Optimization of dark fermentation for biohydrogen production using a hybrid artificial neural network (ANN) and response surface methodology (RSM) approach

doi:10.1002/ep.13485

	Optimization of dark fermentation for biohydrogen production using a hybrid artificial neural network (ANN) and response surface methodology (RSM) approach
	Wang, Yunshan 1; Yang, Gang 1; Sage, Valerie 2; Xu, Jian 3; Sun, Guangzhi 4; He, Jun 5; Sun, Yong 5
刊名	ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY
	2020-08-09
页码	10
关键词	biohydrogen dark hydrogen fermentation hybrid AANs RSM optimization potato peel waste starch
ISSN号	1944-7442
DOI	10.1002/ep.13485
英文摘要	Herein, the production of biohydrogen by dark fermentation was optimized using a novel hybrid approach that combines ANNs (artificial neural networks) with RSM (response surface methodology). Using the limited numbers of data (15 runs) as training data set together with one cross-out method for validation, the complete 29 runs of well-established data matrix were created from ANNs for RSM statistical analysis in order to correlated the critical process parameters with hydrogen production performance. This methodology was found to be robust, cost-effective, reliable, and can be extensively analyzed the critical operational parameters, that is, carbon sources (obtained from potato peel wastes and starchy wastes), metal cofactor Fe-0, pH, and dose levels of microbes on the hydrogen production, along with concentrations of other metabolites, such as acetic acid, propionic acid, butyric acid, valeric acid, and ethanol. The established ANNs-RSM model using Box-Behnken design indicates the significant changes caused by the variations of a few critical operation parameters. The resultant model shows an exceptionally good result in terms of nonlinear noisy processes. Both single and multiple objective optimizations for dark hydrogen fermentation can achieve by using the established hybrid ANN-RSM system. The optimal operating conditions (starch 6.2 kg/m(3), pH 6.7, Fe(0)11.7 g/m(3), sludge 24.6 g/m(3)) could lead to the generation of hydrogen with a yield of 106.2 (cm(3)/g) and metabolites, that is, propionic acid (2.8 kg/m(3)), butyric acid (2E-2 kg/m(3)), valeric acid (4E-4 kg/m(3)) acetic acid (1.9 kg/m(3)), and ethanol (0.1 kg/m(3)) simultaneously.
资助项目	National Key R&D Program of China[2018YFC1903500] ; University of Nottingham Ningbo China FoSE[FIG2019] ; University of Nottingham[QJD1803014]
WOS关键词	CLOSTRIDIUM-BUTYRICUM EB6 ; HYDROGEN-PRODUCTION ; ANAEROBIC FERMENTATION ; NICKEL NANOPARTICLES ; ACTIVATED CARBON ; WASTE-WATER ; IRON ; ENHANCEMENT ; CATALYST ; SLUDGE
WOS研究方向	Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology
语种	英语
出版者	WILEY
WOS记录号	WOS:000557372100001
资助机构	National Key R&D Program of China ; University of Nottingham Ningbo China FoSE ; University of Nottingham
内容类型	期刊论文
源URL	[http://ir.ipe.ac.cn/handle/122111/41627]
专题	中国科学院过程工程研究所
通讯作者	Sun, Yong
作者单位	1.Chinese Acad Sci, Inst Proc Engn, Beijing, Peoples R China 2.CSIRO, Energy Business Unit, Perth, WA, Australia 3.Anhui Univ Technol, Biochem Engn Res Ctr, Maanshan, Peoples R China 4.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun, Peoples R China 5.Univ Nottingham Ningbo China, Dept Chem & Environm Engn, Ningbo 315100, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yunshan,Yang, Gang,Sage, Valerie,et al. Optimization of dark fermentation for biohydrogen production using a hybrid artificial neural network (ANN) and response surface methodology (RSM) approach[J]. ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY,2020:10.
APA	Wang, Yunshan.,Yang, Gang.,Sage, Valerie.,Xu, Jian.,Sun, Guangzhi.,...&Sun, Yong.(2020).Optimization of dark fermentation for biohydrogen production using a hybrid artificial neural network (ANN) and response surface methodology (RSM) approach.ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY,10.
MLA	Wang, Yunshan,et al."Optimization of dark fermentation for biohydrogen production using a hybrid artificial neural network (ANN) and response surface methodology (RSM) approach".ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY (2020):10.