Linhan Ren, Jiayuan Li, Suiqin Li, Kai Li, Yuhang Wang, Jieyu Wang, Ying Chen, Jiahui He, Xing Zhong, Jianguo Wang
Chiral compounds play a pivotal role in pharmaceutical chemistry, and the oxidation of chiral alcohols to corresponding carboxylic acids is a crucial step. However, the enantioselectivity is susceptible to degradation due to sensitivity to enol isomerization and racemization. In this study, Ru/S‐Ni‐MOFs electrocatalysts with high specific surface area were synthesized. After undergoing electrochemical reconfiguration, which combined with 4‐acetamido‐TEMPO (ACT) as co‐catalysts to achieve efficient oxidation of chiral alcohols, with enantioselectivity reaching 99% at industrial‐grade current density of 500 mA/cm2. Additionally, 100 g of chiral acid were successfully synthesized with a yield of 98% and an enantioselectivity of 99% in the large‐scale electrolyzer. In situ experiments and theoretical calculations demonstrated that S doping shifts the center of d‐band toward the Fermi level, which stabilizes ACTH and inhibits the dissociation of OH, thereby enhancing electrocatalytic activity. This study presents an efficient synergistic electrocatalytic strategy for practical large‐scale electrosynthesis of chiral carboxylic acid compounds.
{"title":"Flow electrooxidation of chiral alcohols with high enantioselectivity via integrated metal‐organic frameworks and aminoxyl radicals","authors":"Linhan Ren, Jiayuan Li, Suiqin Li, Kai Li, Yuhang Wang, Jieyu Wang, Ying Chen, Jiahui He, Xing Zhong, Jianguo Wang","doi":"10.1002/aic.18847","DOIUrl":"https://doi.org/10.1002/aic.18847","url":null,"abstract":"Chiral compounds play a pivotal role in pharmaceutical chemistry, and the oxidation of chiral alcohols to corresponding carboxylic acids is a crucial step. However, the enantioselectivity is susceptible to degradation due to sensitivity to enol isomerization and racemization. In this study, Ru/S‐Ni‐MOFs electrocatalysts with high specific surface area were synthesized. After undergoing electrochemical reconfiguration, which combined with 4‐acetamido‐TEMPO (ACT) as co‐catalysts to achieve efficient oxidation of chiral alcohols, with enantioselectivity reaching 99% at industrial‐grade current density of 500 mA/cm<jats:sup>2</jats:sup>. Additionally, 100 g of chiral acid were successfully synthesized with a yield of 98% and an enantioselectivity of 99% in the large‐scale electrolyzer. <jats:italic>In situ</jats:italic> experiments and theoretical calculations demonstrated that S doping shifts the center of d‐band toward the Fermi level, which stabilizes ACTH and inhibits the dissociation of OH, thereby enhancing electrocatalytic activity. This study presents an efficient synergistic electrocatalytic strategy for practical large‐scale electrosynthesis of chiral carboxylic acid compounds.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"37 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782428","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}
Daniel Ovalle, Joshua L. Pulsipher, Yixin Ye, Kyle Harshbarger, Scott Bury, Carl D. Laird, Ignacio E. Grossmann
Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors such as product allocation, delayed shipments, and price renegotiation, among other factors. In such context, we propose a multiperiod mixed‐integer linear programming model that integrates production, scheduling, shipping, and order management to minimize the financial impact of such disruptions. The model accommodates arbitrary supply chain topologies and incorporates various disruption scenarios, offering adaptability to real‐world complexities. A case study from the chemical industry demonstrates the scalability of the model under finer time discretization and explores the influence of disruption types and order management costs on optimal schedules. This approach provides a tractable, adaptable framework for developing responsive operational plans in supply chain and manufacturing networks under uncertainty.
