Optimization methodologies for textile dyeing processes: A systematic literature review

Abstract

Textile dyeing processes are characterized by high complexity due to the interaction of multiple variables such as temperature, pH, dye concentration, time, and fabric type, which significantly influence color quality, resource consumption, and environmental impact. In recent years, various optimization methodologies have been proposed to improve dyeing performance, enhance process efficiency, and reduce energy and chemical usage. This study presents a systematic literature review focused on optimization methodologies applied to textile dyeing processes. The review follows the PIOC framework and the PRISMA 2020 guidelines to ensure a transparent and reproducible selection process. Scientific articles published between 2020 and 2025 were identified through structured searches in major academic databases. After applying inclusion and exclusion criteria, 23 primary studies were selected for detailed analysis. The reviewed literature reveals that response surface methodology, Taguchi-based approaches, artificial neural networks combined with metaheuristic algorithms, and lean–green methodologies are the most frequently applied techniques for dyeing process optimization. These methodologies are primarily used to improve color quality, reduce energy and water consumption, enhance productivity, and minimize environmental impact. The findings provide a structured synthesis of current optimization strategies in textile dyeing and highlight methodological trends, application contexts, and existing research gaps. This review contributes to a clearer understanding of how optimization methodologies are being applied in textile dyeing processes and offers guidance for future research and industrial implementation.