Adaptive Internal Model Control of SCR Denitration System Based on Multi-Objective Optimization

Many thermal power plants use selective catalytic reduction (SCR) systems to complete flue gas denitration. The system has the characteristics of large inertia, large delay and nonlinearity. At the same time, it is difficult to use the controller under a single working condition to meet the control task of the system under the full working conditions. In order to solve the above control difficulties, This paper proposes a two-degree-of-freedom internal model control (2-DOF-IMC) method based on controller parameter adaptation and multi-model adaptation to realize the control of the full working conditions of the SCR system. First, the intelligent identification method based on Differential evolution quantum particle swarm optimization (DEQPSO) is used to obtain the sub-model of ammonia injection-SCR outlet Nitrogen Oxide (NOx) concentration in typical working conditions. A 2-DOF-IMC controller under each sub-model is established. Then, aiming at system set point tracking performance, anti-disturbance performance, and control energy consumption, the Pareto optimal solution of each controller is obtained by using the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) algorithm based on chaotic mutation. Finally, the controller parameter adaptive strategy is designed based on the Takagi-Sugeno (T-S) fuzzy model, and the multi-model adaptive strategy is designed based on the recursive bayesian probability weighting. Through the dynamic performance test and the anti-disturbance performance test, it is verified that the 2-DOF-IMC based on controller parameter adaptation and multi-model adaptation has good control performance and adaptability to all working conditions. The method proposed in this paper combines the advantages of multi-parameter and multi-model strategies, further improves the control quality, and provides a new solution for the control of thermal power plant SCR system.