The typical textile dyeing process calls for a wide range of operational parameters, and it has always been difficult to pinpoint which of these qualities is the most important in dyeing performance. Consequently, this research used a combined design of experiments and machine learning prediction models’ method to offer a sustainable and beneficial reactive cotton fabric dyeing process. To be more precise, we built a least square support vector regression (LSSVR) model based on Taguchi's statistical orthogonal design (L₂₇) to predict exhaustion percentage (E%), fixation rate (F%), and total fixation efficiency (T%) and color strength (K/S) in the reactive cotton dyeing process. The model's prediction accuracy was assessed using many measures, including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). Principal component regression (PCR), partial least square regression (PLSR), and fuzzy modelling were some of the other types of regression models used to compare results. Our findings reveal that the LSSVR model greatly outperformed competing models in predicting the E%, F%, T%, and K/S. This is shown by the LSSVR model's much smaller RMSE and MAE values. Overall, it provided the highest possible R²values, which reached 0.9819.

The dyeing process commonly deteriorates the luster of pre-mercerized cotton fabric, so post-mercerization processes are regularly applied to compensate for this. Herein, the influence of combining pre-mercerization with CS (caustic solution) or LA (liquid ammonia) and post-mercerization with CS or LA on the morphological structure, dyeing performance, tensile strength, and stiffness of woven cotton fabric was investigated. The crystallinity index values greatly decreased from 73.12 to 51.25, 58.73, 38.42, and 40.90% after the combined mercerization processes of LA–LA, CS–CS, LA–CS, and CS–LA, respectively. Additionally, the CS–LA- and LA–CS-treated samples exhibited a mixture of cellulose II and cellulose III allomorphs. The combined mercerization processing of cotton fabric resulted in slightly worse thermal stability. The LA and CS pre-mercerization processes increased the dye exhaustion, although the former decreased the dye fixation rate while the latter increased it by 4% for both dyes. The color strength of the dyed cotton fabric increased after both post-mercerization processes. Moreover, the fabric stiffness and mechanical properties showed an increasing trend due to the combined mercerization efforts.