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

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 PlusRTM and Aspen Plus DynamicsRTM. The dynamic model also facilitates the design of controllers for both traditional proportional-integral-derivative (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 PlusRTM. 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 DynamicsRTM to study transient characteristics and to design the control system. Since Aspen Plus DynamicsRTM 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.;In the control studies, the performance of PID-based, LMPC-based, and NMPC-based approaches are evaluated for maintaining the overall CO2 capture rate and the CO2 stripper reboiler temperature at the desired level in the face of typical input and output disturbances in flue gas flow rate and composition as well as change in the power plant load and variable CO2 capture rate. Scenarios considered include cases using different efficiencies to mimic different conditions between parallel trains in real industrial processes. MPC-based approaches are found to provide superior performance compared to a PID-based one. Especially for parallel trains of CO2 capture processes, the advantage of MPC is observed as the overall extent of CO2 capture for the process is maintained by adjusting the extent of capture for each train based on the absorber efficiencies. The NMPC-based approach is preferred since the optimization problem that must be solved for model predictive control of CO2 capture process is highly nonlinear due to tight performance specifications, environmental and safety constraints, and inherent nonlinearity in the chemical process. In addition, model uncertainties are unavoidable in real industrial processes and can affect the plant performance. Therefore, a robust controller is designed for the CO2 capture process based on ?-synthesis with a DK-iteration algorithm. Effects of uncertainties due to measurement noise and model mismatches are evaluated for both the NMPC and robust controller. The simulation results show that the tradeoff between the fast tracking performance of the NMPC and the superior robust performance of the robust controller must be considered while designing the control system for the CO2 capture units. Different flooding control strategies for the situation when the flue gas flow rate increases are also covered in this work

Flexible operation of coal fired power plant integrated with post combustion CO2 capture using model predictive control

The growing demand for CO2 capture from coal-fired power plant (CFPP) has increased the need to improve the dynamic operability of the integrated power generation-CO2 capture plant. Nevertheless, high-level operation of the entire system is difficult to achieve due to the strong interactions between the CFPP and post combustion CO2 capture (PCC) unit. In addition, the control tasks of power generation and CO2 removal are in conflict, since the operation of both processes requires consuming large amount of steam. For these reasons, this paper develops a model for the integrated CFPP-PCC process and analyzes the dynamic relationships for the key variables within the integrated system. Based on the investigation, a centralized model predictive controller is developed to unify the power generation and PCC processes together, involving the key variables of the two systems and the interactions between them. Three operating modes are then studied for the predictive control system with different focuses on the overall system operation; power generation demand tracking and satisfying the CO2 capture requirement. The predictive controller can achieve a flexible operation of the integrated CFPP- PCC system and fully exert its functions in power generation and CO2 reduction

Non-linear system Identification and control of Solvent-Based Post-Combustion CO2 Capture Process

Solvent-based post-combustion capture (PCC) is a well-developed technology for CO2 capture from power plants and industry. A reliable model that captures the dynamics of the solvent-based capture process is essential to implement suitable control system design. Typically, first principles models are used, however, they usually require comprehensive knowledge and in-depth understanding of the process. In addition, the high computational time required and high complexity of the first principles models makes it unsuitable for control system design implementation. This thesis is aimed at the development of a reliable dynamic model via system identification technique as well as a suitable process control strategy for the solvent-based post-combustion CO2 capture process. The nonlinear autoregressive with exogenous (NARX) inputs model is employed to represent the relationship between the input variables and output variables as two multiple-input single-output (MISO) sub-systems. The forward regression with orthogonal least squares (FROLS) algorithm is implemented to select an accurate model structure that best describes the dynamics within the process. The prediction performance of the identified NARX models is promising and shows that the models capture the underlying dynamics of the CO2 capture process. The model obtained was adopted for various process control system design of the solvent-based PCC process (conventional PI, MPC, and NMPC). For the conventional PI controller design, multivariable control analysis was carried out to determine a suitable control structure. Control performance evaluation of the control schemes reveals that the NMPC scheme was suitable to control the solvent-based PCC process at flexible operations. Findings obtained from the thesis underlines the advancement in dynamic modelling and control implementation of solvent-based PCC process

Design and Implementation of Model Predictive Control Strategies for Improved Power Plant Cycling

