The absorption process of CO${}_2$ by ethanolamine solution is essentially a dynamic system, which is greatly affected by the power plant startup and flue gas load changes. Hence, studying the optimal control of the CO${}_2$ chemical capture process has always been an important part in academic fields. Model predictive control (MPC) is a very effective control strategy used for such process, but the most intractable problem is the lack of accurate and effective model. In this work, Aspen Plus and Aspen Plus Dynamics are used to establish the process of monoethanolamine (MEA) absorption of CO${}_2$ related models based on subspace identification. The nonlinear distribution of the system under steady-state operation is analyzed. Dynamic tests were carried out to understand the dynamic characteristics of the system under variable operating conditions. Systematic subspace identification on open-loop experimental data was performed. We designed a model predictive controller based on the identified model combined with the state-space equation using Matlab/Simulink to analyze the changes of the system under two different disturbances. The simulation results show that the control performance of the MPC algorithm is significantly better than that of the traditional proportion integral differential (PID) system, with excellent setpoint tracking ability and robustness, which improve the stability and flexibility of the system.

中文翻译：

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基于数据识别模型的MEA吸收CO2过程的模型预测控制

一氧化碳的吸收过程${}_2$由乙醇胺溶液本质上是一个动态系统，这在很大程度上受电厂启动和烟气负荷变化的影响。因此，研究CO的最佳控制${}_2$化学捕获过程一直是学术领域的重要组成部分。模型预测控制（MPC）是用于此过程的非常有效的控制策略，但最棘手的问题是缺乏准确有效的模型。在这项工作中，使用Aspen Plus和Aspen Plus Dynamics建立单乙醇胺（MEA）吸收CO的过程${}_2$基于子空间识别的相关模型。分析了系统在稳态下的非线性分布。进行了动态测试以了解系统在可变操作条件下的动态特性。对开环实验数据进行系统的子空间识别。我们使用Matlab / Simulink在已识别的模型与状态空间方程相结合的基础上，设计了模型预测控制器，以分析两种不同扰动下系统的变化。仿真结果表明，MPC算法的控制性能明显优于传统比例积分微分（PID）系统，具有出色的设定点跟踪能力和鲁棒性，提高了系统的稳定性和灵活性。