Tong Zhou, Yunxia Wen, Zhinan Wu, Bei Liu, Yaohao Bi, Xihan Ma, Zhixuan Du, Xin Feng, Xiaohua Lu, Tuo Ji, Jiahua Zhu
The existing state of macromolecular amines in the confined space and their impacts on the kinetic and thermodynamic performance is the frontier issue in the research field of sorbent‐based CO 2 capture. This work studied the amine state in the confined space and the corresponding effects on CO 2 diffusion. The results demonstrated that amine molecules with different molecular weights exhibited two distinct loading states, namely the “spreading” and “stacking” states. By establishing a diffusion model based on the non‐equilibrium thermodynamics theory (NeTD), it was demonstrated that only when the CO 2 intramolecular diffusion and intrapore diffusion in polyethyleneimine were matched, the overall diffusion resistance would be the lowest. This study provides a pathway to understanding the amine molecules in the molded sorbents, which facilitate the advancement of sorbent‐based CO 2 capture in the scale‐up process and guide the design of molded amine sorbents with low mass transfer resistance and high working capacity.
{"title":"Unveiling the states of polyamines in confined spaces and their impacts on the CO 2 adsorption/diffusion process","authors":"Tong Zhou, Yunxia Wen, Zhinan Wu, Bei Liu, Yaohao Bi, Xihan Ma, Zhixuan Du, Xin Feng, Xiaohua Lu, Tuo Ji, Jiahua Zhu","doi":"10.1002/aic.70202","DOIUrl":"https://doi.org/10.1002/aic.70202","url":null,"abstract":"The existing state of macromolecular amines in the confined space and their impacts on the kinetic and thermodynamic performance is the frontier issue in the research field of sorbent‐based CO <jats:sub>2</jats:sub> capture. This work studied the amine state in the confined space and the corresponding effects on CO <jats:sub>2</jats:sub> diffusion. The results demonstrated that amine molecules with different molecular weights exhibited two distinct loading states, namely the “spreading” and “stacking” states. By establishing a diffusion model based on the non‐equilibrium thermodynamics theory (NeTD), it was demonstrated that only when the CO <jats:sub>2</jats:sub> intramolecular diffusion and intrapore diffusion in polyethyleneimine were matched, the overall diffusion resistance would be the lowest. This study provides a pathway to understanding the amine molecules in the molded sorbents, which facilitate the advancement of sorbent‐based CO <jats:sub>2</jats:sub> capture in the scale‐up process and guide the design of molded amine sorbents with low mass transfer resistance and high working capacity.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"11 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801316","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}
Yafeng Liu, Shan Ni, Yue Zhao, Wang Yao, Wenjie Wang, Jianrong Zeng, Huizhou Liu, Liangrong Yang
Effective uranium recovery from wastewater is of significance for the uranium resources utilization and the environmental protection. Herein, the rationally engineered porous organic polymer (POP‐FD) with dual Lewis base sites is fabricated via an aqueous‐phase and scalable method, which achieves synergistic coordination and structural modulation. The decorated dual Lewis base sites can offer a favorable coordinative binding microenvironment. The constructed hierarchical porous channel and interfacial hydrophilic microenvironment can improve the accessibility of Lewis base sites and the diffusion of uranyl ions. Consequently, POP‐FD possesses a saturation U(VI) adsorption uptake of 977.51 mg g −1 within approximately 20 min. The recovery performance remains nearly 95% after scaled‐up synthesis, and POP‐FD delivers excellent selectivity over various metal ions (S.F. = 29–3844) and U(VI) adsorption capacity (316.81 mg g −1 ) in actual leach tailing wastewater. This study offers a promising microenvironment modulation strategy of POPs for green, high‐efficiency, and economic uranium extraction.
