To realize the stable control of furnace temperature in a municipal solid waste incineration(MSWI)process,a nonlinear model predictive control(NMPC)method for furnace temperature is proposed in this paper.First,using the grate temperature and primary air temperature as the intermediate variables,a new MSWI furnace temperature control structure is obtained by integrating the cascade control strategy into NMPC.Then,the stochastic configuration network(SCN)is used to establish the furnace temperature static nonlinear prediction model offline,and the output weights of the hidden layer neurons of the SCN are updated online through the recursive least square method,so the furnace temperature dynamic nonlinear prediction model is established.Finally,an improved rolling optimization strategy is obtained by integrating the improved seagull optimization algorithm with the set value evaluation and learning model,which is used to improve the solution accuracy and efficiency of NMPC rolling optimization.The experimental results show that the dynamic nonlinear prediction model of furnace temperature can predict the furnace temperature accurately.The proposed control method has good adaptability and robustness,and can realize the stable control of furnace temperature in the MSWI process.
关键词
城市固废/炉温/非线性模型预测控制/随机配置网络/海鸥优化算法/设定值评价与学习
Key words
municipal solid waste/furnace temperature/nonlinear model predictive control/stochastic configuration network/seagull optimization algorithm/set value evaluation and learning
维普
万方数据