Muthanna Journal of Engineering and Technology
Volume (13), Issue (3), Year (2025), Pages (1-14)
DOI:10.52113/3/eng/mjet/2025-13-03-/1-14
Research Article By:
Falah A. Barqawi
Corresponding author E-mail: albarqawyfalah@gmail.com
ABSTRACT
Phase change material (PCM) integrated solar water heating systems represent a critical technology for sustainable energy applications, yet face significant performance limitations due to poor thermal conductivity and lack of intelligent control optimization. This study aims to develop and validate a novel machine learning-driven optimization control technique for PCM-based solar water heating systems. The methodology employs a comprehensive three-phase mathematical model encompassing pre-melting, melting transition, and post-melting thermal dynamics, coupled with a neural network controller operating on real-time environmental data to predict optimal pump flow multipliers. Comprehensive simulation validation across five environmental conditions and three PCM materials demonstrated consistent performance improvements with energy storage enhancements of 2.5-4.1% (3.3% average) and heat transfer enhancement ratios of 1.03-1.04×. This research provides the first complete ML-based control system for PCM thermal energy storage with retrofit-compatible optimization requiring no hardware modifications, offering a quantifiable performance benefit for existing installations.
Keywords:
Intelligent Control; Machine Learning Optimization; Phase Change Materials; Renewable Energy Systems; Solar Water Heating.