Applied Gaussian Process in Optimizing Unburned Carbon Content in Fly Ash for Boiler Combustion
Autor: | Richard M. Everson, Yang Liu, Song Zheng, Chunlin Wang, Alma A. M. Rahat |
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Rok vydání: | 2017 |
Předmět: |
Hyperparameter
Engineering Article Subject Waste management business.industry lcsh:Mathematics 020209 energy General Mathematics General Engineering Boiler (power generation) 02 engineering and technology Coal fired lcsh:QA1-939 Combustion symbols.namesake lcsh:TA1-2040 Fly ash 0202 electrical engineering electronic engineering information engineering symbols lcsh:Engineering (General). Civil engineering (General) Process engineering business Gaussian process |
Zdroj: | Mathematical Problems in Engineering, Vol 2017 (2017) |
ISSN: | 1563-5147 1024-123X |
Popis: | 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. |
Databáze: | OpenAIRE |
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