Search

Solvent-based post-combustion carbon capture (PCC) is currently the most promising method to reduce CO2 emission. To achieve a plant-wide controller for flexible operation, it is necessary to develop a data-driven model to understand the dynamic characteristics of PCC plant. This paper aims to: (i) carry out system identification to develop a data-driven model and (ii) provide insights into the nonlinear dynamics among the key variables from the PCC process in a wide operating range. These key variables include: CO2 capture rate, reboiler temperature, condenser temperature and lean solvent temperature. Pilot-scale PCC process implemented in gCCS was used to generate simulation data for system identification and model comparison. Linear single-input-single-output (SISO) transfer function models were firstly developed at different capture rates. Open loop step tests on identified models were then introduced to report the dynamics of key variables in various operating conditions and to indicate the level of system nonlinearity graphically. The nonlinearity analysis was carried out to investigate the system nonlinearity distribution in a quantitative manner. Based on the nonlinearity analysis, a multi-input-multi-output (MIMO) piece-wise model was proposed to simulate the nonlinear characteristics of PCC plant. The piece-wise model shows a satisfactory agreement with gCCS simulation data. Results of this study successfully demonstrate the nonlinear behavior of the solvent-based PCC process, which can be applied in the design of flexible plant-wide controllers.

A flexible operation of the solvent-based post-combustion CO2 capture (PCC) process is of great importance to make the technology widely used in the power industry. However, in case of a wide range of operation, the presence of process nonlinearity may degrade the performance of the pre-designed linear controller. This paper gives a comprehensive analysis of the dynamic behavior and nonlinearity distribution of the PCC process. Three cases are taken into account during the investigation: 1) capture rate change; 2) flue gas flowrate change; and 3) re-boiler temperature change. The investigations show that the CO2 capture process does have strong nonlinearity; however, by selecting a suitable control target and operating range, a single linear controller is possible to control the capture system within this range. Based on the analysis results, a linear model predictive controller is designed for the CO2 capture process. Simulations of the designed controller on an MEA based PCC plant demonstrate the effectiveness of the proposed control approach.

Solvent-based post-combustion CO2 capture plant has to be operated in a flexible manner because of its high energy consumption and the frequent load variation of upstream power plants. Such a flexible operation brings out two objectives for the control system: i) the system should be able to change the CO2 capture rate quickly and smoothly in a wide operating range; ii) the system should effectively remove the disturbances from power plant flue gas. To achieve these goals, this paper proposed a multi-model predictive control (MMPC) strategy for solvent-based post-combustion CO2 capture plant. Firstly, local models of the CO2 capture plant at different operating points are identified through subspace identification method. Nonlinearity analysis of the plant is then performed and according to the results, suitable local models are selected, on which the multi-model predictive controller is designed. To enhance the flue gas disturbance rejection property of the CO2 capture plant and attain a better adaption to the power plant load variation, the flue gas flow rate is considered in the local model identification as an additional measured disturbance, thus the predictive controller can calculate the optimal control input even in the case of flue gas flow rate variation. Simulation results on an MEA-based CO2 capture plant developed on gCCS show the effectiveness and advantages of the proposed MMPC controller over wide range capture rate variation and power plant flue gas variation.

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.

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.

The most mature technology for CO2 capture is absorption with suitable solvents, be it physical or chemical. However, important issues still need to be addressed. Two of the most prominent are: a) the large energetic costs involved, and b) the need for capture plants to operate flexibly. Recently, considerable research efforts have been devoted to both the identification of optimal solvents, and the development of improved capture plant process configurations and operating conditions. These two aspects are generally regarded as the main drivers that can bring down the costs associated with solvent-based CO2 absorption processes. Additionally, an understanding of the dynamic behavior of capture plants is imperative in order to design CCS chains that will be increasingly subjected to variable electricity demand. This work introduces a predictive dynamic modelling framework for solvent-based CO2 absorption, part of the gCCS system modelling environment for CCS chains. The framework aims to serve as a platform to address the issues abovementioned, among others. Applications to optimization and dynamic studies are presented.

