AI-Driven Optimization and Explainable Analytics for Sustainable Cooling Tower Performance

With the advent of advanced-for both continuous and discrete optimization-predictive models in recent years, the optimization of cooling tower performance has played a more significant role. SHAP, LIME, PDP, PID, and PSO together provide a holistic methodology that improves the system’s efficiency. SHAP is good for interpretability via feature importance, dependence, waterfall, and decision plots, but it does not help with interactions (that LIME and PDP complements) Thirdly, the PID controllers have dynamic characteristics on the system’s input/output parameters, and the PSO method can realize global optimization. In short, the behavioral approach of multi-scaling is to view large, complex systems as a collection of smaller systems that we can manipulate to improve performance.