Circular convolution parallel extreme learning machine for modeling boiler efficiency for a 300 MW CFBB

Methodologies and Application
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Abstract

Aiming at the accuracy prediction of combustion efficiency for a 300 MW circulating fluidized bed boiler (CFBB), a circular convolution parallel extreme learning machine (CCPELM) which is a double parallel forward neural network is proposed. In CCPELM, the circular convolution theory is introduced to map the hidden layer information into higher-dimension information; in addition, the input layer information is directly transmitted to its output layer, which makes the whole network into a double parallel construction. In this paper, CCPELM is applied to establish a model for boiler efficiency though data samples collected from a 300 MW CFBB. Some comparative simulation results with other neural network models show that CCPELM owns very high prediction accuracy with fast learning speed and very good repeatability in learning ability.

Keywords

Circular convolution Neural network Extreme learning machine Circulating fluidized bed boiler 

Notes

Acknowledgements

This work was funded by National Natural Science Foundation of China (Grant Nos. 61403331, 61573306), Program for the Top Young Talents of Higher Learning Institutions of Hebei (Grant No. BJ2017033), Natural Science Foundation of Hebei Province (Grant No. F2016203427), China Postdoctoral Science Foundation (Grant No. 2015M571280), the Doctorial Foundation of Yanshan University (Grant No. B847), the natural science foundation for young scientist of Hebei province (Grant No. F2014203099) and the independent research program for young teachers of Yanshan University (Grant No. 13LG006).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

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

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Lab of Industrial Computer Control Engineering of Hebei ProvinceYanshan UniversityQinhuangdaoChina
  2. 2.Department of ComputingThe Hong Kong Polytechnic UniversityHung HomHong Kong

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