Since machine learning and smart methods can be used to study hydrodynamics in the bubble column reactor, it is possible to create highly intelligent bubble column reactors that have not been previously simulated and optimized them with computational fluid dynamics (CFD) methods. The previous studies considered the position of each node (in three directions) inside the bubble column reactor as the input in the artificial intelligence model. Machine learning methods have been used for processing big data related to the bubble column reactor. These big data are associated with the points inside the bubble column reactor, which represent the gas volume fraction and the fluid velocity in the x-direction. In this study, adaptive-network-based fuzzy inference system (ANFIS) was used to find out the relationship between the outputs of the bubble column reactor. The present study also intends to investigate the relationship between two outputs, namely the amount of gas in the bubble column reactor and the velocity of the fluid in the x-direction. Various parameters were investigated in this system, including the number of rules, the type of membership function, and the amount of input data. The mentioned parameters were regularly changed to find out the state where the system can achieve its intelligence. In this study, the best parameter that helped the system was the amount of data in the training process. The results showed that the lower the amount of data used in training, the better the prediction.

由于可以使用机器学习和智能方法来研究鼓泡塔反应器中的流体动力学，因此可以创建高度智能的鼓泡塔反应器，这些反应器以前尚未进行模拟，并已通过计算流体力学（CFD）方法对其进行了优化。先前的研究将气泡塔反应器内部每个节点的位置（三个方向）视为人工智能模型的输入。机器学习方法已用于处理与气泡塔反应器有关的大数据。这些大数据与气泡塔反应器内部的点相关，这些点代表x中的气体体积分数和流体速度-方向。在这项研究中，基于自适应网络的模糊推理系统（ANFIS）用于找出鼓泡塔反应器输出之间的关系。本研究还打算研究两个输出之间的关系，即鼓泡塔反应器中的气体量和x中流体的速度。-方向。在此系统中研究了各种参数，包括规则数量，隶属函数类型和输入数据量。定期更改上述参数，以找出系统可以实现其智能的状态。在这项研究中，帮助系统的最佳参数是训练过程中的数据量。结果表明，训练中使用的数据量越少，预测效果越好。