This page has only limited features, please log in for full access.
Coal-fired power plants with direct air-cooling condensers (DACC-CFPPs) have been widely used in areas rich in coal but short of water since they can significantly reduce water consumption. Despite its water-saving advantages, direct air-cooling condensers have inherent defects such as environmental and load sensitivity, higher costs and poor cooling performance, which deteriorate the economics and flexibility of the integrated system. For safe, efficient and flexible operation, this paper presents the development of a direct air-cooling condenser dynamic model integrated with a 600 MWe coal-fired power plant to study the interactions between heat and power within the entire DACC-CFPP under various load and ambient conditions. The condenser is modeled in a one-dimensional multi-section manner to reflect the impacts of crosswind and ambient temperature disturbances. A detailed turbine-feedwater heater system model is also developed to link the direct air-cooling condenser and power plant together. Steady-state and dynamic validations under various operating conditions are conducted, which demonstrate the high fidelity of the developed model. Simulation studies are then carried out to investigate the dynamic interactions between boiler-turbine, condenser and environmental conditions. We find that manipulating the fan array speed controls the condenser pressure effectively in terms of environmental disturbance rejection and load ramping. Motivated by this finding, a novel variable condenser pressure operating mode is proposed to coordinate the direct air-cooling condenser and coal-fired power plant operation, which can significantly improve the load tracking performance of the integrated plant.
Mingjuan Zhu; Xiao Wu; Jiong Shen; Kwang Lee. Dynamic modeling, validation and analysis of direct air-cooling condenser with integration to the coal-fired power plant for flexible operation. Energy Conversion and Management 2021, 245, 114601 .
AMA StyleMingjuan Zhu, Xiao Wu, Jiong Shen, Kwang Lee. Dynamic modeling, validation and analysis of direct air-cooling condenser with integration to the coal-fired power plant for flexible operation. Energy Conversion and Management. 2021; 245 ():114601.
Chicago/Turabian StyleMingjuan Zhu; Xiao Wu; Jiong Shen; Kwang Lee. 2021. "Dynamic modeling, validation and analysis of direct air-cooling condenser with integration to the coal-fired power plant for flexible operation." Energy Conversion and Management 245, no. : 114601.
Steel industry contributes significantly to the world economy, but is highly energy intensive and CO2 intensive since the coal-based blast furnace route is dominant in steelmaking. Besides efficient utilization of the steel mill gases for power and heat supply, deploying technologies of carbon capture, utilization and renewable power is in urgent need for the transition of the steel industry towards carbon neutrality. To attain this goal, this paper develops a low-carbon steel mill gas utilization system with the integration of solvent-based carbon capture, methanol production based carbon utilization and renewable power. An artificial intelligent based optimal scheduling is then proposed to coordinate the interactions among gas, heat, electricity and carbon under variant weather and load conditions. Gradient boosted regression trees with Bayesian optimization is exploited to identify efficient surrogate models for the complex devices within the system. Heuristic search algorithm of particle swarm optimization is applied to find the low-carbon and economical scheduling within the entire scheduling period. Case studies show that the optimal scheduling can unlock complementary advantages among renewable energy, carbon capture and utilization, leading to 97% renewable energy curtailment reduction, 62% CO2 emission reduction and 126 tons of methanol production in 24 h. Sensitivity analyses are carried out to investigate the effects of additional coal consumption, renewable power installed capacity, CO2 emission penalty coefficient and CO2 capture constraint mode, providing broader insight into the operation of the steel mill gas utilization system towards carbon neutrality.
Han Xi; Xiao Wu; Xianhao Chen; Peng Sha. Artificial intelligent based energy scheduling of steel mill gas utilization system towards carbon neutrality. Applied Energy 2021, 295, 117069 .
AMA StyleHan Xi, Xiao Wu, Xianhao Chen, Peng Sha. Artificial intelligent based energy scheduling of steel mill gas utilization system towards carbon neutrality. Applied Energy. 2021; 295 ():117069.