With increasing penetration of intermittent renewable energy into the electricity grid, one can expect thermal power plants to be required to operate in a more dynamic fashion, with more frequent departures from design point operation. However, the application of optimal control strategies can offer solutions to these operational challenges, associated with the integration of the power plant with the capture plant. In this paper a process control strategy is developed in order to select the optimal control variables for a PCC process. In addition, economically efficient control structures for operation of a post-combustion capture process with minimum energy requirements for coal and natural gas power plant are designed. The results have shown that with an appropriate and well-tuned control strategy, it is possible to maintain critical parameters, such as the degree of CO 2 capture, at the desired set-point, even during periods of significant fluctuation in the power plant load and even if based on simple and well established control technologies, such as PID, avoids the need for more risky solutions such as adding solvent storage tanks to the process.

Development of dynamic models for post-combustion CO2 capture using monoethanolamine solvent has been reported in the literature. Such models are only validated at steady-state, which means the models can predict process performance at different operating points. However, without dynamic validation, there is no guarantee that the model in question would predict dynamic responses accurately. This paper presents a dynamic validation study. The absorber and regenerator were modelled to account for mass transfer with chemical reactions assumed at equilibrium. Plant data logs were provided by the University of Texas at Austin. Three cases were considered: a conventional process and two cases with intercooled absorbers. The absorber temperature profile, capture level and reboiler duty were used for comparison. It is observed that the model satisfactorily predicts the pilot plant behaviour under multiple process inputs and disturbances. The validated model was then used to analyse the effect of increasing inlet flue gas moisture content and the impact of effective intercooling on the process performance. The former marginally influences the capture level, but significantly affects temperature profile, hence is an important parameter in model validation. The latter enhances capture level, and provides evidence that CO2 absorption is mass transfer limited.

Studies have been carried out to show that there could be benefits in employing a hybrid of the post combustion capture and oxyfuel processes. The former is suited for retrofit purposes but involves an energy intensive solvent regeneration process. The oxyfuel process would result in high partial pressures of CO 2 in the flue gas making separation easier. However, the Air Separation Unit is also energy intensive and significant modifications would have to be made on the power generation process. An enhanced- O 2 coal power plant with post combustion CO 2 capture would produce flue gas with reduced mass flow and higher CO 2 partial pressures. The dynamic response of the downstream CO 2 capture plant was studied in this paper.

Power generation from fossil fuel-fired power plants is the largest single source of CO 2 emissions. Post combustion capture via chemical absorption is viewed as the most mature CO 2 capture technique. This paper presents a study of the post combustion CO 2 capture with monoethanolamine (MEA) based on dynamic modelling of the process. The aims of the project were to compare two different approaches (the equilibrium-based approach versus the rate-based approach) in modelling the absorber dynamically and to understand the dynamic behaviour of the absorber during part load operation and with disturbances from the stripper. A powerful modelling and simulation tool gPROMS was chosen to implement the proposed work. The study indicates that the rate-based model gives a better prediction of the chemical absorption process than the equilibrium-based model. The dynamic simulation of the absorber indicates normal absorber column operation could be maintained during part load operation by maintaining the ratio of the flow rates of the lean solvent and flue gas to the absorber. Disturbances in the CO 2 loading of the lean solvent to the absorber significantly affect absorber performance. Further work will extend the dynamic modelling to the stripper for whole plant analysis.

This study aims to provide insights into the design and operation of full-scale post-combustion CO 2 capture for a 500 MWe sub-critical power plant through dynamic modelling and simulation. The development and validation of the dynamic models of the power plant and CO 2 capture plant are described. In addition, the scale-up of the CO 2 capture plant from pilot plant scale (where it was validated) to full scale is discussed. Subsequently the manner in which the two plant models were linked is discussed. A floating IP/LP crossover pressure configuration is used. A throttling valve is included between the LP turbine and draw-off point to prevent pressures at the crossover from dropping below required levels in the reboiler for solvent regeneration. The flue gas from the power plant is treated before it is sent to the CO 2 capture plant. Four case studies are considered. The first investigates the effect of increasing solvent concentration on the performance of the power plant with the capture plant. The second investigates which absorber packing height offers a good balance between capital and operating costs. The two dynamic case studies show that the CO 2 capture plant has a slower response than the power plant. They also reveal an interaction of CO 2 capture level and power plant output control loops making it difficult to achieve steady power output levels quickly.

