Machine Learning-Based Surrogate Modeling and Genetic Algorithm Optimization of Industrial Spray Drying Operations

Abstract

Industrial spray drying is a complex nonlinear process characterized by strong interactions among thermal, mechanical, environmental, and temporal variables, making accurate prediction and operational optimization challenging. This study proposes a hybrid machine learning–evolutionary optimization framework for predictive modeling and intelligent operational optimization in industrial spray drying systems. The framework integrates Random Forest Regression (RFR) for nonlinear process prediction with a Genetic Algorithm (GA) for adaptive exploration of optimal operating conditions under industrial constraints. The RFR model achieved strong predictive performance, with a coefficient of determination of R² = 0.9144 and stable generalization performance (cross-validation R² = 0.8427 ± 0.2027), demonstrating robustness under dynamic industrial variability. Feature importance analysis revealed gas flow and temporal dynamics as the dominant process drivers, enhancing interpretability and supporting operational decision-making. The GA-based optimization identified operating configurations that improved process efficiency by approximately 5.91% compared to baseline conditions while maintaining operational feasibility. The results demonstrate that integrating ensemble learning with evolutionary optimization provides both high predictive accuracy and actionable optimization capability for complex industrial systems. Furthermore, the proposed surrogate-assisted framework reduces computational dependency on physical experimentation by enabling rapid evaluation of candidate operating conditions through data-driven intelligence. The study contributes a deployable data-driven optimization architecture for intelligent and energy-efficient manufacturing operations within Industry 4.0 environments.