Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

Zhuo Zhang,Weiqiang Wang,Shuangchun Li,Fayu Sun,Qingling Li,Dedong Hu

doi:10.3390/pr8121631

Abstract

With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.

Highlights

With numerous advantages compared with conventional water dyeing, such as eliminating effluent discharge, saving water resources and preserving energy, the supercritical carbon dioxide (SC-CO2 ) dyeing process is considered a green and energy-saving process in terms of sustainable development and being environmentally friendly [1,2,3,4,5]
In order to promote the application of supercritical fluid dyeing (SFD), it is necessary to establish the prediction model of the dyeing effect in SC-CO2 in different working conditions such as dyeing time, temperature, pressure, dyestuff type and carrier type
A total of 386 data sets on experimental dyeing belonging to 14 groups collected from the published papers are used for training the Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models, the selected 3 to 4 data sets in each group are used for testing and prediction and the rest are used for training

Summary

Introduction

With numerous advantages compared with conventional water dyeing, such as eliminating effluent discharge, saving water resources and preserving energy, the supercritical carbon dioxide (SC-CO2 ) dyeing process is considered a green and energy-saving process in terms of sustainable development and being environmentally friendly [1,2,3,4,5]. In order to promote the application of supercritical fluid dyeing (SFD), it is necessary to establish the prediction model of the dyeing effect in SC-CO2 in different working conditions such as dyeing time, temperature, pressure, dyestuff type and carrier type. Though the mathematical model derived from Fick’s second law [6,7,8,9,10] and the pseudo–second–order kinetic model [11] have been established to predict the dyeing effect in SC-CO2 , the lack of the parameters such as diffusion coefficient limits their applications. Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are two commonly paradigms [14,15]. GRNN is Processes 2020, 8, 1631; doi:10.3390/pr8121631 www.mdpi.com/journal/processes

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Conclusion