Development of Dye Exhaustion Behavior Prediction Model using Deep Neural Network

Jonghun Lim, Soohwan Jeong, Sungsu Lim, Hyungtae Cho, Jae Yun Shim, Seok Il Hong, Soon Chul Kwon, Heedong Lee, Il Moon, Junghwan Kim

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


The textile dyeing process consumes a significant quantity of energy as it is necessary to maintain the water temperature between 60–120 °C during the dyeing of reactive dyes. Therefore, to reduce the overall cost of the process through reducing the quantity of waste energy, it is crucial to increase the right first time (RFT) rate, which corresponds to the rate at which the target color is imparted through a single dyeing process (Park et al., 2009). To improve the RFT rate, the proper operation with following the optimal dye exhaustion behavior in consideration of the color difference and dyeing uniformity is a critical factor. The color difference is determined according to maximal absorption and the dyeing uniformity is decided by dye exhaustion behavior [Bouatay et al., 2016]. In this study, we developed a model for predicting dyeing exhaustion behavior, and utilized the model to predict optimal dye exhaustion behavior under various dyeing conditions. A deep neural network-based on the dye exhaustion behavior prediction model was developed through regression analysis, the model was further developed and evaluated by dividing the entire dataset into learning and evaluation data. The model's performance was evaluated using the root mean square error (RMSE) parameter alongside the coefficient of determination (R2) which acted as performance evaluation metrics. Using these performance metrics, it was found that the proposed DNN regression exhibited the highest performance and the smallest error in comparison with established models, with root mean square error RMSE and R2 values of 0.016 and 0.994, respectively. The results reported in this study demonstrate that the proposed model exhibits superior performance in predicting the dye exhaustion behavior.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Number of pages6
Publication statusPublished - 2022 Jan

Publication series

NameComputer Aided Chemical Engineering
ISSN (Print)1570-7946

Bibliographical note

Funding Information:
This study has been conducted with the support of the Korea Institute of Industrial Technology with respect to “Development of complex parameter smart analysis modules for color customering (kitech EH-21-0008)” and “Development of Global Optimization System for Energy Process (kitech EM-21-0022), (kitech IZ-21-0052), (kitech IR-21-0029)”.

Publisher Copyright:
© 2022 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications


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