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Volume 10
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Processes, Volume 10, Issue 12 (December 2022) – 282 articles

Cover Story (view full-size image): Essential oils (EOs) from Abies sachalinensis effectively remove nitrogen dioxide and possess antifungal activity. Underwater shockwaves generate an instantaneous high pressure that ruptures cell walls, enhancing the performance of steam distillation and oil extraction. The EO yield significantly increased with the number of shockwave cycles after applying. Pretreatment with shockwaves also reduced total energy consumption. Antioxidant activity increased by more than 30-fold in pretreated leaves compared to untreated dried leaves. This novel process can significantly reduce the energy used for EO extraction in steam distillation, thereby contributing to the development of a low-energy, sustainable EO production system. View this paper
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20 pages, 8397 KiB  
Article
Generalized Mathematical Model of the Grain Drying Process
by Ryszard Myhan and Marek Markowski
Processes 2022, 10(12), 2749; https://doi.org/10.3390/pr10122749 - 19 Dec 2022
Cited by 5 | Viewed by 3375
Abstract
Convective cereal grain drying is an energy-intensive process. Mathematical models are applied to analyze and optimize grain drying processes in different types of dryers and in different stages of drying to improve final grain quality and reduce energy consumption. The aim of the [...] Read more.
Convective cereal grain drying is an energy-intensive process. Mathematical models are applied to analyze and optimize grain drying processes in different types of dryers and in different stages of drying to improve final grain quality and reduce energy consumption. The aim of the present study was to develop a generalized mathematical model of the grain drying process that accounts for all drying stages, including loading and unloading of unprocessed grain, drying, and cooling of dry grain. The developed mathematical model is a system of algebraic equations, where the calculated coefficients are determined by the thermophysical and diffusive properties of dried grain. The model was validated for batch drying of wheat, canola, and corn grain, as well as continuous flow drying of wheat grain. The results were compared with published findings. The relationships between energy consumption during drying and drying time vs. air temperature at the dryer inlet and air stream volume were determined. Dryer capacity and drying conditions specified by the manufacturers, as well as loading and unloading capacity, were considered during batch drying. Continuous flow drying simulations were conducted in counter-flow, parallel-flow, and cross-flow mode. Simulation results indicate that the proposed models correctly depicted process flow in both batch and continuous flow dryers. Full article
(This article belongs to the Special Issue Drying Kinetics and Quality Control in Food Processing)
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<p>Flow and division of streams during the drying process.</p> Full article ">Figure 2
<p>Operations performed by batch dryers and continuous-flow dryers.</p> Full article ">Figure 3
<p>Decomposition of the grain dryer into functional units. 1. Loading unit; 2. drying chamber; 3. cooling unit; 4. unloading unit; 5. source of drying air.</p> Full article ">Figure 4
<p>Classification of drying models. (<b>a</b>) Immobile grain layer; (<b>b</b>) cross-flow; (<b>c</b>) counter-flow; (<b>d</b>) parallel-flow.</p> Full article ">Figure 5
<p>Schematic diagram of the drying model.</p> Full article ">Figure 6
<p>The influence of the number of layers on the drying time of wheat, canola, and corn grain.</p> Full article ">Figure 7
<p>The influence of air temperature and air stream volume on energy consumption and drying time.</p> Full article ">Figure 8
<p>A comparison of the simulated daily capacity of selected batch dryers with their actual capacity. (<b>a</b>) Wheat; (<b>b</b>) canola; (<b>c</b>) corn.</p> Full article ">Figure 9
<p>Simulated changes in the moisture content of successive layers of dried wheat grain.</p> Full article ">Figure 10
<p>Simulated changes in the moisture content of successive grain layers dried in the counter-flow mode.</p> Full article ">Figure 11
<p>Simulation of changes in the moisture content of successive grain layers dried in the parallel-flow mode. (<b>a</b>) Cyclic drying of grain in a very thick layer; (<b>b</b>) overdrying in the initial drying period; (<b>c</b>) simulation of a drying process conducted in 20% in the mixed-flow mode.</p> Full article ">Figure 12
<p>Simulated changes in the moisture content of successive grain layers dried in the cross-flow mode.</p> Full article ">Figure 13
<p>A comparison of the simulated daily capacities of selected continuous-flow dryers and the capacities specified by the manufacturers.</p> Full article ">
22 pages, 7752 KiB  
Article
Mobile Robot Navigation Using Deep Reinforcement Learning
by Min-Fan Ricky Lee and Sharfiden Hassen Yusuf
Processes 2022, 10(12), 2748; https://doi.org/10.3390/pr10122748 - 19 Dec 2022
Cited by 28 | Viewed by 13281
Abstract
Learning how to navigate autonomously in an unknown indoor environment without colliding with static and dynamic obstacles is important for mobile robots. The conventional mobile robot navigation system does not have the ability to learn autonomously. Unlike conventional approaches, this paper proposes an [...] Read more.
Learning how to navigate autonomously in an unknown indoor environment without colliding with static and dynamic obstacles is important for mobile robots. The conventional mobile robot navigation system does not have the ability to learn autonomously. Unlike conventional approaches, this paper proposes an end-to-end approach that uses deep reinforcement learning for autonomous mobile robot navigation in an unknown environment. Two types of deep Q-learning agents, such as deep Q-network and double deep Q-network agents are proposed to enable the mobile robot to autonomously learn about collision avoidance and navigation capabilities in an unknown environment. For autonomous mobile robot navigation in an unknown environment, the process of detecting the target object is first carried out using a deep neural network model, and then the process of navigation to the target object is followed using the deep Q-network or double deep Q-network algorithm. The simulation results show that the mobile robot can autonomously navigate, recognize, and reach the target object location in an unknown environment without colliding with static and dynamic obstacles. Similar results are obtained in real-world experiments, but only with static obstacles. The DDQN agent outperforms the DQN agent in reaching the target object location in the test simulation by 5.06%. Full article
(This article belongs to the Section Manufacturing Processes and Systems)
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<p>Mobile robot hardware configurations for autonomous navigation.</p> Full article ">Figure 2
<p>Proposed block diagram of mobile robot navigation using a DQN/DDQN agent.</p> Full article ">Figure 3
<p>Mobile robot actions direction, where 0 is left (−90°), 1 is left front (−45°), 2 is front (0°), 3 is right front (45°) and 4 is right (90°).</p> Full article ">Figure 4
<p>Angular direction illustration.</p> Full article ">Figure 5
<p>Linear direction illustration.</p> Full article ">Figure 6
<p>Perspective imaging model.</p> Full article ">Figure 7
<p>DQN simulation in the Gazebo.</p> Full article ">Figure 8
<p>Mobile robot searching for the target object using a DQN agent.</p> Full article ">Figure 9
<p>The mobile robot reached the target object once it is labeled using a DQN agent.</p> Full article ">Figure 10
<p>Mobile robot trajectory to the target object using a DQN agent.</p> Full article ">Figure 11
<p>The depth image of the target object from an RGB−D camera.</p> Full article ">Figure 12
<p>Average cumulative reward obtained by a DQN agent in the Gazebo simulator.</p> Full article ">Figure 13
<p>Average cumulative TD error of a DQN agent in the Gazebo simulator.</p> Full article ">Figure 14
<p>Average cumulative reward obtained by a DQN agent for different discount factor values in the Gazebo simulator.</p> Full article ">Figure 15
<p>Average cumulative reward obtained by a DDQN agent in the Gazebo simulator.</p> Full article ">Figure 16
<p>Average cumulative TD error of a DDQN agent in the Gazebo simulator.</p> Full article ">Figure 17
<p>Average cumulative reward obtained by a DDQN agent for different discount factor values in the Gazebo simulator.</p> Full article ">Figure 18
<p>The first arena of autonomous mobile robot navigation in a real-world experiment.</p> Full article ">Figure 19
<p>The Second arena of autonomous mobile robot navigation in a real-world experiment.</p> Full article ">Figure 20
<p>Third-person view of mobile robot reaching the target object in the first arena.</p> Full article ">Figure 21
<p>Third-person view of mobile robot reaching the target object in the second arena.</p> Full article ">
16 pages, 2070 KiB  
Article
The Tobacco Leaf Redrying Process Parameter Optimization Based on IPSO Hybrid Adaptive Penalty Function
by Danping Luo, Yingna Li, Shouguo Tang, Ailian Liu and Liping Zhang
Processes 2022, 10(12), 2747; https://doi.org/10.3390/pr10122747 - 19 Dec 2022
Cited by 1 | Viewed by 2774
Abstract
In the tobacco redrying process, process parameter settings are greatly influenced by ambient temperature and humidity, and the moisture content of the tobacco leaf. In the face of complex and variable tobacco leaf characteristics, it is difficult to accurately adapt the process parameters [...] Read more.
In the tobacco redrying process, process parameter settings are greatly influenced by ambient temperature and humidity, and the moisture content of the tobacco leaf. In the face of complex and variable tobacco leaf characteristics, it is difficult to accurately adapt the process parameters to fluctuations in the incoming material characteristics by manual experience alone. Therefore, an improved optimization method combining an improved particle swarm optimization algorithm (IPSO) and an adaptive penalty function is proposed, which can adaptively recommend the best combination of process parameters according to the dynamic incoming characteristics of the tobacco leaf, to reduce the deviation in the outlet moisture and temperature of the roaster under different processing standards of the tobacco leaf. Firstly, the Radial Basis Function (RBF) Neural Network is used to fit the relationship between process parameters and roaster exit moisture content and temperature. Then, taking the standard tobacco leaf redrying export quality as the optimization goal, the optimization algorithm is used to search for the optimal solution. From the high-dimensional nature of the process operating conditions, the difficulty of this study lies in searching for the optimal solution under complex nonlinear constraints of multiple processes. To improve the convergence speed and accuracy of the searching algorithm, the position update method of the particle swarm optimization algorithm is improved, and the adaptive penalty function is combined to search for the optimal global solution to the optimization problem. Redrying experiments are conducted using the method proposed in this paper. Compared with the manual regulation of outlet moisture and temperature, the fluctuation range values are reduced by 7.5% and 11.8%, respectively, which has good application prospects and promotion value. Full article
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<p>Schematic diagram of tobacco leaf redrying process.</p> Full article ">Figure 2
<p>Changes in ambient temperature and humidity: (<b>a</b>) indoor temperature; (<b>b</b>) indoor humidity.</p> Full article ">Figure 3
<p>Flow chart of RBF neural-network-improved PSO hybrid adaptive penalty function.</p> Full article ">Figure 4
<p>Architecture of RBF neural network to predict tobacco moisture content and temperature.</p> Full article ">Figure 5
<p>RBF neural network error curve.</p> Full article ">Figure 6
<p>Predicted values of the RBF model on the test set: (<b>a</b>) moisture content and (<b>b</b>) temperature.</p> Full article ">Figure 7
<p>Iterative process of each algorithm.</p> Full article ">Figure 8
<p>Moisture and temperature at the outlet of the oven: (<b>a</b>) Optimization algorithm; (<b>b</b>) manual control.</p> Full article ">
13 pages, 2603 KiB  
Article
Transport Behavior of Methane Confined in Nanoscale Porous Media: Impact of Pore Evolution Characteristics
by Shan Wu, Sidong Fang, Liang Ji, Feng Wen, Zheng Sun, Shuhui Yan and Yaohui Li
Processes 2022, 10(12), 2746; https://doi.org/10.3390/pr10122746 - 19 Dec 2022
Cited by 3 | Viewed by 1717
Abstract
As a key technical aspect contributing to shale gas development, nanoconfined methane flow behavior has received tremendous research interest, which remains challenging to understand clearly. The majority of previous contributions put emphasis on the mechanism model for methane confined in a single nanopore; [...] Read more.
As a key technical aspect contributing to shale gas development, nanoconfined methane flow behavior has received tremendous research interest, which remains challenging to understand clearly. The majority of previous contributions put emphasis on the mechanism model for methane confined in a single nanopore; at the same time, the other part focusing on an upscaling approach fails to capture the spatial pore-network characteristics as well as the way to assign pressure conditions to methane flow behavior. In light of the current knowledge gap, pore-network modeling is performed, in which a pore coordination number, indicating the maximum pores a specified pore can connect, gas flow regimes classified by Knudsen numbers, as well as different assigned pressure conditions, are incorporated. Notably, the pore-network modeling is completely self-coded, which is more flexible in adjusting the spatial features of a constructed pore network than a traditional one. In this paper, the nanoconfined methane flow behavior is elaborated first, then the pore network modeling method based on the mass conservation principle is introduced for upscaling, and in-depth analysis is implemented after that. Results show that (a) as for porous media with pore sizes ranging from 5~80 nm, dramatic advancement on apparent gas permeability takes place while pressure is less than 1 MPa; (b) apparent gas permeability evaluated at a specified pressure shall be underestimated by as much as 31.1% on average compared with that under the pressure-difference condition; (c) both a large pore size and a high coordination number are beneficial for strong gas flow capacity through nanoscale porous media, and the rising ratio can reach about 6 times by altering the coordination number from 3 to 7, which is quantified and presented for the first time. Full article
(This article belongs to the Special Issue Advances in Enhancing Unconventional Oil/Gas Recovery)
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<p>Mimicking methane flow behavior with different approaches.</p> Full article ">Figure 2
<p>Construction of porous media with assigned spatial pore network attributes (<b>a</b>): Pore size distribution; (<b>b</b>): Spatial pore structure.</p> Full article ">Figure 3
<p>Apparent gas permeability under the specified pressure condition (<b>a</b>): Gas flow capacity; (<b>b</b>): Spatial pressure distribution feature.</p> Full article ">Figure 4
<p>Apparent gas permeability under the displacement pressure-difference condition (<b>a</b>): Gas transport capacity; (<b>b</b>): Spatial pressure distribution feature.</p> Full article ">Figure 5
<p>Discrepancy caused by different ways to assign pressure condition.</p> Full article ">Figure 6
<p>Different pore size distributions of the nanoscale porous media.</p> Full article ">Figure 7
<p>Impact of PSD on apparent gas permeability versus pressure.</p> Full article ">Figure 8
<p>Impact of pore coordination number on apparent gas permeability.</p> Full article ">
24 pages, 5704 KiB  
Article
Assessing Waste Marble Powder Impact on Concrete Flexural Strength Using Gaussian Process, SVM, and ANFIS
by Nitisha Sharma, Mohindra Singh Thakur, Raj Kumar, Mohammad Abdul Malik, Ahmad Aziz Alahmadi, Mamdooh Alwetaishi and Ali Nasser Alzaed
Processes 2022, 10(12), 2745; https://doi.org/10.3390/pr10122745 - 19 Dec 2022
Cited by 15 | Viewed by 2098
Abstract
The study’s goal is to assess the flexural strength of concrete that includes waste marble powder using machine learning methods, i.e., ANFIS, Support vector machines, and Gaussian processes approaches. Flexural strength has also been studied by using the most reliable approach of sensitivity [...] Read more.
