基于最小二乘支持向量机的棉针织物活性染料湿蒸染色预测模型

Wet-steaming dyeing prediction model of cotton knitted fabric with reactive dye based on least squares support vector machine

针对棉针织物在用活性染料连续湿蒸染色过程中出现的染色条件对织物色光难以控制和预测,易导致染色织物不符合预期产品要求的问题,选用雷马素金黄 RGB 对棉针织物进行湿蒸染色,研究了元明粉和纯碱浓度、汽蒸时间对织物表观染色深度(K/S值)的影响,同时基于最小二乘支持向量机(LS-SVM),将这些影响因素作为预测模型的输入变量,织物K/S值作为输出变量,建立了多因素模型并进行预测。结果表明,织物K/S实验值和模型预测值的相关系数高达0. 999 6,平均相对误差小于 1%,说明该模型具有较高的精度,该建模方法可用于预测织物K/ S 值,为棉针织物活性染料湿蒸染色工艺的优化提供参考。

Aiming at the problem of hard control and prediction of dyeing conditions on the color of dyed fabrics in the continuous wet-steaming dyeing of cotton knitted fabrics with reactive dye, the influences of sodium sulfate concentration, soda concentration, and steaming time on the color depth (K / S value) of the dyed fabrics were studied in the wet-steaming dyeing process of cotton knitted fabrics with Remazol golden yellow RGB. At the same time, based on least squares support vector machine (LS-SVM), using these factors as the input variables of the prediction model and the K / S value of fabric color depth as the output variable, a multi-factor model of K / S value was established to predict K / S value. The experiment results show that the correlation coefficient between the experimental value and the predicted value of the model is 0. 999 6, and the mean relative error is lower than 1%, which indicates that the model has high accuracy. The modeling method can be applied to predict the K / S value of fabric, providing a basis reference for the optimization of the wet-steaming reactive dyeing process conditions for cotton knitted fabric.