优化控制在锅炉燃烧中的应用

The Application of predictive control in the Boiler Burning Control

锅炉燃烧优化控制技术是保证其在最佳情况下运行,提高其经济效益的关键技术。为了实现其过程预测优化控制,利用人工神经网络(ANN)来建立锅炉燃烧优化控制的模型,基于过程测量数据可以反映对象的规律和特性,提出采用过程测量数据检验对象模型的构想。并且提出了一种双ANN结构的锅炉燃烧控制模型,经模型预测一实测对比,其最大误差不过2%,较好地解决了锅炉燃烧控制模型地构造、学习算法和误差校正等问题。

Concerning the predictive control of an Boiler Burning Control reactor, this paper presents and accomplishes the problems as follows: the ANN model is presented, whose main structure is constructed by off-line trained RBFN and expresses the object's wide-change law, whose secondary structure is constructed by on - line - trained BPN, which expresses the main structure's error and can eliminates the model error that was the result of the object's slow-change. The model is founded by linear addition of the main structure and the secondary one. Sinmulation results show that the maximum error between the predictive data and the measuring ones is no bigger than 2 percent and that the method is efficient.