Abstract
Heat exchangers frequently suffer from fouling, which is the accumulation of unwanted deposits on heat-transfer surfaces. This issue reduces thermal performance, increases pressure drop, and raises energy use and operating costs. Predicting fouling resistance remains challenging in process engineering, yet it is important for monitoring, maintenance planning, and mitigation actions that reduce economic losses and environmental impacts. Symbolic regression (SR) is a machine learning approach that searches for an explicit mathematical expression that best represents the relationship between process inputs and a target output. Unlike many black-box models, SR can capture nonlinear behavior while producing compact, interpretable equations that are easier to deploy and analyze in industrial settings. In this work, a methodology to rapidly obtain algebraic models for fouling resistance in industrial heat exchangers using SR was proposed. Plant measurements of hot- and cold-side flow rates and inlet/outlet temperatures were transformed into dimensionless numbers, which were used as model inputs to predict fouling resistance. SR was applied to operational data from industrial heat exchangers, achieving coefficients of determination (R²) above 0.95 and 0.87 for the training and validation datasets, respectively, with low prediction errors. A selected equation was then transferred to a second industrial heat exchanger by re-estimating a reduced set of parameters using plant data. Validation on data not used during parameter estimation showed that the adapted model maintained high predictive accuracy, with R² values of 0.96 and 0.91 for training and validation, respectively. Overall, the proposed approach provided an efficient and interpretable framework for fouling resistance modeling and supported practical deployment across similar units.