A systematic review of thermodynamic modeling and machine learning integration for optimizing plate heat exchanger performance in Uganda’s brewing industry - Edgar, Byamanywoha; Eze, Val Hyginus Udoka; Saadelnour, Saadelnour Abdueljabbar Adam; Yinka, Sanusi; Bubu, Pius Erheyovwe (Frontiers in Mechanical Engineering, 2025)
IntroductionPlate Heat Exchangers (PHEs) play a crucial role in industrial thermal processes, particularly in the brewing industry, where precise temperature regulation influences fermentation efficiency and product quality. In Uganda, PHE performance is constrained by fouling, variable thermal loads, and resource limitations. These challenges highlight the need for advanced optimization approaches tailored to tropical climates and resource-limited settings.MethodsA systematic review was conducted to evaluate the use of thermodynamic modeling and machine learning (ML) for optimizing PHE operation in industrial applications. A total of 199 studies were screened, of which 112 met predefined methodological and quality criteria. Extracted data were synthesized to compare traditional approaches with hybrid physical-ML models, including Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Genetic Algorithms (GA). Performance indicators assessed included predictive accuracy, energy efficiency, fouling behavior, and operational responsiveness.ResultsHybrid models integrating thermodynamic principles with ML techniques consistently outperformed conventional modeling approaches. Significant gains were observed in predictive accuracy across included studies, although effect sizes varied due to dataset diversity and differing evaluation metrics. Real-time fouling prediction using ML contributed to a 22% reduction in maintenance costs and a 15% decrease in operational downtime. Implementations of digital twin architectures and adaptive control algorithms achieved an 18% improvement in energy efficiency and enhanced system responsiveness by up to 30% under dynamic thermal load conditions.DiscussionFindings demonstrate the strong potential of combining thermodynamic modeling with AI-driven methodologies to enhance PHE performance in the brewing sector and related industries. While substantial technological improvements have been reported, context-specific barriers persist, particularly the adaptation of advanced models to tropical environmental conditions and the cost-effective integration of renewable energy sources. Addressing these challenges will be essential for unlocking the full potential of self-optimizing PHE systems that promote energy efficiency, product quality, and sustainable industrial growth in regions such as Uganda.