
<strong>Paper Title</strong><br>
Multi-objective Optimization Integrating Artificial Neural Networks: Design and Performance Analysis of a Solar-Powered System for Simultaneous Production of Hydrogen, Electricity, and Heat<br>
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<strong>Abstract</strong><br>
This study presents a novel approach integrating multi-objective optimization techniques withArtificial Neural
Networks (ANNs) to design and analyze the performance of a solar-powered system aimed atsimultaneously generating
hydrogen, electricity, and heat. The proposed methodology leverages the capability ofANNs to model complex relationships
within the system and employs Grey-wolf optimization to achieveoptimal operation. Results demonstrate the effectiveness of
the integrated approach, with the ANN achieving high accuracy (0.99997) and the TOPSIS was utilized to select the optimal
solution. Moreover, sensitivity analyses highlight the significance of all parameters in influencing system behavior.
Keywords - ANN; Multi-Objective Optimization; Grey-Wolf; Solar Energy; Hydrogen Production