{"ID":2896072,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.07641","arxiv_id":"2507.07641","title":"Machine Learning-Assisted Surrogate Modeling with Multi-Objective Optimization and Decision-Making of a Steam Methane Reforming Reactor","abstract":"This study presents an integrated modeling and optimization framework for a steam methane reforming (SMR) reactor, combining a mathematical model, artificial neural network (ANN)-based hybrid modeling, advanced multi-objective optimization (MOO) and multi-criteria decision-making (MCDM) techniques. A one-dimensional fixed-bed reactor model accounting for internal mass transfer resistance was employed to simulate reactor performance. To reduce the high computational cost of the mathematical model, a hybrid ANN surrogate was constructed, achieving a 93.8% reduction in average simulation time while maintaining high predictive accuracy. The hybrid model was then embedded into three MOO scenarios using the non-dominated sorting genetic algorithm II (NSGA-II) solver: 1) maximizing methane conversion and hydrogen output; 2) maximizing hydrogen output while minimizing carbon dioxide emissions; and 3) a combined three-objective case. The optimal trade-off solutions were further ranked and selected using two MCDM methods: technique for order of preference by similarity to ideal solution (TOPSIS) and simplified preference ranking on the basis of ideal-average distance (sPROBID). Optimal results include a methane conversion of 0.863 with 4.556 mol/s hydrogen output in the first case, and 0.988 methane conversion with 3.335 mol/s hydrogen and 0.781 mol/s carbon dioxide in the third. This comprehensive methodology offers a scalable and effective strategy for optimizing complex catalytic reactor systems with multiple, often conflicting, objectives.","short_abstract":"This study presents an integrated modeling and optimization framework for a steam methane reforming (SMR) reactor, combining a mathematical model, artificial neural network (ANN)-based hybrid modeling, advanced multi-objective optimization (MOO) and multi-criteria decision-making (MCDM) techniques. A one-dimensional fi...","url_abs":"https://arxiv.org/abs/2507.07641","url_pdf":"https://arxiv.org/pdf/2507.07641v1","authors":"[\"Seyed Reza Nabavi\",\"Zonglin Guo\",\"Zhiyuan Wang\"]","published":"2025-07-10T11:10:16Z","proceeding":"physics.chem-ph","tasks":"[\"physics.chem-ph\",\"cs.LG\"]","methods":"[]","has_code":false}