{"title":"Optimal reactive operation of general topology supply chain and manufacturing networks under disruptions","authors":"Daniel Ovalle, Joshua L. Pulsipher, Yixin Ye, Kyle Harshbarger, Scott Bury, Carl D. Laird, Ignacio E. Grossmann","doi":"10.1002/aic.18833","DOIUrl":"https://doi.org/10.1002/aic.18833","url":null,"abstract":"Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors such as product allocation, delayed shipments, and price renegotiation, among other factors. In such context, we propose a multiperiod mixed‐integer linear programming model that integrates production, scheduling, shipping, and order management to minimize the financial impact of such disruptions. The model accommodates arbitrary supply chain topologies and incorporates various disruption scenarios, offering adaptability to real‐world complexities. A case study from the chemical industry demonstrates the scalability of the model under finer time discretization and explores the influence of disruption types and order management costs on optimal schedules. This approach provides a tractable, adaptable framework for developing responsive operational plans in supply chain and manufacturing networks under uncertainty.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"108 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782438","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}
Understanding granular dynamics is essential for many industrial applications, yet significant challenges persist. The discrete element method allows for direct tracking of particle motions, but it suffers from high computational costs, in particular for inverse problems. Recently, machine learning has seen rapid development and brings new possibilities for tackling these challenges. In this work, a differentiable model designed for rapid prediction and inverse optimization of particulate processes is developed. The proposed method is used to improve the maximum discharge rate of hopper flows and automatically optimize the hopper shape based on the target discharge rate. Additionally, controlling the degree of mixing of two particle components is explored and further validated with experiments. The modeling outcomes demonstrate that the differentiable deep learning approach developed in this work can efficiently address inverse optimization challenges in particulate processes, providing a new tool for the design and optimization of particulate manufacturing processes.
{"title":"A differentiable deep learning approach for inverse optimization of hopper flows in particulate manufacturing","authors":"Chengbo Liu, Tingting Liu, Yu Jiang, Yuanye Zhou, Yanjiao Li, Kun Hong, Xizhong Chen","doi":"10.1002/aic.18825","DOIUrl":"https://doi.org/10.1002/aic.18825","url":null,"abstract":"Understanding granular dynamics is essential for many industrial applications, yet significant challenges persist. The discrete element method allows for direct tracking of particle motions, but it suffers from high computational costs, in particular for inverse problems. Recently, machine learning has seen rapid development and brings new possibilities for tackling these challenges. In this work, a differentiable model designed for rapid prediction and inverse optimization of particulate processes is developed. The proposed method is used to improve the maximum discharge rate of hopper flows and automatically optimize the hopper shape based on the target discharge rate. Additionally, controlling the degree of mixing of two particle components is explored and further validated with experiments. The modeling outcomes demonstrate that the differentiable deep learning approach developed in this work can efficiently address inverse optimization challenges in particulate processes, providing a new tool for the design and optimization of particulate manufacturing processes.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"15 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775876","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}
In this work, we develop a carburization strategy to transform hexagonal Ni3In into face-centered cubic Ni3InC0.5 intermetallic carbide, leveraging partially isolated Ni sites for improved acetylene semihydrogenation. The catalyst synthesized via carburization of Ni3In intermetallic compound derived from Ni/In/Mg/Al layered double hydroxides in a C2H2/H2 atmosphere is evidenced to show Ni3InC0.5 intermetallic carbide phase through detailed characterizations, including high-resolution transmission electron microscopy and X-ray absorption spectroscopy. Catalytic tests reveal that the Ni3InC0.5 catalyst achieves 92.0% ethylene selectivity at full acetylene conversion, outperforming the Ni and Ni3In catalysts. Both experimental and theoretical evidence demonstrate that interstitial carbon atoms in Ni3InC0.5 synergize with neighboring In atoms to modify the electronic structure of surface Ni sites via significant hybridization between Ni 3d, In 5p, and C 2p orbitals. These unique features enable higher kinetic favorability of ethylene desorption over its further hydrogenation on the Ni3InC0.5 catalyst and thus contribute to the enhanced semihydrogenation.
