In the present research, artificial neural network (ANN) modelling was utilized to determine the relative importance of effective variables to achieve optimum specific surface areas of a synthesized catalyst. Initially, carbonaceous nanocrystalline mesoporous NiO core–shell solid sphere composites were produced by applying incomplete carbonized glucose (ICG) as the pore directing agent and polyethylene glycol (PEG; 4000) as the surfactant via a hydrothermal-assisted method. The Brunauer–Emmett–Teller (BET) model was applied to ascertain the textural characteristics of the as-prepared mesoporous NiO catalyst. The effects of several key parameters such as metal ratio, surfactant and template concentrations, and calcination temperature on the prediction of the surface areas of the as-synthesized catalyst were evaluated. In order to verify the optimum hydrothermal fabrication conditions, ANN was trained over five different algorithms (QP, BBP, IBP, LM, and GA). Among five different algorithms, LM-4-7-1 representing 4 nodes in the input layer, 7 nodes in the hidden layer, and 1 node in the output layer was verified as the optimum model due to its optimum numerical properties. According to the modelling study, the calcination temperature demonstrated the most effective parameter, while the ICG concentration indicated the least effect. By verifying the optimum hydrothermal fabrication conditions, the thermal decomposition of ammonium sulphate (TDAS) was applied to the functionalized surface areas and mesoporous walls by –SO₃H functional groups. In addition, the catalytic performance and reusability of the produced mesoporous SO₃H–NiO catalyst were evaluated via the transesterification of waste cooking palm oil, resulting in a methyl ester content of 97.4% and excellent stability for nine consecutive transesterification reactions without additional treatments.

中文翻译：

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用于介孔 NiO 纳米晶多变量优化的人工神经网络的实现：生物柴油应用

在本研究中，人工神经网络 (ANN) 建模用于确定有效变量的相对重要性，以实现合成催化剂的最佳比表面积。最初，通过使用不完全碳化葡萄糖（ICG）作为孔导向剂和聚乙二醇（PEG; 4000）作为表面活性剂，制备碳质纳米晶介孔NiO 核壳实心球复合材料。一种水热辅助方法。应用 Brunauer-Emmett-Teller (BET) 模型来确定所制备的介孔 NiO 催化剂的结构特征。评估了金属比例、表面活性剂和模板浓度以及煅烧温度等几个关键参数对预测合成催化剂表面积的影响。为了验证最佳水热制造条件，ANN 接受了五种不同算法（QP、BBP、IBP、LM 和 GA）的训练。在五种不同的算法中，LM-4-7-1 表示输入层中的 4 个节点、隐藏层中的 7 个节点和输出层中的 1 个节点，由于其最优的数值特性，被验证为最优模型。根据建模研究，煅烧温度表现出最有效的参数，而 ICG 浓度表明影响最小。通过验证最佳水热制造条件，硫酸铵 (TDAS) 的热分解被应用于功能化表面区域和介孔壁 - SO₃ H 官能团。此外，通过对废弃的食用棕榈油进行酯交换，_{对所生产的介孔 SO 3} H-NiO 催化剂的催化性能和可重复使用性进行了评估