Chicago/Turabian StyleHan Xi; Xiao Wu; Xianhao Chen; Peng Sha. 2021. "Artificial intelligent based energy scheduling of steel mill gas utilization system towards carbon neutrality." Applied Energy 295, no. : 117069.
Decarbonizing the energy intensive iron and steel industry is in urgent need to meet the ambitious environmental goal. Efficient and clean use of the blast furnace gas (BFG) through combined-cycle gas turbine (CCGT) power plant provides feasible pathway to realize a near-term CO2 reduction when integrated with carbon capture. This paper presents effective control strategies to coordinate the operation of BFG-fired CCGT plant and solvent-based post-combustion CO2 capture (PCC) process based on the in-depth understanding of the interactions among process dynamics in different time-scales for carbon, heat and electricity. The energy storage capability of the PCC process is explored in addition to the CO2 capture and the reboiler steam flowrate used for solvent regeneration is incorporated into the BFG-fired CCGT control loop. Considering this, two coordinated control strategies are developed for the BFG-fired CCGT-PCC, first based on the conventional PI control and then with advanced model predictive control (MPC) approaches. The coordinated strategies are demonstrated to improve the power ramping performance of the CCGT with little degradation on the PCC operation, thus providing better support for the reliability of the power system in the context of increasing penetration of renewable energy resources. Moreover, by considering the impact of disturbances into the predictive models, the MPC-based coordinated control can well alleviate the influence of BFG fluctuations, guaranteeing a stable operation of the integrated plant. This paper points to the new direction of using PCC for more flexible power regulation of adjustable sources in low-carbon energy systems with penetration of intermittent renewable powers.
Xiao Wu; Han Xi; Yuning Ren; Kwang Y. Lee. Power-carbon coordinated control of BFG-fired CCGT power plant integrated with solvent-based post-combustion CO2 capture. Energy 2021, 226, 120435 .
AMA StyleXiao Wu, Han Xi, Yuning Ren, Kwang Y. Lee. Power-carbon coordinated control of BFG-fired CCGT power plant integrated with solvent-based post-combustion CO2 capture. Energy. 2021; 226 ():120435.
Chicago/Turabian StyleXiao Wu; Han Xi; Yuning Ren; Kwang Y. Lee. 2021. "Power-carbon coordinated control of BFG-fired CCGT power plant integrated with solvent-based post-combustion CO2 capture." Energy 226, no. : 120435.
Renewable power and carbon capture are key technologies to transfer the power industry into low carbon generation. Renewables have been developed fast, however, the intermittent nature has imposed higher requirement for the flexibility of the power grid. Retrofitting carbon capture technologies to existing fossil-fuel fired power plants is an important solution to avoid the “lock-in” of emissions, but the high operating costs hinders their large scale application. The coexistence of renewable power and carbon capture opens up a new avenue that the deployment of carbon capture can provide additional flexibility for better accommodation of renewable power while excess renewables can be used to reduce the operating costs of carbon capture. To this end, this paper proposes an artificial intelligence based optimal scheduling strategy for the power plant-carbon capture system in the context of renewable power penetration to show that the mutual benefits between carbon capture and renewable power can be achieved when the carbon capture process is made fully adjustable. An artificial intelligent deep belief neural network is used to reflect the complex interactions between carbon, heat and electricity within the power plant carbon capture system. Multiple operating goals are considered in the scheduling such as minimizing the operating costs, renewable power curtailment and carbon emission, and the particle swarm heuristic optimization is employed to find the optimal solution. The impacts of carbon capture constraint mode, carbon emission penalty coefficient, carbon dioxide production constraints and renewable power installed capacity are investigated to provide broader insight on the potential benefit of carbon capture in future low-carbon energy system. A case study using real world data of weather condition and load demand shows that renewable power curtailment can be reduced by 51% with the integration of post-combustion capture systems and 35% of total carbon emission are captured by the use of excess renewable power through optimal scheduling. This paper points out a new way of using artificial intelligent technologies to coordinate the couplings between carbon and electricity for efficient and environmentally friendly operation of future low-carbon energy system.
