2.8
Journals
Processes
Volume 8
Issue 5
share announcement

Processes, Volume 8, Issue 5 (May 2020) – 136 articles

Cover Story (view full-size image): This paper investigates aquathermolysis of heavy oil in carbonate reservoir rocks from Boca de Jaruco (JSC «Zarubezhneft»), which is developed by the cyclic steam stimulation method. The nickel-based catalyst precursor was introduced in order to intensify the conversion processes of heavy oil components. The active form of such catalysts—nickel sulfides—are achieved after steam treatment of crude oil at reservoir conditions. It is revealed that catalyst particles provide a reduction in the content of resins and asphaltenes due to the destruction of carbon-heteroatom bonds. The oil recovery factor is enhanced due to irreversible viscosity reduction of crude oil. View this paper.
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
26 pages, 5572 KiB  
Review
Graphene-Based Hydrogen Gas Sensors: A Review
by Anna Ilnicka and Jerzy P. Lukaszewicz
Processes 2020, 8(5), 633; https://doi.org/10.3390/pr8050633 - 25 May 2020
Cited by 38 | Viewed by 9685
Abstract
Graphene is a material gaining attention as a candidate for new application fields such as chemical sensing. In this review, we discuss recent advancements in the field of hydrogen gas sensors based on graphene. Accordingly, the main part of the paper focuses on [...] Read more.
Graphene is a material gaining attention as a candidate for new application fields such as chemical sensing. In this review, we discuss recent advancements in the field of hydrogen gas sensors based on graphene. Accordingly, the main part of the paper focuses on hydrogen gas sensors and examines the influence of different manufacturing scenarios on the applicability of graphene and its derivatives as key components of sensing layers. An overview of pristine graphene customization methods is presented such as heteroatom doping, insertion of metal/metal oxide nanosized domains, as well as creation of graphene-polymer blends. Volumetric structuring of graphene sheets (single layered and stacked forms) is also considered as an important modifier of its effective use. Finally, a discussion of the possible advantages and weaknesses of graphene as sensing material for hydrogen detection is provided. Full article
(This article belongs to the Section Materials Processes)
►▼ Show Figures

Figure 1

Figure 1
<p>The increasing number of papers in the field of hydrogen gas sensors from 2001 to 2020 (internet search on the Web of Science, 15 March 2020). Keywords for search: hydrogen gas sensor.</p> Full article ">Figure 2
<p>(<b>a</b>) Gas-sensing response of the P–PANI–GO-based sensor to different concentrations of hydrogen at room temperature; (<b>b</b>) The linear response for gas concentration in the range from 0.01 to 2 vol% [<a href="#B70-processes-08-00633" class="html-bibr">70</a>]. Copyright (2020), with permission from Elsevier.</p> Full article ">Figure 3
<p>(<b>a</b>) Responses of the rGO, PANI, PANI–GO, and Pd–PANI–GO for hydrogen sensing (1%) at room temperature; (<b>b</b>) Response of the Pd-PANI-GO-nanocomposite-based sensor to various gases [<a href="#B70-processes-08-00633" class="html-bibr">70</a>]. Copyright (2020), with permission from Elsevier.</p> Full article ">Figure 4
<p>(<b>a</b>) Photography of the sensor; (<b>b</b>) Resistance response of a Pt@rGO-based hydrogen sensor exposed to 3% H<sub>2</sub> in the temperature range 30–70 °C [<a href="#B80-processes-08-00633" class="html-bibr">80</a>]. Copyright (2020), with permission from Elsevier.</p> Full article ">Figure 5
<p>Sensing mechanism of the Pt@rGO nanocomposite sensor toward hydrogen [<a href="#B80-processes-08-00633" class="html-bibr">80</a>]. Copyright (2020), with permission from Elsevier.</p> Full article ">Figure 6
<p>(<b>a</b>) Resistance response of Pd–rGO device to 3300 ppm hydrogen at different temperature; (<b>b</b>) Sensor response as a function of temperature for the data in (<b>a</b>) [<a href="#B89-processes-08-00633" class="html-bibr">89</a>]. Copyright (2020), with permission from Elsevier.</p> Full article ">Figure 7
<p>Device architecture of the tandem gasochromic-Pd-WO<sub>3</sub>/graphene/Si optoelectronic hydrogen sensor. (<b>a</b>) Tilted schematic device structure; (<b>b</b>) Cross-sectional schematic structure;(<b>c</b>) Cross-sectional SEM image; (<b>d</b>) Optical photograph of a produced device [<a href="#B122-processes-08-00633" class="html-bibr">122</a>]. Copyright (2020), with permission from Elsevier.</p> Full article ">Figure 8
<p>Tandem gasochromic-Pd-WO<sub>3</sub>/graphene/Si device for optoelectronic hydrogen sensors. (<b>a</b>) I-V curves of the device in darkness and under 10 mW laser illumination, with and without H<sub>2</sub>; (<b>b</b>) Response of the device to the 4 vol% H<sub>2</sub>/Ar; (<b>c</b>) Cyclic performance of the device [<a href="#B122-processes-08-00633" class="html-bibr">122</a>]. Copyright (2020), with permission from Elsevier.</p> Full article ">Figure 9
<p>Sensor response vs time graph for ZnO and RGO/ZnO sensors towards 200 ppm of hydrogen at their respective optimum operating temperatures, i.e., 400 °C for ZnO and 150 °C for RGO/ZnO composite [<a href="#B126-processes-08-00633" class="html-bibr">126</a>]. Copyright (2020), with permission from Elsevier.</p> Full article ">Figure 10
<p>(<b>a</b>–<b>c</b>) Responses at different temperatures of ZnO, rGO/ZnO, and Au/rGO/ZnO sensor with and without UV irradiation; (<b>d</b>) Dynamic response at room temperature of rGO, Au/rGO/ZnO, and UV activated Au/rGO/ZnO [<a href="#B128-processes-08-00633" class="html-bibr">128</a>]. Copyright (2020), with permission from Elsevier.</p> Full article ">Figure 11
<p>(<b>a</b>) Dynamic response curve of different hydrogen concentrations; (<b>b</b>) Repeatability measurements of the Au/rGO/ZnO sensor toward 300 ppm of H<sub>2</sub>; (<b>c</b>,<b>d</b>) long term stability, and selectivity test of Au/rGO/ZnO sensor toward 500 ppm of H<sub>2</sub>, tests were realized at room temperature and under UV irradiation [<a href="#B128-processes-08-00633" class="html-bibr">128</a>]. Copyright (2020), with permission from Elsevier.</p> Full article ">Figure 12
<p>Gas sensing mechanism of Au/rGO/ZnO sensor in (<b>a</b>) air (<b>b</b>) air with UV light, and (<b>c</b>) H<sub>2</sub> environment with UV irradiation [<a href="#B128-processes-08-00633" class="html-bibr">128</a>]. Copyright (2020), with permission from Elsevier.</p> Full article ">Figure 13
<p>Schematic representation of sensing mechanism [<a href="#B135-processes-08-00633" class="html-bibr">135</a>]. Copyright (2020), with permission from Elsevier.</p> Full article ">
18 pages, 9325 KiB  
Article
Experimental and Statistical Validation of Data on Mesh-Coupled Annular Distributor Design for Swirling Fluidized Beds
by Shazia Shukrullah, Muhammad Yasin Naz, Abdul Ghaffar, Yasin Khan, Abdulrehman Ali Al-Arainy and Rashed Meer
Processes 2020, 8(5), 632; https://doi.org/10.3390/pr8050632 - 25 May 2020
Cited by 3 | Viewed by 3731
Abstract
In this study, velocimetry and statistical analyses were conducted on a swirling fluidized bed. A bed of spherical particles (4 mm) was fluidized by using an annular distributor covered with mesh. The angles of rectangular blades in the distributor were set at 30°, [...] Read more.
In this study, velocimetry and statistical analyses were conducted on a swirling fluidized bed. A bed of spherical particles (4 mm) was fluidized by using an annular distributor covered with mesh. The angles of rectangular blades in the distributor were set at 30°, 45°, 60°, 75° and 90°, and the cell size of the mesh cover was 2.5 × 2.5 mm2. The weight was varied from 500 to 1250 g and the effect of each variable on bed velocity response was quantified through velocimetry and statistical analysis. The statistical analysis was conducted using NCSS statistical software. The blade angle, bed weight and superficial velocity for 4 mm particles were statistically optimized at 750 g, 58.26° and 1.45 m/s, respectively. On the experimental side, these parameters have been optimized at 750 g, 60° and 1.41 m/s, respectively. A small difference of 1.74° was noticed in experimental and statistical predictions for the blade angle. The bed weights and superficial velocities were found to be same in both cases. The confidence interval (95%) for bed velocity was proposed in the range of 0.513 to 0.519 m/s. The experimentally optimized bed velocity remained within the proposed range. The well-agreeing results indicate good practical value of distributor design and high precision of the experimental measurements. Full article
(This article belongs to the Special Issue Recent Advances in Fluidized Bed Hydrodynamics and Transport Phenomena)
►▼ Show Figures

Figure 1

Figure 1
<p>Geometry of mesh-coupled annular distributor: (<b>a</b>) fixing of rectangular blades between concentric rings, (<b>b</b>) cutaway view of the distributor with mesh cover, (<b>c</b>) complete distributor assembly, and (<b>d</b>) distributor with solid cylinder in the middle.</p> Full article ">Figure 2
<p>Complete design drawing of the bed column.</p> Full article ">Figure 3
<p>The experimental rig consisted of a swirling fluidized bed (SFB), a high-speed camera and a data acquisition setup.</p> Full article ">Figure 4
<p>A vector field of a bed fluidized above <span class="html-italic">U<sub>m</sub>.</span> Inset provides a photographic view of the swirling bed.</p> Full article ">Figure 5
<p>Estimation of <span class="html-italic">U<sub>m</sub></span> for different blade angles by plotting <math display="inline"><semantics> <mrow> <mo>∆</mo> <mi>P</mi> </mrow> </semantics></math> against <span class="html-italic">U<sub>sup</sub></span> for bed weight of 750 g.</p> Full article ">Figure 6
<p>Free-body diagram of forces acting on swirling bed material.</p> Full article ">Figure 7
<p>Estimation of U<sub>m</sub> for different bed weights by fixing the blade angle at 60°.</p> Full article ">Figure 8
<p>Marking of vector field for bed velocity measurements.</p> Full article ">Figure 9
<p>Trend of bed velocity magnitude on radial lines for different superficial velocities.</p> Full article ">Figure 10
<p>Comparison of average velocity profiles of different bed weights at 60°.</p> Full article ">Figure 11
<p>Illustration of bed height and flow response to superficial velocity at different blade angles.</p> Full article ">Figure 12
<p>Statistical analysis of response and independent variables.</p> Full article ">
26 pages, 2584 KiB  
Article
Industrial Processes Management for a Sustainable Society: Global Research Analysis
by Emilio Abad-Segura, Manuel E. Morales, Francisco Joaquín Cortés-García and Luis Jesús Belmonte-Ureña
Processes 2020, 8(5), 631; https://doi.org/10.3390/pr8050631 - 24 May 2020
Cited by 54 | Viewed by 8990
Abstract
Few decades ago, the development of the industrial sector was disconnected from society’s protection. Negative effects awareness emerges from the current industrial processes through the Sustainable Development Goals (SDGs), considering the causal implications to build up a more sustainable society. The aim of [...] Read more.
Few decades ago, the development of the industrial sector was disconnected from society’s protection. Negative effects awareness emerges from the current industrial processes through the Sustainable Development Goals (SDGs), considering the causal implications to build up a more sustainable society. The aim of this study is to analyze the state of the art in industrial processes management to obtain positive and sustainable effects on society. Thus, a bibliometric analysis of 1911 articles was set up during the 1988–2019 period, bringing up the authors’ productivity indicators in the scientific field, that is, journals, authors, research institutions, and countries. We have identified environmental management; the impact assessments of industrial processes on the environment and its relation with a more sustainable society; as well as the study of the sustainable management of water resources as the related axes in the study of environmental protection with political, economic, and educational approaches. The growing trend of world scientific publications let us observe the relevance of industrial processes management in the implementation of efficient models to achieve sustainable societies. This research contributes to the academic, scientific, and social debate on decision-making both in public and private institutions, and in multidisciplinary groups. Full article
(This article belongs to the Special Issue New Processes: Working towards a Sustainable Society)
►▼ Show Figures

Figure 1

Figure 1
<p>Flowchart of the methodology.</p> Full article ">Figure 2
<p>Evolution of the number of articles and percentage of variation between four-year periods.</p> Full article ">Figure 3
<p>Comparison of growth trends of main subject areas (1988–2019).</p> Full article ">Figure 4
<p>Network map of cooperation between authors based on co-authorship (1988–2019).</p> Full article ">Figure 5
<p>Network of cooperation between countries based on co-authorship (1988–2019).</p> Full article ">Figure 6
<p>Keywords network map based on co-occurrence (1998–2019).</p> Full article ">Figure 7
<p>Evolution of keywords network map based on co-occurrence (1998–2019).</p> Full article ">
11 pages, 2105 KiB  
Article
Greening the Gas Grid—Evaluation of the Biomethane Injection Potential from Agricultural Residues in Austria
by Bernhard Stürmer
Processes 2020, 8(5), 630; https://doi.org/10.3390/pr8050630 - 24 May 2020
Cited by 8 | Viewed by 4990
Abstract
In order to implement the Paris Climate Agreement, the current Austrian coalition government has included trend-setting targets in its policy statement. “Green gas” plays a key role in this context, as the natural gas grid shall also gradually become renewable. This article analyses [...] Read more.
In order to implement the Paris Climate Agreement, the current Austrian coalition government has included trend-setting targets in its policy statement. “Green gas” plays a key role in this context, as the natural gas grid shall also gradually become renewable. This article analyses the technical biomethane injection potential for agricultural residues based on Integrated Administration and Control System (IACS) data on a municipal level. While a technical biogas potential of 16.2 TWhCH4 from catch crops, farm manure, straw and beet leaves is available, only about half of it can be fed into the gas grid because of technical and economic reasons. Austria’s biomethane injection potential of 7.4 TWhCH4 is mainly produced in arable farming regions. In order to further increase this potential, the investment costs of biogas upgrading plants must be reduced, the use of biogenic waste and energy crops must be further promoted and an investor-friendly legal framework must be created. Full article
(This article belongs to the Special Issue Current Trends in Anaerobic Digestion Processes)
►▼ Show Figures

