Prediction Control of Biomass Combustion Boiler based on Multilayer Perceptron Neural Network

Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019) Pub Date : 2019-11-01 DOI:10.2991/pntim-19.2019.85

Yilin Shen

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Abstract

The structure of biomass direct fired boiler differs greatly from that of common fuel powder boiler, so the difference of operation process is great, which will inevitably lead to the difference of operation regulation law. Therefore, it is very important to analyze its technological process and combustion process in detail. All data were analyzed by SPSS17.0. We used the IBM SPSS Modeler 14.1data software to carry out modeling and prediction. The results show that there are 100 neurons in hidden layer and the area under the curve. The model accuracy, sensitivity and specific is 91.96%, 81.22% and 93.77%. Through validation data set validating, the model accuracy, sensitivity and specific is 92.15%, 80.32% and 94.01%. Therefore working process of biomass combustion boiler could accurately predict by MLP neural network model based on characteristics as the input layer variables of prediction model. Keywords-Prediction Control; Multilayer Perceptron; Neural Network;Working Process; Biomass Combustion Boiler

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基于多层感知器神经网络的生物质燃烧锅炉预测控制

生物质直燃锅炉的结构与普通燃料粉末状锅炉差异较大，因此运行过程差异较大，必然导致运行调节规律的差异。因此，对其工艺过程和燃烧过程进行详细的分析是十分重要的。所有数据均采用SPSS17.0进行分析。我们使用IBM SPSS Modeler 14.1数据软件进行建模和预测。结果表明，隐藏层和曲线下面积有100个神经元。模型的准确度、灵敏度和特异度分别为91.96%、81.22%和93.77%。通过验证数据集验证，模型的准确率、灵敏度和特异度分别为92.15%、80.32%和94.01%。因此，基于特征的MLP神经网络模型作为预测模型的输入层变量，可以准确预测生物质燃烧锅炉的工作过程。Keywords-Prediction控制;多层感知器;神经网络;工作过程;生物质燃烧锅炉

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Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019)

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