Optimization of dark fermentation for biohydrogen production using a hybrid artificial neural network (ANN) and response surface methodology (RSM) approach
文献类型：期刊论文
| 作者 | Wang, Yunshan5; Yang, Gang5; Sage, Valerie4; Xu, Jian3; Sun, Guangzhi2; He, Jun1; Sun, Yong1 |
| 刊名 | ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY
 |
| 出版日期 | 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. |
| WOS关键词 | CLOSTRIDIUM-BUTYRICUM EB6 ; HYDROGEN-PRODUCTION ; ANAEROBIC FERMENTATION ; NICKEL NANOPARTICLES ; ACTIVATED CARBON ; WASTE-WATER ; IRON ; ENHANCEMENT ; CATALYST ; SLUDGE |
| 资助项目 | National Key R&D Program of China[2018YFC1903500] ; University of Nottingham Ningbo China FoSE[FIG2019] ; University of Nottingham[QJD1803014] |
| WOS研究方向 | Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000557372100001 |
| 出版者 | WILEY |
| 资助机构 | National Key R&D Program of China ; University of Nottingham Ningbo China FoSE ; University of Nottingham |
| 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.Univ Nottingham Ningbo China, Dept Chem & Environm Engn, Ningbo 315100, Peoples R China 2.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun, Peoples R China 3.Anhui Univ Technol, Biochem Engn Res Ctr, Maanshan, Peoples R China 4.CSIRO, Energy Business Unit, Perth, WA, Australia 5.Chinese Acad Sci, Inst Proc Engn, Beijing, 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. |
入库方式： OAI收割
来源：过程工程研究所
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