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
Online-Zugang:	Volltext
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