NOx Emission Model for Coal-Fired Boilers Using Principle Component Analysis and Support Vector Regression
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- Tan Peng
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST)
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- Zhang Cheng
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST)
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- Xia Ji
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST)
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- Fang Qingyan
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST)
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- Chen Gang
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST)
書誌事項
- タイトル別名
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- NO<sub>X</sub> Emission Model for Coal-Fired Boilers Using Principle Component Analysis and Support Vector Regression
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抄録
Combustion optimization is an effective and economical approach for reducing nitrogen oxide (NOX) emissions from coal-fired utility boilers. To implement an online reduction in NOX, a precise and rapid NOX emissions model is required. This study establishes an efficient NOX emission model based on the principle component analysis (PCA) and support vector regression (SVR). Modeling performance comparisons were also conducted using a traditional artificial neural network (ANN) and SVR. A considerable amount of worthwhile real data was acquired from a 1000-MW coal-fired power plant to train and validate the PCA-SVR model, as well as the traditional ANN and SVR models. The predictive accuracy of the PCA-SVR model is considerably greater than that of the ANN and SVR models. The time consumed in the establishment of the PCA-SVR model is also shorter compared with that of the other two models. The proposed PCA-SVR model may be a better choice for the online or real-time modeling of NOX emissions in achieving a reduction of NOX emissions from coal-fired power plants.
収録刊行物
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- JOURNAL OF CHEMICAL ENGINEERING OF JAPAN
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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 49 (2), 211-216, 2016
公益社団法人 化学工学会
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詳細情報 詳細情報について
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- CRID
- 1390282679545940352
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- NII論文ID
- 130005126534
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- NII書誌ID
- AA00709658
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- ISSN
- 18811299
- 00219592
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- NDL書誌ID
- 027204126
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可