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While aiming to create methods for fibre recycling, the question of colours in waste textiles is also in focus; whether the colour should be kept or should be removed while recycling textile fibre. More knowledge is needed for colour management in a circular economy approach.

The research included the use of different dye types in a cotton dyeing process, the process for decolourizing and the results. Two reactive dyes, two direct dyes and one vat dye were used in the study. Four chemical treatment sequences were used to evaluate colour removal from the dyed cotton fabrics, namely, HCE-A, HCE-P-A, HCE-Z-P-A and HCE-Y-A.

The objective was to evaluate how different chemical refining sequences remove colour from direct, reactive and vat dyed cotton fabrics, and how they influence the specific cellulose properties. Dyeing methods and the used refining sequences influence the degree of colour removal. The highest achieved final brightness of refined cotton materials were between 71 and 91 per cent ISO brightness, depending on the dyeing method used.

Only cotton fibre and three different colour types were tested.

With cotton waste, it appears to be easier to remove the colour than to retain it, especially if the textile contains polyester residues, which are desired to be removed in the textile refining stage.

Colour management in the CE context is an important new track to study in the context of the increasing amount of textile waste used as a raw material.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.