Pub Date : 2024-10-13DOI: 10.1016/j.cherd.2024.10.011
The successful design and operation of high-temperature gas-solid fluidized bed reactors require a deep understanding of interparticle forces (IPFs). However, experimentally quantifying IPFs at elevated temperatures has been a significant challenge due to the lack of suitable methods. This study addresses this gap by introducing a simple yet reliable experimental approach to quantify IPFs in a gas-solid fluidized bed across a temperature range from ambient to 1500 °C. The experimental results reveal that IPFs increase gradually with temperatures up to 1200 °C and become more pronounced at higher temperatures. Smaller particles, or those prone to changes in morphological, structural, and chemical properties—such as softening, sintering, or the formation of low-melting-point eutectic compounds at high temperatures—intensify IPFs significantly. This phenomenon is corroborated by our experiments and comparison with literature data across various temperatures and particle types. Finally, two empirical correlations are proposed to predict IPFs as temperature and particle diameter functions for coarse particles in high-temperature fluidized beds. These findings enhance the understanding of IPFs in high-temperature fluidized beds and are valuable for developing such systems for industrial applications.
{"title":"Experimental quantification of interparticle forces in gas-solid fluidized beds operating at temperatures from ambient to 1500 °C","authors":"","doi":"10.1016/j.cherd.2024.10.011","DOIUrl":"10.1016/j.cherd.2024.10.011","url":null,"abstract":"<div><div>The successful design and operation of high-temperature gas-solid fluidized bed reactors require a deep understanding of interparticle forces (IPFs). However, experimentally quantifying IPFs at elevated temperatures has been a significant challenge due to the lack of suitable methods. This study addresses this gap by introducing a simple yet reliable experimental approach to quantify IPFs in a gas-solid fluidized bed across a temperature range from ambient to 1500 °C. The experimental results reveal that IPFs increase gradually with temperatures up to 1200 °C and become more pronounced at higher temperatures. Smaller particles, or those prone to changes in morphological, structural, and chemical properties—such as softening, sintering, or the formation of low-melting-point eutectic compounds at high temperatures—intensify IPFs significantly. This phenomenon is corroborated by our experiments and comparison with literature data across various temperatures and particle types. Finally, two empirical correlations are proposed to predict IPFs as temperature and particle diameter functions for coarse particles in high-temperature fluidized beds. These findings enhance the understanding of IPFs in high-temperature fluidized beds and are valuable for developing such systems for industrial applications.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.cherd.2024.10.013
The reaction-separation-recycle (RSR) systems are an important part of chemical processes. Due to the interaction between reaction and separation sections, the behavior of RSR processes becomes highly complex and non-linear, posing different challenges for their design and optimization. The purpose of this study is to design and optimize RSR processes for irreversible liquid phase reaction systems using the pseudo-transient continuation (PTC) approach within an equation-oriented programing environment. This approach was applied to investigate one binary system and four ternary systems. The differential-algebraic models of the proposed process flowsheets were solved until the steady-state conditions were reached. The tray bypass efficiency method was incorporated into the PTC to circumvent the need for discrete optimization. The results demonstrated that in those cases where the product was heavier than the reactants, employing a stripping column was more economical than using a conventional distillation column for both the binary and ternary systems. In the ternary systems with two recycle streams, when the product was the intermediate component in terms of boiling point, the utilization of a divided-wall distillation column (DWC) resulted in a total annual cost saving of 35 % and 41 % compared to direct and indirect separation methods, respectively.
