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Volume 12
Issue 9
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Processes, Volume 12, Issue 9 (September 2024) – 277 articles

Cover Story (view full-size image): Biomining exploits the potential of microorganisms to facilitate the extraction and recovery of metals from a wide range of waste materials as an appealing alternative, replacing primary raw materials with secondary material resources (thus improving metal recycling rates in the context of the circular economy). Despite encouraging results, the use of biomining for the recovery of metals is limited to laboratory scale applications. Recently, special attention has been paid to the analysis of metal biomining from a process sustainability perspective by using several supporting tools (e.g., life cycle assessment, LCA). The purpose of LCA applications is to establish the potential for a large-scale implementation of the process in question and its possible impacts on the environment. View this paper
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27 pages, 9710 KiB  
Article
A Multi-Scale Numerical Simulation Method Considering Anisotropic Relative Permeability
by Li Wu, Junqiang Wang, Deli Jia, Ruichao Zhang, Jiqun Zhang, Yiqun Yan and Shuoliang Wang
Processes 2024, 12(9), 2058; https://doi.org/10.3390/pr12092058 - 23 Sep 2024
Viewed by 961
Abstract
Most of the oil reservoirs in China are fluvial deposits with firm reservoir heterogeneity, where differences in fluid flow capacity in individual directions should not be ignored; however, the available commercial reservoir simulation software cannot consider the anisotropy of the relative permeability. To [...] Read more.
Most of the oil reservoirs in China are fluvial deposits with firm reservoir heterogeneity, where differences in fluid flow capacity in individual directions should not be ignored; however, the available commercial reservoir simulation software cannot consider the anisotropy of the relative permeability. To handle this challenge, this paper takes full advantage of the parallelism of the multi-scale finite volume (MsFV) method and establishes a multi-scale numerical simulation approach that incorporates the effects of reservoir anisotropy. The methodology is initiated by constructing an oil–water black-oil model considering the anisotropic relative permeability. Subsequently, the base model undergoes decoupling through a sequential solution, formulating the pressure and transport equations. Following this, a multi-scale grid system is configured, within which the pressure and transport equations are progressively developed in the fine-scale grid domain. Ultimately, the improved multi-scale finite volume (IMsFV) method is applied to mitigate low-frequency error in the coarse-scale grid, thereby enhancing computational efficiency. This paper introduces two primary innovations. The first is the development of a multi-scale solution method for the pressure equation incorporating anisotropic relative permeability. Validated using the Egg model, a comparative analysis with traditional numerical simulations demonstrates a significant improvement in computational speed without sacrificing accuracy. The second innovation involves applying the multi-scale framework to investigate the impact of anisotropy relative permeability on waterflooding performance, uncovering distinct mechanisms by which absolute and relative permeability anisotropy influence waterflooding outcomes. Therefore, the IMsFV method can be used as an effective tool for high-resolution simulation and precise residual oil prediction in anisotropic reservoirs. Full article
(This article belongs to the Special Issue New Insight in Enhanced Oil Recovery Process Analysis and Application)
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<p>Flow chart of the technology roadmap.</p> Full article ">Figure 2
<p>Measured relative permeability in <span class="html-italic">x</span>-, <span class="html-italic">y</span>-, and <span class="html-italic">z</span>-directions from a core sample in a lateral bar sedimentary reservoir.</p> Full article ">Figure 3
<p>The schematic diagram used to determine the direction of the grid surface.</p> Full article ">Figure 4
<p>Diagram describing the multi-scale grid (small rectangles of different colors represent fine grids, while black rectangles represent coarse grids. Different colors indicate varying permeability values).</p> Full article ">Figure 5
<p>Reservoir displaying the permeability distribution (shown in red-blue gradient colors), as well as the position of the injectors (blue cylinders) and producers (red cylinders) of the Egg Model (Refer to J. D. Jansen, 2014 [<a href="#B47-processes-12-02058" class="html-bibr">47</a>]).</p> Full article ">Figure 6
<p>Traditional fully implicit solution of pressure at different times for the Egg model.</p> Full article ">Figure 7
<p>Multi-scale solution of pressure at different times for the Egg model.</p> Full article ">Figure 8
<p>Traditional fully implicit solution of saturation at different times for the Egg model.</p> Full article ">Figure 9
<p>Multi-scale solution of saturation at different times for the Egg model.</p> Full article ">Figure 10
<p>Comparison between oil production versus time.</p> Full article ">Figure 11
<p>Comparison between water-cut versus time.</p> Full article ">Figure 12
<p>The comparison of the computation time of each step.</p> Full article ">Figure 13
<p>Anisotropic relative permeability curves in the model.</p> Full article ">Figure 14
<p>Multi-scale solution of pressure at different times for Case 1 (P1 represents the oil production well, and I1 represents the water injection well, the same applies figures below).</p> Full article ">Figure 15
<p>Multi-scale solution of pressure at different times for Case 2.</p> Full article ">Figure 16
<p>Multi-scale solution of pressure at different times for Case 3.</p> Full article ">Figure 17
<p>Multi-scale solution of pressure at different times for Case 4.</p> Full article ">Figure 18
<p>Multi-scale solution of pressure at different times for Case 5.</p> Full article ">Figure 19
<p>Multi-scale solution of oil saturation at different times for Case 1.</p> Full article ">Figure 20
<p>Multi-scale solution of oil saturation at different times for Case 2.</p> Full article ">Figure 21
<p>Multi-scale solution of oil saturation at different times for Case 3.</p> Full article ">Figure 22
<p>Multi-scale solution of oil saturation at different times for Case 4.</p> Full article ">Figure 23
<p>Multi-scale solution of oil saturation at different times for Case 5.</p> Full article ">Figure 24
<p>Water cut curves for different cases.</p> Full article ">Figure 25
<p>Cumulative oil production curves for different cases.</p> Full article ">
24 pages, 5281 KiB  
Review
Induced Casing Deformation in Hydraulically Fractured Shale Gas Wells: Risk Assessment, Early Warning, and Mitigation
by Xiaojin Zhou, Yonggang Duan, Yu Sang, Lang Zhou, Bo Zeng, Yi Song, Yan Dong and Junjie Hu
Processes 2024, 12(9), 2057; https://doi.org/10.3390/pr12092057 - 23 Sep 2024
Viewed by 836
Abstract
In recent years, casing deformation has become a key factor affecting the scale and efficiency of shale gas development. Consequently, a fast and efficient integrated prevention, control, and treatment technology for casing deformation is of great significance in terms of both theory and [...] Read more.
In recent years, casing deformation has become a key factor affecting the scale and efficiency of shale gas development. Consequently, a fast and efficient integrated prevention, control, and treatment technology for casing deformation is of great significance in terms of both theory and application. This paper combines a geological mechanics analysis and multi-cluster fracture propagation to investigate the risk evaluation, early warning and identification, and warning and identification technology relating to casing deformation and its application. It proposes a method for the dynamic and static evaluation of casing deformation risk levels and types, and establishes an index system incorporating stress, fracture, time, and space factors. This four-factor evaluation method is in greater alignment with field conditions. It also proposes a method for the early warning and identification of casing deformation based on fracture monitoring and an operation curve, and clarifies the dominant engineering factors around casing deformation. According to the findings, the total fluid volume per stage has a greater impact on casing deformation than a high pump rate. The prevention and control of casing deformation should preferably be realized by optimizing the fracturing parameters. Moreover, the paper reviews existing technologies for treating casing deformation, several of which are defined as major technologies: small-diameter bridge plug staged fracturing and small-size gun perforation, and long-stage multi-cluster asynchronous fracture initiation and composite temporary plugging and diversion. The study results provide support for a significant reduction in the casing deformation rate during fracturing, improving the effective stimulation degree in the casing deformation section in shale gas wells in the southern Sichuan Basin. These results could serve as references for subsequent research. Full article
(This article belongs to the Special Issue Advances in Technology for Enhancing Oil and Gas Recovery in Shale Reservoirs)
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<p>Distribution and stress of fracture zone.</p> Full article ">Figure 2
<p>Mohr–Coulomb stress circle.</p> Full article ">Figure 3
<p>Model of hydraulic fracture communicating with the fracture zone.</p> Full article ">Figure 4
<p>Production time and casing deformation distribution of shale gas wells in an area of the Luzhou block.</p> Full article ">Figure 5
<p>Relationship between injection rate and casing deformation for shale gas wells in an area of the Luzhou block.</p> Full article ">Figure 6
<p>Flow chart of casing deformation prediction.</p> Full article ">Figure 7
<p>Early warning of casing deformation based on optical fiber monitoring of an adjacent well.</p> Full article ">Figure 8
<p>Correlation between fracturing engineering parameters and casing deformation.</p> Full article ">Figure 9
<p>Grey correlation analysis of fracturing engineering parameters and casing deformation.</p> Full article ">Figure 10
<p>Neural network analysis of fracturing engineering parameters and casing deformation.</p> Full article ">Figure 11
<p>Workover process of seriously deformed shale gas wells.</p> Full article ">Figure 12
<p>Casing deformation in the Luzhou block in the last two years.</p> Full article ">
29 pages, 21121 KiB  
Article
Hydrodynamic Characteristics of Preloading Spiral Case and Concrete in Turbine Mode with Emphasis on Preloading Clearance
by Yutong Luo, Zonghua Li, Shaozheng Zhang, Qingfeng Ren and Zhengwei Wang
Processes 2024, 12(9), 2056; https://doi.org/10.3390/pr12092056 - 23 Sep 2024
Cited by 1 | Viewed by 628
Abstract
A pump-turbine may generate high-amplitude hydraulic excitations during operation, wherein the flow-induced response of the spiral case and concrete is a key factor affecting the stable and safe operation of the unit. The preloading spiral case can enhance the combined bearing capacity of [...] Read more.
A pump-turbine may generate high-amplitude hydraulic excitations during operation, wherein the flow-induced response of the spiral case and concrete is a key factor affecting the stable and safe operation of the unit. The preloading spiral case can enhance the combined bearing capacity of the entire structure, yet there is still limited research on the impact of the preloading pressure on the hydrodynamic response. In this study, the pressure fluctuation characteristics and dynamic behaviors of preloading a steel spiral case and concrete under different preloading pressures at rated operating conditions are analyzed based on fluid–structure interaction theory and contact model. The results show that the dominant frequency of pressure fluctuations in the spiral case is 15 fn, which is influenced by the rotor–stator interaction with a runner rotation of short and long blades. Under preloading pressures of 0.5, 0.7, and 1 times the maximum static head, higher preloading pressures reduce the contact regions, leading to uneven deformation and stress distributions with a near-positive linear correlation. The maximum deformation of the PSSC can reach 2.6 mm, and the stress is within the allowable range. The preloading pressure has little effect on the dominant frequency of the dynamic behaviors in the spiral case (15 fn), but both the maximum and amplitudes of deformation and stress increase with higher preloading pressure. The high-amplitude regions of deformation and stress along the axial direction are located near the nose vane, with maximum values of 0.003 mm and 0.082 MPa, respectively. The contact of concrete is at risk of stress concentrations and cracking under high preloading pressure. The results can provide references for optimizing the structural design and the selection of preloading pressure, which improves operation reliability. Full article
(This article belongs to the Section Process Control and Monitoring)
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<p>Flowchart of the work steps.</p> Full article ">Figure 2
<p>Three-dimensional modeling and mesh of flow passage.</p> Full article ">Figure 3
<p>The flow domain mesh of the pump-turbine unit. (<b>a</b>) Runner; (<b>b</b>) guide vanes and stay vanes.</p> Full article ">Figure 4
<p>Fluid mesh independence analysis.</p> Full article ">Figure 5
<p>Pressure pulsation monitoring points. (<b>a</b>) Spiral case; (<b>b</b>) inter-vane space; (<b>c</b>) zaneless space.</p> Full article ">Figure 6
<p>Pump-turbine model test.</p> Full article ">Figure 7
<p>Three-dimensional modeling and mesh of the preloading spiral case. (<b>a</b>) FEM model; (<b>b</b>) cross-sectional schematic of the boundary condition of the negative pressure.</p> Full article ">Figure 8
<p>Three-dimensional modeling and mesh of the pump-turbine and plant concrete structural model.</p> Full article ">Figure 9
<p>Structural mesh independence analysis.</p> Full article ">Figure 10
<p>Boundary conditions of the pump-turbine unit.</p> Full article ">Figure 11
<p>Streamline distribution of the unit. (<b>a</b>) Whole flow passage; (<b>b</b>) cross-section view.</p> Full article ">Figure 12
<p>Pressure distribution in the flow passage.</p> Full article ">Figure 13
<p>The time history and frequency spectra of the pressure fluctuation. (<b>a</b>) Spiral case; (<b>b</b>) inter-vane space; (<b>c</b>) vaneless space.</p> Full article ">Figure 14
<p>The cross-sections of runner blades.</p> Full article ">Figure 15
<p>Pressure pulsation monitoring points in S1 and S2.</p> Full article ">Figure 16
<p>The pressure amplitude of the monitoring points on the first four dominant frequencies. (<b>a</b>) S1; (<b>b</b>) S2.</p> Full article ">Figure 16 Cont.
<p>The pressure amplitude of the monitoring points on the first four dominant frequencies. (<b>a</b>) S1; (<b>b</b>) S2.</p> Full article ">Figure 17
<p>Deformation and stress monitoring points (red points for total deformation, blue points for maximum principal stress).</p> Full article ">Figure 18
<p>Contact status between the PSSC and concrete under different preloading pressures; (<b>a</b>) 3.2 MPa; (<b>b</b>) 4.564 MPa; (<b>c</b>) 6.52 MPa.</p> Full article ">Figure 18 Cont.
<p>Contact status between the PSSC and concrete under different preloading pressures; (<b>a</b>) 3.2 MPa; (<b>b</b>) 4.564 MPa; (<b>c</b>) 6.52 MPa.</p> Full article ">Figure 19
<p>The total deformation distribution of the PSSC under different preloading pressure; (<b>a</b>) 3.2 MPa; (<b>b</b>) 4.564 MPa; (<b>c</b>) 6.52 MPa.</p> Full article ">Figure 20
<p>The maximum deformation of the PSSC versus the preloading pressure.</p> Full article ">Figure 21
<p>The time history and frequency spectra of dynamic deformation of the PSSC under different preloading pressures; (<b>a</b>) n1; (<b>b</b>) n3; (<b>c</b>) n5; (<b>d</b>) n7.</p> Full article ">Figure 21 Cont.
<p>The time history and frequency spectra of dynamic deformation of the PSSC under different preloading pressures; (<b>a</b>) n1; (<b>b</b>) n3; (<b>c</b>) n5; (<b>d</b>) n7.</p> Full article ">Figure 22
<p>The equivalent stress distribution of the PSSC under different preloading pressures; (<b>a</b>) 3.2 MPa; (<b>b</b>) 4.564 MPa; (<b>c</b>) 6.52 MPa.</p> Full article ">Figure 22 Cont.
<p>The equivalent stress distribution of the PSSC under different preloading pressures; (<b>a</b>) 3.2 MPa; (<b>b</b>) 4.564 MPa; (<b>c</b>) 6.52 MPa.</p> Full article ">Figure 23
<p>The time history and frequency spectra of dynamic stress of the PSSC under different preloading pressures; (<b>a</b>) n2; (<b>b</b>) n4; (<b>c</b>) n6; (<b>d</b>) n8.</p> Full article ">Figure 23 Cont.
<p>The time history and frequency spectra of dynamic stress of the PSSC under different preloading pressures; (<b>a</b>) n2; (<b>b</b>) n4; (<b>c</b>) n6; (<b>d</b>) n8.</p> Full article ">Figure 24
<p>The maximum principal stress distribution of the concrete under different preloading pressures; (<b>a</b>) 3.2 MPa; (<b>b</b>) 4.564 MPa; (<b>c</b>) 6.52 MPa.</p> Full article ">Figure 25
<p>The maximum stress of the PSSC and concrete versus the preloading pressure.</p> Full article ">
15 pages, 11409 KiB  
Article
A Method for Predicting the Action Sites of Regional Mudstone Cap Rock Affecting the Diversion of Hydrocarbons Transported along Oil Source Faults
by Tianqi Zhou, Yachun Wang, Hongqi Yuan, Yinghua Yu and Yunfeng Zhang
Processes 2024, 12(9), 2055; https://doi.org/10.3390/pr12092055 - 23 Sep 2024
Viewed by 505
Abstract
Regional mudstone cap rock has an important influence on the oil and gas distribution of the oil source faults below it. Therefore, studying the influence of these mudstone cap rocks on the hydrocarbon distribution pattern is fundamental to understanding the oil and gas [...] Read more.
