High-precision prediction of unionized hydrogen sulfide generation based on limited datasets and its impact on anaerobic digestion of sulfate-rich wastewater
文献类型：期刊论文
| 作者 | Yin, Wanxin; Yuan, Ye; Chen, Fan; Wang, Hongcheng ; Qiao, Liang; Chen, Tianming; Cheng, Haoyi; Xu, Xijun; Chen, Chuan; Liu, Wenzong
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| 刊名 | JOURNAL OF CLEANER PRODUCTION
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| 出版日期 | 2022-03-20 |
| 卷号 | 341期号:0页码:130875 |
| ISSN号 | 0959-6526 |
| 关键词 | CO-DIGESTION ACTIVATED-SLUDGE UASB TREATMENT PERFORMANCE METHANOGENESIS COMMUNITY BIOGAS |
| 英文摘要 | Data-driven models can simulate the complex anaerobic digestion (AD) process and predict the production of unionized hydrogen sulfide (H2S), thus improving methane (CH4) production by alleviating the unionized H2S inhibition. However, due to the limitations of model structures and small datasets, traditional data-driven models cannot accurately simulate the production of sulfides and their morphological differentiation process. In this study, by integrating method of deep neural network (DNN) model and random standard deviation sampling (RSDS), a method called RSDS-DNN was established to simulate the generation of unionized H2S in the AD process. The virtual data was generated by the RSDS method based on the mean value and standard calculated from the raw dataset collected from the experiment. Compared with the results of the artificial neural network (ANN) models trained with the raw dataset (the lowest MSE of 464.02 and the lowest MAPE of 29.00% for testing dataset), the DNN model trained with a virtual dataset showed obvious advantages. The DNN model with 2 hidden layers and 100 hidden neurons in each layer trained with a virtual dataset with 1060 samples achieved the best performance with a mean R-square (R-2) of 0.978, the minimal MAE of 87.80, and the minimal MAPE of 9.21% for the testing dataset. The Integrated RSDS-DNN model is a potential approach to assist the reducing of unionized H2S production and enhance CH4 production in the AD process. |
| 源URL | [https://ir.rcees.ac.cn/handle/311016/47794]  |
| 专题 | 生态环境研究中心_中国科学院环境生物技术重点实验室 |
| 作者单位 | 1.Harbin Inst Technol, Sch Environm, State Key Lab Urban Water Resources & Environm, Harbin 150001, Peoples R China 2.Northwestern Polytech Univ, Sch Ecol & Environm, Xian 710129, Peoples R China 3.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Key Lab Environm Biotechnol, Beijing 100085, Peoples R China 4.Yancheng Inst Technol, Sch Environm Sci & Engn, Yancheng 224051, Peoples R China 5.Yancheng Inst Technol, Jiangsu Prov Engn Res Ctr Intelligent Environm Pr, Yancheng 224051, Peop |
| 推荐引用方式 GB/T 7714 | Yin, Wanxin,Yuan, Ye,Chen, Fan,et al. High-precision prediction of unionized hydrogen sulfide generation based on limited datasets and its impact on anaerobic digestion of sulfate-rich wastewater[J]. JOURNAL OF CLEANER PRODUCTION,2022,341(0):130875. |
| APA | Yin, Wanxin.,Yuan, Ye.,Chen, Fan.,Wang, Hongcheng.,Qiao, Liang.,...&Wang, Aijie.(2022).High-precision prediction of unionized hydrogen sulfide generation based on limited datasets and its impact on anaerobic digestion of sulfate-rich wastewater.JOURNAL OF CLEANER PRODUCTION,341(0),130875. |
| MLA | Yin, Wanxin,et al."High-precision prediction of unionized hydrogen sulfide generation based on limited datasets and its impact on anaerobic digestion of sulfate-rich wastewater".JOURNAL OF CLEANER PRODUCTION 341.0(2022):130875. |
入库方式： OAI收割
来源：生态环境研究中心
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