{"title":"Design and optimization of reaction-separation-recycle systems using a pseudo-transient continuation model","authors":"","doi":"10.1016/j.cherd.2024.10.013","DOIUrl":"10.1016/j.cherd.2024.10.013","url":null,"abstract":"<div><div>The reaction-separation-recycle (RSR) systems are an important part of chemical processes. Due to the interaction between reaction and separation sections, the behavior of RSR processes becomes highly complex and non-linear, posing different challenges for their design and optimization. The purpose of this study is to design and optimize RSR processes for irreversible liquid phase reaction systems using the pseudo-transient continuation (PTC) approach within an equation-oriented programing environment. This approach was applied to investigate one binary system and four ternary systems. The differential-algebraic models of the proposed process flowsheets were solved until the steady-state conditions were reached. The tray bypass efficiency method was incorporated into the PTC to circumvent the need for discrete optimization. The results demonstrated that in those cases where the product was heavier than the reactants, employing a stripping column was more economical than using a conventional distillation column for both the binary and ternary systems. In the ternary systems with two recycle streams, when the product was the intermediate component in terms of boiling point, the utilization of a divided-wall distillation column (DWC) resulted in a total annual cost saving of 35 % and 41 % compared to direct and indirect separation methods, respectively.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.cherd.2024.10.012
This study focuses on optimizing heat transfer in packed-bed reactors by simplifying the problem to a two-dimensional steady-state heat conduction scenario. The objective is to efficiently arrange a limited volume of high-conductivity material to transport heat from the source to the low-conductivity heat-absorbing materials, representing the reacting fluid phase. The topology optimization problem is tackled using a density-based method that relies on a gradient-based algorithm. The optimized design is extruded and compared to a honeycomb internal structure using high-fidelity simulations for steam methane reforming. Results show a 6.04 % improvement in CH4 conversion for the optimized structure, highlighting the potential of this method to enhance monolithic catalysts, particularly in cases where heat transfer critically influences the reaction.
{"title":"Topology optimization and numerical validation for heat transfer improvement in a packed-bed reactor with monolithic catalyst","authors":"","doi":"10.1016/j.cherd.2024.10.012","DOIUrl":"10.1016/j.cherd.2024.10.012","url":null,"abstract":"<div><div>This study focuses on optimizing heat transfer in packed-bed reactors by simplifying the problem to a two-dimensional steady-state heat conduction scenario. The objective is to efficiently arrange a limited volume of high-conductivity material to transport heat from the source to the low-conductivity heat-absorbing materials, representing the reacting fluid phase. The topology optimization problem is tackled using a density-based method that relies on a gradient-based algorithm. The optimized design is extruded and compared to a honeycomb internal structure using high-fidelity simulations for steam methane reforming. Results show a 6.04 % improvement in CH<sub>4</sub> conversion for the optimized structure, highlighting the potential of this method to enhance monolithic catalysts, particularly in cases where heat transfer critically influences the reaction.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1016/j.cherd.2024.10.006
For environmental safety and sustainability, sulfur concentration in fuel must be minimized. A Commercial desulfurization method, hydrodesulfurization (HDS), offers fine desulfurization of liquid fuels; however, the major challenge of making the process energy efficient remains intact. To address this, various desulfurization approaches are being explored, such as biodesulfurization, adsorption desulfurization, extraction desulfurization, and oxidative desulfurization. Industrial engineers are finding novel reaction routes, while chemical engineers and chemists are working on preparing catalysts and their modifications to optimize process conditions. Research in oxidative desulfurization (ODS) demonstrates that both photocatalytic and thermal-driven ODS processes exhibit significant potential. Thermally driven extraction and catalytic oxidative desulfurization (ECODS) have gained attention using deep eutectic solvents (DESs) and ionic liquids (ILs) as both catalysts and extractants. However, DES overcomes certain limitations of ILs. In the case of DES, the oxidants (H2O2/O2) oxidize the organic acids in DES to peroxy acid, which in turn oxidizes sulfur compounds of fuel into easily removable sulfones, removed by the same DES (acting as extractant as well). DESs are environmentally benign, possess the capability to work synergistically with additional catalysts such as polyoxometalates (POMs) and metal-free catalysts, can be regenerated using only deionized water, and can be reused multiple times with minimal loss of efficiency. This literature review explores the synergistic, catalytic and extractive potential of DES to overcome the major challenge of energy intensive nature of desulfurization process. Furthermore, various methods are critically analyzed, comparative potential of ionic liquids and DESs in ECODS is discussed, and importance of real system (fuel) studies is emphasized.