Regional mudstone cap rock has an important influence on the oil and gas distribution of the oil source faults below it. Therefore, studying the influence of these mudstone cap rocks on the hydrocarbon distribution pattern is fundamental to understanding the oil and gas distribution of the lower generation and upper reservoir reservoirs in the Bohai Bay Basin. This study classified two types of hydrocarbon diversion from oil source faults: blockage diversion and seepage diversion. To locate them, we established a method to predict the areas with blockage diversion and seepage diversion separately by superimposing the sealing and leakage parts of the regional mudstone cap rock with the regions of the connected distribution of sand bodies and the favorable hydrocarbon transport sites of the oil source faults, respectively. We used this approach to predict the locations where hydrocarbons are diverted by the oil source faults under the regional mudstone cap rocks in the first and second sections of the Dongying Formation (E3d1-2) in the Liuchu area of the Raoyang Sag, Bohai Bay Basin. The results show that the regional mudstone cap rock’s blockage diversion occurs mainly in the south-central area of Liuchu, with a localized distribution in the northern part. The seepage diversion site is primarily located in the northeastern area and is also found locally in the west. Both diversions are beneficial for the accumulation of hydrocarbons from the source rocks of the first member of the Shahejie Formation (E3s1) to the upper second member of the Dongying Formation (E3d2U). The latter can also accumulate hydrocarbons in the Guantao Formation (N1g). The results align with the hydrocarbon distribution, demonstrating the feasibility of our method to predict various oil source fault diversion sites under the regional mudstone cap rock. This prediction method offers valuable guidance for exploring the lower generation and upper reservoir hydrocarbon accumulations in hydrocarbon-bearing basins. Full article
(This article belongs to the Section Energy Systems)
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<p>Regional geological map and study area map of the Liuchu area in Raoyang Sag, Jizhong Depression [<a href="#B24-processes-12-02055" class="html-bibr">24</a>]. (<b>a</b>) The situation of Raoyang Sag, China; (<b>b</b>) the situation of Liuchu area in Bohai Bay Basin; (<b>c</b>) the situation of the study area.</p> Full article ">Figure 2
<p>Stratigraphic sequence division of the Liuchu area [<a href="#B23-processes-12-02055" class="html-bibr">23</a>].</p> Full article ">Figure 3
<p>Schematic diagram of the types of regional mudstone cap rock effects on hydrocarbon diversion from the oil source fault: (<b>a</b>) blockage diversion effect; (<b>b</b>) seepage diversion effect.</p> Full article ">Figure 4
<p>Schematic drawing illustrating the hydrocarbon migration sites of the oil source fault affected by the regional mudstone cap rock: (<b>a</b>) blockage diversion site; (<b>b</b>) seepage diversion site.</p> Full article ">Figure 5
<p>Schematic diagram defining the lower cutoff values of the parameters used to predict the oil source fault’s different hydrocarbon diversion migration sites controlled by the regional mudstone cap rock: (<b>a</b>) maximum juxtaposition thickness required for the fault to grow up and down in the regional mudstone cap rock; (<b>b</b>) maximum stratigraphic sand ratio required for the sand bodies to connect; (<b>c</b>) minimum activity rate required to transport hydrocarbons on a fault.</p> Full article ">Figure 6
<p>Thickness map of the E<sub>3</sub><span class="html-italic">d</span><sub>1-2</sub> regional cap rock in the Liuchu area.</p> Full article ">Figure 7
<p>Tables defining the maximum juxtaposition thickness of fault required for the separately developed fault segments to connect upward and downward in the E<sub>3</sub><span class="html-italic">d</span><sub>1-2</sub> regional cap rocks of the Liuchu area.</p> Full article ">Figure 8
<p>The distribution map of the blockage and seepage area of the E<sub>3</sub><span class="html-italic">d</span><sub>1-2</sub> regional cap rocks in the Liuchu area.</p> Full article ">Figure 9
<p>The map of the connected distribution of sand bodies in the E<sub>3</sub><span class="html-italic">d</span><sub>2U</sub> Formation in the Liuchu area.</p> Full article ">Figure 10
<p>The histogram determines the minimum sand-to-shale ratio required for the connected distribution of E<sub>3</sub><span class="html-italic">d</span><sub>2U</sub> sand bodies in the Liuchu area.</p> Full article ">Figure 11
<p>Histogram determining the minimum fault activity rate required for the oil and gas migration through a fault in the Liuchu area.</p> Full article ">Figure 12
<p>The distribution map of the favorable transport sites of the oil source fault in the E<sub>3</sub><span class="html-italic">d</span><sub>2U</sub> reservoir under the E<sub>3</sub><span class="html-italic">d</span><sub>1-2</sub> regional mudstone cap rock in the Liuchu area.</p> Full article ">Figure 13
<p>The relationship map between the distribution of hydrocarbons in the E<sub>3</sub><span class="html-italic">d</span><sub>2U</sub> Formation and the different hydrocarbon diversion migration sites of oil source faults by the E<sub>3</sub><span class="html-italic">d</span><sub>1-2</sub> regional mudstone cap rocks.</p> Full article ">
22 pages, 4616 KiB  
Article
A Study on the Mechanism and Influencing Factors of Interlayer Injection–Production Coupling in a Heterogeneous Sandstone Reservoir
by Wei Zheng, Kai Wang, Jing Li, Juanzhe Jiang, Chenyang Tang, Yufei He, Yuqi Guan and Junjian Li
Processes 2024, 12(9), 2054; https://doi.org/10.3390/pr12092054 - 23 Sep 2024
Viewed by 546
Abstract
To solve the development problems caused by the geological characteristics of heterogeneous sandstone reservoirs, such as uneven interlayer exploitation, a method for improving uneven interlayer exploitation differences by applying interlayer injection–production coupling technology is proposed. A physical model of interlayer injection–production coupling is [...] Read more.
To solve the development problems caused by the geological characteristics of heterogeneous sandstone reservoirs, such as uneven interlayer exploitation, a method for improving uneven interlayer exploitation differences by applying interlayer injection–production coupling technology is proposed. A physical model of interlayer injection–production coupling is elaborated in detail, and its mechanism of enhancing oil recovery is analyzed. The reservoir physical property parameters are measured, and a productivity numerical model for the two-phase flow of oil–water was established based on measurement results. Then, the effectiveness of interlayer injection–production coupling was evaluated. The results showed that the mechanism of interlayer injection–production coupling can be summarized as reservoir elastic energy adjustment and reservoir flow field reconstruction, based on the established physical model. The application of interlayer injection–production coupling technology can significantly improve the interlayer exploitation differences in strongly heterogeneous sandstone reservoirs. The injection rate, liquid production rate, half-period ratio, and coupling period all have a significant influence on the interlayer injection–production coupling effect. Specifically, for the J1 well group, the injection rate and liquid production rate can be appropriately increased by a factor of 2 and 1.5, and corresponding oil recovery will increase by 6.4% and 5%. Meanwhile, when the half-period ratio increases to 3:1, the oil recovery will increase by 7.08%. Therefore, during the design of the interlayer injection–production coupling scheme, the injection rate and liquid production rate can be appropriately increased, the injection time should be increased for the under-exploitation layer, and the optimal coupling period should be selected based on the characteristics of the oilfield. Full article
(This article belongs to the Special Issue Phase Change, Interphase Coupling, and Multiphase Transport in Porous Structures)
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<p>A schematic diagram of the first stage of interlayer injection–production coupling.</p> Full article ">Figure 2
<p>A schematic diagram of the second stage of interlayer injection–production coupling.</p> Full article ">Figure 3
<p>Distribution diagram of inter-well pressure during waterflooding stage.</p> Full article ">Figure 4
<p>Inter-well pressure profile during water injection stage of interlayer injection–production coupling.</p> Full article ">Figure 5
<p>Inter-well pressure profile during production stage of interlayer injection–production coupling.</p> Full article ">Figure 6
<p>The flow field in the later stage of waterflooding (the blue line represents water and the red line represents oil).</p> Full article ">Figure 7
<p>Flow field during the injection stage of interlayer injection–production coupling (the blue line represents water and the red line represents oil).</p> Full article ">Figure 8
<p>Flow field during the production stage of interlayer injection–production coupling (the blue line represents water and the red line represents oil).</p> Full article ">Figure 9
<p>A schematic of the experimental setup for PVT test.</p> Full article ">Figure 10
<p>A schematic of the experimental setup for permeability measurement.</p> Full article ">Figure 11
<p>Location map of J1 well group in P-3 reservoir.</p> Full article ">Figure 12
<p>J1 well group profile map.</p> Full article ">Figure 13
<p>History and matching production dynamic data: (<b>a</b>) field liquid production rate; (<b>b</b>) field oil production rate; (<b>c</b>) field water cut; (<b>d</b>) field water injection rate.</p> Full article ">Figure 14
<p>Relationship between injection rate ratio and enhanced WSV of L20 and L30.</p> Full article ">Figure 15
<p>Relationship between injection ratio and incremental oil and enhanced oil recovery.</p> Full article ">Figure 16
<p>Relationship between production rate ratio and incremental oil and enhanced oil recovery.</p> Full article ">Figure 17
<p>Relationship between Half-Period Ratio and oil recovery of L20 and L30.</p> Full article ">Figure 18
<p>Relationship between Half-Period Ratio and incremental oil and enhanced oil recovery.</p> Full article ">Figure 19
<p>Relationship between coupling period and incremental oil and enhanced oil recovery.</p> Full article ">
19 pages, 2022 KiB  
Article
Characteristic Canonical Analysis-Based Attack Detection of Industrial Control Systems in the Geological Drilling Process
by Mingdi Xu, Zhaoyang Jin, Shengjie Ye and Haipeng Fan
Processes 2024, 12(9), 2053; https://doi.org/10.3390/pr12092053 - 23 Sep 2024
Viewed by 736
Abstract
Modern industrial control systems (ICSs), which consist of sensor nodes, actuators, and buses, contribute significantly to the enhancement of production efficiency. Massive node arrangements, security vulnerabilities, and complex operating status characterize ICSs, which lead to a threat to the industrial processes’ stability. In [...] Read more.
Modern industrial control systems (ICSs), which consist of sensor nodes, actuators, and buses, contribute significantly to the enhancement of production efficiency. Massive node arrangements, security vulnerabilities, and complex operating status characterize ICSs, which lead to a threat to the industrial processes’ stability. In this work, a condition-monitoring method for ICSs based on canonical variate analysis with probabilistic principal component analysis is proposed. This method considers the essential information of the operating data. Firstly, the one-way analysis of variance method is utilized to select the major variables that affect the operating performance. Then, a concurrent monitoring model based on probabilistic principal component analysis is established on both the serially correlated canonical subspace and its residual subspace, which is divided by canonical variate analysis. After that, monitoring statistics and control limits are constructed. Finally, the effectiveness and superiority of the proposed method are validated through comparisons with actual drilling operations. The method has better sensitivity than traditional monitoring methods. The experimental result reveals that the proposed method can effectively monitor the operating performance in a drilling process with its highest accuracy of 92.31% and a minimum monitoring delay of 11 s. The proposed method achieves much better effectiveness through real-world process scenarios due to its distributed structural division and the characteristic canonical analysis conducted in this paper. Full article
(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)
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<p>A typical industrial control system structure for the drilling process.</p> Full article ">Figure 2
<p>Histograms of the drilling data under optimal and non-optimal modes.</p> Full article ">Figure 3
<p>The framework of the proposed CVA–PPCA-based monitoring method.</p> Full article ">Figure 4
<p>Schematic of a geological drilling process.</p> Full article ">Figure 5
<p>The drilling system of a real geological exploration well.</p> Full article ">Figure 6
<p>Time series plots of the drilling process under production.</p> Full article ">Figure 7
<p>Condition monitoring results based on the proposed method: (<b>a</b>) surge attacks; (<b>b</b>) biased attacks; and (<b>c</b>) the normal conditions.</p> Full article ">Figure 8
<p>Condition monitoring results based on the PCA method: (<b>a</b>) surge attacks; (<b>b</b>) biased attacks; (<b>c</b>) the normal conditions.</p> Full article ">
13 pages, 3779 KiB  
Article
Construction of Carbon Dioxide Responsive Graphene Point Imbibition and Drainage Fluid and Simulation of Imbibition Experiments
by Peng Yin, Fang Shi, Mingjian Luo, Jingchun Wu, Yanan Yu, Chunlong Zhang and Bo Zhao
Processes 2024, 12(9), 2052; https://doi.org/10.3390/pr12092052 - 23 Sep 2024
Viewed by 824
Abstract
The global oil and gas exploration targets are gradually moving towards a new field of oil and gas accumulation with nanopore throats, ranging from millimeter scale to micro-nano pore throats. The development method of tight oil reservoirs is different from that of conventional [...] Read more.
The global oil and gas exploration targets are gradually moving towards a new field of oil and gas accumulation with nanopore throats, ranging from millimeter scale to micro-nano pore throats. The development method of tight oil reservoirs is different from that of conventional oil reservoirs, and the development efficiency is constrained. Therefore, it is necessary to construct a nanoscale fluid with strong diffusion and dispersion and improve its permeability, suction, and displacement capabilities. Under the background of CCUS, carbon dioxide flooding is a better way to develop tight reservoirs. However, in order to solve the problem of gas channeling, this paper developed a carbon dioxide-responsive graphene point type surfactant, which has a good gas–liquid synergistic effect. At the same time, graphene nanomaterials are carbon-based and create no environmental damage in oil reservoirs. In this study, graphene quantum dots (GQDs) were prepared using the hydrothermal method, and functional graphene quantum dots (F-GQDs) responsive to carbon dioxide stimulation were synthesized by covalent grafting of amidine functional groups. By characterizing its structure and physical and chemical properties, and by conducting imbibition simulation experiments, its imbibition and drainage ability in nanopore throats is elucidated. Infrared spectrum measurement shows that after functional modification, the quantum dots exhibited new characteristic peaks at 1600 cm−1 to 1300 cm−1, considering the N-H plane-stretching characteristic peak. The fluorescence spectra showed that the fluorescence intensity of F-GQDs was increased after functional modification, which indicated that F-GQDs were successfully synthesized. Through measurements of interfacial activity and adhesion work calculations, the oil–water interfacial tension can achieve ultra-low values within the range of 10−2 to 10−3 mN/m. Oil sand cleaning experiments and indoor simulations of spontaneous imbibition in tight cores demonstrate that F-GQDs exhibit effective oil-washing capabilities and a strong response to carbon dioxide. When combined with carbon dioxide, the system enhances both the rate and efficiency of oil washing. Imbibition recovery can reach more than 50%. The research results provide a certain theoretical basis and data reference for the efficient development of tight reservoirs. Full article
(This article belongs to the Section Chemical Processes and Systems)
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<p>Structural diagram of graphene dots (GQDs).</p> Full article ">Figure 2
<p>Structural diagram of common CO<sub>2</sub> responsive groups.</p> Full article ">Figure 3
<p>Mechanism of graphene dot synthesis by citric acid pyrolysis.</p> Full article ">Figure 4
<p>Contact angle tester (<b>right</b>: measuring sample).</p> Full article ">Figure 5
<p>Instrument diagram of oil-washing effect experiment.</p> Full article ">Figure 6
<p>F-GQD morphology and particle size diagram.</p> Full article ">Figure 7
<p>Comparison of infrared spectral curves of developed GQDs.</p> Full article ">Figure 8
<p>UV Vis absorption spectra of F-GQDs dispersion system.</p> Full article ">Figure 9
<p>Fluorescence spectra of F-GQDs dispersion system.</p> Full article ">Figure 10
<p>Contact angle determination results.</p> Full article ">Figure 11
<p>Imbibition experimental curves of the three dispersion systems.</p> Full article ">
17 pages, 3088 KiB  
Article
The Carrying Behavior of Water-Based Fracturing Fluid in Shale Reservoir Fractures and Molecular Dynamics of Sand-Carrying Mechanism
by Qiang Li, Qingchao Li, Fuling Wang, Jingjuan Wu and Yanling Wang
Processes 2024, 12(9), 2051; https://doi.org/10.3390/pr12092051 - 23 Sep 2024
Cited by 36 | Viewed by 1109
Abstract
Water-based fracturing fluid has recently garnered increasing attention as an alternative oilfield working fluid for propagating reservoir fractures and transporting sand. However, the low temperature resistance and stability of water-based fracturing fluid is a significant limitation, restricting the fracture propagation and gravel transport. [...] Read more.