{"title":"Carburization induced phase transition of Ni3In to Ni3InC0.5 intermetallic carbide for acetylene semihydrogenation","authors":"Xiaohu Ge, Nina Fei, Yueqiang Cao, Hao Jiang, Jing Zhang, Gang Qian, Xinggui Zhou, Xuezhi Duan","doi":"10.1002/aic.18842","DOIUrl":"https://doi.org/10.1002/aic.18842","url":null,"abstract":"In this work, we develop a carburization strategy to transform hexagonal Ni<sub>3</sub>In into face-centered cubic Ni<sub>3</sub>InC<sub>0.5</sub> intermetallic carbide, leveraging partially isolated Ni sites for improved acetylene semihydrogenation. The catalyst synthesized via carburization of Ni<sub>3</sub>In intermetallic compound derived from Ni/In/Mg/Al layered double hydroxides in a C<sub>2</sub>H<sub>2</sub>/H<sub>2</sub> atmosphere is evidenced to show Ni<sub>3</sub>InC<sub>0.5</sub> intermetallic carbide phase through detailed characterizations, including high-resolution transmission electron microscopy and X-ray absorption spectroscopy. Catalytic tests reveal that the Ni<sub>3</sub>InC<sub>0.5</sub> catalyst achieves 92.0% ethylene selectivity at full acetylene conversion, outperforming the Ni and Ni<sub>3</sub>In catalysts. Both experimental and theoretical evidence demonstrate that interstitial carbon atoms in Ni<sub>3</sub>InC<sub>0.5</sub> synergize with neighboring In atoms to modify the electronic structure of surface Ni sites via significant hybridization between Ni 3<i>d</i>, In 5<i>p</i>, and C 2<i>p</i> orbitals. These unique features enable higher kinetic favorability of ethylene desorption over its further hydrogenation on the Ni<sub>3</sub>InC<sub>0.5</sub> catalyst and thus contribute to the enhanced semihydrogenation.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"38 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758205","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}
Miguel J. Bagajewicz, Andre L. M. Nahes, Eduardo M. Queiroz, Diego G. Oliva, Javier A. Francesconi, André L. H. Costa
A novel approach (Complete Set Trimming) to address the globally optimal design of multiple-unit heat exchangers (Shell and Tube, Double Pipe, Plate, etc.) is presented. Three arrangements: Series, Parallel, Series–Parallel, and Parallel–Series, for minimizing area, CAPEX, or total annualized cost are considered. The geometry of all (equal) units is determined together with the number of units and the fluid allocation. The article illustrates the need to minimize CAPEX explicitly instead of using the minimization of Area as its proxy objective function. In addition, the influence of available pressure drop in the final optimal design is also discussed. Finally, the article shows that solutions obtained by minimizing the Total Annualized Cost (TAC) render different solutions than those obtained by minimizing CAPEX, indicating that pumping costs matter, depending on the balance between operational and capital costs.
{"title":"Globally optimal basic design of multiple-unit heat exchangers","authors":"Miguel J. Bagajewicz, Andre L. M. Nahes, Eduardo M. Queiroz, Diego G. Oliva, Javier A. Francesconi, André L. H. Costa","doi":"10.1002/aic.18838","DOIUrl":"https://doi.org/10.1002/aic.18838","url":null,"abstract":"A novel approach (Complete Set Trimming) to address the globally optimal design of multiple-unit heat exchangers (Shell and Tube, Double Pipe, Plate, etc.) is presented. Three arrangements: Series, Parallel, Series–Parallel, and Parallel–Series, for minimizing area, CAPEX, or total annualized cost are considered. The geometry of all (equal) units is determined together with the number of units and the fluid allocation. The article illustrates the need to minimize CAPEX explicitly instead of using the minimization of Area as its proxy objective function. In addition, the influence of available pressure drop in the final optimal design is also discussed. Finally, the article shows that solutions obtained by minimizing the Total Annualized Cost (TAC) render different solutions than those obtained by minimizing CAPEX, indicating that pumping costs matter, depending on the balance between operational and capital costs.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"23 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745613","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}
Surya Prakash Tiwari, Robert L. Thompson, Wei Shi, Nicholas Siefert, David Hopkinson, Janice A. Steckel
Foaming prediction is critical for selecting materials and designing processes in industries such as bioprocessing and gas processing. Existing models lack the generality needed for a wide range of materials and overlook the foaming behavior in pure liquids. This work presents a novel method for predicting foaming in pure liquids based on their density, surface tension, and viscosity, using Reynolds (Re) and Ohnesorge (Oh) numbers. A foaming prediction map, leveraging the theory of fluid drop behavior, was developed by plotting these numbers. This map delineates distinct non-foaming and foaming regions, functioning as a binary classifier for foaming predictions. The map was fitted and validated through shake test experiments on 46 liquids, demonstrating reliable predictions, except for a specific region characterized by small Oh and large Re numbers. This region corresponded to relatively low foam stability and high turbulence, making foaming predictions challenging for liquids in this category.