为了环境安全和可持续发展，必须将燃料中的硫浓度降至最低。加氢脱硫（HDS）是一种商业脱硫方法，可对液体燃料进行精细脱硫。为解决这一问题，人们正在探索各种脱硫方法，如生物脱硫、吸附脱硫、萃取脱硫和氧化脱硫。工业工程师正在寻找新的反应路线，而化学工程师和化学家则致力于制备催化剂及其改性，以优化工艺条件。氧化脱硫（ODS）方面的研究表明，光催化和热驱动 ODS 工艺都具有巨大的潜力。使用深共晶溶剂（DES）和离子液体（IL）作为催化剂和萃取剂的热驱动萃取和催化氧化脱硫（ECODS）已获得广泛关注。然而，DES 克服了离子液体的某些局限性。在 DES 的情况下，氧化剂（H2O2/O2）会将 DES 中的有机酸氧化成过氧酸，过氧酸又会将燃料中的硫化合物氧化成易于去除的砜，并由相同的 DES（同时也是萃取剂）去除。DES 对环境无害，能够与其他催化剂（如聚氧化金属（POM）和无金属催化剂）协同工作，只需使用去离子水即可再生，并且可以多次重复使用，效率损失极小。本文献综述探讨了 DES 的协同、催化和萃取潜力，以克服脱硫过程中能源密集型的主要挑战。此外，还对各种方法进行了批判性分析，讨论了离子液体和 DES 在 ECODS 中的比较潜力，并强调了实际系统（燃料）研究的重要性。
{"title":"Oxidative desulfurization of liquid fuels using deep eutectic solvents as a catalyst and extractant: A review","authors":"","doi":"10.1016/j.cherd.2024.10.006","DOIUrl":"10.1016/j.cherd.2024.10.006","url":null,"abstract":"<div><div>For environmental safety and sustainability, sulfur concentration in fuel must be minimized. A Commercial desulfurization method, hydrodesulfurization (HDS), offers fine desulfurization of liquid fuels; however, the major challenge of making the process energy efficient remains intact. To address this, various desulfurization approaches are being explored, such as biodesulfurization, adsorption desulfurization, extraction desulfurization, and oxidative desulfurization. Industrial engineers are finding novel reaction routes, while chemical engineers and chemists are working on preparing catalysts and their modifications to optimize process conditions. Research in oxidative desulfurization (ODS) demonstrates that both photocatalytic and thermal-driven ODS processes exhibit significant potential. Thermally driven extraction and catalytic oxidative desulfurization (ECODS) have gained attention using deep eutectic solvents (DESs) and ionic liquids (ILs) as both catalysts and extractants. However, DES overcomes certain limitations of ILs. In the case of DES, the oxidants (H<sub>2</sub>O<sub>2</sub>/O<sub>2</sub>) oxidize the organic acids in DES to peroxy acid, which in turn oxidizes sulfur compounds of fuel into easily removable sulfones, removed by the same DES (acting as extractant as well). DESs are environmentally benign, possess the capability to work synergistically with additional catalysts such as polyoxometalates (POMs) and metal-free catalysts, can be regenerated using only deionized water, and can be reused multiple times with minimal loss of efficiency. This literature review explores the synergistic, catalytic and extractive potential of DES to overcome the major challenge of energy intensive nature of desulfurization process. Furthermore, various methods are critically analyzed, comparative potential of ionic liquids and DESs in ECODS is discussed, and importance of real system (fuel) studies is emphasized.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.cherd.2024.10.007
Early online fouling detection is expected to be a real step forward in the operation of continuous tubular reactors. As an online technology, the Rayleigh backscatter based Distributed Optical Fiber Sensor (DOFS) technology was evaluated with respect to temperature measurement resolution, reproducibility and the best online calibration method. Different coatings and terminations were characterized for emulsion polymerization reactors. Commercially available sensors with acrylate primary coatings, dual acrylate coatings, and polyimide coatings were all compared with each other. It is shown that the presence of a secondary coating significantly alters sensor behaviour. Sensors with only a primary coating showed a temperature resolution of 0.1 °C. Online calibration for temperature readout was carried out and validation tested on segments of fiber integrated in a 3D-printed reactor channel (diameter 1.5 mm). Acrylate coated sensors showed a deviation of 20 % for the calibration coefficients when inside the reactor channel compared to the online calibration, leading to errors in temperature measurement. Polyimide coated sensors showed a deviation of just 0.6 % in this validation test, demonstrating good capabilities for online calibration with accurate temperature measurements. The possibility of spatial and temporal monitoring of fouling buildup through a heat exchanging wall equipped with DOFS was evaluated.
