Herein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.

在此，通过 CFD 技术对空气和水之间具有非平衡热条件的气泡塔型反应器进行机械建模和模拟。此外，自适应网络（AN）训练器与模糊推理系统（FIS）的结合作为调用ANFIS的人工智能方法已经在CFD方法的优化中显示出潜力。虽然基于模糊推理系统（PSOFIS）的粒子群优化（PSO）算法的人工智能方法具有优化其他研究领域的良好背景，但没有任何关于该方法与CFD合作的研究。PSOFIS 可以减少所有困难并通过消除额外的 CFD 模拟来简化调查。事实上，在达到最佳智能后，所有预测都可以由 PSOFIS 完成，而无需 CFD 建模所需的大量计算工作。本研究的首要目标是开发用于 CFD 方法应用的 PSOFIS。第二个是比较 PSOFIS 和 ANFIS 对 CFD 结果的准确预测。在本研究中，CFD 数据由 PSOFIS 学习，用于预测气泡柱内的水速。研究输入数量、群体大小和惯性权重的值以获得最佳智能。一旦实现最佳智能，就无需在 CFD 域中进行网格细化。可以增加网格密度，并且可以通过 PSOFIS 以更少的计算量以更简单的方式完成更新的预测。为了进行强有力的验证，将 PSOFIS 在预测液体速度方面的结果与 ANFIS 的结果进行了比较。结果表明，对于相同的模糊集参数，与 ANFIS 相比，PSOFIS 预测更接近 CFD。PSOFIS 的回归数 (R) (0.98) 略高于 ANFIS (0.97)。PSOFIS 显示出网格密度从 9477 增加到 774,468 的强大潜力，以及独立于 CFD 建模的新节点的准确预测。