Xianhao Chen; Xiao Wu; Kwang Y. Lee. The mutual benefits of renewables and carbon capture: Achieved by an artificial intelligent scheduling strategy. Energy Conversion and Management 2021, 233, 113856 .
AMA StyleXianhao Chen, Xiao Wu, Kwang Y. Lee. The mutual benefits of renewables and carbon capture: Achieved by an artificial intelligent scheduling strategy. Energy Conversion and Management. 2021; 233 ():113856.
Chicago/Turabian StyleXianhao Chen; Xiao Wu; Kwang Y. Lee. 2021. "The mutual benefits of renewables and carbon capture: Achieved by an artificial intelligent scheduling strategy." Energy Conversion and Management 233, no. : 113856.
High investment and operating costs are two barriers hindering the commercial deployment of solvent-based post combustion CO2 capture (PCC) technology in power industry. Since upstream power plant are always in flexible operation, conventional approaches which carry out the design based on a fixed condition fail to achieve the best overall performance. To this end, this paper proposes a simultaneous size and operation parameters optimization strategy for the PCC process, in which the flexible operation of the CFPP is taken into account during the design stage. The technique of particle swarm optimization (PSO) is used in a hierarchical manner to find the design and operating conditions of the PCC with minimal total annualized cost. To further explore the optimization potential, the average CO2 capture level in one day is taken as the environmental constraint rather than the conventional instantaneous one. The proposed approach is tested on the PCC design for an existing 660MW supercritical coal-fired power plant. The optimization results of four different design approaches are compared with the economic, efficiency and environmental (3E) performance being fully assessed. The simulation results show that by using the proposed optimization method, 14.45% reduction in TAC can be attained compared with the conventional approach.
Han Xi; Peizhi Liao; Xiao Wu. Simultaneous parametric optimization for design and operation of solvent-based post-combustion carbon capture using particle swarm optimization. Applied Thermal Engineering 2020, 184, 116287 .
AMA StyleHan Xi, Peizhi Liao, Xiao Wu. Simultaneous parametric optimization for design and operation of solvent-based post-combustion carbon capture using particle swarm optimization. Applied Thermal Engineering. 2020; 184 ():116287.
Chicago/Turabian StyleHan Xi; Peizhi Liao; Xiao Wu. 2020. "Simultaneous parametric optimization for design and operation of solvent-based post-combustion carbon capture using particle swarm optimization." Applied Thermal Engineering 184, no. : 116287.
Regulating performance of the main steam temperature (MST) system concerns the economy and safety of the coal-fired power plant (CFPP). This paper develops an offset-free offline robust model predictive control (RMPC) strategy for the MST system of CFPP. Zonotope-type uncertain model is utilized as the prediction model in the proposed RMPC design owing to its features of higher accuracy, compactness of representation and less complexity. An offline RMPC aiming at the system robustness and computational efficiency is then developed to maintain the desired steam temperature in case of wide operating condition change. The proposed RMPC is realized by two stages: in the first stage, the RMPC law set, which is the piecewise affine (PWA) of the MST system state is designed offline; then in the second stage, the explicit control law is selected online according to the current state. To achieve an offset-free tracking performance, a manipulated variable target observer is employed to update the chosen RMPC law. The control simulations using on-site operating data of a 1000 MW ultra-supercritical power plant show that the proposed approach can achieve satisfactory control performance and online computation efficiency even under complicated operating conditions.
Di Wang; Xiao Wu; Jiong Shen. An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant. Energies 2020, 13, 3775 .
AMA StyleDi Wang, Xiao Wu, Jiong Shen. An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant. Energies. 2020; 13 (15):3775.
Chicago/Turabian StyleDi Wang; Xiao Wu; Jiong Shen. 2020. "An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant." Energies 13, no. 15: 3775.
