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.

火用分析通过确定过程中消耗的实际能量来帮助确定不同输入的能量损失。在这方面，本研究采用了一种新方法，通过使用火用分析方法来预测和优化伊朗北部油菜的雨育生产系统（RPS）和灌溉生产系统（IPS）。累积完善度（CDP）和可更新性指数（RI）的两个指标用于评估系统的可持续性。支持向量机（SVM）和多目标遗传算法（MOGA）也分别用于预测和优化油菜籽生产系统。在常规系统的研究中，CDP和RI的平均值分别确定为2.19和0.72，其中RPS中的两个指数值均高于IPS。优化结果表明，通过减少灌溉水，电力和杀生物剂的消耗，以及增加化肥，农家粪肥和柴油的使用，单产提高了24.55％。在最佳情况下的可持续性评估表明，CDP和RI分别增加到2.75和0.81。结果，在最佳条件下，生产过程变得更加环保。