Efficient control of the desulphurization system is challenging in maximizing the economic objective while reducing the SO_(2) emission concentration. The conventional optimization method is generally based on a hiera...Efficient control of the desulphurization system is challenging in maximizing the economic objective while reducing the SO_(2) emission concentration. The conventional optimization method is generally based on a hierarchical structure in which the upper optimization layer calculates the steady-state results and the lower control layer is responsible to drive the process to the target point. However, the conventional hierarchical structure does not take the economic performance of the dynamic tracking process into account. To this end, multi-objective economic model predictive control(MOEMPC) is introduced in this paper, which unifies the optimization and control layers in a single stage. The objective functions are formulated in terms of a dynamic horizon and to balance the stability and economic performance. In the MOEMPC scheme, economic performance and SO_(2) emission performance are guaranteed by tracking a set of utopia points during dynamic transitions. The terminal penalty function and stabilizing constraint conditions are designed to ensure the stability of the system. Finally, an optimized control method for the stable operation of the complex desulfurization system has been established. Simulation results demonstrate that MOEMPC is superior over another control strategy in terms of economic performance and emission reduction, especially when the desulphurization system suffers from frequent flue gas disturbances.展开更多
Nonlinear model predictive control(NMPC)scheme is an effective method of multi-objective optimization control in complex industrial systems.In this paper,a NMPC scheme for the wet limestone flue gas desulphurization(W...Nonlinear model predictive control(NMPC)scheme is an effective method of multi-objective optimization control in complex industrial systems.In this paper,a NMPC scheme for the wet limestone flue gas desulphurization(WFGD)system is proposed which provides a more flexible framework of optimal control and decision-making compared with PID scheme.At first,a mathematical model of the FGD process is deduced which is suitable for NMPC structure.To equipoise the model’s accuracy and conciseness,the wet limestone FGD system is separated into several modules.Based on the conservation laws,a model with reasonable simplification is developed to describe dynamics of different modules for the purpose of controller design.Then,by addressing economic objectives directly into the NMPC scheme,the NMPC controller can minimize economic cost and track the set-point simultaneously.The accuracy of model is validated by the field data of a 1000 MW thermal power plant in Henan Province,China.The simulation results show that the NMPC strategy improves the economic performance and ensures the emission requirement at the same time.In the meantime,the control scheme satisfies the multiobjective control requirements under complex operation conditions(e.g.,boiler load fluctuation and set point variation).The mathematical model and NMPC structure provides the basic work for the future development of advanced optimized control algorithms in the wet limestone FGD systems.展开更多
基金supported by the National Key Research and Development Program of China (2017YFB0601805)。
文摘Efficient control of the desulphurization system is challenging in maximizing the economic objective while reducing the SO_(2) emission concentration. The conventional optimization method is generally based on a hierarchical structure in which the upper optimization layer calculates the steady-state results and the lower control layer is responsible to drive the process to the target point. However, the conventional hierarchical structure does not take the economic performance of the dynamic tracking process into account. To this end, multi-objective economic model predictive control(MOEMPC) is introduced in this paper, which unifies the optimization and control layers in a single stage. The objective functions are formulated in terms of a dynamic horizon and to balance the stability and economic performance. In the MOEMPC scheme, economic performance and SO_(2) emission performance are guaranteed by tracking a set of utopia points during dynamic transitions. The terminal penalty function and stabilizing constraint conditions are designed to ensure the stability of the system. Finally, an optimized control method for the stable operation of the complex desulfurization system has been established. Simulation results demonstrate that MOEMPC is superior over another control strategy in terms of economic performance and emission reduction, especially when the desulphurization system suffers from frequent flue gas disturbances.
基金Financial support from the National Key R&D Program of China(No.2017YFB0601805)。
文摘Nonlinear model predictive control(NMPC)scheme is an effective method of multi-objective optimization control in complex industrial systems.In this paper,a NMPC scheme for the wet limestone flue gas desulphurization(WFGD)system is proposed which provides a more flexible framework of optimal control and decision-making compared with PID scheme.At first,a mathematical model of the FGD process is deduced which is suitable for NMPC structure.To equipoise the model’s accuracy and conciseness,the wet limestone FGD system is separated into several modules.Based on the conservation laws,a model with reasonable simplification is developed to describe dynamics of different modules for the purpose of controller design.Then,by addressing economic objectives directly into the NMPC scheme,the NMPC controller can minimize economic cost and track the set-point simultaneously.The accuracy of model is validated by the field data of a 1000 MW thermal power plant in Henan Province,China.The simulation results show that the NMPC strategy improves the economic performance and ensures the emission requirement at the same time.In the meantime,the control scheme satisfies the multiobjective control requirements under complex operation conditions(e.g.,boiler load fluctuation and set point variation).The mathematical model and NMPC structure provides the basic work for the future development of advanced optimized control algorithms in the wet limestone FGD systems.
