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LSSVM-based color prediction for cotton fabrics with reactive pad-dry-pad-steam dyeing
Journal Pre-proof LSSVM-based color prediction for cotton fabrics with reactive pad-dry-pad-steam dyeing Chengbing Yu, Ziwei Xi, Yilin Lu, Kaixin Tao, Zhong Yi PII:
S0169-7439(19)30790-7
DOI:
https://doi.org/10.1016/j.chemolab.2020.103956
Reference:
CHEMOM 103956
To appear in:
Chemometrics and Intelligent Laboratory Systems
Received Date: 6 December 2019 Revised Date:
21 January 2020
Accepted Date: 24 January 2020
Please cite this article as: C. Yu, Z. Xi, Y. Lu, K. Tao, Z. Yi, LSSVM-based color prediction for cotton fabrics with reactive pad-dry-pad-steam dyeing, Chemometrics and Intelligent Laboratory Systems (2020), doi: https://doi.org/10.1016/j.chemolab.2020.103956. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.
LSSVM-based Color Prediction for Cotton Fabrics with Reactive Pad-Dry-Pad-Steam Dyeing Chengbing Yua,*, Ziwei Xia, Yilin Lua, Kaixin Taoa, Zhong Yib,* a
School of Materials Science and Engineering, Shanghai University, Shanghai 201800, P .R.
China b
College of Chemistry, Chemical Engineering and Biotechnology, Donghua University,
Shanghai 201800, P .R. China *Corresponding authors. [email protected]; [email protected]
ABSTRACT Least-square support vector machines (LSSVM) have been strongly moving from preliminary theoretical investigations into practical industrial applications. Successfully attaining a required color is a crucial task in the dyeing industry. In this paper, an LSSVM-based color quality prediction model for cotton fabrics dyed with a reactive dye using the conventional pad-dry-pad-steam (PDPS) process is proposed. The concentrations of the dye, sodium carbonate and sodium chloride, as well as the drying and steaming times were selected as the model inputs, while the CIE Lab value (L*, a*, b*) and the K/S value of the dyed fabric were used as the model outputs. The two prominent reactive dyes, Remazol Red and Remazol Navy, were used separately to dye cotton fabrics. The proposed LSSVM-based model gave mean absolute errors of less than 0.5 and 1.5 for predicting the K/S value and the CIE Lab value (L*, a*, b*), respectively. As well, the model had an acceptable average color difference ∆E* of less than 2.0 for the fabrics dyed using the PDPS process with both reactive dyes. Keywords: Dyeing, cotton fabric, LSSVM, pad-dry-pad-steam, prediction.
1. Introduction Reactive dyes are commonly-used dyes, which enjoy the advantage that they form strong covalent bonds with cellulose materials (such as cotton fibers) during the dyeing process.
Fabrics dyed with reactive dyes have excellent color fastness, complete chromatographs and low production costs. Therefore, cotton fibers have been widely processed with reactive dyes, mainly through continuous and batch dyeing [1-3]. In practical textile production, it is highly significant to evaluate the color quality of dyed cotton fabrics, and especially determine the color differences between dyed fabric samples and the industry standards [4-5]. Such color differences can be measured in continuous dyeing using a reflective spectrophotometer that horizontally scans the fabric color. In batch dyeing, a colorimeter is typically used to determine the differences between the dyed samples and the standards in terms of the brightness L*, the red-greenness a*, and the yellow-blueness b*. Then, the color difference ∆E* according to the CIE Lab formulas can be calculated [6, 7] or the K/S values of the dyed fabrics are found and compared [8]. When the color difference is found to be unacceptable, the fabric must be re-dyed or stripped, and hence great economic losses are incurred [9]. So, once a dyed fabric sample fails in the color difference test in actual dyeing, the dyeing process should be adjusted in time by calculating the necessary amounts of the dye, the accelerating or fixing agents, and other dyeing conditions in order to satisfy the color difference criteria. Not only does this adjustment reduce the economic loss, but it is also very meaningful for the dyeing of cotton fabrics with reactive dyes. Careful design of the dyeing operations is critical for improving the quality and grade of the knitted products, saving energy, and reducing the industrial wastes and sewage. At present, the design methodology is mainly based on manufacturer-specified dye parameters or empirical dyeing tests [10]. Once an on-site problem in quality or cost emerges, the dyeing process needs to be redesigned and optimized through multiple experiments. This redesign has the disadvantages of long cycles, high costs, inaccurate quantification and susceptibility to human errors. Alternatively, mathematical models can be utilized to predict dyeing results according to process conditions. Thus, based on such models, on-site timely adjustment and optimization of dyeing processes can be carried out correctly and reasonably. Currently, few computer-based approaches have been proposed to apply intelligent algorithms in the optimization of dyeing processes. In particular, an artificial neural network (ANN) can learn real-world complex and unknown processes using experimental data without any actual knowledge of the underlying mathematical, physical, or chemical
principles. Thus, ANN applications in nonlinear and complex systems are quite common [8, 9]. However, ANNs can be easily trapped into local minima and suffer from overfitting in practical applications [11, 12]. In addition to ANNs, support vector machines (SVMs) represent a key machine learning architecture that has attracted much attention in modeling nonlinear and highly complex systems. The SVM can perform nonlinear classification, multivariate function estimation, or nonlinear regression [13]. The LSSVM is an optimized SVM-based algorithm, which applies a set of linear equations, instead of the quadratic programming formulation used in a standard SVM [14, 15]. The LSSVM model does not only reduce the computational complexity, but it also improves the prediction accuracy [16-19]. Furthermore, earlier studies have demonstrated that methods of variable selection (or elimination of less significant variables) can further improve the prediction accuracy and robustness of the regression models, since such methods can obtain relevant variables with the least collinearity, redundancy and noise. These methods have been used in measurements, modeling, control, pattern recognition, and fault diagnosis. For example, those processes have been used in the control and optimization of industrial processes arising in chemical, food, pharmaceutical, financial, and electronic industries [20-22]. The dyeing of cotton fabrics with reactive dyes using the PDPS operation is an extremely complex dyeing process, which involves a large number of physical and chemical changes affected by many factors. Such factors include the structure of the cotton fibers and the reactive dye, the padding pressure, the type and concentration of the dyeing and fixing agents, as well as the steaming temperature and time. In addition, the dyeing process is also affected by random variations in water quality, atmospheric pressure, instrumentation errors of testing and display, dyeing equipment errors, and human experience. To improve the success rate of the dyeing process while avoiding frequent re-dyeing, color correction, stripping and scrapping, it is necessary to properly model the relationship between the various process factors and dyeing outcomes, improve the dyeing equipment performance, and finally achieve real-time control and optimization of the dyeing process and the quality of the dyed products [23, 24]. In this paper for the first time, dye concentration and dyeing conditions of the PDPS
process were used as the inputs of an LSSVM-based prediction model, while the CIE Lab color (L*, a*, b* ) and the K/S value of the dyed cotton fabrics were used as outputs. Two groups of experimental dyeing protocols were designed, in which Remazol Red and Remazol Navy reactive dyes were used respectively to dye the cotton fabrics. Each group consisted of 80 experimental protocols for model training and 20 experimental protocols for model verification. The experimental results were used to evaluate the prediction accuracy and applicability of the LSSVM-based model. 2. Materials and methods 2.1 Materials In recent years, with the worldwide trend of using knitted outerwear, the world production of knitted fabrics has exceeded that of woven fabrics. In this paper, the dyeing tests were performed on scoured, bleached, fluorescent, brightener-free, and tubular cotton knitted fabrics. The fabrics were supplied by the New Sierlk Knitted Garment Company (Nantong, China). The fabrics had a double-interlock knit structure at 40/1s combed compact knitting with a fabric weight of 180 g/m2. The reactive dyes, Remazol Red (RGB) and Remazol Navy (RGB), were kindly supplied by Dystar Trading Co. Ltd. (Shanghai, China). Each of the two dyes is a one-compound recommended reactive dye. The sodium carbonate and sodium chloride compounds were of an analytical grade (Sinopharm Group Co. Ltd., Shanghai, China). All chemicals were used as received without any further purification. Deionized water was used throughout the study. 2.2 Methods 2.2.1 Least-Square Support Vector Machines Support vector machines represent a powerful tool for classification and regression [25]. A SVM is based on the minimization of the structural risk rather than the empirical risk. This enables SVMs to have better generalization performance compared to ANNs [26]. While SVMs have reasonable time costs on limited training samples, the SVM computational complexity becomes prohibitively large with increasing numbers of training samples, and SVMs become unsuitable for real-time applications [27]. In order to overcome the SVM shortcomings, an enhanced variant of it, namely the LSSVM converts the quadratic optimization problem to one of solving linear equations, reduces the computational cost
considerably, and improves the convergence accuracy [28]. There are many factors affecting the dyeing performance, and thus it is generally difficult to establish an appropriate mathematical model. Model selection depends mainly on the problem complexity. In this work, a multi-factor model is used, and LSSVM is exploited to deal with nonlinearities in the dyeing process. Fig. 1 shows a flow chart of the LSSVM-based multi-input prediction model of the dye color quality. In Fig. 1, the 2D output vector Y has the K/S value and the CIE Lab value (L*, a*, b*). The mean absolute error (MAE), the root-mean-square error (RMSE), the precision (Ep) and the coefficient of determination (R2) are utilized to evaluate the prediction performance of the LSSVM-based model [29, 30]. The MAE, RMSE, Ep and R2 are defined, respectively, as: =
1
1
= = 1− = 1−
∑ ∑
| − | 1 , −
2 ,
× 100% ,
− −
=
1
3 ,
4 .
