Improving ADM1 predictions via Bayesian analysis for continuous anaerobic digestion
文献类型：期刊论文
| 作者 | Liu, Yanxin1,2; Jiang, Ying1; Nasar, Nasreen1; Bajon-Fernandez, Yadira1; Longhurst, Philip1; Guo, Weisi1; Lei, Mei3; Bortone, Immacolata1 |
| 刊名 | JOURNAL OF ENVIRONMENTAL MANAGEMENT
 |
| 出版日期 | 2026-01-15 |
| 卷号 | 398页码:128537 |
| 关键词 | Anaerobic digestion ADM1 Steady-state performance Global sensitivity analysis Bayesian calibration |
| ISSN号 | 0301-4797 |
| DOI | 10.1016/j.jenvman.2025.128537 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | The Anaerobic Digestion Model No.1 (ADM1) application for continuous anaerobic digestion is often constrained by challenges in reliably calibrating model parameters, especially when long-term data are unavailable. This study presents a Bayesian inference-based framework that enables ADM1 calibration using only initial-stage digester performance data. A custom Python implementation was developed, integrating modules for global sensitivity analysis, Bayesian calibration and parameter identifiability evaluation. Key microbial and ionic parameters were refined through Random Balance Designs-Fourier Amplitude Sensitivity Test (RBD-FAST), identifying sugar/acetate degraders and cation/anion levels in the inoculum as critical drivers of steady-state performance. With informative priors derived from similar ADM1 studies, the model was calibrated with less than two hydraulic retention times of data and validated against steady-state performance data. It predicted pH and total chemical oxygen demand (tCOD) with mean percentage errors of 1.10 % and 5.38 % respectively. Biogas production trends were captured within the 95 % credible interval for 63.14 % of observations. Compared to uniform priors, the Bayesian approach with informative priors improved predictive accuracy. Jensen-Shannon divergence revealed that hydrolysis rates is the most identifiable for thermally hydrolysed sludge. Unlike conventional ADM1 calibration approaches that require long-term steady-state data, this Bayesian framework achieves reliable predictions using only early-stage observations. By enabling accurate simulation of organic contaminant degradation and system stability from limited data, the framework supports risk-informed design and operation of anaerobic digesters and offers a solution for data-scarce industrial settings to improve safety, sustainability, and optimisation. |
| URL标识 | 查看原文 |
| WOS关键词 | WASTE-WATER ; SENSITIVITY-ANALYSIS ; CO-DIGESTION ; THERMAL PRETREATMENT ; BIOGAS PRODUCTION ; SEWAGE-SLUDGE ; PERFORMANCE ; CALIBRATION ; NETWORK ; MODELS |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001662672100001 |
| 出版者 | ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219626]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Bortone, Immacolata |
| 作者单位 | 1.Cranfield Univ, Fac Engn & Appl Sci, Coll Rd, Cranfield MK43 0AL, England; 2.Univ Exeter, Fac Environm Sci & Econ, Stocker Rd, Exeter EX4 4PY, England; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Yanxin,Jiang, Ying,Nasar, Nasreen,et al. Improving ADM1 predictions via Bayesian analysis for continuous anaerobic digestion[J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT,2026,398:128537. |
| APA | Liu, Yanxin.,Jiang, Ying.,Nasar, Nasreen.,Bajon-Fernandez, Yadira.,Longhurst, Philip.,...&Bortone, Immacolata.(2026).Improving ADM1 predictions via Bayesian analysis for continuous anaerobic digestion.JOURNAL OF ENVIRONMENTAL MANAGEMENT,398,128537. |
| MLA | Liu, Yanxin,et al."Improving ADM1 predictions via Bayesian analysis for continuous anaerobic digestion".JOURNAL OF ENVIRONMENTAL MANAGEMENT 398(2026):128537. |
入库方式： OAI收割
来源：地理科学与资源研究所
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