The study’s goal is to assess the flexural strength of concrete that includes waste marble powder using machine learning methods, i.e., ANFIS, Support vector machines, and Gaussian processes approaches. Flexural strength has also been studied by using the most reliable approach of sensitivity analysis in order to determine the influential independent variable to predict the dependent variable. The entire dataset consists of 202 observations, of which 120 were experimental and 82 were readings from previous research projects. The dataset was then arbitrarily split into two subsets, referred to as the training dataset and the testing dataset, each of which contained a weighted percentage of the total observations (70–30). Output was concrete mix flexural strength, whereas inputs comprised cement, fine and coarse aggregates, water, waste marble powder, and curing days. Using statistical criteria, an evaluation of the efficacy of the approaches was carried out. In comparison to other algorithms, the results demonstrate that the Gaussian process technique has a lower error bandwidth, which contributes to its superior performance. The Gaussian process is capable of producing more accurate predictions of the results of an experiment due to the fact that it has a higher coefficient of correlation (0.7476), a lower mean absolute error value (1.0884), and a smaller root mean square error value (1.5621). The number of curing days was identified as a significant predictor, in addition to a number of other factors, by sensitivity analysis. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems)
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<p>The architecture of ANFIS model.</p> Full article ">Figure 2
<p>Chemical Analysis of Marble Powder.</p> Full article ">Figure 3
<p>Experimental Setup for Flexural Strength [<a href="#B58-processes-10-02745" class="html-bibr">58</a>].</p> Full article ">Figure 4
<p>FS Results Based on Experimental Studies.</p> Full article ">Figure 5
<p>Pair Plot of all Variables.</p> Full article ">Figure 6
<p>The scatter graph shows observed and expected ANFIS FS values.</p> Full article ">Figure 7
<p>The scatter graph shows SVM-predicted and observed FS values.</p> Full article ">Figure 7 Cont.
<p>The scatter graph shows SVM-predicted and observed FS values.</p> Full article ">Figure 8
<p>The scatter graph displays the observed and GP-predicted FS.</p> Full article ">Figure 8 Cont.
<p>The scatter graph displays the observed and GP-predicted FS.</p> Full article ">Figure 9
<p>The scatter plot shows observed and predicted values of FS from SVM, GP, and ANFIS.</p> Full article ">Figure 9 Cont.
<p>The scatter plot shows observed and predicted values of FS from SVM, GP, and ANFIS.</p> Full article ">Figure 10
<p>Performance Evaluation by Statistical Parameters.</p> Full article ">Figure 10 Cont.
<p>Performance Evaluation by Statistical Parameters.</p> Full article ">Figure 11
<p>Box plots for all Testing Dataset models.</p> Full article ">Figure 12
<p>Relationship between Flexural Strength and MAE.</p> Full article ">Figure 13
<p>Relationship between Removed Parameters and CC Value Based on GP Model.</p> Full article ">
19 pages, 12933 KiB  
Article
Developing Design Approaches for Tile Pattern Designs Inspired by Traditional Textile Patterns
by Arus Kunkhet and Disaya Chudasri
Processes 2022, 10(12), 2744; https://doi.org/10.3390/pr10122744 - 19 Dec 2022
Cited by 2 | Viewed by 4546
Abstract
This article presents multidisciplinary research that involved design (i.e., textiles, tiles, pattern design), mathematics (i.e., symmetry and seven frieze groups) and a viewpoint on product design and development for business opportunities. This research comprised a design experiment and a survey. In the design [...] Read more.
This article presents multidisciplinary research that involved design (i.e., textiles, tiles, pattern design), mathematics (i.e., symmetry and seven frieze groups) and a viewpoint on product design and development for business opportunities. This research comprised a design experiment and a survey. In the design experiment, two design approaches were created to translate the characteristics of traditional textile patterns into new pattern designs for floor tiles. These two design approaches were entitled: “partial replication”, and “combination and simplification”. The seven frieze groups were used as a transformation rule in both design approaches, resulting in two sets of frieze patterns. Although they were derived from the same origin, they looked different. A survey was conducted with 61 respondents to gain outsiders’ perspectives on these new pattern designs. The findings include: (i) positive responses to applying traditional textile patterns to other products, (ii) plausible products for pattern designs, (iii) preferences for design approaches and frieze patterns and (iv) opportunities for design research and education with other disciplines. This paper concludes with theoretical and practical implications for further research. Full article
(This article belongs to the Special Issue Reinventing Processes for Sustainability via Process Intensification and Integration)
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Figure 1
<p>“Sin tin chok” from Komol Phaboraan Museum, Long district, Phrae province. (Photo by Chudasri).</p> Full article ">Figure 2
<p>A square tile arranged in various layouts. (Illustration by Kunkhet).</p> Full article ">Figure 3
<p>Seven frieze groups. Red arrows indicate translation distance. Red lines indicate axes of symmetry (horizontal and vertical). Dashed red lines indicate horizontal glide reflection axes of symmetry. Red dots indicate twofold rotation axes in each part. (Illustration by Kunkhet and Chudasri).</p> Full article ">Figure 4
<p>Research methodology. (Illustration by Kunkhet and Chudasri).</p> Full article ">Figure 5
<p>Three pattern sets shown with original photographs and computer-generated images (images from Chudasri).</p> Full article ">Figure 6
<p>Asymmetric units and adjustments on a square tile. (Illustration by Kunkhet and Chudasri).</p> Full article ">Figure 7
<p>Design derivations from ‘design approach 1’. F1–F7 represent sets of symbols for the seven frieze groups as indicated in <a href="#processes-10-02744-f003" class="html-fig">Figure 3</a>. Red arrows indicate tile placement in a horizontal orientation, similarly to translation. (Illustration by Kunkhet and Chudasri).</p> Full article ">Figure 8
<p>Asymmetric units from three main parts and adjustments on square tiles. Red arrows indicate stretching in a horizontal orientation. Blue arrows indicate the unit adjustments or compliance to the square. (Illustration by Kunkhet and Chudasri).</p> Full article ">Figure 9
<p>Mapping asymmetric units in the design structure, followed by simplification. (Illustration by Kunkhet and Chudasri).</p> Full article ">Figure 10
<p>Design derivations from ‘design approach 2’. F1–F7 represent sets of symbols for the seven frieze groups as indicated in <a href="#processes-10-02744-f003" class="html-fig">Figure 3</a>. Red arrows indicate tile placement in a horizontal orientation, similarly to translation. (Illustration by Kunkhet and Chudasri).</p> Full article ">Figure 11
<p>Numbers of respondents classified by generation, shown in percentage.</p> Full article ">Figure 12
<p>Two design approaches for selection. (Illustration by Kunkhet).</p> Full article ">Figure 13
<p>Percentage of respondents’ preferences for two design approaches.</p> Full article ">Figure 14
<p>Design approach 1: seven frieze patterns and respondents’ preferences. (Illustration by Kunkhet and Chudasri).</p> Full article ">Figure 15
<p>Design approach 2: seven frieze patterns and respondents’ preferences. (Illustration by Kunkhet and Chudasri).</p> Full article ">
15 pages, 6087 KiB  
Article
Analysis of Laser Cutting Process for Different Diagonal Material Shapes
by Jungsoo Choi, Ryoonhan Kim, Danbi Song, Dae-Won Cho, Jeong Suh, Seonmin Kim and Sang-Hyun Ahn
Processes 2022, 10(12), 2743; https://doi.org/10.3390/pr10122743 - 19 Dec 2022
Cited by 8 | Viewed by 2404
Abstract
In this study, the laser cutting characteristics were analyzed according to the shape of the back side of the specimen, and the laser cutting characteristics were compared according to the thickness of the edge (10 mm, 20 mm, and 30 mm). A Yb-YAG [...] Read more.
In this study, the laser cutting characteristics were analyzed according to the shape of the back side of the specimen, and the laser cutting characteristics were compared according to the thickness of the edge (10 mm, 20 mm, and 30 mm). A Yb-YAG laser was used in this study, and the cutting target was STS304 with a thickness of 50 mm, and the cutting process was analyzed using a high-speed camera. In the experiment, it was found through image analysis that the cutting performance was excellent at 30 mm thickness of the edge. In order to analyze this reason, a thermal conduction analysis (numerical simulation) was performed, and it was confirmed that the thicker thickness of the edge caused a preheating effect during laser cutting due to a large amount of heat accumulation. This effect can be used as a reference for the initial processing state while cutting thick metals as it is a characteristic that has not been revealed before. Full article
(This article belongs to the Special Issue Advanced Technologies in Laser Materials Processing)
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<p>Specimen shapes.</p> Full article ">Figure 2
<p>Experimental setup: (<b>a</b>) Schematic and (<b>b</b>) laser cutting experimental set-up.</p> Full article ">Figure 3
<p>Cut specimen images: (<b>a</b>) case 1, (<b>b</b>) case 2, (<b>c</b>) case 3, and (<b>d</b>) case 4.</p> Full article ">Figure 4
<p>Longitudinal cross section of cut specimen: (<b>a</b>) case 2, (<b>b</b>) case 3, and (<b>c</b>) case 4. (<b>d</b>) Schematic of case 2 cause analysis.</p> Full article ">Figure 5
<p>Molten pool behavior and evacuation captured by the high-speed camera in case 1 at (<b>a</b>) t: 4.0 s, (<b>b</b>) t: 6.0 s, (<b>c</b>) t: 8.0 s, (<b>d</b>) t: 10.0 s, (<b>e</b>) t: 8.3 s, and (<b>f</b>) t: 8.5 s.</p> Full article ">Figure 6
<p>Molten pool behavior and evacuation captured by the high-speed camera in case 2 at (<b>a</b>) t: 2.0 s, (<b>b</b>) t: 4.0 s, (<b>c</b>) t: 6.0 s, (<b>d</b>) t: 8.0 s, (<b>e</b>) t: 10.0 s, (<b>f</b>) t: 12.0 s, (<b>g</b>) t: 14.0 s, (<b>h</b>) t: 16.0 s, (<b>i</b>) t: 18.0 s, (<b>j</b>) t: 20.0 s, (<b>k</b>) t: 20.7 s, (<b>l</b>) t: 20.8 s, and (<b>m</b>) t: 20.9 s.</p> Full article ">Figure 7
<p>Molten pool behavior and evacuation captured by the high-speed camera in case 3 at (<b>a</b>) t: 2.0 s, (<b>b</b>) t: 4.0 s, (<b>c</b>) t: 6.0 s, (<b>d</b>) t: 8.0 s, (<b>e</b>) t: 10.0 s, (<b>f</b>) t: 12.0 s, (<b>g</b>) t: 14.0 s, (<b>h</b>) t: 16.0 s, (<b>i</b>) t: 18.0 s, (<b>j</b>) t: 20.0 s, (<b>k</b>) t: 22.0 s, and (<b>l</b>) 24.0 s.</p> Full article ">Figure 8
<p>Molten pool behavior and evacuation captured by the high-speed camera in case 4 at (<b>a</b>) t: 2.0 s, (<b>b</b>) t: 4.0 s, (<b>c</b>) t: 6.0 s, (<b>d</b>) t: 8.0 s, (<b>e</b>) t: 10.0 s, (<b>f</b>) t: 12.0 s, (<b>g</b>) t: 14.0 s, (<b>h</b>) t: 16.0 s, (<b>i</b>) t: 18.0 s, (<b>j</b>) t: 20.0 s, (<b>k</b>) t: 22.0 s, and (<b>l</b>) t: 24.0 s.</p> Full article ">Figure 9
<p>Location of molten pool evacuation on the bottom surface along the x-direction for a variable time.</p> Full article ">Figure 10
<p>Boundary condition of simulation (symmetry).</p> Full article ">Figure 11
<p>Numerical simulation of heat conduction for different cases: (<b>a</b>) half scale of case 2 and (<b>b</b>) half scale of case 4.</p> Full article ">Figure 12
<p>Laser cutting simulation result of cutting surface. (<b>a</b>) Cut surface at 10 mm in the x-direction of case 2 (<b>b</b>) Cut at 10 mm in the x-direction of case 4.</p> Full article ">Figure 13
<p>Laser cutting simulation results (<b>a</b>) Enlarged section of case 2 (<b>b</b>) enlarged section of case 4.</p> Full article ">Figure 14
<p>Comparison of simulation temperature for each position along x-direction for case 2 and case 4.</p> Full article ">Figure 15
<p>Visualization of thermal conduction according to the cut position of the specimen. (side-view) (<b>a</b>) case 2: 15 mm cut point, (<b>b</b>) case 2: 30 mm cut point, (<b>c</b>) case 2: 40 mm cut point, (<b>d</b>) case 4: 15 mm cut point, (<b>e</b>) case 4: 30 mm cut point, and (<b>f</b>) case 4: 40 mm cut point.</p> Full article ">Figure 16
<p>Visualization of thermal conduction according to the cut position of the specimen. (front side view). (<b>a</b>) case 2: 15 mm cut point, (<b>b</b>) case 2: 30 mm cut point, (<b>c</b>) case 2: 40 mm cut point, (<b>d</b>) case 4: 15 mm cut point, (<b>e</b>) case 4: 30 mm cut point, and (<b>f</b>) case 4: 40 mm cut point.</p> Full article ">
12 pages, 2244 KiB  
Article
Interaction between Iron Fluoride and Molten FLiBe
by Stepan P. Arkhipov, Yury P. Zaikov, Pavel A. Arkhipov and Albert R. Mullabaev
Processes 2022, 10(12), 2742; https://doi.org/10.3390/pr10122742 - 19 Dec 2022
Cited by 4 | Viewed by 2060
Abstract
The equilibrium potentials of iron in a LiF-BeF2-FeF2 melt were measured using the EMF method and were dependent upon the temperature and iron fluoride concentrations. The empirical equations for the isotherms and equilibrium polytherms of the iron fluoride concentration were [...] Read more.
The equilibrium potentials of iron in a LiF-BeF2-FeF2 melt were measured using the EMF method and were dependent upon the temperature and iron fluoride concentrations. The empirical equations for the isotherms and equilibrium polytherms of the iron fluoride concentration were obtained. The cathode polarization of iron fluoride in the molten mixture of lithium and beryllium fluoride was measured using the current switch off method from the stationary state. It was found that in the studied temperature and concentration ranges of iron fluoride in the LiF-BeF2 electrolyte, the valence state of iron in the melt is mainly +2. According to the experimental values of the equilibrium potentials of the iron electrode in the LiF-BeF2-FeF2 melt, the conditional standard potentials of iron were calculated relative to the fluoride reference electrode in the molten mixture of lithium and beryllium fluoride. The conditional standard values of the Gibbs energy change were calculated at the formation of iron difluoride from the element in the form of dilute solutions, as were the thermodynamic values (enthalpy and entropy) when iron difluoride was mixed with LiF-BeF2. Full article
(This article belongs to the Special Issue Electrochemical Technology for New Materials Synthesis and Reprocessing)
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<p>Schematic of the electrochemical cell. 1—molybdenum electrodes; 2—alundum straw; 3—molybdenum current lead; 4—nickel current lead; 5—fluoroplastic cover; 6—quartz retort; 7—glassy carbon ampule; 8—heat-resistant boron nitride screen; 9—nickel electrode; 10—nitride-boron isolator; 11—iron electrode; 12—melt; 13—graphite platform.</p> Full article ">Figure 2
<p>Time–potential dependence in the 0.66LiF–0.34BeF<sub>2</sub> melt. Concentration of Fe was 0.0338 wt.%, T = 923 K; the time between the measurements was 15 min.</p> Full article ">Figure 3
<p>Cyclic voltammograms in the 0.66LiF–0.34BeF<sub>2</sub>–FeF<sub>2</sub> melt at T = 923 K and iron concentrations, wt.%: 1—0.0992; 2—0.0651; 3—0.0551; 4—0.0338; 5—0.0031.</p> Full article ">Figure 4
<p>Temperature dependence of electrode potentials of iron in the LiF–BeF<sub>2</sub> melt. The values of conditional standard potentials of iron: 1—present paper; 2—[<a href="#B32-processes-10-02742" class="html-bibr">32</a>]. Equilibrium potentials at iron concentrations in the melt, wt.%: 3—0.0338; 4—0.0559; 5—0.0651; 6—0.0992.</p> Full article ">Figure 5
<p>Cathode polarization of iron electrodes relative to the beryllium reference electrodes at a temperature of 923 K in the 0.66LiF–0.34BeF<sub>2</sub> melt. The iron concentration, wt.%: 1—0.0031; 2—0.0338; 3—0.0651; 4—0.0992.</p> Full article ">Figure 6
<p>Straight line regions of iron electrode polarization in the 0.66LiF–0.34BeF<sub>2</sub> melt<sub>,</sub> wt.%: 1—0.0031; 2—0.0338; 3—0.0651; 4—0.0992.</p> Full article ">
14 pages, 2825 KiB  
Article
Regional Geological Disasters Emergency Management System Monitored by Big Data Platform
by Xiaoping Qian
Processes 2022, 10(12), 2741; https://doi.org/10.3390/pr10122741 - 19 Dec 2022
Cited by 2 | Viewed by 1834
Abstract
In order to deal with the hazards caused by geological disasters in time, an emergency management system is proposed based on association rule data mining. With the support of a big data platform, a regional geological disaster emergency management system is built based [...] Read more.
