{
  "abstract": "Anaerobic digestion (AD) models can be challenging to calibrate against real data due to limited practical identifiability, data scarcity, and model complexity. Weighted least squares objective functions and gradient-based solvers using finite differences are often used in literature, but result in suboptimal parameter estimates and large uncertainty bands. This paper identifies the parameters of a reduced-order ADM1 by comparing the frameworks of maximum likelihood estimation (MLE) and Bayesian inference (BI), and quantifies uncertainties of parameters and outputs. Multirate experimental data are taken from a lab-scale AD reactor fed with agricultural substrates and operated at organic loading rates of 3$\\,$g VS $\\mathrm{m^{-3}d^{-1}}$. Global optima are found via global and gradient-based local search algorithms using forward sensitivities. Enhanced scatter search provides the best global search results. These are refined by the Gauss-Newton algorithm as the best local search, which delivers $R^2$ values of 0.95 for acetic acid and 0.77 for biogas production. BI yields substantially narrower uncertainty bands than MLE and avoids the need for a global search by incorporating prior knowledge about parameter distributions. The search space is captured efficiently by log10 scaling of parameters. Algorithmic details on its implications are provided. Despite a high-resolution lab-scale dataset, the ADM1-specific inhibition parameter $K_\\mathrm{I,nh3}$ is practically not identifiable with MLE, and shows a low output sensitivity, whereas a parameter for the effective ion concentration shows superb practical identifiabilty at high confidence with both MLE and BI. The present work lays a scalable base for calibration of nonlinear AD models by providing reliable simulations. It thus advances model-based monitoring and control applications of multirate processes such as agricultural AD plants.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.19098",
  "author_total": 9,
  "authors": [
    {
      "name": "Simon Hellmann"
    },
    {
      "name": "Leander Lerch"
    },
    {
      "name": "Terrance Wilms"
    },
    {
      "name": "Moritz Reumschüssel"
    },
    {
      "name": "Alberto Meola"
    },
    {
      "name": "Félix Delory"
    },
    {
      "name": "Steffi Knorn"
    },
    {
      "name": "Stefan Streif"
    },
    {
      "name": "Sören Weinrich"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cgkvt9n19csak.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.19098",
  "fulltext_url": null,
  "id": "cgkvt9n19csak",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.19098",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CE"
    ],
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "2215|Anaerobic Digestion and Biogas Production"
    ]
  ],
  "openalex": "W7222245964",
  "pdf_url": "https://arxiv.org/pdf/2610.19098",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cgkvt9n19csak.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:41Z",
      "native_id": "2610.19098",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.19098"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Global Parameter Identification and Uncertainty Quantification of reduced-order ADM1 using Maximum Likelihood Estimation and Bayesian Inference",
  "title_text": "Global Parameter Identification and Uncertainty Quantification of reduced-order ADM1 using Maximum Likelihood Estimation and Bayesian Inference",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:35Z",
  "url": "https://research.draconitic.com/paper/cgkvt9n19csak",
  "venue": "arXiv",
  "versions": []
}