Water-based fracturing fluid has recently garnered increasing attention as an alternative oilfield working fluid for propagating reservoir fractures and transporting sand. However, the low temperature resistance and stability of water-based fracturing fluid is a significant limitation, restricting the fracture propagation and gravel transport. To effectively ameliorate the temperature resistance and sand-carrying capacity, a modified cross-linker with properties adaptable to varying reservoir conditions and functional groups was synthesized and chemically characterized. Meanwhile, a multifunctional collaborative progressive evaluation device was developed to investigate the rheology and sand-carrying capacity of fracturing fluid. Utilizing molecular dynamics simulations, the thickening mechanism of the modified cross-linker and the sand-carrying mechanism of the fracturing fluid were elucidated. Results indicate that the designed cross-linker provided a high viscosity stability of 130 mPa·s and an excellent sand-carrying capacity of 15 cm2 at 0.3 wt% cross-linker content. Additionally, increasing reservoir pressure exhibited enhanced thickening and sand-carrying capacities. However, a significant inverse relationship was observed between reservoir temperature and sand-carrying capacity, attributed to changes in the drag coefficient and thickener adsorption. These results verified the effectiveness of the cross-linker in enhancing fluid viscosity and sand-carrying capacity as a modified cross-linker for water-based fracturing fluid. Full article
(This article belongs to the Special Issue Environmentally Friendly Production of Energy from Natural Gas Hydrates)
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<p>Collaborative evaluation device of water-based fracturing fluid performance.</p> Full article ">Figure 2
<p>Synthesis process (<b>a</b>) and chemical characterization (<b>b</b>) of nano-titanium cross-linker.</p> Full article ">Figure 3
<p>Sand-carrying experimental device and schematic diagram of water-based fracturing fluid.</p> Full article ">Figure 4
<p>Effect of cross-linker content on fracturing fluid viscosity and sand-carrying capacity (453 K, 170 s<sup>−1</sup> and 20 MPa). (<b>a</b>): fracturing fluid viscosity. (<b>b</b>): sand-carrying capacity.</p> Full article ">Figure 5
<p>Micro mechanism of sand carrying in fracturing fluid by the cross-linker type and cross-linker content.</p> Full article ">Figure 6
<p>Effect of reservoir temperature on fracturing fluid viscosity and sand-carrying capacity (0.3% cross-linker content, 170 s<sup>−1</sup> and 20 MPa). (<b>a</b>): fracturing fluid viscosity. (<b>b</b>): sand-carrying capacity.</p> Full article ">Figure 7
<p>Microscopic model differences of fracturing fluid at different reservoir temperatures. (<b>a</b>): Microscopic grid variation. (<b>b</b>): sand-carrying capacity of synthetic crosslinker.</p> Full article ">Figure 8
<p>Effect of reservoir pressure on fracturing fluid viscosity and sand-carrying capacity (0.3% cross-linker content, 170 s<sup>−1</sup> and 453 K). (<b>a</b>): fracturing fluid viscosity. (<b>b</b>): sand-carrying capacity.</p> Full article ">Figure 9
<p>Effect of reservoir pressure on fracturing fluid viscosity and sand -carrying capacity (0.3% cross-linker content, 20 MPa and 453 K). (<b>a</b>): Synthetic. (<b>b</b>): Commercial.</p> Full article ">
23 pages, 4933 KiB  
Article
Design of Oil Mist and Volatile-Organic-Compound Treatment Equipment in the Manufacturing Plant
by Chengguo Fu, Weiwei He, Qianfen Wang, Yuhao Li, Hui Yang, Haibo Li, Ting Chen, Yaqi Zhang, Ming Yu and Yuguang Wang
Processes 2024, 12(9), 2050; https://doi.org/10.3390/pr12092050 - 23 Sep 2024
Viewed by 835
Abstract
To effectively confront the acute challenge of global warming, at the present stage, the Chinese government has designated carbon reduction as the core objective to accomplish the coordinated control of greenhouse gas and pollutant emissions. As China is a major manufacturing country, with [...] Read more.
To effectively confront the acute challenge of global warming, at the present stage, the Chinese government has designated carbon reduction as the core objective to accomplish the coordinated control of greenhouse gas and pollutant emissions. As China is a major manufacturing country, with the continuous improvement of air emission standards, it is particularly necessary to carry out the design of more efficient volatile organic pollutant emission devices. This study takes a treatment system with a waste gas ventilation volume of 6 × 104 m3·h−1 as an example, adopts the end treatment approach of adsorption and catalytic combustion coupling, and designs a purification device composed of multistage oil-mist recovery, electrostatic adsorption, dry filtration, activated-carbon adsorption and desorption, catalytic combustion, etc. It also employs the fuzzy proportional-integral-derivative fine temperature control algorithm, and the temperature overshoot was decreased by 85%. The average emission concentration of volatile organic compounds at the equipment outlet is 6.56 mg·m−3, and the average removal rate is 93.99%, far surpassing the national emission standards. The device operates efficiently and stably, confirming that the end-coupled treatment system based on the adaptive fuzzy proportional-integral-derivative temperature control strategy can effectively handle volatile organic compounds with oil mist and holds significant promotion and research value. Full article
(This article belongs to the Special Issue New Research on Adsorbent Materials in Environmental Protection)
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<p>Overall structure diagram.</p> Full article ">Figure 2
<p>Schematic diagram of dry filter box.</p> Full article ">Figure 3
<p>Structure diagram of activated-carbon box.</p> Full article ">Figure 4
<p>Schematic diagram of the catalytic combustion chamber structure.</p> Full article ">Figure 5
<p>Schematic diagram of the program control flow.</p> Full article ">Figure 6
<p>Logic schematic diagram based on fuzzy PID control scheme.</p> Full article ">Figure 7
<p>Schematic diagram of the PID simulation structure.</p> Full article ">Figure 8
<p>Response curve of PID control system.</p> Full article ">Figure 9
<p>Fuzzy PID control system frame diagram.</p> Full article ">Figure 10
<p>Response curve of fuzzy PID system.</p> Full article ">Figure 11
<p>Equipment installation site.</p> Full article ">Figure 12
<p>Gas detection result: (<b>a</b>) comparison of VOCs concentration, (<b>b</b>) air volume comparison diagram, (<b>c</b>) VOCs rate comparison diagram.</p> Full article ">
22 pages, 4186 KiB  
Article
Optimal Reactive Power Dispatch and Demand Response in Electricity Market Using Multi-Objective Grasshopper Optimization Algorithm
by Punam Das, Subhojit Dawn, Sadhan Gope, Diptanu Das and Ferdinando Salata
Processes 2024, 12(9), 2049; https://doi.org/10.3390/pr12092049 - 23 Sep 2024
Viewed by 901
Abstract
Optimal Reactive Power Dispatch (ORPD) is a power system optimization tool that modifies system control variables such as bus voltage and transformer tap settings, and it compensates devices’ Volt Ampere Reactive (VAR) output. It is used to decrease real power loss, enhance the [...] Read more.
Optimal Reactive Power Dispatch (ORPD) is a power system optimization tool that modifies system control variables such as bus voltage and transformer tap settings, and it compensates devices’ Volt Ampere Reactive (VAR) output. It is used to decrease real power loss, enhance the voltage profile, and promote stability. Furthermore, several issues have been faced in electricity markets, such as price volatility, transmission line congestion, and an increase in the cost of electricity during peak hours. Programs such as demand response (DR) provide system operators with more control over how small customers participate in lowering peak-hour energy prices and demand. This paper presents an extensive study on ORPD methodologies and DR programs for lowering voltage deviation, limiting cost, and minimizing power losses to create effective and economical operations systems. The main objectives of this work are to minimize costs and losses in the system and reduce voltage variation. The Grasshopper Optimization Algorithm (GOA) and Dragonfly Algorithm (DA) have been implemented successfully to solve this problem. The proposed technique has been evaluated by using the IEEE-30 bus system. The results obtained by the implementation of demand response systems show a considerable reduction in costs and load demands that benefit consumers through DR considerations. The results obtained from the GOA and DA are compared with those generated by other researchers and published in the literature to ascertain the algorithm’s efficiency. Full article
(This article belongs to the Special Issue Advances in Renewable Energy Systems (2nd Edition))
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<p>Flow chart of GOA.</p> Full article ">Figure 2
<p>IEEE 30 bus system.</p> Full article ">Figure 3
<p>Comparison results of TVD for IEEE 30 bus system [<a href="#B1-processes-12-02049" class="html-bibr">1</a>].</p> Full article ">Figure 4
<p>Load voltage profile of IEEE 30 bus system.</p> Full article ">Figure 5
<p>Comparative convergence characteristics.</p> Full article ">Figure 6
<p>Response bus load reduction with DR.</p> Full article ">Figure 7
<p>Load bus voltage profile with and without DR program.</p> Full article ">Figure 8
<p>Comparison of cost before and after DR.</p> Full article ">Figure 9
<p>Incentives paid at different load buses.</p> Full article ">Figure 10
<p>Convergence curve comparison of DR for IEEE-30 bus.</p> Full article ">Figure 11
<p>Load bus voltage profile with and without DR program using MOGOA.</p> Full article ">Figure 12
<p>Pareto optimal curve comparison for Scenario 1 and Scenario 2.</p> Full article ">
16 pages, 5220 KiB  
Article
Modification of Sulfur Cake—Waste from Sulfuric Acid Production
by Yerdos Ongarbayev, Yerbol Tileuberdi, Aigul Baimagambetova, Yerzhan Imanbayev, Yernar Kanzharkan, Ainur Zhambolova, Aliya Kenzhegaliyeva and Aksaule Kydyrali
Processes 2024, 12(9), 2048; https://doi.org/10.3390/pr12092048 - 22 Sep 2024
Viewed by 813
Abstract
In the production of sulfuric acid, sulfur cake—a waste product of the sulfur purification process—is formed in large quantities, which requires its disposal and use. For its use in composite materials, modification is necessary to convert sulfur into a polymer form. The aim [...] Read more.
In the production of sulfuric acid, sulfur cake—a waste product of the sulfur purification process—is formed in large quantities, which requires its disposal and use. For its use in composite materials, modification is necessary to convert sulfur into a polymer form. The aim of the study was to develop a method for modifying sulfur cake—a waste product of sulfuric acid production—for its disposal. Available reagents—styrene, glycerol, and oleic acid—were tested as modifiers in the work. The sample compositions consisted of 100% sulfur cake (no. 1) and its mixtures: 97% sulfur cake + 3% styrene (no. 2), 97% sulfur cake + 3% glycerol (no. 3), 97% sulfur cake + 3% oleic acid (no. 4), 95% sulfur cake + 3% styrene, 1% glycerol, and 1% oleic acid (no. 5). Modification of sulfur cake was carried out at a temperature of 140 °C for 30 min. The composition, crystal structure, and thermal properties of the samples of the original and modified sulfur cake were studied using X-ray phase and X-ray structural analyses, IR spectroscopy, differential scanning calorimetry, differential thermal and thermogravimetric analysis. The optimal modifier for sulfur cake was a mixture of styrene, glycerol, and oleic acid, which led to the formation of acetal (polyoxymethylene) and an improvement in the structure due to a decrease in the content of impurities. Modification of sulfur cake with styrene resulted in the appearance of a CAr–S bond band at 571 cm−1, and modification with oleic acid a C–S band in the region of 694 cm−1 in the IR spectra. The results of differential scanning calorimetric analysis showed an increase in the heat of fusion of sulfur by 12.45 J/g in the samples of sulfur cake modified with glycerol and styrene. Modification of sulfur cake with oleic acid and a mixture of reagents resulted in the appearance of a third peak with maxima at 244.2 and 264.0 °C, which demonstrated a significant effect of the indicated additives on the thermal behavior of the sulfur cake. Proposed schemes for modifying sulfur cake with styrene and oleic acid are presented. Full article
(This article belongs to the Special Issue Development and Utilization of Biomass, Coal and Organic Solid Wastes)
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<p>Diffraction patterns of samples based on the results of X-ray phase analysis: (<b>a</b>)—sulfur cake; sulfur cake samples modified with (<b>b</b>)—styrene, (<b>c</b>)—glycerol, (<b>d</b>)—oleic acid, (<b>e</b>)—styrene, glycerol, and oleic acid.</p> Full article ">Figure 1 Cont.
<p>Diffraction patterns of samples based on the results of X-ray phase analysis: (<b>a</b>)—sulfur cake; sulfur cake samples modified with (<b>b</b>)—styrene, (<b>c</b>)—glycerol, (<b>d</b>)—oleic acid, (<b>e</b>)—styrene, glycerol, and oleic acid.</p> Full article ">Figure 2
<p>Diffraction patterns of samples based on the results of X-ray structural analysis: (<b>a</b>)—sulfur cake; sulfur cake samples modified with: (<b>b</b>)—styrene, (<b>c</b>)—glycerol, (<b>d</b>)—oleic acid, (<b>e</b>)—styrene, glycerol, and oleic acid.</p> Full article ">Figure 2 Cont.
<p>Diffraction patterns of samples based on the results of X-ray structural analysis: (<b>a</b>)—sulfur cake; sulfur cake samples modified with: (<b>b</b>)—styrene, (<b>c</b>)—glycerol, (<b>d</b>)—oleic acid, (<b>e</b>)—styrene, glycerol, and oleic acid.</p> Full article ">Figure 3
<p>IR spectra of samples: 1—sulfur cake; sulfur cake samples modified with 2—styrene, 3—glycerol, 4—oleic acid, 5—styrene, glycerol, and oleic acid.</p> Full article ">Figure 4
<p>Differential scanning calorimetric analysis curves of samples: 1—sulfur cake; sulfur cake samples modified with 2—styrene, 3—glycerol, 4—oleic acid, 5—styrene, glycerol, and oleic acid.</p> Full article ">Figure 5
<p>Thermograms of samples based on the results of differential thermal analysis and thermogravimetry: (<b>a</b>)—sulfur cake; sulfur cake samples modified with (<b>b</b>)—styrene, (<b>c</b>)—glycerol, (<b>d</b>)—oleic acid, (<b>e</b>)—styrene, glycerol, and oleic acid.</p> Full article ">Figure 6
<p>Scheme of transformation of sulfur forms.</p> Full article ">Figure 7
<p>Scheme of the reaction of sulfur with styrene.</p> Full article ">Figure 8
<p>Scheme of the reaction of sulfur with oleic acid.</p> Full article ">
17 pages, 3682 KiB  
Article
Research on the Phase Behavior of Multi-Component Thermal-Fluid-Heavy Oil Systems
by Xiangji Dou, Mingjie Liu, Xinli Zhao, Yanfeng He, Erpeng Guo, Jiahao Lu, Borui Ma and Zean Chen
Processes 2024, 12(9), 2047; https://doi.org/10.3390/pr12092047 - 22 Sep 2024
Viewed by 627
Abstract
Multi-component thermal luid technology optimizes development effects and has a strong adaptability, providing a new choice for the efficient development of heavy oil reservoirs. However, due to the significant differences between the phase behavior of multi-component thermal-fluid-heavy oil systems and conventional systems, and [...] Read more.
