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

文献类型：期刊论文

作者Jin, Nani1,2; Guo, Li1,2; Liu, Xinhua1
刊名COMPUTERS & CHEMICAL ENGINEERING
出版日期2022-06-01
卷号162页码:11
关键词Machine learning Deep neural network Gated recurrent unit Decoupling combustion Pollutant emission Coal stove
ISSN号0098-1354
DOI10.1016/j.compchemeng.2022.107822
英文摘要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.
WOS关键词THERMAL-DECOMPOSITION ; REDUCTION ; SEARCH ; BOILER
资助项目"Transformational Technologies for Clean Energy and Demonstration", Strategic Priority Research Program of Chinese Academy of Sciences[XDA21040400]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000831313200006
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构"Transformational Technologies for Clean Energy and Demonstration", Strategic Priority Research Program of Chinese Academy of Sciences
源URL[http://ir.ipe.ac.cn/handle/122111/54233]  
专题中国科学院过程工程研究所
通讯作者Guo, Li; Liu, Xinhua
作者单位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
推荐引用方式
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.

入库方式： OAI收割

来源：过程工程研究所

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