{"title":"Distributed optical fiber sensors for real-time tracking of fouling buildup for tubular continuous polymerization reactors","authors":"","doi":"10.1016/j.cherd.2024.10.007","DOIUrl":"10.1016/j.cherd.2024.10.007","url":null,"abstract":"<div><div>Early online fouling detection is expected to be a real step forward in the operation of continuous tubular reactors. As an online technology, the Rayleigh backscatter based <strong>D</strong>istributed <strong>O</strong>ptical <strong>F</strong>iber <strong>S</strong>ensor (DOFS) technology was evaluated with respect to temperature measurement resolution, reproducibility and the best online calibration method. Different coatings and terminations were characterized for emulsion polymerization reactors. Commercially available sensors with acrylate primary coatings, dual acrylate coatings, and polyimide coatings were all compared with each other. It is shown that the presence of a secondary coating significantly alters sensor behaviour. Sensors with only a primary coating showed a temperature resolution of 0.1 °C. Online calibration for temperature readout was carried out and validation tested on segments of fiber integrated in a 3D-printed reactor channel (diameter 1.5 mm). Acrylate coated sensors showed a deviation of 20 % for the calibration coefficients when inside the reactor channel compared to the online calibration, leading to errors in temperature measurement. Polyimide coated sensors showed a deviation of just 0.6 % in this validation test, demonstrating good capabilities for online calibration with accurate temperature measurements. The possibility of spatial and temporal monitoring of fouling buildup through a heat exchanging wall equipped with DOFS was evaluated.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.cherd.2024.10.008
Improving the quality of dynamic cell culture in laboratories is an important field in bioengineering. In this study, a novel lab-scale bioreactor using a vibrating agitator and a modified flask has been introduced to create a strong mixing at low shear stress. This bioreactor has been optimized using Box-Behnken design based on three dimensionless important structural factors including disc diameter, vibration amplitude, and the height of the disc placement. Three growth indicators including the specific growth rate, the natural logarithm of the maximum cell density, and productivity have been considered as biological responses. The results show that the disc diameter has the most important role in these indicators. If the disc diameter, vibration amplitude, and the height of disc placement are set to 0.24, 0.02, and 0.4 of the flask diameter, respectively, the values of the specific growth rate, the maximum cell density, and productivity at this optimum settings are 0.033 (h−1), 13.11, and 5133 (cells/(mL.h)), respectively. These values of the indicators are high and indicate the better performance of this bioreactor than other lab-scale bioreactors. In addition, investigating Reynolds number in the fluid flow indicates that in the range of 780 up to 1150, growth indices are high.
{"title":"Design, experimental optimization, and flow analysis of a novel bioreactor for dynamic mammalian cell culture at laboratory scale using Box-Behnken design","authors":"","doi":"10.1016/j.cherd.2024.10.008","DOIUrl":"10.1016/j.cherd.2024.10.008","url":null,"abstract":"<div><div>Improving the quality of dynamic cell culture in laboratories is an important field in bioengineering. In this study, a novel lab-scale bioreactor using a vibrating agitator and a modified flask has been introduced to create a strong mixing at low shear stress. This bioreactor has been optimized using Box-Behnken design based on three dimensionless important structural factors including disc diameter, vibration amplitude, and the height of the disc placement. Three growth indicators including the specific growth rate, the natural logarithm of the maximum cell density, and productivity have been considered as biological responses. The results show that the disc diameter has the most important role in these indicators. If the disc diameter, vibration amplitude, and the height of disc placement are set to 0.24, 0.02, and 0.4 of the flask diameter, respectively, the values of the specific growth rate, the maximum cell density, and productivity at this optimum settings are 0.033 (h<sup>−1</sup>), 13.11, and 5133 (cells/(mL.h)), respectively. These values of the indicators are high and indicate the better performance of this bioreactor than other lab-scale bioreactors. In addition, investigating Reynolds number in the fluid flow indicates that in the range of 780 up to 1150, growth indices are high.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.cherd.2024.10.009
Pyrite, a common iron mineral in refractory iron ores, emits SO2 during oxidative roasting, contributing to environmental pollution. This study investigated the effect of calcite on the thermal decomposition of pyrite, focusing on thermodynamics, phase transformation, microstructural evolution, and non-isothermal kinetics, with emphasis on SO2 formation inhibition. Results showed that pyrite decomposed first to pyrrhotite, then to magnetite and hematite, with SO2 as the primary gaseous product. Higher temperatures and lower oxygen concentrations favored S2 gas formation. Non-isothermal decomposition of pyrite occurred between 400–725°C, initiated at the particle surface, and significantly increased product porosity, resulting in butterfly-shaped hematite. The addition of calcite resulted in the reaction of SO2 with calcite to form anhydrite on the particle surface, inhibiting the release of SO2. Initially, the thermal decomposition of pyrite proceeded with a low apparent activation energy, making the reaction relatively easy. However, the presence of calcite significantly increased the apparent activation energy and inhibited the thermal decomposition reaction.
