Vegetable oils are a very important feedstock for many industries such as biofuels. There is the need to source for novel and underexploited plant oilseeds to meet the world demand for oils. Thus, the extraction of oil from Hura crepitans (sandbox) seeds was conducted using the solvent extraction method. Modeling of the extraction process was carried out using response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). The effects of the nature of the solvent (non-polar (n-hexane) and polar (acetone and ethyl acetate)), solid-solvent ratio (0.1–0.3 g/mL), extraction time (2–6 h), and their interactions on the oil yield were investigated using the D-optimal design technique. Performance assessment of the developed models was carried out to check their effectiveness in predicting the H. crepitans seed oil (HCSO) yield using various fit statistics. The coefficient of determination (R²) observed for the RSM and ANFIS models was 0.9720 and 0.9988, respectively, with corresponding mean relative percent deviation (MRPD) of 2.50 and 0.37%. Maximum HCSO yield of 62.95 wt% was achieved by ANFIS coupled with genetic algorithm (GA) using 0.1 g/mL solid-solvent ratio, extraction time of 4.19 h, and acetone, while maximum HCSO yield of 62.50 wt% was observed by RSM with a solid-solvent ratio of 0.1 g/mL, extraction time of 4.04 h, and acetone. Characteristics of the HCSO indicated that it could serve as a good feedstock for the production of oleochemicals such as biodiesel. The results obtained in this study demonstrated that ANFIS is marginally superior to RSM in the modeling of the HCSO extraction process, while GA was slightly better than the numerical tool of RSM in the optimization of the process.

植物油是生物燃料等许多行业非常重要的原料。为了满足世界对石油的需求，需要寻找新的和开发不足的植物油籽。因此，使用溶剂提取方法从Hura crepitans（沙盒）种子中提取油。使用响应表面方法（RSM）和自适应神经模糊推理系统（ANFIS）对提取过程进行建模。溶剂性质的影响（非极性（n-D最佳设计法研究了正己烷和极性溶剂（丙酮和乙酸乙酯），固溶比（0.1–0.3 g / mL），萃取时间（2–6 h）及其对油收率的影响技术。对开发的模型进行了性能评估，以检验它们在预测H方面的有效性。crepitans使用各种拟合统计籽油（HCSO）产率。测定系数（R ²）对RSM和ANFIS模型的观察值分别为0.9720和0.9988，相应的平均相对百分偏差（MRPD）为2.50和0.37％。通过ANFIS和遗传算法（GA）结合使用0.1 g / mL固-溶剂比，4.19 h的萃取时间和丙酮，获得了HCSO的最大产率为62.95 wt％，而通过RSM观察到的最大HCSO产率为62.50 wt％。固溶剂比为0.1 g / mL，萃取时间为4.04 h，使用丙酮。HCSO的特征表明，它可以用作生产油脂化合物（如生物柴油）的良好原料。在这项研究中获得的结果表明，在HCSO萃取过程的建模中，ANFIS略胜于RSM，而在过程优化方面，GA略优于RSM的数值工具。