In order to deal with the hazards caused by geological disasters in time, an emergency management system is proposed based on association rule data mining. With the support of a big data platform, a regional geological disaster emergency management system is built based on monitoring data. In the result analysis, the association rule algorithm demonstrates high computing power in the test, which can filter the data with strong association rules. In addition, the big data platform can allow data visualization, which has good data storage capacity and disaster early warning capacity. In the simulation test of the emergency management system, it was found that the system is feasible in theory. When it is applied to the actual disaster emergency management, it wasfound that, in the face of geological disasters, the processing speed of relevant departments increased by 59.4%, and the allocation of personnel and materials wasmore reasonable. The above results show that the big data platform monitoring data can improve the regional geological disasters emergency management capacity and ensure the safety of people’s lives and property. Full article
(This article belongs to the Special Issue Modeling, Operation and Planning in Engineering System Problems)
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<p>Geological hazard collection and treatment process.</p> Full article ">Figure 2
<p>Big data platform environment construction.</p> Full article ">Figure 3
<p>Emergency management system based on big data platform.</p> Full article ">Figure 4
<p>Apriori algorithm execution time.</p> Full article ">Figure 5
<p>Running time of association rule algorithm.</p> Full article ">Figure 6
<p>Data sensitivity analysis of big data platform.</p> Full article ">Figure 7
<p>Visualization effect analysis of big data platform.</p> Full article ">Figure 8
<p>Early warning performance analysis of big data platform.</p> Full article ">Figure 9
<p>Simulation analysis of emergency management system.</p> Full article ">Figure 10
<p>Practical application analysis of emergency management system.</p> Full article ">
15 pages, 4212 KiB  
Article
Analysis of Influencing Factors in Pilot Experiment for Synthesis of Natural Gas Hydrate by Spray Method
by Yun Ma, Jinzhao Zhu, Qingguo Meng, Chunxiao Ding, Jinbing Teng, Xin Wang and Qian Lu
Processes 2022, 10(12), 2740; https://doi.org/10.3390/pr10122740 - 19 Dec 2022
Cited by 1 | Viewed by 1764
Abstract
In recent years, the technology of storing and transporting natural gas in the form of hydrate has received a lot of attention. At present, the research on the synthesis of natural gas hydrate for the purpose of storage and transportation is still in [...] Read more.
In recent years, the technology of storing and transporting natural gas in the form of hydrate has received a lot of attention. At present, the research on the synthesis of natural gas hydrate for the purpose of storage and transportation is still in the laboratory stage, and its synthesis process is in the design and conception stage. The influencing factors of natural gas hydrate synthesis under pilot-scale conditions are more complex. Moreover, pilot experiments are oriented to actual production, and its economic feasibility and operational convenience have higher requirements. This paper aimed to study the influencing factors of gas hydrate synthesis by spray method under pilot-scale conditions. Under specific conditions of surfactant and pressure, we carried out research on the effects of reaction temperature, different forms of atomizers, high-pressure pump flow, experimental water, and other factors. Experiments show that the optimal synthesis conditions were a temperature of −5 °C, a pressure of 5 MPa, a conical nozzle, a generated gas hydrate as the hydrate of type I structure, and a gas storage capacity of 1:123 (gas–water ratio). Full article
(This article belongs to the Special Issue Production of Energy-Efficient Natural Gas Hydrate)
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<p>Reactor equipment.</p> Full article ">Figure 2
<p>Schematic diagram of main structure of the reactor.</p> Full article ">Figure 3
<p>Gas storage capacity measurement device.</p> Full article ">Figure 4
<p>Laser Raman spectrum of gas hydrate.</p> Full article ">Figure 5
<p>Curves of temperature and pressure change with time in the reactor.</p> Full article ">Figure 6
<p>The synthetic products and their distribution and state in the reactor.</p> Full article ">
12 pages, 701 KiB  
Article
Bt and G10evo-EPSPS Protein Expressed in ZDAB3 Corn Has No Impact on Nutritional Composition and Toxicological Safety
by Xiaoxing Yu, Guo Chen, Ziying Zhou, Xiaoyun Chen, Xiaoyun He, Yue Jiao and Pengfei Wang
Processes 2022, 10(12), 2739; https://doi.org/10.3390/pr10122739 - 19 Dec 2022
Cited by 2 | Viewed by 1875
Abstract
Genetically modified (GM) crops expressing insecticidal and herbicide-tolerant traits provide a new approach to agriculture production, but concerns about food safety were often raised by the public. The present research shows the findings of the nutritional assessment of ZDAB3 expressing insecticidal Cry proteins [...] Read more.
Genetically modified (GM) crops expressing insecticidal and herbicide-tolerant traits provide a new approach to agriculture production, but concerns about food safety were often raised by the public. The present research shows the findings of the nutritional assessment of ZDAB3 expressing insecticidal Cry proteins (Cry1Ab and Cry2Ab) and EPSPS protein (G10evo-EPSPS). The key nutrients and anti-nutrients of ZDAB3 maize were examined and contrasted with those of its non-transgenic control maize grown at the same locations during three planting seasons. The values for proximates, amino acids, fatty acids, minerals, vitamins, phytic acid, and trypsin inhibitor assessed for ZDAB3 were comparable to those of its non-transgenic control maize or within the range of values reported for other commercial lines. In addition, no adverse effects related to the G10evo-EPSPS protein in mammals were observed. These data indicated that the expression of Cry1Ab, Cry2Ab, and G10evo-EPSPS proteins in ZDAB3 maize does not affect the nutritional compositions, and ZDAB3 maize is equivalent to non-transgenic maize regarding those important compositions. Full article
(This article belongs to the Special Issue Genetically Modified Organisms Testing: Current Technology and Applications)
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<p>Safety assessment process of ZDAB3 in this study.</p> Full article ">Figure 2
<p>Identification of exogenous genes in GM maize ZDAB3 grain. (<b>a</b>) RT-PCR detection of the exogenous gene <span class="html-italic">cry1Ab</span>. Lane M: DL 5000 DNA Marker; Lane 1–2: non-GM maize; Lane 3–4: GM maize ZDAB3. (<b>b</b>) RT-PCR detection of the exogenous gene <span class="html-italic">cry2Ab</span>. Lane M: DL 5000 DNA Marker; Lane 1–2: non-GM maize; Lane 3–4: GM maize ZDAB3. (<b>c</b>) RT-PCR detection of the exogenous gene <span class="html-italic">g10evo-epsps</span>. Lane M: DL 5000 DNA Marker; Lane 1–2: non-GM maize; Lane 3–4: GM maize ZDAB3. (<b>d</b>) Protein levels of Cry1Ab, Cry2Ab, and G10evo-EPSPS in ZDAB3 grains.</p> Full article ">
20 pages, 8627 KiB  
Article
Modeling Study on Melt Flow, Heat Transfer, and Inclusion Motion in the Funnel-shaped Molds for Two Thin-Slab Casters
by Lin Xu, Qun-Wu Pei, Ze-Feng Han, Shuo Yang, Jian-Yu Wang and Yan-Tao Yao
Processes 2022, 10(12), 2738; https://doi.org/10.3390/pr10122738 - 19 Dec 2022
Cited by 7 | Viewed by 1941
Abstract
For the purpose of studying compact strip production (CSP) funnel-shaped mold and flexible thin-slab rolling (FTSR) funnel-shaped mold, a three-dimensional (3D) multi-field coupling mathematical model was established to describe the electromagnetic braking (EMBr) continuous casting process. To investigate the metallurgical effect of EMBr [...] Read more.
For the purpose of studying compact strip production (CSP) funnel-shaped mold and flexible thin-slab rolling (FTSR) funnel-shaped mold, a three-dimensional (3D) multi-field coupling mathematical model was established to describe the electromagnetic braking (EMBr) continuous casting process. To investigate the metallurgical effect of EMBr in the CSP and FTSR funnel-shaped thin-slab molds, a Reynolds-averaged Navier–Stokes (RANS) turbulence model, together with an enthalpy–porosity approach, was established to numerically simulate the effect of ruler EMBr on the behaviors of melt flow, heat transfer, solidification, and inclusion movement in high-speed casting. The simulation results indicate that the application of ruler EMBr in the CSP and FTSR molds shows great potential to improve the surface temperature of molten steel and reduce the penetration depth of downward backflow. This contributes to the melting of the slag rim near the meniscus region and facilitates the floating removal of the inclusions in the molten pool. In addition, in comparison with the case of no EMBr, the parametric study shows that the braking effect of ruler EMBr with an electromagnetic parameter of 0.5 T can enhance the upward backflow in the two high-speed thin-slab molds. The enhanced upward backflow can successfully entrain the inclusions to the top of the mold and improve the activity of surface fluctuations to avoid the formation of the slag rim. For instance, for the ruler EMBr applied to the FTSR mold, the maximum amplitude of surface fluctuation and the floatation removal quantity of inclusions with a diameter of 100 μm are increased by 4.6 percent and 51 percent, respectively. Full article
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<p>Schematic diagram of ruler EMBr device: (<b>a</b>) the geometric model of the ruler EMBr device and (<b>b</b>) the dimensions of the ruler EMBr device.</p> Full article ">Figure 2
<p>Schematic diagram of computational domain and mesh: (<b>a</b>) CSP mold and (<b>b</b>) FTSR mold.</p> Full article ">Figure 3
<p>Schematic diagram of submerged entry nozzle of the CSP mold: (<b>a</b>) the geometric model of the SEN and (<b>b</b>) the dimensions of the SEN.</p> Full article ">Figure 4
<p>Schematic diagram of submerged entry nozzle of the FTSR mold: (<b>a</b>) the geometric model of the SEN and (<b>b</b>) the dimensions of the SEN.</p> Full article ">Figure 5
<p>Schematic diagram of particle transport process in the CSP and FTSR molds.</p> Full article ">Figure 6
<p>Schematic distribution of shell surface velocity on the funnel region of the CSP mold: (<b>a</b>) <span class="html-italic">X</span>-velocity and (<b>b</b>) <span class="html-italic">Y</span>-velocity.</p> Full article ">Figure 7
<p>Schematic distribution of shell surface velocity on the funnel region of the FTSR mold: (<b>a</b>) <span class="html-italic">X</span>-velocity and (<b>b</b>) <span class="html-italic">Y</span>-velocity.</p> Full article ">Figure 8
<p>Distribution of magnetic flux density along the mold width direction.</p> Full article ">Figure 9
<p>Comparison between the predicted shell thickness and experimental measurements.</p> Full article ">Figure 10
<p>Electromagnetic characteristics in the central plane of the CSP mold: (<b>a</b>) magnetic flux density, (<b>b</b>) induced current density, and (<b>c</b>) electromagnetic force.</p> Full article ">Figure 11
<p>Electromagnetic characteristics in the central plane of the FTSR mold: (<b>a</b>) magnetic flux density, (<b>b</b>) induced current density, and (<b>c</b>) electromagnetic force.</p> Full article ">Figure 12
<p>Characteristics of molten steel flow field in the mold: (<b>a</b>) CSP mold and (<b>b</b>) FTSR mold.</p> Full article ">Figure 13
<p>Velocity (left half of each subfigure) and temperature (right half of each subfigure) distribution of molten steel in the central plane of the CSP mold thickness: (<b>a</b>) <span class="html-italic">B</span><sub>max</sub> = 0 T and (<b>b</b>) <span class="html-italic">B</span><sub>max</sub> = 0.5 T.</p> Full article ">Figure 14
<p>Velocity (left half of each subfigure) and temperature (right half of each subfigure) distribution of molten steel in the central plane of the FTSR mold thickness: (<b>a</b>) <span class="html-italic">B</span><sub>max</sub> = 0 T and (<b>b</b>) <span class="html-italic">B</span><sub>max</sub> = 0.5 T.</p> Full article ">Figure 15
<p>Effect of magnetic flux density on molten steel surface velocity in the two casters.</p> Full article ">Figure 16
<p>Effect of magnetic flux density on level fluctuation in the CSP mold: (<b>a</b>) <span class="html-italic">B</span><sub>max</sub> = 0 T and (<b>b</b>) <span class="html-italic">B</span><sub>max</sub> = 0.5 T.</p> Full article ">Figure 17
<p>Effect of magnetic flux density on level fluctuation in the FTSR mold: (<b>a</b>) <span class="html-italic">B</span><sub>max</sub> = 0 T and (<b>b</b>) <span class="html-italic">B</span><sub>max</sub> = 0.5 T.</p> Full article ">Figure 18
<p>Effect of magnetic flux density on solidified shell thickness in the two casters: (<b>a</b>) at the mold exit on the mold wide face and (<b>b</b>) at the center line on the mold narrow face.</p> Full article ">Figure 19
<p>Inclusion distribution on the free surface in the CSP mold: (<b>a</b>) <span class="html-italic">B</span><sub>max</sub> = 0 T and (<b>b</b>) <span class="html-italic">B</span><sub>max</sub> = 0.5 T.</p> Full article ">Figure 20
<p>Inclusion distribution on the free surface in the FTSR mold: (<b>a</b>) <span class="html-italic">B</span><sub>max</sub> = 0 T and (<b>b</b>) <span class="html-italic">B</span><sub>max</sub> = 0.5 T.</p> Full article ">
15 pages, 9119 KiB  
Article
Electrospinning Composites as Carriers of Natural Pigment: Screening of Polymeric Blends
by Sergiana dos Passos Ramos, Letícia Guerreiro da Trindade, Tatiana Martelli Mazzo, Elson Longo, Fabiana Perrechil Bonsanto, Veridiana Vera de Rosso and Anna Rafaela Cavalcante Braga
Processes 2022, 10(12), 2737; https://doi.org/10.3390/pr10122737 - 19 Dec 2022
Cited by 7 | Viewed by 2094
Abstract
Several studies have already demonstrated that electrospinning is an excellent tool for forming nano/microfibers. However, the number of parameters affecting the formation of the structures has become a great challenge, including the polymeric solutions’ rheological properties, directly affecting the morphology of the fibers [...] Read more.
