Residential College | false |
Status | 已發表Published |
Uncertainty and sensitivity analysis of algal-bacterial model under different ranges of parameter variation | |
Sheng, Huajun1,2; Ni, Shenzhou1; Wang, Yuyin3; Yuan, Rui4; Su, Kuizu1,5; Hao, Tianwei6 | |
2022-02-01 | |
Source Publication | Biochemical Engineering Journal |
ISSN | 1369-703X |
Volume | 179Pages:108334 |
Abstract | Based on the Activated Sludge Model No.3, a comprehensive model consisting of autotrophic bacteria, algae, and heterotrophic bacteria was presented in this work to describe the relationship among microorganisms and substrates in photo-sequencing batch reactors. The proposed model, which is called the algal-bacteria model, has involved a total of 31 parameters. The standard regression coefficient method was used to conduct global sensitivity analysis of the algal-bacteria model proposed was used to reduce the experiment time and energy of the experimenters, which determined that eight sensitivity parameters from analysis results according to the absolute value of sensitivity indices SRC. Analyzing the influence of different parameter value ranges on the sensitivity analysis, it provides a reference for the parameter sensitivity and uncertainty analysis of the algae-bacteria model. The results show that the algal-bacteria model simulates the removal process of complex nutrients well, and the parameter values affect the sensitivity analysis, but the identification of the most sensitive parameters will not be affected. Moreover, in terms of predicting the removal of dissolved oxygen and pollutants, the simulation results were proved to be in good agreement with the experimental data. It was demonstrated that the established model could be used as a valuable tool for optimizing the parameters of photo-sequencing batch reactors. Key findings of this paper include (1) representation of a comprehensive model to describe the interactions among bacteria, algae, and substrates; (2) assessment of the reliability of model parameters; (3) adoption of a novel method to estimate the most sensitive parameters and evaluate the influence of the change of parameters on the sensitivity analysis results. Our findings suggest that the application of the algal-bacterial consortia system may provide a viable technology in wastewater treatment. |
Keyword | Algal-bacterial Model Global Sensitivity Analysis Psbr |
DOI | 10.1016/j.bej.2022.108334 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Biotechnology & Applied Microbiology ; Engineering |
WOS Subject | Biotechnology & Applied Microbiology ; Engineering, Chemical |
WOS ID | WOS:000761024300005 |
Scopus ID | 2-s2.0-85122510880 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING |
Corresponding Author | Su, Kuizu; Hao, Tianwei |
Affiliation | 1.Department of Civil Engineering, Hefei University of Technology, Hefei, 230009, China 2.HeFei Water, Hefei, 230011, China 3.Department of Environmental Engineering, Hefei University of Technology, Hefei, 230009, China 4.College of Civil and Architecture Engineering, Chuzhou University, Chuzhou, 239000, China 5.Anhui Provincial Engineering Laboratory for Rural Water Environment and Resources, Hefei, 230009, China 6.Department of Civil & Environmental Engineering, University of Macau, Macau, 999078, China |
Corresponding Author Affilication | University of Macau |
Recommended Citation GB/T 7714 | Sheng, Huajun,Ni, Shenzhou,Wang, Yuyin,et al. Uncertainty and sensitivity analysis of algal-bacterial model under different ranges of parameter variation[J]. Biochemical Engineering Journal, 2022, 179, 108334. |
APA | Sheng, Huajun., Ni, Shenzhou., Wang, Yuyin., Yuan, Rui., Su, Kuizu., & Hao, Tianwei (2022). Uncertainty and sensitivity analysis of algal-bacterial model under different ranges of parameter variation. Biochemical Engineering Journal, 179, 108334. |
MLA | Sheng, Huajun,et al."Uncertainty and sensitivity analysis of algal-bacterial model under different ranges of parameter variation".Biochemical Engineering Journal 179(2022):108334. |
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