{
  "abstract": "The growing need for flexible operation and reliable steam supply challenges coal-fired boilers to maintain an operating economy through fixed or single-variable combustion adjustments. This study develops a condition-aware, data-driven framework for coordinated combustion optimization. Historical stable-operation samples from the boiler were first classified according to the main steam flow rate and apparent fuel-related operating state. For each operating category, an MSE-constrained RF-NGBoost surrogate model was developed to predict the coal fan variable operating cost. The trained surrogate models then served as the offline response environment for a twin delayed deep deterministic policy gradient (TD3) agent, which searched for coordinated target set points of flue-gas oxygen content, bed temperature, and bed pressure drop under operating constraints. The RF-NGBoost models achieved R2 values of 0.967–0.982, RMSE values of 0.387–0.483 CNY/t, and mean absolute error values of 0.255–0.396 CNY/t across the six operating categories. For representative low-, medium-, and high-load conditions, the offline TD3 optimization identified condition-specific target set points associated with predicted coal-electricity operating cost reductions of 0.36–1.07% within the historical feasible domain. In an on-site comparison comprising two representative two-day operating periods, the TD3-derived target-set point strategy was associated with a 0.51% reduction in the actual coal-electricity operating cost compared with manual experience-based adjustment while maintaining main-steam supply and predefined operational constraints. The proposed framework links condition-specific cost modeling, offline coordinated optimization, and field verification to support economically oriented combustion adjustment in circulating fluidized bed boilers.",
  "abstract_shown": true,
  "affiliations": [
    "Shaoxing Shangyu Hangxie Thermoelectricity Co., Ltd. , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yilei Sui"
    },
    {
      "affs": [
        0
      ],
      "name": "Qiong Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Cheng Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Weifeng Cai"
    },
    {
      "affs": [
        0
      ],
      "name": "Hongjie Pan"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaojun Lei"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiayu Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Liting Ding"
    },
    {
      "affs": [
        0
      ],
      "name": "Maoyong Xu"
    },
    {
      "affs": [
        0
      ],
      "name": "Songwei Shi",
      "orcid": "0009-0002-5769-398X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bzhaj2zk31w2p.bib",
  "cited_by": 0,
  "doi": "10.1021/acsomega.6c06404",
  "fulltext_url": "https://europepmc.org/articles/PMC13689164",
  "id": "bzhaj2zk31w2p",
  "issns": [
    "2470-1343"
  ],
  "landing_url": "https://pubs.acs.org/acsodf/article/11/41/63454/5448450/Enhancing-Boiler-Operational-Economy-via-Data",
  "language": "eng",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2208|Advanced Power Generation Technologies"
    ]
  ],
  "openalex": "W7221316658",
  "pdf_url": "https://pubs.acs.org/doi/pdf/10.1021/acsomega.6c06404",
  "pmcid": "PMC13689164",
  "pmid": "42924113",
  "published": "2026-10-10",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/bzhaj2zk31w2p.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:39Z",
      "native_id": "10.1021/acsomega.6c06404",
      "source": "crossref",
      "url": "https://pubs.acs.org/acsodf/article/11/41/63454/5448450/Enhancing-Boiler-Operational-Economy-via-Data"
    },
    {
      "first_seen": "2026-10-30T12:20:33Z",
      "native_id": "MED:42924113",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42924113"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2470-1343",
      "subfield": "general"
    }
  ],
  "title": "Enhancing Boiler Operational Economy via Data-Driven Collaborative Combustion Optimization",
  "title_text": "Enhancing Boiler Operational Economy via Data-Driven Collaborative Combustion Optimization",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:33Z",
  "url": "https://research.draconitic.com/paper/bzhaj2zk31w2p",
  "venue": "ACS Omega",
  "versions": []
}