<ul data-eligibleForWebStory="false"><li>This study focused on a steam methane reforming (SMR) reactor and introduced an integrated modeling and optimization framework combining a mathematical model, artificial neural network (ANN)-based hybrid modeling, multi-objective optimization (MOO), and multi-criteria decision-making (MCDM) techniques.</li><li>A hybrid ANN surrogate model was developed to reduce computational costs by 93.8% while maintaining high predictive accuracy, embedded in three MOO scenarios that aimed to maximize methane conversion, hydrogen output, and simultaneously minimize carbon dioxide emissions.</li><li>The optimal trade-off solutions were ranked and selected using MCDM methods like technique for order of preference by similarity to the ideal solution (TOPSIS) and simplified preference ranking on the basis of ideal-average distance (sPROBID).</li><li>Overall, this methodology provides an efficient strategy for optimizing intricate catalytic reactor systems with conflicting objectives, offering scalable solutions for such complex systems.</li></ul>

Machine Learning-Assisted Surrogate Modeling with Multi-Objective Optimization and Decision-Making of a Steam Methane Reforming Reactor

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