The construction of the dyeing model can be divided into training and testing phases. Out of 100 data samples, 80 samples were used for training while 20 samples were used for testing. The LSSVM prediction model was trained based on the input and output matrices of the training set. In brief, the model training involves, first, importing training data, initializing the model, finding the optimal parameters, and finally obtaining the trained LSSVM prediction model. In the testing phase, the trained LSSVM prediction model is fed with the test input matrix to obtain output predictions. Then the predicted values are compared with actual experimental values. If the prediction result is accurate enough, the model is
considered to be satisfactory and the modeling process is complete. Otherwise, further model refinements are carried on until good prediction performance is reached. 2.2.2 Dyeing using the PDPS process The pad-dry process. The cotton fabrics were initially immersed in a freshly-prepared dyeing solution containing Reactive dye (>0-20 g/L) at room temperature for a 30-second interval, then passed through a vertical two-roll padder (Weida Machinery Co., Ltd., Shaoxing, China) to obtain 65±1% pick-up using a two-dip two-nip procedure. Then, the wet cotton samples were immediately dried with a hot-air laboratory machine (Weida Machinery Co., Ltd., Shaoxing, China) at 100±1 oC for an interval of 70-160 s (Fig. 2). The pad-steam process. After the pad-dry process, the samples were immersed in a freshly-prepared dyeing solution containing sodium chloride (125-200 g/L) and sodium carbonate (10-50 g/L) at room temperature for a 30-second interval. Then, the samples were passed through the afore-mentioned vertical two-roll padder to obtain 60 ± 1% pick-up using again a two-dip two-nip procedure. The wet cotton samples were then steamed in a laboratory steamer (Mathis, Switzerland) with saturated atmospheric steam pressure at 100±1 oC for an interval of 40-180 s (Fig. 2). 2.2.3 Washing process All dyed fabric samples were washed first with cold water, then with hot water at 50-60 oC for a 5-minute interval. These samples were thus soaped at 95±1 oC for 15 minutes in a water bath with 5-g/L standard soap flakes and a liquor ratio of 50:1. Then, the samples were washed with hot water at 80±1 oC for 5 minutes and then with cold water. Finally, all samples were dried at 60 oC in a vacuum oven. 2.2.4 Color measurements The CIE Lab value (L*, a*, b*) [31] and the color strength (K/S value) [32] of the dyed fabrics were determined using a Color i5 Benchtop Spectrophotometer (X-Rite Co., USA) with a D65 illuminant and a 10o standard observer. The color differences ∆E* were calculated with experimental L*, a*, b* values of the measured and predicted values. Each fabric sample was folded into four layers and placed in front of the aperture of the spectrophotometer. The sample color characteristics were measured in four different random locations and the average values were calculated and adopted.
2.2.5 LSSVM-based Model Design The LSSVM methodology is considered to be highly successful in the design of nonlinear and complex prediction models. This methodology has been widely applied in electronic equipment analysis, bearing degradation prediction, wind power prediction, and many other fields [20-22]. However, LSSVM applications in dyeing prediction have been limited so far. Since the color of a dyed fabric has a nonlinear complex relationship with the PDPS dyeing conditions, the LSSVM methodology was successfully adopted in this paper to predict the K/S value and the CIE Lab value (L*, a*, b*) of the dyed fabrics. For the proposed LSSVM-based model, five main PDPS factors were selected as model inputs. These factors are the sodium chloride concentration in g/L (A), the sodium carbonate concentration in g/L (B), the drying time in seconds (C), the steaming time in seconds (D), and either Remazol Red dye concentration in g/L or Remazol Navy dye concentration in g/L (E). The model outputs were selected to be the CIE Lab value (L*, a*, b*) and the K/S value of the dyed fabrics. Based on the actual dyeing of the PDPS process [33, 34], each experimental input was determined randomly within their reasonable ranges, and 100 groups of experimental conditions were designed for Remazol Red and Remazol Navy. In every 100 groups, 80 experimental conditions were used to train and establish the LSSVM-based model, and the other 20 experimental conditions were used to verify and evaluate the LSSVM-based model. 3. Results and Discussion 3.1 The K/S and CIE Lab values The K/S and CIE Lab values are two important distinct measures for the color of dyed fabrics. The K/S value can be calculated based on the Kubelka-Munk equation [20], $⁄ = 1 −
⁄2 5 ,
where K and S are, respectively, the absorption and scattering coefficients of a dyed fabric
sample, while R is the sample reflectance at the wavelength corresponding to the maximum dye absorption. This wavelength is 540 nm and 620 nm for Remazol Red and Remazol Navy, respectively. In fact, the K/S value is only meaningful at that maximum-absorption wavelength.
The CIE Lab 1976 color scale is a comprehensive result of measuring color in the full visible range, not only at the wavelength of the maximum dye absorption. The CIE Lab value is based on the CIELAB color scale published by the Commission International Eclairage (CIE). This scale is a device-independent complete model of all visible colors for the human eye. The CIE Lab uniform color space is a nonlinear transformation of the CIE 1976 standard colorimetric system. Specifically, CIE Lab values are obtained from CIE 1976 ones by converting rectangular-coordinate XYZ tristimulus components into cylindrical-coordinate color components of the brightness L*, hue a*, and saturation b*. These color components are more consistent with the human eye vision [31]. The transformation between the CIE 1976 and CIE Lab color components is given as / − 16 *+∗ = 116 × . / ( ( 2 / −. 3 6 ， 0∗ = 500 × 1. 2 / ) ( (4 ∗ = 200 × 1. / − . 5 3 ' / 5 7 > 0.008856 . 7 = 78, 6 7 , 16 . 7 = 7.787 × 7 + , 7 < 0.008856 116
where X, Y, Z are the tristimulus values of the target object; Xn, Yn, Zn are the tristimulus values of the white stimulus such that the standard illuminator is applied to a full diffuse reflector which makes a complete diffuse reflection into the observer's eyes. It can be seen that the L*, a*, b* values in the CIE Lab formula express colors by using a uniform color space in which the brightness L* and the chromaticity components a* and b* are considered to be uniformly varied [31] . The color difference ∆Eab* between two chrominance values (L1* a1*, b1*) and (L2*, a2*, b2*) in the CIE Lab color space is calculated as ∆
?@
∗
=A +
∗
−+
∗
+ 0
∗
−0
∗
+ 4
∗
−4
∗
8 .
