基于神经网络模型及预测控制DMC的火电机组脱硝控制策略

燃煤火电机组的NO_x排放值是受国家环保部门实时监督考核的重要环保安全指标。由于NO_x被控对象的纯时延大时滞特性，常规的PID控制很难将烟气NO_x排放指标控制到理想范围内。介绍了一种基于BP神经网络模型和预测控制的动态矩阵控制（DMC）算法相结合的新型火电机组脱硝控制策略，其中BP神经网络可逼近DMC算法中脱硝对象的零输入响应，利用神经网络的泛化能力，逼近实际工业过程在不同负荷下模型参数时变的特性，使预测控制中的模型预测部分可以更精确地逼近实际过程对象，提高整个预测控制算法的控制精度。现场应用表明，这种新型脱硝控制策略可有效提高火电机组NO_x的控制品质。

The Denitration Strategy of Predictive Dynamic Matrix Control (DMC) Combined with BP Neural Network in Fossil Fuel Power Plant

As one of the major environmental protection indicators, the value of NO_x emission from fossil fuel power plants has been supervised and assessed strictly in real time by the national environment protection department. Unfortunately, due to the intrinsic long duration of pure time delay dynamics in the NO_x controlled model, regulating the NO_x emissions within the ideal range by conventional PID controls would not be easy. In this paper, a strategy of the predictive dynamic matrix control (DMC) algorithm combined with BP neural network model is proposed for the denitration control, in which the BP neural network is implemented to approach the response of the zero predictive input in the DMC control. Therefore, by taking advantage of the BP generalization ability, the time-variant characteristics of the model at different loads in real industrial processes can be approximated to make the predictive model precisely closer to the practical object and consequently improve the precision of the predictive model output in the DMC algorithm. The application in a fossil fuel power plant shows that the new control strategy can effectively improve the NO_x control performance.