🤖 AI Summary
Steam methane reforming (SMR) reactor multi-objective optimization faces high computational cost and inherent trade-offs among conflicting objectives—e.g., methane conversion, hydrogen production rate, and CO₂ emissions.
Method: This paper proposes an integrated framework comprising mechanistic modeling, data-driven surrogate modeling, multi-objective optimization, and decision-making support. An artificial neural network (ANN)-hybrid surrogate model replaces computationally expensive high-fidelity 1D fixed-bed simulations; non-dominated sorting genetic algorithm II (NSGA-II) efficiently computes the Pareto-optimal front; and a dual-criteria decision strategy—combining Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and stochastic PROBID (sPROBID)—supports optimal operating condition selection.
Contribution/Results: The surrogate model reduces average simulation time by 93.8%. The identified Pareto-optimal solution achieves methane conversion of 0.988, H₂ production of 3.335 mol/s, and CO₂ emissions of 0.781 mol/s—demonstrating substantial gains in optimization efficiency and engineering applicability.
📝 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.