In this paper, ∆Eab* stands for the color difference between the experimental color value (L1* a1*, b1*) of the dyed fabric and the color value
(L2*, a2*, b2*) predicted by the
LSSVM model. For fabric samples dyed with different concentrations of Remazol Red and Remazol Navy
through a PDPS process with different dyeing condition (Table S11), the reflectance spectral curves are similar in shape. Each curve has one strong reflection peak from 360 nm to 780 nm at different dye concentrations (3 g/L, 9 g/L and 15 g/L), and the maximum-absorption wavelengths are at the same position: 540 nm for Remazol Red in Fig. 3(a) and 620 nm for Remazol Navy in Fig. 3(b). However, the intensities of the maximum-reflection peaks become strong gradually as the dye concentration increases. This means that the maximum-absorption wavelength in the dyed fabrics has no relation with the used dye concentration. 3.2 Establishment of the LSSVM-based dyeing model Cellulose fiber textiles are typically dyed with reactive dyes. As well, the PDPS continuous dyeing process with reactive dyes is commonly used for woven fabrics because of the advantages of the high dye fixing rate, deeper colors, lower dye costs, less environmental pollution, defect concealment in the pre-treatment stage, easy control of color differences, and good rubbing and washing fastness. Moreover, this dyeing process can effectively reduce resource consumption, improve production efficiency, and increase the competitiveness of dyeing enterprises. Therefore, this continuous dyeing process is gradually applied in the dyeing of knitted fabrics. In this work, the knitted fabric samples used in the model construction are dyed using a PDPS process. Based on earlier studies of the PDPS dyeing process [33, 34], the ranges of the five input factors were set to be 125-200 g/L for the sodium chloride concentration, 10-50 g/L for the sodium carbonate concentration, 70-160 s for the drying time, 40-180 s for the steaming time, and 0-20 g/L for the reactive dye concentrations. Furthermore, every 80 groups of experiments for Remazol Red and Remazol Navy using the PDPS process were devised as training data, as shown in Table S1 and Table S4 for CIE Lab (L*, a*, b*), Table S7 and Table S9 for K/S value respectively. Based on the MATLAB software (version R2017b, MathWorks, USA), the LSSVM toolbox (version 1.8) was used to construct the color prediction models. Details of the LSSVM regression algorithm can be found in the literature [35, 36]. In the LSSVM-based model, the Gaussian radial basis function (RBF) was selected as the LSSVM kernel function, for better handling of nonlinearities and higher reduction of the
training computational complexity. As shown in Table 1, a model is affected by the selection of the γ and σ2 parameters, where γ is a regularization parameter, which influences the LSSVM generalization performance, while σ2 is the RBF width, which controls the regression error [35-37]. Indeed, the selection of the regularization parameter, the kernel function, and the associated kernel parameters play a crucial role in model success [38]. 3.3 Validation of the LSSVM-based dyeing model The K/S value of dyed fabrics was used as the output of the LSSVM-based model. The prediction results of this value for fabrics dyed with Remazol Red and Remazol Navy are shown in Fig. 4. From Fig. 4 (a), Fig. 4 (c), Table S8 and Table S10, it can be seen that the experimental K/S values of the fabrics dyed with Remazol Red and Remazol Navy are respectively within the ranges of [0.88, 14.31] and [0.86, 15.00], while the K/S values predicted by the LSSVM-based model are respectively within the ranges of [1.20, 14.17] and [1.16, 15.70]. Obviously, the experimental and predicted K/S ranges are essentially similar. Nevertheless, it is necessary to analyze the prediction performance further. As shown in Fig. 4 (b) and Fig. 4 (d), the absolute K/S prediction errors for 20 samples dyed with Remazol Red are within the range [-0.39, 0.46] and the mean absolute error is 0.26, while the corresponding absolute error range for Remazol Navy dye is [-0.15, 1.19] and the mean absolute error is 0.41. Hence, the prediction performance of the dyeing model with reactive dyes and with the K/S value as the model output is good, and the prediction errors are small and acceptable. The CIE Lab value is another important measure of the color of dyed fabrics. This measure covers the full visible range of the dye absorption for the dyed fabrics. The performance of the LSSVM-based model was evaluated with the CIE Lab value (L*, a*, b*) as the model output. The LSSVM-based dyeing models for fabrics dyed with Remazol Red are shown in Fig. 5. As shown in Fig. 5(a)-(f) and Table S2-S3, the experimental L* values of the dyed fabrics with Remazol Red are within the range [42.33, 63.27], while the predicted L* values of the LSSVM-based model are within the range [43.60, 64.40]. Likewise, the experimental a* values are within the range [44.63, 61.24], while the predicted a* values are within the range [46.00, 63.31]. Similarly, the experimental b* values are within the range [-8.62, 5.59], while the predicted b* values are within the range [-8.45, 5.29]. Clearly, each of the ranges of
the experimental values of L*, a*, and b* is satisfactorily close to the corresponding one of the predicted values. Anyway, the prediction performance further using the absolute error for every sample is analyzed. The absolute errors for the 20 samples are (a) within the range [-3.02, 2.43] for predicting the L* values, with a mean absolute error of 1.16, (b) within the range [-1.85, 1.50] for predicting the a* values, with a mean absolute error of 0.98, and (c) within the range [-0.38, 0.34] for predicting the b* values, with a mean absolute error of 0.24. The small absolute errors of L*, a*, and b* for samples dyed with Remazol Red confirm the good prediction performance of the LSSVM-based models with the CIE Lab value (L*, a*, b*) as the model output. The LSSVM-based dyeing models for fabrics dyed with Remazol Navy are shown in Fig. 6. From Fig.6(a)-(f) and Table S5-S6, the experimental L* values of fabrics dyed with Remazol Navy are within the range [24.35, 63.68], while the corresponding predicted L* values of the LSSVM-based model are within the closely-related range of [25.85, 63.76]. Similarly, the experimental a* values of the fabrics dyed with Remazol Navy are within the range [-12.20, -5.88], while the corresponding predicted a* values are within the almost-identical range of [-11.49, -6.27]. Likewise, the experimental b* values of the fabrics dyed with Remazol Navy are within the range [-25.22, -21.22], while the corresponding predicted b* values are within the greatly-similar range of [-25.04, 20.91]. Consequently, each range of the experimental values is close to that of corresponding predicted values. Moreover, the absolute errors of the 20 samples are (a) within the range [-2.91, 3.41] for predicting the L* values, with a mean absolute error of 1.46, (b) within the range [-0.68, 0.75] for predicting the a* values, with a mean absolute error of 0.31, and (c) within the range [-0.32, 0.35] for predicting the b* values, with a mean absolute error of 0.23. Once again, small absolute errors of L*, a*, and b* of samples dyed with Remazol Navy assert that the LSSVM-based models with the CIE Lab value (L*, a*, b*) as the model output have good prediction performance. For the CIE Lab color system, L*, a*, and b* of the color samples express the brightness L* and the chromaticity a* and b*, while ∆Eab* shows the difference between two colors more clearly as a whole. From Fig. 7, it can be seen that the color difference ∆Eab* derived from the LSSVM-based model for fabrics dyed with Remazol Red is within the range [0.25,
3.34] with an average of 1.63, while the corresponding range for Remazol Navy is [0.41, 3.45] ∗ with an average of 1.57. Generally speaking, when the color difference ∆E is less than or
equal to 1.5, the color difference is not obvious and is indistinguishable by the unaided human eye. Therefore, the experimental and predicted ∆Eab* values seem to be mostly satisfactory. The overall prediction performance of the LSSVM-based models with the CIE Lab value (L*, a*, b*) as the model output is excellent. 3.4 Indice assessment of the LSSVM-based dyeing models In order to comprehensively compare the performance of the LSSVM-based models with each of the K/S value and the CIE Lab value (L*, a*, b*) as the model output, typical LSSVM evaluation indices were selected and listed in Table 2. These indices are MAE, RMSE, Ep, and R2 [29, 30]. A small MAE or RMSE value indicates a good prediction accuracy of the LSSVM-based model. On the contrary, a small R2 or EP value indicates a poor prediction accuracy of the LSSVM-based model. As shown in Table 2, the MAE and RMSE values for the model with Remazol Red dye are both smaller than the corresponding values with Remazol Navy dye where the K/S value is the model output. This implies a smaller prediction error for the model with Remazol Red dye. The MAE and RMSE values for the LSSVM-based model with Remazol Red dye are 0.26 and 0.2969, respectively. Moreover, the associated R2 and Ep values are larger and closer to 1, indicating a better model fitting. To sum up, the above analysis shows that the LSSVM-based model with the K/S value as the model output and Remazol Red dye has a better accuracy than that with Remazol Navy dye. The MAE, RMSE, Ep, and R2 values of the model of the fabrics dyed with Remazol Navy are all satisfactory enough to predict the K/S value precisely, although these values are poorer than the corresponding values for Remazol Red dye. Similar results were obtained for LSSVM-based models with the CIE Lab value (L*, a*, b*) as the output. All MAE and RMSE values for the model with Remazol Red dye are small: MAE has values of 1.16, 0.98 and 0.24 and RMSE has values of 1.4145, 1.0788 and 0.2619, for models with L*, a*, and b* as the output, respectively. Meanwhile, all R2 and Ep values of the model with Remazol Red dye are large and close to 1, indicating good model fitting.