Multi-component thermal luid technology optimizes development effects and has a strong adaptability, providing a new choice for the efficient development of heavy oil reservoirs. However, due to the significant differences between the phase behavior of multi-component thermal-fluid-heavy oil systems and conventional systems, and the lack of targeted and large-scale research, key issues such as the phase behavior of these systems are unclear. This research studies the phase behavior and influencing factors of emulsions and foamy oil in a multi-component thermal-fluid-heavy oil system through high-temperature and high-pressure PVT experiments, revealing the characteristics of the system’s special phase behavior. In the heavy oil emulsion system, the water content directly affects changes in the system’s phase state. The higher the temperature, the larger the phase transition point, and the two are positively correlated. As the stirring speed increases, the phase transition point first increases and then decreases. The amount of dissolved gas is negatively correlated with the size of the phase transition point, and dissolution can form foamy oil. In the heavy oil–foamy oil system, the dissolution capacity of CO2 is greater than that of multi-component gases, which is greater than that of N2. A high water content and high temperature are not conducive to the dissolution of multi-component gases. While an increase in stirring speed is beneficial for the dissolution of gases, there are limitations to its enhancement ability. Therefore, the development of multi-component thermal fluids should avoid the phase transition point of emulsions and promote the dissolution of multi-component gases. Full article
(This article belongs to the Special Issue Chemical Flooding in EOR: Practical and Simulation Insights)
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<p>Appearance drawing of high-temperature sealing structure components.</p> Full article ">Figure 2
<p>Concentric magnetic force for the coupling of the rotor.</p> Full article ">Figure 3
<p>A schematic diagram of the PVT experiment.</p> Full article ">Figure 4
<p>Effects of different water contents on the emulsion viscosity.</p> Full article ">Figure 5
<p>Contrast the effects of different temperatures on the emulsion viscosity.</p> Full article ">Figure 6
<p>Effects of different stirring rates on the emulsion viscosity (50 °C).</p> Full article ">Figure 7
<p>Effects of different mixing rates on the water rate of emulsion.</p> Full article ">Figure 8
<p>Comparison of different transition points of dissolved gas to oil ratio (30 °C).</p> Full article ">Figure 9
<p>Pictures of the PVT experimental products. (<b>a</b>) Discharged heavy oil. (<b>b</b>) Emulsified heavy oil with entrained bubbles (the reflective spots in the figure are bubbles).</p> Full article ">Figure 10
<p>Gas dissolution degree of different components (30 °C).</p> Full article ">Figure 11
<p>Effects of temperature on the viscosity of different injected-fluid-heavy oil systems (15 MPa).</p> Full article ">Figure 12
<p>The influences of different water contents on the gas dissolution capacity.</p> Full article ">Figure 13
<p>Saturated pressure of a multicomponent gas at different temperatures.</p> Full article ">Figure 13 Cont.
<p>Saturated pressure of a multicomponent gas at different temperatures.</p> Full article ">Figure 14
<p>Comparison of heavy oils with different viscosities.</p> Full article ">Figure 14 Cont.
<p>Comparison of heavy oils with different viscosities.</p> Full article ">Figure 15
<p>Dissolved gas–oil ratios at different stirring rates (50 °C).</p> Full article ">Figure 16
<p>Water content and heavy oil viscosity changes under different water supply quantities.</p> Full article ">
18 pages, 3822 KiB  
Article
The Effect of Water on Gas-Containing Coal with Cyclic Loading: An Experimental Study
by Yang Yang, Changbao Jiang, Diandong Hou, Fazhi Yan and Shaojie Chen
Processes 2024, 12(9), 2046; https://doi.org/10.3390/pr12092046 - 22 Sep 2024
Viewed by 515
Abstract
In deep mining engineering, coal often is subjected to the influence of cyclic loading and water. In order to study the effect of water on gas-containing coal with cyclic loading, the pore evolution, permeability and energy dissipation of gas-containing coal under the effects [...] Read more.
In deep mining engineering, coal often is subjected to the influence of cyclic loading and water. In order to study the effect of water on gas-containing coal with cyclic loading, the pore evolution, permeability and energy dissipation of gas-containing coal under the effects of water and cyclic axial stress were analyzed in depth. The results showed that with the increase of water content, dissipation energy and permeability of coal samples decreased gradually. The presence of water weakened the increase in micropores and mesopores and promoted the increase in macropores during the cyclic loading process. Compared to dry coal samples, the ratio of micropores and mesopores in saturated coal samples decreased by 0.8872%, while the proportion of macropores increased by 0.0341%. On the basis of the above, a new formula for calculating the dissipation energy of gas-containing coal under the effects of water was proposed. A new damage variable based on energy dissipation was proposed. Finally, a theoretical model was derived to describe the permeability of gas-containing coal under the effects of water and cyclic axial stress. Full article
(This article belongs to the Section Chemical Processes and Systems)
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<p>The experimental apparatus of the cyclic loading test.</p> Full article ">Figure 2
<p>Nuclear magnetic resonance analysis apparatus.</p> Full article ">Figure 3
<p>Loading paths for cyclic loading tests.</p> Full article ">Figure 4
<p>The flow chart for experimentation and analysis.</p> Full article ">Figure 5
<p>The stress–strain curve for coal specimens with cyclic loading: axial strain (<span class="html-italic">ε</span><sub>a</sub>); radial strain (<span class="html-italic">ε</span><sub>r</sub>); volumetric strain (<span class="html-italic">ε</span><sub>v</sub>).</p> Full article ">Figure 6
<p>The evolution of axial stress and permeability with axial strain.</p> Full article ">Figure 7
<p>The stress state under the joint action of gas pressure and loading stress.</p> Full article ">Figure 8
<p>The dissipation energy of each cycle for three coal samples.</p> Full article ">Figure 9
<p>The cumulative dissipation energy of three coal samples.</p> Full article ">Figure 10
<p>The Evolution of damage variables under cyclic loading.</p> Full article ">Figure 11
<p>The T<sub>2</sub> distribution for coal specimens before and after the experiments.</p> Full article ">Figure 12
<p>The fractal dimension of coal specimens before and after the experiments.</p> Full article ">Figure 13
<p>Permeability data-matching results under cyclic loading conditions.</p> Full article ">
17 pages, 9733 KiB  
Article
Effect of Heat-Treatment Process on Magnetic Characteristics of Grain-Oriented Electrical Steel
by Claudia-Olimpia Stasac, Andrei-Dan Tomșe, Mircea-Nicolae Arion, Livia Bandici and Francisc-Ioan Hathazi
Processes 2024, 12(9), 2045; https://doi.org/10.3390/pr12092045 - 22 Sep 2024
Viewed by 749
Abstract
This paper explores the effects and impacts of the metallurgical process of quenching on grain-oriented strips of electrical steel. Experimental findings reveal that quenching resulted in increased hardness and an increased Young’s modulus. An analysis of the material structure post-quenching indicates significant alterations [...] Read more.
This paper explores the effects and impacts of the metallurgical process of quenching on grain-oriented strips of electrical steel. Experimental findings reveal that quenching resulted in increased hardness and an increased Young’s modulus. An analysis of the material structure post-quenching indicates significant alterations in grain spacing and reduced height differences between grains. However, the magnetic properties of the steel deteriorated following quenching. Full article
(This article belongs to the Section Materials Processes)
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<p>The surface of the GOES sample seen at 125× magnification.</p> Full article ">Figure 2
<p>The surface of the GOES sample seen at 250× magnification.</p> Full article ">Figure 3
<p>(<b>a</b>) Epstein frame; (<b>b</b>) simulated Epstein frame using FEMM with the corner-joint effect.</p> Full article ">Figure 4
<p>Epstein Frame method for measuring magnetic proprieties.</p> Full article ">Figure 5
<p>Epstein frame wattmeter method for measuring power loss.</p> Full article ">Figure 6
<p>Samples before quenching, taken via a 3D laser scan at 3750×.</p> Full article ">Figure 7
<p>Profile analysis (red area) of the height difference between different grains on the sample from <a href="#processes-12-02045-f006" class="html-fig">Figure 6</a>.</p> Full article ">Figure 8
<p>Quenched samples taken via a 3D laser scan at 3750×.</p> Full article ">Figure 9
<p>Profile analysis (red area) of the height difference between different grains on the quenched sample from <a href="#processes-12-02045-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 10
<p>Before (<b>a</b>) and after (<b>b</b>) quenching, taken via a microscope at 1250× magnification.</p> Full article ">Figure 11
<p>(<b>a</b>) Before (red) and after (blue) quenching comparison of the sample BH hysteresis at 1.5 T; (<b>b</b>) before (red) and after (blue) quenching comparison of the sample BH hysteresis at 1 T.</p> Full article ">Figure 12
<p>Before (red) and after (blue) quenching comparison of the sample BH hysteresis at 0.5 T.</p> Full article ">Figure 13
<p>(<b>a</b>) Secondary (blue) and primary (red) voltage in transformer before quenching at 50 Hz; (<b>b</b>) secondary (blue) and primary (red) voltage in transformer after quenching at 50 Hz.</p> Full article ">Figure 14
<p>(<b>a</b>) Secondary (blue) and primary (red) voltage in transformer before quenching at 100 Hz; (<b>b</b>) secondary (blue) and primary (red) voltage in transformer after quenching at 100 Hz.</p> Full article ">Figure 15
<p>(<b>a</b>) Secondary (blue) and primary (red) voltage in transformer before quenching at 150 Hz; (<b>b</b>) secondary (blue) and primary (red) voltage in transformer after quenching at 150 Hz.</p> Full article ">Figure 16
<p>Specific loss of power before (red) and after (blue) quenching.</p> Full article ">Figure 17
<p>The load–displacement curve.</p> Full article ">Figure 18
<p>Ramp-load graph and a scratch test at 45° from the rolling direction (RD).</p> Full article ">
20 pages, 10098 KiB  
Article
Adsorption of Methylene Blue and Eriochrome Black T onto Pinecone Powders (Pinus nigra Arn.): Equilibrium, Kinetics, and Thermodynamic Studies
by Alper Solmaz
Processes 2024, 12(9), 2044; https://doi.org/10.3390/pr12092044 - 22 Sep 2024
Viewed by 843
Abstract
In this study, methylene blue (MB) and eriochrome black T (EBT) dyes were removed with the waste Pinus nigra Arn. powders from Anatolian black pinecone (PC-PnA) within the framework of sustainability. UV–Vis spectroscopy, X-ray diffraction (XRD), scanning electron microscope (SEM), energy [...] Read more.
In this study, methylene blue (MB) and eriochrome black T (EBT) dyes were removed with the waste Pinus nigra Arn. powders from Anatolian black pinecone (PC-PnA) within the framework of sustainability. UV–Vis spectroscopy, X-ray diffraction (XRD), scanning electron microscope (SEM), energy dispersive X-ray (EDX), fourier transform infrared spectroscopy (FTIR), thermogravimetry–differential thermal analysis (TGA-DTA), Brunauer–Emmett–Teller (BET) surface area, and point of zero charge (pHpzc) analyses were performed for the characterization of PC-PnAs. The effects of pH, amount of adsorbent, time, initial concentration and temperature were determined by batch adsorption experiments. Four kinetic and isotherm models were examined, and error function tests were used for the most suitable model. According to this, the average pore diameters, mass losses at 103.9 and 721.6 °C and pHpzc values of PC-PnAs were found as 61.661 Å, 5.9%, 30%, and 5.77, respectively. Additionally, the most suitable kinetic and isotherm models for the removal of both dyes were Langmuir and pseudo-second-order. The maximum removal efficiencies (qmax) for MB and EBT dyes was calculated as 91.46 and 15.85 mg/g, respectively and the adsorption process was found to be endothermic. As a result, PC-PnA particles can be used as an alternative sorbent for the removal of MB and EBT dyes. Full article
(This article belongs to the Special Issue Integrated Approaches to Eco-Friendly Processes for Persistent Pollutants Contamination)
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<p>SEM-EDX diagrams of PC-<span class="html-italic">Pn</span>A before and after the reaction; (<b>a</b>) Raw PC-<span class="html-italic">Pn</span>A, (<b>b</b>) MB charged PC-<span class="html-italic">Pn</span>A, (<b>c</b>) EBT charged PC-<span class="html-italic">Pn</span>A.</p> Full article ">Figure 2
<p>FTIR diagram of PC-<span class="html-italic">Pn</span>A before and after reaction; Black line: Raw PC-PnA, Red line: MB charged PC-PnA, Blue line: EBT charged PC-PnA.</p> Full article ">Figure 3
<p>Nitrogen adsorption–desorption isotherm of the adsorbent PC-<span class="html-italic">Pn</span>A.</p> Full article ">Figure 4
<p>pH<sub>pzc</sub> of PC-<span class="html-italic">Pn</span>A.</p> Full article ">Figure 5
<p>Effect of the initial pH value for MB (V: 10 mL, C<sub>0</sub>: 10 mg/L, T: 298 K, dosage of PC-<span class="html-italic">Pn</span>A: 1.0 g/L, pH: 3–11, reaction time: 60 min) and for EBT (V: 10 mL, C<sub>0</sub>: 5 mg/L, T: 298 K, dosage of PC-<span class="html-italic">Pn</span>A: 1.0 g/L, pH: 3–11, reaction time: 60 min).</p> Full article ">Figure 6
<p>Effect of the PC-<span class="html-italic">Pn</span>A dosage; (<b>a</b>) for MB; V: 50 mL, C<sub>0</sub>: 10 mg/L; T: 298 K, dosage of PC-<span class="html-italic">Pn</span>A: 0.4–1.8 g/L, pH: 3.0, reaction time: 60 min, and (<b>b</b>) for EBT; V: 50 mL, C<sub>0</sub>: 5.0 mg/L; T: 298 K, dosage of PC-<span class="html-italic">Pn</span>A: 0.4–1.8 g/L, pH: 3.0, reaction time: 60 min.</p> Full article ">Figure 7
<p>Effect of the contact time; (<b>a</b>) for MB; V:100 mL; C<sub>0</sub>:10 mg/L, dosage of PC-<span class="html-italic">Pn</span>A: 1.6 g/L; T: 298 K; pH: 3.0, and (<b>b</b>) for EBT; V:100 mL; C<sub>0</sub>: 5.0 mg/L, dosage of PC-<span class="html-italic">Pn</span>A: 1.4 g/L; T: 298 K; pH: 3.0.</p> Full article ">Figure 8
<p>Effect of the initial dye concentration; (<b>a</b>) for MB; V:10 mL; C<sub>0</sub>:13.17–150 mg/L, dosage of PC-<span class="html-italic">Pn</span>A: 1.0 g/L; T: 298 K; pH: 3.0, time: 60 min. and (<b>b</b>) for EBT; V:10 mL; C<sub>0</sub>:13.17–150 mg/L, dosage of PC-<span class="html-italic">Pn</span>A: 1.0 g/L; T: 298 K; pH: 3.0. time: 60 min.</p> Full article ">Figure 9
<p>Linear form graphics of kinetic models; (<b>a</b>) pseudo-first-order, (<b>b</b>) pseudo-second-order, (<b>c</b>) Elovich, (<b>d</b>) intra-particle diffusion.</p> Full article ">Figure 10
<p>Variation of experimental and theoretical <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>q</mi> </mrow> <mrow> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> values versus <span class="html-italic">t</span>, (<b>a</b>) For MB, (<b>b</b>) For EBT.</p> Full article ">Figure 11
<p>Linear form graphics of isotherm models; (<b>a</b>) Freundlich, (<b>b</b>) Langmuir, (<b>c</b>) Temkin, and (<b>d</b>) Sips.</p> Full article ">Figure 12
<p>Variation of experimental and theoretical <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>q</mi> </mrow> <mrow> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> values versus <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> values, (<b>a</b>) For MB, (<b>b</b>) For EBT.</p> Full article ">Figure 13
<p>Regression plot of thermodynamic results.</p> Full article ">
28 pages, 10657 KiB  
Article
Fluoride Removal from Aqueous Solutions by Using Super-Adsorbents of Chitosan/Orange Peels/Activated Carbon@MgO: Synthesis, Characterization, and Adsorption Evaluation
by Athanasia K. Tolkou, Apostolos Posantzis, Konstantinos N. Maroulas, Ramonna I. Kosheleva, Ioanna Koumentakou, Margaritis Kostoglou and George Z. Kyzas
Processes 2024, 12(9), 2043; https://doi.org/10.3390/pr12092043 - 22 Sep 2024
Viewed by 946
Abstract
Exposure to excessive concentrations of fluoride in potable water is harmful to human health; therefore, its limitation is deemed necessary. Among the commonly applied technologies, adsorption is selected, as it is a highly effective, simple, and economically efficient treatment. In the present study, [...] Read more.
