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Optimization of dark fermentation for biohydrogen production using a hybrid artificial neural network (ANN) and response surface methodology (RSM) approach
Wang, Yunshan1; Yang, Gang1; Sage, Valerie2; Xu, Jian3; Sun, Guangzhi4; He, Jun5; Sun, Yong5
2020-08-09
Source PublicationENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY
ISSN1944-7442
Pages10
AbstractHerein, 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.
Keywordbiohydrogen dark hydrogen fermentation hybrid AANs RSM optimization potato peel waste starch
DOI10.1002/ep.13485
Language英语
WOS KeywordCLOSTRIDIUM-BUTYRICUM EB6 ; HYDROGEN-PRODUCTION ; ANAEROBIC FERMENTATION ; NICKEL NANOPARTICLES ; ACTIVATED CARBON ; WASTE-WATER ; IRON ; ENHANCEMENT ; CATALYST ; SLUDGE
Funding ProjectNational Key R&D Program of China[2018YFC1903500] ; University of Nottingham Ningbo China FoSE[FIG2019] ; University of Nottingham[QJD1803014]
WOS Research AreaScience & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology
WOS SubjectGreen & Sustainable Science & Technology ; Engineering, Environmental ; Engineering, Chemical ; Environmental Sciences
Funding OrganizationNational Key R&D Program of China ; University of Nottingham Ningbo China FoSE ; University of Nottingham
WOS IDWOS:000557372100001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/41628
Collection中国科学院过程工程研究所
Corresponding AuthorSun, Yong
Affiliation1.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
Recommended Citation
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.
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