Compared with the LSSVM-based model for Remazol Red dye, the model with Remazol Navy dye has similar MAE, RMSE, Ep, and R2 values. All these results confirm that the models with the CIE Lab values (L*, a*, b*) as the outputs are also all satisfactory enough to predict the CIE Lab value precisely. This is consistent with the color difference ∆Eab* calculated by experimental and predicted L*, a*, and b*. 4. Conclusions Based on the conventional PDPS process and the LSSVM methodology, a comprehensive experimental scheme for dyeing cotton fabrics with a reactive dye was designed. The two reactive dyes, Remazol Red and Remazol Navy, were used independently to dye cotton fabrics. The CIE Lab value and the K/S value of each dyed fabric were measured and used as the outputs of the LSSVM-based model. The sodium chloride concentration, sodium carbonate concentration, drying time, steaming time and dye concentration were used as the model inputs. The model predicted results with the K/S value and the CIE Lab value (L*, a*, b*) as the outputs were all satisfactory. Also, the absolute errors were all small, and the color difference ∆Eab* was less than 2.0 for fabrics dyed with both reactive dyes using the PDPS process. The model evaluation indices, namely MAE, RMSE, Ep, and R2 , were all acceptable. Therefore, the K/S value and CIE Lab value (L*, a*, b*) can be used as the outputs of the LSSVM-based model in reactive dyeing of cotton fabrics using the PDPS process to meet the needs of actual dyeing operations. Acknowledgements This work was supported by the National Major Science and Technology Projects of China [grant number 2017YFB0309700]. Compliance with ethical standards Conflicts of interest The authors declare that they have no conflict of interest. References 1. A.K.R. Choudhury. Green chemistry and textile industry. J. Text. Engin. & Fashion Techn. 2(3) (2017) 351-361. 2. R. Shamey, T. Hussein. Critical Solutions in the Dyeing of Cotton Textile Materials. Text. Prog. 37 (2010) 1-84.
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Table captions Table 1 Parameter values of LSSVM-based models with different reactive dyes and model outputs. Table 2 Performance measures of LSSVM-based models with the K/S value and CIE Lab value as the outputs and with different reactive dyes.
Figure captions Fig. 1. A flow chart of the LSSVM-based dye color model. Fig. 2. The dyeing flow chart of pad-dry-pad-steam process. Fig. 3. Reflectance spectra of fabric samples dyed with (a) Remazol Red and (b) Remazol Navy at dye concentrations of 3 g/L, 9 g/L and 15 g/L. Fig. 4. The K/S value prediction performance for the LSSVM-based model: (a) experimental and predicted color values for fabrics dyed with Remazol Red, (b) absolute errors of the predictions in (a), (c) experimental and predicted color values for fabrics dyed with Remazol Navy, and (d) absolute errors of the predictions in (c). Fig. 5. The CIE Lab prediction performance of the LSSVM-based model for fabrics dyed with Remazol Red: (a) experimental and predicted L* values, (b), absolute errors of the L* predictions, (c) experimental and predicted a* values, (d) absolute errors of the a* predictions, (e) experimental and predicted b* values, and (f) absolute errors of the b* predictions. Fig. 6. The CIE Lab value prediction performance of the LSSVM-based model for fabrics dyed with Remazol Navy: (a) experimental and predicted L* values, (b) the absolute errors of the L* predictions, (c) experimental and predicted a* values, (d) absolute errors of the a* predictions, (e) experimental and predicted b* values, and (f) absolute errors of the b* predictions. Fig. 7. The color difference between the experimental and predicted values for fabrics dyed with (a) Remazol Red, and (b) Remazol Navy.
Table 1 Parameter values of LSSVM-based models with different reactive dyes and model outputs. Model output
Evaluation Dye index
K/S value
L*
a*
b*
Remazol
γ
63.0596
26127.5566
33166.3760
20.8425
Red
σ2
20.3398
269.6401
397.4164
10.1132
Remazol
γ
6212.0887
3825.8233
4.1800
1.9560
Navy
σ2
67.5748
19.7921
15.8973
1.0868
Table 2 Performance measures of LSSVM-based models with the K/S value and CIE Lab value as the outputs and with different reactive dyes. Dye Remazol Red
Remazol Navy
Output K/S L* a* b* K/S L* a* b*
Experimental value [0.88, 14.31] [42.33, 63.27] [44.63, 61.91] [-8.62, 5.59] [0.86, 15.00] [24.35, 63.68] [-12.20, -5.88] [-25.22, -21.22]
MAE
RMSE
EP
R2
0.26 1.16 0.98 0.24 0.41 1.46 0.31 0.23
0.2969 1.4145 1.0788 0.2619 0.5331 1.7655 0.3733 0.2450
0.9608 0.9715 0.9813 0.9164 0.9325 0.9491 0.9573 0.9895
0.9922 0.9445 0.9488 0.9952 0.9863 0.9645 0.9530 0.9368
Fig. 1. A flow chart of the LSSVM-based dye color model.
Fig. 2. The dyeing flow chart of pad-dry-pad-steam process.
Fig. 3. Reflectance spectra of fabric samples dyed with (a) Remazol Red and (b) Remazol Navy at dye concentrations of 3 g/L, 9 g/L and 15 g/L. Detailed dyeing conditions in Table S11.
Fig. 4. The K/S value prediction performance for the LSSVM-based model: (a) experimental and predicted color values for fabrics dyed with Remazol Red, (b) absolute errors of the predictions in (a), (c) experimental and predicted color values for fabrics dyed with Remazol Navy, and (d) absolute errors of the predictions in (c).
Fig. 5. The CIE Lab prediction performance of the LSSVM-based model for fabrics dyed with Remazol Red: (a) experimental and predicted L* values, (b), absolute errors of the L* predictions, (c) experimental and predicted a* values, (d) absolute errors of the a* predictions, (e) experimental and predicted b* values, and (f) absolute errors of the b* predictions.
Fig. 6. The CIE Lab value prediction performance of the LSSVM-based model for fabrics dyed with Remazol Navy: (a) experimental and predicted L* values, (b) the absolute errors of the L* predictions, (c) experimental and predicted a* values, (d) absolute errors of the a* predictions, (e) experimental and predicted b* values, and (f) absolute errors of the b* predictions.
Fig. 7. The color difference between the experimental and predicted values for fabrics dyed with (a) Remazol Red, and (b) Remazol Navy.
Highlights
Dye concentration was used as the inputs of an prediction model. The K/S value and CIE Lab value (L*, a*, b*) were used as the outputs of the model. The model can meet the needs of actual dyeing operations. The model evaluation indices were all acceptable.
SCHOOL OF MATERIALS SCIENCE AND ENGINEERING, SHANGHAI UNIVERSITY
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Prof. Dr. Chengbing Yu Department of Polymer Materials, School of Materials Science and Engineering, Shanghai University Tel: +86-21-65038646 Fax: +86-21-66138039 E-mail: [email protected] Shanghai, Jan 22th, 2020
Dear editorial team, It is with great pleasure to submit the revised manuscript entitled by “LSSVM-based Color Prediction for Cotton Fabrics with Reactive Pad-Dry-Pad-Steam Dyeing” for publication in Chemometrics and Intelligent Laboratory Systems. We already studied the comments of the reviewers carefully and answered them point to point in “Responses for the comments”, all revised words and sentences were marked in “marked manuscript”. Revised manuscript and updated figures and all tables in Supplement are renewed in the meantime. The main findings and works of the paper are: 1.Dye concentration and dyeing conditions of the PDPS process were used for the first time as the inputs of a prediction model. 2.The K/S value and CIE Lab value (L*, a*, b*) can be used as the outputs of the LSSVM-based model in reactive dyeing of cotton fabrics using the PDPS process to meet the needs of actual dyeing operations. 3.The LSSVM-based model predicted results with the K/S value and the CIE Lab value (L*, a*, b*) as the outputs were all satisfactory. 2
4.The model evaluation indices, namely MAE, RMSE, Ep, and R , were all acceptable. We strongly believe that this work will draw broad interests for the readers working on textile industry. The topic fits the scope of Chemometrics and Intelligent Laboratory Systems well because it is an effective control and prediction method in fabric dyeing production, and we’d like to submit this important work for publication in Chemometrics and Intelligent Laboratory Systems. We declare that this paper is not or will not be considered for publication in any other journals, meetings, or media before a decision has been made.
Sincerely Yours, Prof.Dr. Chengbing Yu.
Declaration of Interest Statement
Article Title:
LSSVM-based Color Prediction for Cotton Fabrics with Reactive
Pad-Dry-Pad-Steam Dyeing Authors:
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Chengbing Yu, Ziwei Xi, Yilin Lu, Kaixin Tao, Yi Zhong
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previously. Yes
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elsewhere while acceptance by Industrial Crops and Products. Yes
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by any authors of this manuscript. Yes
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attesting to the above. Signature: Date:
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DOI:
https://doi.org/10.1016/j.chemolab.2020.103956
Reference:
CHEMOM 103956
To appear in:
Chemometrics and Intelligent Laboratory Systems
Received Date: 6 December 2019 Revised Date:
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Accepted Date: 24 January 2020
Please cite this article as: C. Yu, Z. Xi, Y. Lu, K. Tao, Z. Yi, LSSVM-based color prediction for cotton fabrics with reactive pad-dry-pad-steam dyeing, Chemometrics and Intelligent Laboratory Systems (2020), doi: https://doi.org/10.1016/j.chemolab.2020.103956. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.