Several studies have already demonstrated that electrospinning is an excellent tool for forming nano/microfibers. However, the number of parameters affecting the formation of the structures has become a great challenge, including the polymeric solutions’ rheological properties, directly affecting the morphology of the fibers formed. The present work aimed to produce polymeric composites and determine their rheological properties, comparing them to the morphology of the fibers formed by electrospinning. Also, to evaluate their potential use as the carriers of natural pigments. To this end, a distinct combination of solutions containing Chitosan/Gelatin, Chitosan/poly(ethylene) oxide (PEO) and Zein/PEO was produced and submitted to electrospinning. The sample containing zein manufactured the structures smaller in diameter (201.3 ± 58.6 nm) among those studied. Besides, it was observed that adding PEO to the solutions impacts the increase in viscosity and shear thinning behavior, guaranteeing uniformity in the structures formed. Natural pigments were successfully incorporated into the chosen zein/PEO solution, and it was observed that adding these compounds led to changes in the rheological characteristics, as expected. Nevertheless, it was possible to produce uniform fibers with diameters ranging from 665.68 ± 249.56 to 2874.44 ± 1187.40 nm, opening the possibility of using these natural pigments in biotechnological processes. Full article
(This article belongs to the Special Issue Plants as Functional Food Ingredients and Food Preservative)
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<p>Flow curves of (<b>A</b>) polymeric solutions and (<b>B</b>) solutions containing the bioactive compounds.</p> Full article ">Figure 2
<p>Tan δ obtained from frequency sweeps of (<b>A</b>) polymeric solutions and (<b>B</b>) solutions containing the bioactive compounds.</p> Full article ">Figure 3
<p>Field emission scanning microscopy images of samples 1 (<b>a</b>,<b>b</b>), 2 (<b>c</b>,<b>d</b>), 3 (<b>e</b>,<b>f</b>), 4 (<b>g</b>,<b>h</b>), 5 (<b>i</b>,<b>j</b>), 6 (<b>k</b>,<b>l</b>), 7 (<b>m</b>,<b>n</b>).</p> Full article ">Figure 3 Cont.
<p>Field emission scanning microscopy images of samples 1 (<b>a</b>,<b>b</b>), 2 (<b>c</b>,<b>d</b>), 3 (<b>e</b>,<b>f</b>), 4 (<b>g</b>,<b>h</b>), 5 (<b>i</b>,<b>j</b>), 6 (<b>k</b>,<b>l</b>), 7 (<b>m</b>,<b>n</b>).</p> Full article ">Figure 3 Cont.
<p>Field emission scanning microscopy images of samples 1 (<b>a</b>,<b>b</b>), 2 (<b>c</b>,<b>d</b>), 3 (<b>e</b>,<b>f</b>), 4 (<b>g</b>,<b>h</b>), 5 (<b>i</b>,<b>j</b>), 6 (<b>k</b>,<b>l</b>), 7 (<b>m</b>,<b>n</b>).</p> Full article ">Figure 4
<p>ATR-FTIR of samples 1 (<b>a</b>), 2 (<b>a</b>), 3 (<b>b</b>), 4 (<b>a</b>), 5 (<b>b</b>), 6 (<b>b</b>) and 7 (<b>a</b>).</p> Full article ">Figure 5
<p>Field emission scanning microscopy images of samples 8 (<b>a</b>,<b>b</b>), 9 (<b>c</b>,<b>d</b>), 10 (<b>e</b>,<b>f</b>) and 11 (<b>g</b>,<b>h</b>).</p> Full article ">Figure 5 Cont.
<p>Field emission scanning microscopy images of samples 8 (<b>a</b>,<b>b</b>), 9 (<b>c</b>,<b>d</b>), 10 (<b>e</b>,<b>f</b>) and 11 (<b>g</b>,<b>h</b>).</p> Full article ">Figure 6
<p><b>The</b> ATR-FTIR spectrum of the composite Zein and PEO composite, <span class="html-italic">Spirulina</span> composite, C-PC composite, carotenoids composite and anthocyanins composite samples.</p> Full article ">Figure 7
<p>Histogram of average fiber diameter of samples 8 (<b>a</b>), 9 (<b>b</b>), 10 (<b>c</b>) and 11 (<b>d</b>).</p> Full article ">
11 pages, 1907 KiB  
Article
Production of Ultra-Clean Coal by the Combined Method of Grinding and Collector Gasification Flotation
by Liang Shen, Jiabao Gong and Yifang Liu
Processes 2022, 10(12), 2736; https://doi.org/10.3390/pr10122736 - 19 Dec 2022
Cited by 2 | Viewed by 2156
Abstract
The preparation and application of ultra-clean coal is one of the important aspects of clean energy technology. However, the preparation of ultra-clean coal is mainly chemical methods, which are low in efficiency, high in energy consumption and expensive. It is urgent to find [...] Read more.
The preparation and application of ultra-clean coal is one of the important aspects of clean energy technology. However, the preparation of ultra-clean coal is mainly chemical methods, which are low in efficiency, high in energy consumption and expensive. It is urgent to find an effective method to prepare ultra-clean coal. In this paper, the combined method of grinding and the collector gasification flotation method was used to obtain ultra-clean coal. The effects of grinding time on the particle size composition, mineral dissociation, surface properties and flotation results of coal samples were studied. The grinding test results show that with the increase in grinding time, the particle size and the pore diameter of coal samples decreased gradually, while the specific surface area and pore volume of coal samples gradually increased. When the grinding time was 20 min, the D90 and D[4,3] of grinding products were 5.20 um and 4.23 um, respectively. The ash content of −1.3 g/cm3 was less than 1% when the grinding time was 20 min. Compared with the traditional flotation method, the collector gasification flotation method can obtain a higher concentrate yield and lower concentrate ash content. When the amount of collector was 2.0 kg/t, the yield of clean coal obtained by the collector gasification flotation method was 4.1% higher than that by the traditional flotation method, while the ash content of clean coal was 0.3% lower. Full article
(This article belongs to the Section Separation Processes)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Diagram of the collector gasification flotation method.</p> Full article ">Figure 2
<p>XRD analysis results of the coal samples.</p> Full article ">Figure 3
<p>Effect of grinding time on coal particle size distribution. (<b>a</b>) Particle size distribution curves. (<b>b</b>) D90 and D[4,3] of the particles.</p> Full article ">Figure 4
<p>Adsorption/desorption equilibrium curves of liquid nitrogen on coal samples with different grinding times. (<b>a</b>) 0 min, (<b>b</b>) 10 min, (<b>c</b>) 20 min.</p> Full article ">Figure 5
<p>Flotation results of collector gasification flotation method and traditional flotation method under different collector dosages.</p> Full article ">Figure 6
<p>SEM image of flotation-cleaned coal.</p> Full article ">
23 pages, 5895 KiB  
Article
Experimental Study and an RSM Modelling on Drilling Characteristics of the Sheep Horn Particle Reinforced Epoxy Composites for Structural Applications
by Chandrashekar Anjinappa, Manjunath Y. J, Omar Shabbir Ahmed, Mohamed Abbas, Ahmad Aziz Alahmadi, Mamdooh Alwetaishi and Ali Nasser Alzaed
Processes 2022, 10(12), 2735; https://doi.org/10.3390/pr10122735 - 19 Dec 2022
Cited by 4 | Viewed by 2044
Abstract
Recent environmental concern has been raised about the development of biocomposites because of their low cost, eco-friendliness, and biodegradability. Machining of polymeric composite is inevitable during assembly of structural components. In view of creating holes in structural composites, drilling is necessary and it [...] Read more.
Recent environmental concern has been raised about the development of biocomposites because of their low cost, eco-friendliness, and biodegradability. Machining of polymeric composite is inevitable during assembly of structural components. In view of creating holes in structural composites, drilling is necessary and it is essential to carry out research to find the optimal machining parameters. The experimental assessment and prediction of the thrust force and torque involved in drilling composites reinforced with sheep horn are presented in this work. The matrix and sheep horn particles were combined in the right proportions before being moulded and poured into a mould, then allowed to cure at room temperature. Investigated properties included ultimate tensile strength, flexural strength, and hardness. To evaluate the quality of the hole, micrographs of the drilled hole were employed. When the mixture was optimised based on the properties, it was found that a 70:30 ratio produced the best results. Thrust force and torque of 58 N and 4.8 N-mm, respectively, were observed for sheep horn filler laminates which were drilled using the combination of 6 mm diameter, 0.1 mm/rev feed rate, and 400 rpm speed. This is by far the best among the combinations used in the experiment. Additionally, the experimental outcomes indicate that the feed rate and spindle speed are the most significant factors affecting the thrust force. Since there were minimal errors in the comparison, the central composite design modelling is consummate. Overall, the extensive experimental effort offers several options to utilise this composite material in future applications across a wide range of fields. Full article
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<p>Experimental procedure.</p> Full article ">Figure 2
<p>Experimental setup.</p> Full article ">Figure 3
<p>Actual setup of drilling operation with dynamometer.</p> Full article ">Figure 4
<p>Fabricated composite with different diameter holes.</p> Full article ">Figure 5
<p>Light microscope used for hole quality assessment.</p> Full article ">Figure 6
<p>EDS analysis of sheep horn powder.</p> Full article ">Figure 7
<p>Effect of filler loading on (<b>a</b>) tensile strength, (<b>b</b>) flexural strength, and (<b>c</b>) hardness of sheep horn particle-reinforced composites.</p> Full article ">Figure 8
<p>Measured thrust force for input factors using dynamometer on composites. (<b>a</b>) Composition 70:30. (<b>b</b>) Composition 75:25. (<b>c</b>) Composition 80:20.</p> Full article ">Figure 8 Cont.
<p>Measured thrust force for input factors using dynamometer on composites. (<b>a</b>) Composition 70:30. (<b>b</b>) Composition 75:25. (<b>c</b>) Composition 80:20.</p> Full article ">Figure 9
<p>The 3D surface plots of the factors that effect thrust force, including the interactions between spindle speed and diameter (<b>a</b>), feed rate and spindle speed (<b>b</b>), and feed and diameter (<b>c</b>). Actual vs. predicted thrust force (<b>d</b>).</p> Full article ">Figure 9 Cont.
<p>The 3D surface plots of the factors that effect thrust force, including the interactions between spindle speed and diameter (<b>a</b>), feed rate and spindle speed (<b>b</b>), and feed and diameter (<b>c</b>). Actual vs. predicted thrust force (<b>d</b>).</p> Full article ">Figure 10
<p>The 3D surface plot of the variables controlling torque. (<b>a</b>) Interactions between the diameter and the feed rate, (<b>b</b>) diameter and the spindle speed, (<b>c</b>) spindle speed and feed rate. (<b>d</b>) Estimated vs. actual torque.</p> Full article ">Figure 11
<p>The 3D surface maps. (<b>a</b>) The interaction between feed rate and diameter on thrust force. (<b>b</b>) The interaction between feed and spindle speed. (<b>c</b>) The interaction between spindle speed and diameter. (<b>d</b>) Actual vs. predicted thrust force.</p> Full article ">Figure 12
<p>(<b>a</b>) The interaction between feed rate and diameter. (<b>b</b>) The interaction between feed and spindle speed. (<b>c</b>) Spindle speed and diameter interaction. (<b>d</b>) Actual vs. predicted torque.</p> Full article ">Figure 13
<p>(<b>a</b>) The interaction between feed rate and diameter with thrust force, whereas (<b>b</b>) shows the interaction between feed and spindle speed, and (<b>c</b>) shows the interaction between spindle speed and diameter. (<b>d</b>) Actual vs. predicted thrust force.</p> Full article ">Figure 14
<p>The 3D surface maps of the variables controlling torque. (<b>a</b>) Diameter and feed rate interaction. (<b>b</b>) Feed rate and spindle speed interaction. (<b>c</b>) Spindle speed and diameter interaction. (<b>d</b>) Actual vs. predicted torque.</p> Full article ">Figure 15
<p>Pareto chart for multiobjective optimisation. Objective (f1) = thrust force, objective (f2) = torque.</p> Full article ">Figure 16
<p>Hole quality assessment using light microscopy. (<b>a</b>) A 6 mm diameter drill, (<b>b</b>) 8 mm diameter drill, (<b>c</b>) 10 mm diameter drill.</p> Full article ">
29 pages, 5942 KiB  
Article
Processes in Doping System: Quantification Reports in Mixed Martial Arts Fighters
by Shyla Del-Aguila-Arcentales, Aldo Alvarez-Risco, Mercedes Rojas-Osorio, Hugo Meza-Perez, Gloria Rojas-Cangahuala, John Simbaqueba-Uribe, Niria Goñi Avila, Rosa Talavera-Aguirre, Luis Mayo-Alvarez and Jaime A. Yáñez
Processes 2022, 10(12), 2734; https://doi.org/10.3390/pr10122734 - 18 Dec 2022
Viewed by 3006
Abstract
Mixed martial arts (MMA) has always been surrounded by controversy due to the unusual muscle development of its participants, so it is crucial to know the strategies that have been implemented to reduce doping cases. The main purpose of this paper is to [...] Read more.