Design and Implementation of Model Predictive Control Strategies for Improved Power Plant Cycling Xin He With the increasing focus on renewable energy sources, traditional power plants such as coal-fired power plants will have to cycle their load to accommodate the penetration of renewables into the power grid. Significant overshooting and oscillatory performance may occur during cycling operations if classical feedback control strategies are employed for plantwide control. To minimize the impact when power plants are operating away from their designed conditions, model-based optimal control strategies would need to be developed for improved power plant performance during cycling. In this thesis, model predictive control (MPC) strategies are designed and implemented for improved power plant cycling. The MPC strategies addressed correspond to a dynamic matrix control (DMC)-based linear MPC, a classical sequential quadratic programming (SQP)-based nonlinear MPC, a direct transcription-based nonlinear MPC and a proposed modified SQP-based nonlinear MPC. The proposed modified SQP algorithm is based on the backtracking line search framework, which employs a group of relaxed step acceptance conditions for faster convergence. The numerical results for motivating examples, which are selected from literature problem sets, served as proof of concept to verify that the proposed modified SQP has the potential for implementation on high-dimensional systems. To illustrate the tracking performance and computational efficiency of the developed MPC strategies, three processes of different dimensionalities are addressed. The first process is an integrated gasification combined cycling power plant with a water-gas shift membrane reactor (IGCC-MR), which is represented by a first-principles and simplified systems-level nonlinear model in MATLAB. For this application, a setpoint tracking scenario simulating a step increase in power demand, a disturbance rejection scenario simulating a coal feed quality change, and a trajectory tracking scenario simulating a wind power penetration into the power grid are presented. The second application is an aqueous monoethanolamine (MEA)-based carbon capture process as part of a supercritical pulverized coal-fired (SCPC) power plant, whose model is built in Aspen Plus Dynamics. For this system, disturbance rejection scenarios considering a ramp decrease in the flue gas flow rate as well as wind power penetration, and a scenario considering a combination of disturbance rejection and setpoint tracking are addressed. The third process is the entire SCPC power plant with MEA-based carbon capture (SCPC-MEA), which simulation is also built in Aspen Plus Dynamics. Trajectory tracking and disturbance rejection scenarios associated with wind and solar power penetrations are presented for this process. The MPC implementations on the three processes for the different scenarios addressed are successful. The closed-loop results show that the proposed modified SQP-based nonlinear MPC enhances the tracking performance by up to 96% when compared to the DMC-based linear MPC in terms of integral squared error results. The novel approach also improves the MPC computational efficiency by 20% when compared to classical SQP-based and direct transcription-based nonlinear MPCs

Reinforced coordinated control of coal-fired power plant retrofitted with solvent based CO2 capture using model predictive controls

Solvent-based post-combustion CO2 capture (PCC) provides a promising technology for the CO2 removal of coal-fired power plant (CFPP). However, there are strong interactions between the CFPP and the PCC system, which makes it challenging to attain a good control for the integrated plant. The PCC system requires extraction of large amounts of steam from the intermediate/low pressure steam turbine to provide heat for solvent regeneration, which will reduce power generation. Wide-range load variation of power plant will cause strong fluctuation of the flue gas flow and brings in a significant impact on the PCC system. To overcome these issues, this paper presents a reinforced coordinated control scheme for the integrated CFPP-PCC system based on the investigation of the overall plant dynamic behavior. Two model predictive controllers are developed for the CFPP and PCC plants respectively, in which the steam flow rate to re-boiler and the flue-gas flow rate are considered as feed-forward signals to link the two systems together. Three operating modes are considered for designing the coordinated control system, which are: (1) normal operating mode; (2) rapid power load change mode; and (3) strict carbon capture mode. The proposed coordinated controller can enhance the overall performance of the CFPP-PCC plant and achieve a flexible trade-off between power generation and CO2 reduction. Simulation results on a small-scale subcritical CFPP-PCC plant developed on gCCS demonstrates the effectiveness of the proposed controller

Dynamic behavior investigations and disturbance rejection predictive control of solvent-based post-combustion CO2 capture process

