Textile products with a faded effect achieved via ozonation are increasingly popular nowadays. In order to better understand and apply this process, the complex factors and effects of color fading ozonation are investigated via process modeling in terms of pH, temperature, water pick-up, time (of process) and original color (of textile) affecting the color performance (K/S, L*, a*, b* values) of reactive-dyed cotton using the Extreme Learning Machine (ELM), Support Vector Regression (SVR) and Random Forest (RF), respectively. It is found that the RF and SVR perform better than the ELM as the latter were very unstable in the case of predicting a certain single output. Both the RF and SVR are potentially applicable, but SVR would be more recommended to be used in the real application due to its balancer predicting performance and lower training time cost.
KanCWCheungHFChanQ. A study of plasma-induced ozone treatment on the colour fading of dyed cotton. J Clean Prod2016; 112: 3514–3524.
2.
Paul R. Denim: manufacture, finishing and applications. Cambridge: Elsevier, 2015.
3.
Hoigné J. The chemistry of ozone in water. In: Process technologies for water treatment. In Samuel Stucki (ed.), Boston, MA: Springer, 1988, pp.121–141.
4.
ErenHAOzturkD. The evaluation of ozonation as an environmentally friendly alternative for cotton preparation. Text Res J2011; 81: 512–519.
5.
MaoYGuanYLuoD, et al.Investigation of a homogeneous activating ozonation method in the rinsing procedure of cotton fabric dyed with reactive dye. Color Technol2011; 127: 256–267.
6.
YigitIErenSErenHA. Ozone utilisation for discharge printing of reactive dyed cotton. Color Technol2018; 134: 13–23.
7.
ErenSGümüsBErenHA. Colour stripping of reactive-dyed cotton by ozone treatment. Color Technol2016; 132: 466–471.
8.
KıcıkHErenHA. Application of ozone gas for the stripping of fabric ink-jet-printed with reactive dyes. Color Technol2017; 133: 485–490.
9.
He Z, Li M, Zuo D, et al. The effect of denim color fading ozonation on yarns. Ozone: Science & Engineering 2018; 40:5: 377–384.
10.
HeZLiMZuoD, et al.Color fading of reactive-dyed cotton using UV-assisted ozonation. Ozone Sci Eng2019; 41: 60–68.
11.
He Z, Li M, Zuo D, et al. Effects of color fading ozonation on the color yield of reactive-dyed cotton. Dyes and Pigments 2019; 164: 417–427.
12.
Abd JelilRZengXKoehlL, et al.Modeling plasma surface modification of textile fabrics using artificial neural networks. Eng Appl Artif Intell2013; 26: 1854–1864.
13.
WuLYickKLNgSP, et al.Parametric design and process parameter optimization for bra cup molding via response surface methodology. Expert Syst Appl2012; 39: 162–171.
14.
BalciOOǧulataRT. Prediction of the changes on the CIELab values of fabric after chemical finishing using artificial neural network and linear regression models. Fibers Polym2009; 10: 384–393.
15.
ElemenSAkçakoca KumbasarEPYaparS. Modeling the adsorption of textile dye on organoclay using an artificial neural network. Dye Pigment2012; 95: 102–111.
16.
Zervent ÜnalB. The prediction of seam strength of denim fabrics with mathematical equations. J Text Inst2012; 103: 744–751.
17.
SetteSVan LangenhoveL. An overview of soft computing in textiles. J Text Inst2003; 94: 103–109.
18.
Tehran MA, and Maleki M. Artificial Neural Network Prosperities in Textile Applications, In: Suzuki Kenji (ed.), Artificial Neural Networks Industrial and Control Engineering Applications, 2011. 35–64. Rijeka, IntechOpen.
19.
Wu Y, Zhang Y, Liu X, Cai Z, and Cai Y. A multiobjective optimization-based sparse extreme learning machine algorithm. Neurocomputing 2018; 317: 88–100.
20.
SunZChoiTAuK, et al.Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst2008; 46: 413–421.
21.
Vapnik V. The nature of statistical learning theory. New York: Springer Science & Business Media, 2013.
22.
BasakDPalSPatranabisDC. Support vector regression. Neural Inf Proc Rev2007; 11: 203–224.
23.
Smola AJ and Schölkopf B. A tutorial on support vector regression. Statistics and computing 2004; 14(3): 199–222.
24.
GhoshAChatterjeeP. Prediction of cotton yarn properties using support vector machine. Fibers Polym2010; 11: 84–88.
25.
NurwahaDWangX. Prediction of rotor spun yarn strength using support vector machines method. Fibers Polym2011; 12: 546–549.
26.
GüneşoğluSYüceerM. A modeling study of micro-cracking processes of polyurethane coated cotton fabrics. Text Res J2018; 88: 2766–2781.
27.
YapPHWangXWangL, et al.Prediction of wool knitwear pilling propensity using Support Vector Machines. Text Res J2010; 80: 77–83.
28.
LiawAWienerM. Classification and regression by randomForest. R News2002; 2: 18–22.
29.
Breiman, L. Random forest. Machine Learning 2001; 45: 5–32.
30.
KwonBKWonJSKangDJ. Fast defect detection for various types of surfaces using random forest with VOV features. Int J Precis Eng Manuf2015; 16: 965–970.
31.
VenkatramanVAlsbergBK. A quantitative structure-property relationship study of the photovoltaic performance of phenothiazine dyes. Dye Pigment2015; 114: 69–77.
32.
KhatriAPeerzadaMHMohsinM, et al.A review on developments in dyeing cotton fabrics with reactive dyes for reducing effluent pollution. J Clean Prod2015; 87: 50–57.
33.
HuangG-BZhuQ-YSiewC-K. Extreme learning machine: theory and applications. Neurocomputing2006; 70: 489–501.
34.
Rao CR and Mitra SK. Generalized inverse of a matrix and its applications. Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Theory of Statistics, 601–620, University of California Press, Berkeley, Calif., 1972. Available at: https://projecteuclid.org/euclid.bsmsp/1200514113.
35.
MuKSmolaAJSchoB. The connection between regularization operators and support vector kernels. Neural Network1998; 11: 637–649.
36.
Scholkopf B and Smola AJ. Learning with kernels: support vector machines, regularization, optimization, and beyond. London: MIT Press, 2001.
37.
XuSAnXQiaoX, et al.Multi-output least-squares support vector regression machines. Pattern Recognit Lett2013; 34: 1078–1084.
38.
LiawAWienerM. Classification and regression by randomForest. R News2002; 2: 18–22.
39.
Breiman L. Classification and regression trees. New York: Routledge, 2017.
40.
Rahman R, Otridge J and Pal R. Gene expression IntegratedMRF: random forest-based framework for integrating prediction from different data types. Bioinformatics 2017; 33: 1407–1410.
41.
KubelkaP. Ein Beitrag zur Optik der Farbanstriche (Contribution to the optic of paint). Zeitschrift fur Tech Phys1931; 12: 593–601.
42.
SinghRBalasundaramS. Application of extreme learning machine method for time series analysis. Int J Intell Technol2007; 2: 256–262.
43.
Smits GF and Jordaan EM. Improved SVM regression using mixtures of kernels. In Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No. 02CH37290) (Vol. 3, pp. 2785–2790), 2002, May, IEEE.