LSSVM-based Color Prediction for Cotton Fabrics with Reactive Pad-Dry-Pad-Steam Dyeing Chengbing Yua,*, Ziwei Xia, Yilin Lua, Kaixin Taoa, Zhong Yib,* a
School of Materials Science and Engineering, Shanghai University, Shanghai 201800, P .R.
China b
College of Chemistry, Chemical Engineering and Biotechnology, Donghua University,
Shanghai 201800, P .R. China *Corresponding authors. [email protected]; [email protected]
ABSTRACT Least-square support vector machines (LSSVM) have been strongly moving from preliminary theoretical investigations into practical industrial applications. Successfully attaining a required color is a crucial task in the dyeing industry. In this paper, an LSSVM-based color quality prediction model for cotton fabrics dyed with a reactive dye using the conventional pad-dry-pad-steam (PDPS) process is proposed. The concentrations of the dye, sodium carbonate and sodium chloride, as well as the drying and steaming times were selected as the model inputs, while the CIE Lab value (L*, a*, b*) and the K/S value of the dyed fabric were used as the model outputs. The two prominent reactive dyes, Remazol Red and Remazol Navy, were used separately to dye cotton fabrics. The proposed LSSVM-based model gave mean absolute errors of less than 0.5 and 1.5 for predicting the K/S value and the CIE Lab value (L*, a*, b*), respectively. As well, the model had an acceptable average color difference ∆E* of less than 2.0 for the fabrics dyed using the PDPS process with both reactive dyes. Keywords: Dyeing, cotton fabric, LSSVM, pad-dry-pad-steam, prediction.
1. Introduction Reactive dyes are commonly-used dyes, which enjoy the advantage that they form strong covalent bonds with cellulose materials (such as cotton fibers) during the dyeing process.
Fabrics dyed with reactive dyes have excellent color fastness, complete chromatographs and low production costs. Therefore, cotton fibers have been widely processed with reactive dyes, mainly through continuous and batch dyeing [1-3]. In practical textile production, it is highly significant to evaluate the color quality of dyed cotton fabrics, and especially determine the color differences between dyed fabric samples and the industry standards [4-5]. Such color differences can be measured in continuous dyeing using a reflective spectrophotometer that horizontally scans the fabric color. In batch dyeing, a colorimeter is typically used to determine the differences between the dyed samples and the standards in terms of the brightness L*, the red-greenness a*, and the yellow-blueness b*. Then, the color difference ∆E* according to the CIE Lab formulas can be calculated [6, 7] or the K/S values of the dyed fabrics are found and compared [8]. When the color difference is found to be unacceptable, the fabric must be re-dyed or stripped, and hence great economic losses are incurred [9]. So, once a dyed fabric sample fails in the color difference test in actual dyeing, the dyeing process should be adjusted in time by calculating the necessary amounts of the dye, the accelerating or fixing agents, and other dyeing conditions in order to satisfy the color difference criteria. Not only does this adjustment reduce the economic loss, but it is also very meaningful for the dyeing of cotton fabrics with reactive dyes. Careful design of the dyeing operations is critical for improving the quality and grade of the knitted products, saving energy, and reducing the industrial wastes and sewage. At present, the design methodology is mainly based on manufacturer-specified dye parameters or empirical dyeing tests [10]. Once an on-site problem in quality or cost emerges, the dyeing process needs to be redesigned and optimized through multiple experiments. This redesign has the disadvantages of long cycles, high costs, inaccurate quantification and susceptibility to human errors. Alternatively, mathematical models can be utilized to predict dyeing results according to process conditions. Thus, based on such models, on-site timely adjustment and optimization of dyeing processes can be carried out correctly and reasonably. Currently, few computer-based approaches have been proposed to apply intelligent algorithms in the optimization of dyeing processes. In particular, an artificial neural network (ANN) can learn real-world complex and unknown processes using experimental data without any actual knowledge of the underlying mathematical, physical, or chemical
principles. Thus, ANN applications in nonlinear and complex systems are quite common [8, 9]. However, ANNs can be easily trapped into local minima and suffer from overfitting in practical applications [11, 12]. In addition to ANNs, support vector machines (SVMs) represent a key machine learning architecture that has attracted much attention in modeling nonlinear and highly complex systems. The SVM can perform nonlinear classification, multivariate function estimation, or nonlinear regression [13]. The LSSVM is an optimized SVM-based algorithm, which applies a set of linear equations, instead of the quadratic programming formulation used in a standard SVM [14, 15]. The LSSVM model does not only reduce the computational complexity, but it also improves the prediction accuracy [16-19]. Furthermore, earlier studies have demonstrated that methods of variable selection (or elimination of less significant variables) can further improve the prediction accuracy and robustness of the regression models, since such methods can obtain relevant variables with the least collinearity, redundancy and noise. These methods have been used in measurements, modeling, control, pattern recognition, and fault diagnosis. For example, those processes have been used in the control and optimization of industrial processes arising in chemical, food, pharmaceutical, financial, and electronic industries [20-22]. The dyeing of cotton fabrics with reactive dyes using the PDPS operation is an extremely complex dyeing process, which involves a large number of physical and chemical changes affected by many factors. Such factors include the structure of the cotton fibers and the reactive dye, the padding pressure, the type and concentration of the dyeing and fixing agents, as well as the steaming temperature and time. In addition, the dyeing process is also affected by random variations in water quality, atmospheric pressure, instrumentation errors of testing and display, dyeing equipment errors, and human experience. To improve the success rate of the dyeing process while avoiding frequent re-dyeing, color correction, stripping and scrapping, it is necessary to properly model the relationship between the various process factors and dyeing outcomes, improve the dyeing equipment performance, and finally achieve real-time control and optimization of the dyeing process and the quality of the dyed products [23, 24]. In this paper for the first time, dye concentration and dyeing conditions of the PDPS
process were used as the inputs of an LSSVM-based prediction model, while the CIE Lab color (L*, a*, b* ) and the K/S value of the dyed cotton fabrics were used as outputs. Two groups of experimental dyeing protocols were designed, in which Remazol Red and Remazol Navy reactive dyes were used respectively to dye the cotton fabrics. Each group consisted of 80 experimental protocols for model training and 20 experimental protocols for model verification. The experimental results were used to evaluate the prediction accuracy and applicability of the LSSVM-based model. 2. Materials and methods 2.1 Materials In recent years, with the worldwide trend of using knitted outerwear, the world production of knitted fabrics has exceeded that of woven fabrics. In this paper, the dyeing tests were performed on scoured, bleached, fluorescent, brightener-free, and tubular cotton knitted fabrics. The fabrics were supplied by the New Sierlk Knitted Garment Company (Nantong, China). The fabrics had a double-interlock knit structure at 40/1s combed compact knitting with a fabric weight of 180 g/m2. The reactive dyes, Remazol Red (RGB) and Remazol Navy (RGB), were kindly supplied by Dystar Trading Co. Ltd. (Shanghai, China). Each of the two dyes is a one-compound recommended reactive dye. The sodium carbonate and sodium chloride compounds were of an analytical grade (Sinopharm Group Co. Ltd., Shanghai, China). All chemicals were used as received without any further purification. Deionized water was used throughout the study. 2.2 Methods 2.2.1 Least-Square Support Vector Machines Support vector machines represent a powerful tool for classification and regression [25]. A SVM is based on the minimization of the structural risk rather than the empirical risk. This enables SVMs to have better generalization performance compared to ANNs [26]. While SVMs have reasonable time costs on limited training samples, the SVM computational complexity becomes prohibitively large with increasing numbers of training samples, and SVMs become unsuitable for real-time applications [27]. In order to overcome the SVM shortcomings, an enhanced variant of it, namely the LSSVM converts the quadratic optimization problem to one of solving linear equations, reduces the computational cost
considerably, and improves the convergence accuracy [28]. There are many factors affecting the dyeing performance, and thus it is generally difficult to establish an appropriate mathematical model. Model selection depends mainly on the problem complexity. In this work, a multi-factor model is used, and LSSVM is exploited to deal with nonlinearities in the dyeing process. Fig. 1 shows a flow chart of the LSSVM-based multi-input prediction model of the dye color quality. In Fig. 1, the 2D output vector Y has the K/S value and the CIE Lab value (L*, a*, b*). The mean absolute error (MAE), the root-mean-square error (RMSE), the precision (Ep) and the coefficient of determination (R2) are utilized to evaluate the prediction performance of the LSSVM-based model [29, 30]. The MAE, RMSE, Ep and R2 are defined, respectively, as: =
1
1
= = 1− = 1−
∑ ∑
| − | 1 , −
2 ,
× 100% ,
− −
=
1
3 ,
4 .
