Physics-Guided Bioinspired Multi-Objective Optimization for Explainable Drying-Stage Identification in an R290/R600a...

Integrating low-global-warming-potential refrigerants, waste-heat recovery, and intelligent process monitoring offers a promising route to energy-efficient drying systems. This study experimentally compares the natural refrigerants R290 and R600a in a vapor-compression refrigeration system and utilizes condenser waste heat for apple drying. Refrigeration tests were conducted at an evaporator temperature of -16 °C and condenser temperatures of 25, 30, 35, and 40 °C. R290 provided 73% higher cooling capacity and 59% greater condenser heat rejection than R600a, while reducing the drying time from 300 to 265 min. Conversely, R600a exhibited lower specific energy consumption and a higher specific moisture extraction rate. To identify drying progress without continuous product weighing, a physics-guided bioinspired multi-objective evolutionary algorithm, termed Physics-Guided Ordinal Chaotic Rule-Based SPEA2 (P-CRb-SPEA2), was developed. The proposed framework integrates Pareto-based evolutionary search, chaotic initialization, ordinal error, physical consistency, and rule complexity to extract interpretable IF-THEN rules from six thermal and environmental features. Ten-fold cross-validation showed physically consistent, interpretable drying-stage classification, with T₁₀ and T₈ emerging as dominant features for R290 and R600a, respectively. The proposed approach demonstrates the potential of bioinspired evolutionary intelligence for explainable monitoring of integrated refrigeration-drying energy systems.