Call For Papers
Smart Chemical Engineering
Innovations and Applications of Reliable AI in Chemical Engineering
Data-driven Artificial Intelligence (AI) has been rapidly transforming Chemical Engineering (ChE). Nevertheless, the transition from high-performing AI models to reliable engineering tools remains a critical challenge. ChE applications often consist of systems that intersect with human health and safety, environmental health, and high profitability, and thus AI models require a high degree of reliability to be safely implemented within these critical domains. In addition, ChE systems often involve complex multiscale phenomena, stringent physical constraints, limited data, and operating conditions extending beyond available datasets. These characteristics demand AI approaches that are not only accurate, but also transparent, interpretable, physically consistent, and transferable to real-world engineering applications. In response to the increasing demand for reliable AI implementation within ChE, this Call for Papers on “Innovations and Applications of Reliable AI in Chemical Engineering” in Smart Chemical Engineering (SCE) aims to highlight next-generation AI methodologies and applications that advance the reliability and practicality of AI in ChE. Particular emphasis is placed on approaches that bridge data-driven AI with ChE knowledge for reliable implementation, and on innovative applications of transparent and explainable AI methodologies to challenging ChE applications. We hope to showcase recent methodological advances, scientific breakthroughs, and forward perspectives enabled by the reliable implementation of AI within ChE. We welcome original research articles, reviews, and perspectives focusing on the development of explainable and interpretable AI, physics-informed and knowledge-guided learning, gray-box and hybrid mechanistic-AI models, uncertainty propagation and robust modeling, training accurate AI models within data-limited domains, and intelligent digital twins in Chemical Engineering. We also welcome innovative applications of explainable AI to ChE problems including reaction and materials engineering, biomedical and pharmaceutical engineering, process systems engineering, and energy and sustainability. Studies demonstrating rigorous analysis of the underlying methodology, excellent generalization, physical insight, or translation toward safe implementation within practical and industrial applications are particularly encouraged. By bringing together methodological advances and emerging applications, this Call for Papers seeks to explore how AI can evolve from a powerful predictive technology into a reliable and scientifically impactful tool within ChE. Topics of interest include, but are not limited to: Reliable, robust, and transparent AI for ChE Explainable and interpretable AI Physics-informed and knowledge-guided machine learning Graybox (Hybrid mechanistic and data-driven) modeling Uncertainty quantification, uncertainty propagation, and robust AI Generalization, extrapolation, and transfer learning Data-limited and data-efficient learning Multiscale and multimodal AI AI-assisted reaction, catalyst, and materials discovery AI for multiphase flows, transport phenomena, and reaction engineering Intelligent process modeling, monitoring, optimization, and control AI-enabled digital twins and reliable smart manufacturing Safe AI implementation for energy, environmental, and sustainable chemical processes Safe AI applications in biomedical and pharmaceutical engineering Benchmarking, validation, and reproducibility of AI models Industrial deployment and practical real-world applications of AI in ChE Keywords: AI in chemical engineering; reliable AI; explainable AI; transparent AI; physics-informed AI; hybrid modeling; robust machine learning; data-limited AI learning; intelligent process systems engineering Submission Guideline: Author submission guidelines can be found at: https://www.sciltp.com/journals/sce/instructionForAuthors When submitting your manuscript via the online submission portal: https://sciflux.org/authors/submissions/add-submissions?journalCode=1909189275485560833 , please select the relevant Thematic Issue title. Article Processing Charges (APCs) are waived before March 2027. All manuscripts will undergo peer review in accordance with the journal’s established policies and procedures. Final acceptance will be based on the peer-review reports and the evaluation of the Academic Editor and the Editor-in-Chief. Decisions will be made by the Editor-in-Chief or Academic Editor who has no conflicts of interest with any of the authors. Academic Editor: Name: Donovan Chaffart Email: drgchaff@eitech.edu.cn Affiliation: School of Chemical and Biomolecular Engineering, Eastern Institute of Technology, Ningbo, China. Homepage: https://faculty.eitech.edu.cn/bme/yy/main.htm
Smart Chemical Engineering
Deadline: 01 March 2027AI-Enabled Catalytic Engineering: From Catalyst Design to Process Optimization
Smart Chemical Engineering (SCE) is announcing a Call for Papers for the topic “AI-Enabled Catalytic Engineering: From Catalyst Design to Process Optimization”. Catalysis plays an important role in energy conversion, advanced materials manufacturing, and health-related applications, while also presenting major challenges associated with complex reaction networks, multiscale process coupling, and precise regulation. Recent advances in artificial intelligence (AI), particularly in high-dimensional data mining, complex relationship modeling, and optimization-driven decision-making, are reshaping catalyst design, mechanistic understanding, and process optimization. In this context, Smart Chemical Engineering (SCE) is pleased to announce a Call for Papers and invites submissions of high-quality contributions that highlight methodological advances and emerging applications of AI in catalytic engineering. We welcome original research articles, reviews, and perspective papers addressing theoretical developments, methodological innovation, and practical applications of AI in catalyst design, mechanistic analysis, and related process engineering. Topics of interest include, but are not limited to: AI-driven catalyst design, screening, and optimization AI-assisted mechanistic understanding and analysis of complex catalytic reaction networks AI-driven research in computational catalysis Integration of multimodal characterization and AI for catalytic spectral analysis and mechanistic studies AI-driven multi-objective optimization, process regulation, and engineering applications of catalytic systems Keywords: Artificial Intelligence, Catalytic Engineering, Catalyst Design, Mechanistic Analysis, Process Regulation Academic Editors Prof. Yuanhui Ji Affiliation: School of Chemistry and Chemical Engineering, Southeast University Prof. Jiahua Zhu Affiliation: College of Chemical Engineering, Nanjing Tech University Prof. Xiaonan Wang Affiliation: Department of Chemical Engineering, Tsinghua University Submission Details: Submission Deadline: 30 November 2026 Office Contact: sce@sciltp.com Instruction for Authors: https://www.sciltp.com/journals/sce/instructionForAuthors Submission Link: https://sciflux.org/authors/submissions/add-submissions?journalCode=1909189275485560833
Smart Chemical Engineering
Deadline: 30 November 2026
