Chemical Engineering Science最新文献_第2页

Numerical & experimental validation of Vanadium-Titanium flow battery for general flow battery simulation model development

IF 4.7 2区工程技术

Chemical Engineering Science Pub Date : 2026-09-01 DOI: 10.1016/j.ces.2026.125100

Chivukula Kalyan Sundar Krishna,Boris Balakin,Yansong Zhao

引用次数: 0

A spatiotemporally switchable polyphenol–MOF redox hydrogel for dual pH/temperature-regulated release and ROS modulation in infected microenvironments

IF 4.7 2区工程技术

Chemical Engineering Science Pub Date : 2026-09-01 DOI: 10.1016/j.ces.2026.125092

Qin Zeng,Yuancheng Liu,Huiyun Wu,Chunyan Deng

引用次数: 0

Multiscale modeling of bubble cross-interface dynamics in a water–oil-gas multiphase reactor using a coupled VOF-DBM-LES approach

IF 4.7 2区工程技术

Chemical Engineering Science Pub Date : 2026-09-01 DOI: 10.1016/j.ces.2026.125078

Xue Yuxi,Jian Zeng,Wan Zhanghao,Xie Xueqing,Liu Xiyi,Li Mingzhou

引用次数: 0

Influence of surface hydrophobicity and roughness on residual microbubble during the detachment of bubble and solid plane 表面疏水性和粗糙度对气泡与固面分离过程中残余微泡的影响

IF 4.7 2区工程技术

Chemical Engineering Science Pub Date : 2026-09-01 DOI: 10.1016/j.ces.2026.125068

Xiaofeng Jiang, Jing Cui, Xiaoying Wang, Zixuan Fu, Jiacheng Bai, Xianliang Meng, Enle Xu

{"title":"Influence of surface hydrophobicity and roughness on residual microbubble during the detachment of bubble and solid plane","authors":"Xiaofeng Jiang, Jing Cui, Xiaoying Wang, Zixuan Fu, Jiacheng Bai, Xianliang Meng, Enle Xu","doi":"10.1016/j.ces.2026.125068","DOIUrl":"https://doi.org/10.1016/j.ces.2026.125068","url":null,"abstract":"Bubble-particle detachment is critical to flotation efficiency, and previous studies have clarified the influence of factors such as hydrodynamics and surface properties. However, the phenomenon of residual microbubbles remaining on solid surfaces after detachment is still not well understood. In this study, the effect of surface hydrophobicity and roughness on the residual microbubble as well as three-phase contact line (TPL) during bubble detaching from the solid plane were investigated. It was found that the enhancement of solid surface hydrophobicity leads to an increase in the volume of residual microbubble. Whereas, increasing surface roughness induces a nonlinear variation in the volume of residual microbubble, characterized by an initial increase, followed by a decline, and then a subsequent rise. In addition, a similar trend is observed for the TPL length and dynamical contact angles, both of which increase with enhanced hydrophobicity and exhibit the same nonlinear response to surface roughness. These results are expected to provide new insights into the detachment theory, highlighting the potential role of surface physic-chemical structure in bubble-particle stability.","PeriodicalId":271,"journal":{"name":"Chemical Engineering Science","volume":"47 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep reinforcement learning-driven mesh adaptation for supercritical water fluidized bed simulations 超临界水流化床模拟的深度强化学习驱动网格自适应

IF 4.7 2区工程技术

Chemical Engineering Science Pub Date : 2026-09-01 DOI: 10.1016/j.ces.2026.125053

Haozhe Su, Jie Zhang, Hui Jin, Liejin Guo

{"title":"Deep reinforcement learning-driven mesh adaptation for supercritical water fluidized bed simulations","authors":"Haozhe Su, Jie Zhang, Hui Jin, Liejin Guo","doi":"10.1016/j.ces.2026.125053","DOIUrl":"https://doi.org/10.1016/j.ces.2026.125053","url":null,"abstract":"This study develops a reinforcement learning-based adaptive mesh refinement (RL-AMR) framework integrated into a computational fluid dynamics − discrete element method (CFD-DEM) solver to achieve autonomous mesh control in the dense phase region of a supercritical water fluidized bed reactor (SCWFBR). The proposed method reformulates mesh adaptation as an optimal control problem by quantifying the adaptation parameters into a compact vector, which dramatically compresses the action space compared to conventional direct-addressing RL-AMR approaches. This parameterization enables the model to scale efficiently to large computational domains without compromising precision. Using the high-resolution fixed-mesh results as the reference, comparative simulations with heuristic AMR strategies demonstrate that the proposed RL-AMR achieves comparable particle similarity (average 0.748) and superior accuracy in predicting gas-phase products (<ce:italic>R</ce:italic><ce:sup loc=\"post\">2</ce:sup> > 98.8 %), while reducing computational time by nearly 50 %. Analysis of the training dynamics reveals the RL agent’s evolution from random exploration to stable policy optimization, resulting in smoother and more strategic mesh adjustments that minimize numerical instabilities. The scale-up investigation reveals that while a direct zero-shot deployment preserves baseline computational efficiency and global flow statistics, a rapid few-shot calibration successfully restores optimal species predictive accuracy with minimal retraining cost. This work develops a scalable, parameterized RL-AMR framework that overcomes the action-space explosion inherent in existing cell-level RL-AMR methods, establishing a practical and transferable pathway for intelligent mesh control in complex multi-physics systems.","PeriodicalId":271,"journal":{"name":"Chemical Engineering Science","volume":"6 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable machine learning for spatial regression of bubble characteristics in a coal and biomass co-gasifier

IF 4.7 2区工程技术

Chemical Engineering Science Pub Date : 2026-09-01 DOI: 10.1016/j.ces.2026.125051

Ju Wang,Siyuan Chen,Shiliang Yang,Hua Wang

引用次数: 0

Construction of Lewis acid sites and oxygen vacancy for directed conversion of lignin to guaiacol

IF 4.7 2区工程技术

Chemical Engineering Science Pub Date : 2026-09-01 DOI: 10.1016/j.ces.2026.125088

Mingqiang Chen,Mengyu Cai,Yishuang Wang,Defang Liang,Chang Li,Haosheng Xin,Xiaoyu Zhao,Jiawen Hu,Jun Wang

引用次数: 0

Clean vanadium extraction from vanadium slag via low-temperature co-roasting with sodium oxalate and sodium carbonate

IF 4.7 2区工程技术

Chemical Engineering Science Pub Date : 2026-09-01 DOI: 10.1016/j.ces.2026.125096

Zekun Li,Changqing Li,Tao Jiang,Jing Wen,Xinyu An,Lan Zhang

引用次数: 0

Biomass carbon quantum dot-modified polyaspartic acid, used as an efficient scale inhibitor and corrosion inhibitor with fluorescent properties

IF 4.7 2区工程技术

Chemical Engineering Science Pub Date : 2026-09-01 DOI: 10.1016/j.ces.2026.125083

Zhigang Chen,Zhengnan Sun,Guolin Jing,Mingjing Yu

引用次数: 0

Low-rank physics-constrained dynamic mode decomposition for data-mechanism fusion in high-dimensional dynamical systems 高维动力系统数据机制融合的低阶物理约束动态模式分解

IF 4.7 2区工程技术

Chemical Engineering Science Pub Date : 2026-09-01 DOI: 10.1016/j.ces.2026.125056

Xing Qian, Jiajun Hou, Yuhui Yin, Shengkun Jia, Yiqing Luo, Xigang Yuan

引用次数: 0