Exposure to excessive concentrations of fluoride in potable water is harmful to human health; therefore, its limitation is deemed necessary. Among the commonly applied technologies, adsorption is selected, as it is a highly effective, simple, and economically efficient treatment. In the present study, several combinations of chitosan (CS), orange peels (OP), activated carbon (AC), and MgO were synthesized and tested as adsorbents in order to find the most effective derivative for fluoride extraction. The impact of the adsorbent dosage, pH level, contact time, and initial concentration was investigated to assess the feasibility of the chitosan/orange peels/activated carbon@MgO composite. According to the results, the modification of chitosan with AC, OP, and MgO in a unique adsorbent (CS/OP/AC@MgO), especially in acidic conditions (pH 3.0 ± 0.1) by using 1.0 g/L of the adsorbent, demonstrated the highest efficiency in F removal, up to 97%. The pseudo-second (PSO) order model and Langmuir isotherm model fit better to the experimental results, especially for CS/OP/AC@MgO, providing a Qm = 26.92 mg/g. Thermodynamic analysis confirmed the spontaneous nature of the adsorption process. The structure and morphology of the modified OP/CS@AC-Mg were extensively characterized using BET, XRD, FTIR, and SEM techniques. Full article
(This article belongs to the Special Issue Advances in Adsorption of Wastewater Pollutants)
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<p>Impact of starting solution pH and comparison of materials for F<sup>−</sup> adsorption (C<sub>0</sub> of 5 mg/L, dose of 0.5 g/L, 293 K, contact time of 24 h).</p> Full article ">Figure 2
<p>Determination of pH<sub>pzc</sub> of the optimal CS/OP/AC@MgO material using the pH drift method [<a href="#B45-processes-12-02043" class="html-bibr">45</a>].</p> Full article ">Figure 3
<p>Dose effect on F<sup>−</sup> adsorption on the optimal materials (C<sub>0</sub> of 5 mg/L, pH 3.0 ± 0.1, 293 K, contact time of 24 h).</p> Full article ">Figure 4
<p>Impact of contact time for F<sup>−</sup> adsorption on the optimal materials (C<sub>0</sub> of 5 mg/L, dose of 0.5 g/L, pH 3.0 ± 0.1, 293 K, contact time of 24 h).</p> Full article ">Figure 5
<p>PFO and PSO kinetic models for F<sup>−</sup> adsorption on the optimal materials: (<b>a</b>) Cs/OP, (<b>b</b>) CS/AC, (<b>c</b>) CS/AC@MgO and (<b>d</b>) CS/OP/AC@MgO.</p> Full article ">Figure 5 Cont.
<p>PFO and PSO kinetic models for F<sup>−</sup> adsorption on the optimal materials: (<b>a</b>) Cs/OP, (<b>b</b>) CS/AC, (<b>c</b>) CS/AC@MgO and (<b>d</b>) CS/OP/AC@MgO.</p> Full article ">Figure 6
<p>Comparison between phenomenological model (continuous lines) and experimental (symbols) solute concentration evolution data for F<sup>−</sup> adsorption on optimal materials: (<b>a</b>) Cs/OP, (<b>b</b>) CS/AC, (<b>c</b>) CS/AC@MgO and (<b>d</b>) CS/OP/AC@MgO.</p> Full article ">Figure 6 Cont.
<p>Comparison between phenomenological model (continuous lines) and experimental (symbols) solute concentration evolution data for F<sup>−</sup> adsorption on optimal materials: (<b>a</b>) Cs/OP, (<b>b</b>) CS/AC, (<b>c</b>) CS/AC@MgO and (<b>d</b>) CS/OP/AC@MgO.</p> Full article ">Figure 7
<p>Langmuir and Freundlich adsorption isotherm models for F<sup>−</sup> adsorption on optimal materials: (<b>a</b>) Cs/OP, (<b>b</b>) CS/AC, (<b>c</b>) CS/AC@MgO and (<b>d</b>) CS/OP/AC@MgO.</p> Full article ">Figure 7 Cont.
<p>Langmuir and Freundlich adsorption isotherm models for F<sup>−</sup> adsorption on optimal materials: (<b>a</b>) Cs/OP, (<b>b</b>) CS/AC, (<b>c</b>) CS/AC@MgO and (<b>d</b>) CS/OP/AC@MgO.</p> Full article ">Figure 8
<p>Effect of temperature on F<sup>−</sup> adsorption on optimal materials (C<sub>0</sub> of 5 mg/L; pH 3.0 ± 0.1.0; dose of 0.5 g/L; 293, 303, 313, and 323 K; and 1.5 h).</p> Full article ">Figure 9
<p>Adsorption of F<sup>−</sup> onto CS/OP/AC@MgO for 5 adsorption–desorption cycles.</p> Full article ">Figure 10
<p>SEM micrographs: (<b>a</b>) CS/OP, (<b>b</b>) CS/AC, (<b>c</b>) CS/AC@MgO, and (<b>d</b>) CS/OP/AC@MgO. The left column shows the materials before adsorption, while the right column displays the same materials after fluoride adsorption.</p> Full article ">Figure 10 Cont.
<p>SEM micrographs: (<b>a</b>) CS/OP, (<b>b</b>) CS/AC, (<b>c</b>) CS/AC@MgO, and (<b>d</b>) CS/OP/AC@MgO. The left column shows the materials before adsorption, while the right column displays the same materials after fluoride adsorption.</p> Full article ">Figure 11
<p>SEM/EDS analysis of (<b>a</b>) CS/AC@MgO and (<b>b</b>) CS/OP/AC@MgO adsorbents.</p> Full article ">Figure 11 Cont.
<p>SEM/EDS analysis of (<b>a</b>) CS/AC@MgO and (<b>b</b>) CS/OP/AC@MgO adsorbents.</p> Full article ">Figure 12
<p>FTIR spectra for (<b>a</b>) CS/OP, (<b>b</b>) CS/AC, (<b>c</b>) CS/AC@MgO, and (<b>d</b>) CS/OP/AC@MgO before and after fluoride adsorption.</p> Full article ">Figure 12 Cont.
<p>FTIR spectra for (<b>a</b>) CS/OP, (<b>b</b>) CS/AC, (<b>c</b>) CS/AC@MgO, and (<b>d</b>) CS/OP/AC@MgO before and after fluoride adsorption.</p> Full article ">Figure 13
<p>XRD spectra of Cs/OP, CS/AC, CS/AC@MgO, and CS/OP/AC@MgO adsorbents.</p> Full article ">
12 pages, 2451 KiB  
Article
Optimization of Ultrasound-Assisted Extraction of Phenolics from Satureja hortensis L. and Antioxidant Activity: Response Surface Methodology Approach
by Jelena M. Mašković, Vladimir Jakovljević, Vladimir Živković, Milan Mitić, Luka V. Kurćubić, Jelena Mitić and Pavle Z. Mašković
Processes 2024, 12(9), 2042; https://doi.org/10.3390/pr12092042 - 22 Sep 2024
Viewed by 891
Abstract
The extract of the plant species Satureja hortensis L. (often used as traditional ethno-therapy and in food processing) was prepared using the ultrasonic extraction technique, and contains a large quantity of secondary metabolites, with scientific evidence for antioxidant, antimicrobial, sedative, antispastic and antidiarrheal [...] Read more.
The extract of the plant species Satureja hortensis L. (often used as traditional ethno-therapy and in food processing) was prepared using the ultrasonic extraction technique, and contains a large quantity of secondary metabolites, with scientific evidence for antioxidant, antimicrobial, sedative, antispastic and antidiarrheal activities. Process optimization was carried out using a mathematical–statistical method (response surface methodology—RSM), which models and examines the effects of three levels and three variables on the observed response. The investigated responses were the content of total phenolic components (TPC) and total flavonoids (TFC), as well as tests of antioxidant activity at the level of radicals. The independent variables were ethanol concentration (40–80%), temperature (40–80 °C) and the liquid–solid ratio (10–30 mL/g). Results from this study were entered into a second-degree polynomial with multiple non-linear regression. Analysis of variance (ANOVA) was applied to find the most favorable environment for assessing the model’s performance and effectiveness with an ethanol concentration of 20%, temperature of 80 °C and LSR of 21.4 mL/g. ANOVA assessed the model’s significance, and a second-order polynomial model described the relationships between variables and responses. Diagnostic plots confirmed the model’s adequacy and reliability. The estimated values were 4.11 mg chlorogenic acid equivalents per gram of dry weight (CEA/g), 2.18 mg of rutin equivalents per gram of dry weight (RE/g), and 0.030 mg/mL and 0.030 mg/mL for TPC, TFC, IC50 and EC50, respectively. High-Performance Liquid Chromatography with diode array detection (HPLC-DAD) examination revealed that the prominent substance in the tested extract is rosmarinic acid (46,172 µg/mL), followed by chlorogenic acid (1519 µg/mL). Full article
(This article belongs to the Special Issue Innovative Strategies and Applications in Sustainable Food Processing)
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<p>Effect of ethanol concentration, temperature and TPC on RSM.</p> Full article ">Figure 2
<p>Effect of ethanol concentration, temperature and TFC on RSM.</p> Full article ">Figure 3
<p>Effect of ethanol concentration, temperature and IC<sub>50</sub> on RSM.</p> Full article ">Figure 4
<p>Effect of ethanol concentration, temperature and EC<sub>50</sub> on RSM.</p> Full article ">Figure 5
<p>HPLC chromatogram recorded at 320 nm: 1—p-hydroxybenzoic acid; 2—vanillic acid; 3—p-coumaric acid; 4—ferulic acid; 5—rosmarinic acid.</p> Full article ">Figure 6
<p>HPLC chromatogram recorded at 360 nm: 5—rosmarinic acid; 6—rutin; 7—apigenin glucoside; 8—quercetin; 9—luteolin; 10—kaempferol; 11—apigenin.</p> Full article ">
12 pages, 2113 KiB  
Article
Development of a Selective Spectrophotometric Method for Deltamethrin Using Silver Nanoparticles
by Giovana A. Ferrari, Mayra A. Nascimento, Esteffany L. Bernardo, Marcela O. B. Cortêz, Alvaro V. N. C. Teixeira, André F. Oliveira and Renata P. L. Moreira
Processes 2024, 12(9), 2041; https://doi.org/10.3390/pr12092041 - 22 Sep 2024
Viewed by 699
Abstract
The present work proposes a spectrophotometric method for deltamethrin determination using silver nanoparticles (AgNPs). The AgNPs are spherical with a diameter of~11 nm and a negative surface charge with zeta potential ranging from −4.1 mV (pH 2) to −48 mV (pH 10). The [...] Read more.
The present work proposes a spectrophotometric method for deltamethrin determination using silver nanoparticles (AgNPs). The AgNPs are spherical with a diameter of~11 nm and a negative surface charge with zeta potential ranging from −4.1 mV (pH 2) to −48 mV (pH 10). The AgNP colloidal system showed greater stability at higher pH values and for a molar ratio of 6 between the sodium borohydride and silver nitrate in the synthesis. This is because the borate ions from the oxidation of borohydride are present on the surface of the nanoparticles, promoting an electrostatic repulsion between them which keeps them dispersed. The method was validated, obtaining satisfactory results of veracity and precision, and the limits of detection and quantification were 0.17 and 0.51 mg L−1, respectively. The method was selective for deltamethrin compared to the compounds cypermethrin, endosulfan, thiamethoxam, atrazine, chlorpyrifos and parathion. Deltamethrin promotes the formation of dendritic silver nanostructures, changing the color of the system. The results demonstrate the development of a reliable and selective method for the detection of deltamethrin using AgNPs. Full article
(This article belongs to the Section Chemical Processes and Systems)
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<p>Image of suspensions of silver nanoparticles obtained by TEM. (<b>A</b>) typical shape and size of a single AgNP; (<b>B</b>) agglomerated AgNPs.</p> Full article ">Figure 2
<p>Kinetic study of AgNPs in contact with deltamethrin. (<b>A</b>) Molecular absorption spectrum monitored from 0–20 min. The different colors represent the band’s position over time; the insert in (<b>A</b>) shows the λ<sub>ABS/2</sub> monitoring; (<b>B</b>) monitoring of the SPR band at 400 nm (■) and monitoring of the of the half-height band of the isosbestic point—λ<sub>ABS/2</sub> (<span style="color:red">●</span>). Experimental conditions: [AgNPs]<sub>initial</sub> = 250 µmol L<sup>−1</sup>; [deltamethrin]<sub>initial</sub> = 2.50 mg L<sup>−1</sup>; room temperature = 23 ± 2 °C.</p> Full article ">Figure 3
<p>(<b>A</b>) Temporal correlation curve obtained by DLS for AgNPs in the presence of deltamethrin as a function of time. The different colors represent the curve’s position over time. The continuous lines are fit using the sum of two exponentials decays. (<b>B</b>) Size distributions of AgNPs in the presence of deltamethrin as a function of time. Experimental conditions: [AgNPs]<sub>initial</sub>: 250 µmol L<sup>−1</sup>; [deltamethrin]<sub>initial</sub> = 250 mg L<sup>−1</sup>; room temperature = 23 ± 2 °C. The results for population d<sub>1</sub> are shown in black, and the arrow points to the axis that should be observed. The results for population d<sub>2</sub> are shown in red, and the arrow points to the axis that should be observed.</p> Full article ">Figure 4
<p>Images obtained by TEM. (<b>A</b>,<b>B</b>) AgNPs in the presence of ACN, without deltamethrin; (<b>C</b>,<b>D</b>) AgNPs in the presence of deltamethrin.</p> Full article ">Figure 5
<p>Method selectivity employing AgNPs: (<b>A</b>) water; (<b>B</b>) ACN; (<b>C</b>) cypermethrin; (<b>D</b>) deltamethrin; (<b>E</b>) atrazine; (<b>F</b>) endosulfan; (<b>G</b>) chlorpyrifos; (<b>H</b>) thiamethoxam; (<b>I</b>) parathion. Experimental conditions: ACN = 25% (<span class="html-italic">v</span>/<span class="html-italic">v</span>); pesticides concentration = 10 mg L<sup>−1</sup>.</p> Full article ">
18 pages, 11094 KiB  
Article
Simulation and Experimental Design of Magnetic Fluid Seal Safety Valve for Pressure Vessel
by Zhenggui Li, Ziyue Wang, Changrong Shen, Wangxu Li, Yanxiong Jiao, Chuanshi Cheng, Jie Min and Yuanyuan Li
Processes 2024, 12(9), 2040; https://doi.org/10.3390/pr12092040 - 21 Sep 2024
Viewed by 1056
Abstract
This article focuses on the safety valve of pressure vessels, and a new ferrofluid sealing device for pressure vessel safety valves is developed based on a special magnetic circuit. A combined method of numerical calculation and experimental analysis is used to study the [...] Read more.