{"title":"Foaming prediction in pure liquids from dimensionless numbers inspired by the theory of fluid behavior for drops","authors":"Surya Prakash Tiwari, Robert L. Thompson, Wei Shi, Nicholas Siefert, David Hopkinson, Janice A. Steckel","doi":"10.1002/aic.18836","DOIUrl":"https://doi.org/10.1002/aic.18836","url":null,"abstract":"Foaming prediction is critical for selecting materials and designing processes in industries such as bioprocessing and gas processing. Existing models lack the generality needed for a wide range of materials and overlook the foaming behavior in pure liquids. This work presents a novel method for predicting foaming in pure liquids based on their density, surface tension, and viscosity, using Reynolds (<i>Re</i>) and Ohnesorge (<i>Oh</i>) numbers. A foaming prediction map, leveraging the theory of fluid drop behavior, was developed by plotting these numbers. This map delineates distinct non-foaming and foaming regions, functioning as a binary classifier for foaming predictions. The map was fitted and validated through shake test experiments on 46 liquids, demonstrating reliable predictions, except for a specific region characterized by small <i>Oh</i> and large <i>Re</i> numbers. This region corresponded to relatively low foam stability and high turbulence, making foaming predictions challenging for liquids in this category.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"103 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745615","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}
Yuting Wu, Shikun Zhong, Bona Lu, Shanglin Liu, Youhao Xu, Wei Wang
This study pioneers a three-dimensional, transient reactive simulation of an industrial fluid catalytic cracking full-loop system. Within a two-fluid model framework, the simulation incorporates the Energy Minimization Multiscale (EMMS)-based models to account for the effects of mesoscale flow structures on drag and heat transfer, and integrates a 12-lumped kinetics model and a coke combustion model to describe catalytic cracking reactions and catalyst regeneration, respectively. It finds the significant impact of reactions on solid concentration and gas velocity distributions throughout the system, particularly in the first reaction zone. The first reaction zone achieves 80% conversion of feedstock oil, with the second reaction zone contributing an additional 19% conversion. These variations in product concentration along the bed height reflect substantial differences in reaction types under varying environments. Furthermore, the simulation captures temperature changes along the solid circulation path, facilitating the determination of the heat exchanger power required to control the reaction temperature.
{"title":"Reactive simulation of an industrial-scale FCC reaction-regeneration full loop system","authors":"Yuting Wu, Shikun Zhong, Bona Lu, Shanglin Liu, Youhao Xu, Wei Wang","doi":"10.1002/aic.18845","DOIUrl":"https://doi.org/10.1002/aic.18845","url":null,"abstract":"This study pioneers a three-dimensional, transient reactive simulation of an industrial fluid catalytic cracking full-loop system. Within a two-fluid model framework, the simulation incorporates the Energy Minimization Multiscale (EMMS)-based models to account for the effects of mesoscale flow structures on drag and heat transfer, and integrates a 12-lumped kinetics model and a coke combustion model to describe catalytic cracking reactions and catalyst regeneration, respectively. It finds the significant impact of reactions on solid concentration and gas velocity distributions throughout the system, particularly in the first reaction zone. The first reaction zone achieves 80% conversion of feedstock oil, with the second reaction zone contributing an additional 19% conversion. These variations in product concentration along the bed height reflect substantial differences in reaction types under varying environments. Furthermore, the simulation captures temperature changes along the solid circulation path, facilitating the determination of the heat exchanger power required to control the reaction temperature.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"13 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745612","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}
Nitrocellulose (NC) is essential in high-energy propellants, with nitrogen content affecting its pyrolysis rate and thermal stability. This study creates all-atom models of NC with varying nitrogen levels to explore pyrolysis mechanisms and validate them against experimental thermal response data. Results show that RO − NO2 bond cleavage initiates NC decomposition. Lower nitration levels convert nitrogen oxides into carbon-nitrogen compounds, primarily HCN. Additionally, HCHO production is linked to CH2ONO2 group transformation, with low-nitration, high-hydrogen NC reducing HCHO yield. Kinetic parameters for cellulose thermal decomposition indicate that pyrolysis activation energies decrease with nitration levels, demonstrating that nitration significantly lowers the energy barrier for ring-opening. Molecular dynamics simulations reveal pathways for HCHO, NO2, and NO generation during combustion, enhancing understanding of NC combustion mechanisms and safety in explosive applications.