从废水中有效回收铀对铀资源利用和环境保护具有重要意义。本文通过水相和可扩展方法制备了具有双刘易斯碱基的合理工程多孔有机聚合物（POP‐FD），实现了协同配合和结构调制。修饰后的双Lewis碱基可以提供良好的协同结合微环境。层状多孔通道的构建和界面亲水微环境的形成提高了Lewis碱基的可达性和铀酰离子的扩散。因此，POP‐FD在大约20分钟内具有977.51 mg g - 1的饱和U（VI）吸附量。放大合成后，POP‐FD对各种金属离子（S.F. = 29-3844）和U（VI）的吸附量（316.81 mg g - 1）均有良好的选择性，回收率接近95%。该研究为绿色、高效、经济的铀矿开采提供了一种有前景的持久性有机污染物微环境调节策略。
{"title":"Scalable and aqueous‐phase fabrication of engineered porous polymers with dual Lewis base sites for uranium extraction","authors":"Yafeng Liu, Shan Ni, Yue Zhao, Wang Yao, Wenjie Wang, Jianrong Zeng, Huizhou Liu, Liangrong Yang","doi":"10.1002/aic.70199","DOIUrl":"https://doi.org/10.1002/aic.70199","url":null,"abstract":"Effective uranium recovery from wastewater is of significance for the uranium resources utilization and the environmental protection. Herein, the rationally engineered porous organic polymer (POP‐FD) with dual Lewis base sites is fabricated via an aqueous‐phase and scalable method, which achieves synergistic coordination and structural modulation. The decorated dual Lewis base sites can offer a favorable coordinative binding microenvironment. The constructed hierarchical porous channel and interfacial hydrophilic microenvironment can improve the accessibility of Lewis base sites and the diffusion of uranyl ions. Consequently, POP‐FD possesses a saturation U(VI) adsorption uptake of 977.51 mg g <jats:sup>−1</jats:sup> within approximately 20 min. The recovery performance remains nearly 95% after scaled‐up synthesis, and POP‐FD delivers excellent selectivity over various metal ions (S.F. = 29–3844) and U(VI) adsorption capacity (316.81 mg g <jats:sup>−1</jats:sup> ) in actual leach tailing wastewater. This study offers a promising microenvironment modulation strategy of POPs for green, high‐efficiency, and economic uranium extraction.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"22 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801313","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}
Flocculation–thickening is widely used in mineral processing and various chemical engineering fields. The flocculation in the thickener feedwell plays a key role in the tailings slurry thickening process. Hydrodynamic conditions directly affect particle flocculation kinetics and subsequent settling rates, thus determining the overall performance of the thickener. This study employs a multiscale modeling approach to investigate how feed solid concentration affects flow characteristics and flocculation–settling performance in a pilot‐scale deep cone thickener, in which Computational Fluid Dynamics‐Population Balance Model (CFD‐PBM) and a Two‐Fluid Model with Kinetic Theory of Granular Flow (TFM‐KTGF) were applied to simulate flocculation and settling behavior, respectively. Results show that medium solid concentration promotes particle aggregation via optimal turbulence dissipation. Increasing concentration reduces both the initial settling rate ratio of flocs and the settling differential between particle sizes. These findings enhance the understanding of flocculation–thickening mechanisms and support process optimization in solid–liquid separation fields.
