Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network

Ahmad KhazaiePoul; M. Soleimani; S. Salahi

doi:10.1016/j.cjche.2015.11.027

Your Location：

Home >

Browse articles >

Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network

第25届中国过程控制会议专栏 | Updated：2026-01-06

- Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network
- Chinese Journal of Chemical Engineering Vol. 24, Issue 4, Pages: 491-498(2016)
- Affiliations：
  
  1. 2 Amol University of Special Modern Technologies, Amol 7,Iran,46168-4976
  2. 3 Chemical Engineering Department, Islamic Azad University, Shahrood Branch, Shahrood,Iran
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2015.11.027
  CLC：
- Published：2016
- Accepted：
Scan QR Code
Ahmad KhazaiePoul, M. Soleimani, S. Salahi. Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network[J]. Chin.J.Chem.Eng., 2016, 24(4): 491-498.
DOI：

Ahmad KhazaiePoul, M. Soleimani, S. Salahi. Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network[J]. Chin.J.Chem.Eng., 2016, 24(4): 491-498. DOI： 10.1016/j.cjche.2015.11.027.

摘要

Abstract

Nowadays artificial neural networks (ANNs) with strong ability have been applied widely for prediction of nonlinear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature

pressure

critical temperature

critical pressure

density

molecular weight and acentric factor has been used for solubility prediction of three disperse dyes in supercritical carbon dioxide (SC-CO

) and ethanol as co-solvent. Itwas shown how a multi-layer perceptron network can be trained to represent the solubility of disperse dyes in SC-CO

. Numeric Sensitivity Analysis and Garson equation were utilized to find out the degree of effectiveness of different input variables on the efficiency of the proposedmodel. Results showed that our proposed ANN model has correlation coefficient

Nash-Sutcliffe model efficiency coefficient and discrepancy ratio about 0.998

0.992

and 1.053 respectively.

关键词

Keywords

references

Views

191

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Solubility of red palm oil in supercritical carbon dioxide: Measurement and modelling

A soft sensing method for biomanufacturing processes based on physics-informed variational learning with applications

From data to insight: Building a knowledge graph for risk analysis of hazardous chemical accidents

Quality related fault detection based on dynamic-inner convolutional autoencoder and partial least squares and its application to ironmaking process

Attention-enhanced multi-time scale LSTM for soft sensor modeling of corn starch liquefaction

Related Author

Wan Jun Lee

Chin Ping Tan

Rabiha Sulaiman

Gun Hean Chong

Yu Zhenhua

Cheng Xinyue

Wang Guan

Sun Lihua

Related Institution

2 Supercritical Fluid Centre(SFC), Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang

Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology

School of Computer Science and Technology, Xinjiang University

Xinjiang Research Institute of Huairou Laboratory

⁰