This paper presents a controller design study for the supercritical coal fired power plant (CFPP) integrated with solvent-based post-combustion CO2 capture (PCC) system. The focus of the study is on the steam drawn-off from turbine to the re-boiler, which is the key interaction between the CFPP and PCC plants. The simulation study of a 660 MW supercritical CFPP-PCC unit model has shown that the impact of re-boiler steam change on the power generation of CFPP is more than 100 times faster than that on the PCC operation. Considering this finding, a collaborative predictive control strategy is proposed for the CFPP-PCC system where the re-boiler steam flowrate is manipulated for the CFPP load ramping and then gradually set to the required value for CO2 capture. The PCC is thereby exploited as an energy storage device, which can quickly store/release extra energy for the CFPP in addition to the primary function of carbon emission reduction. The simulation results show that the proposed collaborative predictive controller can effectively improve the load ramping performance of CFPP without much performance degradation on the PCC operation.
Xiao Wu; Meihong Wang; Kwang Y. Lee. Flexible operation of supercritical coal-fired power plant integrated with solvent-based CO2 capture through collaborative predictive control. Energy 2020, 206, 118105 .
AMA StyleXiao Wu, Meihong Wang, Kwang Y. Lee. Flexible operation of supercritical coal-fired power plant integrated with solvent-based CO2 capture through collaborative predictive control. Energy. 2020; 206 ():118105.
Chicago/Turabian StyleXiao Wu; Meihong Wang; Kwang Y. Lee. 2020. "Flexible operation of supercritical coal-fired power plant integrated with solvent-based CO2 capture through collaborative predictive control." Energy 206, no. : 118105.
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.
Peizhi Liao; Yiguo Li; Xiao Wu; Meihong Wang; Eni Oko. Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control. International Journal of Greenhouse Gas Control 2020, 95, 102985 .
AMA StylePeizhi Liao, Yiguo Li, Xiao Wu, Meihong Wang, Eni Oko. Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control. International Journal of Greenhouse Gas Control. 2020; 95 ():102985.
Chicago/Turabian StylePeizhi Liao; Yiguo Li; Xiao Wu; Meihong Wang; Eni Oko. 2020. "Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control." International Journal of Greenhouse Gas Control 95, no. : 102985.
This paper develops an intelligent predictive controller (IPC) for a large-scale solvent-based post-combustion CO2 capture (PCC) process. An artificial neural network (NN) model is trained to represent the dynamics of the PCC process based on an in-depth behavior investigation of the process under different operating conditions. The resulting NN model can portray the PCC characteristics very well in terms of dynamic trend, response time and steady-state gain. An intelligent predictive controller is thus developed based on the NN model to track the desired CO2 capture level and maintain the given re-boiler temperature, in which the particle swarm optimization (PSO) algorithm is applied to find the best future control sequence for the PCC process. A warm start scheme is proposed in the IPC to improve the quality of initial swarm in the PSO. Dynamic simulations to change CO2 capture level set-point and flue gas flow rate are carried out on the PCC process. The results show that the IPC can adjust CO2 capture level fast and significantly reduce the fluctuations in re-boiler temperature. It is concluded that the proposed IPC is helpful for flexible operation of the solvent-based PCC process.
Xiao Wu; Jiong Shen; Meihong Wang; Kwang Y. Lee. Intelligent predictive control of large-scale solvent-based CO2 capture plant using artificial neural network and particle swarm optimization. Energy 2020, 196, 117070 .
AMA StyleXiao Wu, Jiong Shen, Meihong Wang, Kwang Y. Lee. Intelligent predictive control of large-scale solvent-based CO2 capture plant using artificial neural network and particle swarm optimization. Energy. 2020; 196 ():117070.
Chicago/Turabian StyleXiao Wu; Jiong Shen; Meihong Wang; Kwang Y. Lee. 2020. "Intelligent predictive control of large-scale solvent-based CO2 capture plant using artificial neural network and particle swarm optimization." Energy 196, no. : 117070.