{"title":"Particle flocculation and thickening by multiscale CFD modeling: A focus on wide solid concentration field","authors":"Zhiran Mao, Xuetao Wang, Yuchen Shao, Yulian Wang, Yangyang Liu, Yisheng Jiang, Baoyu Cui, Andrew Bayly","doi":"10.1002/aic.70182","DOIUrl":"https://doi.org/10.1002/aic.70182","url":null,"abstract":"Flocculation–thickening is widely used in mineral processing and various chemical engineering fields. The flocculation in the thickener feedwell plays a key role in the tailings slurry thickening process. Hydrodynamic conditions directly affect particle flocculation kinetics and subsequent settling rates, thus determining the overall performance of the thickener. This study employs a multiscale modeling approach to investigate how feed solid concentration affects flow characteristics and flocculation–settling performance in a pilot‐scale deep cone thickener, in which Computational Fluid Dynamics‐Population Balance Model (CFD‐PBM) and a Two‐Fluid Model with Kinetic Theory of Granular Flow (TFM‐KTGF) were applied to simulate flocculation and settling behavior, respectively. Results show that medium solid concentration promotes particle aggregation via optimal turbulence dissipation. Increasing concentration reduces both the initial settling rate ratio of flocs and the settling differential between particle sizes. These findings enhance the understanding of flocculation–thickening mechanisms and support process optimization in solid–liquid separation fields.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"114 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785456","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}
The cavern effect strongly impacts mixing efficiency in pseudoplastic fluids stirred in tanks. Perturbed six‐bent‐blade turbine impellers suppress cavern formation effectively, yet existing models cannot predict cavern size and morphology consistently. To overcome this, we develop a high‐fidelity framework coupling the lattice Boltzmann method with the immersed boundary method, enabling direct numerical simulations of pseudoplastic fluid mixing driven by a rotating perturbed six‐bent‐blade turbine. By varying mass concentration and rotational speed, we identify three distinct flow regimes. Based on these results, we propose an elongated heart‐shaped cavern model that predicts cavern geometry and size across regimes and apparent Reynolds numbers. Incorporating impeller perturbation effects, we further introduce a six‐petal rose model that captures the periodicity of the phase‐averaged flow field, achieving unprecedented accuracy in reproducing cavern morphology. Together, these models provide physical insights and practical tools for optimizing pseudoplastic fluid mixing.
{"title":"Direct numerical simulations and modeling of pseudoplastic fluid mixing driven by a perturbed six‐bent‐blade turbine","authors":"Juanjuan Qiao, Tian Liu, Longhao Xiang, Cheng Peng, Songying Chen","doi":"10.1002/aic.70189","DOIUrl":"https://doi.org/10.1002/aic.70189","url":null,"abstract":"The cavern effect strongly impacts mixing efficiency in pseudoplastic fluids stirred in tanks. Perturbed six‐bent‐blade turbine impellers suppress cavern formation effectively, yet existing models cannot predict cavern size and morphology consistently. To overcome this, we develop a high‐fidelity framework coupling the lattice Boltzmann method with the immersed boundary method, enabling direct numerical simulations of pseudoplastic fluid mixing driven by a rotating perturbed six‐bent‐blade turbine. By varying mass concentration and rotational speed, we identify three distinct flow regimes. Based on these results, we propose an elongated heart‐shaped cavern model that predicts cavern geometry and size across regimes and apparent Reynolds numbers. Incorporating impeller perturbation effects, we further introduce a six‐petal rose model that captures the periodicity of the phase‐averaged flow field, achieving unprecedented accuracy in reproducing cavern morphology. Together, these models provide physical insights and practical tools for optimizing pseudoplastic fluid mixing.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"52 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765173","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}
A two‐dimensional model of coupled transfer processes is developed for a supercapacitive swing adsorption module, encompassing both the adsorption layer and parallel plate electrochemical supercapacitor. The separation process involves heat and mass transfer in combination with adsorption and desorption under the influence of an external electric field (charge transfer). The simulation results suggest the potential for intensifying gas mixture separation by adsorption under an external electric field. The periodic variation in the electric potential causes a periodic variation in the adsorbed component concentration, gas phase component concentration, temperature, velocity, and pressure, together with the coupled momentum, mass, and charge transfer in the porous adsorption layer (carbon cloth) under the external electric field during the capacitor's charging and discharging. The main conclusion is that a new gas separation process, defined as an electric field swing adsorption process, is feasible by combining adsorption and charge transfer.
