Data analytics-based model for optimizing cationic retarder and acetic acid in polyacrylic yarn dyeing

The study aims to develop a model for optimizing the concentration of solution parameters in the dyeing process of polyacrylic yarns. Specifically, the study examines the use of cationic retaiders and acetic acid, which affect the wavelength as an indicator of yarn color aging. By optimizing these parameters, the objective is to improve the color stability and longevity of dyed polyacrylic yarns. The application of Response Surface Methodology (RSM) encompassed two distinct types of data distribution properties: linear and non-linear. The R-squared ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:math> ) value for the non-linear RSM model was 0.96, compared to 0.86 for the linear RSM model. These results indicate that the model formed based on the non-linear RSM offers superior predictive ability in optimizing solution concentration as a parameter in the polyacrylic yarn dyeing process compared to the linear RSM-based model. In addition to providing practical implications for textile practitioners, this study contributes theoretically by emphasizing the effectiveness of statistical methods such as RSM in manufacturing process analysis.