Increasing demand for flexible operation has posed significant challenges to the control system design of solvent-based post-combustion CO2 capture (PCC) process: 1) the capture system itself has very slow dynamics; 2) in the case of wide range of operation, dynamic behavior of the PCC process will change significantly at different operating points; and 3) the frequent variation of upstream flue gas flowrate will bring in strong disturbances to the capture system. For these reasons, this paper provides a comprehensive study on the dynamic characteristics of the PCC process. The system dynamics under different CO2 capture rates, re-boiler temperatures, and flue gas flow rates are analyzed and compared through step-response tests. Based on the in-depth understanding of the system behavior, a disturbance rejection predictive controller (DRPC) is proposed for the PCC process. The predictive controller can track the desired CO2 capture rate quickly and smoothly in a wide operating range while tightly maintaining the re-boiler temperature around the optimal value. Active disturbance rejection approach is used in the predictive control design to improve the control property in the presence of dynamic variations or disturbances. The measured disturbances, such as the flue gas flow rate, is considered as an additional input in the predictive model development, so that accurate model prediction and timely control adjustment can be made once the disturbance is detected. For unmeasured disturbances, including model mismatches, plant behavior variations, etc., a disturbance observer is designed to estimate the value of disturbances. The estimated signal is then used as a compensation to the predictive control signal to remove the influence of disturbances. Simulations on a monoethanolamine (MEA) based PCC system developed on gCCS demonstrates the excellent effect of the proposed controller

MANAGEMENT DECISION SUPPORT SYSTEM OF SOLVENT-BASED POST-COMBUSTION CARBON CAPTURE

A management decision-support framework for a coal-fired power plant with solvent based post combustion CO2 capture (PCC) (integrated plant) is proposed and developed in this thesis. A brief introduction pertaining to the solvent-based PCC technology, thesis motivations and objectives are given in Chapter 1. Chapter 2 comprises a comprehensive literature review of solvent-based PCC plant from the bottom level (PCC instrumentation level) until the top level (managerial decision of PCC system). Chapter 3 describes the development of solvent-based PCC dynamic model via empirical methods. Open-loop dynamic analyses are presented to provide a deeper understanding of the dynamic behaviour of key variables in solvent-based PCC plant. Chapter 4 presents the design of the control architecture for solvent-based PCC plant. Two control algorithms developed, which utilise conventional proportional, integral and derivative (PID) controller and advanced model predictive control (MPC). Chapter 5 proposes a conceptual framework for optimal operation of the integrated plant. The MPC scheme is chosen as the control algorithm while mixed integer non-linear programming (MINLP) using genetic algorithm (GA) function is employed in the optimization algorithm. Both algorithms are integrated to produce a hybrid MPC-MINLP algorithm. Capability and applicability of the algorithm is evaluated based on 24 hours and annual operation of integrated plant. Chapter 6 extends the scope of Chapter 5 by evaluating the relevance of solvent-based PCC technology in the operation of black coal-fired power plant in Australia. This chapter considers a prevailing climate policy established in Australia namely Emission Reduction Fund (ERF). Finally, the concluding remarks and future extensions of this research are presented in Chapter 7

Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control

Post-combustion carbon capture (PCC) with chemical absorption has strong interactions with coal-fired power plant (CFPP). It is necessary to investigate dynamic characteristics of the integrated CFPP-PCC system to gain knowledge for flexible operation. It has been demonstrated that the integrated system exhibits large time inertial and this will incur additional challenge for controller design. Conventional PID controller cannot effectively control CFPP-PCC process. To overcome these barriers, this paper presents an improved neural network inverse control (NNIC) which can quickly operate the integrated system and handle with large time constant. Neural network (NN) is used to approximate inverse dynamic relationships of integrated CFPP-PCC system. The NN inverse model uses setpoints as model inputs and gets predictions of manipulated variables. The predicted manipulated variables are then introduced as feed-forward signals. In order to eliminate steady-state bias and to operate the integrated CFPP-PCC under different working conditions, improvements have been achieved with the addition of PID compensator. The improved NNIC is evaluated in a large-scale supercritical CFPP-PCC plant which is implemented in gCCS toolkit. Case studies are carried out considering variations in power setpoint and capture level setpoint. Simulation results reveal that proposed NNIC can track setpoints quickly and exhibit satisfactory control performances

Investigation of control strategies for adsorption-based CO2 capture from a thermal power plant under variable load operation

This work considers the closed-loop behavior of a moving bed temperature swing adsorption process designed to capture CO2 from a coal-fired power plant. Four decentralized control strategies were studied based on step changes and ramps of flue gas feed flow rate and controller setpoint changes. A proportional-integral (PI) control configuration, where CO2 purity was controlled by hot fluid velocity to the desorption section and CO2 recovery was controlled by the sorbent flow rate, demonstrated the overall best performance. The 99% settling time for higher-level control variables varied from 0 to 13 min for most control configurations and the settling time for CO2 purity was generally longer than for CO2 recovery. The simulations show that using ratio controllers lead to larger offsets but can give around 10 times faster purity response compared to PI-control. All investigated control combinations were able to keep the controlled variables relatively close to the setpoints and the largest relative steady state setpoint offset was 2%.publishedVersio