Flexible operation of post-combustion solvent-based carbon capture for coal-fired power plants using multi-model predictive control: A simulation study

Wu, Xiao; Shen, Jiong; Li, Yiguo; Wang, Meihong; Lawal, Adekola

doi:10.1016/j.fuel.2018.02.061

Cited by 49 publications

(34 citation statements)

References 45 publications

(84 reference statements)

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“…To make a better use of the interactions between the CFPP and PCC systems and combine them together, the flue gas flowrate and steam flow rate to re-boiler are utilized as additional feedforward signals in the PCC and CFPP controller designs, respectively. Different from the conventional design approaches, which only send the measured flue gas flow rate signals at current sampling time to the PCC controllers [24,30], a method for deep reinforcement integration and coordination of the two systems is proposed in this paper. The method makes full use of the prediction feature of MPC, that takes the current and future estimation of flue gas and re-boiler steam flow rates as respective feedforward signals in MPC_PCC and MPC_CFPP developments.…”

Section: Reinforced Coordinated Control System Design For the Cfpp-pcmentioning

confidence: 99%

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

Wang

Shen

et al. 2019

Applied Energy

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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.

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Section: Reinforced Coordinated Control System Design For the Cfpp-pcmentioning

confidence: 99%

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

Wang

Shen

et al. 2019

Applied Energy

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“…Then, the prediction model (24) is transformed into the augmented style to impose integral action on the MPC, so that an offset-free tracking performance can be obtained in the presence of model-plant mismatch [21]:…”

Section: A Construction Of Prediction Modelmentioning

confidence: 99%

Transient Analysis and Execution-Level Power Tracking Control of the Concentrating Solar Thermal Power Plant

Liang

2019

Energies

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Concentrating solar power (CSP) is a promising technology for exploiting solar energy. A major advantage of CSP plants lies in their capability of integrating with thermal energy storage; hence, they can have a similar operability to that of fossil-fired power plants, i.e., their power output can be adjusted as required. For this reason, the power output of such CSP plants is generally scheduled to maximize the operating revenue by participating in electric markets, which can result in frequent changes in the power reference signal and introduces challenges to real-time power tracking. To address this issue, this paper systematically studies the execution-level power tracking control strategy of an CSP plant, primarily aiming at coordinating the control of the sluggish steam generator (including the economizer, the boiler, and the superheater) and the fast steam turbine. The governing equations of the key energy conversion processes in the CSP plant are first presented and used as the simulation platform. Then, the transient behavior of the CSP plant is analyzed to gain an insight into the system dynamic characteristics and control difficulties. Then, based on the step-response data, the transfer functions of the CSP plant are identified, which form the prediction model of the model predictive controller. Finally, two control strategies are studied through simulation experiments: (1) the heuristic PI control with two operation modes, which can be conveniently implemented but cannot coordinate the control of the power tracking speed and the main steam parameters, and (2) advanced model predictive control (MPC), which overcomes the shortcoming of PI (Proportional-Integral) control and can significantly improve the control performance.

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“…The linear model's failure to capture the nonlinear dynamics of the PCC plant will deteriorate the control performance. In order to develop a model predictive controller for wide-range capture rate change, Wu et al [10] proposed a simple nonlinear distribution analysis for solvent-based PCC plant. However, since multi-variable model is used in the analysis, the nonlinearity for a certain input-output loop cannot be revealed in detail.…”

Section: Motivationmentioning

confidence: 99%

“…Using the local models developed in Section 3.3, this section provides a nonlinearity analysis based on gap metric to quantify the nonlinearity degree of PCC process (as modelled in gCCS). Compared with [10], this paper put more key variables (As listed in Table 1) in consideration to investigate their nonlinear characteristics in a quantitative manner. Nonlinearity measurement is also carried out in SISO model to reveal the relationship between input and output variables at varying capture rates.…”

Section: Nonlinearity Analysismentioning

confidence: 99%

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Application of piece-wise linear system identification to solvent-based post-combustion carbon capture

Liao

et al. 2018

Fuel

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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.

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Flexible operation of post-combustion solvent-based carbon capture for coal-fired power plants using multi-model predictive control: A simulation study

Cited by 49 publications

References 45 publications

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

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

Transient Analysis and Execution-Level Power Tracking Control of the Concentrating Solar Thermal Power Plant

Application of piece-wise linear system identification to solvent-based post-combustion carbon capture

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