Prediction of colour strength in environmentally-friendly dyeing of polyester fabric with madder using supercritical carbon dioxide

Abstract

The textile industry is one of the significant reasons for global water pollution, with dyeing processes being particularly environmentally detrimental. Researchers have explored alternative approaches to address this issue, such as using natural dyes, supercritical fluids and so forth. In addition to environment-friendly approaches, reducing the number of experiments in studies, accurate production straightaway and using artificial intelligence (AI), one of the technologies of the present and the future that will provide significant support. Reaching clearer results with AI technology will not necessarily contribute to environment-friendly technologies. However, AI techniques, including artificial neural networks (ANNs) and adaptive neuro fuzzy interface system (ANFIS) were employed to predict the colour strength (K/S) of the dyed fabric based on process parameters. A comprehensive experimental design involving pressure, temperature, and time variations was conducted, and the results were analysed using multi-factor analysis of variance (MANOVA). The study demonstrates that supercritical carbon dioxide (scCO₂) dyeing with madder on polyester fabric is a promising and environmentally friendly approach. Additionally, the optimised ANN and ANFIS models, aided by genetic algorithms (GAs), exhibit high predictive accuracy (less than 3%), providing insights into the impact of process parameters on colour strength. This research underscores the potential of AI-driven automation in textile dyeing, offering solutions for dye formula prediction, colour matching, and defect detection, reducing the need for human intervention in these processes.