From prediction to process control: a critical review of machine learning in reactive dyeing of cotton
Year: 2026
Type: preprint
Study design: Systematic Review
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.
Source: ChemRxiv
Institution: Bangladesh University of Textiles
Language: English
Subfield: Polymers and Plastics
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7214958919