The integrated energy system (IES) plays an important role in the development of clean energy through the complementary advantages of multi energy and the absorption capacity of renewable energy. However, because of the multi energy coupling and the intermittence of renewable energy, the risk of system dynamic instability increases. In order to solve the above issues, this paper considers the influence of the dynamic characteristics of the system from the level of planning and design stage. Based on the solar intensity and load demand curve of typical winter days and considering the daily economic operation of typical days and the dynamic characteristics of the system, an IES capacity configuration optimization model is established, which takes into account the system investment cost and the dynamic characteristics of the system. Then the genetic algorithm with penalty function is used to optimize the solution. Finally, according to the typical winter day data of a certain area in Nanjing, P.R.China, the rationality and validity of the model are verified, and the scientific configuration of an IES capacity considering dynamic performance is realized, which provides ideas and support for the planning and design of an IES later.
Yuxuan Li; Junli Zhang; Xiao Wu; Jiong Shen; Kwang Y. Lee. Capacity Configuration of Integrated Energy System Considering Equipment Inertia. IFAC-PapersOnLine 2020, 53, 13088 -13093.
AMA StyleYuxuan Li, Junli Zhang, Xiao Wu, Jiong Shen, Kwang Y. Lee. Capacity Configuration of Integrated Energy System Considering Equipment Inertia. IFAC-PapersOnLine. 2020; 53 (2):13088-13093.
Chicago/Turabian StyleYuxuan Li; Junli Zhang; Xiao Wu; Jiong Shen; Kwang Y. Lee. 2020. "Capacity Configuration of Integrated Energy System Considering Equipment Inertia." IFAC-PapersOnLine 53, no. 2: 13088-13093.
Most stand-alone integrated energy systems (IES) with renewable energy can only meet the demand of electrical load, not both electrical load and thermal load. Those studies on combined heat and power cogeneration systems mainly focus on the optimal scheduling of each source, ignoring the difference in dynamic response between electrical and thermal processes. In fact, the different response speeds of electrical and thermal objects will bring in challenges to control. In order to specify and solve the issue, this paper proposes a detailed mechanism model of a standard IES. A model predictive control (MPC) controller is designed and tuned based on the state-space form of the system. The control simulation results imply the feasibility of the MPC controller in coordinating both electricity and heat.
Yuhui Jin; Junli Zhang; Xiao Wu; Jiong Shen; Kwang Y. Lee. Coordinated Control for Combined Heat and Power Load of an Integrated Energy System. IFAC-PapersOnLine 2020, 53, 13184 -13189.
AMA StyleYuhui Jin, Junli Zhang, Xiao Wu, Jiong Shen, Kwang Y. Lee. Coordinated Control for Combined Heat and Power Load of an Integrated Energy System. IFAC-PapersOnLine. 2020; 53 (2):13184-13189.
Chicago/Turabian StyleYuhui Jin; Junli Zhang; Xiao Wu; Jiong Shen; Kwang Y. Lee. 2020. "Coordinated Control for Combined Heat and Power Load of an Integrated Energy System." IFAC-PapersOnLine 53, no. 2: 13184-13189.
Solvent-based post-combustion CO2 capture (PCC) appears to be the most effective choice to overcome the CO2 emission issue of fossil fuel fired power plants. To make the PCC better suited for power plants, growing interest has been directed to the flexible operation of PCC in the past ten years. The flexible operation requires the PCC system to adapt to the strong flue gas flow rate change and to adjust the carbon capture level rapidly in wide operating range. In-depth study of the dynamic characteristics of the PCC process and developing a suitable control approach are the keys to meet this challenge. This paper provides a critical review for the dynamic research of the solvent–based PCC process including first-principle modelling, data-driven system/process identification and the control design studies, with their main features being listed and discussed. The existent studies have been classified according to the approaches used and their advantages and limitations have been summarized. Potential future research opportunities for the flexible operation of solvent-based PCC are also given in this review.
Xiao Wu; Meihong Wang; Peizhi Liao; Jiong Shen; Yiguo Li. Solvent-based post-combustion CO2 capture for power plants: A critical review and perspective on dynamic modelling, system identification, process control and flexible operation. Applied Energy 2019, 257, 113941 .
