{
  "abstract": "Anaerobic digestion (AD) is a cornerstone of energy recovery and decarbonization at wastewater resource recovery facilities (WRRFs), yet full-scale digesters are often operated conservatively due to delayed process feedback, nonlinear stability constraints, and limited observability. This study presents a novel reinforcement learning (RL)-enabled supervisory control framework for full-scale AD that integrates a calibrated mechanistic digital twin model (DTM) with offline policy learning and counterfactual uncertainty evaluation. Specifically, the DTM was calibrated to reproduce control-relevant digester behavior. Using 2.7 years of simulated operation, a batch-constrained Q (BCQ)-reinforcement learning policy was trained to maximize biogas production while penalizing instability associated with pH excursions and elevated volatile fatty acids (VFAs)/alkalinity (ALK) ratios. Counterfactual evaluation against historical, greedy, and heuristic controls used 1 year unseen DTM simulation and 1 year of full-scale operational data. The RL policy achieved comparable mean daily biogas production (+0.35%) in simulated testing. Under the 1 year operational data, it achieved plant-equivalent mean production of 9802 m3/day, corresponding to a modest potential cumulative improvement (+2.7%, 93 405 m3/year) subject to substantial uncertainty P(Δ > 0) = 0.524. These results indicate that offline-trained RL, combined with a mechanistic digital twin and counterfactual evaluation, provides a feasible and interpretable framework for developing and assessing supervisory AD control without live-plant experimentation while showing that the resulting benefit must be interpreted under explicit model-plant uncertainty.",
  "abstract_shown": true,
  "affiliations": [
    "School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia30318, United States.",
    "Jimmy and Rosalynn Carter School of Public Policy, Georgia Institute of Technology, Atlanta, Georgia30332, United States."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ahmed I Yunus",
      "orcid": "0000-0003-3110-6257"
    },
    {
      "affs": [
        0
      ],
      "name": "Srinivas Jalla"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Joe F Bozeman",
      "orcid": "0000-0001-9791-1043"
    },
    {
      "affs": [
        0
      ],
      "name": "Yongsheng Chen",
      "orcid": "0000-0002-9519-2302"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0gwv46drhtm5y.bib",
  "cited_by": 0,
  "doi": "10.1021/acs.est.6c05822",
  "fulltext_url": "https://europepmc.org/articles/PMC13648226",
  "id": "0gwv46drhtm5y",
  "issns": [
    "0013-936X",
    "1520-5851"
  ],
  "landing_url": "https://europepmc.org/article/MED/42917018",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2215|Anaerobic Digestion and Biogas Production"
    ]
  ],
  "openalex": "W7214187510",
  "pdf_url": "https://europepmc.org/articles/PMC13648226?pdf=render",
  "pmcid": "PMC13648226",
  "pmid": "42917018",
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0gwv46drhtm5y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:36Z",
      "native_id": "MED:42917018",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42917018"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "journal-openalex:F23",
      "subfield": "environmental-science"
    }
  ],
  "title": "Reinforcement-Learning-Enabled Supervisory Control of a Full-Scale Anaerobic Digestion Facility Using a Calibrated Digital Twin.",
  "title_text": "Reinforcement-Learning-Enabled Supervisory Control of a Full-Scale Anaerobic Digestion Facility Using a Calibrated Digital Twin.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:36Z",
  "url": "https://research.draconitic.com/paper/0gwv46drhtm5y",
  "venue": "Environmental science & technology",
  "versions": []
}