Research On Modeling And Optimization Control Technology Of Post-Combustion CO₂ Capture System

Nowadays,a series of ecological problems such as global warming and sea level rise caused by CO₂-based greenhouse gas emissions are getting worse.Under the background of energy conservation,emission reduction and green development,Carbon Capture Utilization and Storage（CCUS）technology is adopted for power plants that rely on fossil fuels to generate electricity has become urgent,and Post-Pombustion CO₂ Papture（PCC）based on Monoethanolamine（MEA）solution is recognized as a highly efficient one of the carbon capture technologies.Research related to modeling and optimal control of PCC systems,which have the characteristics of nonlinearity,large inertia,strong coupling between multiple variables,and high operating and operational costs and energy consumption,is also becoming increasingly popular.It is important to be familiar with the process flow of PCC system,correctly grasp the dynamic characteristic change law of PCC system,and adopt advanced optimal control strategy to ensure the flexible operation of the system,carry out energy saving and emission reduction,and take the green and low-carbon sustainable development road.Based on the above background,the main research contents of this thesis include:（1）A complete model of the PCC system was built based on Aspen Plus and Aspen Plus Dynamics software for steady-state design based on the kinetic behavior of the PCC system.On the basis of this model,two sets of dynamic tests with step changes of flue gas flow and flue gas content at typical operating points were performed to obtain the input and output experimental data,and the dynamic characteristics of the system and the variation patterns among its main variables were analyzed qualitatively.（2）To address the problem of CO₂ capture efficiency affected by flue gas fluctuations in PCC system,this thesis proposes DMC-PID cascade control strategy.Firstly,the system model of controller design is obtained by implementing the subspace identification method using the input and output data,and then the PID controller is used as the inner loop in the control system to quickly eliminate the flue gas disturbance,and the DMC controller is used in the outer loop to realize the optimal regulation of the lean liquid flow under the constraint conditions to achieve the optimal operation of the PCC system.The simulation verifies that the DMC-PID series control strategy applied to the PCC system has good dynamic performance such as small overshoot,small steady-state error and short response time when optimized operation is achieved.（3）According to the operation requirements of PCC system and the coupling characteristics between multivariable,this thesis applies the multivariable Model Predictive Control（MPC）strategy to the system.Firstly,three typical operating point models of the system are selected,and different MPC controllers are designed for each linearized model.Then,the lean solvent flow and extraction steam flow are used as operating variables.The simulation results show that in the case of interference of flue gas flow,the system can fully meet the constraints and optimal control requirements for CO2 capture rate and reboiler temperature.（4）Aiming at the problem of strong nonlinearity of PCC system under large-scale variable working conditions,based on the MPC controller designed according to three working conditions,this thesis constructs the Gain Scheduling Model Predictive Control（GS-MPC）strategy.The simulation experiment shows that GS-MPC control can obtain better control effect than MPC and realize the control requirements of three input and two output of PCC system,it is of great significance to the efficient operation of the whole system and energy conservation and emission reduction.