Hybrid Thermodynamic and Machine Learning Optimization of Plate Heat Exchanger Performance for Brewery Cooling Systems in Uganda

The study highlights a significant performance shortfall in the brewery's cooling system, driven primarily by throughput-efficiency trade-offs, diminishing thermal returns, and progressive fouling. By integrating classical thermodynamic analysis with machine learning, the research demonstrates a robust and scalable framework for enhancing energy efficiency and adaptive process control in brewing and other energy-intensive industries. This work advances the application of intelligent thermal management strategies in Sub-Saharan industrial contexts, with future research recommended in real-time fouling detection, economic optimization of flow control, and hybrid predictive modeling for dynamic production environments.