{"title":"A two‐dimensional model of the coupled transfer processes for a supercapacitive swing adsorption module","authors":"Valery A. Danilov, Gunther Kolb","doi":"10.1002/aic.70200","DOIUrl":"https://doi.org/10.1002/aic.70200","url":null,"abstract":"A two‐dimensional model of coupled transfer processes is developed for a supercapacitive swing adsorption module, encompassing both the adsorption layer and parallel plate electrochemical supercapacitor. The separation process involves heat and mass transfer in combination with adsorption and desorption under the influence of an external electric field (charge transfer). The simulation results suggest the potential for intensifying gas mixture separation by adsorption under an external electric field. The periodic variation in the electric potential causes a periodic variation in the adsorbed component concentration, gas phase component concentration, temperature, velocity, and pressure, together with the coupled momentum, mass, and charge transfer in the porous adsorption layer (carbon cloth) under the external electric field during the capacitor's charging and discharging. The main conclusion is that a new gas separation process, defined as an electric field swing adsorption process, is feasible by combining adsorption and charge transfer.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"21 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765171","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}
The erosion of bed materials and coal ash on water‐cooled walls presents a persistent technical challenge in circulating fluidized bed boiler systems, manifesting as increased frequency of unplanned shutdowns and elevated maintenance requirements. Traditional metal anti‐wear devices, due to inherent structural limitations, struggle to achieve optimal coordination between velocity and pressure gradient fields, making them prone to erosion and limiting their overall wear resistance. This study proposes bionic anti‐wear devices inspired by squid fin and shark dorsal fin. The experimental and simulation results show that bionic devices can optimize the coordination between the velocity and pressure gradient fields. Compared to the traditional right‐angle triangular device, the shark dorsal fin‐inspired device reduces the windward surface area by 3.25%, maximum pressure coefficient by 50%–60%, and the erosion rate by 93.55%. This study provides an innovative approach for developing next‐generation anti‐wear devices with enhanced wear resistance.
{"title":"Synergistic mitigation of erosion on vertical water‐cooled walls with bionic anti‐wear devices","authors":"Yiwei Gao, Xin Li, Hao Song, Yong Zhan, Kaigang Guo, Liping Wei","doi":"10.1002/aic.70185","DOIUrl":"https://doi.org/10.1002/aic.70185","url":null,"abstract":"The erosion of bed materials and coal ash on water‐cooled walls presents a persistent technical challenge in circulating fluidized bed boiler systems, manifesting as increased frequency of unplanned shutdowns and elevated maintenance requirements. Traditional metal anti‐wear devices, due to inherent structural limitations, struggle to achieve optimal coordination between velocity and pressure gradient fields, making them prone to erosion and limiting their overall wear resistance. This study proposes bionic anti‐wear devices inspired by squid fin and shark dorsal fin. The experimental and simulation results show that bionic devices can optimize the coordination between the velocity and pressure gradient fields. Compared to the traditional right‐angle triangular device, the shark dorsal fin‐inspired device reduces the windward surface area by 3.25%, maximum pressure coefficient by 50%–60%, and the erosion rate by 93.55%. This study provides an innovative approach for developing next‐generation anti‐wear devices with enhanced wear resistance.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"46 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759843","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}
Claudemi A. Nascimento, San Dinh, David S. Mebane, Fernando V. Lima