AMA StyleXiao Wu, Meihong Wang, Peizhi Liao, Jiong Shen, Yiguo Li. Solvent-based post-combustion CO2 capture for power plants: A critical review and perspective on dynamic modelling, system identification, process control and flexible operation. Applied Energy. 2019; 257 ():113941.
Chicago/Turabian StyleXiao Wu; Meihong Wang; Peizhi Liao; Jiong Shen; Yiguo Li. 2019. "Solvent-based post-combustion CO2 capture for power plants: A critical review and perspective on dynamic modelling, system identification, process control and flexible operation." Applied Energy 257, no. : 113941.
Solid oxide fuel cell (SOFC) is of great importance to renewable energy generation system. In practice its output voltage should be held constant and fuel utilization rate should be guaranteed in a reasonable range respectively when the resistance load varies over a large area. In order to overcome the issues in practice, a fuzzy model predictive control with zone tracking for a SOFC power generation system is proposed. The nonlinearity and multivariable coupling are mitigated by fuzzy model and predictive control approaches respectively. The feedforward compensation is adopted to improve with the dynamic response. Zone control is integrated with fuzzy model predictive control for the purposes of satisfying fuel utilization within a desired range. A performance index with a weight function is developed to optimize controlled variables trajectory in the desired range so that the undulations of the controlled variables can be alleviated within the range. The advantages of the proposed method are manifested by simulations.
Long Wu; Xiao Wu; Lei Pan; Jiong Shen; Yiguo Li; Junli Zhang. Fuzzy Model Predictive Control of Solid Oxide Fuel Cell with Zone Tracking. IFAC-PapersOnLine 2019, 52, 210 -215.
AMA StyleLong Wu, Xiao Wu, Lei Pan, Jiong Shen, Yiguo Li, Junli Zhang. Fuzzy Model Predictive Control of Solid Oxide Fuel Cell with Zone Tracking. IFAC-PapersOnLine. 2019; 52 (4):210-215.
Chicago/Turabian StyleLong Wu; Xiao Wu; Lei Pan; Jiong Shen; Yiguo Li; Junli Zhang. 2019. "Fuzzy Model Predictive Control of Solid Oxide Fuel Cell with Zone Tracking." IFAC-PapersOnLine 52, no. 4: 210-215.
During the scram or load rejection of nuclear power plant, the excess steam of steam generator needs to be dumped to the main condenser through the steam conditioning device to reduce the temperature rising of the primary loop and prevent the steam generator from over-pressure. For study of the dynamics and control strategy of the steam dump system, this paper established a simulation model of nuclear power plant using Gsuite simulation platform, and then studied the dynamical-varying of some important-variables in nuclear power plants during scram. The results indicated that the model built can approximately predict the dynamic changes of important parameters such as the average temperature of the primary loop and the main steam pressure. Therefrom it is concluded that the joint operation of the steam dump system and atmospheric steam dump system can effectively prevent the rising of the temperature of the primary loop and the overpressure of the secondary circuit, and thus it can avoid the opening of the main-steam safety valve.
Nianci Lu; Yanjun Li; Lei Pan; Xiao Wu; Jiong Shen; Zhenxiang Liu; Kwang_Y_ Lee. Study on Dynamics of Steam Dump System in Scram Condition of Nuclear Power Plant. IFAC-PapersOnLine 2019, 52, 360 -365.
AMA StyleNianci Lu, Yanjun Li, Lei Pan, Xiao Wu, Jiong Shen, Zhenxiang Liu, Kwang_Y_ Lee. Study on Dynamics of Steam Dump System in Scram Condition of Nuclear Power Plant. IFAC-PapersOnLine. 2019; 52 (4):360-365.
Chicago/Turabian StyleNianci Lu; Yanjun Li; Lei Pan; Xiao Wu; Jiong Shen; Zhenxiang Liu; Kwang_Y_ Lee. 2019. "Study on Dynamics of Steam Dump System in Scram Condition of Nuclear Power Plant." IFAC-PapersOnLine 52, no. 4: 360-365.
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.