Figure 1

Figure 1
<p>Spatial distribution of the technical biogas potential (own analysis).</p> Full article ">Figure 2
<p>Regional distribution of the possible biomethane injection potential (own analysis).</p> Full article ">Figure 3
<p>Frequency distribution of average haul distance (own analysis).</p> Full article ">Figure 4
<p>Changes in the biomethane injection and electrification potential based on changes in the minimum amounts for farm manure (<b>left</b>) and catch crops (<b>right</b>) (own analysis).</p> Full article ">Figure 5
<p>Change in biomethane injection potential based on changes in the minimum feed-in capacity (own analysis).</p> Full article ">
15 pages, 2870 KiB  
Article
Improved Catalytic Properties of Thermomyces lanuginosus Lipase Immobilized onto Newly Fabricated Polydopamine-Functionalized Magnetic Fe3O4 Nanoparticles
by Yanhong Bi, Zhaoyu Wang, Rui Zhang, Yihan Diao, Yaoqi Tian and Zhengyu Jin
Processes 2020, 8(5), 629; https://doi.org/10.3390/pr8050629 - 24 May 2020
Cited by 13 | Viewed by 3537
Abstract
In this study, magnetic Fe3O4 nanoparticles coated with polydopamine possessing abundant amino groups (Fe3O4@PDA) were conveniently prepared, detailed, and characterized, and then firstly used as a supporting matrix for immobilizing Thermomyces lanuginosus lipase (Fe3O [...] Read more.
In this study, magnetic Fe3O4 nanoparticles coated with polydopamine possessing abundant amino groups (Fe3O4@PDA) were conveniently prepared, detailed, and characterized, and then firstly used as a supporting matrix for immobilizing Thermomyces lanuginosus lipase (Fe3O4@PDA@TLL). The effects of some crucial factors on the immobilization efficiency were investigated and the optimal protein loading and activity recovery were found to be 156.4 mg/g and 90.9%, respectively. Characterization studies revealed that Fe3O4@PDA@TLL displayed a broader pH and temperature adaptability as compared to the free TLL, which allows its use at wider ranges of reaction conditions. With regard to the stabilities, the immobilized TLL clearly displayed improved pH, thermal, and solvent tolerance stabilities compared to the free enzyme, suggesting that the biocompatible Fe3O4@PDA might be an outstanding material for immobilizing TLL and acting as alternative support for different enzymes. Full article
(This article belongs to the Special Issue Biocatalysis, Enzyme and Process Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>TEM images of Fe<sub>3</sub>O<sub>4</sub> (<b>A</b>), Fe<sub>3</sub>O<sub>4</sub> nanoparticles coated with polydopamine possessing abundant amino groups (Fe<sub>3</sub>O<sub>4</sub>@PDA) (<b>B</b>), and the aforementioned used as a supporting matrix for immobilizing Thermomyces lanuginosus lipase (Fe<sub>3</sub>O<sub>4</sub>@PDA@TLL) (<b>C</b>).</p> Full article ">Figure 2
<p>Size distribution of Fe<sub>3</sub>O<sub>4</sub> (<b>A</b>), Fe<sub>3</sub>O<sub>4</sub>@PDA (<b>B</b>), and Fe<sub>3</sub>O<sub>4</sub>@PDA@TLL (<b>C</b>).</p> Full article ">Figure 3
<p>XPS spectra (<b>A</b>), FT-IR spectra (<b>B</b>), and XRD patterns (<b>C</b>) of Fe<sub>3</sub>O<sub>4</sub> (<b>1</b>), Fe<sub>3</sub>O<sub>4</sub>@PDA (<b>2</b>) and Fe<sub>3</sub>O<sub>4</sub>@PDA@TLL (<b>3</b>).</p> Full article ">Figure 3 Cont.
<p>XPS spectra (<b>A</b>), FT-IR spectra (<b>B</b>), and XRD patterns (<b>C</b>) of Fe<sub>3</sub>O<sub>4</sub> (<b>1</b>), Fe<sub>3</sub>O<sub>4</sub>@PDA (<b>2</b>) and Fe<sub>3</sub>O<sub>4</sub>@PDA@TLL (<b>3</b>).</p> Full article ">Figure 4
<p>Effect of immobilization conditions on activity recovery (<b>1</b>) and protein loading (<b>2</b>). (<b>A</b>) Effect of enzyme-support ratio (12 mL phosphate buffer (50 mM, pH 8.5) containing 0.4 g Fe<sub>3</sub>O<sub>4</sub>@PDA, different amount of TLL, 25 °C, 10 h); (<b>B</b>) Effect of immobilization time (12 mL phosphate buffer (50 mM, pH 8.5) containing 0.4 g Fe<sub>3</sub>O<sub>4</sub>@PDA, 2.4 mL TLL, 25 °C, different immobilization time); (<b>C</b>) Effect of immobilization pH (12 mL phosphate buffer (50 mM, pH 6.5–9.0) containing 0.4 g Fe<sub>3</sub>O<sub>4</sub>@PDA, 2.4 mL TLL, 25 °C, 4.0 h); (<b>D</b>) Effect of immobilization temperature (12 mL phosphate buffer (50 mM, pH 8.0) containing 0.4 g Fe<sub>3</sub>O<sub>4</sub>@PDA, 2.4 mL TLL, 10–55 °C, 4.0 h).</p> Full article ">Figure 4 Cont.
<p>Effect of immobilization conditions on activity recovery (<b>1</b>) and protein loading (<b>2</b>). (<b>A</b>) Effect of enzyme-support ratio (12 mL phosphate buffer (50 mM, pH 8.5) containing 0.4 g Fe<sub>3</sub>O<sub>4</sub>@PDA, different amount of TLL, 25 °C, 10 h); (<b>B</b>) Effect of immobilization time (12 mL phosphate buffer (50 mM, pH 8.5) containing 0.4 g Fe<sub>3</sub>O<sub>4</sub>@PDA, 2.4 mL TLL, 25 °C, different immobilization time); (<b>C</b>) Effect of immobilization pH (12 mL phosphate buffer (50 mM, pH 6.5–9.0) containing 0.4 g Fe<sub>3</sub>O<sub>4</sub>@PDA, 2.4 mL TLL, 25 °C, 4.0 h); (<b>D</b>) Effect of immobilization temperature (12 mL phosphate buffer (50 mM, pH 8.0) containing 0.4 g Fe<sub>3</sub>O<sub>4</sub>@PDA, 2.4 mL TLL, 10–55 °C, 4.0 h).</p> Full article ">Figure 5
<p>Effect of pH (<b>A</b>) and temperature (<b>B</b>) on the activity of free TLL and Fe<sub>3</sub>O<sub>4</sub>@PDA@TLL.</p> Full article ">Figure 5 Cont.
<p>Effect of pH (<b>A</b>) and temperature (<b>B</b>) on the activity of free TLL and Fe<sub>3</sub>O<sub>4</sub>@PDA@TLL.</p> Full article ">Figure 6
<p>pH (<b>A</b>), thermal (<b>B</b>), solvent tolerance (<b>C</b>), and operational (<b>D</b>) stabilities of the enzyme.</p> Full article ">Figure 6 Cont.
<p>pH (<b>A</b>), thermal (<b>B</b>), solvent tolerance (<b>C</b>), and operational (<b>D</b>) stabilities of the enzyme.</p> Full article ">
15 pages, 3735 KiB  
Article
Organoboron Ionic Liquids as Extractants for Distillation Process of Binary Ethanol + Water Mixtures
by Ilsiya M. Davletbaeva, Alexander V. Klinov, Alina R. Khairullina, Alexander V. Malygin, Sergey E. Dulmaev, Alisa R. Davletbaeva and Timur A. Mukhametzyanov
Processes 2020, 8(5), 628; https://doi.org/10.3390/pr8050628 - 24 May 2020
Cited by 10 | Viewed by 3632
Abstract
Aminoethers of boric acid, which are organoboron ionic liquids, were synthesized by using boric acid, triethanolamine, and triethylene glycol/diethylene glycol. Due to the formation of intermolecular complexes of borates, the structure of aminoethers of boric acid contains ion pairs separated in space, giving [...] Read more.
Aminoethers of boric acid, which are organoboron ionic liquids, were synthesized by using boric acid, triethanolamine, and triethylene glycol/diethylene glycol. Due to the formation of intermolecular complexes of borates, the structure of aminoethers of boric acid contains ion pairs separated in space, giving these compounds the properties inherent to ionic liquids. It is established that the thermal stability of aminoethers under normal atmospheric conditions increases with an increase in the size of the glycol. According to measurements of fast scanning calorimetry, density, dynamic viscosity, and electrical conductivity, water is involved in the structural organization of aminoethers of boric acid. The impact of the most thermostable organoboron ionic liquids on the phase equilibrium conditions of the vapor–liquid azeotropic ethanol–water mixture is studied. It is shown that the presence of these substances leads to increase in the relative volatility of ethanol. In general, the magnitude of this effect is at the level shown by imidazole ionic liquids, which provide high selectivity in the separation of aqueous alcohol solutions. A large separation factor, high resistance to thermal oxidative degradation processes, accompanied by low cost start reagents, make aminoethers of boric acid on the basis of triethylene glycol a potentially effective extractant for the extractive distillation of water–alcohol mixtures. Full article
(This article belongs to the Special Issue Green Separation and Extraction Processes)
►▼ Show Figures

Figure 1

Figure 1
<p>Scheme of synthesis of aminoethers of boric acid based on triethylene glycol (AEBA–TEG) and subsequent intermolecular complexation.</p> Full article ">Figure 2
<p>TGA curves of AEBA–MEG (<b>1</b>), AEBA–DEG (<b>2</b>), and AEBA–TEG (<b>3</b>) in air.</p> Full article ">Figure 3
<p>Mass-spectra of thermo-oxidative degradation products of AEBA.</p> Full article ">Figure 4
<p>Fast scanning calorimetry curves of AEBA–TEG.</p> Full article ">Figure 5
<p>Dynamic viscosity of aqueous solutions of AEBA–TEG (<b>a</b>) and AEBA–DEG (<b>b</b>).</p> Full article ">Figure 6
<p>Dependences of the electrical conductivity of AEBA–DEG (<b>1</b>) and AEBA–TEG (<b>2</b>) on their concentration in aqueous solutions (T = 25 °C).</p> Full article ">Figure 7
<p>Change in the concentration of ethanol in the ethanol–water liquid mixture during open evaporation: line is solution (3); geometric figures—experimental data.</p> Full article ">Figure 8
<p>Change in the concentration of ethanol in a liquid mixture during open evaporation: red color is 0.1 wt.fr. of AEBA; green—0.2 wt.fr.; blue—0.6 wt.fr.</p> Full article ">
14 pages, 5140 KiB  
Article
Spark Plasma Sintering of Cobalt Powders in Conjunction with High Energy Mechanical Treatment and Nanomodification
by Van Minh Nguyen, Rita Khanna, Yuri Konyukhov, Tien Hiep Nguyen, Igor Burmistrov, Vera Levina, Ilya Golov and Gopalu Karunakaran
Processes 2020, 8(5), 627; https://doi.org/10.3390/pr8050627 - 23 May 2020
Cited by 9 | Viewed by 3772
Abstract
Spark plasma sintering (SPS) investigations were carried out on three sets of Co specimens: untreated, high energy mechanically (HEMT) pre-treated, and nanomodified powders. The microstructure, density, and mechanical properties of sintered pellets were investigated as a function of various pre-treatments and sintering temperatures [...] Read more.
Spark plasma sintering (SPS) investigations were carried out on three sets of Co specimens: untreated, high energy mechanically (HEMT) pre-treated, and nanomodified powders. The microstructure, density, and mechanical properties of sintered pellets were investigated as a function of various pre-treatments and sintering temperatures (700–1000 °C). Fine-grained sinters were obtained for pre-treated Co powders; nano-additives tended to inhibit grain growth by reinforcing particles at grain boundaries and limiting grain-boundary movement. High degree of compaction was also achieved with relative densities of sintered Co pellets ranging between 95.2% and 99.6%. A direct co-relation was observed between the mechanical properties and densities of sintered Co pellets. For a comparable sinter quality, sintering temperatures for pre-treated powders were lower by 100 °C as compared to untreated powders. Highest values of bending strength (1997 MPa), microhardness (305 MPa), and relative density (99.6%) were observed for nanomodified HEMT and SPS processed Co pellets, sintered at 700 °C. Full article
(This article belongs to the Special Issue Synthesis and Application of Nano- and Microdispersed Systems)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>A</b>) X-ray diffraction (Cu Kα), and (<b>B</b>) selected area electron diffraction (SAED) results on synthesized Co nanopowders.</p> Full article ">Figure 2
<p>(<b>a</b>,<b>b</b>) SEM and (<b>c</b>,<b>d</b>) TEM micrographs of synthesized Co nanopowders.</p> Full article ">Figure 3
<p>SEM images of Co powders after HEMT operation. (<b>a</b>) t = 0 min, (<b>b</b>) t = 1 min, (<b>c</b>) t = 2 min.</p> Full article ">Figure 4
<p>An overview of the experimental plan.</p> Full article ">Figure 5
<p>The SEM micrographs of three sets of Co micropowders: (<b>a</b>) untreated Co powder, (<b>b</b>) HEMT treated Co powder, (<b>c</b>) nanomodification, and HEMT treatments on Co powders.</p> Full article ">Figure 6
<p>Microstructure of sintered Co pellets (700 °C) for sets (<b>a</b>) I, (<b>b</b>) II, (<b>c</b>) III of Co powders as determined through optical microscopy.</p> Full article ">Figure 7
<p>Influence of sintering temperature on (<b>A</b>) absolute densities and (<b>B</b>) relative densities of three sets of Co powders.</p> Full article ">Figure 8
<p>Impact of pre-treatments and sintering temperatures on (<b>A</b>) bending strength and (<b>B</b>) microhardness of sintered pellets.</p> Full article ">Figure 9
<p>Impact of surface carbon diffusion on the microhardness of sintered Co pellets (set III, 1000 °C).</p> Full article ">Figure 10
<p>(<b>A</b>–<b>C</b>) Linear dependence of bending strength and microhardness on the relative density of sintered pellets.</p> Full article ">
15 pages, 2697 KiB  
Article
Environmental Assessment of Olive Mill Solid Waste Valorization via Anaerobic Digestion Versus Olive Pomace Oil Extraction
by Bernabé Alonso-Fariñas, Armando Oliva, Mónica Rodríguez-Galán, Giovanni Esposito, Juan Francisco García-Martín, Guillermo Rodríguez-Gutiérrez, Antonio Serrano and Fernando G. Fermoso
Processes 2020, 8(5), 626; https://doi.org/10.3390/pr8050626 - 23 May 2020
Cited by 23 | Viewed by 5089
Abstract
Anaerobic digestion is a promising alternative to valorize agrifood wastes, which is gaining interest under an environmental sustainability overview. The present research aimed to compare anaerobic digestion with olive pomace oil extraction, by using life cycle assessment, as alternatives for the valorization of [...] Read more.
Anaerobic digestion is a promising alternative to valorize agrifood wastes, which is gaining interest under an environmental sustainability overview. The present research aimed to compare anaerobic digestion with olive pomace oil extraction, by using life cycle assessment, as alternatives for the valorization of the olive mill solid waste generated in the centrifugation process with a two-outlet decanter from oil mills. In the case of olive pomace oil extraction, two cases were defined depending on the type of fuel used for drying the wet pomace before the extraction: natural gas or a fraction of the generated extracted pomace. The anaerobic digestion alternative consisted of the production of biogas from the olive mill solid waste, heat and electricity cogeneration by the combustion of the generated biogas, and composting of the anaerobic digestate. The life cycle assessment showed that anaerobic digestion was the best alternative, with a global environmental impact reduction of 88.1 and 85.9% respect to crude olive pomace oil extraction using natural gas and extracted pomace, respectively, as fuel. Full article
(This article belongs to the Special Issue Production of Biofuels and Numerical Modeling of Chemical Combustion Systems)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>System boundaries.</p> Full article ">Figure 2
<p>Life cycle environmental impacts of olive mill solid waste valorization via anaerobic digestion and olive pomace oil extraction. AD: anaerobic digestion; OPOE-A: crude olive pomace oil extraction with natural gas as fuel for olive pomace drying; OPOE-B: crude olive pomace oil extraction with extracted pomace as fuel for olive pomace drying. The values shown on top of each bar represent the total impact after the system credits have been applied. Some impacts have been scaled to fit. To obtain the original values, multiply by the factor shown on the x-axis for the relevant impacts.</p> Full article ">Figure 3
<p>Percentage contribution to the impacts for anaerobic digestion scheme (AD): (<b>a</b>) positive contribution; (<b>b</b>) credits.</p> Full article ">Figure 3 Cont.
<p>Percentage contribution to the impacts for anaerobic digestion scheme (AD): (<b>a</b>) positive contribution; (<b>b</b>) credits.</p> Full article ">Figure 4
<p>Percentage contribution to the impacts for crude olive pomace oil extraction, burning natural gas (OPOE-A): (<b>a</b>) positive contribution; (<b>b</b>) credits.</p> Full article ">Figure 4 Cont.
<p>Percentage contribution to the impacts for crude olive pomace oil extraction, burning natural gas (OPOE-A): (<b>a</b>) positive contribution; (<b>b</b>) credits.</p> Full article ">Figure 5
<p>Percentage contribution to the impacts for crude olive pomace oil extraction, burning a fraction of the extracted olive pomace (OPOE-B): (<b>a</b>) positive contribution; (<b>b</b>) credits.</p> Full article ">Figure 5 Cont.
<p>Percentage contribution to the impacts for crude olive pomace oil extraction, burning a fraction of the extracted olive pomace (OPOE-B): (<b>a</b>) positive contribution; (<b>b</b>) credits.</p> Full article ">Figure 6
<p>Percentage contribution to the impacts of the crude pomace oil refining (OPOE-A and OPOE-B).</p> Full article ">Figure 7
<p>Influence of the reduction of the biogas production from olive mill solid waste in the anaerobic digestion (AD) on the environmental LCA (life cycle assessment) comparison with OPOE employing natural gas (<b>a</b>) and a fraction of the extracted olive pomace (<b>b</b>) as fuel for drying.</p> Full article ">
13 pages, 3051 KiB  
Article
Improvement of the Mechanical Properties of Thermosetting-Binding-System-Based Composites by Means of Kneading Procedure Modification and Composite Formulation
by Adis Šahinović and Branka Mušič
Processes 2020, 8(5), 625; https://doi.org/10.3390/pr8050625 - 23 May 2020
Cited by 3 | Viewed by 3112
Abstract
By understanding the effects of the physical properties of individual input materials (e.g., binding system) on the physical and thermal properties of a composite material, the latter can be engineered in advance according to the desired properties and application. Often, a need to [...] Read more.
By understanding the effects of the physical properties of individual input materials (e.g., binding system) on the physical and thermal properties of a composite material, the latter can be engineered in advance according to the desired properties and application. Often, a need to replace a specific component in a composite material arises, due to various reasons such as high raw material prices, product price reduction, environmental issues, improvement of properties, and others. In this study, we focused on the substitution of a phenolic novolac resin binding system and the reduction of compounding process temperature in combination with material throughput and screw speed variation of a phenolic-novolac-resin-based composite material, manufactured by kneading process using a co-kneader single screw extruder. Modifications were carried out in the interest of reducing production process cost and positive environmental effect due to reduction of energy consumption in the compounding process. We achieved great success in improvement of mechanical properties with all four substituted phenolic molding compounds (PMCs), while the decrease in thermal stability was the lowest for PMCs prepared at higher screw speeds and material throughput. The results indicated that higher screw speeds produce the best combination of mechanical and thermal properties of PMCs. Full article
(This article belongs to the Section Materials Processes)
►▼ Show Figures