Mixed martial arts (MMA) has always been surrounded by controversy due to the unusual muscle development of its participants, so it is crucial to know the strategies that have been implemented to reduce doping cases. The main purpose of this paper is to describe the various cases of doping detected by USADA in UFC MMA participants. In addition, strategies that are being developed to reduce cases of positive doping are proposed. From the UFC USADA database, doping cases were extracted, obtaining the substance or substances involved; the formula, physiological effect and the athletes involved; the dates of the sampling; if it was out of competition or in-competition and the sanction time. The substances that were most involved were found to be Ostarine (22), Clomiphene (9), Diuretics (10) and Stanozolol (9). Some sanctions were diminished because they were treated with contamination of supplements (cases of Ostarine) and cases of contamination of meat (Clomiphene). When contaminated supplements were reported, they were added to the list of high-risk supplements maintained as part of USADA’s online dietary supplement safety education and awareness resource—Supplement 411. There were also cases in which positive doping could be avoided through the early report of therapeutic use exemptions. The methodology that the USADA has implemented allows us to register the athletes with positive doping, check the risk of the supplements before being bought and provide a teaching portal. These efforts are necessary to implement in all countries in which MMA is practiced, avoiding the participation of doped martial artists. Full article
(This article belongs to the Section Pharmaceutical Processes)
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Figure 1
<p>Chemical structure of 1-androstenedione. Formula: C<sub>19</sub>H<sub>26</sub>O<sub>2</sub>.</p> Full article ">Figure 2
<p>Chemical structure of amphetamine. Formula: C<sub>9</sub>H<sub>13</sub>N.</p> Full article ">Figure 3
<p>Chemical structure of anastrazole. Formula: C<sub>17</sub>H<sub>19</sub>N<sub>5</sub>.</p> Full article ">Figure 4
<p>Chemical structure of arimistane. Formula: C<sub>19</sub>H<sub>24</sub>O<sub>2</sub>.</p> Full article ">Figure 5
<p>Chemical structure of boldenone. Formula: C<sub>19</sub>H<sub>26</sub>O<sub>2</sub>.</p> Full article ">Figure 6
<p>Chemical structure of cardarine. Formula: C<sub>21</sub>H<sub>18</sub>F<sub>3</sub>NO<sub>3</sub>S<sub>2</sub>.</p> Full article ">Figure 7
<p>Chemical structure of clenbuterol. Formula: C<sub>12</sub>H<sub>18</sub>C<sub>l2</sub>N<sub>2</sub>O.</p> Full article ">Figure 8
<p>Chemical structure of clomiphene. Formula: C<sub>26</sub>H<sub>28</sub>ClNO.</p> Full article ">Figure 9
<p>Chemical structure of cocaine. Formula: C<sub>17</sub>H<sub>21</sub>NO<sub>4</sub>.</p> Full article ">Figure 10
<p>Chemical structure of DHEA. Formula: C<sub>19</sub>H<sub>28</sub>O<sub>2</sub>.</p> Full article ">Figure 11
<p>Chemical structure of DHCMT. Formula: C<sub>20</sub>H<sub>27</sub>ClO<sub>2</sub>.</p> Full article ">Figure 12
<p>Chemical structure of Furosemide.</p> Full article ">Figure 13
<p>Chemical structure of Hydrochlorothiazide.</p> Full article ">Figure 14
<p>Chemical structure of 4-amino-6-chloro-1,3-benzenedisulfonamide (ACB).</p> Full article ">Figure 15
<p>Chemical structure of drostanolone. Formula: C<sub>20</sub>H<sub>32</sub>O<sub>2</sub>.</p> Full article ">Figure 16
<p>Chemical structure of EPO. Formula: C<sub>815</sub>H<sub>1317</sub>N<sub>233</sub>O<sub>241</sub>S<sub>5</sub>.</p> Full article ">Figure 17
<p>Chemical structure of GHRP-6. Formula: C<sub>46</sub>H<sub>56</sub>N<sub>12</sub>O<sub>6</sub>.</p> Full article ">Figure 18
<p>Chemical structure of GHRP-2. Formula: C<sub>45</sub>H<sub>55</sub>N<sub>9</sub>O<sub>6</sub>.</p> Full article ">Figure 19
<p>Chemical structure of higenamine. Formula: C<sub>16</sub>H<sub>17</sub>NO<sub>3</sub>.</p> Full article ">Figure 20
<p>Chemical structure of hGH. Formula: C<sub>19</sub>H<sub>26</sub>O<sub>3</sub>.</p> Full article ">Figure 21
<p>Chemical structure of ibutamoren. Formula: C<sub>27</sub>H<sub>36</sub>N<sub>4</sub>O<sub>5</sub>S.</p> Full article ">Figure 22
<p>Chemical structure of IGF-1. Formula: C<sub>71</sub>H<sub>119</sub>N<sub>17</sub>O<sub>19</sub>S.</p> Full article ">Figure 23
<p>Chemical structure of ipamorelin. Formula: C<sub>38</sub>H<sub>49</sub>N<sub>9</sub>O<sub>5</sub>.</p> Full article ">Figure 24
<p>Chemical structure of ligandrol. Formula: C<sub>14</sub>H<sub>12</sub>F<sub>6</sub>N<sub>2</sub>O.</p> Full article ">Figure 25
<p>Chemical structure of meldonium. Formula: C<sub>6</sub>H<sub>14</sub>N<sub>2</sub>O<sub>2</sub>.</p> Full article ">Figure 26
<p>Chemical structure of methyltestosterone. Formula: C<sub>20</sub>H<sub>30</sub>O<sub>2</sub>.</p> Full article ">Figure 27
<p>Chemical structure of mesterolone. Formula: C<sub>20</sub>H<sub>32</sub>O<sub>2</sub>.</p> Full article ">Figure 28
<p>Chemical structure of methandienone. Formula: C<sub>20</sub>H<sub>28</sub>O<sub>2</sub>.</p> Full article ">Figure 29
<p>Chemical structure of modafinil. Formula: C<sub>15</sub>H<sub>15</sub>NO<sub>2</sub>S.</p> Full article ">Figure 30
<p>Chemical structure of nandrolone. Formula: C<sub>18</sub>H<sub>26</sub>O<sub>2</sub>.</p> Full article ">Figure 31
<p>Chemical structure of 19-norandrosterone. Formula: C<sub>18</sub>H<sub>28</sub>O<sub>2</sub>.</p> Full article ">Figure 32
<p>Chemical structure of ostarine. Formula: C<sub>19</sub>H<sub>14</sub>F<sub>3</sub>N<sub>3</sub>O<sub>3</sub>.</p> Full article ">Figure 33
<p>Chemical structure of ozone. Formula: O<sub>3</sub>.</p> Full article ">Figure 34
<p>Chemical structure of stanozolol. Formula: C<sub>21</sub>H<sub>32</sub>N<sub>2</sub>O.</p> Full article ">Figure 35
<p>Chemical structure of tamoxifen. Formula: C<sub>26</sub>H<sub>29</sub>NO.</p> Full article ">Figure 36
<p>Chemical structure of testosterone. Formula: C<sub>19</sub>H<sub>28</sub>O<sub>2</sub>.</p> Full article ">Figure 37
<p>Chemical structure of tetrahidrocannabinol. Formula: C<sub>21</sub>H<sub>30</sub>O<sub>2</sub>.</p> Full article ">Figure 38
<p>Chemical structure of trenbolone. Formula: C<sub>18</sub>H<sub>22</sub>O<sub>2</sub>.</p> Full article ">
13 pages, 2939 KiB  
Article
Synthesis and Characterization of Nanoformulation of the Broad-Spectrum Enzyme Inhibitor Mancozeb by Polyethylene Glycol Capping and Its Dissipation Kinetics in Water Using TiO2 Nanoparticles
by Wafa Mahmoud Daqa, Adil Alshoaibi, Faheem Ahmed and Tentu Nageswara Rao
Processes 2022, 10(12), 2733; https://doi.org/10.3390/pr10122733 - 18 Dec 2022
Cited by 2 | Viewed by 2042
Abstract
The poly(ethylene) glycol (PEG) capped mancozeb nanoformulation was prepared by the ultrasonic method using a 1% mancozeb solution and 20% capping agent, PEG-4000. The synthesized nanoformulation was characterized using UV-visible, FTIR, SEM and TEM techniques. The photolytic and photo catalytic experiments were carried [...] Read more.
The poly(ethylene) glycol (PEG) capped mancozeb nanoformulation was prepared by the ultrasonic method using a 1% mancozeb solution and 20% capping agent, PEG-4000. The synthesized nanoformulation was characterized using UV-visible, FTIR, SEM and TEM techniques. The photolytic and photo catalytic experiments were carried out in a Borosil glass bottle in the presence of sunlight, varying the pH proportions at a single fortification level (1.0 g/mL) in ground water, under sunlight. The optimal catalyst concentration for complete degradation was observed to be 0.1 percent. The mancozeb nanoformulation in water was determined using the HPLC-PDA method, and the rate constant and the 50% degradation (DT50) values were calculated based on the results. The photolytic results show that there is no significant loss of residues due to adsorption. Titanium dioxide (TiO2) was discovered to be an excellent decontaminating catalyst in a variety of water samples. The compound survives for several days in the absence of a catalyst. Full article
(This article belongs to the Section Pharmaceutical Processes)
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<p>X-Ray diffraction pattern of TiO<sub>2</sub> nanoparticles.</p> Full article ">Figure 2
<p>FTIR spectrum of TiO<sub>2</sub> nanoparticles.</p> Full article ">Figure 3
<p>FTIR spectrum of mancozeb nanoformulation.</p> Full article ">Figure 4
<p>SEM image of TiO<sub>2</sub> nanoparticles.</p> Full article ">Figure 5
<p>SEM image of mancozeb nanoformulation.</p> Full article ">Figure 6
<p>(<b>a</b>) TEM image of TiO<sub>2</sub> nanoparticles and (<b>b</b>) size distribution curve.</p> Full article ">Figure 7
<p>(<b>a</b>) TEM image of mancozeb nanoformulation and (<b>b</b>) size distribution curve.</p> Full article ">Figure 8
<p>Plot representing the effect of catalyst amount on decontamination of mancozeb in water under direct sunlight.</p> Full article ">Figure 9
<p>Graph representing the dissipation curve of photolytic decontamination of mancozeb in water under direct sunlight.</p> Full article ">Figure 10
<p>Graph representing the dissipation curve of photocatalytic decontamination of mancozeb in water under direct sunlight.</p> Full article ">Figure 11
<p>Representative photocatalytic 0th hour mancozeb standard, control and sample chromatograms.</p> Full article ">
21 pages, 1551 KiB  
Article
Smart Technology Prioritization for Sustainable Manufacturing in Emergency Situation by Integrated Spherical Fuzzy Bounded Rationality Decision-Making Approach
by Chia-Nan Wang, Thuy-Duong Thi Pham, Nhat-Luong Nhieu and Ching-Chien Huang
Processes 2022, 10(12), 2732; https://doi.org/10.3390/pr10122732 - 18 Dec 2022
Cited by 5 | Viewed by 1837
Abstract
The delays and disruptions during the pandemic have awakened interest in the sustainability and resilience of production systems to emergencies. In that context, the deployment of smart technologies has emerged as an almost mandatory development orientation to ensure the stability of manufacturing. The [...] Read more.
The delays and disruptions during the pandemic have awakened interest in the sustainability and resilience of production systems to emergencies. In that context, the deployment of smart technologies has emerged as an almost mandatory development orientation to ensure the stability of manufacturing. The core value of smart technologies is to reduce the dependence on human labor in production systems. Thereby, the negative impacts caused by emergency situations are mitigated. However, the implementation of smart technologies in a specific production system that already exists requires a high degree of suitability. Motivated by this fact, this study proposes an integrated spherical fuzzy bounded rationality decision-making approach, which is composite of the spherical fuzzy decision-making trial and evaluation laboratory (SF DEMATEL) and the spherical fuzzy regret theory-based combined compromise solution (R-SF CoCoSo) method. The proposed approach reflects both the ambiguities and psychological behaviors of decision-makers in prioritization problems. It was applied to prioritize seven smart technologies for manufacturing in Vietnam. The results show that reliability, costs, and maturity are the most important criteria for choosing smart technology which is suitable for an existing production system in Vietnam. Our findings seem to suggest that the automatic inspection, remote machine operation, and robots are the most suitable smart technologies to stabilize and sustain production in Vietnam for emergency situations. Full article
(This article belongs to the Special Issue Green Manufacturing and Sustainable Supply Chain Management)
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<p>The proposed spherical fuzzy bounded rationality decision-making approach.</p> Full article ">Figure 2
<p>Smart technologies and objectives in sustainable manufacturing.</p> Full article ">Figure 3
<p>Decision-makers’ weighting results.</p> Full article ">Figure 4
<p>Psychological behavior coefficients of decision-makers.</p> Full article ">Figure 5
<p>Criteria weighting results.</p> Full article ">Figure 6
<p>Stability coefficient sensitivity analysis results.</p> Full article ">
13 pages, 4063 KiB  
Article
Derivation and Verification of Gaussian Terrain Wake Model Based on Wind Field Experiment
by Wei Liu, Xiaoxun Zhu, Kaike Wang, Xiaoxia Gao, Shaohai Zhang, Lijiang Dong, Zeqi Shi, Hongkun Lu and Jie Zhou
Processes 2022, 10(12), 2731; https://doi.org/10.3390/pr10122731 - 17 Dec 2022
Cited by 2 | Viewed by 1909
Abstract
Aiming at the problem where the current engineering wake model does not describe the wind speed distribution of the wake in the complex terrain wind farm completely, based on the three-dimensional full wake model (3DJGF wake model), this paper proposed a wake model [...] Read more.
Aiming at the problem where the current engineering wake model does not describe the wind speed distribution of the wake in the complex terrain wind farm completely, based on the three-dimensional full wake model (3DJGF wake model), this paper proposed a wake model that can predict the three-dimensional wind speed distribution of the entire wake region in the complex wind farm, taking into account the Coanda effect, wind shear effect, and wake subsidence under the Gaussian terrain. Two types of Doppler lidar were used to conduct wind field experiments, and the inflow wind profile and three-dimensional expansion of the wake downstream of the wind turbine on the Gaussian terrain were measured. The experimental results showed that the wake centerline and terrain curve showed similar variation characteristics, and the near wake profile was similar to a super-Gaussian shape (asymmetric super-Gaussian shape) under low-wind-speed conditions, while the near wake profile presented a bimodal shape (asymmetric bimodal shape) under high-wind-speed conditions. The predicted profiles of the Gaussian terrain wake model were compared with the experimental data and the three typical wake models. The comparison results showed that the newly proposed Gaussian terrain wake model fit well with the experimental data in both near wake and far wake regions, and it had better performance in predicting the wake speed of the Gaussian terrain wind farm than the other three wake models. It can effectively predict the three-dimensional velocity distribution in the whole wake region of complex terrain. Full article
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<p>Schematic diagram of a double-Gaussian function.</p> Full article ">Figure 2
<p>Schematic diagram of the Coanda effect.</p> Full article ">Figure 3
<p>Schematic diagram of wind turbine wake center sinking on Gaussian terrain.</p> Full article ">Figure 4
<p>Schematic diagram of two types of lidar measurement.</p> Full article ">Figure 5
<p>Layout of lidars.</p> Full article ">Figure 6
<p>Wake sinking height curve.</p> Full article ">Figure 7
<p>Cloud image of 3-2 wind turbine horizontal profile measured by W3D6000.</p> Full article ">Figure 8
<p>Comparison results of horizontal profiles at four downstream locations.</p> Full article ">Figure 9
<p>Cloud image of 3-2 wind turbine vertical profile measured by W3D6000.</p> Full article ">Figure 10
<p>Comparison results of vertical profiles at four downstream locations.</p> Full article ">
19 pages, 5439 KiB  
Article
Numerical Modelling and Validation of Mixed-Mode Fracture Tests to Adhesive Joints Using J-Integral Concepts
by Luís F. R. Neves, Raul D. S. G. Campilho, Isidro J. Sánchez-Arce, Kouder Madani and Chander Prakash
Processes 2022, 10(12), 2730; https://doi.org/10.3390/pr10122730 - 17 Dec 2022
Cited by 7 | Viewed by 2280
Abstract
The interest in the design and numerical modelling of adhesively-bonded components and structures for industrial application is increasing as a research topic. Although research on joint failure under pure mode is widespread, applied bonded joints are often subjected to a mixed mode loading [...] Read more.
