A novel effluent quality predicting model based on genetic-deep belief network algorithm for cleaner production in a full-scale paper-making wastewater treatment

doi:10.1016/j.jclepro.2020.121787

	A novel effluent quality predicting model based on genetic-deep belief network algorithm for cleaner production in a full-scale paper-making wastewater treatment
	Niu, Guoqiang; Yi, Xiaohui; Chen, Chen; Li, Xiaoyong; Han, Donghui; Yan, Bo; Huang, Mingzhi; Ying, Guangguo
刊名	JOURNAL OF CLEANER PRODUCTION
	2020
卷号	265 页码:121787
ISSN号	0959-6526
DOI	10.1016/j.jclepro.2020.121787
WOS研究方向	Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology
语种	英语
WOS记录号	WOS:000552097000014
内容类型	期刊论文
源URL	[http://ir.gig.ac.cn/handle/344008/57343]
专题	中国科学院广州地球化学研究所
推荐引用方式 GB/T 7714	Niu, Guoqiang,Yi, Xiaohui,Chen, Chen,et al. A novel effluent quality predicting model based on genetic-deep belief network algorithm for cleaner production in a full-scale paper-making wastewater treatment[J]. JOURNAL OF CLEANER PRODUCTION,2020,265:121787.
APA	Niu, Guoqiang.,Yi, Xiaohui.,Chen, Chen.,Li, Xiaoyong.,Han, Donghui.,...&Ying, Guangguo.(2020).A novel effluent quality predicting model based on genetic-deep belief network algorithm for cleaner production in a full-scale paper-making wastewater treatment.JOURNAL OF CLEANER PRODUCTION,265,121787.
MLA	Niu, Guoqiang,et al."A novel effluent quality predicting model based on genetic-deep belief network algorithm for cleaner production in a full-scale paper-making wastewater treatment".JOURNAL OF CLEANER PRODUCTION 265(2020):121787.