Figure 1

Figure 1
<p>Stages of preparation process of phenolic-novolac-resin-based composite material.</p> Full article ">Figure 2
<p>Differential scanning calorimetry (DSC) thermograms of the four studied PF resins.</p> Full article ">Figure 3
<p>Differential scanning calorimetry (DSC) thermograms of both studied binding systems.</p> Full article ">Figure 4
<p>Flow-curing behavior of granulates from phenolic molding compound 1 (PMC 1) to phenolic molding compound 5 (PMC 5).</p> Full article ">Figure 5
<p>Thermomechanical (TMA) thermograms of standard test specimens from PMC 1 to PMC 5.</p> Full article ">Figure 6
<p>X-ray computed tomography scans of standard test specimens PMC 1 (<b>a</b>), PMC 2 (<b>b</b>), PMC 3 (<b>c</b>), PMC 4 (<b>d</b>), and PMC 5 (<b>e</b>).</p> Full article ">
26 pages, 2358 KiB  
Review
Bacterial Cellulose as a Versatile Platform for Research and Development of Biomedical Materials
by Selestina Gorgieva
Processes 2020, 8(5), 624; https://doi.org/10.3390/pr8050624 - 22 May 2020
Cited by 82 | Viewed by 11653
Abstract
The unique pool of features found in intracellular and extracellular bacterial biopolymers attracts a lot of research, with bacterial cellulose (BC) being one of the most versatile and common. BC is an exopolysaccharide consisting solely of cellulose, and the variation in the production [...] Read more.
The unique pool of features found in intracellular and extracellular bacterial biopolymers attracts a lot of research, with bacterial cellulose (BC) being one of the most versatile and common. BC is an exopolysaccharide consisting solely of cellulose, and the variation in the production process can vary its shape or even its composition when compounding is applied in situ. Together with ex situ modification pathways, including specialised polymers, particles or exclusively functional groups, BC provides a robust platform that yields complex multifunctional compounds that go far beyond ultra-high purity, intrinsic hydrophilicity, mechanical strength and biocompatibility to introduce bioactive, (pH, thermal, electro) responsive, conductive and ‘smart’ properties. This review summarises the research outcomes in BC-medical applications, focusing mainly on data from the past decade (i.e., 2010–2020), with special emphasis on BC nanocomposites as materials and devices applicable in medicine. The high purity and unique structural/mechanical features, in addition to its capacity to closely adhere to irregular skin surfaces, skin tolerance, and demonstrated efficacy in wound healing, all stand as valuable attributes advantageous in topical drug delivery. Numerous studies prove BC compatibility with various human cells, with modifications even improving cell affinity and viability. Even BC represents a physical barrier that can reduce the penetration of bacteria into the tissue, but in its native form does not exhibit antimicrobial properties, therefore carious modifications have been made or specific compounds added to confer antimicrobial or anti-inflammatory properties. Progress in the use of BC-compounds as wound dressings, vascular grafts, and scaffolds for the treatment of cartilage, bone and osteochondral defects, the role as a basement membrane in blood-brain barrier models and many more are discussed to particular extent, emphasising the need for BC compounding to meet specific requirements. Full article
(This article belongs to the Special Issue Preparation of Bacterial Cellulose and Its Biomedical Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Macro-, micro- and molecular structure of bacterial cellulose (BC) membrane. Molecular structure image adapted from [<a href="#B2-processes-08-00624" class="html-bibr">2</a>].</p> Full article ">Figure 2
<p>Trend of annual publications considering BC for 2010–2020 period. Search engine is Web of Science Core Collection and search terms (<b>a</b>), ‘bacterial cellulose’ (<b>b</b>), ‘bacterial cellulose’ AND ‘medicine’, (<b>c</b>) and ‘bacterial cellulose’ AND ‘medicine’ AND ‘review’.</p> Full article ">Figure 3
<p>Schematic illustration of biosynthesis of BC/potato starch (PS) composites and experimental design in fabrication of tissue engineered urethra. Reprinted with permission from Ref. [<a href="#B10-processes-08-00624" class="html-bibr">10</a>] Copyright (2016) American Chemical Society.</p> Full article ">Figure 4
<p>Chemical modifications of BC. Reprinted with permission from [<a href="#B23-processes-08-00624" class="html-bibr">23</a>]. Copyright (2017) Elsevier.</p> Full article ">Figure 5
<p>Processing of BC crystals by acid hydrolysis and cationic surface modification of sulfated BC by physical adsorption using amines and amine-containing polymers. Reprinted with permission from [<a href="#B25-processes-08-00624" class="html-bibr">25</a>]. Copyright (2018) American Chemical Society.</p> Full article ">Figure 6
<p>BC used as basement membrane in BBB model. Reprinted with permission from [<a href="#B99-processes-08-00624" class="html-bibr">99</a>]. Copyright (2019) Elsevier.</p> Full article ">Figure 7
<p>BC included into (<b>a</b>) esterase biosensor and (<b>b</b>) glucose oxidase biosensor. Reprinted with permission from (<b>a</b>) [<a href="#B129-processes-08-00624" class="html-bibr">129</a>] and (<b>b</b>) [<a href="#B130-processes-08-00624" class="html-bibr">130</a>]. Copyright (2016) American Chemical Society and (2018) Elsevier.</p> Full article ">Figure 8
<p>Physical de-hydration method for inclusion of photosensitiser C<sub>60</sub> in BC for photodynamic therapy in cancer treatment. Reprinted with permission from [<a href="#B136-processes-08-00624" class="html-bibr">136</a>]. Copyright (2018) The Royal Society of Chemistry.</p> Full article ">
24 pages, 1789 KiB  
Article
Assessment of the SM12, SM8, and SMD Solvation Models for Predicting Limiting Activity Coefficients at 298.15 K
by Sydnee N. Roese, Justin D. Heintz, Cole B. Uzat, Alexa J. Schmidt, Griffin V. Margulis, Spencer J. Sabatino and Andrew S. Paluch
Processes 2020, 8(5), 623; https://doi.org/10.3390/pr8050623 - 22 May 2020
Cited by 7 | Viewed by 4584
Abstract
The SMx (x = 12, 8, or D) universal solvent models are implicit solvent models which using electronic structure calculations can compute solvation free energies at 298.15 K. While solvation free energy is an important thermophysical property, within the thermodynamic modeling [...] Read more.
The SMx (x = 12, 8, or D) universal solvent models are implicit solvent models which using electronic structure calculations can compute solvation free energies at 298.15 K. While solvation free energy is an important thermophysical property, within the thermodynamic modeling of phase equilibrium, limiting (or infinite dilution) activity coefficients are preferred since they may be used to parameterize excess Gibbs free energy models to model phase equilibrium. Conveniently, the two quantities are related. Therefore the present study was performed to assess the ability to use the SMx universal solvent models to predict limiting activity coefficients. Two methods of calculating the limiting activity coefficient where compared: (1) the solvation free energy and self-solvation free energy were both predicted and (2) the self-solvation free energy was computed using readily available vapor pressure data. Overall the first method is preferred as it results in a cancellation of errors, specifically for the case in which water is a solute. The SM12 model was compared to both the Universal Quasichemical Functional-group Activity Coefficients (UNIFAC) and modified separation of cohesive energy density (MOSCED) models. MOSCED was the highest performer, yet had the smallest available compound inventory. UNIFAC and SM12 exhibited comparable performance. Therefore further exploration and research should be conducted into the viability of using the SMx models for phase equilibrium calculations. Full article
(This article belongs to the Special Issue Thermodynamics: Modeling and Simulation)
►▼ Show Figures