This article focuses on the safety valve of pressure vessels, and a new ferrofluid sealing device for pressure vessel safety valves is developed based on a special magnetic circuit. A combined method of numerical calculation and experimental analysis is used to study the relationship between seal clearance, number of seals, pole slot width, pole tooth height, pole tooth width, and the sealing pressure of the ferrofluid sealing device. The research results show that seal clearance and pole tooth width have a significant impact on the sealing performance, and as the dimensions increase, the sealing pressure decreases. As the number of seals, pole tooth height, and slot width increase, the sealing performance initially improves and then decreases. This phenomenon is attributed to the increase in magnetic reluctance in the magnetic circuit. In experimental studies, when the excitation current of the electromagnet is 240 mA and the coil turns number 30, the sealing capacity is 61.22 kPa. When the excitation current is 200 mA and the coil turns number 80, the sealing capacity is 168.24 kPa. The experiments demonstrate the compensating ability of magnetic fluid seals in combination with safety valve seals, confirming that combined seals have higher reliability compared to conventional mechanical seals. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)
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<p>Samples of ferrofluid preparation process ((<b>a</b>) core–shell nano-magnetic particles; (<b>b</b>) dried nano-magnetic particles; (<b>c</b>) silicone-based ferrofluid).</p> Full article ">Figure 2
<p>Hydrolysis and polycondensation of ethyl orthosilicate.</p> Full article ">Figure 3
<p>XRD of Fe<sub>3</sub>O<sub>4</sub>@SiO<sub>2</sub>@A1120 nanoparticles.</p> Full article ">Figure 4
<p>Infrared spectrum of Fe<sub>3</sub>O<sub>4</sub>@SiO<sub>2</sub>@A1120 nanoparticles.</p> Full article ">Figure 5
<p>TG diagram of Fe<sub>3</sub>O<sub>4</sub>@SiO<sub>2</sub>@A1120 nanoparticles.</p> Full article ">Figure 6
<p>A static diagram of a silicone oil-based ferrofluid over a period of time.</p> Full article ">Figure 7
<p>Distribution of magnetic liquid in the seal gap under static pressure seal.</p> Full article ">Figure 8
<p>Ferrofluid seal safety valve model diagram ((<b>a</b>) mechanical seal concept diagram; (<b>b</b>) composite seal concept diagram; (<b>c</b>) section diagram of sealing structure).</p> Full article ">Figure 9
<p>Structural diagram of the magnetic fluid seal part of the safety valve device.</p> Full article ">Figure 10
<p>Cloud map of magnetic field distribution of sealing device at different gaps.</p> Full article ">Figure 11
<p>Magnetic induction intensity of cut-off line under different seal gap conditions.</p> Full article ">Figure 12
<p>Cloud map of magnetic induction intensity distribution at different sealing stages.</p> Full article ">Figure 13
<p>Magnetic induction intensity of truncated line under different sealing stages.</p> Full article ">Figure 14
<p>Seal pressure diagram under different seal clearance values.</p> Full article ">Figure 15
<p>Magnetic field cloud image of a sealing device with different slot widths.</p> Full article ">Figure 16
<p>Magnetic induction intensity at different slot widths.</p> Full article ">Figure 17
<p>Magnetic field cloud image of the sealing device under different pole tooth heights.</p> Full article ">Figure 18
<p>Magnetic induction intensity of the truncated line under different pole tooth heights.</p> Full article ">Figure 19
<p>Magnetic field cloud image of a sealing device with different pole tooth widths.</p> Full article ">Figure 20
<p>Magnetic induction intensity of truncated line under different pole tooth widths.</p> Full article ">Figure 21
<p>Test site diagram (① data acquisition computer; ②—DC power supply; ③—gas storage tank; ④—pressure gauge; ⑤—pressure sensor; ⑥—magnetic fluid seal safety valve; ⑦—data collection card; ⑧—pressure reducing valve).</p> Full article ">Figure 22
<p>Experimental correlation parameter ((<b>A</b>) relation between sealing performance and excitation current; (<b>B</b>) relation between sealing performance and coil turns).</p> Full article ">
16 pages, 6383 KiB  
Article
The Microstructure, Mechanical Properties, and Precipitation Behavior of 1000 MPa Grade GEN3 Steel after Various Quenching Processes
by Angang Ning, Rui Gao, Stephen Yue and Timothy Skszek
Processes 2024, 12(9), 2039; https://doi.org/10.3390/pr12092039 - 21 Sep 2024
Viewed by 854
Abstract
This study examines the microstructure, mechanical properties, and precipitation behavior of 1000 MPa grade GEN3 steel when subjected to various quenching processes, with a focus on the quench and partition (Q&P) technique. The Q&P-treated samples achieved 1300 MPa tensile strength and demonstrated superior [...] Read more.
This study examines the microstructure, mechanical properties, and precipitation behavior of 1000 MPa grade GEN3 steel when subjected to various quenching processes, with a focus on the quench and partition (Q&P) technique. The Q&P-treated samples achieved 1300 MPa tensile strength and demonstrated superior yield strength, attributed to their refined substructure and their large amounts of precipitates. The quenched samples exhibited the thinnest martensite laths due to the highest martensite volume. Despite the as-annealed samples having the smallest grain size, the Q&P treatment resulted in optimal microstructural refinement results and a high dislocation density, reaching 1.15 × 1015 m−2. Analysis of the precipitates revealed the presence of V8C7, M7C3, M2C, and Ti(C, N) across various heat treatments. The application of the McCall–Boyd method and the Ashby–Orowan correction model indicated that quench and tempered (Q&T) samples contained the largest volume of fine precipitates, contributing to their high yield strengths. These findings offer valuable insights for optimizing heat treatment processes to develop advanced high-strength steels for industrial applications. Full article
(This article belongs to the Special Issue Metallurgical Process: Optimization and Control)
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<p>The heating process and dilatometer results of the GEN3 steel.</p> Full article ">Figure 2
<p>Schematic diagram of Sample 2#, 3#, and 4#.</p> Full article ">Figure 3
<p>The microstructure of steel after different heat treatments.</p> Full article ">Figure 3 Cont.
<p>The microstructure of steel after different heat treatments.</p> Full article ">Figure 4
<p>EBSD results of steel after different heat treatments: (<b>a1</b>–<b>a4</b>) show Inverse Pole Figure (IPF) maps for body-centered cubic (bcc) structures; (<b>b1</b>–<b>b4</b>) illustrate misorientation boundary maps with color codes: red for 2–5°, green for 5–15°, and blue for &gt;15°; (<b>c1</b>–<b>c4</b>) phase distributions (green: fcc; red: bcc); (<b>d1</b>–<b>d4</b>) represents the KAM (kernel average misorientation maps) of the body-centered cubic (bcc) structures.</p> Full article ">Figure 5
<p>Stress–strain and work-hardening curves of steels after heat treatment.</p> Full article ">Figure 6
<p>Change in phase distribution of the steel as a function of temperature.</p> Full article ">Figure 7
<p>Elements in M<sub>7</sub>C<sub>3</sub> and M(C, N).</p> Full article ">Figure 8
<p>M<sub>2</sub>C after quench and temper: (<b>a</b>) Morphology by TEM; (<b>b</b>) SAED (Selected Area Electron Diffraction) analysis; (<b>c</b>) EDS (Energy Dispersive X-ray Spectrum) analysis.</p> Full article ">Figure 9
<p>M<sub>7</sub>C<sub>3</sub> after quench and partition: (<b>a</b>) Morphology; (<b>b</b>) SAED analysis; (<b>c</b>) EDS result.</p> Full article ">Figure 10
<p>V<sub>8</sub>C<sub>7</sub> after quench and partition: (<b>a</b>) Morphology; (<b>b</b>) SAED analysis; (<b>c</b>) EDS result.</p> Full article ">Figure 11
<p>Ti(C, N) after quenching by water: (<b>a</b>) Morphology under STEM mode; (<b>b</b>) EDS result by line scanning.</p> Full article ">Figure 12
<p>The comparison of typical martensite microstructure for Samples 3 and 4: (<b>a</b>) Tempered martensite in Q&amp;T sample; (<b>b</b>) Martensite microstructure after Q&amp;P process.</p> Full article ">
16 pages, 4940 KiB  
Article
Potato Beetle Detection with Real-Time and Deep Learning
by Abdil Karakan
Processes 2024, 12(9), 2038; https://doi.org/10.3390/pr12092038 - 21 Sep 2024
Viewed by 610
Abstract
In this study, deep learning methods were used to detect potato beetles (Leptinotarsa decemlineata) on potato plants. High-resolution images were taken of fields with the help of a drone. Since these images were large in size, each one was divided into [...] Read more.
In this study, deep learning methods were used to detect potato beetles (Leptinotarsa decemlineata) on potato plants. High-resolution images were taken of fields with the help of a drone. Since these images were large in size, each one was divided into six equal parts. Then, according to the image, the potato beetles were divided into three classes: adult, late-stage potato beetle, and no beetles. A data set was created with 3000 images in each class, making 9000 in total. Different filters were applied to the images that made up the data set. In this way, problems that may have arisen from the camera in real-time detection were minimized. At the same time, the accuracy rate was increased. The created data set was used with six different deep learning models: MobileNet, InceptionV3, ResNet101, AlexNet, DenseNet121, and Xception. The deep learning models were tested with Sgd, Adam, and Rmsprop optimization methods and their performances were compared. In order to evaluate the success of the models more accurately, they were tested on a second data set created with images taken from a different field. As a result of this study, the highest accuracy of 99.81% was obtained. In the test results from a second field that did not exist in the data set, 92.95% accuracy was obtained. The average accuracy rate was 96.30%. Full article
(This article belongs to the Section Advanced Digital and Other Processes)
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<p>General working principle of the system.</p> Full article ">Figure 2
<p>The original image and the image divided into 6 equal parts.</p> Full article ">Figure 3
<p>Average blur filters in 3 × 3 and 5 × 5 sizes.</p> Full article ">Figure 4
<p>Applying a 3 × 3 median filter on the image.</p> Full article ">Figure 5
<p>Sharpening filter examples.</p> Full article ">Figure 6
<p>Horizontal and vertical weight coefficients for Sobel, Prewitt, and Roberts edge detection methods.</p> Full article ">Figure 7
<p>(<b>a</b>) Original image, (<b>b</b>) Grayscale, (<b>c</b>) Add noise, (<b>d</b>) Add blur, (<b>e</b>) Rotate left and right, (<b>f</b>) Increase and decrease brightness, (<b>g</b>) Add crop, (<b>h</b>) Rotate clockwise and counterclockwise.</p> Full article ">Figure 8
<p>AlexNet architecture.</p> Full article ">Figure 9
<p>Residual block, which is the building block of the ResNet model.</p> Full article ">Figure 10
<p>Accuracy graphs of deep learning architectures; (<b>a</b>) AlexNet, (<b>b</b>) InceptionV3, (<b>c</b>) ResNet101, (<b>d</b>) DenseNet121, (<b>e</b>) MobileNet, (<b>f</b>) Xception.</p> Full article ">Figure 11
<p>Complexity matrices of the test results with the highest success of the six different deep learning models used in this study.</p> Full article ">
22 pages, 7040 KiB  
Article
Study of Noise Reduction and Identification of Internal Damage Signals in Wire Ropes
by Pengbo Li and Jie Tian
Processes 2024, 12(9), 2037; https://doi.org/10.3390/pr12092037 - 21 Sep 2024
Viewed by 781
Abstract
Mining wire rope, a frequently used load-bearing element, suffers various forms of damage over extended periods of operation. Damage occurring within the wire rope, which is not visible to the naked eye and is difficult to detect accurately with current technology, is of [...] Read more.
Mining wire rope, a frequently used load-bearing element, suffers various forms of damage over extended periods of operation. Damage occurring within the wire rope, which is not visible to the naked eye and is difficult to detect accurately with current technology, is of particular concern. Consequently, the identification of internal damage assumes paramount importance in ensuring mine safety. This study proposes a wire rope internal damage noise reduction and identification method, first of all, through a three-dimensional magnetic dipole model to achieve the detection and analysis of the internal damage of the wire rope. Simultaneously, a sensor system capable of accurately detecting the internal damage of wire rope is developed and validated through experimentation. A novel approach is proposed to address the noise reduction issue in the design process. This approach utilizes a particle swarm optimization variational modal decomposition method to enhance the signal-to-noise ratio. Additionally, a dual-attention mode, which combines channel attention and spatial attention, is integrated into the CNN-GRU network model. This network model is specifically designed for the detection of internal damage in steel wire ropes. The proposed method successfully achieves quantitative identification of internal damage in steel wire ropes. The experimental findings demonstrate that this approach is capable of efficiently detecting internal damage in wire rope and possesses the capacity to quantitatively identify such damage, enabling adaptive identification of wire rope. Full article
(This article belongs to the Section Process Control and Monitoring)
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<p>Cross-section of the theoretical model for internal inspection of wire rope.</p> Full article ">Figure 2
<p>Schematic diagram of internal damage in three-dimensional coordinates in which (<b>a</b>) shows the overall schematic diagram and (<b>b</b>) shows the longitudinal cross section.</p> Full article ">Figure 3
<p>Magnetic field distribution of transverse magnetic pole microfacet elements.</p> Full article ">Figure 4
<p>Magnetic field distribution in the longitudinal magnetic pole microelement surface.</p> Full article ">Figure 5
<p>Structure of channel attention.</p> Full article ">Figure 6
<p>Structure of spatial attention.</p> Full article ">Figure 7
<p>GRU structure.</p> Full article ">Figure 8
<p>GRU- Attention structure.</p> Full article ">Figure 9
<p>General architecture of the CNN-GRU -Attention network model.</p> Full article ">Figure 10
<p>Overall flow chart of PSO-VMD-CNN-GRU algorithm.</p> Full article ">Figure 11
<p>Variation graphs for different damage lengths: (<b>a</b>) Variation of internal damage magnetic susceptibility; (<b>b</b>) Internal damage detection model graph; (<b>c</b>) Variation of internal damage magnetic susceptibility; (<b>d</b>) External damage detection model.</p> Full article ">Figure 12
<p>Wire rope testing overall experimental system.</p> Full article ">Figure 13
<p>Hall effect sensor circuit diagram.</p> Full article ">Figure 14
<p>Internal damage of fabricated wire rope.</p> Full article ">Figure 15
<p>Internal damage signal detected by the sensor.</p> Full article ">Figure 16
<p>Comparison of noise reduction of internal damage signal of wire rope.</p> Full article ">Figure 17
<p>Recognition results of CNN-GRU-Attention network (<b>a</b>) Length damage recognition results (<b>b</b>) Length damage loss function results.</p> Full article ">
17 pages, 16970 KiB  
Article
Effects of Media and Processes on the Aromas of White Truffle Solid-State Fermented Products
by Chih-Yuan Cheng and Su-Der Chen
Processes 2024, 12(9), 2036; https://doi.org/10.3390/pr12092036 - 21 Sep 2024
Viewed by 591
Abstract
This study aimed to formulate a black bean soy sauce using black beans and black rice as media for the solid-state fermentation of white truffle. Various proportions of these media (4:0, 3:1, 2:2, 1:3, and 0:4) were prepared, with methionine concentrations (0, 0.3, [...] Read more.
This study aimed to formulate a black bean soy sauce using black beans and black rice as media for the solid-state fermentation of white truffle. Various proportions of these media (4:0, 3:1, 2:2, 1:3, and 0:4) were prepared, with methionine concentrations (0, 0.3, 0.6, 0.9, 1.2, and 1.5%) serving as precursors for a 4-week solid-state fermentation to analyze the aroma profiles. GC-MS analysis showed that samples with 1.5% methionine exhibited significantly higher levels of sulfur-containing volatile compounds compared to those without methionine. GC-IMS analysis revealed that a 2:2 ratio of black beans to black rice produced the most enriched aroma. Lower methionine levels improved mycelial growth, with 0.3% methionine yielding the richest aroma components. After fermentation, the white truffle products were sterilized using autoclaving, hot air assisted radio frequency (HARF), and high pressure processing (HPP), followed by freeze drying. GC-IMS analysis showed that HPP samples had an aroma closest to fresh samples, whereas HARF and autoclave resulted in similar aromas. However, 24 h freeze drying significantly diminished the aroma, resulting in no significant difference in aroma among the freeze-dried products treated with different sterilization methods. Full article
(This article belongs to the Special Issue Applications of Non-Thermal and Thermal Technologies in Food Processing)
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<p>Experimental design of aroma study in <span class="html-italic">Tuber magnatum</span> solid-state fermentation.</p> Full article ">Figure 2
<p>GC-MS spectrum of <span class="html-italic">Tuber magnatum</span> four-week solid-state fermented products using different ratios of black bean and black rice as media.</p> Full article ">Figure 3
<p>GC-MS spectrum of <span class="html-italic">Tuber magnatum</span> four-week solid-state fermented products using different ratios of black bean and black rice as media with 1.5% methionine.</p> Full article ">Figure 4
<p>Aroma fingerprints of <span class="html-italic">Tuber magnatum</span> solid-state fermented products using different ratios of black bean and black rice as media.</p> Full article ">Figure 5
<p>The characteristic aroma fingerprints of <span class="html-italic">Tuber magnatum</span> solid-state fermented products using different ratios of black bean and black rice as media.</p> Full article ">Figure 6
<p>Principal component analysis of <span class="html-italic">Tuber magnatum</span> solid-state fermented products using different ratios of black bean and black rice as media.</p> Full article ">Figure 7
<p>Aroma fingerprints of <span class="html-italic">Tuber magnatum</span> solid-state fermented products using different concentrations of methionine in media.</p> Full article ">Figure 8
<p>The characteristic aroma fingerprints of <span class="html-italic">Tuber magnatum</span> solid-state fermented products with different concentrations of methionine in media.</p> Full article ">Figure 9
<p>Aroma fingerprints of unfermented products with different concentrations of methionine in media.</p> Full article ">Figure 10
<p>The characteristic aroma fingerprints of (<b>a</b>) before and (<b>b</b>) after <span class="html-italic">Tuber magnatum</span> solid-state fermented products with different concentrations of methionine in media.</p> Full article ">Figure 11
<p>Principal component analysis of (<b>a</b>) before and (<b>b</b>) after <span class="html-italic">Tuber magnatum</span> solid-state fermented products with different concentrations of methionine in media.</p> Full article ">Figure 12
<p>Aroma fingerprints of different pasteurization methods and their freeze-dried (<b>a</b>) before and (<b>b</b>) after <span class="html-italic">Tuber magnatum</span> solid-state fermented products.</p> Full article ">Figure 12 Cont.
