Operation space design of microbial fuel cells combined anaerobic-anoxic-oxic process based on support vector regression inverse model
文献类型：期刊论文
| 作者 | Wang, Jing1; Wang, Qilun1; Zhou, Jinglin1; Wang, Xiaohui2; Cheng, Long3
|
| 刊名 | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
 |
| 出版日期 | 2018-06-01 |
| 卷号 | 72页码:340-349 |
| 关键词 | Mfc-a(2)/o Operation Space Design Support Vector Regression Inverse Model Prediction Uncertainty Estimation |
| DOI | 10.1016/j.engappai.2018.04.005 |
| 文献子类 | Article |
| 英文摘要 | Microbial Fuel Cells (MFCs) can produce power at the same time of wastewater treatment, which is a new technique for environmental protection and new energy. An appropriate space design of operation variables is very important to improve the performance of MFC process. This paper presents a space design method based on data-driven model but not the traditional mechanism model, which is easy to accomplish in a fast and cost-effective mode. The support vector regression (SVR) forward and inverse model are deduced with the quadratic kernel function, in which the quadratic kernel function is suitable for the mathematical formula in the inversion stage. And the space design of operation variables are proposed to calculate directly from the inverse model with the effect of confidence interval when the model prediction uncertainty are considered. The proposed design method is verified in the real MFC-A(2)/O equipment. It is shown that the designated operation space is a narrow and effective region of the knowledge space which brackets the entire fraction of the MFC experiment space. And in general terms, the possible product quality from the designated operation space is more densely concentrated on the desired value compared to the tradition forward model design method. |
| WOS关键词 | WASTE-WATER TREATMENT ; REMOVAL ; SYSTEM ; PERFORMANCE ; NITROGEN |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000434239000028 |
| 资助机构 | National Natural Science Foundation of China(61573050 ; Beijing Natural Science Foundation(4162066) ; open-project grant - State Key Laboratory of Synthetical Automation for Process Industry at the Northeastern University(PAL-N201702) ; 61473025 ; 61633016) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/22046]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 作者单位 | 1.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China 2.Beijing Univ Chem Technol, Beijing Engn Res Ctr Environm Mat Water Purificat, Coll Chem Engn, Beijing 100029, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Jing,Wang, Qilun,Zhou, Jinglin,et al. Operation space design of microbial fuel cells combined anaerobic-anoxic-oxic process based on support vector regression inverse model[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2018,72:340-349. |
| APA | Wang, Jing,Wang, Qilun,Zhou, Jinglin,Wang, Xiaohui,&Cheng, Long.(2018).Operation space design of microbial fuel cells combined anaerobic-anoxic-oxic process based on support vector regression inverse model.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,72,340-349. |
| MLA | Wang, Jing,et al."Operation space design of microbial fuel cells combined anaerobic-anoxic-oxic process based on support vector regression inverse model".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 72(2018):340-349. |
入库方式： OAI收割
来源：自动化研究所
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