OPTIMIZATION OF HYBRID SOLAR THERMAL SYSTEMS FOR INDUSTRIAL ENERGY EFFICIENCY IN PAKISTAN

Abstract

The industrial sector in Pakistan is characterized by high energy intensity, heavy reliance on fossil fuels, and persistent supply constraints, resulting in elevated production costs and reduced operational efficiency. In response, Hybrid Solar Thermal Systems (HSTSs) have emerged as a promising solution for sustainable industrial process heat by integrating solar collectors, thermal energy storage, and auxiliary energy sources. This study developed and evaluated an optimization framework for HSTSs aimed at improving industrial energy efficiency under Pakistan’s climatic and operational conditions. A quantitative simulation-based research design was employed, incorporating thermodynamic modeling and advanced optimization techniques, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Model Predictive Control (MPC). Performance was assessed using key indicators such as solar fraction, exergy efficiency, fuel savings, system reliability, and levelized cost of heat. The results revealed that MPC outperformed GA and PSO across all performance metrics, achieving the highest solar fraction (71.5%), exergy efficiency (58.9%), and fuel savings (53.8%), while minimizing energy cost. Sector-wise analysis further confirmed strong applicability in textile, food, chemical, and pharmaceutical industries. The findings demonstrate that intelligent optimization significantly enhances the feasibility and effectiveness of hybrid solar thermal systems, offering a viable pathway for reducing fossil fuel dependence and improving industrial sustainability in Pakistan.