Modelling and optimising the impact of process variables on brown rice quality and overall energy consumption

Abstract

This study aimed to optimise the impact of process variables on brown rice quality (brown rice recovery and head brown rice) and overall energy consumption. The applicability of predictive polynomial regression analysis (PRA) and artificial neural network (ANN) models were evaluated. Process variables [paddy moisture content (12-16%), soaking time (10-16 h), steaming time (20-30 min) and soaking temperature (65-75°C)] were interacted using response surface methodology and their impact on brown rice quality and overall energy consumption were determined. The influence of process variables differs significantly with brown rice recovery and head brown rice. However, a decrease in soaking temperature and paddy moisture content was observed to increase overall energy consumption. ANN shows better predictive accuracy than the PRA model. Optimum conditions that can guarantee maximum quality and minimum overall energy consumption was established at 80°C soaking temperature, 7 h soaking time, 35 min steaming time and 14% paddy moisture content.