甲烷化反应器的仿真模型可以指导甲烷化工艺流程的优化，具有重要的研究意义。然而甲烷化反应器仿真模型的构建涉及反应器建模与反应动力学建模两部分，两个部分相互耦合，如果忽略反应器传递作用单独考虑动力学模型或不关注动力学仅从化学平衡的角度求解反应器模型会导致仿真精度较低，难以有效指导流程优化。采用多目标优化算法对Aspen Plus中建立的甲烷化反应器模型的动力学参数进行辨识，可以通过较少的数据点位实现高精度的动力学方程组参数辨识，该方法同时考虑反应器作用和反应动力学，可以有效解决复杂反应过程中的动力学方程组参数辨识问题。结果表明，甲烷化反应器流程仿真模型的多目标参数辨识方法可以使CO转化率和CH4选择性仿真结果的均方根误差分别降低至1.96%和4.59%，低于目前已有的动力学模型。

The simulation model of the methanation reactor can guide the optimization of the methanation process, which is of important research significance. However, the construction of the methanation reactor simulation model involves two parts: reactor modeling and reaction kinetics modeling, and the two models are coupled with each other. Neglecting the reactor transferring role and considering the kinetic model alone or solving the reactor model from the chemical equilibrium point of view without focusing on the kinetics will lead to low simulation accuracy, which makes it difficult to guide the optimization of the process effectively. The multi-objective optimization algorithm is used to identify the kinetic parameters of the methanation reactor model built in Aspen Plus, which can achieve high-precision identification of the parameters of the kinetic equation set with fewer data points, and the method can effectively solve the problem of identifying the parameters of the kinetic equation set in the complex reaction process by considering the reactor action and reaction kinetics at the same time. The results show that the multi-objective parameter identification method of the methanation reactor process simulation model can reduce the root-mean-square errors of CO conversion and CH4 selectivity simulation results to 1.96% and 4.59%, respectively, which are lower than those of the existing kinetic models.