Sustainable Machine-Learning Framework for Efficient Color Prediction in Dope-Dyed Recycled PET/PCT

Abstract

Dope dyeing offers an eco–efficient alternative to conventional textile coloration by incorporating pigments directly into the polymer melt, minimizing water and chemical use. This study presents a sustainable data–driven framework that reduces repetitive dyeing trials and resource consumption during color matching of dope–dyed recycled PET/PCT microfiber fabrics. A two–stage hybrid machine learning model─combining k–nearest neighbors (kNN), feature expansion, and residual modeling─was developed to predict subtle color variations within the narrow CIELAB output range inherent to dope–dyed systems. The model achieved R2 values above 0.83 for L*, a*, and b*, and external validation with untrained dyeing recipes yielded a mean ΔE of 0.65 with visually negligible deviation. By accurately pre–estimating color outcomes, this approach minimizes iterative experiments, energy use, and wastewater generation, contributing to sustainable textile manufacturing. The proposed framework demonstrates that data–driven color prediction can enhance process efficiency and environmental performance in dope–dyed fabric production, supporting circular and low–impact coloration technologies.