International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 17, No. 6, 8 Dec. 2025
Cover page and Table of Contents: PDF (size: 1923KB)
Improving Boiler Performance Using Machine Learning: A Predictive Approach to Steam Demand Optimization
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Author(s)
Hermanus M. Scholtz
1,*
Hadi Harb
1
Index Terms
Steam Predictions, Machine Learning, Data Driven Approaches, Boiler Efficiency, Extra Trees
Abstract
This paper explores the application of machine learning to enhance boiler efficiency and cost management at a Uranium Mine in Africa. The current steam control system relies on a feedforward loop, which adjusts based on slurry flow into the leach tank, and a feedback loop, which regulates steam to a setpoint. However, this method is inefficient, as it does not account for slurry temperature variations, leading to unstable control and suboptimal steam usage. To address these limitations, this study applies the Extra Trees algorithm to predict steam demand more accurately. The data-driven approach achieves a 6.6% reduction in steam consumption and a 2% decrease in heavy fuel oil (HFO) usage, resulting in cost savings and improved sustainability. Based on multiple evaluation metrics, the Extra Trees model proved to be the most accurate and consistent algorithm, achieving a 96.67% R-squared score and a Root Mean Square Error (RMSE) of 1131.37 kg, indicating minimal deviation between actual and predicted values. The findings highlight the shortcomings of traditional control strategies under fluctuating conditions and demonstrate how advanced feature engineering enhances predictive accuracy. By integrating machine learning into operational workflows, this research provides actionable insights to improve boiler performance, process stability, and overall efficiency.
Cite This Paper
Hermanus M. Scholtz, Hadi Harb, "Improving Boiler Performance Using Machine Learning: A Predictive Approach to Steam Demand Optimization", International Journal of Intelligent Systems and Applications(IJISA), Vol.17, No.6, pp.1-15, 2025. DOI:10.5815/ijisa.2025.06.01
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