Xiao Wu; Meihong Wang; Jiong Shen; Yiguo Li; Adekola Lawal; Kwang Y. Lee. Reinforced coordinated control of coal-fired power plant retrofitted with solvent based CO2 capture using model predictive controls. Applied Energy 2019, 238, 495 -515.
AMA StyleXiao Wu, Meihong Wang, Jiong Shen, Yiguo Li, Adekola Lawal, Kwang Y. Lee. Reinforced coordinated control of coal-fired power plant retrofitted with solvent based CO2 capture using model predictive controls. Applied Energy. 2019; 238 ():495-515.
Chicago/Turabian StyleXiao Wu; Meihong Wang; Jiong Shen; Yiguo Li; Adekola Lawal; Kwang Y. Lee. 2019. "Reinforced coordinated control of coal-fired power plant retrofitted with solvent based CO2 capture using model predictive controls." Applied Energy 238, no. : 495-515.
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.
Xiao Wu; Jiong Shen; Yiguo Li; Meihong Wang; Adekola Lawal; Kwang Y. Lee. Dynamic behavior investigations and disturbance rejection predictive control of solvent-based post-combustion CO2 capture process. Fuel 2019, 242, 624 -637.
AMA StyleXiao Wu, Jiong Shen, Yiguo Li, Meihong Wang, Adekola Lawal, Kwang Y. Lee. Dynamic behavior investigations and disturbance rejection predictive control of solvent-based post-combustion CO2 capture process. Fuel. 2019; 242 ():624-637.
Chicago/Turabian StyleXiao Wu; Jiong Shen; Yiguo Li; Meihong Wang; Adekola Lawal; Kwang Y. Lee. 2019. "Dynamic behavior investigations and disturbance rejection predictive control of solvent-based post-combustion CO2 capture process." Fuel 242, no. : 624-637.
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.
Xiao Wu; Meihong Wang; Jiong Shen; Yiguo Li; Adekola Lawal; Kwang Y. Lee. Flexible operation of coal fired power plant integrated with post combustion CO2 capture using model predictive control. International Journal of Greenhouse Gas Control 2019, 82, 138 -151.
AMA StyleXiao Wu, Meihong Wang, Jiong Shen, Yiguo Li, Adekola Lawal, Kwang Y. Lee. Flexible operation of coal fired power plant integrated with post combustion CO2 capture using model predictive control. International Journal of Greenhouse Gas Control. 2019; 82 ():138-151.
Chicago/Turabian StyleXiao Wu; Meihong Wang; Jiong Shen; Yiguo Li; Adekola Lawal; Kwang Y. Lee. 2019. "Flexible operation of coal fired power plant integrated with post combustion CO2 capture using model predictive control." International Journal of Greenhouse Gas Control 82, no. : 138-151.
It is extremely challenging to control ultra-supercritical boiler–turbine unit on account of its nonlinearity, coupling among multi-variables, hard input constraints, and unknown disturbances. An offset-free output-feedback stable model predictive control (MPC) (OFOF-SMPC) for nonlinear constrained ultra-supercritical boiler-turbine units is investigated. It is shown that the OFOF-SMPC is composed of a disturbance and state observer, a steady-state target calculator as well as a stable MPC, which can remove unknown disturbances from output channels and ensure the stability of the closed-loop system with input constraints. Simulation results demonstrate the effectiveness of the proposed algorithm.
Chen Chen; Lei Pan; Jiong Shen; Kwang Y. Lee; Fan Zhang; Li Sun; Xiao Wu; Junli Zhang; Wenchao Xue. Control of Nonlinear Constrained Ultra-Supercritical Boiler–Turbine Units Using Offset-Free Output-Feedback Stable MPC. IFAC-PapersOnLine 2018, 51, 155 -160.
AMA StyleChen Chen, Lei Pan, Jiong Shen, Kwang Y. Lee, Fan Zhang, Li Sun, Xiao Wu, Junli Zhang, Wenchao Xue. Control of Nonlinear Constrained Ultra-Supercritical Boiler–Turbine Units Using Offset-Free Output-Feedback Stable MPC. IFAC-PapersOnLine. 2018; 51 (28):155-160.
