Computational intelligence applications in predicting energy consumption, greenhouse gas emissions, and drying performance of hybrid infrared dryer

Efficient dehydration of heat-sensitive crops remains a major challenge due to the trade-off between drying time, energy demand, and product quality. This study investigated the hybrid infrared-hot air drying of Moringa oleifera leaves in a continuous conveyor-belt dryer, focusing on the joint effects of air temperature (35-55 °C), airflow velocity (0.3-1.0 m/s), and infrared intensity (0.08-0.15 W/cm²). Experimental results demonstrated that higher air temperatures and infrared intensities significantly reduced drying time (from 210 min at 35 °C, 0.08 W/cm², and 1.0 m/s to 95 min at 55 °C, 0.15 W/cm², and 0.3 m/s) and lowered specific energy consumption (SEC) from 5.2 to 3.9 MJ/kg. In contrast, increasing airflow velocity extended the drying period and higher SEC by up to 18%. The maximum thermal and drying efficiencies reached 42.96% and 27.0%, respectively, under optimized conditions. Among eleven thin-layer drying models evaluated, the Midilli-Kucuk model achieved the best performance (R² > 0.999; RMSE < 0.0003). Artificial intelligence (ANN, PCA, and SOM) further enhanced process interpretation, confirming that high infrared intensity and air temperature minimized SEC while maximizing energy efficiency. An environmental assessment revealed that optimized hybrid drying reduced CO₂ emissions by approximately 20% compared to conventional hot-air drying, corresponding to a carbon mitigation potential of 0.45-0.52 kg CO₂ per kg dried product. These findings establish a predictive and sustainable framework for intelligent hybrid drying, offering industrial relevance for energy-efficient processing of moringa and other heat-sensitive crops.