{"title":"Microscopic kinetics of scission and reformation in the pyrolysis of nitrocellulose","authors":"Changwei Liu, Haojie Qian, Qing Wang, Jinkai Qiu, Yajun Ding, Cheng Lian, Honglai Liu","doi":"10.1002/aic.18844","DOIUrl":"https://doi.org/10.1002/aic.18844","url":null,"abstract":"Nitrocellulose (NC) is essential in high-energy propellants, with nitrogen content affecting its pyrolysis rate and thermal stability. This study creates all-atom models of NC with varying nitrogen levels to explore pyrolysis mechanisms and validate them against experimental thermal response data. Results show that RO − NO<sub>2</sub> bond cleavage initiates NC decomposition. Lower nitration levels convert nitrogen oxides into carbon-nitrogen compounds, primarily HCN. Additionally, HCHO production is linked to CH<sub>2</sub>ONO<sub>2</sub> group transformation, with low-nitration, high-hydrogen NC reducing HCHO yield. Kinetic parameters for cellulose thermal decomposition indicate that pyrolysis activation energies decrease with nitration levels, demonstrating that nitration significantly lowers the energy barrier for ring-opening. Molecular dynamics simulations reveal pathways for HCHO, NO<sub>2</sub>, and NO generation during combustion, enhancing understanding of NC combustion mechanisms and safety in explosive applications.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"58 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745614","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}
Hong-Da Zhang, Wei-Yao Yang, Miao Pang, Ya-Qiao Tian, Shi-Chao Su, Zhi-Ping Zhao, Le Sang
Non-modified Pd/PDA/Ni foam, hydrophobic modified F9-Pd/PDA/Ni foam, and F17-Pd/PDA/Ni foam catalysts are successfully prepared and used for NB hydrogenation in micropacked bed reactors (μPBRs). The catalytic performance increases with the addition of water in the water–methanol mixed solvent. In the mixed solvent with 50 v/v% water, F17-Pd/PDA/Ni foam can almost completely convert nitrobenzene (NB) with a yield of 95.9% at a mild 45°C. As the solvent contact angle of catalysts increases (87.5–141.5°), the enhancement ratios of NB conversion and AN yield are 28.9%–92.4% and 31.4%–106.5%, respectively. The space–time yield of AN in μPBRs reaches 1.873 kg·L−1·h−1·g−1, which is 1–2 orders of magnitude higher than that of conventional reactors. The kinetic model of NB hydrogenation is established at the water–methanol system in μPBRs. The hydrophobicity of catalysts significantly improves the reaction rate of NB hydrogenation, and the reaction rate constant is increased by 69.9%.