Chicago/Turabian StyleChen Chen; Lei Pan; Jiong Shen; Kwang Y. Lee; Fan Zhang; Li Sun; Xiao Wu; Junli Zhang; Wenchao Xue. 2018. "Control of Nonlinear Constrained Ultra-Supercritical Boiler–Turbine Units Using Offset-Free Output-Feedback Stable MPC." IFAC-PapersOnLine 51, no. 28: 155-160.
In order to optimize the economic objectives while regulating the ultra-supercritical boiler-turbine unit, an improved utopia tracking based multiobjective fuzzy model predictive control is proposed in this paper. This method uses a hierarchical structure, in which the quasi-infinite horizon fuzzy model predictive control is designed for the steady-state compromise point in the upper layer and the utopia tracking based multiobjective control is devised in the lower layer. To ensure the closed-loop stability, a constraint with respect to the optimal value function about the compromise point is enforced. The simulation results on a 1000MW USC boiler-turbine unit model verify the merits of the proposed strategy in achieving less fuel consumption and less throttling loss during the load regulation.
Fan Zhang; Yali Xue; Donghai Li; Jiong Shen; Xiao Wu; Zhenlong Wu; Ting He. Multiobjective Operation of Ultra-Supercritical Boiler-Turbine Unit. IFAC-PapersOnLine 2018, 51, 592 -597.
AMA StyleFan Zhang, Yali Xue, Donghai Li, Jiong Shen, Xiao Wu, Zhenlong Wu, Ting He. Multiobjective Operation of Ultra-Supercritical Boiler-Turbine Unit. IFAC-PapersOnLine. 2018; 51 (28):592-597.
Chicago/Turabian StyleFan Zhang; Yali Xue; Donghai Li; Jiong Shen; Xiao Wu; Zhenlong Wu; Ting He. 2018. "Multiobjective Operation of Ultra-Supercritical Boiler-Turbine Unit." IFAC-PapersOnLine 51, no. 28: 592-597.
Fossil fuel power plants dominate the energy structure in China. However, the water scarcity, in Southeast/Southwest China, results in the development of the dry cooling power plants. Direct air cooling condenser (DACC) is appealing for its water saving ability. In DACC, the exhaust steam is cooled and condensed to water by the air flow, which is provided by the fan array at the cost of electricity. There exists a varying optimal exhaust steam pressure under different air temperatures and loads. The static model of the turbine-DACC system is built to quantify the influences of the air temperatures and loads. An on-line optimization is used here to set the reference value of the exhaust steam pressure for the turbine-DACC system for economic operation. The objective is to maximize the net power production while satisfying the constraints on the safety pressure and the available air flowrate. The optimization is conducted in a model 660MW power plant selected on a random summer/winter day. The hourly optimal exhaust steam pressure and the operation instructions are calculated as the reference values for the control layer. The optimization results show that the optimal exhaust steam pressure may be too close to the safety limits in winter, suggesting the safety also to be considered in the optimization. In summer, the DACC system cannot satisfy the condensation load sometimes. Thus, a complementary cooling method, for example, the wet cooling, is needed to reduce the burden on the DACC system.
Mingjuan Zhu; Jiong Shen; Kwang Y. Lee; Xiao Wu. Modelling and Optimization of the Direct Air Cooling Power Generation Unit. IFAC-PapersOnLine 2018, 51, 696 -701.
AMA StyleMingjuan Zhu, Jiong Shen, Kwang Y. Lee, Xiao Wu. Modelling and Optimization of the Direct Air Cooling Power Generation Unit. IFAC-PapersOnLine. 2018; 51 (28):696-701.
Chicago/Turabian StyleMingjuan Zhu; Jiong Shen; Kwang Y. Lee; Xiao Wu. 2018. "Modelling and Optimization of the Direct Air Cooling Power Generation Unit." IFAC-PapersOnLine 51, no. 28: 696-701.