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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 6138930, 8 pages
Research Article

Applied Gaussian Process in Optimizing Unburned Carbon Content in Fly Ash for Boiler Combustion

1Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China
2Department of Computer Science, University of Exeter, Exeter EX4 4QF, UK

Correspondence should be addressed to Yang Liu

Received 24 September 2016; Accepted 12 April 2017; Published 11 May 2017

Academic Editor: J.-C. Cortés

Copyright © 2017 Chunlin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recently, Gaussian Process (GP) has attracted generous attention from industry. This article focuses on the application of coal fired boiler combustion and uses GP to design a strategy for reducing Unburned Carbon Content in Fly Ash (UCC-FA) which is the most important indicator of boiler combustion efficiency. With getting rid of the complicated physical mechanisms, building a data-driven model as GP is an effective way for the proposed issue. Firstly, GP is used to model the relationship between the UCC-FA and boiler combustion operation parameters. The hyperparameters of GP model are optimized via Genetic Algorithm (GA). Then, served as the objective of another GA framework, the predicted UCC-FA from GP model is utilized in searching the optimal operation plan for the boiler combustion. Based on 670 sets of real data from a high capacity tangentially fired boiler, two GP models with 21 and 13 inputs, respectively, are developed. In the experimental results, the model with 21 inputs provides better prediction performance than that of the other. Choosing the results from 21-input model, the UCC-FA decreases from 2.7% to 1.7% via optimizing some of the operational parameters, which is a reasonable achievement for the boiler combustion.