The conversion of sorrel oil to biodiesel through transesterification was conducted in the presence of calcined kola nut husk pod ash as a base heterogeneous catalyst. Thus, to predict the biodiesel production yield, two models based on neural intelligence and neuro‐fuzzy techniques were established. The predictive capability and accuracy of the models were compared using various statistics. The neuro‐fuzzy‐based model (adaptive neuro‐fuzzy inference system, ANFIS) obtained for the transesterification process had a lower (0.32%) mean relative percent deviation (MRPD) and a higher coefficient of determination—R ² (0.9991) compared to the neural intelligence‐based model (artificial neural network, ANN) with MRPD of 0.42% and R ² of 0.9971. Also, the models developed were coupled with genetic algorithm (GA) in order to maximize the sorrel oil biodiesel (SOB) yield at optimum values of the process input parameters. SOB yield of >99.0 wt% was obtained when both developed models were subjected to optimization. The results of the process modeling confirm that neuro‐fuzzy model performed slightly better than neural intelligence model. The sensitivity analysis performed on both models shows that reaction time was the most important input variable while other input variables could not be neglected. The characteristics of the synthesized SOB demonstrate that it satisfied the biodiesel standard limits.

中文翻译：

---

基于可乐果荚果皮的碱多相催化剂优化浆油生物柴油的生产：神经智能遗传算法与神经模糊遗传算法

通过酯交换反应将or浆油转化为生物柴油是在煅烧的可乐果壳荚果灰作为基础非均相催化剂的情况下进行的。因此，为了预测生物柴油的产量，建立了两个基于神经智能和神经模糊技术的模型。使用各种统计数据比较了模型的预测能力和准确性。与酯交换过程相比，基于神经模糊的模型（自适应神经模糊推理系统，ANFIS）具有较低的（0.32％）平均相对百分比偏差（MRPD）和较高的确定系数-R ²（0.9991）基于神经智能的模型（人工神经网络，ANN），MRPD为0.42％，R ²为0.9971。此外，开发的模型与遗传算法（GA）结合在一起，以在工艺输入参数的最佳值下最大化浆生物柴油（SOB）的产量。当两个开发的模型都进行优化时，SOB收率> 99.0 wt％。过程建模的结果证实，神经模糊模型的性能略好于神经智能模型。在两个模型上进行的敏感性分析表明，反应时间是最重要的输入变量，而其他输入变量则不能忽略。合成的SOB的特性表明它满足了生物柴油标准限值。