<p>Aroma fingerprints of different pasteurization methods and their freeze-dried (<b>a</b>) before and (<b>b</b>) after <span class="html-italic">Tuber magnatum</span> solid-state fermented products.</p> Full article ">Figure 13
<p>(<b>a</b>) Before and (<b>b</b>) after characteristic aroma compound fingerprints of <span class="html-italic">Tuber magnatum</span> solid-state fermented products by different pasteurization methods and freeze-drying.</p> Full article ">Figure 13 Cont.
<p>(<b>a</b>) Before and (<b>b</b>) after characteristic aroma compound fingerprints of <span class="html-italic">Tuber magnatum</span> solid-state fermented products by different pasteurization methods and freeze-drying.</p> Full article ">Figure 14
<p>Principal component analysis of <span class="html-italic">Tuber magnatum</span> solid-state fermented products using different pasteurization methods and freeze-drying.</p> Full article ">
21 pages, 2693 KiB  
Article
A Numerical Study on the Flow Field and Classification Performance of an Industrial-Scale Micron Air Classifier under Various Outlet Mass Airflow Rates
by Nang Xuan Ho, Hoi Thi Dinh, Nhu The Dau and Bang Hai Nguyen
Processes 2024, 12(9), 2035; https://doi.org/10.3390/pr12092035 - 21 Sep 2024
Viewed by 682
Abstract
In this study, the gas−particle flow field in a real-size industrial-scale micron air classifier manufactured by Phenikaa Group using 3D transient simulations with the FWC-RSM–DPM (Four-Way Coupling-Reynold Stress Model-Discrete Phase Model) in ANSYS Fluent 2022 R2 and with the assistance of High-Performance Computing [...] Read more.
In this study, the gas−particle flow field in a real-size industrial-scale micron air classifier manufactured by Phenikaa Group using 3D transient simulations with the FWC-RSM–DPM (Four-Way Coupling-Reynold Stress Model-Discrete Phase Model) in ANSYS Fluent 2022 R2 and with the assistance of High-Performance Computing (HPC) systems is explored. A comparison among three coupling models is carried out, highlighting the significant influence of the interactions between solid and gas phases on the flow field. The complex two-phase flow, characterized by the formation of multiple vortices with different sizes, positions, and rotation directions, is successfully captured on the real-size model of the classifier. Additionally, analyzing the effects of the vortices on the flow field provides a comprehensive understanding of the gas–solid flow field and the classification mechanism. The effect of the outlet mass airflow rate is also investigated. The classifier’s Key Performance Indicators (KPIs: d50, K, η, ΔP) and the constrained condition of the particle size distribution curve of the final product are used to evaluate the classification efficiency. The contributions of this work are as follows: (i) a simulation analysis of a real-size industrial-scale classifier is conducted that highlights its advantages over a lab-scale one; (ii) a comparison is conducted among three coupling models, showing the advancement of four-way coupling in providing accurate results for simulations of interactions between the gas phase and particles; and (iii) the particle size distribution curve performances of a classified product under different simulation models and outlet airflow rates are addressed, from which optimal parameters can be selected in the design and operation processes to achieve the required efficiency of an air classifier. Full article
(This article belongs to the Section Separation Processes)
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<p>A schematic outline of the classification process by a micron air classifier.</p> Full article ">Figure 2
<p>The computational grid (<b>a</b>) and dimensions (<b>b</b>) of the micron air classifier.</p> Full article ">Figure 3
<p>Vector graphics of the flow field (vectors are colored according to the velocity magnitude). (<b>a</b>) Vortices forming inside the classifier; (<b>b</b>) comparison of the flow field under one-way coupling, two-way coupling, and four-way coupling; (<b>c</b>) large vortex forming in the classification chamber; (<b>d</b>) vortex forming in the coarse material cone.</p> Full article ">Figure 4
<p>Axial velocity distributions (<b>a</b>) and radial velocity distributions (<b>b</b>) in the horizontal section (Z = 310 mm) between one-way coupling (V<sub>1</sub>), two-way coupling (V<sub>2</sub>), and four-way coupling (V<sub>3</sub>).</p> Full article ">Figure 5
<p>The solid flow patterns with 1-way coupling, 2-way coupling, and 4-way coupling. The snapshots were taken at <span class="html-italic">t</span> = 3 s. The particles are colored according to the particle diameters.</p> Full article ">Figure 6
<p>Tromp curves of the classifier with one-way, two-way, and four-way coupling CFD–DEM models.</p> Full article ">Figure 7
<p>Particle size distributions with one-way, two-way, and four-way coupling models (CFD–DEM).</p> Full article ">Figure 8
<p>Vortices of airflow into the investigated classifier: (<b>a</b>) M<sub>1</sub> = 4.934 kg/s, (<b>b</b>) M<sub>2</sub> = 6.125 kg/s, and (<b>c</b>) M<sub>3</sub> = 6.806 kg/s.</p> Full article ">Figure 9
<p>Contours of the tangential velocity distribution under different outlet mass airflow rates: (<b>a</b>) M<sub>1</sub> = 4.934 kg/s, (<b>b</b>) M<sub>2</sub> = 6.125 kg/s, and (<b>c</b>) M<sub>3</sub> = 6.806 kg/s.</p> Full article ">Figure 10
<p>A comparison of air tangential velocities (<span class="html-italic">V<sub>t</sub></span>) at Z = 310 mm. <span class="html-italic">V<sub>t</sub></span><sub>1</sub>, <span class="html-italic">V<sub>t</sub></span><sub>2</sub>, and <span class="html-italic">V<sub>t</sub></span><sub>3</sub> correspond to M<sub>1</sub>, M<sub>2</sub>, and M<sub>3</sub>, respectively.</p> Full article ">Figure 11
<p>A comparison of air radial velocities at <span class="html-italic">Z</span> = 310 mm. <span class="html-italic">V<sub>r</sub></span><sub>1</sub>, <span class="html-italic">V<sub>r</sub></span><sub>2</sub>, and <span class="html-italic">V<sub>r</sub></span><sub>3</sub> correspond to M<sub>1</sub>, M<sub>2</sub>, and M<sub>3</sub>, respectively.</p> Full article ">Figure 12
<p>A comparison of air axial velocities (<span class="html-italic">V<sub>a</sub></span>) at <span class="html-italic">Z</span> = 310 mm. <span class="html-italic">V<sub>a</sub></span><sub>1</sub>, <span class="html-italic">V<sub>a</sub></span><sub>2</sub>, and <span class="html-italic">V<sub>a</sub></span><sub>3</sub> correspond to M<sub>1</sub>, M<sub>2</sub>, and M<sub>3</sub>, respectively.</p> Full article ">Figure 13
<p>Solid flow patterns under different outlet mass airflow rates. Snapshots were captured at <span class="html-italic">t</span> = 3 s, with the particles colored based on their diameter.</p> Full article ">Figure 14
<p>Tromp curves of the classifier with different outlet mass flow rates.</p> Full article ">Figure 15
<p>Particle size distributions at different outlet mass flow rates.</p> Full article ">Figure 16
<p>A photograph of the micron air classifier manufactured by Phenikaa Group.</p> Full article ">
13 pages, 12510 KiB  
Article
Optimization of Ansys CFX Input Parameters for Numerical Modeling of Pump Performance in Turbine Operation
by Jan Černý and Martin Polák
Processes 2024, 12(9), 2034; https://doi.org/10.3390/pr12092034 - 21 Sep 2024
Viewed by 841
Abstract
The paper deals with the issue of determining the optimal setting of input variables in Ansys CFX for modeling pump flow in turbine operation (PAT). The pump model was created in Autodesk Inventor. The mesh for numerical simulations was created using Ansys Fluent [...] Read more.
The paper deals with the issue of determining the optimal setting of input variables in Ansys CFX for modeling pump flow in turbine operation (PAT). The pump model was created in Autodesk Inventor. The mesh for numerical simulations was created using Ansys Fluent Meshing, considering the mesh quality parameters’ skewness and aspect ratio. The Ansys CFX computational model was experimentally verified on an actual pump by measuring the performance parameters on a test circuit and using the PIV (particle image velocimetry) method. The research indicated that the most suitable setting for the model input variables was the inlet pressure and PAT flow rate combination. Another option was to adjust the pressure at the pump inlet and outlet. However, the calculation time in this case was up to 30% longer. The comparison of the model results with the experiment showed that the deviations in the numerical model performance values did not exceed 10% of the values measured on the test circuit. Only the calculated torque was 1.2 ± 0.13 Nm higher on average than the torque measured on the test circuit. This difference is most likely due to the simplification of the geometry of the computational mesh in order to reduce the computation time. Full article
(This article belongs to the Section Energy Systems)
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<p>Ansys Fluent mesh.</p> Full article ">Figure 2
<p>Hydraulic circuit for testing turbines/pumps; Q, flowmeter; FP, feed pump; PAT, pump as turbine; V<sub>1</sub>, and V<sub>2</sub>, control valves; p<sub>p</sub> and p<sub>s</sub>, pressure measurement; Δh, vertical distance; D, dynamometer; FC, frequency inverter; C, PIV camcoder [<a href="#B27-processes-12-02034" class="html-bibr">27</a>].</p> Full article ">Figure 3
<p>META Plus 5 pump [<a href="#B27-processes-12-02034" class="html-bibr">27</a>].</p> Full article ">Figure 4
<p>PIV method used for the experiment [<a href="#B28-processes-12-02034" class="html-bibr">28</a>].</p> Full article ">Figure 5
<p>Dependence of head on shaft speed.</p> Full article ">Figure 6
<p>Dependence of torque on shaft speed.</p> Full article ">Figure 7
<p>Dependence of efficiency on shaft speed.</p> Full article ">Figure 8
<p>Location of monitored profiles.</p> Full article ">Figure 9
<p>Velocity field comparison, PIV vs. Ansys.</p> Full article ">Figure 10
<p>Velocity field in radial section B in the draft tube for measured velocities and direction of impeller rotation in setting 1.</p> Full article ">Figure 11
<p>Pressure fields in impeller and spiral casing in A section in setting 1.</p> Full article ">Figure 12
<p>Pressure course in the mid-height of the impeller blades in setting 1.</p> Full article ">
22 pages, 9333 KiB  
Article
Refractured Well Hydraulic Fractures Optimization in Tight Sandstone Gas Reservoirs: Application in Linxing Gas Field
by Zhengrong Chen, Yantao Xu, Bumin Guo, Zhihong Zhao, Haozeng Jin, Wei Liu and Ran Zhang
Processes 2024, 12(9), 2033; https://doi.org/10.3390/pr12092033 - 21 Sep 2024
Viewed by 757
Abstract
Poorly producing wells in sandstone gas reservoirs are often refractured to enhance production. Considering the mutual interference of initial/refractured fractures, conductivity dynamic evolution, non-uniform inflow, and variable mass flow in the fracture comprehensively, a semi-analytical reservoir-fracture coupled production model fusing spatial and time [...] Read more.
Poorly producing wells in sandstone gas reservoirs are often refractured to enhance production. Considering the mutual interference of initial/refractured fractures, conductivity dynamic evolution, non-uniform inflow, and variable mass flow in the fracture comprehensively, a semi-analytical reservoir-fracture coupled production model fusing spatial and time separation methods is introduced to model refractured well performance. The proposed model is verified by CMG. The field applications indicate that the refracture job should be carried out when production is lower than the desired value. Restoring the Cf-ini and constructing the Cf-ref can increase productivity, which increases over 8 D•cm. The production growth rate just obtained a slight improvement. The production increased significantly with Lf-ini increasing from 120~270 m and Lf-ref increasing from 100~150 m. Hence, it is essential to extend the Lf-ini under engineering conditions. The ks/km = 10 can obviously increase production, but further enlarging ks does not contribute to well performance. Conversely, further producing larger bs is vital to enhancing production. Subsequently, the optimal parameter combinations (ds > Lf-ini > Lf-ref > Cf-ini > ks > Cf-ref) for well(X1) are carried out by orthogonal experiments. This work proposes a novel method to simulate refractured vertical well performance in tight gas reservoirs for refracture optimization. Full article
(This article belongs to the Special Issue Innovations in Hydraulic Fracturing Technology for Unconventional Reservoirs)
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<p>Schematic of the refracture and discretization segments.</p> Full article ">Figure 2
<p>Experimental device and schematic diagram of the component modules.</p> Full article ">Figure 3
<p>Images of the slate test sample and holder. (<b>a</b>) Slate holder; (<b>b</b>) Slate before the test; (<b>c</b>) Slate after the test.</p> Full article ">Figure 4
<p>Schematic diagram of the experimental data.</p> Full article ">Figure 5
<p>Solution program flowchart of the semi-analytical model.</p> Full article ">Figure 6
<p>Schematics of the initial CMG simulation model and refracture model. (<b>a</b>) Initial fracture; (<b>b</b>) Refracture.</p> Full article ">Figure 7
<p>Simulation results of the proposed model versus commercial software.</p> Full article ">Figure 8
<p>Daily and cumulative production versus different refracturing timing scenarios. (<b>a</b>) Impact of the refracture timing; (<b>b</b>) Growth rate.</p> Full article ">Figure 9
<p>Daily and cumulative production versus different initial fracture conductivity scenarios. (<b>a</b>) Impact of the initial fracture conductivity; (<b>b</b>) Growth rate.</p> Full article ">Figure 10
<p>Daily and cumulative gas production versus different refracture conductivity scenarios. (<b>a</b>) Impact of the refracture conductivity; (<b>b</b>) Growth rate.</p> Full article ">Figure 11
<p>Daily and cumulative production versus different extended initial fracture length scenarios. (<b>a</b>) Impact of the extended initial fracture length; (<b>b</b>) Growth rate.</p> Full article ">Figure 12
<p>Daily and cumulative production versus different refracture length scenarios. (<b>a</b>) Impact of the refracture length; (<b>b</b>) Growth rate.</p> Full article ">Figure 13
<p>Daily and cumulative production for the different <span class="html-italic">k</span><sub>s</sub>/<span class="html-italic">k</span><sub>m</sub> scenarios. (<b>a</b>) Impact of the fracture network permeability; (<b>b</b>) Growth rate.</p> Full article ">Figure 14
<p>Daily and cumulative production for the different <span class="html-italic">b</span><sub>s</sub> scenarios. (<b>a</b>) Impact of the fracture network width; (<b>b</b>) Growth rate.</p> Full article ">Figure 15
<p>History match and refracturing production prediction to well(X1).</p> Full article ">Figure 16
<p>Orthogonal experiment results and fracture parameter importance range based on 4-year cumulative gas production. (<b>a</b>) 4-year cumulative gas production; (<b>b</b>) fracture parameter importance range.</p> Full article ">Figure 17
<p>Pressure diffusion characteristics before and after refracturing based on optimal design.</p> Full article ">Figure A1
<p>Diagram of the closed boundary slab source function in the <span class="html-italic">x</span> direction.</p> Full article ">Figure A2
<p>The diagram of the spatial point source.</p> Full article ">
19 pages, 2901 KiB  
Article
Fault Diagnosis of an Excitation System Using a Fuzzy Neural Network Optimized by a Novel Adaptive Grey Wolf Optimizer
by Xinghe Fu, Dingyu Guo, Kai Hou, Hongchao Zhu, Wu Chen and Da Xu
Processes 2024, 12(9), 2032; https://doi.org/10.3390/pr12092032 - 20 Sep 2024
Cited by 1 | Viewed by 783
Abstract
As the excitation system is the core control component of a synchronous condenser system, its fault diagnosis is crucial for maximizing the reactive power compensation capability of the synchronous condenser. To achieve accurate diagnosis of excitation system faults, this paper proposes a novel [...] Read more.
