LAPSE:2021.0637
Published Article
LAPSE:2021.0637
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, Fayu Sun, Qingling Li, Weiqiang Wang, Dedong Hu, Shuangchun Li
July 26, 2021
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.
Keywords
back propagation neural network, generalized regression neural network, prediction model, supercritical carbon dioxide, the dyeing effect
Suggested Citation
Zhang Z, Sun F, Li Q, Wang W, Hu D, Li S. 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. (2021). LAPSE:2021.0637
Author Affiliations
Zhang Z: College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
Sun F: School of Mechanical Engineering, Shandong University, Jinan 250061, China
Li Q: College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
Wang W: School of Mechanical Engineering, Shandong University, Jinan 250061, China
Hu D: College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China [ORCID]
Li S: College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
Journal Name
Processes
Volume
8
Issue
12
Article Number
E1631
Year
2020
Publication Date
2020-12-11
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr8121631, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2021.0637
This Record
External Link

doi:10.3390/pr8121631
Publisher Version
Download
Files
[Download 1v1.pdf] (1.5 MB)
Jul 26, 2021
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
117
Version History
[v1] (Original Submission)
Jul 26, 2021
 
Verified by curator on
Jul 26, 2021
This Version Number
v1
Citations
Most Recent
This Version
URL Here
http://psecommunity.org/LAPSE:2021.0637
 
Original Submitter
Calvin Tsay
Links to Related Works
Directly Related to This Work
Publisher Version