The interest in the design and numerical modelling of adhesively-bonded components and structures for industrial application is increasing as a research topic. Although research on joint failure under pure mode is widespread, applied bonded joints are often subjected to a mixed mode loading at the crack tip, which is more complex than the pure mode and affects joint strength. Failure of these joints under loading is the objective of predictions through mathematical and numerical models, the latter based on the Finite Element Method (FEM), using Cohesive Zone Modelling (CZM). The Single leg bending (bending) testing is among those employed to study mixed mode loading. This work aims to validate the application of FEM-CZM to SLB joints. Thus, the geometries used for experimental testing were reproduced numerically and experimentally obtained properties were employed in these models. Upon the validation of the numerical technique, a parametric study involving the cohesive laws’ parameters is performed, identifying the parameters with the most influence on the joint behaviour. As a result, it was possible to numerically model SLB tests of adhesive joints and estimate the mixed-mode behaviour of different adhesives, which enables mixed-mode modelling and design of adhesive structures. Full article
(This article belongs to the Special Issue Design of Adhesive Bonded Joints)
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<p>Geometry and dimensions of the SLB specimens, adapted from [<a href="#B10-processes-10-02730" class="html-bibr">10</a>].</p> Full article ">Figure 2
<p>Painting of the specimen face and scale location to aid measuring the crack propagation.</p> Full article ">Figure 3
<p>Experimental setup employed.</p> Full article ">Figure 4
<p>Schematic representation of <span class="html-italic">δ</span><sub>n</sub>, <span class="html-italic">δ</span><sub>s</sub> and <span class="html-italic">θ</span><sub>p</sub>.</p> Full article ">Figure 5
<p>Mesh details and boundary conditions for the SLB model. The upper right close-up shows the horizontal constraint at the centre span.</p> Full article ">Figure 6
<p>Schematic of a triangular cohesive law, adapted from [<a href="#B20-processes-10-02730" class="html-bibr">20</a>].</p> Full article ">Figure 7
<p><span class="html-italic">P</span>-<span class="html-italic">δ</span> curves for the Araldite<sup>®</sup> 2015 (<b>a</b>) and sample <span class="html-italic">P</span>-<span class="html-italic">δ</span> curves for each adhesive (<b>b</b>).</p> Full article ">Figure 8
<p>Sample <span class="html-italic">G</span><sub>I</sub>-<span class="html-italic">δ</span><sub>n</sub> and <span class="html-italic">G</span><sub>II</sub>-<span class="html-italic">δ</span><sub>s</sub> curves (<b>a</b>) and <span class="html-italic">R</span>-curves (<b>b</b>) for the Araldite<sup>®</sup> 2015.</p> Full article ">Figure 9
<p>Experimental fracture envelopes for the adhesives Araldite<sup>®</sup> AV138 (<b>a</b>), Araldite<sup>®</sup> 2015 (<b>b</b>), and SikaForce<sup>®</sup> 7752 (<b>c</b>).</p> Full article ">Figure 10
<p>Experimental CZM laws for the Araldite<sup>®</sup> 2015 data: tensile (<b>a</b>) and shear (<b>b</b>).</p> Full article ">Figure 11
<p>Comparison between numerical and experimental <span class="html-italic">P</span>-<span class="html-italic">δ</span> curves for the three adhesives studied: Araldite<sup>®</sup> AV138 (<b>a</b>), Araldite<sup>®</sup> 2015 (<b>b</b>), and SikaForce<sup>®</sup> 7752 (<b>c</b>).</p> Full article ">Figure 12
<p>Comparison between experimental and numerical values of <span class="html-italic">G</span><sub>IC</sub> (<b>a</b>) and <span class="html-italic">G</span><sub>IIC</sub> (<b>b</b>), by adhesive type.</p> Full article ">Figure 13
<p>Fracture envelopes for the three adhesives studied: Araldite<sup>®</sup> AV138 (<b>a</b>), Araldite<sup>®</sup> 2015 (<b>b</b>), and SikaForce<sup>®</sup> 7752 (<b>c</b>).</p> Full article ">Figure 14
<p>Effect of <span class="html-italic">G</span><sub>IC</sub> over the <span class="html-italic">P</span>-<span class="html-italic">δ</span> curve for the three adhesives: Araldite<sup>®</sup> AV138 (<b>a</b>), Araldite<sup>®</sup> 2015 (<b>b</b>), and SikaForce<sup>®</sup> 7752 (<b>c</b>). 0% corresponds to the reference case.</p> Full article ">Figure 15
<p>Effect of <span class="html-italic">t</span><sub>n</sub><sup>0</sup> on the <span class="html-italic">P</span>-<span class="html-italic">δ</span> curves for the three studied adhesives: Araldite<sup>®</sup> AV138 (<b>a</b>), Araldite<sup>®</sup> 2015 (<b>b</b>), and SikaForce<sup>®</sup> 7752 (<b>c</b>). 0% corresponds to the reference case.</p> Full article ">Figure 16
<p>Combined effect of the variation of <span class="html-italic">G</span><sub>IC</sub>, <span class="html-italic">G</span><sub>IIC</sub>, <span class="html-italic">t</span><sub>n</sub><sup>0</sup>, and <span class="html-italic">t</span><sub>s</sub><sup>0</sup> for the three adhesives studied: Araldite<sup>®</sup> AV138 (<b>a</b>), Araldite<sup>®</sup> 2015 (<b>b</b>), and SikaForce<sup>®</sup> 7752 (<b>c</b>). 0% corresponds to the reference case.</p> Full article ">
23 pages, 4294 KiB  
Article
Adsorption Characteristics and Mechanism of Methylene Blue in Water by NaOH-Modified Areca Residue Biochar
by Yixin Lu, Yujie Liu, Chunlin Li, Haolin Liu, Huan Liu, Yi Tang, Chenghan Tang, Aojie Wang and Chun Wang
Processes 2022, 10(12), 2729; https://doi.org/10.3390/pr10122729 - 17 Dec 2022
Cited by 7 | Viewed by 2187
Abstract
To solve the water pollution problem caused by methylene blue (MB), areca residue biochar (ARB) was prepared by pyrolysis at 600 °C, and modified areca residue biochar (M-ARB) was obtained by modifying ARB with 1.5 mol/L NaOH, and they were utilized to adsorb [...] Read more.
To solve the water pollution problem caused by methylene blue (MB), areca residue biochar (ARB) was prepared by pyrolysis at 600 °C, and modified areca residue biochar (M-ARB) was obtained by modifying ARB with 1.5 mol/L NaOH, and they were utilized to adsorb and eliminate MB from water. The structural characteristics of ARB and M-ARB were examined, and the main influencing factors and adsorption mechanism of MB adsorption process were investigated. The outcomes demonstrated an increase in M-ARB’s specific surface area and total pore volume of 66.67% and 79.61%, respectively, compared with ARB, and the pore structure was more abundant, and the content of oxygen element was also significantly increased. When the reaction temperature was 25 °C, starting pH of the mixture was 10, the initial MB concentration was 50 mg/L, the ARB and M-ARB dosages were 0.07 g/L and 0.04 g/L, respectively, the adsorption equilibrium was achieved at about 210 min, and the elimination rate for MB exceeded 94%. The adsorption behaviors of ARB and M-ARB on MB were more in line with the Langmuir isotherm model (R2 > 0.95) and the quasi-secondary kinetic model (R2 > 0.97), which was characterized by single-molecule layer chemisorption. The highest amount of MB that may theoretically be absorbed by M-ARB in water ranging from 136.81 to 152.72 mg/g was 74.99–76.59% higher than that of ARB. The adsorption process was a spontaneous heat absorption reaction driven by entropy increase, and the adsorption mechanism mainly involved electrostatic gravitational force, pore filling, hydrogen bonding, and π–π bonding, which was a complex process containing multiple mechanisms of action. NaOH modification can make the ARB have more perfect surface properties and more functional group structures that can participate in the adsorption reaction, which can be used as an advantageous adsorption material for MB removal in water. Full article
(This article belongs to the Special Issue Biomass Conversion and Organic Waste Utilization)
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<p>Scanning electron microscope of ARB (<b>a</b>) and M-ARB (<b>b</b>).</p> Full article ">Figure 2
<p>EDS analysis results of ARB (<b>a</b>) and M-ARB (<b>b</b>).</p> Full article ">Figure 3
<p>Effect of initial pH on MB adsorption performance.</p> Full article ">Figure 4
<p>Effect of biochar dosage on MB adsorption performance.</p> Full article ">Figure 5
<p>Effect of contact time on MB adsorption performance.</p> Full article ">Figure 6
<p>Effect of initial MB concentration on its adsorption performance.</p> Full article ">Figure 7
<p>Adsorption kinetics fitting of MB onto ARB and M-ARB: (<b>a</b>) quasi-primary kinetic model, (<b>b</b>) quasi-secondary kinetic model, and (<b>c</b>) Elovich model.</p> Full article ">Figure 8
<p>Segmented fit of the Weber–Morris model.</p> Full article ">Figure 9
<p>Isothermal adsorption fitting of Langmuir model (<b>a</b>), Freundlich model (<b>b</b>), and Temkin model (<b>c</b>).</p> Full article ">Figure 10
<p>Adsorption thermodynamic fitting of MB onto ARB and M-ARB.</p> Full article ">Figure 11
<p>FTIR spectra of ARB (<b>a</b>) and M-ARB (<b>b</b>) before and after MB adsorption.</p> Full article ">Figure 12
<p>Adsorption mechanism of ARB and M-ARB for MB.</p> Full article ">Figure 13
<p>Regeneration performance of ARB and M-ARB for MB adsorption.</p> Full article ">
11 pages, 1508 KiB  
Article
Temperature-Related N2O Emission and Emission Potential of Freshwater Sediment
by Shuai Li, Ang Yue, Selina Sterup Moore, Fei Ye, Jiapeng Wu, Yiguo Hong and Yu Wang
Processes 2022, 10(12), 2728; https://doi.org/10.3390/pr10122728 - 16 Dec 2022
Cited by 3 | Viewed by 1899
Abstract
Nitrous oxide (N2O) is a major radiative forcing and stratospheric ozone-depleting gas. Among natural sources, freshwater ecosystems are significant contributors to N2O. Although temperature is a key factor determining the N2O emissions, the respective effects of temperature [...] Read more.
Nitrous oxide (N2O) is a major radiative forcing and stratospheric ozone-depleting gas. Among natural sources, freshwater ecosystems are significant contributors to N2O. Although temperature is a key factor determining the N2O emissions, the respective effects of temperature on emitted and dissolved N2O in the water column of freshwater ecosystems remain unclear. In this study, 48 h incubation experiments were performed at three different temperatures; 15 °C, 25 °C, and 35 °C. For each sample, N2O emission, dissolved N2O in the overlying water and denitrification rates were measured, and N2O-related functional genes were quantified at regular intervals. The highest N2O emission was observed at an incubation of 35 °C, which was 1.5 to 2.1 factors higher than samples incubated at 25 °C and 15 °C. However, the highest level of dissolved N2O and estimated exchange flux of N2O were both observed at 25 °C and were both approximately 2 factors higher than those at 35 °C and 15 °C. The denitrification rates increased significantly during the incubation period, and samples at 25 °C and 35 °C exhibited much greater rates than those at 15 °C, which is in agreement with the N2O emission of the three incubation temperatures. The NO3− decreased in relation to the increase of N2O emissions, which confirms the dominant role of denitrification in N2O generation. Indeed, the nirK type denitrifier, which constitutes part of the denitrification process, dominated the nirS type involved in N2O generation, and the nosZ II type N2O reducer was more abundant than the nosZ I type. The results of the current study indicate that higher temperatures (35 °C) result in higher N2O emissions, but incubation at moderate temperatures (25 °C) causes higher levels of dissolved N2O, which represent a potential source of N2O emissions from freshwater ecosystems. Full article
(This article belongs to the Special Issue Nitrogen Cycling Processes in Coastal Ecosystems)
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<p>N<sub>2</sub>O emission (<b>a</b>), dissolved N<sub>2</sub>O (<b>b</b>), estimated N<sub>2</sub>O exchange flux (<b>c</b>) and N<sub>2</sub>O saturation (<b>d</b>) at incubations of 15 °C, 25 °C and 35 °C.</p> Full article ">Figure 2
<p>Denitrification and anammox rates at hours 0, 24 and 48 in 15 °C (<b>a</b>), 25 °C (<b>b</b>) and 35 °C (<b>c</b>). The concentration of NO<sub>3</sub><sup>−</sup> (<b>d</b>), NO<sub>2</sub><sup>−</sup> (<b>e</b>) and NH<sub>4</sub><sup>+</sup> (<b>f</b>) in the overlying water during incubation at 15, 25 and 35 °C. The a, b and c above the columns were the results tested by ANOVA. Different letters indicate significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3
<p>The abundance of N<sub>2</sub>O-related functional genes (<span class="html-italic">nirS</span>, <span class="html-italic">nirK</span>) (<b>a</b>), and (<span class="html-italic">nosZ</span> I, <span class="html-italic">nosZ</span> II) (<b>b</b>) at different temperatures and the ratio of (<span class="html-italic">nirS</span> + <span class="html-italic">nirK</span>)/(<span class="html-italic">nosZ</span> I + <span class="html-italic">nosZ</span> II) (<b>c</b>). (The a, b and c above the columns were the results tested by ANOVA. Different letters indicate significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Correlation of the dissolved N<sub>2</sub>O and NO<sub>3</sub><sup>−</sup> at 15 °C (<b>a</b>), dissolved N<sub>2</sub>O and <span class="html-italic">nirS</span>/<span class="html-italic">nirK</span> ratio at 25 °C (<b>b</b>), N<sub>2</sub>O emission and NO<sub>3</sub><sup>−</sup> at 35 °C (<b>c</b>). Dark points represent the mean values for each sampling time during 48-incubation, and light points represent all survey data.</p> Full article ">
18 pages, 1188 KiB  
Article
Agent-Based and Stochastic Optimization Incorporated with Machine Learning for Simulation of Postcombustion CO2 Capture Process
by Huilan Zheng, Gaurav Mirlekar and Lars O. Nord
Processes 2022, 10(12), 2727; https://doi.org/10.3390/pr10122727 - 16 Dec 2022
Cited by 1 | Viewed by 2319
Abstract
In this paper, a novel method is proposed for the incorporation of data-driven machine learning techniques into process optimization. Such integration improves the computational time required for calculations during optimization and benefits the online application of advanced control algorithms. The proposed method is [...] Read more.
In this paper, a novel method is proposed for the incorporation of data-driven machine learning techniques into process optimization. Such integration improves the computational time required for calculations during optimization and benefits the online application of advanced control algorithms. The proposed method is illustrated via the chemical absorption-based postcombustion CO2 capture process, which plays an important role in the reduction of CO2 emissions to address climate challenges. These processes simulated in a software environment are typically based on first-principle models and calculate physical properties from basic physical quantities such as mass and temperature. Employing first-principle models usually requires a long computation time, making process optimization and control challenging. To overcome this challenge, in this study, machine learning algorithms are used to simulate the postcombustion CO2 capture process. The extreme gradient boosting (XGBoost) and support vector regression (SVR) algorithms are employed to build models for prediction of carbon capture rate (CR) and specific reboiler duty (SRD). The R2 (a statistical measure that represents the fitness) of these models is, on average, greater than 90% for all the cases. XGBoost and SVR take 0.022 and 0.317 s, respectively, to predict CR and SRD of 1318 cases, whereas the first-principal process simulation model needs 3.15 s to calculate one case. The models built by XGBoost are employed in the optimization methods, such as an agent-based approach represented by the particle swarm optimization and stochastic technique indicated by the simulated annealing, to find specific optimal operating conditions. The most economical case, in which the CR is 72.2% and SRD is 4.3 MJ/kg, is obtained during optimization. The results show that computations with the data-driven models incorporated in the optimization technique are faster than first-principle modeling approaches. Thus, the application of machine learning techniques in the optimization of carbon capture technologies is demonstrated successfully. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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<p>Workflow diagram of the proposed method for data-driven process modeling and optimization.</p> Full article ">Figure 2
<p>Schematic of chemical absorption process representing postcombustion CO<sub>2</sub> capture (PCC).</p> Full article ">Figure 3
<p>Schematic of evolution from decision tree to XGBoost.</p> Full article ">Figure 4
<p>Plot of data correlation coefficients.</p> Full article ">Figure 5
<p>Plots of original data collected and smoothed curve for each independent variable and dependent variable. (<b>a</b>) Flue gas flow rate vs. capture rate/specific reboiler duty. (<b>b</b>) Lean amine flow rate vs. capture rate/specific reboiler duty. (<b>c</b>) CO<sub>2</sub> molar fraction in flue gas vs. capture rate/specific reboiler duty. (<b>d</b>) Lean amine loading vs. capture rate/specific reboiler duty.</p> Full article ">Figure 6
<p>Plots of errors for model prediction. (<b>a</b>) Error of capture rate prediction (XGBoost). (<b>b</b>) Error of SRD prediction (XGBoost). (<b>c</b>) Error of capture rate prediction (SVR). (<b>d</b>) Error of SRD prediction (SVR). All errors for model prediction are based on original data.</p> Full article ">
14 pages, 1803 KiB  
Article
Dynamics of Pressure Variation in Closed Vessel Explosions of Diluted Fuel/Oxidant Mixtures
by Venera Giurcan, Domnina Razus, Maria Mitu and Codina Movileanu
Processes 2022, 10(12), 2726; https://doi.org/10.3390/pr10122726 - 16 Dec 2022
Viewed by 2175
Abstract
Nitrous oxide is widely used as oxidizer or nitriding agent in numerous industrial activities such as production of adipic acid and caprolactam and even for production of some semiconductors. Further, it is used as an additive in order to increase the power output [...] Read more.