As the excitation system is the core control component of a synchronous condenser system, its fault diagnosis is crucial for maximizing the reactive power compensation capability of the synchronous condenser. To achieve accurate diagnosis of excitation system faults, this paper proposes a novel adaptive grey wolf optimizer (AGWO) to optimize the initial weights and biases of the fuzzy neural network (FNN), thereby enhancing the diagnostic performance of the FNN model. Firstly, an improved nonlinear convergence factor is introduced to balance the algorithm’s global exploration and local exploitation capabilities. Secondly, a new adaptive position update strategy that enhances the interaction capability of the position information is proposed to improve the algorithm’s ability to jump out of the local optimum and accelerate the convergence speed. In addition, it is demonstrated that the proposed AGWO algorithm has global convergence. By selecting real fault waveforms of the excitation system for case validation, the results show that the proposed AGWO has a better convergence performance compared to the grey wolf optimizer (GWO), particle swarm optimization (PSO), whale optimization algorithm (WOA), and marine predator algorithm (MPA). Specifically, compared to the FNN and GWO-FNN models, the AGWO-FNN model improves average diagnostic accuracy on the test set by 4.2% and 2.5%, respectively. Therefore, the proposed AGWO-FNN effectively enhances the accuracy of fault diagnosis in the excitation system and exhibits stronger diagnostic capability. Full article
(This article belongs to the Special Issue Motor Drive Systems: Control Technology, Fault Diagnosis and Fault Tolerance)
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<p>Basic structure of self-shunt excitation control system for synchronous condenser.</p> Full article ">Figure 2
<p>The overall diagnostic process of the excitation system.</p> Full article ">Figure 3
<p>Variation curves of different convergence factors.</p> Full article ">Figure 4
<p>Iterative convergence curves of GWO algorithm with different convergence factors.</p> Full article ">Figure 5
<p>The structure of the serial-type FNN.</p> Full article ">Figure 6
<p>Flowchart of the AGWO-optimized FNN algorithm.</p> Full article ">Figure 7
<p>ReliefF method feature weight ordering.</p> Full article ">Figure 8
<p>Fitness iteration curves of different optimization algorithms.</p> Full article ">Figure 9
<p>Comparison of diagnostic accuracy of different diagnostic models.</p> Full article ">Figure 10
<p>Comparison of confusion matrices for different diagnostic models.</p> Full article ">
13 pages, 3240 KiB  
Article
Mass Transfer Kinetics of Volatile Organic Compound Desorption from a Novel Adsorbent
by Jiale Zheng, Chuanruo Yang, Ming Xue, Xingchun Li and Xinglei Zhao
Processes 2024, 12(9), 2031; https://doi.org/10.3390/pr12092031 - 20 Sep 2024
Viewed by 816
Abstract
The adsorption isotherms and intraparticle mass transfer coefficients of a novel adsorbent with various VOCs at different temperatures during the desorption process are investigated. Firstly, the adsorption isotherms of an HCP-5 adsorbent with o-xylene and ethyl acetate systems were determined at temperatures ranging [...] Read more.
The adsorption isotherms and intraparticle mass transfer coefficients of a novel adsorbent with various VOCs at different temperatures during the desorption process are investigated. Firstly, the adsorption isotherms of an HCP-5 adsorbent with o-xylene and ethyl acetate systems were determined at temperatures ranging from 30 to 160 °C, and the data were fitted using the Langmuir adsorption isotherm equation. Subsequently, a mathematical model for the fixed-bed desorption breakthrough of VOCs was established. By combining with fixed-bed desorption breakthrough experiments, the intraparticle mass transfer coefficients of o-xylene and ethyl acetate during the desorption process at different temperatures were obtained through the least squares method. This study revealed that the intraparticle mass transfer coefficients of o-xylene and ethyl acetate during the desorption process were basically equal. The intraparticle mass transfer coefficients increased and then decreased with temperature during the desorption process. Compared with the adsorption process, the contribution of surface diffusion inside the adsorbent pores to intraparticle mass transfer decreased during the desorption process, leading to a significant decrease in the intraparticle mass transfer coefficients, which were approximately one-twentieth of those during the adsorption process. Full article
(This article belongs to the Topic Advanced Heat and Mass Transfer Technologies)
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<p>Experimental measuring system.</p> Full article ">Figure 2
<p>Reusability of HCP-5: weight change curve.</p> Full article ">Figure 3
<p>Mass transfer process of desorption.</p> Full article ">Figure 4
<p>Comparison of calculated (line) and experimental (point) adsorption isotherm of HCP-5 and o-xylene system.</p> Full article ">Figure 5
<p>Comparison of calculated (line) and experimental (point) adsorption isotherm of HCP-5 and ethyl acetate system.</p> Full article ">Figure 6
<p>Comparison of calculated (line) and experimental (point) breakthrough curve of HCP-5 adsorbent and o-xylene system.</p> Full article ">Figure 7
<p>Comparison of calculated (line) and experimental (point) breakthrough curve of HCP-5 adsorbent and ethyl acetate system.</p> Full article ">Figure 8
<p>Variation in overall mass transfer coefficient with gas velocity in desorption process.</p> Full article ">Figure 9
<p>Comparison of calculated (line) and experimental (point) breakthrough curve of HCP-5 adsorbent and o-xylene system.</p> Full article ">Figure 10
<p>Comparison of calculated (line) and experimental (point) breakthrough curve of HCP-5 adsorbent and ethyl acetate system.</p> Full article ">Figure 11
<p>Variation in intraparticle mass transfer coefficient with desorption temperature.</p> Full article ">
27 pages, 46094 KiB  
Article
Study on Hydraulic Fracture Propagation in Mixed Fine-Grained Sedimentary Rocks and Practice of Volumetric Fracturing Stimulation Techniques
by Hong Mao, Yinghao Shen, Yao Yuan, Kunyu Wu, Lin Xie, Jianhong Huang, Haoting Xing and Youyu Wan
Processes 2024, 12(9), 2030; https://doi.org/10.3390/pr12092030 - 20 Sep 2024
Viewed by 592
Abstract
Yingxiongling shale oil is considered a critical area for future crude oil production in the Qaidam Basin. However, the unique features of the Yingxiongling area, such as extraordinary thickness, hybrid sedimentary, and extensive reformation, are faced with several challenges, including an unclear understanding [...] Read more.
Yingxiongling shale oil is considered a critical area for future crude oil production in the Qaidam Basin. However, the unique features of the Yingxiongling area, such as extraordinary thickness, hybrid sedimentary, and extensive reformation, are faced with several challenges, including an unclear understanding of the main controlling factors for hydraulic fracturing propagation, difficulties in selecting engineering sweet layers, and difficulties in optimizing the corresponding fracturing schemes, which restrict the effective development of production. This study focuses on mixed fine-grained sedimentary rocks, employing a high-resolution integrated three-dimensional geological-geomechanical model to simulate fracture propagation. By combining laboratory core experiments, a holistic investigation of the controlling factors was conducted, revealing that hydraulic fracture propagation in mixed fine-grained sedimentary rocks is mainly influenced by rock brittleness, natural fractures, stress, varying lithologies, and fracturing parameters. A comprehensive compressibility evaluation standard was established, considering brittleness, stress contrast, and natural fracture density, with weights of 0.3, 0.23, and 0.47. In light of the high brittleness, substantial interlayer stress differences, and localized developing natural microfractures in the Yingxiongling mixed fine-grained sedimentary rock reservoir, this study examined the influence of various construction parameters on the propagation of hydraulic fractures and optimized these parameters accordingly. Based on the practical application in the field, a “three-stage” stimulation strategy was proposed, which involves using high-viscosity fluid in the front to create the main fracture, low-viscosity fluid with sand-laden slugs to create volume fractures, and continuous high-viscosity fluid carried sand to maintain the conductivity of the fracture network. The resulting oil and gas seepage area corresponding to the stimulated reservoir volume (SRV) matched the actual well spacing of 500 m, achieving the effect of full utilization. The understanding of the controlling factors for fracture expansion, the compressibility evaluation standard, and the main process technology developed in this study effectively guide the optimization of transformation programs for mixed fine-grained sedimentary rocks. Full article
(This article belongs to the Special Issue Phase Change, Interphase Coupling, and Multiphase Transport in Porous Structures)
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<p>Interpretation of well chai13 logging data.</p> Full article ">Figure 2
<p>3D geological-geomechanical model.</p> Full article ">Figure 3
<p>Influence of brittle nature on the hydraulic fracture propagation mechanism.</p> Full article ">Figure 4
<p>Analysis of the correlation between brittleness index and average daily fluid production.</p> Full article ">Figure 5
<p>The impact of stress differences on the hydraulic fracture expansion patterns.</p> Full article ">Figure 6
<p>Correlation analysis between stress difference coefficient and average daily fluid production.</p> Full article ">Figure 7
<p>Correlation analysis between stress anisotropy coefficient and fracture propagation.</p> Full article ">Figure 8
<p>Influence of natural fracture lengths on hydraulic fracturing.</p> Full article ">Figure 8 Cont.
<p>Influence of natural fracture lengths on hydraulic fracturing.</p> Full article ">Figure 9
<p>Correlation analysis of microfracture development index with average daily fluid production.</p> Full article ">Figure 10
<p>Stress–strain curves of different lithologies in the Yingxiongling shale oil formation.</p> Full article ">Figure 11
<p>Correlation analysis of compressibility index and average daily fluid production.</p> Full article ">Figure 12
<p>Stimulated volume of hydraulic fracture extension under different pumping rates.</p> Full article ">Figure 13
<p>Impact of cluster spacing on hydraulic fracture expansion.</p> Full article ">Figure 14
<p>Hydraulic fracture propagation patterns at different proppant loading.</p> Full article ">Figure 15
<p>Comparison of hydraulic fracture propagation rules under different fluid pumping rates.</p> Full article ">Figure 16
<p>Segmented and clustered approach based on comprehensive quality assessment.</p> Full article ">Figure 17
<p>Analysis of typical well pressure fracturing curves.</p> Full article ">Figure 18
<p>Distribution of attributes of oil and gas seepage areas and characterization of volume fracture network on the YY2H platform.</p> Full article ">Figure 19
<p>Production curve of the YY2H Platform.</p> Full article ">
17 pages, 10333 KiB  
Article
Multiphysics to Investigate the Thermal and Mechanical Responses in Hard Disk Drive Components Due to the Reflow Soldering Process
by Napatsorn Kimaporn, Chawit Samakkarn and Jatuporn Thongsri
Processes 2024, 12(9), 2029; https://doi.org/10.3390/pr12092029 - 20 Sep 2024
Cited by 1 | Viewed by 689
Abstract
In hard disk drive (HDD) manufacturing, a reflow soldering process (RSP) implements heat generated by the welding tip to melt a solder ball for bonding the following essential HDD components: a flexible printed circuit (FPC) and a printed circuit cable (PCC). Since the [...] Read more.
In hard disk drive (HDD) manufacturing, a reflow soldering process (RSP) implements heat generated by the welding tip to melt a solder ball for bonding the following essential HDD components: a flexible printed circuit (FPC) and a printed circuit cable (PCC). Since the mentioned components are tiny and comprise many thin material layers, an experiment to study thermal and mechanical responses is complex and not worth it. Therefore, a static state multiphysics consisting of thermal analysis (TA) and structural analysis (SA) was employed to investigate both responses. First, the experiment was established to mimic the RSP, measuring the temperature generated by the actual welding tip. Then, the measured temperature was defined as the boundary conditions with the pressing force (F) for the TA and SA based on the actual operating conditions. As expected, the TA results revealed the temperature distribution in the HDD components, which was consistent with the theory and results from previous work and confirmed this work’s credibility. Significantly, the SA reported severe total deformation (δ) in FPC’s top and bottom ends. The maximum δ was 0.72–0.88 mm for the F of 0–1 N. The stronger the F, the greater the δ. This research highlights that multiphysics can investigate both responses in HDD components as slight as 0.1–100 microns thick, which can be used to develop a high-efficacy RSP. Full article
(This article belongs to the Special Issue Thermal Analysis, Modeling and Simulation in Engineering Processes)
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<p>The reflow soldering process: (<b>a</b>) a location in the HDD and (<b>b</b>) an enlarged picture [<a href="#B6-processes-12-02029" class="html-bibr">6</a>].</p> Full article ">Figure 2
<p>Materials and the RSP principle.</p> Full article ">Figure 3
<p>The 1D heat transfer, boundary conditions, and temperature gradient in x direction. The color gradient in the arrows represents the decrease in temperature from top to bottom.</p> Full article ">Figure 4
<p>The methodology flowchart.</p> Full article ">Figure 5
<p>The WT: (<b>a</b>) the designed model with rough dimensions and (<b>b</b>) the actual model.</p> Full article ">Figure 6
<p>The measurement setup in a laboratory to measure temperature: (<b>a</b>) an actual image and (<b>b</b>) a schematic image.</p> Full article ">Figure 7
<p>The HDD components: (<b>a</b>) with and (<b>b</b>) without the WT.</p> Full article ">Figure 8
<p>The HDD components and rough dimensions without the WT (<b>a</b>) a disassembled model and (<b>b</b>) an enlarged picture of an assembled model.</p> Full article ">Figure 9
<p>The hexahedral mesh model.</p> Full article ">Figure 10
<p>The boundary conditions: (<b>a</b>) top view and (<b>b</b>) bottom view.</p> Full article ">Figure 11
<p>The temperature at the head tip after processing by the analysis software.</p> Full article ">Figure 12
<p>The temperature distribution in the HDD components from an isometric view.</p> Full article ">Figure 13
<p>The temperature distribution in the HDD components in a side view of the focused plane.</p> Full article ">Figure 14
<p>The total deformation (<span class="html-italic">δ</span>) in HDD components as a disassembled model for <span class="html-italic">T</span><sub>01</sub> of 410.0 °C and <span class="html-italic">F</span> of 0.5 N.</p> Full article ">Figure 15
<p>The temperature distribution in HDD components as a disassembled model for <span class="html-italic">T<sub>01</sub></span> of 410.0 °C and <span class="html-italic">F</span> of 0.5 N.</p> Full article ">Figure 16
<p>The temperature distribution in the copper layer for <span class="html-italic">T</span><sub>01</sub> of 410.0 °C and <span class="html-italic">F</span> of 0.5 N.</p> Full article ">Figure 17
<p>The total deformation (<span class="html-italic">δ</span>) in HDD components as a disassembled model for <span class="html-italic">T</span><sub>01</sub> of 410.0 °C and <span class="html-italic">F</span> of 1.0 N.</p> Full article ">Figure 18
<p>The total deformation (<span class="html-italic">δ</span>) in the copper layer for <span class="html-italic">F</span> of (<b>a</b>) 0.5 N and (<b>b</b>) 1.0 N.</p> Full article ">Figure 19
<p>The heatmap of maximum total deformation for varying <span class="html-italic">T</span><sub>01</sub> and <span class="html-italic">F</span> by multiphysics.</p> Full article ">
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