In this work, a novel generalizable framework is proposed for obtaining dynamic discrepancy reduced-order models (DD-ROMs) that balance the differences between high-fidelity models (HFMs) and reduced-order models (ROMs) using Gaussian Processes (GPs). The proposed framework encompasses fundamental criteria for addressing missing underlying physics and is the first-of-its-kind to offer a comprehensive insight guided by sensitivity and correlation analyses into where the discrepancy terms must be incorporated. The proposed framework is employed to correct dynamic mismatches between a reduced-order model and a high-fidelity microkinetic model of the steam methane reforming (SMR) reactions. The validation results demonstrate that with the discrepancy function added to the equilibrium constant, the DD-ROM is capable of mimicking the dynamic trajectories of the microkinetic model with high accuracy, exhibiting an <span data-altimg="/cms/asset/1a832197-55e2-41df-bccf-e4632cb0b7bc/aic70194-math-0001.png"></span><mjx-container ctxtmenu_counter="2" ctxtmenu_oldtabindex="1" jax="CHTML" role="application" sre-explorer- style="font-size: 103%; position: relative;" tabindex="0"><mjx-math aria-hidden="true" location="graphic/aic70194-math-0001.png"><mjx-semantics><mjx-mrow><mjx-msup data-semantic-children="0,1" data-semantic- data-semantic-role="latinletter" data-semantic-speech="normal upper R squared" data-semantic-type="superscript"><mjx-mrow><mjx-mi data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic- data-semantic-parent="2" data-semantic-role="latinletter" data-semantic-type="identifier"><mjx-c></mjx-c></mjx-mi></mjx-mrow><mjx-script style="vertical-align: 0.363em;"><mjx-mrow size="s"><mjx-mn data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic- data-semantic-parent="2" data-semantic-role="integer" data-semantic-type="number"><mjx-c></mjx-c></mjx-mn></mjx-mrow></mjx-script></mjx-msup></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display="inline" unselectable="on"><math altimg="urn:x-wiley:00011541:media:aic70194:aic70194-math-0001" display="inline" location="graphic/aic70194-math-0001.png" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup data-semantic-="" data-semantic-children="0,1" data-semantic-role="latinletter" data-semantic-speech="normal upper R squared" data-semantic-type="superscript"><mrow><mi data-semantic-="" data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic-parent="2" data-semantic-role="latinletter" data-semantic-type="identifier" mathvariant="normal">R</mi></mrow><mrow><mn data-semantic-="" data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic-parent="2" data-semantic-role="integer" data-semantic-type="number" mathvariant="normal">2</mn></mrow></msup></mrow>$$ {mathrm{R}}^2 $$</annotation></semantics></math></mjx-assistive-mml></mjx-container> of 97.86% and an <span data-altim
在这项工作中，提出了一个新的可推广的框架，用于获得动态差异降阶模型（dd - rom），该模型利用高斯过程（GPs）平衡高保真模型（HFMs）和降阶模型（ROMs）之间的差异。提出的框架包含了解决缺失的底层物理的基本标准，并且是同类中第一个在敏感性和相关性分析的指导下提供全面见解的框架，以确定必须将差异项纳入其中。该框架用于修正蒸汽甲烷重整（SMR）反应的降阶模型和高保真微动力学模型之间的动力学不匹配。验证结果表明，在平衡常数中加入差异函数后，cd - rom能够高精度地模拟微动力学模型的动态轨迹，R2 $$ {mathrm{R}}^2 $$为97.86% and an NRMSE$$ mathrm{NRMSE} $$ of 0.123, while obtaining a significant computational gain, being 104 times faster per model execution than integrating the HFM model.
{"title":"Embedding Dynamic Microkinetic Modeling Information Into Reduced-Order Models Using Gaussian Processes","authors":"Claudemi A. Nascimento, San Dinh, David S. Mebane, Fernando V. Lima","doi":"10.1002/aic.70194","DOIUrl":"https://doi.org/10.1002/aic.70194","url":null,"abstract":"In this work, a novel generalizable framework is proposed for obtaining dynamic discrepancy reduced-order models (DD-ROMs) that balance the differences between high-fidelity models (HFMs) and reduced-order models (ROMs) using Gaussian Processes (GPs). The proposed framework encompasses fundamental criteria for addressing missing underlying physics and is the first-of-its-kind to offer a comprehensive insight guided by sensitivity and correlation analyses into where the discrepancy terms must be incorporated. The proposed framework is employed to correct dynamic mismatches between a reduced-order model and a high-fidelity microkinetic model of the steam methane reforming (SMR) reactions. The validation results demonstrate that with the discrepancy function added to the equilibrium constant, the DD-ROM is capable of mimicking the dynamic trajectories of the microkinetic model with high accuracy, exhibiting an <span data-altimg=\"/cms/asset/1a832197-55e2-41df-bccf-e4632cb0b7bc/aic70194-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"2\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/aic70194-math-0001.png\"><mjx-semantics><mjx-mrow><mjx-msup data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"normal upper R squared\" data-semantic-type=\"superscript\"><mjx-mrow><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow><mjx-script style=\"vertical-align: 0.363em;\"><mjx-mrow size=\"s\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\"><mjx-c></mjx-c></mjx-mn></mjx-mrow></mjx-script></mjx-msup></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00011541:media:aic70194:aic70194-math-0001\" display=\"inline\" location=\"graphic/aic70194-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><msup