Volume 19, Issue 6 e3151
RESEARCH ARTICLE

Estimation of density and viscosity of deep eutectic solvents: Experimental and machine learning approach

Dhruv Patel

Dhruv Patel

Chemical Engineering Department, Shroff S. R. Rotary Institute of Chemical Technology, UPL University of Sustainable Technology, Ankleshwar, India

Search for more papers by this author
Krunal J. Suthar

Corresponding Author

Krunal J. Suthar

Chemical Engineering Department, Shroff S. R. Rotary Institute of Chemical Technology, UPL University of Sustainable Technology, Ankleshwar, India

Correspondence

Krunal J. Suthar, Associate Professor, Chemical Engineering Department, Shroff S. R. Rotary Institute of Chemical Technology, UPL University of Sustainable Technology, No: 402, Ankleshwar - Valia Rd, Vataria, Gujarat 393135, India.

Email: [email protected]

Search for more papers by this author
Hemant Kumar Balsora

Hemant Kumar Balsora

Chemical Engineering Department, Shroff S. R. Rotary Institute of Chemical Technology, UPL University of Sustainable Technology, Ankleshwar, India

Search for more papers by this author
Dhara Patel

Dhara Patel

Chemical Engineering Department, Shroff S. R. Rotary Institute of Chemical Technology, UPL University of Sustainable Technology, Ankleshwar, India

Search for more papers by this author
Swapna Rekha Panda

Swapna Rekha Panda

Chemical Engineering Department, Shroff S. R. Rotary Institute of Chemical Technology, UPL University of Sustainable Technology, Ankleshwar, India

Search for more papers by this author
Nirav Bhavsar

Nirav Bhavsar

Department of Chemical Engineering, Dharmsinh Desai University, Nadiad, India

Search for more papers by this author
First published: 23 August 2024
Citations: 7

Abstract

Deep eutectic solvents (DESs) are increasingly recognized as sustainable alternatives suitable for a range of industrial applications. A precise comprehension of their properties is important for progress in science and engineering. In this study, we synthesized four novel ternary DESs using mandelic acid and measured their densities and viscosities at temperatures ranging from 298 to 353 K. Subsequently, an artificial neural network model was developed to predict DES density and viscosity based on temperature, critical properties, acentric factor, and molar ratio. The neural network parameters were optimized using experimental data from synthesized DESs and literature sources, both collectively over 500 data points for density and viscosity. Additionally, we investigated the influence of input parameters on model accuracy and assessed their significance. The results show that the average percentage relative error was 0.501 for density and 4.81 for viscosity. This research helps advance science and engineering applications of DESs.

The full text of this article hosted at iucr.org is unavailable due to technical difficulties.