The construction of the dyeing model can be divided into training and testing phases. Out of 100 data samples, 80 samples were used for training while 20 samples were used for testing. The LSSVM prediction model was trained based on the input and output matrices of the training set. In brief, the model training involves, first, importing training data, initializing the model, finding the optimal parameters, and finally obtaining the trained LSSVM prediction model. In the testing phase, the trained LSSVM prediction model is fed with the test input matrix to obtain output predictions. Then the predicted values are compared with actual experimental values. If the prediction result is accurate enough, the model is
considered to be satisfactory and the modeling process is complete. Otherwise, further model refinements are carried on until good prediction performance is reached. 2.2.2 Dyeing using the PDPS process The pad-dry process. The cotton fabrics were initially immersed in a freshly-prepared dyeing solution containing Reactive dye (>0-20 g/L) at room temperature for a 30-second interval, then passed through a vertical two-roll padder (Weida Machinery Co., Ltd., Shaoxing, China) to obtain 65±1% pick-up using a two-dip two-nip procedure. Then, the wet cotton samples were immediately dried with a hot-air laboratory machine (Weida Machinery Co., Ltd., Shaoxing, China) at 100±1 oC for an interval of 70-160 s (Fig. 2). The pad-steam process. After the pad-dry process, the samples were immersed in a freshly-prepared dyeing solution containing sodium chloride (125-200 g/L) and sodium carbonate (10-50 g/L) at room temperature for a 30-second interval. Then, the samples were passed through the afore-mentioned vertical two-roll padder to obtain 60 ± 1% pick-up using again a two-dip two-nip procedure. The wet cotton samples were then steamed in a laboratory steamer (Mathis, Switzerland) with saturated atmospheric steam pressure at 100±1 oC for an interval of 40-180 s (Fig. 2). 2.2.3 Washing process All dyed fabric samples were washed first with cold water, then with hot water at 50-60 oC for a 5-minute interval. These samples were thus soaped at 95±1 oC for 15 minutes in a water bath with 5-g/L standard soap flakes and a liquor ratio of 50:1. Then, the samples were washed with hot water at 80±1 oC for 5 minutes and then with cold water. Finally, all samples were dried at 60 oC in a vacuum oven. 2.2.4 Color measurements The CIE Lab value (L*, a*, b*) [31] and the color strength (K/S value) [32] of the dyed fabrics were determined using a Color i5 Benchtop Spectrophotometer (X-Rite Co., USA) with a D65 illuminant and a 10o standard observer. The color differences ∆E* were calculated with experimental L*, a*, b* values of the measured and predicted values. Each fabric sample was folded into four layers and placed in front of the aperture of the spectrophotometer. The sample color characteristics were measured in four different random locations and the average values were calculated and adopted.
2.2.5 LSSVM-based Model Design The LSSVM methodology is considered to be highly successful in the design of nonlinear and complex prediction models. This methodology has been widely applied in electronic equipment analysis, bearing degradation prediction, wind power prediction, and many other fields [20-22]. However, LSSVM applications in dyeing prediction have been limited so far. Since the color of a dyed fabric has a nonlinear complex relationship with the PDPS dyeing conditions, the LSSVM methodology was successfully adopted in this paper to predict the K/S value and the CIE Lab value (L*, a*, b*) of the dyed fabrics. For the proposed LSSVM-based model, five main PDPS factors were selected as model inputs. These factors are the sodium chloride concentration in g/L (A), the sodium carbonate concentration in g/L (B), the drying time in seconds (C), the steaming time in seconds (D), and either Remazol Red dye concentration in g/L or Remazol Navy dye concentration in g/L (E). The model outputs were selected to be the CIE Lab value (L*, a*, b*) and the K/S value of the dyed fabrics. Based on the actual dyeing of the PDPS process [33, 34], each experimental input was determined randomly within their reasonable ranges, and 100 groups of experimental conditions were designed for Remazol Red and Remazol Navy. In every 100 groups, 80 experimental conditions were used to train and establish the LSSVM-based model, and the other 20 experimental conditions were used to verify and evaluate the LSSVM-based model. 3. Results and Discussion 3.1 The K/S and CIE Lab values The K/S and CIE Lab values are two important distinct measures for the color of dyed fabrics. The K/S value can be calculated based on the Kubelka-Munk equation [20], $⁄ = 1 −
⁄2 5 ,
where K and S are, respectively, the absorption and scattering coefficients of a dyed fabric
sample, while R is the sample reflectance at the wavelength corresponding to the maximum dye absorption. This wavelength is 540 nm and 620 nm for Remazol Red and Remazol Navy, respectively. In fact, the K/S value is only meaningful at that maximum-absorption wavelength.
The CIE Lab 1976 color scale is a comprehensive result of measuring color in the full visible range, not only at the wavelength of the maximum dye absorption. The CIE Lab value is based on the CIELAB color scale published by the Commission International Eclairage (CIE). This scale is a device-independent complete model of all visible colors for the human eye. The CIE Lab uniform color space is a nonlinear transformation of the CIE 1976 standard colorimetric system. Specifically, CIE Lab values are obtained from CIE 1976 ones by converting rectangular-coordinate XYZ tristimulus components into cylindrical-coordinate color components of the brightness L*, hue a*, and saturation b*. These color components are more consistent with the human eye vision [31]. The transformation between the CIE 1976 and CIE Lab color components is given as / − 16 *+∗ = 116 × . / ( ( 2 / −. 3 6 ， 0∗ = 500 × 1. 2 / ) ( (4 ∗ = 200 × 1. / − . 5 3 ' / 5 7 > 0.008856 . 7 = 78, 6 7 , 16 . 7 = 7.787 × 7 + , 7 < 0.008856 116
where X, Y, Z are the tristimulus values of the target object; Xn, Yn, Zn are the tristimulus values of the white stimulus such that the standard illuminator is applied to a full diffuse reflector which makes a complete diffuse reflection into the observer's eyes. It can be seen that the L*, a*, b* values in the CIE Lab formula express colors by using a uniform color space in which the brightness L* and the chromaticity components a* and b* are considered to be uniformly varied [31] . The color difference ∆Eab* between two chrominance values (L1* a1*, b1*) and (L2*, a2*, b2*) in the CIE Lab color space is calculated as ∆
?@
∗
=A +
∗
−+
∗
+ 0
∗
−0
∗
+ 4
∗
−4
∗
8 .