data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"latinletter\" data-semantic-speech=\"normal upper R squared\" data-semantic-type=\"superscript\"><mrow><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\" mathvariant=\"normal\">R</mi></mrow><mrow><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" mathvariant=\"normal\">2</mn></mrow></msup></mrow>$$ {mathrm{R}}^2 $$</annotation></semantics></math></mjx-assistive-mml></mjx-container> of 97.86% and an <span data-altim","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"148 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145752935","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}
This study explores the design, optimization, and simulation of membrane modules for effective ammonia-N recovery from biogas slurry using vacuum membrane distillation technology. Three distinct modules are specifically constructed, involving the original membrane module (OMM), aeration-enhanced membrane module (AMM), and stirring-enhanced membrane module (SMM). Compared to OMM, the flux of AMM and SMM increased by 85% and 72%, respectively, along with the ammonia-N recovery rate increasing by 43% and 40%, respectively, attributing to their enhanced turbulence and reduced concentration polarization. Computational fluid dynamics simulations unravel that both AMM and SMM exhibit optimized parameters compared to OMM, involving flow dynamics, shear stress distribution, and temperature gradients across the membrane interfaces, leading to improved ammonia-N flux and recovery rate. Through systematic comparisons, this study identifies optimal operating conditions for improved ammonia-N recovery efficiency, membrane longevity, and provides insights into membrane module modifications to address challenges regarding ammonia-N recovery from real-life biogas slurry.
{"title":"Design, optimization, and simulation of vacuum membrane distillation module recovering ammonia-N from biogas slurry","authors":"Yuchen Sun, Yicong Chen, Zeyang Zhang, Jingqi Lin, Dong Xia, Qingbiao Li, Yuanpeng Wang","doi":"10.1002/aic.70201","DOIUrl":"https://doi.org/10.1002/aic.70201","url":null,"abstract":"This study explores the design, optimization, and simulation of membrane modules for effective ammonia-N recovery from biogas slurry using vacuum membrane distillation technology. Three distinct modules are specifically constructed, involving the original membrane module (OMM), aeration-enhanced membrane module (AMM), and stirring-enhanced membrane module (SMM). Compared to OMM, the flux of AMM and SMM increased by 85% and 72%, respectively, along with the ammonia-N recovery rate increasing by 43% and 40%, respectively, attributing to their enhanced turbulence and reduced concentration polarization. Computational fluid dynamics simulations unravel that both AMM and SMM exhibit optimized parameters compared to OMM, involving flow dynamics, shear stress distribution, and temperature gradients across the membrane interfaces, leading to improved ammonia-N flux and recovery rate. Through systematic comparisons, this study identifies optimal operating conditions for improved ammonia-N recovery efficiency, membrane longevity, and provides insights into membrane module modifications to address challenges regarding ammonia-N recovery from real-life biogas slurry.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"18 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145752915","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}
Yuhui Yin, Chenhui Kou, Shengkun Jia, Xigang Yuan, Yiqing Luo
The standard Dynamic Mode Decomposition (DMD), when used in complex fluid flow modeling, often suffers from situations like noisy data and translational motion, leading to high errors and non‐physical results. Meanwhile, purely physics‐based numerical methods offer high accuracy but are computationally intensive. To bridge this gap, this paper proposes a Physics‐Constrained Dynamic Mode Decomposition (PCDMD) framework, which integrates governing physical laws into the DMD to constrain predicted results by using Kalman correction. This hybrid approach retains the speed of DMD while improving accuracy by ensuring that predictions obey the underlying physics. We systematically evaluated the PCDMD on flow problems with increasing complexity, including lid‐driven cavity flow, flow around a cylinder with concentration transport, and a rising bubble system. In each case, PCDMD significantly improves both the predictive accuracy and physical consistency. By balancing between the data‐driven modeling and physical correction, the PCDMD remains robust under imperfect data and physical equations.
