Model NOx emission and thermal efficiency of CFBB based on an ameliorated extreme learning machine

Methodologies and Application
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Abstract

Extreme learning machine (ELM) is a novel single hidden layer feed-forward network, which has become a research hotspot in various domains. Through in-depth analysis on ELM, there are four factors mainly affect its model performance, such as the input data, the input weights, the number of hidden layer nodes and the hidden layer activation function. In order to enhance the performance of ELM, an ameliorated extreme learning machine, namely AELM, is proposed based on the aforementioned four factors. The proposed method owns new way to generate input weights and bias of hidden layer and has a new-type hidden layer activation function. Simulations on many UCI benchmark regression problems have demonstrated that the AELM generally outperforms the original ELM as well as several variants of ELM. Simultaneously, the AELM is adopted to build thermal efficiency model and NOx emission model of a 330MW circulating fluidized bed boiler. The results demonstrate the AELM is a useful machine learning tool.

Keywords

Artificial intelligence Extreme learning machine Thermal efficiency model NOx emission model Circulating fluidized bed boiler 

Notes

Acknowledgements

Project Supported by the National Natural Science Foundation of China (Grant Nos. 61573306, 61403331).

Compliance with ethical standards

Conflict of interest

Peifeng Niu declares that he has no conflict of interest. Yunpeng Ma declares that he has no conflict of interest. Guoqiang Li declares that he has no conflict of interest.

Human Participants

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Key Lab of Industrial Computer Control Engineering of Hebei ProvinceYanshan UniversityQinhuangdaoChina

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