Data-driven prediction and thermodynamic performance assessment of industrial cooling towers using advanced machine learning algorithms

Cooling towers are an important part of the thermal system in industries, where they are used to remove unwanted heat and help maintain the proper performance of the machines. Four machine learning algorithms, namely random forest, support vector machine (SVM), decision tree, and AdaBoost are proposed in this paper for the performance forecasting of cooling towers. for the performance forecasting of cooling towers. These models were built in Python with the help of the following operational parameters: inlet water temperature (32–41°C), ambient air temperature (14–32°C), and relative humidity (35–92%). All the essential performance measures like outlet water temperature, water losses, the effectiveness, and the second law efficiency were predicted and assessed by statistical indicators such as coefficient of determination (R²), root mean square error (RMSE) and mean absolute percentage error (MAPE). The SVM algorithm had the best predictive accuracy and lowest prediction errors of all the tested models with a value of R2 of 0.985 and RMSE of 1.25 kg/s. Parametric analysis had indicated that the increase in relative humidity between 35% and 92% decreased the evaporation losses by about 55–70% and makeup water demand by about 58–68%. Thermodynamic analysis further revealed that the second-law efficiency improved by approximately 65–75% as the ambient temperature increased. The results indicate that predictive modeling with machine learning offers a useful method in the optimization of cooling tower operation and minimizing water use in industrial systems.