{"title":"Hydrophobic Ni foam catalyst for nitrobenzene hydrogenation enhancement in micropacked bed reactors","authors":"Hong-Da Zhang, Wei-Yao Yang, Miao Pang, Ya-Qiao Tian, Shi-Chao Su, Zhi-Ping Zhao, Le Sang","doi":"10.1002/aic.18846","DOIUrl":"https://doi.org/10.1002/aic.18846","url":null,"abstract":"Non-modified Pd/PDA/Ni foam, hydrophobic modified F<sub>9</sub>-Pd/PDA/Ni foam, and F<sub>17</sub>-Pd/PDA/Ni foam catalysts are successfully prepared and used for NB hydrogenation in micropacked bed reactors (μPBRs). The catalytic performance increases with the addition of water in the water–methanol mixed solvent. In the mixed solvent with 50 v/v% water, F<sub>17</sub>-Pd/PDA/Ni foam can almost completely convert nitrobenzene (NB) with a yield of 95.9% at a mild 45°C. As the solvent contact angle of catalysts increases (87.5–141.5°), the enhancement ratios of NB conversion and AN yield are 28.9%–92.4% and 31.4%–106.5%, respectively. The space–time yield of AN in μPBRs reaches 1.873 kg·L<sup>−1</sup>·h<sup>−1</sup>·g<sup>−1</sup>, which is 1–2 orders of magnitude higher than that of conventional reactors. The kinetic model of NB hydrogenation is established at the water–methanol system in μPBRs. The hydrophobicity of catalysts significantly improves the reaction rate of NB hydrogenation, and the reaction rate constant is increased by 69.9%.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745616","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}
Jingxuan Xue, Xiaojie Feng, Qingzhu Jia, Qiang Wang, Fangyou Yan
Classical group contribution method, as one of the main methods for estimating thermodynamic properties, is developed with the number of groups, ignoring the influence of group characters. In this work, the spatial group contribution (SGC) method combining Euclidean distance and quantum properties is proposed, which uses the spatial group factor (SGF) and the spatial position factor (SPF) to reflect the spatial differences of the groups, thereby improving the limitations of the previous methods that only rely on topological structures. Five SGC models are established, including critical temperature (Tc), critical pressure (Pc), critical volume (Vc), boiling point (Tb), and melting point (Tm), and the squared correlation coefficients (R2training) of 0.9935, 0.9925, 0.9988, 0.9828, and 0.8690 are obtained, respectively. After a series of rigorous validation procedures (external validation and internal validation), all models present excellent predictability (R2test: 0.8690–0.9988) and stability (Q2: 0.8344–0.9981). Compared with the atomic adjacent group (AAG) model, which is a traditional group contribution method, the absolute mean relative errors (AAREtraining) of five models are reduced by 24.67%–69.26%. The position factor and spatial group factor crucially improve the models based on the number of groups. The spatiality-based SGC method is of great significance for the prediction of thermodynamic properties and has the potential to be extended to more thermodynamic properties such as phase transition properties of enthalpy and entropy as well as saturated vapor pressure.
{"title":"A universal spatial group contribution method by 3D-structures for predicting the thermodynamic properties","authors":"Jingxuan Xue, Xiaojie Feng, Qingzhu Jia, Qiang Wang, Fangyou Yan","doi":"10.1002/aic.18823","DOIUrl":"https://doi.org/10.1002/aic.18823","url":null,"abstract":"Classical group contribution method, as one of the main methods for estimating thermodynamic properties, is developed with the number of groups, ignoring the influence of group characters. In this work, the spatial group contribution (SGC) method combining Euclidean distance and quantum properties is proposed, which uses the spatial group factor (SGF) and the spatial position factor (SPF) to reflect the spatial differences of the groups, thereby improving the limitations of the previous methods that only rely on topological structures. Five SGC models are established, including critical temperature (<i>T</i><sub><i>c</i></sub>), critical pressure (<i>P</i><sub><i>c</i></sub>), critical volume (<i>V</i><sub><i>c</i></sub>), boiling point (<i>T</i><sub><i>b</i></sub>), and melting point (<i>T</i><sub><i>m</i></sub>), and the squared correlation coefficients (<i>R</i><sup>2</sup><sub>training</sub>) of 0.9935, 0.9925, 0.9988, 0.9828, and 0.8690 are obtained, respectively. After a series of rigorous validation procedures (external validation and internal validation), all models present excellent predictability (<i>R</i><sup>2</sup><sub>test</sub>: 0.8690–0.9988) and stability (<i>Q</i><sup>2</sup>: 0.8344–0.9981). Compared with the atomic adjacent group (AAG) model, which is a traditional group contribution method, the absolute mean relative errors (AARE<sub>training</sub>) of five models are reduced by 24.67%–69.26%. The position factor and spatial group factor crucially improve the models based on the number of groups. The spatiality-based SGC method is of great significance for the prediction of thermodynamic properties and has the potential to be extended to more thermodynamic properties such as phase transition properties of enthalpy and entropy as well as saturated vapor pressure.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"24 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734463","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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