Dynamic modelling for post-combustion CO 2 capture using monoethanolamine (MEA) solvent has been validated at steady state in the past. This paper presents a dynamic validation. The absorber and regenerator were modelled using the two-film theory in gPROMS®. Electrolyte NRTL model in Aspen Properties® is used to describe the chemical equilibrium and vapour-liquid equilibrium. The CO 2 capture process was simulated. The temperature profile of the absorber, the capture level and the regenerator duty were compared with pilot plant data logs. It is observed that the model satisfactorily predicts the behaviour of the pilot plant due to a number of process inputs and disturbances, producing trends in close agreement with the data logs.

Post-combustion capture by chemical absorption using MEA solvent remains the only commercial technology for large scale CO2 capture for coal-fired power plants. This paper presents a study of the dynamic responses of a post- combustion CO2 capture plant by modelling and simulation. Such a plant consists mainly of the absorber (where CO2 is chemically absorbed) and the regenerator (where the chemical solvent is regenerated). Model development and validation are described followed by dynamic analysis of the absorber and regenerator columns linked together with recycle. The gPROMS (Process Systems Enterprise Ltd.) advanced process modelling environment has been used to implement the proposed work. The study gives insights into the operation of the absorber- regenerator combination with possible disturbances arising from integrated operation with a power generation plant. It is shown that the performance of the absorber is more sensitive to the molar L/G ratio than the actual flow rates of the liquid solvent and flue gas. In addition, the importance of appropriate water balance in the absorber column is shown. A step change of the reboiler duty indicates a slow response. A case involving the combination of two fundamental CO2 capture technologies (the partial oxyfuel mode in the furnace and the post-combustion solvent scrubbing) is studied. The flue gas composition was altered to mimic that observed with the combination. There was an initial sharp decrease in CO2 absorption level which may not be observed in steady-state simulations. (C) 2010 Elsevier Ltd. All rights reserved.

Global concentration of CO2 in the atmosphere is increasing rapidly. CO2 emissions have an impact on global climate change. Effective CO2 emission abatement strategies such as Carbon Capture and Storage (CCS) are required to combat this trend. There are three major approaches for CCS: post-combustion capture, pre-combustion capture and oxyfuel process. Post-combustion capture offers some advantages as existing combustion technologies can still be used without radical changes on them. This makes post-combustion capture easier to implement as a retrofit option (to existing power plants) compared to the other two approaches. Therefore, post-combustion capture is probably the first technology that will be deployed. This paper aims to provide a state-of-the-art assessment of the research work carried out so far in post-combustion capture with chemical absorption. The technology will be introduced first, followed by required preparation of flue gas from power plants to use this technology. The important research programmes worldwide and the experimental studies based on pilot plants will be reviewed. This is followed by an overview of various studies based on modelling and simulation. Then the focus is turned to review development of different solvents and process intensification. Based on these, we try to predict challenges and potential new developments from different aspects such as new solvents, pilot plants, process heat integration (to improve efficiency), modelling and simulation, process intensification and government policy impact.

Post combustion capture via chemical absorption is viewed as the most mature CO2 capture technique. The effects of the addition of CO2 chemical absorption process on power plant performance have been studied using various steady-state models. However, there are several gaps in the understanding of the impact of post combustion capture on the operability of the power plant. These questions could be addressed by studying the dynamic behavior of such plants. In this study, dynamic models of the CO2 chemical absorption process were developed and validated. Dynamic analyses of the process reveal that absorber performance is sensitive to L/G ratio and that changes in reboiler duty significantly affect the regenerator performance.