Nitrous oxide is widely used as oxidizer or nitriding agent in numerous industrial activities such as production of adipic acid and caprolactam and even for production of some semiconductors. Further, it is used as an additive in order to increase the power output of engines, and as an oxidizer in propulsion systems of rockets, because it has a large heat of formation (+81.6 kJ mol−1). N2O is highly exothermic, and during its decomposition a supplementary heat amount is released, so it needs special handling conditions. The combustion of fuels in nitrous oxide atmosphere can lead to high unstable and turbulent deflagrations that speedily self-accelerate and therefore a deflagration can change to a detonation. The peak explosion pressure and the maximum rate of pressure rise of explosions in confined spaces are key safety parameters to evaluate the hazard of processes running in closed vessels and for design of enclosures able to withstand explosions or of their vents used as relief devices. The present study reports some major explosion parameters such as the maximum (peak) explosion pressures pmax, explosion times θmax, maximum rates of pressure rise (dp/dt)max and severity factors KG for ethylene-nitrous oxide mixtures (lean and stoichiometric) diluted with various amounts of N2, at various initial pressures (p0 = 0.50–1.50 bar), in experiments performed in a spherical vessel centrally ignited by inductive-capacitive electric sparks. The influence of the initial pressure and composition on pmax, θmax and (dp/dt)max is discussed. The data are compared with similar values referring to ethylene-air mixtures measured in the same initial conditions. It was found that at identical C/O ratios with ethylene-air, ethylene-N2O-N2 mixtures develop higher explosion pressures and higher rates of pressure rise, due to the exothermic dissociation of N2O under flame conditions. Full article
(This article belongs to the Special Issue Chemical Looping Combustion, Gasification and Fuel Conversion Technology)
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<p>Experimental system.</p> Full article ">Figure 2
<p>Comparison of pressure-time records for stoichiometric C<sub>2</sub>H<sub>4</sub>-N<sub>2</sub>O-N<sub>2</sub> (60 vol.% N<sub>2</sub>) and stoichiometric C<sub>2</sub>H<sub>4</sub>-air [<a href="#B30-processes-10-02726" class="html-bibr">30</a>] explosions in a spherical vessel having central ignition at <span class="html-italic">p</span><sub>0</sub> = 1 bar and <span class="html-italic">T</span><sub>0</sub> = 298 K.</p> Full article ">Figure 3
<p>Pressure trajectories of stoichiometric C<sub>2</sub>H<sub>4</sub>/N<sub>2</sub>O/N<sub>2</sub> mixtures at <span class="html-italic">p</span><sub>0</sub> = 1 bar.</p> Full article ">Figure 4
<p>Maximum explosion pressures of stoichiometric C<sub>2</sub>H<sub>4</sub>-N<sub>2</sub>O-N<sub>2</sub> at various initial pressures.</p> Full article ">Figure 5
<p>Maximum explosion pressure as a function of inert concentrations at ambient initial temperature and pressure.</p> Full article ">Figure 6
<p>Pressure evolution for deflagration of stoichiometric C<sub>2</sub>H<sub>4</sub>-N<sub>2</sub>O-N<sub>2</sub> with 60 vol.% N<sub>2</sub>, at ambient initial conditions.</p> Full article ">Figure 7
<p>Maximum rates of pressure rise for stoichiometric (<b>a</b>) and lean (<b>b</b>) C<sub>2</sub>H<sub>4</sub>-N<sub>2</sub>O-N<sub>2</sub> mixtures with various amount of N<sub>2</sub>.</p> Full article ">Figure 8
<p>Severity factors for N<sub>2</sub> diluted lean and stoichiometric C<sub>2</sub>H<sub>4</sub>-N<sub>2</sub>O-N<sub>2</sub> mixtures.</p> Full article ">
21 pages, 8502 KiB  
Article
Digital Twin Modelling Method of Five-Axis Machine Tool for Predicting Continuous Trajectory Contour Error
by Dun Lyu, Jian Liu, Shiyou Luo, Shuo Liu, Qunlin Cheng and Hui Liu
Processes 2022, 10(12), 2725; https://doi.org/10.3390/pr10122725 - 16 Dec 2022
Cited by 1 | Viewed by 2510
Abstract
The CNC machine tool is the passive executor of machining code. It cannot predict the machining accuracy during machining. If the error is found to be out of tolerance after processing, it will not only scrap the parts, but also greatly affect the [...] Read more.
The CNC machine tool is the passive executor of machining code. It cannot predict the machining accuracy during machining. If the error is found to be out of tolerance after processing, it will not only scrap the parts, but also greatly affect the processing efficiency. This phenomenon is very prominent when machining sculptured surface parts with five-axis machine tools. Therefore, this paper proposes a Digital Twin (DT) modeling method of five-axis machine tools for predicting Continuous Trajectory Contour Error (CTCE) caused by tracking errors and geometric errors. The DT consists of three parts: the Setpoints Trajectory (ST) model, the Actual Trajectory (AT) model considering tracking errors and geometric errors and the CTCE model. For a specific machine tool, according to the basic geometric information of the machine tool (tool length, kinematic chain information, etc.) and 41 geometric errors, the DT can be established. Inputting the Setpoints Positions (SPs) and the Linear Encoder Detection Positions (LEDPs), the DT can be used to predict the Tool-Tip Position Trajectory (TTPT) contour error and the Tool Orientation Trajectory (TOT) contour error. In order to verify the proposed method experimentally, the KMC400S U five-axis machine tool is selected to establish its DT by which the contour error of the S-shaped trajectory are predicted offline. Then, the DMU50 five-axis machine tool is selected to establish its DT to predict the contour error of the circular trajectory in real time. Combined with the deep motion mechanism, this paper proposes a DT modeling method for the vertical application scene of parts machining accuracy prediction, which is of great significance to developing the DT application theory and ensuring the machining accuracy of parts. Full article
(This article belongs to the Special Issue High-Performance Machining Processes: From Mechanisms to Equipment)
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<p>The DT framework of five-axis CNC machine tool.</p> Full article ">Figure 2
<p>Error twist.</p> Full article ">Figure 3
<p>The geometric relationship diagram of CTCE.</p> Full article ">Figure 4
<p>The calculation model of the TTPT contour error.</p> Full article ">Figure 5
<p>The geometric relationship between <math display="inline"><semantics> <mrow> <mover> <mrow> <msub> <mi>ε</mi> <mi>p</mi> </msub> </mrow> <mo>→</mo> </mover> </mrow> </semantics></math> and the vector <math display="inline"><semantics> <mi>p</mi> </semantics></math> of <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <msub> <mi>P</mi> <mrow> <mi>r</mi> <mo>,</mo> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msub> <msub> <mi>P</mi> <mi>a</mi> </msub> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> in three cases. (<b>a</b>) Case 1; (<b>b</b>) Case 2; (<b>c</b>) Case 3.</p> Full article ">Figure 6
<p>The calculation model of the TOT contour error.</p> Full article ">Figure 7
<p>The structure of the five-axis machine tool used in the experiment.</p> Full article ">Figure 8
<p>The kinematic chain of the KMC400S U five-axis machine tool.</p> Full article ">Figure 9
<p>The relative position of control points between A-axis and C-axis.</p> Full article ">Figure 10
<p>Measurement of the geometric errors of the machine tool. (<b>a</b>) Laser interferometer measures geometric errors of translational axes; (<b>b</b>) R-Test measures geometric errors of rotary axes.</p> Full article ">Figure 11
<p>3rd order fitting curve of X-axis geometric error <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mrow> <mi>y</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p>The error twist of the error <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mrow> <mi>y</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 13
<p>The geometry of the S-shaped edge strip.</p> Full article ">Figure 14
<p>Contour error prediction process of S-shaped trajectory.</p> Full article ">Figure 15
<p>Partial G code for finishing S-shaped edge trip.</p> Full article ">Figure 16
<p>SP of each axis of the KMC400S U five-axis machine tool. (<b>a</b>) SP of translational axes; (<b>b</b>) SP of rotary axes.</p> Full article ">Figure 17
<p>LEDP of each axis of the KMC400S U five-axis machine tool. (<b>a</b>) LEDP of translational axes; (<b>b</b>) LEDP of rotary axes.</p> Full article ">Figure 18
<p>The synthesized ST and AT. (<b>a</b>) TTPT of ST and AT; (<b>b</b>) TOT of ST and AT.</p> Full article ">Figure 19
<p>The predicted contour errors. (<b>a</b>) The TTPT contour error; (<b>b</b>) The TOT contour error.</p> Full article ">Figure 20
<p>The ST, AT, and MT.</p> Full article ">Figure 21
<p>Predicted and measured contour errors of TTPT.</p> Full article ">Figure 22
<p>Experiment of real-time prediction of contour error of DMU 50 five-axis machine tool.</p> Full article ">Figure 23
<p>The results of real-time data collection and CTCE prediction.</p> Full article ">
24 pages, 7501 KiB  
Article
A Resilience-Oriented Bidirectional ANFIS Framework for Networked Microgrid Management
by Muhammad Zeshan Afzal, Muhammad Aurangzeb, Sheeraz Iqbal, Atiq ur Rehman, Hossam Kotb, Kareem M. AboRas, Elmazeg Elgamli and Mokhtar Shouran
Processes 2022, 10(12), 2724; https://doi.org/10.3390/pr10122724 - 16 Dec 2022
Cited by 8 | Viewed by 3078
Abstract
This study implemented a bidirectional artificial neuro-fuzzy inference system (ANFIS) to solve the problem of system resilience in synchronized and islanded grid mode/operation (during normal operation and in the event of a catastrophic disaster, respectively). Included in this setup are photovoltaics, wind turbines, [...] Read more.
This study implemented a bidirectional artificial neuro-fuzzy inference system (ANFIS) to solve the problem of system resilience in synchronized and islanded grid mode/operation (during normal operation and in the event of a catastrophic disaster, respectively). Included in this setup are photovoltaics, wind turbines, batteries, and smart load management. Solar panels, wind turbines, and battery-charging supercapacitors are just a few of the sustainable energy sources ANFIS coordinates. The first step in the process was the development of a mode-specific control algorithm to address the system’s current behavior. Relative ANFIS will take over to greatly boost resilience during times of crisis, power savings, and routine operations. A bidirectional converter connects the battery in order to keep the DC link stable and allow energy displacement due to changes in generation and consumption. When combined with the ANFIS algorithm, PV can be used to meet precise power needs. This means it can safeguard the battery from extreme conditions such as overcharging or discharging. The wind system is optimized for an island environment and will perform as designed. The efficiency of the system and the life of the batteries both improve. Improvements to the inverter’s functionality can be attributed to the use of synchronous reference frame transformation for control. Based on the available solar power, wind power, and system state of charge (SOC), the anticipated fuzzy rule-based ANFIS will take over. Furthermore, the synchronized grid was compared to ANFIS. The study uses MATLAB/Simulink to demonstrate the robustness of the system under test. Full article
(This article belongs to the Special Issue Sustainable Microgrid Systems: Technologies, Applications and Trends)
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<p>Resilience in microgrids.</p> Full article ">Figure 2
<p>Networked microgrids.</p> Full article ">Figure 3
<p>Hierarchal control strategies in microgrids.</p> Full article ">Figure 4
<p>Proposed flow of study.</p> Full article ">Figure 5
<p>Setting up ANFIS with MPPT control schemes.</p> Full article ">Figure 6
<p>ANFIS inverter control scheme.</p> Full article ">Figure 7
<p>Schematic diagram of ANFIS inverter control scheme.</p> Full article ">Figure 8
<p>ANFIS MATLAB diagram.</p> Full article ">Figure 9
<p>Microgrid setup with distributed energy resources (DERs) present: a case study.</p> Full article ">Figure 10
<p>Active and reactive power.</p> Full article ">Figure 11
<p>Automatic ANFIS power system selection.</p> Full article ">Figure 12
<p>Distribution load reactive power.</p> Full article ">Figure 13
<p>ANFIS controller three-phase voltage and current.</p> Full article ">Figure 14
<p>Management of load.</p> Full article ">Figure 15
<p>Wind-stabilized reactive and active power.</p> Full article ">Figure 16
<p>Input actual Watts of power.</p> Full article ">Figure 17
<p>Size determination state of the microgrid in its resilience condition during the case study.</p> Full article ">Figure 18
<p>Economic analysis of the smart microgrid.</p> Full article ">Figure 19
<p>Charging and discharging at different state of charge (SOC). (<b>a</b>) Solar/PV (x axis = BESS, y axis = time in seconds), (<b>b</b>) solar–wind hybrid (x axis = BESS, y axis = time in seconds), (<b>c</b>) wind (x axis = BESS, y axis = time in hours) and (<b>d</b>) battery (x axis = BESS, y axis = time in hours).</p> Full article ">Figure 19 Cont.
<p>Charging and discharging at different state of charge (SOC). (<b>a</b>) Solar/PV (x axis = BESS, y axis = time in seconds), (<b>b</b>) solar–wind hybrid (x axis = BESS, y axis = time in seconds), (<b>c</b>) wind (x axis = BESS, y axis = time in hours) and (<b>d</b>) battery (x axis = BESS, y axis = time in hours).</p> Full article ">Figure 20
<p>Optimal exploitation of power in islanding mode.</p> Full article ">
28 pages, 8477 KiB  
Article
Dynamic Performance Assessment of PMSG and DFIG-Based WECS with the Support of Manta Ray Foraging Optimizer Considering MPPT, Pitch Control, and FRT Capability Issues
by Mohamed Metwally Mahmoud, Basiony Shehata Atia, Almoataz Y. Abdelaziz and Noura A. Nour Aldin
Processes 2022, 10(12), 2723; https://doi.org/10.3390/pr10122723 - 16 Dec 2022
Cited by 27 | Viewed by 3048
Abstract
Wind generators have attracted a lot of attention in the realm of renewable energy systems, but they are vulnerable to harsh environmental conditions and grid faults. The influence of the manta ray foraging optimizer (MRFO) on the dynamic performance of the two commonly [...] Read more.
Wind generators have attracted a lot of attention in the realm of renewable energy systems, but they are vulnerable to harsh environmental conditions and grid faults. The influence of the manta ray foraging optimizer (MRFO) on the dynamic performance of the two commonly used variable speed wind generators (VSWGs), called the permanent magnet synchronous generator (PMSG) and doubly-fed induction generator (DFIG), is investigated in this research article. The PMSG and DFIG were exposed to identical wind speed changes depending on their wind turbine characteristics, as well as a dangerous three-phase fault, to evaluate the durability of MRFO-based wind side controllers. To protect VSWGs from hazardous gusts and obtain the optimum power from incoming wind speeds, we utilized a pitch angle controller and optimal torque controller, respectively, in our study. During faults, the commonly utilized industrial approach (crowbar system) was exclusively employed to aid the studied VSWGs in achieving fault ride-through (FRT) capability and control of the DC link voltage. Furthermore, an MRFO-based PI controller was used to develop a crowbar system. The modeling of PMSG, DFIG, and MRFO was performed using the MATLAB/Simulink toolbox. We compared performances of PMSG and DFIG in reference tracking and resilience against changes in system parameters under regular and irregular circumstances. The effectiveness and reliability of the optimized controllers in mitigating the adverse impacts of faults and wind gusts were demonstrated by the simulation results. Without considering the exterior circuit of VSWGs or modifying the original architecture, MRFO-PI controllers in the presence of a crowbar system may help cost-effectively alleviate FRT concerns for both studied VSWGs. Full article
(This article belongs to the Special Issue Advances in Renewable Energy Systems)
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<p>Modeling of a WT.</p> Full article ">Figure 2
<p>PAC system.</p> Full article ">Figure 3
<p>OTC method for MPPT operation.</p> Full article ">Figure 4
<p>PMSWG with its proposed control system.</p> Full article ">Figure 5
<p>DFIWG with its proposed control system.</p> Full article ">Figure 6
<p>Failure in WE system components.</p> Full article ">Figure 7
<p>MRFO strategy.</p> Full article ">Figure 8
<p>MRFO flowchart.</p> Full article ">Figure 9
<p>Control of crowbar system based on MRFO.</p> Full article ">Figure 10
<p>PMSWG system parameter responses as a result of wind speed changes: (<b>a</b>) wind speed profile; (<b>b</b>) tip speed ratio; (<b>c</b>) power coefficient; (<b>d</b>) pitch angle; (<b>e</b>) angular speed; (<b>f</b>) electromagnetic torque; (<b>g</b>) injected active and reactive power to the grid; and (<b>h</b>) DC-link capacitor voltage.</p> Full article ">Figure 10 Cont.
