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Environmentally Benign Dyeing of Polyester Fabric with Madder: Modelling by Artificial Neural Network and Fuzzy Logic Optimized by Genetic Algorithm
Fibers and Polymers ( IF 2.5 ) Pub Date : 2021-07-21 , DOI: 10.1007/s12221-021-1161-0
Aminoddin Haji 1 , Morteza Vadood 1
Affiliation  

In this study, polyester fabric was dyed with madder as an environmentally friendly natural dye. According to Box-Behnken experimental design, 46 samples were dyed under various levels of five parameters including dye concentration, dyebath pH, temperature, time, and liquor ratio, and the color strength (K/S) of the dyed samples was measured. To evaluate the effect of each parameter on the color strength, the data was evaluated using multiple analysis of variance. Then, the artificial neural network (ANN) and fuzzy logic models were used to predict the measured K/S values. As both models contain different parameters, the genetic algorithm was implemented to optimize the model accuracy. It was observed that the best obtained ANN and fuzzy models can predict the K/S values with mean absolute percentage error of 2.52 and 3.01, respectively. Also, the effect of each input parameter on ANN was determined according to the partial derivative method and it was found that the maximum and minimum effects on ANN corresponds to dye concentration and liquor ratio, respectively. Finally, the effects of each dyeing parameter on the color strength was investigated based on the established optimal ANN model.



中文翻译:

涤纶织物的环保染色茜草:人工神经网络建模和遗传算法优化的模糊逻辑

在这项研究中,涤纶织物用茜草作为一种环保的天然染料染色。根据Box-Behnken实验设计,46个样品在染料浓度、染浴pH值、温度、时间和浴比5个参数的不同水平下染色,并测量染色样品的色强度(K/S)。为了评估每个参数对颜色强度的影响,使用多重方差分析来评估数据。然后,人工神经网络 (ANN) 和模糊逻辑模型用于预测测得的K/S值。由于两种模型包含不同的参数,因此采用遗传算法来优化模型精度。据观察,最佳获得的 ANN 和模糊模型可以预测K/S值的平均绝对百分比误差分别为 2.52 和 3.01。此外,根据偏导数方法确定了每个输入参数对 ANN 的影响,发现对 ANN 的最大和最小影响分别对应于染料浓度和浴比。最后,基于建立的最优人工神经网络模型研究了每个染色参数对颜色强度的影响。

更新日期:2021-07-22
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