Stochastic algorithm-based optimization using artificial intelligence/machine learning models for sorption enhanced steam methane reformer reactor

•A novel approach to real time optimization of SESMR is introduced.•It combines the strength of stochastic algorithms with data-driven models.•Solver can navigate complex solution spaces in real-world applications.•The proposed approach greatly improves the overall optimization process. There is a n...

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Veröffentlicht in:Computers & chemical engineering Jg. 196; S. 109060
Hauptverfasser: Bishnu, Sumit K., Alnouri, Sabla Y., Al Mohannadi, Dhabia M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.05.2025
Schlagworte:
Artificial intelligence
Machine learning
Optimization
Simulated annealing
Solver
Stochastic algorithm
Stochastic algorithm
Solver
Artificial intelligence
Optimization
Machine learning
Simulated annealing
ISSN:0098-1354
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Zusammenfassung:•A novel approach to real time optimization of SESMR is introduced.•It combines the strength of stochastic algorithms with data-driven models.•Solver can navigate complex solution spaces in real-world applications.•The proposed approach greatly improves the overall optimization process. There is a need for comprehensive tools that combine data-driven modeling with optimization techniques. In this work, a robust Random Forest Regression (RFR) model was developed to capture the behavior and characteristics of a Sorption Enhanced Steam Methane Reformer (SE-SMR) Reactor system. This model was then integrated into a Simulated Annealing (SA) optimization framework that helped identify the optimal operating conditions for the unit. The combined approach demonstrates the potential of using machine learning models in conjunction with optimization techniques to improve the solving process. The proposed methodology achieved an optimal methane conversion rate of 0.99979, and was successful in effectively identifying the optimal operating conditions that were required for near-complete conversion.
ISSN:0098-1354
DOI:10.1016/j.compchemeng.2025.109060