{"title":"Effect of calcite on the thermal decomposition of pyrite: Thermodynamics, phase transformation, microstructure evolution and kinetics","authors":"","doi":"10.1016/j.cherd.2024.10.009","DOIUrl":"10.1016/j.cherd.2024.10.009","url":null,"abstract":"<div><div>Pyrite, a common iron mineral in refractory iron ores, emits SO<sub>2</sub> during oxidative roasting, contributing to environmental pollution. This study investigated the effect of calcite on the thermal decomposition of pyrite, focusing on thermodynamics, phase transformation, microstructural evolution, and non-isothermal kinetics, with emphasis on SO<sub>2</sub> formation inhibition. Results showed that pyrite decomposed first to pyrrhotite, then to magnetite and hematite, with SO<sub>2</sub> as the primary gaseous product. Higher temperatures and lower oxygen concentrations favored S<sub>2</sub> gas formation. Non-isothermal decomposition of pyrite occurred between 400–725°C, initiated at the particle surface, and significantly increased product porosity, resulting in butterfly-shaped hematite. The addition of calcite resulted in the reaction of SO<sub>2</sub> with calcite to form anhydrite on the particle surface, inhibiting the release of SO<sub>2</sub>. Initially, the thermal decomposition of pyrite proceeded with a low apparent activation energy, making the reaction relatively easy. However, the presence of calcite significantly increased the apparent activation energy and inhibited the thermal decomposition reaction.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.cherd.2024.10.005
Efficient water management in agriculture is important for mitigating the growing freshwater scarcity crisis. Mixed-integer Model Predictive Control (MPC) has emerged as an effective approach for addressing the complex scheduling problem in agricultural irrigation. However, the computational complexity of mixed-integer MPC still poses a significant challenge, particularly in large-scale applications. This study proposes an approach to enhance the computational efficiency of mixed-integer MPC-based irrigation schedulers by employing Rectified Linear Unit (ReLU) surrogate models to describe the soil moisture dynamics of the agricultural field. By leveraging the mixed-integer linear representation of the ReLU operator, the proposed approach transforms the mixed-integer MPC-based scheduler with a quadratic cost function into a mixed-integer quadratic program, which is the simplest class of mixed-integer nonlinear programming problems that can be efficiently solved using global optimization solvers. The effectiveness of this approach is demonstrated through comparative studies conducted on a large-scale agricultural field across two growing seasons, involving other machine learning surrogate models, specifically Long Short-Term Memory (LSTM) networks, and the triggered irrigation scheduling method. The ReLU-based approach significantly reduces solution times — by up to 99.5% — while achieving comparable performance to the LSTM approach in terms of water savings and Irrigation Water Use Efficiency (IWUE). Moreover, the ReLU-based approach achieves enhanced performance in terms of irrigation water savings and IWUE compared to the triggered approach.
