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

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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.

## References

- Antoulas AC (2005) An overview of approximation methods for large-scale dynamical systems. Annu Rev Control 29(2):181–190CrossRefGoogle Scholar
- Arsie I, Pianese C, Sorrentino M (2010) Development of recurrent neural networks for virtual sensing of NOx emissions in internal combustion engines. SAE Int J Fuels Lubr 2(2):354–361CrossRefGoogle Scholar
- Bartlett PL (1998) The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans Neural Netw 44(2):525–536MathSciNetzbMATHGoogle Scholar
- Deng W, Zheng Q, Chen L (2009) Regularized extreme learning machine. In: Proceedings of the IEEE symposium on computational intelligence and data mining, pp 389–395Google Scholar
- Deng WY, Zheng QH, Chen L et al (2010) Research on extreme learning of neural networks. Chin J Comput 33(2):279–287MathSciNetCrossRefGoogle Scholar
- Gao XQ, Ding YM (2009) Digital signal processing. Xidian University Press, Xi’anGoogle Scholar
- Green M, Ekelund U, Edenbrandt L et al (2009) Exploring new possibilities for case-based explanation of artificial neural network ensembles. Neural Netw 22(1):75–81CrossRefGoogle Scholar
- Hernandez L, Baladrón C, Aguiar JM et al (2013) Short-term load forecasting for microgrids based on artificial neural networks. Energies 6:1385–1408CrossRefGoogle Scholar
- Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70:3056–3062CrossRefGoogle Scholar
- Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71:3460–3468CrossRefGoogle Scholar
- Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feed forward neural networks. In: Proceedings of international joint conference on neural networks, pp 25–29Google Scholar
- Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRefGoogle Scholar
- Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892CrossRefGoogle Scholar
- Huang GB, Zhou H, Ding X et al (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42(2):513–529CrossRefGoogle Scholar
- Iliya S, Goodyer E, Gow J, et al (2015) Application of artificial neural network and support vector regression in cognitive radio networks for RF power prediction using compact differential evolution algorithm. In: Federated conference on computer science and information systems, pp 55–66Google Scholar
- Iosifidis A, Tefas A, Pitas I (2013) Minimum class variance extreme learning machine for human action recognition. IEEE Trans Circuits Syst Video Technol 5427–5431Google Scholar
- Li MB, Huang GB, Saratchandran P et al (2005) Fully complex extreme learning machine. Neurocomputing 68(1):306–314CrossRefGoogle Scholar
- Li G, Niu P, Wang H et al (2013) Least square fast learning network for modeling the combustion efficiency of a 300 MW coal-fired boiler. Neural Netw Off J Int Neural Netw Soc 5(3):57–66zbMATHGoogle Scholar
- Luo Z, Wang F, Zhou H et al (2011) Principles of optimization of combustion by radiant energy signal and its application in a 660 MWe down- and coal-fired boiler. Korean J Chem Eng 28(12):2336–2343CrossRefGoogle Scholar
- May RJ, Maier HR, Dandy GC (2010) Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Netw Off J Int Neural Netw Soc 23(2):283–294CrossRefGoogle Scholar
- Pangaribuan JJ, Suharjito (2014) Diagnosis of diabetes mellitus using extreme learning machine. In: International conference on information technology systems and innovation, pp 33–38Google Scholar
- Pao YH, Park GH, Sobajic DJ (1994) Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2):163–180CrossRefGoogle Scholar
- Qiu X, Suganthan PN, Gehan AJ (2017) Amaratunga. Electricity load demand time series forecasting with empirical mode decomposition based random vector functional link network. In: IEEE international conference on systems, man, and cybernetics. IEEE, pp 1394–1399Google Scholar
- Qu BY, Lang BF, Liang JJ et al (2016) Two-hidden-layer extreme learning machine for regression and classification. Neurocomputing 175:826–834CrossRefGoogle Scholar
- Schoukens J, Pintelon R (1991) Identification of linear systems: a practical guideline to accurate modeling. Pergamon Press, Inc, OxfordzbMATHGoogle Scholar
- Suresh S, Babu RV, Kim HJ (2009) No-reference image quality assessment using modified extreme learning machine classifier. Appl Soft Comput 9:541–552CrossRefGoogle Scholar
- Wang J, Wu W, Li Z et al (2012) Convergence of gradient method for double parallel feedforward neural network. Int J Nume Anal Model 8(3):484–495MathSciNetzbMATHGoogle Scholar
- Zhang H, Yin Y, Zhang S (2016) An improved ELM algorithm for the measurement of hot metal temperature in blast furnace. Neurocomputing 174(4):232–237MathSciNetCrossRefGoogle Scholar
- Zheng Y, Gao X, Sheng C (2017) Impact of co-firing lean coal on NOx emission of a large-scale pulverized coal-fired utility boiler during partial load operation. Korean J Chem Eng 34(4):1273–1280CrossRefGoogle Scholar
- Zhou H, Zhao JP, Zheng LG et al (2012) Modeling NOx, emissions from coal-fired utility boilers using support vector regression with ant colony optimization. Eng Appl Artif Intell 25(1):147–158CrossRefGoogle Scholar
- Zhu Q-Y, Qin A-K, Suganthan P-N, Huang G-B (2005) Evolutionary extreme learning machine. Pattern Recognit 38(10):1759–1763CrossRefzbMATHGoogle Scholar