In this paper, ∆Eab* stands for the color difference between the experimental color value (L1* a1*, b1*) of the dyed fabric and the color value
(L2*, a2*, b2*) predicted by the
LSSVM model. For fabric samples dyed with different concentrations of Remazol Red and Remazol Navy
through a PDPS process with different dyeing condition (Table S11), the reflectance spectral curves are similar in shape. Each curve has one strong reflection peak from 360 nm to 780 nm at different dye concentrations (3 g/L, 9 g/L and 15 g/L), and the maximum-absorption wavelengths are at the same position: 540 nm for Remazol Red in Fig. 3(a) and 620 nm for Remazol Navy in Fig. 3(b). However, the intensities of the maximum-reflection peaks become strong gradually as the dye concentration increases. This means that the maximum-absorption wavelength in the dyed fabrics has no relation with the used dye concentration. 3.2 Establishment of the LSSVM-based dyeing model Cellulose fiber textiles are typically dyed with reactive dyes. As well, the PDPS continuous dyeing process with reactive dyes is commonly used for woven fabrics because of the advantages of the high dye fixing rate, deeper colors, lower dye costs, less environmental pollution, defect concealment in the pre-treatment stage, easy control of color differences, and good rubbing and washing fastness. Moreover, this dyeing process can effectively reduce resource consumption, improve production efficiency, and increase the competitiveness of dyeing enterprises. Therefore, this continuous dyeing process is gradually applied in the dyeing of knitted fabrics. In this work, the knitted fabric samples used in the model construction are dyed using a PDPS process. Based on earlier studies of the PDPS dyeing process [33, 34], the ranges of the five input factors were set to be 125-200 g/L for the sodium chloride concentration, 10-50 g/L for the sodium carbonate concentration, 70-160 s for the drying time, 40-180 s for the steaming time, and 0-20 g/L for the reactive dye concentrations. Furthermore, every 80 groups of experiments for Remazol Red and Remazol Navy using the PDPS process were devised as training data, as shown in Table S1 and Table S4 for CIE Lab (L*, a*, b*), Table S7 and Table S9 for K/S value respectively. Based on the MATLAB software (version R2017b, MathWorks, USA), the LSSVM toolbox (version 1.8) was used to construct the color prediction models. Details of the LSSVM regression algorithm can be found in the literature [35, 36]. In the LSSVM-based model, the Gaussian radial basis function (RBF) was selected as the LSSVM kernel function, for better handling of nonlinearities and higher reduction of the
training computational complexity. As shown in Table 1, a model is affected by the selection of the γ and σ2 parameters, where γ is a regularization parameter, which influences the LSSVM generalization performance, while σ2 is the RBF width, which controls the regression error [35-37]. Indeed, the selection of the regularization parameter, the kernel function, and the associated kernel parameters play a crucial role in model success [38]. 3.3 Validation of the LSSVM-based dyeing model The K/S value of dyed fabrics was used as the output of the LSSVM-based model. The prediction results of this value for fabrics dyed with Remazol Red and Remazol Navy are shown in Fig. 4. From Fig. 4 (a), Fig. 4 (c), Table S8 and Table S10, it can be seen that the experimental K/S values of the fabrics dyed with Remazol Red and Remazol Navy are respectively within the ranges of [0.88, 14.31] and [0.86, 15.00], while the K/S values predicted by the LSSVM-based model are respectively within the ranges of [1.20, 14.17] and [1.16, 15.70]. Obviously, the experimental and predicted K/S ranges are essentially similar. Nevertheless, it is necessary to analyze the prediction performance further. As shown in Fig. 4 (b) and Fig. 4 (d), the absolute K/S prediction errors for 20 samples dyed with Remazol Red are within the range [-0.39, 0.46] and the mean absolute error is 0.26, while the corresponding absolute error range for Remazol Navy dye is [-0.15, 1.19] and the mean absolute error is 0.41. Hence, the prediction performance of the dyeing model with reactive dyes and with the K/S value as the model output is good, and the prediction errors are small and acceptable. The CIE Lab value is another important measure of the color of dyed fabrics. This measure covers the full visible range of the dye absorption for the dyed fabrics. The performance of the LSSVM-based model was evaluated with the CIE Lab value (L*, a*, b*) as the model output. The LSSVM-based dyeing models for fabrics dyed with Remazol Red are shown in Fig. 5. As shown in Fig. 5(a)-(f) and Table S2-S3, the experimental L* values of the dyed fabrics with Remazol Red are within the range [42.33, 63.27], while the predicted L* values of the LSSVM-based model are within the range [43.60, 64.40]. Likewise, the experimental a* values are within the range [44.63, 61.24], while the predicted a* values are within the range [46.00, 63.31]. Similarly, the experimental b* values are within the range [-8.62, 5.59], while the predicted b* values are within the range [-8.45, 5.29]. Clearly, each of the ranges of
the experimental values of L*, a*, and b* is satisfactorily close to the corresponding one of the predicted values. Anyway, the prediction performance further using the absolute error for every sample is analyzed. The absolute errors for the 20 samples are (a) within the range [-3.02, 2.43] for predicting the L* values, with a mean absolute error of 1.16, (b) within the range [-1.85, 1.50] for predicting the a* values, with a mean absolute error of 0.98, and (c) within the range [-0.38, 0.34] for predicting the b* values, with a mean absolute error of 0.24. The small absolute errors of L*, a*, and b* for samples dyed with Remazol Red confirm the good prediction performance of the LSSVM-based models with the CIE Lab value (L*, a*, b*) as the model output. The LSSVM-based dyeing models for fabrics dyed with Remazol Navy are shown in Fig. 6. From Fig.6(a)-(f) and Table S5-S6, the experimental L* values of fabrics dyed with Remazol Navy are within the range [24.35, 63.68], while the corresponding predicted L* values of the LSSVM-based model are within the closely-related range of [25.85, 63.76]. Similarly, the experimental a* values of the fabrics dyed with Remazol Navy are within the range [-12.20, -5.88], while the corresponding predicted a* values are within the almost-identical range of [-11.49, -6.27]. Likewise, the experimental b* values of the fabrics dyed with Remazol Navy are within the range [-25.22, -21.22], while the corresponding predicted b* values are within the greatly-similar range of [-25.04, 20.91]. Consequently, each range of the experimental values is close to that of corresponding predicted values. Moreover, the absolute errors of the 20 samples are (a) within the range [-2.91, 3.41] for predicting the L* values, with a mean absolute error of 1.46, (b) within the range [-0.68, 0.75] for predicting the a* values, with a mean absolute error of 0.31, and (c) within the range [-0.32, 0.35] for predicting the b* values, with a mean absolute error of 0.23. Once again, small absolute errors of L*, a*, and b* of samples dyed with Remazol Navy assert that the LSSVM-based models with the CIE Lab value (L*, a*, b*) as the model output have good prediction performance. For the CIE Lab color system, L*, a*, and b* of the color samples express the brightness L* and the chromaticity a* and b*, while ∆Eab* shows the difference between two colors more clearly as a whole. From Fig. 7, it can be seen that the color difference ∆Eab* derived from the LSSVM-based model for fabrics dyed with Remazol Red is within the range [0.25,
3.34] with an average of 1.63, while the corresponding range for Remazol Navy is [0.41, 3.45] ∗ with an average of 1.57. Generally speaking, when the color difference ∆E is less than or
equal to 1.5, the color difference is not obvious and is indistinguishable by the unaided human eye. Therefore, the experimental and predicted ∆Eab* values seem to be mostly satisfactory. The overall prediction performance of the LSSVM-based models with the CIE Lab value (L*, a*, b*) as the model output is excellent. 3.4 Indice assessment of the LSSVM-based dyeing models In order to comprehensively compare the performance of the LSSVM-based models with each of the K/S value and the CIE Lab value (L*, a*, b*) as the model output, typical LSSVM evaluation indices were selected and listed in Table 2. These indices are MAE, RMSE, Ep, and R2 [29, 30]. A small MAE or RMSE value indicates a good prediction accuracy of the LSSVM-based model. On the contrary, a small R2 or EP value indicates a poor prediction accuracy of the LSSVM-based model. As shown in Table 2, the MAE and RMSE values for the model with Remazol Red dye are both smaller than the corresponding values with Remazol Navy dye where the K/S value is the model output. This implies a smaller prediction error for the model with Remazol Red dye. The MAE and RMSE values for the LSSVM-based model with Remazol Red dye are 0.26 and 0.2969, respectively. Moreover, the associated R2 and Ep values are larger and closer to 1, indicating a better model fitting. To sum up, the above analysis shows that the LSSVM-based model with the K/S value as the model output and Remazol Red dye has a better accuracy than that with Remazol Navy dye. The MAE, RMSE, Ep, and R2 values of the model of the fabrics dyed with Remazol Navy are all satisfactory enough to predict the K/S value precisely, although these values are poorer than the corresponding values for Remazol Red dye. Similar results were obtained for LSSVM-based models with the CIE Lab value (L*, a*, b*) as the output. All MAE and RMSE values for the model with Remazol Red dye are small: MAE has values of 1.16, 0.98 and 0.24 and RMSE has values of 1.4145, 1.0788 and 0.2619, for models with L*, a*, and b* as the output, respectively. Meanwhile, all R2 and Ep values of the model with Remazol Red dye are large and close to 1, indicating good model fitting.
