Artificial neural network was introduced for modeling and verifying the relative significance of operational variables to attain the best possible average pore diameter (D_p) of the mesoporous TiO₂‐ZnO nanocrystalline synthesized by microwave‐assisted hydrothermal method. To optimize the system, the autonomous variables calcination temperature and concentrations of D‐glucose, polyethylene glycol, and Zn‐Ti were employed as input factors, D_p was designated as output. D_p was achieved from the implementation of the experimental model of the variables by the Barrett‐Joyner‐Halenda method. The network was trained by IBP, BBP, LM, QP, and GA algorithms as a model. The BBP‐4‐7‐1 model provided the optimum numerical properties among four other algorithms. Furthermore, the calcination temperature acted as the most efficient variable whilst D‐glucose concentration showed the least effect. By validating the microwave‐assisted hydrothermal conditions, in situ polymerization of poly(sodium 4‐styrene sulfonate) was introduced to activate mesopore walls by –SO₃H functional groups used for ester production by transesterification of used cooking oil, resulting in a methyl ester content of 97.4 % and excellent reusability for ten sequential reactions without any further regeneration.

中文翻译：

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人工神经网络模型估算介孔TiO2-ZnO纳米晶的孔隙率

引入了人工神经网络来建模和验证操作变量的相对重要性，以达到微波辅助水热法合成的介孔TiO ₂ -ZnO纳米晶体的最佳可能的平均孔径（D _p）。为了优化系统，将煅烧温度和D-葡萄糖，聚乙二醇和Zn-Ti浓度的自主变量用作输入因子，将D _p指定为输出。d _p通过使用Barrett-Joyner-Halenda方法实施变量的实验模型来实现。该网络由IBP，BBP，LM，QP和GA算法作为模型进行了训练。BBP‐4‐7-1‐1模型在其他四种算法中提供了最佳的数值特性。此外，煅烧温度是最有效的变量，而D-葡萄糖浓度的影响最小。通过验证微波辅助的水热条件，引入了聚（4-苯乙烯磺酸钠）的原位聚合反应，以–SO ₃活化中孔壁。通过对使用过的食用油进行酯交换反应而用于酯生产的H官能团，其甲酯含量为97.4％，十次连续反应具有出色的可重复使用性，而无需任何进一步的再生。