Open AccessMain Parameters Prediction of the Hot Water Boiler Based on the LSTM Neural Networks
Author(s) -
Sijie Liu,
Chuangxin Guo,
Ning Mei
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/677/3/032100
Subject(s) - boiler (water heating) , artificial neural network , coal water , principal component analysis , computer science , slurry , coal , process engineering , data mining , artificial intelligence , petroleum engineering , environmental science , engineering , waste management , environmental engineering
This paper presents a data-driven method for the main parameter’s prediction of a hot water boiler. Principal component analysis is used to compress the input dimensions of the model and reserves the main information of the monitored parameters. The validity of the model is demonstrated by a case study of a coal water slurry circulating fluidized bed hot water oiler belong to a heating company. The historical data of the boiler is employed to establish a deep long short memory cell neural network as the prediction. The prediction results of the main parameters could fulfil the demand of the actual engineering.