EXERGY PARAMETRIC ANALYSIS AND PREDICTION OF TURMERIC RHIZOME SLICES DRYING USING NEURO-FUZZY, NEURAL-NETWORK AND REGRESSION TECHNIQUES

This study presents exergy parametric analysis and prediction of turmeric rhizome drying using first and second law of thermodynamics as well as soft-computing techniques. The drying experiments were conducted at inlet drying temperature: (40-650C), air velocity (1.5-3m/s), drying time: (30-240 minutes) and sample thickness: (2-5mm). The Neuro-Fuzzy Exhaustive Search (NFES) parametric analysis results revealed that drying time (RMSE=0.0031), temperature (RMSE=0.096), temperature (RMSE=0.046) and sample thickness (RMSE=0.748) are the most single relevant parameters for Exergy Loss (EL), Exergy Efficiency (EE), Exergetic Improvement Potential (EIP) and Sustainability Index (SI) respectively. Whereas temperature-time (RMSE=0.0031), temperature-velocity (RMSE=0.0945), temperature-time (RMSE=0.046) and time-thickness (RMSE=0.7534) are the most important two-input combinations for EL, EE, EIP and SI correspondingly. NFES also revealed that time-temperature-velocity (RMSE=0.004), temperature-velocity-thickness (RMSE=0.082), time-temperature-velocity (RMSE=0.0436) and time-temperature-thickness (RMSE=0.758) are the three-input significant combination for EL, EE, EIP and SI respectively. The ANN results show that two-input combination architectures gave the highest R2 with minimum RMSE for the exergy-sustainability indicators. Therefore, this study shows that NFES and ANN are reliable tools for the analyses of turmeric rhizome drying thermo-sustainability indicators