Multi-objective prediction of coal-fired boiler with a deep hybrid neural networks
| Autor: | Jianmei Wang, Xiaobin Hu, Peifeng Niu, Xinxin Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Atmospheric Science
Thermal efficiency 010504 meteorology & atmospheric sciences Artificial neural network Power station Computer science 020209 energy Boiler (power generation) Control engineering 02 engineering and technology Coal fired 01 natural sciences Pollution Hybrid neural network 0202 electrical engineering electronic engineering information engineering Deep neural networks Waste Management and Disposal Hybrid model 0105 earth and related environmental sciences |
| Zdroj: | Atmospheric Pollution Research. 11:1084-1090 |
| ISSN: | 1309-1042 |
| Popis: | Our works frequently examine the emission of pollutants and the prediction of the thermal efficiency of boilers from power plants. Power plant systems are strongly coupled. Thus, multi-objective modelling and prediction is always a difficult problem. Artificial neural network (ANN) modelling is one of the methods used to meet this challenge. With the increasing requirements of environmental protection, the classical shallow neural network can no longer meet the needs of high precision. In recent years, deep neural networks have gradually demonstrated their powerful capabilities. However, can deep neural networks be used to improve model prediction performance? After many experiments, we successfully construct a sophisticated and stable deep hybrid neural network model to achieve this requirement. The experimental results show that the performance of the hybrid model is superior to that of the classical model; we diagram the detailed structure of the model and provide the corresponding parameter settings in this paper. |
| Databáze: | OpenAIRE |
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