Adaptive Predictive Control of Oxygen Content in Flue Gas for Municipal Solid Waste Incineration Process
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摘要: 在城市固废焚烧过程中, 烟气含氧量是影响焚烧效果的重要工艺参数. 由于固废焚烧过程的复杂性, 实际应用过程中难以实现烟气含氧量的有效控制. 面向城市固废焚烧过程烟气含氧量控制的实际需求, 文中提出了一种基于数据驱动的烟气含氧量自适应预测控制方法. 首先, 采用自适应模糊C均值 (Fuzzy C-means, FCM) 算法辅助确定径向基函数 (Radial basis function, RBF) 神经网络隐含层神经元个数及初始中心, 建立基于FCM算法的RBF神经网络预测模型, 并在控制过程中通过自适应更新策略在线调节预测模型参数; 然后, 利用梯度下降算法求解控制律, 并基于李亚普诺夫理论分析了所提控制方法的稳定性; 最后, 基于城市固废焚烧厂实际数据, 验证了所提控制方法的有效性.Abstract: Oxygen content in flue gas is an important process parameter for incineration efficiency in municipal solid waste incineration (MSWI). Due to the complexity of MSWI process, it is difficult to achieve effective control of oxygen content in flue gas in practical application. A data-driven adaptive predictive control method for oxygen content in flue gas in MSWI process is proposed in this paper. Firstly, an adaptive fuzzy C-means (FCM) algorithm is used to determine the number of hidden layer neurons and the initial clustering center of radial basis function (RBF) neural network model, and the RBF neural network prediction model based on FCM algorithm is established. During the control process, the prediction model parameters are adjusted adaptively by an online updating strategy. Then, the gradient descent method is exploited to solve the control law, and the stability of the control system is analyzed based on Lyapunov theory. Finally, the effectiveness of the proposed control method is verified based on the actual data of the MSWI plant.
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表 1 输入输出变量变化范围
Table 1 Range of input and output variables
变量名 变量符号 变化范围 干燥段一次风流量 xp1 10.75~14.76 km3/h 燃烧1段一次风流量 xp2 25.90~35.56 km3/h 燃烧2段一次风流量 xp3 11.25~15.72 km3/h 燃烬段一次风流量 xp4 2.27~5.06 km3/h 二次风流量 xs 18.91~21.84 km3/h 烟气含氧量 yo 4.6~7.6% 表 2 操作变量与烟气含氧量的皮尔森相关系数
Table 2 Pearson correlation coefficient between manipulated variables and oxygen content in flue gas
操作变量名 操作变量符号 r 干燥段一次风流量 xp1 −0.4303 燃烧1段一次风流量 xp2 0.3015 燃烧2段一次风流量 xp3 −0.1034 燃烬段一次风流量 xp4 0.0697 二次风流量 xs 0.1413 表 3 不同建模方法的烟气含氧量预测评价指标对比
Table 3 Comparison of prediction evaluation indexes of oxygen content in flue gas with different modeling methods
模型 MAE MAPE RMSE MLP网络 0.0467 0.0086 0.0585 RBF网络 0.0357 0.0065 0.0449 FCM-RBF网络 0.0307 0.0056 0.0417 表 4 不同RBF神经网络预测控制器性能指标对比
Table 4 Comparison of evaluation indexes of different RBF neural network predictive controllers
控制器 IAE ITAE Devmax RBF-MPC 0.0056 4.3874 0.4991 FCM-RBF-MPC 0.0043 3.0002 0.4949 自适应RBF-MPC 0.0045 3.4617 0.4335 自适应FCM-RBF-MPC 0.0031 2.4152 0.4226 -
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