High free fatty acids (FFA) content in oils poses challenges such as soap formation and difficulty in the separation of by-products in direct transesterification of oil to biodiesel, which is of environmental concern and also increases the cost of production. Thus, in this study, the ferric sulfate-catalyzed esterification of neem seed oil (NSO) with an FFA of 5.84% was investigated to reduce it to the recommended level of ≤1%. The esterification process for the NSO was modeled using response surface methodology (RSM) and artificial neural network (ANN). The effect of the pertinent process input variables viz. methanol/NSO molar ratio (10:1–30:1), ferric sulfate dosage (2–6 wt%), and reaction time (30–90 min) and their interactions on the reduction of the FFA of the NSO, were examined using Box Behnken design. The optimal condition for the process for reducing the FFA content of the oil was established using RSM and ANN-genetic algorithm (ANN-GA). The results showed that the models developed described the process accurately with the coefficient of determination (R²) of 0.9656 and 0.9908 and the mean relative percent deviation (MRPD) of 6.5 and 2.9% for RSM and ANN, respectively. The ANN-GA established the optimum reduction of FFA of 0.58% with methanol/NSO molar ratio of 18.51, ferric sulfate dosage of 6 wt%, and reaction time of 62.8 min as against the corresponding values of 0.62% FFA, 23.5, 5.03, and 75 min established by the RSM. Based on the statistics considered in the study, ANN and GA outperformed RSM in modeling and optimization of the NSO esterification process.

油中高含量的游离脂肪酸（FFA）带来了诸如皂类形成的挑战，以及在将油直接酯交换为生物柴油时难以分离副产物的问题，这是环境问题，也增加了生产成本。因此，在这项研究中，研究了FFA为5.84％的印度em种子油（NSO）的硫酸铁催化酯化反应，以将其降低至推荐水平≤1％。使用响应面方法（RSM）和人工神经网络（ANN）对NSO的酯化过程进行建模。相关过程输入变量的影响即研究了甲醇/ NSO摩尔比（10：1–30：1），硫酸铁用量（2–6 wt％）和反应时间（30–90分钟）以及它们与减少NSO FFA的相互作用。使用Box Behnken设计。使用RSM和ANN遗传算法（ANN-GA）建立了降低油中FFA含量的最佳条件。结果表明，所开发的模型以确定系数（R ²）分别为0.9656和0.9908，RSM和ANN的平均相对百分比偏差（MRPD）为6.5和2.9％。ANN-GA确定了FFA的最佳降低为0.58％，甲醇/ NSO摩尔比为18.51，硫酸铁用量为6 wt％，反应时间为62.8分钟，而FFA的相应值为0.62％，23.5、5.03，和RSM建立的75分钟。根据研究中的统计数据，在NSO酯化过程的建模和优化中，人工神经网络和遗传算法优于RSM。