Scholarlink

<p>This study introduces an integrated modeling and optimization framework for a steam methane reforming (SMR) reactor, merging mathematical modeling, artificial neural network (ANN), 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 accuracy. The ANN surrogate uses operating variables as inputs to predict axial temperature and species profiles. This hybrid strategy accelerates the reactor simulation while preserving the mechanistic structure of the model, enabling efficient integration into the multi-objective optimization framework. The framework 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.</p><p>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 0.988, 3.335&#xa0;mol/s hydrogen, and 0.781&#xa0;mol/s carbon dioxide. The proposed methodology effectively manages complex reactor optimization problems, offering significant computational efficiency, clear insights into variable sensitivities, and informed decision-making among conflicting objectives, demonstrating broad applicability to other catalytic reactor systems. </p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning assisted surrogate modeling with multi objective optimization and decision making of a steam methane reforming reactor

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月31日

查看全文
  • Seyed Reza Nabavi,
  • Zonglin Guo,
  • Zhiyuan Wang

摘要

This study introduces an integrated modeling and optimization framework for a steam methane reforming (SMR) reactor, merging mathematical modeling, artificial neural network (ANN), 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 accuracy. The ANN surrogate uses operating variables as inputs to predict axial temperature and species profiles. This hybrid strategy accelerates the reactor simulation while preserving the mechanistic structure of the model, enabling efficient integration into the multi-objective optimization framework. The framework 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 0.988, 3.335 mol/s hydrogen, and 0.781 mol/s carbon dioxide. The proposed methodology effectively manages complex reactor optimization problems, offering significant computational efficiency, clear insights into variable sensitivities, and informed decision-making among conflicting objectives, demonstrating broad applicability to other catalytic reactor systems.