{
  "abstract": "Coke accumulation on radiant coils sets the run length of steam-cracking furnaces, yet the thickness field cannot be measured in service, and rigorous simulators are proprietary and non-differentiable. We introduce a grey-box surrogate of a plant-calibrated SPYRO model for an industrial ethane cracker, forecasting the complete coke field across 52 axial segments over a 90-day run. It learns the coking rate as an explicit Arrhenius core modulated by a positive neural component and coke self-feedback, integrated over time to inherently ensure monotonic growth. On six held-out cases outside the training window in at least two operating variables, it reaches 0.034 ± 0.001 mm mean absolute error (R² = 0.9994, five seeds) with zero monotonicity violations; ablations show the guarantee costs no accuracy. Plant infrared data reproduce the encoded temperature trend. We close with limitations (unidentifiable embedded kinetics, narrow validity window, exogenous wall temperature) and demonstrate an online-estimator deployment using the analytic Jacobian.",
  "abstract_shown": true,
  "affiliations": [
    "Tianjin Haitai Testing Technology Co., Ltd.",
    "Refining, Petrochemical and New Materials Business Unit",
    "SINOPEC",
    "School of Astronautics",
    "Beihang University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hongtao Li",
      "orcid": "0009-0004-3016-752X"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Hao Zhou",
      "orcid": "0009-0003-1914-8734"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Jinshen Wang",
      "orcid": "0009-0003-7047-9010"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Xiaochen Sun",
      "orcid": "0009-0007-0732-9078"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fghhas6xqq6wy.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009054/v1",
  "fulltext_url": null,
  "id": "fghhas6xqq6wy",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009054/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7213532276",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009054/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/fghhas6xqq6wy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15009054",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009054/v1"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3109|Model Reduction and Neural Networks",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Predicting spatiotemporal coke growth in an industrial ethane cracker with a hybrid mechanistic–neural surrogate of its plant-tuned SPYRO model",
  "title_text": "Predicting spatiotemporal coke growth in an industrial ethane cracker with a hybrid mechanistic–neural surrogate of its plant-tuned SPYRO model",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/fghhas6xqq6wy",
  "venue": "ChemRxiv",
  "versions": []
}