Economic Model Predictive Control for Post-Combustion CO2 Capture System Based on MEA

Chenbin Ma,Wenzhao Zhang,Aimin An,Yu Zheng

doi:10.3390/en14238160

Abstract

For the post-combustion CO2 capture (PCC) system, the time variability of the economic performance is key to the production process of such an actual industrial process. However, the performance index used by the conventional model predictive control (MPC) does not reflect the economy of the production process, so the economic cost function is used instead of the traditional performance index to measure the economy of the production process. In this paper, a complete dynamic model of the PCC system is constructed in Aspen Plus Dynamics. The effectiveness of the model is verified by dynamic testing; subspace identification is carried out using experimental data, a state-space equation between flue gas flow and lean solvent flow; the CO2 capture rate is obtained; and dynamic models and control algorithm models of accused objects are established in Matlab/Simulink. Under the background of the environmental protection policy, an economic model predictive control (EMPC) strategy is proposed to manipulate the PCC system through seeking the optimal function of the economic performance, and the system is guaranteed to operate under the economic optimal and excellent quality of the MPC control strategy. The simulation results verify the effectiveness of the proposed method.

Highlights

As one of the highest concentrations of greenhouse gases in the atmosphere, carbon dioxide (CO2 ) has caused global climate change, which has become one of the major environmental problems that humanity faces at present
The carbon emission index of the system and the energy consumption index of lean solvent are taken into account in the post-combustion CO2 capture (PCC) system, and the economic optimization control of the carbon capture system can be realized under the condition of terminal constraints; the employed economic model predictive control (EMPC) controller can dynamically and flexibly adjust the relationship between its own carbon emission reduction function and lean solvent energy consumption
Through three simulation experiment scenarios, the economic performance of the proposed EMPC is verified, and the economic optimization control of the carbon capture system is realized; Based on the subspace identification method, the state space model of a PCC process based on MEA is identified on the Aspen Plus platform, which produces the identification data

Summary

Introduction

As one of the highest concentrations of greenhouse gases in the atmosphere, carbon dioxide (CO2 ) has caused global climate change, which has become one of the major environmental problems that humanity faces at present. By constructing a model of the post-combustion CO2 capture system in Aspen Plus software, the simulation study of the capture system under different absorbents, absorption towers and de-absorption towers is carried out; the economic performance under the optimal process parameters selected is analyzed; and the design scheme is obtained in order to make the carbon capture rate the lowest it can be [7,8]. A state space model based on the subspace identification method is implemented to obtain the predictive model and the control of a post-combustion CO2 capture process based on MEA, consisting of an absorber, a stripper, a heat exchanger and a reboiler. Concluding remarks are presented at the end of this paper

Carbon Capture Technology and Main Unit Models

Algorithm of Economic Model Predictive Control

EMPC for Post-Combustion CO2 Capture System Based on MEA

Simulation and Analysis

Findings

Conclusions