{"title":"ReLU surrogates in mixed-integer MPC for irrigation scheduling","authors":"","doi":"10.1016/j.cherd.2024.10.005","DOIUrl":"10.1016/j.cherd.2024.10.005","url":null,"abstract":"<div><div>Efficient water management in agriculture is important for mitigating the growing freshwater scarcity crisis. Mixed-integer Model Predictive Control (MPC) has emerged as an effective approach for addressing the complex scheduling problem in agricultural irrigation. However, the computational complexity of mixed-integer MPC still poses a significant challenge, particularly in large-scale applications. This study proposes an approach to enhance the computational efficiency of mixed-integer MPC-based irrigation schedulers by employing Rectified Linear Unit (ReLU) surrogate models to describe the soil moisture dynamics of the agricultural field. By leveraging the mixed-integer linear representation of the ReLU operator, the proposed approach transforms the mixed-integer MPC-based scheduler with a quadratic cost function into a mixed-integer quadratic program, which is the simplest class of mixed-integer nonlinear programming problems that can be efficiently solved using global optimization solvers. The effectiveness of this approach is demonstrated through comparative studies conducted on a large-scale agricultural field across two growing seasons, involving other machine learning surrogate models, specifically Long Short-Term Memory (LSTM) networks, and the triggered irrigation scheduling method. The ReLU-based approach significantly reduces solution times — by up to 99.5% — while achieving comparable performance to the LSTM approach in terms of water savings and Irrigation Water Use Efficiency (IWUE). Moreover, the ReLU-based approach achieves enhanced performance in terms of irrigation water savings and IWUE compared to the triggered approach.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.cherd.2024.09.031
Rapid and accurate system evolution predictions are crucial in scientific and engineering research. However, the complexity of processing systems, involving multiple physical field couplings and slow convergence of iterative numerical algorithms, leads to low computational efficiency. Hence, this paper introduces a systematic deep-learning-based surrogate modeling methodology for multi-physics-coupled process systems with limited data and long-range time evolution, accurately predicting physics dynamics and considerably improving computational efficiency and generalization. The methodology comprises three main components: (1) generating datasets using a sequential sampling strategy, (2) modeling multi-physics spatio-temporal dynamics by designing a heterogeneous Convolutional Autoencoder and Recurrent Neural Network, and (3) training high-precision models with limited data and long-range time evolution via a dual-phase training strategy. A holistic evaluation using a 2D low-temperature plasma processing example demonstrates the methodology’s superior computational efficiency, accuracy, and generalization capabilities. It predicts plasma dynamics approximately times faster than traditional numerical solvers, with a consistent 2% relative error across different generalization tasks. Furthermore, the potential for transferability across various geometries is explored, and the model’s transfer capability is demonstrated with two distinct geometric domain examples.
{"title":"A deep-learning-based surrogate modeling method with application to plasma processing","authors":"","doi":"10.1016/j.cherd.2024.09.031","DOIUrl":"10.1016/j.cherd.2024.09.031","url":null,"abstract":"<div><div>Rapid and accurate system evolution predictions are crucial in scientific and engineering research. However, the complexity of processing systems, involving multiple physical field couplings and slow convergence of iterative numerical algorithms, leads to low computational efficiency. Hence, this paper introduces a systematic deep-learning-based surrogate modeling methodology for multi-physics-coupled process systems with limited data and long-range time evolution, accurately predicting physics dynamics and considerably improving computational efficiency and generalization. The methodology comprises three main components: (1) generating datasets using a sequential sampling strategy, (2) modeling multi-physics spatio-temporal dynamics by designing a heterogeneous Convolutional Autoencoder and Recurrent Neural Network, and (3) training high-precision models with limited data and long-range time evolution via a dual-phase training strategy. A holistic evaluation using a 2D low-temperature plasma processing example demonstrates the methodology’s superior computational efficiency, accuracy, and generalization capabilities. It predicts plasma dynamics approximately <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>5</mn></mrow></msup></mrow></math></span> times faster than traditional numerical solvers, with a consistent 2% relative error across different generalization tasks. Furthermore, the potential for transferability across various geometries is explored, and the model’s transfer capability is demonstrated with two distinct geometric domain examples.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.cherd.2024.10.004
To address the limitations of static models and gain insight into the processes of extractive leaching and chemical precipitation, a data-driven dynamic modeling strategy is proposed using a Lithium-ion battery recycling case study. The data correlations among pH, temperature, redox potential, conductivity and system state are investigated. Predictive models are then developed to describe the system state online and are employed as surrogate models for time-intensive offline chemical analyses. This enables further process optimization, such as time-saving measures and improved process efficiency through dynamic parameter studies. The proposed strategy serves as a guideline for dynamic modeling and integrates big data methodologies into chemical engineering.
{"title":"Enabling data-driven process dynamic modeling for extractive leaching and chemical precipitation","authors":"","doi":"10.1016/j.cherd.2024.10.004","DOIUrl":"10.1016/j.cherd.2024.10.004","url":null,"abstract":"<div><div>To address the limitations of static models and gain insight into the processes of extractive leaching and chemical precipitation, a data-driven dynamic modeling strategy is proposed using a Lithium-ion battery recycling case study. The data correlations among pH, temperature, redox potential, conductivity and system state are investigated. Predictive models are then developed to describe the system state online and are employed as surrogate models for time-intensive offline chemical analyses. This enables further process optimization, such as time-saving measures and improved process efficiency through dynamic parameter studies. The proposed strategy serves as a guideline for dynamic modeling and integrates big data methodologies into chemical engineering.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号