Compared with the LSSVM-based model for Remazol Red dye, the model with Remazol Navy dye has similar MAE, RMSE, Ep, and R2 values. All these results confirm that the models with the CIE Lab values (L*, a*, b*) as the outputs are also all satisfactory enough to predict the CIE Lab value precisely. This is consistent with the color difference ∆Eab* calculated by experimental and predicted L*, a*, and b*. 4. Conclusions Based on the conventional PDPS process and the LSSVM methodology, a comprehensive experimental scheme for dyeing cotton fabrics with a reactive dye was designed. The two reactive dyes, Remazol Red and Remazol Navy, were used independently to dye cotton fabrics. The CIE Lab value and the K/S value of each dyed fabric were measured and used as the outputs of the LSSVM-based model. The sodium chloride concentration, sodium carbonate concentration, drying time, steaming time and dye concentration were used as the model inputs. The model predicted results with the K/S value and the CIE Lab value (L*, a*, b*) as the outputs were all satisfactory. Also, the absolute errors were all small, and the color difference ∆Eab* was less than 2.0 for fabrics dyed with both reactive dyes using the PDPS process. The model evaluation indices, namely MAE, RMSE, Ep, and R2 , were all acceptable. Therefore, the K/S value and CIE Lab value (L*, a*, b*) can be used as the outputs of the LSSVM-based model in reactive dyeing of cotton fabrics using the PDPS process to meet the needs of actual dyeing operations. Acknowledgements This work was supported by the National Major Science and Technology Projects of China [grant number 2017YFB0309700]. Compliance with ethical standards Conflicts of interest The authors declare that they have no conflict of interest. References 1. A.K.R. Choudhury. Green chemistry and textile industry. J. Text. Engin. & Fashion Techn. 2(3) (2017) 351-361. 2. R. Shamey, T. Hussein. Critical Solutions in the Dyeing of Cotton Textile Materials. Text. Prog. 37 (2010) 1-84.
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Table captions Table 1 Parameter values of LSSVM-based models with different reactive dyes and model outputs. Table 2 Performance measures of LSSVM-based models with the K/S value and CIE Lab value as the outputs and with different reactive dyes.
Figure captions Fig. 1. A flow chart of the LSSVM-based dye color model. Fig. 2. The dyeing flow chart of pad-dry-pad-steam process. Fig. 3. Reflectance spectra of fabric samples dyed with (a) Remazol Red and (b) Remazol Navy at dye concentrations of 3 g/L, 9 g/L and 15 g/L. Fig. 4. The K/S value prediction performance for the LSSVM-based model: (a) experimental and predicted color values for fabrics dyed with Remazol Red, (b) absolute errors of the predictions in (a), (c) experimental and predicted color values for fabrics dyed with Remazol Navy, and (d) absolute errors of the predictions in (c). Fig. 5. The CIE Lab prediction performance of the LSSVM-based model for fabrics dyed with Remazol Red: (a) experimental and predicted L* values, (b), absolute errors of the L* predictions, (c) experimental and predicted a* values, (d) absolute errors of the a* predictions, (e) experimental and predicted b* values, and (f) absolute errors of the b* predictions. Fig. 6. The CIE Lab value prediction performance of the LSSVM-based model for fabrics dyed with Remazol Navy: (a) experimental and predicted L* values, (b) the absolute errors of the L* predictions, (c) experimental and predicted a* values, (d) absolute errors of the a* predictions, (e) experimental and predicted b* values, and (f) absolute errors of the b* predictions. Fig. 7. The color difference between the experimental and predicted values for fabrics dyed with (a) Remazol Red, and (b) Remazol Navy.
Table 1 Parameter values of LSSVM-based models with different reactive dyes and model outputs. Model output
Evaluation Dye index
K/S value
L*
a*
b*
Remazol
γ
63.0596
26127.5566
33166.3760
20.8425
Red
σ2
20.3398
269.6401
397.4164
10.1132
Remazol
γ
6212.0887
3825.8233
4.1800
1.9560
Navy
σ2
67.5748
19.7921
15.8973
1.0868
Table 2 Performance measures of LSSVM-based models with the K/S value and CIE Lab value as the outputs and with different reactive dyes. Dye Remazol Red
Remazol Navy
Output K/S L* a* b* K/S L* a* b*
Experimental value [0.88, 14.31] [42.33, 63.27] [44.63, 61.91] [-8.62, 5.59] [0.86, 15.00] [24.35, 63.68] [-12.20, -5.88] [-25.22, -21.22]
MAE
RMSE
EP
R2
0.26 1.16 0.98 0.24 0.41 1.46 0.31 0.23
0.2969 1.4145 1.0788 0.2619 0.5331 1.7655 0.3733 0.2450
0.9608 0.9715 0.9813 0.9164 0.9325 0.9491 0.9573 0.9895
0.9922 0.9445 0.9488 0.9952 0.9863 0.9645 0.9530 0.9368
Fig. 1. A flow chart of the LSSVM-based dye color model.
Fig. 2. The dyeing flow chart of pad-dry-pad-steam process.
Fig. 3. Reflectance spectra of fabric samples dyed with (a) Remazol Red and (b) Remazol Navy at dye concentrations of 3 g/L, 9 g/L and 15 g/L. Detailed dyeing conditions in Table S11.
Fig. 4. The K/S value prediction performance for the LSSVM-based model: (a) experimental and predicted color values for fabrics dyed with Remazol Red, (b) absolute errors of the predictions in (a), (c) experimental and predicted color values for fabrics dyed with Remazol Navy, and (d) absolute errors of the predictions in (c).
Fig. 5. The CIE Lab prediction performance of the LSSVM-based model for fabrics dyed with Remazol Red: (a) experimental and predicted L* values, (b), absolute errors of the L* predictions, (c) experimental and predicted a* values, (d) absolute errors of the a* predictions, (e) experimental and predicted b* values, and (f) absolute errors of the b* predictions.
Fig. 6. The CIE Lab value prediction performance of the LSSVM-based model for fabrics dyed with Remazol Navy: (a) experimental and predicted L* values, (b) the absolute errors of the L* predictions, (c) experimental and predicted a* values, (d) absolute errors of the a* predictions, (e) experimental and predicted b* values, and (f) absolute errors of the b* predictions.
Fig. 7. The color difference between the experimental and predicted values for fabrics dyed with (a) Remazol Red, and (b) Remazol Navy.
Highlights
Dye concentration was used as the inputs of an prediction model. The K/S value and CIE Lab value (L*, a*, b*) were used as the outputs of the model. The model can meet the needs of actual dyeing operations. The model evaluation indices were all acceptable.
SCHOOL OF MATERIALS SCIENCE AND ENGINEERING, SHANGHAI UNIVERSITY
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Prof. Dr. Chengbing Yu Department of Polymer Materials, School of Materials Science and Engineering, Shanghai University Tel: +86-21-65038646 Fax: +86-21-66138039 E-mail: [email protected] Shanghai, Jan 22th, 2020
Dear editorial team, It is with great pleasure to submit the revised manuscript entitled by “LSSVM-based Color Prediction for Cotton Fabrics with Reactive Pad-Dry-Pad-Steam Dyeing” for publication in Chemometrics and Intelligent Laboratory Systems. We already studied the comments of the reviewers carefully and answered them point to point in “Responses for the comments”, all revised words and sentences were marked in “marked manuscript”. Revised manuscript and updated figures and all tables in Supplement are renewed in the meantime. The main findings and works of the paper are: 1.Dye concentration and dyeing conditions of the PDPS process were used for the first time as the inputs of a prediction model. 2.The K/S value and CIE Lab value (L*, a*, b*) can be used as the outputs of the LSSVM-based model in reactive dyeing of cotton fabrics using the PDPS process to meet the needs of actual dyeing operations. 3.The LSSVM-based model predicted results with the K/S value and the CIE Lab value (L*, a*, b*) as the outputs were all satisfactory. 2
4.The model evaluation indices, namely MAE, RMSE, Ep, and R , were all acceptable. We strongly believe that this work will draw broad interests for the readers working on textile industry. The topic fits the scope of Chemometrics and Intelligent Laboratory Systems well because it is an effective control and prediction method in fabric dyeing production, and we’d like to submit this important work for publication in Chemometrics and Intelligent Laboratory Systems. We declare that this paper is not or will not be considered for publication in any other journals, meetings, or media before a decision has been made.
Sincerely Yours, Prof.Dr. Chengbing Yu.
Declaration of Interest Statement
Article Title:
LSSVM-based Color Prediction for Cotton Fabrics with Reactive
Pad-Dry-Pad-Steam Dyeing Authors:
Yes
Chengbing Yu, Ziwei Xi, Yilin Lu, Kaixin Tao, Yi Zhong
All authors of this manuscript have directly participated in planning, execution,
and/or analysis of this study (if not, specify). Yes
The contents of this manuscript have not been copyrighted or published
previously. Yes
The contents of this manuscript are not now under consideration for publication
elsewhere. Yes
The contents of this manuscript will not be copyrighted, submitted, or published
elsewhere while acceptance by Industrial Crops and Products. Yes
There are no directly related manuscripts or abstracts, published or unpublished,
by any authors of this manuscript. Yes
I am sole author of this manuscript
Yes
I am one author signing on behalf of all co-authors of this manuscript, and
attesting to the above. Signature: Date:
2019-12-16
Printed Name: Institution:
Chengbing Yu Shanghai University