ArticleOpen research Africa2025
Hybrid Thermodynamic and Machine Learning Optimization of Plate Heat Exchanger Performance for Brewery Cooling Systems in Uganda.
Article in Open research Africa, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Hybrid Intelligence for Fouling Prediction and Adaptive Clean-in-Place Optimization in Food Processing Heat Exchangers: A Systematic Review.Journal of food science · 2026Pooled it
- Hybrid Thermodynamic and Machine Learning Optimization of Plate Heat Exchanger Performance for Brewery Cooling Systems in Uganda.Open research Africa · 2025Article
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Authors and funding
5 authors.
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Abstract
Background: Efficient thermal management in brewing is critical for product quality, energy efficiency, and sustainability. At Nile Breweries Limited, Mbarara, Uganda, the Plate Heat Exchanger (PHE) cooling system has faced persistent challenges in achieving the target wort outlet temperature of 283 K (10 °C) or lower. Understanding and optimizing the thermodynamic and operational performance of this system is essential, particularly in resource-constrained Sub-Saharan production environments. Methods: An integrated optimization framework was developed by combining thermodynamic modeling, empirical diagnostics, and machine learning. Over 50 production batches were monitored using a high-resolution (1 Hz) dataset to assess short-term performance, while a longer dataset of 480 batches was analyzed for fouling trends. Statistical regression, LMTD analysis, and correlation tests were applied to evaluate thermal relationships, and a feedforward Artificial Neural Network (ANN) was trained on seven operational parameters in MATLAB/Simulink to enable predictive control. Results: Only 14.60% of production batches achieved the cooling target (95% CI: 11.2%-18.7%). Regression analysis showed a significant positive association between wort mass flow rate and outlet temperature (slope = 1.24 K/(kg/s), p < 0.001, R Conclusions: 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.