Figure 1

Figure 1
<p>Parity plot of the predicted versus reference values of the negative dimensionless self-solvation free energy, <math display="inline"><semantics> <mrow> <mo>−</mo> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> <mo>/</mo> <mfenced separators="" open="(" close=")"> <mi>R</mi> <mi>T</mi> </mfenced> </mrow> </semantics></math>, using the SM12, SM8, and SMD universal solvent model, as indicated. The blue lines corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math> line, and the dashed blue lines correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> <mo>.</mo> <mn>5</mn> </mrow> </semantics></math> and are drawn as a reference; the value of 1.5 was used based on the <span class="html-italic">RMSE</span> for the SM12 predictions.</p> Full article ">Figure 2
<p>Parity plot of the predicted versus reference values of the negative dimensionless solvation free energy, <math display="inline"><semantics> <mrow> <mo>−</mo> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mi>solv</mi> </msubsup> <mo>/</mo> <mfenced separators="" open="(" close=")"> <mi>R</mi> <mi>T</mi> </mfenced> </mrow> </semantics></math>, using the SM12, SM8, and SMD universal solvent model, as indicated. The blue lines corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math> line, and the dashed blue lines correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics></math> and are drawn as a reference; the value of 1.1 was used based on the <span class="html-italic">RMSE</span> for the SM12 predictions. The reference values are from the Minnesota Solvation Database.</p> Full article ">Figure 3
<p>Parity plot of the predicted versus reference values of <math display="inline"><semantics> <mrow> <mo>−</mo> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mi>solv</mi> </msubsup> <mo>/</mo> <mfenced separators="" open="(" close=")"> <mi>R</mi> <mi>T</mi> </mfenced> </mrow> </semantics></math> using the SM12 universal solvent model for the case where water was the solvent, water was the solute, and where water was neither the solvent or solute, as indicated. The blue lines corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math> line, and the dashed blue lines correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics></math> and are drawn as a reference; the value of 1.1 was used based on the <span class="html-italic">RMSE</span> for the SM12 predictions. The reference values are from the Minnesota Solvation Database.</p> Full article ">Figure 4
<p>Parity plot of the predicted versus reference values of <math display="inline"><semantics> <mrow> <mo form="prefix">ln</mo> <msubsup> <mi>γ</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mo>∞</mo> </msubsup> </mrow> </semantics></math> from the Minnesota Solvation Database version 2012 [<a href="#B47-processes-08-00623" class="html-bibr">47</a>]. Predictions are made using the SM12, SM8, and SMD universal solvent model, as indicated. The blue lines corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math> line, and the dashed blue lines correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics></math> and are drawn as a reference; the value of 1.1 was used based on the <span class="html-italic">RMSE</span> for the SM12 predictions. In the top pane we present results when <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> </mrow> </semantics></math> was predicted using the SM12, SM8, or SMD universal solvent model. In the bottom pane we present results when <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> </mrow> </semantics></math> was computed using reference vapor pressure data.</p> Full article ">Figure 5
<p>Parity plot of the predicted versus reference values of <math display="inline"><semantics> <mrow> <mo form="prefix">ln</mo> <msubsup> <mi>γ</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mo>∞</mo> </msubsup> </mrow> </semantics></math> using the SM12 universal solvent model for the case where water was the solvent, water was the solute, and where water was neither the solvent or solute, as indicated. The blue lines corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math> line, and the dashed blue lines correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics></math> and are drawn as a reference; the value of 1.1 was used based on the <span class="html-italic">RMSE</span> for the SM12 predictions. The reference values are from the Minnesota Solvation Database. In the top pane we present results when <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> </mrow> </semantics></math> was predicted using the SM12 universal solvent model. In the bottom pane we present results when <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> </mrow> </semantics></math> was computed using reference vapor pressure data.</p> Full article ">Figure 6
<p>Parity plot of the predicted versus reference values of <math display="inline"><semantics> <mrow> <mo form="prefix">ln</mo> <msubsup> <mi>γ</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mo>∞</mo> </msubsup> </mrow> </semantics></math> from the DECHEMA. Predictions are made using the SM12, SM8, and SMD universal solvent model, as indicated. The blue lines corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math> line, and the dashed blue lines correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics></math> and are drawn as a reference. In the top pane we present results when <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> </mrow> </semantics></math> was predicted using the SM12, SM8, or SMD universal solvent model. In the bottom pane we present results when <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> </mrow> </semantics></math> was computed using reference vapor pressure data.</p> Full article ">Figure 7
<p>Parity plot of the predicted versus reference values of <math display="inline"><semantics> <mrow> <mo form="prefix">ln</mo> <msubsup> <mi>γ</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mo>∞</mo> </msubsup> </mrow> </semantics></math> using the SM12 universal solvent model for the case where water was the solvent, water was the solute, and where water was neither the solvent or solute, as indicated. The blue lines corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math> line, and the dashed blue lines correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics></math> and are drawn as a reference. The reference values are from DECHEMA. In the top pane we present results when <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> </mrow> </semantics></math> was predicted using the SM12 universal solvent model. In the bottom pane we present results when <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> </mrow> </semantics></math> was computed using reference vapor pressure data.</p> Full article ">Figure 8
<p>Parity plot of the predicted versus reference values of <math display="inline"><semantics> <mrow> <mo form="prefix">ln</mo> <msubsup> <mi>γ</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mo>∞</mo> </msubsup> </mrow> </semantics></math> using the SM12 universal solvent model for the case where water was the solvent and water was the solute, as indicated. The blue lines corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math> line, and the dashed blue lines correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics></math> and are drawn as a reference. The reference values are from “Yaws’ Handbook of Properties for Aqueous Systems” [<a href="#B56-processes-08-00623" class="html-bibr">56</a>]. In the top pane we present results when <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> </mrow> </semantics></math> was predicted using the SM12 universal solvent model. In the bottom pane we present results when <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> </mrow> </semantics></math> was computed using reference vapor pressure data.</p> Full article ">Figure 9
<p>Parity plot of the predicted versus reference values of <math display="inline"><semantics> <mrow> <mo form="prefix">ln</mo> <msubsup> <mi>γ</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mo>∞</mo> </msubsup> </mrow> </semantics></math> at 298.15 K using the SM12 universal solvent model, Universal Quasichemical Functional-group Activity Coefficients (UNIFAC), and modified separation of cohesive energy density (MOSCED) versus reference data from the Minnesota Solvation Database version 2012 (top pane) [<a href="#B47-processes-08-00623" class="html-bibr">47</a>] and DECHEMA (bottom pane) [<a href="#B50-processes-08-00623" class="html-bibr">50</a>,<a href="#B51-processes-08-00623" class="html-bibr">51</a>,<a href="#B52-processes-08-00623" class="html-bibr">52</a>,<a href="#B53-processes-08-00623" class="html-bibr">53</a>,<a href="#B54-processes-08-00623" class="html-bibr">54</a>,<a href="#B55-processes-08-00623" class="html-bibr">55</a>], as indicated. The blue lines corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math> line, and the dashed blue lines correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics></math> and are drawn as a reference. For the SM12 universal solvent model predictions, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> </mrow> </semantics></math> was predicted.</p> Full article ">Figure 10
<p>Parity plot of the predicted versus reference values of <math display="inline"><semantics> <mrow> <mo form="prefix">ln</mo> <msubsup> <mi>γ</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mo>∞</mo> </msubsup> </mrow> </semantics></math> at 298.15 K using the SM12 universal solvent model, UNIFAC, and MOSCED versus reference data from “Yaws’ Handbook of Properties for Aqueous Systems” [<a href="#B56-processes-08-00623" class="html-bibr">56</a>]. In the top pane results as shown for the case of water as the solute (water in organics), and in the bottom pane results are shown for the case of water as the solvent (organics in water). The blue lines corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math> line, and the dashed blue lines correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics></math> and are drawn as a reference. For the SM12 universal solvent model predictions, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>self</mi> </msubsup> </mrow> </semantics></math> was predicted.</p> Full article ">
26 pages, 3324 KiB  
Article
Assessment and Prediction of Complex Industrial Steam Network Operation by Combined Thermo-Hydrodynamic Modeling
by Kristián Hanus, Miroslav Variny and Peter Illés
Processes 2020, 8(5), 622; https://doi.org/10.3390/pr8050622 - 22 May 2020
Cited by 10 | Viewed by 4586
Abstract
Steam network operation stability and reliability is vital for any industrial branch. A combined steam network model comprising a balance and a coupled thermo-hydrodynamic model, including seasonal variations impact and system specificities, is presented. A balance model can readily be used by a [...] Read more.
Steam network operation stability and reliability is vital for any industrial branch. A combined steam network model comprising a balance and a coupled thermo-hydrodynamic model, including seasonal variations impact and system specificities, is presented. A balance model can readily be used by a refinery’s operators. The thermo-hydrodynamic model identifies system bottlenecks and cold spots and evaluates proposed operation and investment measures including heat loss reduction. A three-pressure levels refinery steam network served for model testing and validation. Balance model results reveal significant misbalance in steam production and consumption, reaching 30.5% in the low-pressure steam system, and heat balance differences in the range of 9.2% to 29.5% on individual pressure levels, attributable both to flow measurement accuracy issues and to heat losses. The thermo-hydrodynamic model results differ from the measured steam parameters by less than 5% (temperature) and by less than 4% (pressure), respectively, with the estimated operational insulation heat conductivity exceeding 0.08 W/m/K. Its comparison with that of 0.03 W/m/K for dry insulation material yields the need for pipelines re-insulation and a partial revamp of the steam network. The model is sufficiently general for any type of industry, pursuing the goal of cleaner and energy-efficient steam transport and consumption. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
►▼ Show Figures

Figure 1

Figure 1
<p>A part of simplified simulation scheme of high-pressure steam (HPS) network for the balance model (S = steam supplier, C = steam consumer).</p> Full article ">Figure 2
<p>Calculation algorithm of the thermo-hydrodynamic model.</p> Full article ">Figure 3
<p>A part of simplified simulation scheme of HPS network after implementation of proposed changes (S = steam supplier, C = steam consumer; existing connection of main pipelines = dot-dashed line, new connection of pipelines = dashed line).</p> Full article ">Figure 4
<p>The amount of HPS supplied from the combined heat and power (CHP) unit during year 2017.</p> Full article ">Figure 5
<p>A time course of LPS steam supply from the CHP unit and its total supply throughout the year 2017.</p> Full article ">Figure 6
<p>A difference between the amount of steam supplied and consumed within the LPS network during the year 2017.</p> Full article ">Figure 7
<p>A difference between the amount of steam supplied and consumed within the MPS network during the year 2017.</p> Full article ">Figure 8
<p>A difference between the amount of steam supplied and consumed within the HPS network during the year 2017.</p> Full article ">Figure 9
<p>Time course of HPS flow velocity at S2 unit and HPS pressure at battery limit of units S2 and S1 during the time period from 1.7.2017 to 17.7.2017.</p> Full article ">Figure 10
<p>Algorithm used to formulate recommendations for steam system inspections and metering devices check.</p> Full article ">
21 pages, 11173 KiB  
Article
Performance Evaluation of Elimination of Stagnation of Solar Thermal Systems
by Miroslav Rimar, Marcel Fedak, Jakub Vahovsky, Andrii Kulikov, Peter Oravec, Olha Kulikova, Michal Smajda and Miroslav Kana
Processes 2020, 8(5), 621; https://doi.org/10.3390/pr8050621 - 22 May 2020
Cited by 8 | Viewed by 3748
Abstract
The study deals with the possibility of elimination of stagnation of thermal systems. The state of stagnation of thermal systems leads to overheating and evaporation of the heat transfer medium, which increases pressure and can lead to damage to the solar thermal system. [...] Read more.
The study deals with the possibility of elimination of stagnation of thermal systems. The state of stagnation of thermal systems leads to overheating and evaporation of the heat transfer medium, which increases pressure and can lead to damage to the solar thermal system. Stagnation can occur due to a fault and stopping of the circulation pump, which causes the circulation of the heat transfer medium to stop. Another possibility is to achieve thermal saturation in the system, which can be affected by low heat consumption from the system. Elimination of stagnation is possible by various construction designs of collectors or by using other technical means. This study describes an experiment verifying the usability of a thermal collector’s tilting system to eliminate thermal stagnation of the system. The system is fully automatic, and when recording the limit values, ensures that the panel is rotated out of the ideal position, thus reducing the amount of received energy. In this way, the temperature of the medium in the system can be reduced by up to 10% in one hour. In the case of thermal saturation of the system, the solution is the automatic circulation of heat-transfer fluid through the system during the night and the release of thermal energy to the outside. These results suggest that the methods used actively eliminate stagnation of thermal systems. Full article
(This article belongs to the Special Issue Thermal Safety of Chemical Processes)
►▼ Show Figures

Figure 1

Figure 1
<p>Solar panels installed on the tracker construction allowing the collectors to be tilted.</p> Full article ">Figure 2
<p>Control system based on Siemens Desigo Insight.</p> Full article ">Figure 3
<p>(<b>a</b>) Diagram of solar radiation incident on a titled plane. S, N—cardinal points, β—tilt angle, γ—plane azimuth angle, γ<sub>s</sub>—solar azimuth angle, θ—incidence angle, θ<sub>z</sub>—solar zenith angle; (<b>b</b>) Scheme of sun position in time. SolEtHor—solar hour, SolAzimuth—solar azimuth, N,W,E,S—cardinal points.</p> Full article ">Figure 4
<p>Temperature—pressure diagram water and glycol.</p> Full article ">Figure 5
<p>Diagram of a part of the multivalent laboratory system used in experiments. Blue line—inlet pipes (cold water), Red line—outlet pipes (heated water).</p> Full article ">Figure 6
<p>Position control system.</p> Full article ">Figure 7
<p>Block diagram of a control system of a construction rotation.</p> Full article ">Figure 8
<p>Diagram of the pump system control.</p> Full article ">Figure 9
<p>Solar radiation intensity and usable solar radiation.</p> Full article ">Figure 10
<p>Incident solar radiation on the panel—noon.</p> Full article ">Figure 11
<p>Incident solar radiation on the panel—east and west.</p> Full article ">Figure 12
<p>Tilting of the panel to the east at the noon.</p> Full article ">Figure 13
<p>Illumination intensity and amount of incident solar radiation during a sunny day.</p> Full article ">Figure 14
<p>Flow of system inlet and outlet temperatures, outside and storage temperatures during a sunny day.</p> Full article ">Figure 15
<p>Circulation pump failure simulation and its effect on the system outlet temperature.</p> Full article ">Figure 16
<p>Simulation of circulation pump failure with and without tilting the panel to the end position.</p> Full article ">Figure 17
<p>Influence of panel tilting on the system temperatures and liquid cooling process in the tank.</p> Full article ">Figure 18
<p>Outlet temperature curve depending on panel tilting.</p> Full article ">
15 pages, 1232 KiB  
Article
TLC-Densitometric Determination of Five Coxibs in Pharmaceutical Preparations
by Paweł Gumułka, Monika Dąbrowska and Małgorzata Starek
Processes 2020, 8(5), 620; https://doi.org/10.3390/pr8050620 - 22 May 2020
Cited by 8 | Viewed by 4949
Abstract
A class of drugs called coxibs (COX-2 inhibitors) were created to help relieve pain and inflammation of osteoarthritis and rheumatoid arthritis with the lowest amount of side effects possible. The presented paper describes a new developed, optimized and validated thin layer chromatographic (TLC)-densitometric [...] Read more.
A class of drugs called coxibs (COX-2 inhibitors) were created to help relieve pain and inflammation of osteoarthritis and rheumatoid arthritis with the lowest amount of side effects possible. The presented paper describes a new developed, optimized and validated thin layer chromatographic (TLC)-densitometric procedure for the simultaneous assay of five coxibs: celecoxib, etoricoxib, firecoxib, rofecoxib and cimicoxib. Chromatographic separation was conducted on HPTLC F254 silica gel chromatographic plates as a stationary phase using chloroform–acetone–toluene (12:5:2, v/v/v) as a mobile phase. Densitometric detection was carried out at two wavelengths of 254 and 290 nm. The method was tested according to ICH guidelines for linearity, recovery and specificity. The presented method was linear in a wide range of concentrations for all analyzed compounds, with correlation coefficients greater than 0.99. The method is specific, precise (%RSD < 1) and accurate (more than 95%, %RSD < 2). Low-cost, simple and rapid, it can be used in laboratories for drug monitoring and quality control. Full article
(This article belongs to the Special Issue Applications of Chromatographic Separation Techniques in Food and Chemistry)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Chemical structure and CAS numbers of analyzed compounds.</p> Full article ">Figure 2
<p>An example of densitograms obtained for a mixture of standard substances at 254 nm (<b>A</b>) and 290 nm (<b>B</b>) (1—cimicoxib; 2—robenacoxib; 3—etoricoxib; 4—celecoxib; 5—firocoxib).</p> Full article ">Figure 3
<p>An absorption spectra registered for analyzed compounds obtained directly from the chromatogram (C—celecoxib; E—etoricoxib; CI—cimicoxib; F—firocoxib; R—robenacoxib).</p> Full article ">Figure 4
<p>An example of chromatograms obtained for Onsior preparation containing robenacoxib. (1—cimicoxib).</p> Full article ">Figure 5
<p>An example of the plot of residuals for firocoxib at 254 nm (<b>A</b>) and celecoxib at 290 nm (<b>B</b>).</p> Full article ">
9 pages, 1438 KiB  
Article
High Enzymatic Recovery and Purification of Xylooligosaccharides from Empty Fruit Bunch via Nanofiltration
by Hans Wijaya, Kengo Sasaki, Prihardi Kahar, Nanik Rahmani, Euis Hermiati, Yopi Yopi, Chiaki Ogino, Bambang Prasetya and Akihiko Kondo
Processes 2020, 8(5), 619; https://doi.org/10.3390/pr8050619 - 21 May 2020
Cited by 19 | Viewed by 4633
Abstract
Xylooligosaccharides (XOS) are attracting an ever-increasing amount of interest for use as food prebiotics. In this study, we used efficient membrane separation technology to convert lignocellulosic materials into a renewable source of XOS. This study revealed a dual function of nanofiltration membranes by [...] Read more.
Xylooligosaccharides (XOS) are attracting an ever-increasing amount of interest for use as food prebiotics. In this study, we used efficient membrane separation technology to convert lignocellulosic materials into a renewable source of XOS. This study revealed a dual function of nanofiltration membranes by first achieving a high yield of xylobiose (a main component of XOS) from alkali-pretreated empty fruit bunch (EFB) hydrolysate, and then by achieving a high degree of separation for xylose as a monosaccharide product. Alkali pretreatment could increase the xylan content retention of raw EFB from 23.4% to 26.9%, which eventually contributed to higher yields of both xylobiose and xylose. Nanofiltration increased the total amount of XYN10Ks_480 endoxylanase produced from recombinant Streptomyces lividans 1326 without altering its specific activity. Concentrated XYN10Ks_480 endoxylanase was applied to the recovery of both xylobiose and xylose from alkali-pretreated EFB hydrolysate. Xylobiose and xylose yields reached 41.1% and 17.3%, respectively, and when unconcentrated XYN10Ks_480 endoxylanase was applied, those yields reached 35.1% and 8.3%, respectively. The last step in nanofiltration was to separate xylobiose over xylose, and 41.3 g.L−1 xylobiose (90.1% purity over xylose) was achieved. This nanofiltration method should shorten the processes used to obtain XOS as a high-value end product from lignocellulosic biomass. Full article
(This article belongs to the Special Issue Biomass Processing and Conversion Systems)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Thin-layer chromatography (TLC) results showing the primary hydrolysis products of alkali-pretreated EFB using XYN10Ks_480 endoxylanase produced from recombinant <span class="html-italic">S. lividans</span> 1326 in 72 h. Lane 1: Using the unconcentrated XYN10Ks_480 endoxylanase; Lane 2: Using XYN10Ks_480 endoxylanase concentrated by NF xylooligosaccharides (XOS) standards (STD); X1, xylose; X2, xylobiose; X3, xylotriose; X4, xylotetraose; X5, xylopentaose.</p> Full article ">Figure 2
<p>The yield from primary hydrolysis using alkali-pretreated EFB from either unconcentrated or concentrated XYN10Ks_480 endoxylanase produced by recombinant <span class="html-italic">S. lividans</span> 1326 in 72 h. X1, xylose; X2, xylobiose; X5, xylopentaose.</p> Full article ">Figure 3
<p>Flow chart showing the distribution of xylose and one of the xylooligosaccharides (XOS), xylobiose. Sugar recovery is shown in the parentheses. Membrane separations using ultrafiltration (UF) and NF were applied to the enzymatic hydrolysate obtained from an alkali-pretreated EFB. Data are presented as the average of the results of triplicate samples.</p> Full article ">
24 pages, 800 KiB  
Article
An Adjective Selection Personality Assessment Method Using Gradient Boosting Machine Learning
by Bruno Fernandes, Alfonso González-Briones, Paulo Novais, Miguel Calafate, Cesar Analide and José Neves
Processes 2020, 8(5), 618; https://doi.org/10.3390/pr8050618 - 21 May 2020
Cited by 8 | Viewed by 6746
Abstract
Goldberg’s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performed by presenting a set of [...] Read more.
Goldberg’s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performed by presenting a set of markers, or adjectives, to the subject, requesting him to quantify each marker using a 9-point rating scale. Consequently, the goal of this study is to conduct experiments and propose a shorter alternative where the subject is only required to identify which adjectives describe him the most. Hence, a web platform was developed for data collection, requesting subjects to rate each adjective and select those describing him the most. Based on a Gradient Boosting approach, two distinct Machine Learning architectures were conceived, tuned and evaluated. The first makes use of regressors to provide an exact score of the Big Five while the second uses classifiers to provide a binned output. As input, both receive the one-hot encoded selection of adjectives. Both architectures performed well. The first is able to quantify the Big Five with an approximate error of 5 units of measure, while the second shows a micro-averaged f1-score of 83%. Since all adjectives are used to compute all traits, models are able to harness inter-trait relationships, being possible to further reduce the set of adjectives by removing those that have smaller importance. Full article
(This article belongs to the Special Issue Bioinformatics Applications Based On Machine Learning)
►▼ Show Figures