{"title":"Data–physics fusion for complex fluid systems based on Physics‐Constrained Dynamic Mode Decomposition","authors":"Yuhui Yin, Chenhui Kou, Shengkun Jia, Xigang Yuan, Yiqing Luo","doi":"10.1002/aic.70170","DOIUrl":"https://doi.org/10.1002/aic.70170","url":null,"abstract":"The standard Dynamic Mode Decomposition (DMD), when used in complex fluid flow modeling, often suffers from situations like noisy data and translational motion, leading to high errors and non‐physical results. Meanwhile, purely physics‐based numerical methods offer high accuracy but are computationally intensive. To bridge this gap, this paper proposes a Physics‐Constrained Dynamic Mode Decomposition (PCDMD) framework, which integrates governing physical laws into the DMD to constrain predicted results by using Kalman correction. This hybrid approach retains the speed of DMD while improving accuracy by ensuring that predictions obey the underlying physics. We systematically evaluated the PCDMD on flow problems with increasing complexity, including lid‐driven cavity flow, flow around a cylinder with concentration transport, and a rising bubble system. In each case, PCDMD significantly improves both the predictive accuracy and physical consistency. By balancing between the data‐driven modeling and physical correction, the PCDMD remains robust under imperfect data and physical equations.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"13 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731332","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}
Pengli Chen, Saier Liu, Zhangyi Gao, Min Qiu, You Ma, Zhenlun Wang, Xin Jin, Zhiling Xin, Minjing Shang, Yuanhai Su
Flow pattern prediction in multiphase systems is essential for characterizing hydrodynamic properties and optimizing mass/heat transfer efficiency. Herein, we propose a generative artificial intelligence (GenAI) flow pattern prediction framework for rapidly processing and analyzing large–scale flow pattern image data, with the first application to predictive modeling of gas–liquid Taylor flow in microchannels. The forecasting results of this GenAI–based prediction framework do not consist of discrete flow pattern classification labels but rather intuitive, spatially resolved high‐fidelity visualization results comparable to experimental observations under steady–state operating conditions (e.g., high–resolution flow pattern images captured by high–speed cameras). Notably, the proposed prediction framework overcomes the limitations of conventional methods that only provide category information of flow patterns. More importantly, the model evaluation results demonstrate that this framework can effectively model the correlation between operating conditions and corresponding flow characteristics within microchannels, thereby validating the great potential of this GenAI technology for multiphase flow research.
{"title":"Prediction of Taylor flow in microchannels based on generative artificial intelligence","authors":"Pengli Chen, Saier Liu, Zhangyi Gao, Min Qiu, You Ma, Zhenlun Wang, Xin Jin, Zhiling Xin, Minjing Shang, Yuanhai Su","doi":"10.1002/aic.70181","DOIUrl":"https://doi.org/10.1002/aic.70181","url":null,"abstract":"Flow pattern prediction in multiphase systems is essential for characterizing hydrodynamic properties and optimizing mass/heat transfer efficiency. Herein, we propose a generative artificial intelligence (GenAI) flow pattern prediction framework for rapidly processing and analyzing large–scale flow pattern image data, with the first application to predictive modeling of gas–liquid Taylor flow in microchannels. The forecasting results of this GenAI–based prediction framework do not consist of discrete flow pattern classification labels but rather intuitive, spatially resolved high‐fidelity visualization results comparable to experimental observations under steady–state operating conditions (e.g., high–resolution flow pattern images captured by high–speed cameras). Notably, the proposed prediction framework overcomes the limitations of conventional methods that only provide category information of flow patterns. More importantly, the model evaluation results demonstrate that this framework can effectively model the correlation between operating conditions and corresponding flow characteristics within microchannels, thereby validating the great potential of this GenAI technology for multiphase flow research.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"42 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731327","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}
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