Modeling color fading ozonation of reactive-dyed cotton using the Extreme Learning Machine, Support Vector Regression and Random Forest

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.