Figure 1

Figure 1
<p>Platform for data collection allowing the subject to perform Saucier’s Mini-Marker test and, at the same time, select a set of adjectives that describe him the most.</p> Full article ">Figure 2
<p>Number of times each adjective was selected.</p> Full article ">Figure 3
<p>Mean rating values to set an adjective as selected.</p> Full article ">Figure 4
<p>Distribution of observations per bin and personality trait.</p> Full article ">Figure 5
<p>Architecture I—Big Five regressors.</p> Full article ">Figure 6
<p>Architecture II—Big Five bin classifiers.</p> Full article ">Figure 7
<p>Graphical view of Architecture’s I RMSE and MAE for both datasets.</p> Full article ">Figure 8
<p>Graphical view of Architecture’s II micro and macro-averaged f1-score and precision for both datasets.</p> Full article ">Figure 9
<p>Feature importance heat-map of Architecture I.</p> Full article ">Figure 10
<p>Feature importance heat-map of Architecture II.</p> Full article ">
16 pages, 3053 KiB  
Article
A Data-Driven-Based Industrial Refrigeration Optimization Method Considering Demand Forecasting
by Josep Cirera, Jesus A. Carino, Daniel Zurita and Juan A. Ortega
Processes 2020, 8(5), 617; https://doi.org/10.3390/pr8050617 - 21 May 2020
Cited by 4 | Viewed by 3820
Abstract
One of the main concerns of industry is energy efficiency, in which the paradigm of Industry 4.0 opens new possibilities by facing optimization approaches using data-driven methodologies. In this regard, increasing the efficiency of industrial refrigeration systems is an important challenge, since this [...] Read more.
One of the main concerns of industry is energy efficiency, in which the paradigm of Industry 4.0 opens new possibilities by facing optimization approaches using data-driven methodologies. In this regard, increasing the efficiency of industrial refrigeration systems is an important challenge, since this type of process consume a huge amount of electricity that can be reduced with an optimal compressor configuration. In this paper, a novel data-driven methodology is presented, which employs self-organizing maps (SOM) and multi-layer perceptron (MLP) to deal with the (PLR) issue of refrigeration systems. The proposed methodology takes into account the variables that influence the system performance to develop a discrete model of the operating conditions. The aforementioned model is used to find the best PLR of the compressors for each operating condition of the system. Furthermore, to overcome the limitations of the historical performance, various scenarios are artificially created to find near-optimal PLR setpoints in each operation condition. Finally, the proposed method employs a forecasting strategy to manage the compressor switching situations. Thus, undesirable starts and stops of the machine are avoided, preserving its remaining useful life and being more efficient. An experimental validation in a real industrial system is performed in order to validate the suitability and the performance of the methodology. The proposed methodology improves refrigeration system efficiency up to 8%, depending on the operating conditions. The results obtained validates the feasibility of applying data-driven techniques for the optimal control of refrigeration system compressors to increase its efficiency. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
►▼ Show Figures

Figure 1

Figure 1
<p>Refrigeration system scheme.</p> Full article ">Figure 2
<p>Theoretical cooling capacity–slide valve curve of a screw compressor.</p> Full article ">Figure 3
<p>The theoretical three stages curve of the partial load ratio (PLR) problem of two screw compressors C1 and C2 working in parallel to supply the cooling demand of a refrigeration system.</p> Full article ">Figure 4
<p>Proposed methodology diagram. Note that the diagram illustrates the example case in which two compressors, C1 and C2, are used to supply the cooling demand. * BH stands for best historical.</p> Full article ">Figure 5
<p>Process operation modelling. * BH stands for best historical performance.</p> Full article ">Figure 6
<p>Proliferation steps in order to obtain a near-optimal PLR setpoint for each compressor in each specific operation conditions discretized by each best-matching unit (BMU). * BH stands for best historical performance.</p> Full article ">Figure 7
<p>Switching management methodology overview.</p> Full article ">Figure 8
<p>Comparison of the actual control and the proposed methodology in the scenario where two compressors are needed. (<b>a</b>) Actual PLRs control strategy. (<b>b</b>) Proposed methodology PLRs. (<b>c</b>) Accumulative electrical power consumed by the actual control and the proposed method. (<b>d</b>) Accumulative savings in kWh of the proposed method.</p> Full article ">Figure 9
<p>Comparison of the actual control and the proposed methodology in the switching scenario. (<b>a</b>) Actual and proposed PLRs. (<b>b</b>) Accumulative electrical power consumed by the actual control and the proposed method. (<b>c</b>) Accumulative savings in kWh of the proposed method.</p> Full article ">Figure 10
<p>Savings achieved by the proposed methodology during the eight testing days. (<b>a</b>) Boxplot of the instantaneous saving per minute of each testing day. (<b>b</b>) Accumulative saving of the proposed method during the evaluation period of each day.</p> Full article ">
12 pages, 4152 KiB  
Article
Robust Design of PC/ABS Filled with Nano Carbon Black for Electromagnetic Shielding Effectiveness and Surface Resistivity
by Wipoo Sriseubsai, Arsarin Tippayakraisorn and Jun Wei Lim
Processes 2020, 8(5), 616; https://doi.org/10.3390/pr8050616 - 21 May 2020
Cited by 12 | Viewed by 3980 | Correction
Abstract
This study focuses on the electromagnetic interference shielding effectiveness (EMI SE), dissipation of electrostatic discharge (ESD), and surface resistivity of polymer blends between polycarbonate (PC) and acrylonitrile–butadiene–styrene (ABS) filled with carbon black powder (CBp) and carbon black masterbatch (CBm). The mixtures of PC/ABS/CB [...] Read more.
This study focuses on the electromagnetic interference shielding effectiveness (EMI SE), dissipation of electrostatic discharge (ESD), and surface resistivity of polymer blends between polycarbonate (PC) and acrylonitrile–butadiene–styrene (ABS) filled with carbon black powder (CBp) and carbon black masterbatch (CBm). The mixtures of PC/ABS/CB composites were prepared by the injection molding for the 4-mm thickness of the specimen. The D-optimal mixture design was applied in this experiment. The EMI SE was measured at the frequency of 800 and 900 MHz with a network analyzer, MIL-STD-285. The result showed that the EMI SE was increased when the amount of filler increased. The surface resistivity of the composites was determined according to the ASTM D257. It was found that the surface resistivity of the plastic with no additives was 1012 Ω/ square. When the amount of fillers was added, the surface resistivity of plastic composites decreased to the range of 106–1011 Ω/square, which was suitable for the application without the electrostatic discharge. The optimization of multi-response showed using high amounts of PC and CB was the best mixture of this research. Full article
(This article belongs to the Special Issue Green Technologies: Bridging Conventional Practices and Industry 4.0)
►▼ Show Figures

Figure 1

Figure 1
<p>The mechanism of electromagnetic interference shielding effectiveness (EMI SE).</p> Full article ">Figure 2
<p>Mixture design.</p> Full article ">Figure 3
<p>Source and receiver of the network analyzer.</p> Full article ">Figure 4
<p>Surface resistivity measurement following the ASTM D257.</p> Full article ">Figure 5
<p>Shielding effectiveness (SE) at 800 MHz with the carbon black.</p> Full article ">Figure 6
<p>SE at 900 MHz with the carbon black.</p> Full article ">Figure 7
<p>SEM image of the 16 wt % carbon black powder (CBp) in the PC/ABS.</p> Full article ">Figure 8
<p>SEM image of the 8 wt % carbon black masterbatch (CBm) in the PC/ABS.</p> Full article ">Figure 9
<p>The relationship between dielectric constant and percentage of carbon black.</p> Full article ">Figure 10
<p>The relationship between surface resistivity and percentage of carbon black.</p> Full article ">Figure 11
<p>Overlay mapping @ 800 MHz with carbon black masterbatch.</p> Full article ">Figure 12
<p>Overlay mapping @ 900 MHz with carbon black masterbatch.</p> Full article ">Figure 13
<p>Overlay mapping @ 800 MHz with carbon black particles.</p> Full article ">Figure 14
<p>Overlay mapping @ 900 MHz with carbon black particles.</p> Full article ">
18 pages, 5647 KiB  
Review
A Comparison of Bioactive Glass Scaffolds Fabricated ‎by Robocasting from Powders Made by Sol–Gel and Melt-Quenching Methods
by Basam A. E. Ben-Arfa and Robert C. Pullar
Processes 2020, 8(5), 615; https://doi.org/10.3390/pr8050615 - 21 May 2020
Cited by 29 | Viewed by 7210
Abstract
Bioactive glass scaffolds are used in bone and tissue biomedical implants, and there is great interest in their fabrication by additive manufacturing/3D printing techniques, such as robocasting. Scaffolds need to be macroporous with voids ≥100 m to allow cell growth and vascularization, biocompatible [...] Read more.
Bioactive glass scaffolds are used in bone and tissue biomedical implants, and there is great interest in their fabrication by additive manufacturing/3D printing techniques, such as robocasting. Scaffolds need to be macroporous with voids ≥100 m to allow cell growth and vascularization, biocompatible and bioactive, with mechanical properties matching the host tissue (cancellous bone for bone implants), and able to dissolve/resorb over time. Most bioactive glasses are based on silica to form the glass network, with calcium and phosphorous content for new bone growth, and a glass modifier such as sodium, the best known being 45S5 Bioglass®. 45S5 scaffolds were first robocast in 2013 from melt-quenched glass powder. Sol–gel-synthesized bioactive glasses have potential advantages over melt-produced glasses (e.g., greater porosity and bioactivity), but until recently were never robocast as scaffolds, due to inherent problems, until 2019 when high-silica-content sol–gel bioactive glasses (HSSGG) were robocast for the first time. In this review, we look at the sintering, porosity, bioactivity, biocompatibility, and mechanical properties of robocast sol–gel bioactive glass scaffolds and compare them to the reported results for robocast melt-quench-synthesized 45S5 Bioglass® scaffolds. The discussion includes formulation of the printing paste/ink and the effects of variations in scaffold morphology and inorganic additives/dopants. Full article
(This article belongs to the Special Issue Synthesis and Characterization of Biomedical Materials)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic illustration and in-situ images of the robocasting process within an oil bath, and an example of a sintered scaffold for human mandibular defect reconstruction. [<a href="https://www.euroceram.org/en/technologies/material-extrusion/robocasting-direct-ink-writing.html" target="_blank">https://www.euroceram.org/en/technologies/material-extrusion/robocasting-direct-ink-writing.html</a>, accessed on January 2020].</p> Full article ">Figure 2
<p>The typical “wood pile” or “log-cabin” scaffold structure.</p> Full article ">Figure 3
<p>A depiction of the rapid sol–gel bioactive glass synthesis route developed by Ben-Arfa et al. [<a href="#B25-processes-08-00615" class="html-bibr">25</a>].</p> Full article ">Figure 4
<p>Particle size distributions of the bioactive sol–gel glass powders heat-treated at different temperatures followed by wet-ball milling in ethanol under given values of balls-to-powder ratio (BPR): (<b>a</b>) 5; (<b>b</b>) 10; (<b>c</b>) 15; (<b>d</b>) 20. Reproduced with permission from [<a href="#B55-processes-08-00615" class="html-bibr">55</a>], Copyright Elsevier Ltd and Techna Group S.r.l., 2018.</p> Full article ">Figure 5
<p>Effects of the processing parameters on the nitrogen sorption isotherms for the bioactive sol–gel glass powders: (<b>a</b>,<b>b</b>) Comparison of two BPRs (5 and 10) used in wet-ball milling in ethanol for samples heat-treated at different temperatures (550, 675, 800, 925 °C); (<b>c</b>,<b>d</b>) comparison of two heat treatment temperatures for powders wet-ball milling in ethanol under different BPR values (5, 10, 15, 20). Reproduced with permission from [<a href="#B55-processes-08-00615" class="html-bibr">55</a>], Copyright Elsevier Ltd and Techna Group S.r.l., 2018.</p> Full article ">Figure 6
<p>Effects of the processing parameters on the rheological properties of the bioactive glass suspensions. The viscosity behavior of 25% SL (<b>a</b>) and 40% SL (<b>b</b>) and the viscoelastic properties of the paste at 25% SL (<b>c</b>) and 40% SL (<b>d</b>). Reproduced with permission from [<a href="#B31-processes-08-00615" class="html-bibr">31</a>], Copyright the American Ceramic Society, 2018.</p> Full article ">Figure 7
<p>(<b>a</b>) Robocast 45S5 scaffold sintered at 1000 °C/1 h. (<b>b</b>) Shrinkage of the 45S5 scaffold during sintering. (<b>c</b>) Change in porosity of the scaffold with sintering temperature. (<b>d</b>) Variation in compressive strength with sintering temperature, and comparison to other 45S5 scaffolds made by alternative additive manufacturing methods. Reproduced with permission from [<a href="#B48-processes-08-00615" class="html-bibr">48</a>], Copyright Elsevier Ltd. 2014.</p> Full article ">Figure 8
<p>Robocast scaffolds of high-silica-content sol–gel bioactive glasses (HSSGG) sol–gel bioactive glass. (<b>a</b>) Optical and SEM images of scaffolds with 300 μm as-printed void size, after sintering at 800 °C/2 h. (<b>b</b>) Compressive strength of the scaffolds with different void sizes (300, 400, and 500 μm as printed) sintered at 800 °C/2 h. Structural and morphological features of the scaffolds with 300 μm printed void size after immersion in SBF for 72 h (<b>c</b>) and 4 weeks (<b>d</b>). Reproduced with permission from [<a href="#B24-processes-08-00615" class="html-bibr">24</a>], Copyright Elsevier Ltd. 2018.</p> Full article ">Figure 9
<p>SEM images of robocast HSSGG sol–gel bioactive glass scaffolds, with 300 μm as-printed void size and after sintering at 800 °C/2 h, with addition of (<b>a</b>,<b>c</b>) 5 wt% Cu<sup>2+</sup> and (<b>b</b>,<b>d</b>) 5 wt% La<sup>3+</sup>. (<b>e</b>) Compressive strength of the scaffolds with different void sizes (300, 400, and 500 μm as printed) sintered at 800 °C/2 h. Reproduced with permission from [<a href="#B23-processes-08-00615" class="html-bibr">23</a>], Copyright Acta Materialia Inc. 2019.</p> Full article ">
14 pages, 6226 KiB  
Article
The Effect of Variations of Flow from Tributary Channel on the Flow Behavior in a T-Shape Confluence
by Aliasghar Azma and Yongxiang Zhang
Processes 2020, 8(5), 614; https://doi.org/10.3390/pr8050614 - 21 May 2020
Cited by 5 | Viewed by 3537
Abstract
Channel confluences are of the common structures in fluid transport channels. In this study, a series of numerical simulations were performed, utilizing a 3D code to investigate the reaction of the flow parameters and vortical structure to the variations in flow discharge and [...] Read more.
Channel confluences are of the common structures in fluid transport channels. In this study, a series of numerical simulations were performed, utilizing a 3D code to investigate the reaction of the flow parameters and vortical structure to the variations in flow discharge and its Froude number from both main channel and tributary branch in a T-shape junction. The code was calibrated with the experimental data. Parameters, including the velocity, the turbulence energy, stream surface profile, head losses, and the transverse flow motions, were considered in different situations. It was concluded that increasing the ratio of discharge of flow from side-channel to the main channel (Q*) increased the area and power of the recirculation zone, as well as the width of separation plate downstream of the confluence, while it reduced the area of the stagnation zone (or the wake vortex) within the side-channel. It was also indicated that increasing the discharge ratio from side-channel resulted in an increase in the upstream water level in the main channels, which was dependent on the upstream discharge. Full article
(This article belongs to the Special Issue Fluid Flow and Heat Transfer of Nanofluids)
►▼ Show Figures

