First, the effects of ultrasound-related variables on the crystallinity and particle size of synthesised SAPO-34 catalysts were modelled using a genetic programming (GP) method. The results confirm that GP has good predictive power. Secondly, optimisation of the ultrasound parameters was considered using a genetic algorithm (GA) to obtain SAPO-34 catalysts with high crystallinity and minimum particle size for the best performance in the methanol to light olefins process. The GP models were used as the fitness functions inside the GA. Finally, the optimum solution was validated experimentally and the results indicate that there is a good agreement between experimental and predicted values.

中文翻译：

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进化算法在合成 SAPO-34 催化剂上超声相关参数建模和优化中的应用：结晶度和粒度

首先，超声相关变量对合成 SAPO-34 催化剂的结晶度和粒度的影响使用遗传编程 (GP) 方法进行建模。结果证实GP具有良好的预测能力。其次，考虑使用遗传算法（GA）优化超声参数以获得具有高结晶度和最小粒径的 SAPO-34 催化剂，以在甲醇制轻烯烃过程中获得最佳性能。GP 模型被用作 GA 内部的适应度函数。最后，对最优解进行了实验验证，结果表明实验值和预测值之间存在良好的一致性。