<p>PMSWG system parameter responses as a result of wind speed changes: (<b>a</b>) wind speed profile; (<b>b</b>) tip speed ratio; (<b>c</b>) power coefficient; (<b>d</b>) pitch angle; (<b>e</b>) angular speed; (<b>f</b>) electromagnetic torque; (<b>g</b>) injected active and reactive power to the grid; and (<b>h</b>) DC-link capacitor voltage.</p> Full article ">Figure 11
<p>PMSG system parameter responses as a result of an 85% voltage dip: (<b>a</b>) system voltage; (<b>b</b>) system currents; (<b>c</b>) injected active power to the grid; (<b>d</b>) injected reactive power to the grid; (<b>e</b>) DC-link voltage; (<b>f</b>) electromagnetic torque; and (<b>g</b>) angular speed.</p> Full article ">Figure 11 Cont.
<p>PMSG system parameter responses as a result of an 85% voltage dip: (<b>a</b>) system voltage; (<b>b</b>) system currents; (<b>c</b>) injected active power to the grid; (<b>d</b>) injected reactive power to the grid; (<b>e</b>) DC-link voltage; (<b>f</b>) electromagnetic torque; and (<b>g</b>) angular speed.</p> Full article ">Figure 12
<p>DFIWG system parameter responses as a result of wind speed changes: (<b>a</b>) wind speed profile; (<b>b</b>) tip speed ratio; (<b>c</b>) power coefficient; (<b>d</b>) pitch angle; (<b>e</b>) angular speed; (<b>f</b>) electromagnetic torque; (<b>g</b>) delivered active and reactive powers to the grid; and (<b>h</b>) DC-link voltage.</p> Full article ">Figure 12 Cont.
<p>DFIWG system parameter responses as a result of wind speed changes: (<b>a</b>) wind speed profile; (<b>b</b>) tip speed ratio; (<b>c</b>) power coefficient; (<b>d</b>) pitch angle; (<b>e</b>) angular speed; (<b>f</b>) electromagnetic torque; (<b>g</b>) delivered active and reactive powers to the grid; and (<b>h</b>) DC-link voltage.</p> Full article ">Figure 12 Cont.
<p>DFIWG system parameter responses as a result of wind speed changes: (<b>a</b>) wind speed profile; (<b>b</b>) tip speed ratio; (<b>c</b>) power coefficient; (<b>d</b>) pitch angle; (<b>e</b>) angular speed; (<b>f</b>) electromagnetic torque; (<b>g</b>) delivered active and reactive powers to the grid; and (<b>h</b>) DC-link voltage.</p> Full article ">Figure 13
<p>DFIWG system parameter responses as a result of 85% voltage dip: (<b>a</b>) system voltage; (<b>b</b>) system current; (<b>c</b>) supplied active power to the grid; (<b>d</b>) supplied reactive power to the grid; (<b>e</b>) DC-link voltage; (<b>f</b>) electromagnetic torque; and (<b>g</b>) angular speed.</p> Full article ">Figure 13 Cont.
<p>DFIWG system parameter responses as a result of 85% voltage dip: (<b>a</b>) system voltage; (<b>b</b>) system current; (<b>c</b>) supplied active power to the grid; (<b>d</b>) supplied reactive power to the grid; (<b>e</b>) DC-link voltage; (<b>f</b>) electromagnetic torque; and (<b>g</b>) angular speed.</p> Full article ">Figure 13 Cont.
<p>DFIWG system parameter responses as a result of 85% voltage dip: (<b>a</b>) system voltage; (<b>b</b>) system current; (<b>c</b>) supplied active power to the grid; (<b>d</b>) supplied reactive power to the grid; (<b>e</b>) DC-link voltage; (<b>f</b>) electromagnetic torque; and (<b>g</b>) angular speed.</p> Full article ">
10 pages, 1680 KiB  
Article
Development of Co-Amorphous Loratadine–Citric Acid Orodispersible Drug Formulations
by Emőke Margit Rédai, Emese Sipos, Robert Alexandru Vlad, Paula Antonoaea, Nicoleta Todoran and Adriana Ciurba
Processes 2022, 10(12), 2722; https://doi.org/10.3390/pr10122722 - 16 Dec 2022
Cited by 3 | Viewed by 2166
Abstract
This study aimed at the preparation and characterization of co-amorphous loratadine–citric acid orally disintegrating dosage forms (ODx). A co-amorphous loratadine–citric acid was prepared by solvent evaporation method in three different molecular ratios. DSC, FTIR, and dissolution studies have been conducted for the binary [...] Read more.
This study aimed at the preparation and characterization of co-amorphous loratadine–citric acid orally disintegrating dosage forms (ODx). A co-amorphous loratadine–citric acid was prepared by solvent evaporation method in three different molecular ratios. DSC, FTIR, and dissolution studies have been conducted for the binary system. The co-amorphous system was used to obtain oral lyophilizates and orally disintegrating tablets by direct compression. Diameter, thickness, hardness, disintegration time, uniformity of mass, and dissolution was determined for the dosage forms. DSC curves showed a lack of sharp endothermic peaks for the binary systems. FTIR spectra presented a hypsochromic modification of the characteristic peaks. Dissolution studies indicated a five-fold increase in the dissolved amount compared to pure loratadine in water. Disintegration times of direct compression ODx varied in the range of 34–41 s and for freeze-dried ODx in the range of 8–9 s. Friability was under 1% in all cases. The dissolution of loratadine in buffer solution at pH = 1 was almost complete. In conclusion binary systems of loratadine and citric acid enhance solubility and combined with the orally disintegrating pharmaceutical form also increase patient compliance. Full article
(This article belongs to the Section Pharmaceutical Processes)
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Figure 1

Figure 1
<p>The co-amorphous system (a—citric acid, b—loratadine bonded with hydrogen bonds as a dotted line).</p> Full article ">Figure 2
<p>DSC curves of loratadine, citric acid, and binary mixtures 1:1, 2:1, 3:1 of loratadine–citric acid.</p> Full article ">Figure 3
<p>FTIR spectra of loratadine, citric acid, and binary mixtures 1:1, 2:1, 3:1 of loratadinecitric acid.</p> Full article ">Figure 4
<p>Dissolution curves of loratadine and binary mixtures 1:1, 2:1, 3:1 of loratadine–citric acid in water.</p> Full article ">Figure 5
<p>The physical appearance of ODx ((<b>a</b>)—prepared by direct compression, (<b>b</b>)—prepared by freeze-drying).</p> Full article ">Figure 6
<p>Dissolution curves of ODx.</p> Full article ">
14 pages, 3782 KiB  
Article
3D Computational Fluid Dynamics Analysis of a Convective Drying Chamber
by Miguel Andrés Mauricio Daza-Gómez, Carlos Andrés Gómez Velasco, Juan Carlos Gómez Daza and Nicolás Ratkovich
Processes 2022, 10(12), 2721; https://doi.org/10.3390/pr10122721 - 16 Dec 2022
Cited by 6 | Viewed by 2659
Abstract
The drying industry has grown considerably due to the tremendous demand for non-perishable food. Convective drying is one of the most popular equipment in the drying industry (food, chemical, pharmaceutical, etc.). One of the drawbacks of this equipment, when used for convective drying, [...] Read more.
The drying industry has grown considerably due to the tremendous demand for non-perishable food. Convective drying is one of the most popular equipment in the drying industry (food, chemical, pharmaceutical, etc.). One of the drawbacks of this equipment, when used for convective drying, is the non-uniformity in the final product quality. This study presents the development of a numerical model through Computational Fluid Dynamics (CFD). The drying chamber of a heat pump dryer is assessed from the perspective of drying air velocity and temperature profiles. The model was developed by solving different transport phenomena-related equations. The established methodology was set up to evaluate how the drying air velocity and temperature distribution affect the drying chamber. These results will define if there is a need to redesign it. The air velocity and temperature profile results show a need to redesign the chamber. Only trays 2, 3, and 4 are the ones that would achieve the drying of the products. The proposed solution is to implement air distributors or modify the tray positioning to make the drying air and temperature distribution homogeneous. Full article
(This article belongs to the Section Food Process Engineering)
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Figure 1

Figure 1
<p>Dimensions of the convective drying chamber. (<b>a</b>) Lateral view and (<b>b</b>) Diagonal view.</p> Full article ">Figure 2
<p>Visualisation of the polyhedral-type mesh in the studied chamber.</p> Full article ">Figure 3
<p>Visualisation of the grid in the studied chamber with a sample.</p> Full article ">Figure 4
<p>Comparison of the proposed meshes with a time of 45 s: (<b>a</b>) first mesh, (<b>b</b>) second mesh, and (<b>c</b>) third mesh.</p> Full article ">Figure 4 Cont.
<p>Comparison of the proposed meshes with a time of 45 s: (<b>a</b>) first mesh, (<b>b</b>) second mesh, and (<b>c</b>) third mesh.</p> Full article ">Figure 5
<p>Comparison between experimental and numerical data.</p> Full article ">Figure 6
<p>The velocity profile in the drying chamber is (<b>a</b>) normal to <span class="html-italic">X</span>, (<b>b</b>) normal to <span class="html-italic">Y</span>, and (<b>c</b>) normal to <span class="html-italic">Z</span>.</p> Full article ">Figure 7
<p>Temperature distribution at different times. (<b>a</b>) 10.087 s; (<b>b</b>) 44.59 s. At 44.59 s, it is also shown (<b>b</b>) normal to <span class="html-italic">X</span>, (<b>c</b>) normal to <span class="html-italic">Y</span>, and (<b>d</b>) normal to <span class="html-italic">Z</span>.</p> Full article ">Figure 8
<p>Velocity vs. temperature in the drying chamber.</p> Full article ">Figure 9
<p>The temperature of the sample.</p> Full article ">Figure 10
<p>Mass fraction of water vapour.</p> Full article ">Figure 11
<p>Mass fraction of water vapour distribution.</p> Full article ">
20 pages, 7095 KiB  
Article
Magnetic Activated Biochar Fe3O4-MOS Made from Moringa Seed Shells for the Adsorption of Methylene Blue
by Meiping Li, Cheng Dong, Caixia Guo and Ligang Yu
Processes 2022, 10(12), 2720; https://doi.org/10.3390/pr10122720 - 16 Dec 2022
Cited by 6 | Viewed by 2107
Abstract
In recent years, more and more biochars have been employed to treat dye wastewater. In order to increase the utilization of moringa seed shell resources and enrich the removal method of methylene blue (MB) in solution, in the current study, the magnetic moringa [...] Read more.
In recent years, more and more biochars have been employed to treat dye wastewater. In order to increase the utilization of moringa seed shell resources and enrich the removal method of methylene blue (MB) in solution, in the current study, the magnetic moringa seed shells biochar was prepared through ultrasonic-assisted impregnation and pyrolysis, while Fe3O4 was used to activate the material to obtain adsorption (Fe3O4-MOS). The prepared adsorbents were characterized by SEM-EDS, XRD, XPS, FTIR, N2 adsorption and desorption and VSM. Under the suitable experimental conditions, the removal rate can be close to 100% and the maximum adsorption capacity of MB could be 219.60 mg/g. The Freundlich model provided a good match to the data presented by the adsorption isotherm, and the adsorption of MB on Fe3O4-MOS was a spontaneous and endothermic reaction. Study of the mechanism indicated that pore adsorption, electrostatic interaction, hydrogen bond, and π-π interaction were the major adsorption mechanisms. After five cycles, it was found that Fe3O4-MOS had a high removal rate for MB, which was close to 90%. This work provides a new idea for moringa seed shells and the results confirm that Fe3O4-MOS has substantial potential for dye wastewater treatment. Full article
(This article belongs to the Section Environmental and Green Processes)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>SEM images of BC (<b>a</b>,<b>b</b>,<b>c</b>) and Fe<sub>3</sub>O<sub>4</sub>-MOS (<b>d</b>,<b>e</b>,<b>f</b>); EDS elemental analysis of Fe<sub>3</sub>O<sub>4</sub>-MOS (<b>g</b>); and hysteresis curves of Fe<sub>3</sub>O<sub>4</sub>-MOS (<b>h</b>).</p> Full article ">Figure 1 Cont.
<p>SEM images of BC (<b>a</b>,<b>b</b>,<b>c</b>) and Fe<sub>3</sub>O<sub>4</sub>-MOS (<b>d</b>,<b>e</b>,<b>f</b>); EDS elemental analysis of Fe<sub>3</sub>O<sub>4</sub>-MOS (<b>g</b>); and hysteresis curves of Fe<sub>3</sub>O<sub>4</sub>-MOS (<b>h</b>).</p> Full article ">Figure 2
<p>XRD patterns for biochar and magnetic moringa seed shell biochar.</p> Full article ">Figure 3
<p>XPS patterns of the Fe<sub>3</sub>O<sub>4</sub>-MOS full scan (<b>a</b>), C1S (<b>b</b>), O1S (<b>c</b>), and Fe2p (<b>d</b>).</p> Full article ">Figure 4
<p>FT–IR spectra of magnetic Fe<sub>3</sub>O<sub>4</sub> nanoparticles, Moringa waste, BC, Fe<sub>3</sub>O<sub>4</sub>-MOS and Fe<sub>3</sub>O<sub>4</sub>-MOS adsorption.</p> Full article ">Figure 5
<p>N<sub>2</sub> adsorption isotherm (<b>a</b>) and pore size distribution (<b>b</b>) of Fe<sub>3</sub>O<sub>4</sub>-MOS.</p> Full article ">Figure 6
<p>pH<sub>pzc</sub> of Fe<sub>3</sub>O<sub>4</sub>-MOS (<b>a</b>) and the effect of pH on the adsorption of MB by BC and Fe<sub>3</sub>O<sub>4</sub>-MOS (<b>b</b>).</p> Full article ">Figure 7
<p>Dosage-dependent change in MB adsorption by BC and Fe<sub>3</sub>O<sub>4</sub>-MOS.</p> Full article ">Figure 8
<p>Non-Linear plots of the PFO model, PSO model, the IPD model and the Elovich model for the adsorption of MB onto BC (<b>a</b>,<b>c</b>) and Fe<sub>3</sub>O<sub>4</sub>-MOS (<b>b</b>,<b>d</b>) composites at 20, 40 and 60 mg/L dye solutions.</p> Full article ">Figure 9
<p>Effect of the temperature for the adsorption of MB onto BC (<b>a</b>) and Fe<sub>3</sub>O<sub>4</sub>-MOS (<b>b</b>) composites at 20, 40, 60, 80and 100 mg/L dye solutions.</p> Full article ">Figure 10
<p>Effect of the concentration for the adsorption of MB onto BC (<b>a</b>) and Fe<sub>3</sub>O<sub>4</sub>-MOS (<b>b</b>) at 20, 40, 60, 80and 100 mg/L dye solutions.</p> Full article ">Figure 11
<p>Non-Linear plot of Langmuir, Fredunlich, Temkin and D-R models for the adsorption of MB onto BC and Fe<sub>3</sub>O<sub>4</sub>-MOS composites at 298 K, 308 K, 318 K, 328 K and 388 K.</p> Full article ">Figure 11 Cont.
<p>Non-Linear plot of Langmuir, Fredunlich, Temkin and D-R models for the adsorption of MB onto BC and Fe<sub>3</sub>O<sub>4</sub>-MOS composites at 298 K, 308 K, 318 K, 328 K and 388 K.</p> Full article ">Figure 12
<p>The influence of cycle times on adsorption.</p> Full article ">Scheme 1
<p>The process of preparation of Fe<sub>3</sub>O<sub>4</sub>-MOS and BC.</p> Full article ">
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