Modeling and Control of Post-Combustion CO2 Capture Process Integrated with a 550MWe Supercritical Coal-fired Power Plant

Abstract

This work focuses on the development of both steady-state and dynamic models for an monoethanolamine (MEA)-based CO2 capture process for a commercial-scale supercritical pulverized coal (PC) power plant, using Aspen Plus ® and Aspen Plus Dynamics ®. The dynamic model also facilitates the design of controllers for both traditional proportional-integralderivative (PID) and advanced controllers, such as linear model predictive control (LMPC), nonlinear model predictive control (NMPC) and H∞ robust control. A steady-state MEA-based CO2 capture process is developed in Aspen Plus ®. The key process units, CO2 absorber and stripper columns, are simulated using the rate-based method. The steady-state simulation results are validated using experimental data from a CO2 capture pilot plant. The process parameters are optimized with the goal of minimizing the energy penalty. Subsequently, the optimized rate-based, steady-state model with appropriate modifications, such as the inclusion of the size and metal mass of the equipment, is exported into Aspen Plus Dynamics ® to study transient characteristics and to design the control system. Since Aspen Plus Dynamics ® does not support the rate-based model, modifications to the Murphree efficiencies in the columns and a rigorous pressure drop calculation method are implemented in the dynamic model to ensure consistency between the design and off-design results from the steady-state and dynamic models. The results from the steady-state model indicate that between three and six parallel trains of CO2 capture processes are required to capture 90% CO2 from a 550MWe supercritical PC plant depending on the maximum column diameter used and the approach to flooding at the design condition. However, in this work, only two parallel trains of CO2 capture process are modeled and integrated with a 550MWe post-combustion, supercritical PC plant in the dynamic simulation due to the high calculation expense of simulating more than two trains. v Acknowledgement I would like to express my deepest gratitude to Dr. Richard Turton, who served as my graduate advisor and a great mentor during my stay in Morgantown, West Virginia. His guidance, patience and support helped immensely in completing my research. I would also like to thank my advisor Dr. Debangsu Bhattacharyya for his keen insight and guidance. His knowledge and expertise helped significantly with understanding the Aspen simulation packages and process control. There were many times when I was stuck with lack of progress and was able to find new directions after discussions with them. I also want to thank my committee members, Dr.