Optimization of rapeseed production using exergy analysis methodology

Abstract

The exergy analysis helps to determine the energy losses of different inputs through identifying the actual amount of energy consumed in a process. In this regard, a new approach was applied in this study to predict and optimize the rainfed production systems (RPS) and irrigated production systems (IPS) of rapeseed in the north of Iran, by using exergy analysis methodology. Two indices of cumulative degree of perfection (CDP) and renewability index (RI) were used to evaluate the sustainability of systems. The Support Vector Machine (SVM) and Multi-Objective Genetic Algorithm (MOGA) were also employed to predict and optimize the rapeseed production systems, respectively. In the study of conventional system, CDP and RI were determined to be 2.19 and 0.72, on average, respectively, in which, the values of both indices were higher in the RPS than IPS. Optimization results indicated that by reducing the consumption of irrigation water, electricity and biocides as well as increasing the use of chemical fertilizers, farmyard manure and diesel fuel, the yield increased by 24.55%. Sustainability assessment in optimal scenario revealed that CDP and RI increased to 2.75 and 0.81, respectively. As a result, under optimal conditions, the production process becomes more environmentally friendly.