Figure 1

Figure 1
<p>Plan and front view of mesh domain along with boundary conditions.</p> Full article ">Figure 2
<p>A comparison of the accuracy of velocity calculations from different turbulence models in estimating U-component of velocity at x = 8.82 (x* = 2 Wc) and (<b>a</b>) y = 0.25 Wc, and (<b>b</b>) y = 0.5 Wc.</p> Full article ">Figure 3
<p>The validation of transverse velocity distribution at x = 8.82 (x* = 2 Wc).</p> Full article ">Figure 4
<p>The distribution of the stream-wise component of velocity (U) for (<b>a</b>–<b>d</b>) case 1, and (<b>e</b>–<b>h</b>) case 4, and (<b>i</b>–<b>l</b>) case 7.</p> Full article ">Figure 5
<p>The distribution of the normal component of velocity (V) for (<b>a</b>–<b>d</b>) case 1, and (<b>e</b>–<b>h</b>) case 4, and (<b>i</b>–<b>l</b>) case 7.</p> Full article ">Figure 6
<p>The comparison of velocity components at transverse planes (<b>a</b>) U at x = 7.91, (<b>b</b>) V at x = 7.91, (<b>c</b>) U at x = 8.35m, and (<b>d</b>) V at x = 8.35m.</p> Full article ">Figure 7
<p>Longitudinal flow surface profiles at (<b>a</b>) Y = 0.905m and (<b>b</b>) Y = 0.455m (channel centerline).</p> Full article ">Figure 8
<p>Three-dimensional streamlines at the confluence (<b>a</b>) downstream view of recirculation, (<b>b</b>) upstream view, and (<b>c</b>) tributary flow.</p> Full article ">Figure 9
<p>The effect of Fr* on the flow pattern at different elevations for (<b>a</b>–<b>d</b>) case 1 and (<b>e</b>–<b>h</b>) case 4, and (<b>i</b>–<b>l</b>) case 7.</p> Full article ">Figure 10
<p>Transverse profile of recirculation zone at x = 8.35 (x = 0.5 Wc downstream of confluence).</p> Full article ">Figure 11
<p>Transverse profile of recirculation zone at x = 8.35 (x = 0.5 Wc downstream of confluence).</p> Full article ">Figure 12
<p>Transverse distribution of turbulent kinetic energy (TKE) at (<b>a</b>) x = Wc and (<b>b</b>) x = 1.5 Wc.</p> Full article ">
21 pages, 958 KiB  
Review
MOF-Based Adsorbents for Atmospheric Emission Control: A Review
by Nicola Gargiulo, Antonio Peluso and Domenico Caputo
Processes 2020, 8(5), 613; https://doi.org/10.3390/pr8050613 - 21 May 2020
Cited by 26 | Viewed by 6436
Abstract
This review focuses on the use of metal–organic frameworks (MOFs) for adsorbing gas species that are known to weaken the thermal self-regulation capacities of Earth’s atmosphere. A large section is dedicated to the adsorption of carbon dioxide, while another section is dedicated to [...] Read more.
This review focuses on the use of metal–organic frameworks (MOFs) for adsorbing gas species that are known to weaken the thermal self-regulation capacities of Earth’s atmosphere. A large section is dedicated to the adsorption of carbon dioxide, while another section is dedicated to the adsorption of other different gas typologies, whose emissions, for various reasons, represent a “wound” for Earth’s atmosphere. High emphasis is given to MOFs that have moved enough ahead in their development process to be currently considered as potentially usable in “real-world” (i.e., out-of-lab) adsorption processes. As a result, there is strong evidence of a wide gap between laboratory results and the industrial implementation of MOF-based adsorbents. Indeed, when a MOF that performs well in a specific process is commercially available in large quantities, economic observations still make designers tend toward more traditional adsorbents. Moreover, there are cases in which a specific MOF remarkably outperforms the currently employed adsorbents, but it is not industrially produced, thus strongly limiting its possibilities in large-scale use. To overcome such limitations, it is hoped that the chemical industry will be able to provide more and more mass-produced MOFs at increasingly competitive costs in the future. Full article
(This article belongs to the Section Materials Processes)
►▼ Show Figures

Figure 1

Figure 1
<p>Crystal structures of some mass-produced metal–organic frameworks (MOFs): (<b>a</b>) MOF-177; (<b>b</b>) Cu-BTC; (<b>c</b>) Mg-MOF-74; (<b>d</b>) ZIF-8; (<b>e</b>) PCN-250 (Fe<sub>3</sub>); (<b>f</b>) UiO-66.</p> Full article ">
15 pages, 1292 KiB  
Article
Evaluation of Toxicity on Ctenopharyngodon idella Due to Tannery Effluent Remediated by Constructed Wetland Technology
by Sobia Ashraf, Muhammad Naveed, Muhammad Afzal, Sana Ashraf, Sajid Rashid Ahmad, Khadeeja Rehman, Zahir Ahmad Zahir and Avelino Núñez-Delgado
Processes 2020, 8(5), 612; https://doi.org/10.3390/pr8050612 - 20 May 2020
Cited by 16 | Viewed by 4408
Abstract
Aquatic pollution caused by industrial effluents is an environmental issue, imposing deleterious impacts on the overall environment, specifically, on humans, by disrupting the balance of the ecosystem. Among all the industries, tanneries are considered some of the most polluting due to heavy use [...] Read more.
Aquatic pollution caused by industrial effluents is an environmental issue, imposing deleterious impacts on the overall environment, specifically, on humans, by disrupting the balance of the ecosystem. Among all the industries, tanneries are considered some of the most polluting due to heavy use of toxic organic and inorganic compounds during leather processing, most of which find their way into rivers, lakes, and streams, thus exerting adverse effects on aquatic life, particularly on fish. Considering the huge concentrations of pollutants present in tannery effluents, toxicity evaluation is of prime importance. Therefore, bioassays are usually employed to assess the acute toxicity of industrial effluents and efficiency of effluent clean-up technologies as they provide a thorough response of test species to the substances present in the tested media. In the present study, the toxic effects of tannery effluent on common grass carp (Ctenopharyngodon idella) were studied for 96 h in laboratory conditions. The effluent was added at different concentrations, before and after treatment by constructed wetlands (CWs). During this period, mortality data was collected to calculate the 96 h-LC50 (lethal concentration inducing 50% mortality) and acute toxicity of C. idella. In addition to this, observations on change in morphological, physiological, and behavioural patterns were also made every 24 h. The present toxicity assay revealed that the raw tannery effluent changed the morphology, physiology, and behavioural response of fish. Moreover, fish exposure to raw/untreated effluent caused high acute toxicity and 100% mortality, due to the presence of high concentrations of salts and chromium (Cr) metal. While treatment of tannery effluent by CWs vegetated with different plants (B. mutica, L. fusca, and T. domingensis) significantly reduced its toxicity and fish mortality as well, and inoculation of salt and Cr-tolerant endophytic bacteria (Enterobacter sp. HU38, Microbacterium arborescens HU33, and Pantoea stewartii ASI11) further reduced (up to 90%) its toxicity level. Hence, the use of CWs for tannery effluent treatment can be recommended to favour public health and promote the overall safety of the environment. Full article
(This article belongs to the Special Issue Study of Biodegradation and Bioremediation)
►▼ Show Figures

Figure 1

Figure 1
<p>Mortality in fish on exposure to different concentrations of untreated and treated effluent by CWs using <span class="html-italic">Typha domingensis.</span> Tannery effluent treated in CWs with only <span class="html-italic">T. domingensis</span> (T2), effluent treated in CWs with <span class="html-italic">T. domingensis</span> and bacteria (T3), effluent treated in CWs without vegetation (T4), and raw tannery effluent (T5). Each value is mean ± standard error and number of replicates for each treatment (<span class="html-italic">n</span>) = 3.</p> Full article ">Figure 2
<p>Mortality in fish on exposure to different concentrations of untreated and treated effluent by CWs using <span class="html-italic">Leptochloa fusca.</span> Tannery effluent treated in CWs with <span class="html-italic">L. fusca</span> only (T2), effluent treated in CWs with <span class="html-italic">L. fusca</span> and bacteria (T3), effluent treated in CWs without vegetation (T4), and raw tannery effluent (T5). Each value is mean ± standard error and number of replicates for each treatment (<span class="html-italic">n</span>) = 3.</p> Full article ">Figure 3
<p>Mortality in fish on exposure to different concentrations of untreated and treated effluent by CWs using <span class="html-italic">Brachiaria mutica.</span> Tannery effluent treated in CWs with <span class="html-italic">B. mutica</span> only (T2), effluent treated in CWs with <span class="html-italic">B. mutica</span> and bacteria (T3), effluent treated in CWs without vegetation (T4), and raw tannery effluent (T5). Each value is mean ± standard error and number of replicates for each treatment (<span class="html-italic">n</span>) = 3. Stocked in T3 (effluent treatment by combined use of <span class="html-italic">L. fusca</span> and bacteria) at 50% effluent concentration, while the maximum percentage mortality observed was only 10% in higher effluent concentrations (100%). Observations for T4 treatment were the same as described above.</p> Full article ">Figure 4
<p>Relative performance of different plant species in toxicity reduction of tannery effluent in constructed wetland systems. Tannery effluent treated in CWs with plants only (T2), and with plants and bacteria (T3). Labels (a) and (b) indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) among plant species for toxicity reduction at a 5% level of significance. Each value is mean ± standard error and number of replicates for each treatment (<span class="html-italic">n</span>) = 3. Different letters on each error bar show significant differences among different treatments.</p> Full article ">Figure 5
<p>Morphological change in <span class="html-italic">C. idella</span> on exposure to tannery effluent before (<b>A</b>) and after treatment (<b>B</b>) by CW technology.</p> Full article ">Figure 6
<p>Variation in behavioural response of <span class="html-italic">C. idella</span> on exposure to untreated tannery effluent at 50% dilution showing irregular swimming and zig-zag movements (<b>A</b>), and normal movements and swimming behaviour in treated tannery effluent at the same dilution level (<b>B</b>), and in tap water set as control (<b>C</b>).</p> Full article ">
19 pages, 1916 KiB  
Article
Mathematical Modelling of Blanch-Assisted Drying of Pomegranate (Punica granatum) Arils in a Hot-Air Drier
by Adegoke Olusesan Adetoro, Alemayehu Ambaw Tsige, Umezuruike Linus Opara and Olaniyi Amos Fawole
Processes 2020, 8(5), 611; https://doi.org/10.3390/pr8050611 - 20 May 2020
Cited by 6 | Viewed by 3966
Abstract
The effect of blanching conditions on the hot-air drying kinetics of three pomegranates (cvs. “Acco”, “Herskawitz” and “Wonderful”) were assessed. Water blanching conditions considered were 90 °C for 30 s, 90 °C for 60 s, 100 °C for 30 s and 100 °C [...] Read more.
The effect of blanching conditions on the hot-air drying kinetics of three pomegranates (cvs. “Acco”, “Herskawitz” and “Wonderful”) were assessed. Water blanching conditions considered were 90 °C for 30 s, 90 °C for 60 s, 100 °C for 30 s and 100 °C for 60 s. The drying experiments were carried out at 60 °C, 19.6% relative humidity and at a constant air velocity of 1.0 m s−1. The experimental curves were fitted to seven different drying models. For the Acco cultivar, the drying behaviour was best predicted by the Logarithmic and Page model for blanched (R2 ranging between 0.9966 and 0.9989) and unblanched (R2 = 0.9918) samples, respectively. Furthermore, for the Herskawitz cultivar, Logarithm, Page and Midili models were most suitable for predicting drying behaviour of both blanched and unblanched samples. Also, for the Wonderful cultivar, Logarithm and Midili models were most accurate for predicting the drying behaviour for both blanched and unblanched samples amongst other models. The blanched samples dried faster with shorter drying times: “Acco” (7 h), “Herskawitz” (8 h), and “Wonderful” (7 h), compared to the unblanched samples, which dried after 15, 20 and 11 h, respectively. Effective diffusion coefficient of moisture in pomegranate arils ranged from 4.81 × 10−9 and 1.11 × 10−8 m2 s−1 for the Acco cultivar, for the Herskawitz cultivar; 3.29 × 10−9 and 1.01 × 10−8 m2 s−1 and for the Wonderful cultivar; 5.83 × 10−9 and 1.09 × 10−8 m2 s−1. Overall, blanching resulted in low energy consumption during drying of pomegranate arils. In addition, the Logarithmic model generally showed an appropriate model for blanched samples regardless of cultivar. For unblanched samples, the Page model was more appropriate for “Acco” and “Herskawitz”, while the Midili model was appropriate for “Wonderful”. Therefore, this study provided science-based and practical drying conditions for the investigated pomegranate cultivars. Full article
(This article belongs to the Special Issue Drying Kinetics and Quality Control in Food Processing)
►▼ Show Figures

