Machine Learning and Computational Fluid Dynamics Applications for the Modeling of the Decompression‐Induced Innovative Steaming Treatment of Paddy at the Bulk Level

ABSTRACT Instant controlled pressure drop (ICPD) treatment is a novel approach for the quality improvement of milled rice. In this investigation, temperature and moisture profiling for the bulk‐level ICPD treatment of paddy and its effect on the gelatinization kinetics of milled rice were studied using machine learning approaches, namely, artificial neural network (ANN), adaptive neuro‐fuzzy interface system (ANFIS), and computational fluid dynamics (CFD). For CFD‐based temperature profiles, the coefficient of determination ( R 2 ) values ranged from 0.91 to 0.95, while for moisture profiles, the values were between 0.97 and 0.99. In case of ANN predictions, the 2‐5‐3 architecture showed adequate performance based on an R 2 value of 0.99 and a mean squared error (MSE) of 0.514. For ANFIS‐based predictions, the R 2 values exceeded 0.99, while the MSE values ranged from 0.0034 to 0.0084. The 2‐3‐3‐1 ANFIS architecture with gaussmf membership function and nine fuzzy rules showed the best results. These results indicated that the ANFIS‐based model exhibited more accuracy as compared to ANN‐ and CFD‐based predictions. In addition, the broken rice percentage (BRP) was determined to assess the impact of temperature and moisture profiles on the quality of milled rice, showing a decrease in BRP with the increase of treatment time. The parboiled rice treated for 40 s had the highest quality by considering BRP obtained from all the nodes.