{
  "abstract": "Reactive exhaust dyeing of cotton uses large amounts of water, salt, alkali and heat, partly because the recipe and programme are fixed when a batch starts. This review asks whether learned models, acting on the machine during a batch, have been shown to reduce that consumption. An OpenAlex search (2003 to September 2026, 862 records) and web searching identified 27 primary studies of learned, rule-based, model-based or search-based methods, or inline monitoring, in reactive dyeing of cotton. Each was coded by setting and by how far its output reached the process. Twenty-two stop at offline prediction, three only measure the bath, one advises an operator and one acts automatically; 24 are laboratory studies. No included study documents a learned model automatically changing an outcome-level decision (dye, salt or alkali quantity or timing, temperature programme, or rinse endpoint) during a batch. The one automatic controller, a fixed rule timing alkali injection at pilot scale, is not learned. A check on 135 studies of all fibres agreed. Reported fixation spans 40 to 70%, and no study has measured how much of the shortfall control could recover. Two priorities follow: measuring that share, and a matched-batch trial of learned, rule-based and operator control.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Sk. Mainuddin"
    },
    {
      "name": "Tinon Turja Majumder",
      "orcid": "0009-0000-0684-398X"
    },
    {
      "name": "Md Shajjad Khan Faisal"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c9wd1crxn3j8v.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009673/v1",
  "fulltext_url": null,
  "id": "c9wd1crxn3j8v",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009673/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2215|Dyeing and Modifying Textile Fibers"
    ]
  ],
  "openalex": "W7214958919",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009673/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/c9wd1crxn3j8v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009673",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009673/v2"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "From prediction to process control: a critical review of machine learning in reactive dyeing of cotton",
  "title_text": "From prediction to process control: a critical review of machine learning in reactive dyeing of cotton",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/c9wd1crxn3j8v",
  "venue": "ChemRxiv",
  "versions": []
}