Figure 1

Figure 1
<p>Experimental setup for the drying experiment. (<b>a</b>) Schematic diagram showing the laboratory hot-air convective drying system and (<b>b</b>) demonstrating the mechanism of heat and mass transfer through the sample.</p> Full article ">Figure 2
<p>Pomegranate arils at different stages (<b>a</b>) samples before processing, (<b>b</b>) blanch-assisted dried arils and (<b>c</b>) unblanched dried arils (control).</p> Full article ">Figure 3
<p>Drying time (min) of arils extracted from pomegranate cultivars (Acco, Herskawitz and Wonderful), blanched at 100 °C for 60 s and dried in a hot-air flow rate of 1 m s<sup>−1</sup> at 60 °C and 19.6% RH.</p> Full article ">Figure 4
<p>Drying curves for pomegranate aril cultivars (<b>a</b>) Acco, (<b>b</b>) Herskawitz and (<b>c</b>) Wonderful at a hot-air flow rate of 1 m s<sup>−1</sup>, temperature condition 60 °C and 19.6% RH.</p> Full article ">Figure 5
<p>Drying rate versus drying time of pomegranate arils for cultivars (<b>a</b>) Acco, (<b>b</b>) Herskawitz and (<b>c</b>) Wonderful at hot-air flow rate of 1 m s<sup>−1</sup>, temperature condition 60 °C and 19.6% RH.</p> Full article ">
11 pages, 2176 KiB  
Article
Compound Identification and In Vitro Cytotoxicity of the Supercritical Carbon Dioxide Extract of Papaya Freeze-Dried Leaf Juice
by Kooi-Yeong Khaw, Paul Nicholas Shaw, Marie-Odile Parat, Saurabh Pandey and James Robert Falconer
Processes 2020, 8(5), 610; https://doi.org/10.3390/pr8050610 - 20 May 2020
Cited by 6 | Viewed by 3885
Abstract
Carica papaya leaves are used as a remedy for the management of cancer. Freeze-dried C. papaya leaf juice was extracted using a supercritical fluid extraction system. Compound identification was carried out using analytical techniques including liquid chromatography coupled to high-resolution quadrupole time-of-flight mass [...] Read more.
Carica papaya leaves are used as a remedy for the management of cancer. Freeze-dried C. papaya leaf juice was extracted using a supercritical fluid extraction system. Compound identification was carried out using analytical techniques including liquid chromatography coupled to high-resolution quadrupole time-of-flight mass spectrometry (LC–QToF-MS) and gas chromatography–mass spectrometry (GC–MS). The cytotoxic activities of the scCO2 extract and its chemical constituents were determined using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay on squamous cell carcinoma (SCC25) and human keratinocyte (HaCaT) cell lines. The chemical constituents were quantified by QToF-MS. The supercritical carbon dioxide (scCO2) extract of papaya freeze-dried leaf juice showed cytotoxic activity against SCC25. Three phytosterols, namely, β-sitosterol, campesterol, and stigmasterol, together with α-tocopherol, were confirmed to be present in the scCO2 extract. Quantitative analysis showed that β-sitosterol was the major phytosterol present followed by α-tocopherol, campesterol, and stigmasterol. β-Sitosterol and campesterol were active against SCC25 (half maximal inhibitory concentration (IC50) ≈ 1 µM), while stigmasterol was less active (~33 µM) but was biologically more selective against SCC25. Interestingly, an equimolar mixture of phytosterols was not more effective (no synergistic effect was observed) but was more selective than the individual compounds. The compounds identified are likely accountable for at least part of the cytotoxicity and selectivity effects of C. papaya. Full article
(This article belongs to the Special Issue Innovation in Chemical Plant Design)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>ai</b>) Extracted ion chromatogram (EIC) of <span class="html-italic">m/z</span> 431.3884 for the scCO<sub>2</sub> extract. (<b>aii</b>) EIC of <span class="html-italic">m/z</span> 431.3823 for the <span class="html-small-caps">dl</span>-α-tocopherol standard; (<b>bi</b>) EIC of <span class="html-italic">m/z</span> 383.3662 for the scCO<sub>2</sub> extract; (<b>bii</b>) EIC of <span class="html-italic">m/z</span> 383.3683 for the campesterol standard; (<b>ci</b>) EIC of <span class="html-italic">m/z</span> 395.3668 for the scCO<sub>2</sub> extract; (<b>cii</b>) EIC of <span class="html-italic">m/z</span> 395.3669 for the stigmasterol standard; (<b>di</b>) EIC of <span class="html-italic">m/z</span> 397.1361for the scCO<sub>2</sub> extract; (<b>dii</b>) EIC of <span class="html-italic">m/z</span> 397.1347 for the β-sitosterol standard.</p> Full article ">Figure 2
<p>Effect of scCO<sub>2</sub> extract on the viability of SCC25 and HaCaT cell lines. Results are expressed as means ± standard error of the mean (SEM) (<span class="html-italic">n</span> = 3 independent experiments). Statistical significance was determined by two-way ANOVA with the Sidak post hoc test, comparing the survival of HaCaT vs, scc25 (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p> Full article ">Figure 3
<p>Effect of stigmasterol (<b>a</b>), <span class="html-small-caps">dl</span>-α-tocopherol (<b>b</b>), campesterol (<b>c</b>), and β-sitosterol (<b>d</b>) on the cell viabilities of SCC25 and HaCaT cell lines. Results are expressed as means ± SEM (<span class="html-italic">n</span> = 3 independent experiments). Statistical significance was determined by two-way ANOVA with the Sidak post hoc test, comparing the survival of HaCaT vs. SCC25 (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p> Full article ">Figure 4
<p>Effect of an equimolar mix of the three phytosterols on SCC25 and HaCaT cell lines. Statistical significance was determined by two-way ANOVA with the Sidak post hoc test, comparing the survival of HaCaT vs. SCC25 (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure A1
<p>GC–MS analysis of scCO<sub>2</sub> extract.</p> Full article ">
19 pages, 6016 KiB  
Article
Research on State Recognition and Failure Prediction of Axial Piston Pump Based on Performance Degradation Data
by Rui Guo, Zhiqian Zhao, Saiyu Huo, Zhijie Jin, Jingyi Zhao and Dianrong Gao
Processes 2020, 8(5), 609; https://doi.org/10.3390/pr8050609 - 20 May 2020
Cited by 15 | Viewed by 3927
Abstract
Degradation state recognition and failure prediction are the key steps of prognostic and health management (PHM), which directly affect the reliability of the equipment and the selection of preventive maintenance strategy. Given the problem that the distinction between feature vectors is not obvious [...] Read more.
Degradation state recognition and failure prediction are the key steps of prognostic and health management (PHM), which directly affect the reliability of the equipment and the selection of preventive maintenance strategy. Given the problem that the distinction between feature vectors is not obvious and the accuracy of fault prediction is low, a method based on multi-class Gaussian process classification and Gaussian process regression (GPR) is studied by the vibration signal and flow signal in six degraded states of the axial piston pump. For degradation state recognition, the variational mode decomposition (VMD) was used to decompose the vibration signal, and obtaining intrinsic mode function (IMF) components with rich information. Subsequently, multi-scale permutation entropy (MPE) was employed to select feature vectors of IMF components in different states. In order to reduce feature dimensions and improve recognition performance, ReliefF was used to select feature vectors with high weight, then a method based on multi-class Gaussian process classification was established by using these feature vectors to realize the research on the degradation state recognition. The test results demonstrate that the method can effectively identify the degradation state. Its recognition rate reaches 98.9%. Besides, for failure prediction, through the analysis of the wear process and wear mechanism of the valve plate, the curve fitting between the flow and the wear amount was performed by GPR to realize the failure prediction of the axial piston pump. Depending on the evaluation index, the GPR obtained a better failure prediction effect. The results will assist in the realization of predictive maintenance, and which also has significant practical value in project items. Full article
(This article belongs to the Special Issue Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis)
►▼ Show Figures

Figure 1

Figure 1
<p>Flow chart of technical route.</p> Full article ">Figure 2
<p>Test bench physical map.</p> Full article ">Figure 3
<p>Hydraulic system diagram of the performance degratation test of the piston pump.</p> Full article ">Figure 4
<p>Valve plates in six different wear states. (<b>a</b>) Normal valve plate and local amplification of its transition area. (<b>b</b>) No. 1 valve plate and local amplification of its transition area. (<b>c</b>) No. 2 valve plate and local amplification of its transition area. (<b>d</b>) No. 3 valve plate and local amplification of its transition area. (<b>e</b>) No. 4 valve plate and local amplification of its transition area. (<b>f</b>) No. 5 valve plate and local amplification of its transition area.</p> Full article ">Figure 5
<p>The profilometer measures the wear degree of valve plate on site.</p> Full article ">Figure 6
<p>VMD decomposition results of vibration signals in six states.</p> Full article ">Figure 7
<p>Relation of mutual information and delay time.</p> Full article ">Figure 8
<p>The curve of the embedding dimension.</p> Full article ">Figure 9
<p>MPE in different degenerate states.</p> Full article ">Figure 10
<p>The result of feature vector selection.</p> Full article ">Figure 11
<p>VMD decomposition results of flow signals in six states.</p> Full article ">Figure 12
<p>Gaussian Process regression fit curve.</p> Full article ">
16 pages, 10532 KiB  
Article
Experimental Analysis of the Performance and Load Cycling of a Polymer Electrolyte Membrane Fuel Cell
by Andrea Ramírez-Cruzado, Blanca Ramírez-Peña, Rosario Vélez-García, Alfredo Iranzo and José Guerra
Processes 2020, 8(5), 608; https://doi.org/10.3390/pr8050608 - 20 May 2020
Cited by 9 | Viewed by 5484
Abstract
In this work, a comprehensive experimental analysis on the performance of a 50 cm2 polymer electrolyte membrane (PEM) fuel cell is presented, including experimental results for a dedicated load cycling test. The harmonized testing protocols defined by the Joint Research Centre (JRC) [...] Read more.
In this work, a comprehensive experimental analysis on the performance of a 50 cm2 polymer electrolyte membrane (PEM) fuel cell is presented, including experimental results for a dedicated load cycling test. The harmonized testing protocols defined by the Joint Research Centre (JRC) of the European Commission for automotive applications were followed. With respect to a reference conditions representative of automotive applications, the impact of variations in the cell temperature, reactants pressure, and cathode stoichiometry was analyzed. The results showed that a higher temperature resulted in an increase in cell performance. A higher operating pressure also resulted in higher cell voltages. Higher cathode stoichiometry values negatively affected the cell performance, as relatively dry air was supplied, thus promoting the dry-out of the cell. However, a too low stoichiometry caused a sudden drop in the cell voltage at higher current densities, and also caused significant cell voltage oscillations. No significant cell degradation was observed after the load cycling tests. Full article
(This article belongs to the Special Issue Representative Model and Flow Characteristics of Fuel Cells)
►▼ Show Figures

