Machine learning-aided optimization of coal decoupling combustion for lowering NO and CO emissions simultaneously

doi:10.1016/j.compchemeng.2022.107822

CAS OpenIR

	Machine learning-aided optimization of coal decoupling combustion for lowering NO and CO emissions simultaneously
	Jin, Nani 1,2; Guo, Li1,2 ; Liu, Xinhua 1
	2022-06-01
Source Publication	COMPUTERS & CHEMICAL ENGINEERING
ISSN	0098-1354
Volume	162 Pages:11
Abstract	Decoupling combustion technology enables significant suppression of NOx and CO emissions from solid fuel combustion, but calls for optimizing reactor structure to make full use of its superiority. Taking a coal stove as an example, three different network models were established and trained to predict the steady-state NO and CO emissions from coal decoupling combustion well. The two GRU-DNN models have higher prediction accuracy and better generalization ability than the DNN model, but they both need to be fed with complex sequence data, leading to long training and response time to new inputs. The DNN model with simple fuel properties and structural parameters as the inputs was used to forecast the steady-state NO and CO emissions from various coal-stove combinations with acceptable accuracy, so facilitating the optimization of stove structure and further coal decoupling combustion to lower the NO and CO emissions simultaneously. (C) 2022 Elsevier Ltd. All rights reserved.
Keyword	Machine learning Deep neural network Gated recurrent unit Decoupling combustion Pollutant emission Coal stove
DOI	10.1016/j.compchemeng.2022.107822
Language	英语
WOS Keyword	THERMAL-DECOMPOSITION ; REDUCTION ; SEARCH ; BOILER
Funding Project	"Transformational Technologies for Clean Energy and Demonstration", Strategic Priority Research Program of Chinese Academy of Sciences[XDA21040400]
WOS Research Area	Computer Science ; Engineering
WOS Subject	Computer Science, Interdisciplinary Applications ; Engineering, Chemical
Funding Organization	"Transformational Technologies for Clean Energy and Demonstration", Strategic Priority Research Program of Chinese Academy of Sciences
WOS ID	WOS:000831313200006
Publisher	PERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type	期刊论文
Identifier	http://ir.ipe.ac.cn/handle/122111/54234
Collection	中国科学院过程工程研究所
Corresponding Author	Guo, Li; Liu, Xinhua
Affiliation	1.Chinese Acad Sci, Inst Proc Engn, State Key Lab Multiphase Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Chem Engn, Beijing 100049, Peoples R China
Recommended Citation GB/T 7714	Jin, Nani,Guo, Li,Liu, Xinhua. Machine learning-aided optimization of coal decoupling combustion for lowering NO and CO emissions simultaneously[J]. COMPUTERS & CHEMICAL ENGINEERING,2022,162:11.
APA	Jin, Nani,Guo, Li,&Liu, Xinhua.(2022).Machine learning-aided optimization of coal decoupling combustion for lowering NO and CO emissions simultaneously.COMPUTERS & CHEMICAL ENGINEERING,162,11.
MLA	Jin, Nani,et al."Machine learning-aided optimization of coal decoupling combustion for lowering NO and CO emissions simultaneously".COMPUTERS & CHEMICAL ENGINEERING 162(2022):11.

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