Figure 1

Figure 1
<p>Test bench (<b>a</b>) front view showing the main components; and (<b>b</b>) back view showing the BoP (Balance of Plant).</p> Full article ">Figure 2
<p>(<b>Top</b>) Graphite bipolar plates (anode side and cathode side) with serpentine flow field. (<b>Bottom left</b>) External fan (black) and film heater attached to the terminal plates for temperature control. (<b>Bottom right</b>) Thermocouple inserted into the cathode monopolar plate for cell temperature measurement.</p> Full article ">Figure 3
<p>Polarization curve for reference conditions. Forward curve (increasing current) in the bottom; Backward curve (decreasing current) at the top. Error bars corresponding to three repetitions are included for each measurement point.</p> Full article ">Figure 4
<p>Polarization curve for temperature tests. Forward curve (increasing current) at the bottom; backward curve (decreasing current) at the top. Error bars corresponding to the three repetitions are included for each measurement point.</p> Full article ">Figure 5
<p>Polarization curve for pressure tests. Forward curve (increasing current) at the bottom; backward curve (decreasing current) at the top. Error bars corresponding to the three repetitions are included for each measurement point.</p> Full article ">Figure 6
<p>Polarization curve for cathode stoichiometry tests (λ<sub>c</sub> = 1.3, 1.5, 2.0, 3.5). λ<sub>c</sub> = 1.5 corresponds to the reference conditions. Forward curve (increasing current) at the bottom; backward curve (decreasing current) at the top. Error bars corresponding to the three repetitions are included for each measurement point.</p> Full article ">Figure 7
<p>Evolution of cell voltage (in blue) during the load cycle test. Current density profile defined according to <a href="#app2-processes-08-00608" class="html-app">Appendix B</a> is shown in red.</p> Full article ">Figure 8
<p>Superposition of the evolution of the cell voltage corresponding to the five cycles during the load cycle test. (<b>Top</b>) complete load cycles; (<b>Bottom</b>) high load (15 min to 21 min) close-up.</p> Full article ">Figure 9
<p>Polarization curves recorded before load cycling and after load cycling. Error bars corresponding to three repetitions are included for each measurement point.</p> Full article ">Figure 10
<p>Time evolution of the cell voltage for the cathode stoichiometry tests at 1.0 A/cm<sup>2</sup>.</p> Full article ">Figure 11
<p>Cell efficiency curve and power curve for reference conditions (<a href="#processes-08-00608-t001" class="html-table">Table 1</a>); λ<sub>a</sub> = 1.3.</p> Full article ">Figure A1
<p>Method for identifying the 100% load in a polarization curve, as described in [<a href="#B11-processes-08-00608" class="html-bibr">11</a>]. The 100% load value is obtained by averaging the two current values (in red) corresponding to the forward and backward curves (in blue) at 0.65 V.</p> Full article ">Figure A2
<p>Evolution of the cell voltage during the cell conditioning step. The +/−5 mV range is marked in red.</p> Full article ">
16 pages, 829 KiB  
Article
An Innovative Design of an Integrated MED-TVC and Reverse Osmosis System for Seawater Desalination: Process Explanation and Performance Evaluation
by Omer Mohamed Abubaker Al-hotmani, Mudhar Abdul Alwahab Al-Obaidi, Yakubu Mandafiya John, Raj Patel and Iqbal Mohammed Mujtaba
Processes 2020, 8(5), 607; https://doi.org/10.3390/pr8050607 - 20 May 2020
Cited by 13 | Viewed by 5239
Abstract
In recent times two or more desalination processes have been combined to form integrated systems that have been widely used to resolve the limitations of individual processes as well as producing high performance systems. In this regard, a simple integrated system of the [...] Read more.
In recent times two or more desalination processes have been combined to form integrated systems that have been widely used to resolve the limitations of individual processes as well as producing high performance systems. In this regard, a simple integrated system of the Multi Effect Distillation (MED)/Thermal Vapour Compression (TVC) and Permeate Reprocessing Reverse Osmosis (PRRO) process was developed by the same authors and confirmed its validity after a comparison study against other developed configurations. However, this design has a considerable amount of retentate flowrate and low productivity. To resolve this issue, two novel designs of MED and double reverse osmosis (RO) processes including Permeate and Retentate Reprocessing designs (PRRP and RRRO) are developed and modelled in this paper. To systematically assess the consistency of the presented designs, the performance indicators of the novel designs are compared against previous simple designs of MED and PRRO processes at a specified set of operating conditions. Results show the superiority of the integrated MED and double permeate reprocessing design. This has specifically achieved both economic and environmental advantages where total productivity is increased by around 9% and total retentate flowrate (disposed to water bodies) is reduced by 5% with a marginally reduced energy consumption. Full article
(This article belongs to the Special Issue Redesign Processes in the Age of the Fourth Industrial Revolution)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic diagram of a simple integrated system of multi effect distillation and thermal vapour compression and permeate reprocessing reverse osmosis processes.</p> Full article ">Figure 2
<p>Schematic diagram of an integrated system of multi effect distillation and thermal vapour compression and double permeate reprocessing reverse osmosis processes.</p> Full article ">Figure 3
<p>Schematic diagram of an integrated system of multi effect distillation and thermal vapour compression and double permeate reprocessing and retentate reprocessing reverse osmosis processes.</p> Full article ">
17 pages, 6091 KiB  
Article
AOC-OPTICS: Automatic Online Classification for Condition Monitoring of Rolling Bearing
by Hassane Hotait, Xavier Chiementin and Lanto Rasolofondraibe
Processes 2020, 8(5), 606; https://doi.org/10.3390/pr8050606 - 20 May 2020
Cited by 7 | Viewed by 3065
Abstract
Bearings are essential components in rotating machines. They ensure the rotation and power transmission. So, these components are essential elements for industrial machines. Thus, real-time monitoring is required to detect a possible anomaly, diagnose the failure of rolling bearing and follow its evolution. [...] Read more.
Bearings are essential components in rotating machines. They ensure the rotation and power transmission. So, these components are essential elements for industrial machines. Thus, real-time monitoring is required to detect a possible anomaly, diagnose the failure of rolling bearing and follow its evolution. This paper presents a methodology for automatic online implementation of fault diagnosis of rolling bearings, by AOC-OPTICS (automatic online classification monitoring based on ordering points to identify clustering structure, OPTICS). The algorithm consists of three phases namely: initialization, detection and follow-up. These phases use the combination of features extraction methods, smart ranking, features weighting and classification by the OPTICS method. Two methods have been integrated in the dimension reduction step to improve the efficiency of detection and the followed of the defect (relief method and t-distributed stochastic neighbor embedding method). Thus, the determination of the internal parameters of the OPTICS method is improved. A regression model and exponential model are used to track the fault. The analytical simulations discuss the influence of parameters automation. Experimental validation shows detection with 100% accuracy and regression models of monitoring reaching R 2 = 0.992 . Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Representation of the core distance and reachability distance for <math display="inline"><semantics> <mrow> <mrow> <mi>M</mi> <mi>i</mi> <mi>n</mi> <mi>P</mi> <mi>t</mi> <mi>s</mi> </mrow> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>. (<b>b</b>) Reachability plot.</p> Full article ">Figure 2
<p>Flowchart of AOC-OPTICS.</p> Full article ">Figure 3
<p><math display="inline"><semantics> <mrow> <mi>ε</mi> </mrow> </semantics></math> as a function of <math display="inline"><semantics> <mrow> <mi>M</mi> <mi>i</mi> <mi>n</mi> <mi>P</mi> <mi>t</mi> <mi>s</mi> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mn>0.1</mn> <mi>b</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> level noise.</p> Full article ">Figure 4
<p>Effect of amplitude 0.1<span class="html-italic">b</span> (<span class="html-italic">t</span>): (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mrow> <mn>10</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mrow> <mn>51</mn> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Effect of amplitude 0.3<span class="html-italic">b</span> (<span class="html-italic">t</span>): (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mrow> <mn>10</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mrow> <mn>51</mn> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Effect of amplitude 0.5<span class="html-italic">b</span> (<span class="html-italic">t</span>): (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mrow> <mn>12</mn> </mrow> </msub> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mrow> <mn>51</mn> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>(<b>a</b>) Calinski index, (<b>b</b>) Davies–Bouldin index, (<b>c</b>) density, (<b>d</b>) distance and (<b>e</b>) contour.</p> Full article ">Figure 8
<p>(<b>a</b>) Test bench (<b>b</b>) profilometer with Plastiform<sup>TM</sup> paste.</p> Full article ">Figure 9
<p>Experimental validation (<b>a</b>) Iteration 10, (<b>b</b>) Iteration 11 and (<b>c</b>) Iteration 90.</p> Full article ">Figure 10
<p>Follow-up of the detected cluster: evolution of (<b>a</b>) Calinski index, (<b>b</b>) Davies–Bouldin index. (<b>c</b>) Density, (<b>d</b>) distance and (<b>e</b>) contour.</p> Full article ">
20 pages, 7444 KiB  
Article
Model Calibration of Stochastic Process and Computer Experiment for MVO Analysis of Multi-Low-Frequency Electromagnetic Data
by Muhammad Naeim Mohd Aris, Hanita Daud, Khairul Arifin Mohd Noh and Sarat Chandra Dass
Processes 2020, 8(5), 605; https://doi.org/10.3390/pr8050605 - 19 May 2020
Cited by 4 | Viewed by 3159
Abstract
An electromagnetic (EM) technique is employed in seabed logging (SBL) to detect offshore hydrocarbon-saturated reservoirs. In risk analysis for hydrocarbon exploration, computer simulation for subsurface modelling is a crucial task. It can be expensive and time-consuming due to its complicated mathematical equations, and [...] Read more.
An electromagnetic (EM) technique is employed in seabed logging (SBL) to detect offshore hydrocarbon-saturated reservoirs. In risk analysis for hydrocarbon exploration, computer simulation for subsurface modelling is a crucial task. It can be expensive and time-consuming due to its complicated mathematical equations, and only a few realizations of input-output pairs can be generated after a very lengthy computational time. Understanding the unknown functions without any uncertainty measurement could be very challenging as well. We proposed model calibration between a stochastic process and computer experiment for magnitude versus offset (MVO) analysis. Two-dimensional (2D) Gaussian process (GP) models were developed for low-frequencies of 0.0625–0.5 Hz at different hydrocarbon depths to estimate EM responses at untried observations with less time consumption. The calculated error measurements revealed that the estimates were well-matched with the computer simulation technology (CST) outputs. Then, GP was fitted in the MVO plots to provide uncertainty quantification. Based on the confidence intervals, hydrocarbons were difficult to determine especially when their depth was 3000 m from the seabed. The normalized magnitudes for other frequencies also agreed with the resulting predictive variance. Thus, the model resolution for EM data decreases as the hydrocarbon depth increases even though multi-low frequencies were exercised in the SBL application. Full article
(This article belongs to the Collection Process Data Analytics)
►▼ Show Figures

Figure 1

Figure 1
<p>Stratified illustration of the SBL models. There are two types of SBL models: (<b>a</b>) SBL model with the presence of hydrocarbon layer; (<b>b</b>) SBL model without the hydrocarbon layer. For the target model, depth of the hydrocarbon (thickness of overburden) was varied, which were 1000 m, 2000 m, and 3000 m. The height and length of both the SBL models were 5000 m and 20,000 m, respectively.</p> Full article ">Figure 2
<p>Simplified methodological flow of GP regression modelling in SBL data generated through the CST software.</p> Full article ">Figure 3
<p>EM responses obtained from the target models, where the hydrocarbon layer was located at 1000 m from the seabed, for multiple frequencies (0.0625–0.5 Hz with an increment of 0.0625 Hz each). The total source-receiver separation distance is 20,000 m.</p> Full article ">Figure 4
<p>EM responses obtained from the target models, where the hydrocarbon layer was located at 2000 m from the seabed, for multiple frequencies (0.0625–0.5 Hz with an increment of 0.0625 Hz each). The total source-receiver separation distance is 20,000 m.</p> Full article ">Figure 5
<p>EM responses obtained from the target models, where the hydrocarbon layer was located at 3000 m from the seabed, for multiple frequencies (0.0625–0.5 Hz with an increment of 0.0625 Hz each). The total source-receiver separation distance is 20,000 m.</p> Full article ">Figure 6
<p>Contour plot of the 2D forward GP model. EM responses versus source-receiver separation distance for all tried and untried transmission frequencies (0.0625–0.5 Hz) at overburden thickness of 1000 m.</p> Full article ">Figure 7
<p>Contour plot of the 2D forward GP model. EM responses versus source-receiver separation distance for all tried and untried transmission frequencies (0.0625–0.5 Hz) at overburden thickness of 2000 m.</p> Full article ">Figure 8
<p>Contour plot of the 2D forward GP model. EM responses versus source-receiver separation distance for all tried and untried transmission frequencies (0.0625–0.5 Hz) at overburden thickness of 3000 m.</p> Full article ">Figure 9
<p>Histogram of the average percentage difference between the target EM responses and the reference responses. There were three histograms which represented the depths of hydrocarbon. The average percentage difference was calculated for every transmission frequency used in this research work (0.0625–0.5 Hz with an increment of 0.0625 Hz).</p> Full article ">Figure 10
<p>1D GP models of MVO responses observed along the receiver line from four different SBL models at a frequency of 0.0625 Hz: (i) model with hydrocarbon depth of 1000 m (blue); (ii) model with hydrocarbon depth of 2000 m (green); (iii) model with hydrocarbon depth of 3000 m (red); (iv) model with no hydrocarbon layer (magenta).</p> Full article ">Figure 11
<p>Zoomed-in scale of the 1D GP models for a frequency of 0.0625 Hz. The grey-colored bar is the 95% confidence interval (predictive variance) provided by the GP. The “cross” denotes the testing data involved in the GP regression.</p> Full article ">Figure 12
<p>Normalized MVO responses for a transmission frequency of 0.0625 Hz observed along the receiver line for three different hydrocarbon depths: (i) normalized MVO for a hydrocarbon depth of 1000 m (blue); (ii) normalized MVO for a hydrocarbon depth of 2000 m (reddish-yellow); (iii) normalized MVO for a hydrocarbon depth of 3000 m (grey).</p> Full article ">Figure 13
<p>Normalized MVO responses for transmission frequency of 0.09375 Hz: (i) normalized MVO for a hydrocarbon depth of 1000 m (blue); (ii) normalized MVO for a hydrocarbon depth of 2000 m (reddish-yellow); (iii) normalized MVO for a hydrocarbon depth of 3000 m (grey).</p> Full article ">Figure 14
<p>Normalized MVO responses for transmission frequency of 0.28125 Hz: (i) normalized MVO for a hydrocarbon depth of 1000 m (blue); (ii) normalized MVO for a hydrocarbon depth of 2000 m (reddish-yellow); (iii) normalized MVO for a hydrocarbon depth of 3000 m (grey).</p> Full article ">Figure 15
<p>Normalized MVO responses for transmission frequency of 0.46875 Hz: (i) normalized MVO for a hydrocarbon depth of 1000 m (blue); (ii) normalized MVO for a hydrocarbon depth of 2000 m (reddish-yellow); (iii) normalized MVO for a hydrocarbon depth of 3000 m (grey).</p> Full article ">
17 pages, 1941 KiB  
Article
Inside–Out Method for Simulating a Reactive Distillation Process
by Liang Wang, Xiaoyan Sun, Li Xia, Jianping Wang and Shuguang Xiang
Processes 2020, 8(5), 604; https://doi.org/10.3390/pr8050604 - 19 May 2020
Cited by 3 | Viewed by 7190
Abstract
Reactive distillation is a technical procedure that promotes material strengthening and its simulation plays an important role in the design, research, and optimization of reactive distillation. The solution to the equilibrium mathematical model of the reactive distillation process involves the calculation of a [...] Read more.
Reactive distillation is a technical procedure that promotes material strengthening and its simulation plays an important role in the design, research, and optimization of reactive distillation. The solution to the equilibrium mathematical model of the reactive distillation process involves the calculation of a set of nonlinear equations. In view of the mutual influence between reaction and distillation, the nonlinear enhancement of the mathematical model and the iterative calculation process are prone to fluctuations. In this study, an improved Inside–Out method was proposed to solve the reaction distillation process. The improved Inside–Out methods mainly involved—(1) the derivation of a new calculation method for the K value of the approximate thermodynamic model from the molar fraction summation equation and simplifying the calculation process of the K value, as a result; and (2) proposal for an initial value estimation method suitable for the reactive distillation process. The algorithm was divided into two loop iterations—the outer loop updated the relevant parameters and the inside loop solved the equations, by taking the isopropyl acetate reactive distillation column as an example for verifying the improved algorithm. The simulation results presented a great agreement with the reference, and only the relative deviation of the reboiler heat duty reached 2.57%. The results showed that the calculation results were accurate and reliable, and the convergence process was more stable. Full article
(This article belongs to the Special Issue Chemical Process Design, Simulation and Optimization)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic representation of the reactive distillation column.</p> Full article ">Figure 2
<p>The jth equilibrium stage of the reactive distillation column.</p> Full article ">Figure 3
<p>Schematic representation of input and output streams in all stages of a distillation column.</p> Full article ">Figure 4
<p>Calculation of the block diagram of the improved Inside–Out method.</p> Full article ">Figure 5
<p>Schematic diagram of the isopropyl acetate reactive distillation column.</p> Full article ">Figure 6
<p>Comparison of the initial values of the extent of reaction and simulation results.</p> Full article ">Figure 7
<p>Error at each iteration for the simulation of isopropyl acetate reactive distillation column.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-136
Previous Issue
Next Issue
Back to TopTop