Development of machine learning models for the prediction of binary diffusion coefficients of gases

Authors

Ismail Adewale Olumegbon, University of Maryland, Baltimore County (UMBC)
Ibrahim Olanrewaju Alade, King Fahd University of Petroleum and Minerals
Mojeed Opeyemi Oyedeji, King Fahd University of Petroleum and Minerals
Talal F. Qahtan, Prince Sattam Bin Abdulaziz University
Aliyu Bagudu, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

Engineering Applications of Artificial Intelligence

Abstract

Diffusion coefficient (D12) is an important transport property in the petrochemical and pharmaceutical industries for the design and optimization of processes. The process of measuring the D12 for gas mixtures via an experimental approach is time-consuming and technically challenging. Consequently, many empirical models have been developed to circumvent these challenges. Though these models exhibit good agreement with the experiment, nonetheless, improvement is still needed in terms of increasing the model performance, and the flexibility of their applications because some of these models require extensive calculations to obtain their input parameters. Motivated by this, the work presents a simple and accurate approach for the estimation of diffusion coefficient using machine learning (ML) algorithms. This study employs support vector regression (SVR), Gaussian process regression (GPR), and artificial neural networks (ANN) to estimate the D12 of molecular gas systems over a wide range of temperature (293- 313K), and pressure (0.05–12.0 MPa). The proposed ML models were built using simple descriptors such as temperature, pressure, molar masses of constituent mixtures, and mole fractions of the gases. On the testing dataset, the following overall correlation coefficients were obtained 90.7 %, 99.1 %, and 99.97 % percent for the SVR, GPR, and ANN, respectively. The ANN model did better than the other ML algorithms presented in terms of its generalization capability. The authors believe that the ANN model presented is a viable alternative for the evaluation of the binary diffusion coefficient of gases due to the simplicity of the descriptors.

DOI

10.1016/j.engappai.2023.106279

Publication Date

8-1-2023

Keywords

Artificial neural network, Diffusion coefficient, Gaussian process regression, Machine learning, Support vector regression

Comments

IR Deposit conditions:

OA version (pathway b) Accepted version

24 months embargo

License: CC BY-NC-ND

Must link to publisher version with DOI

Recommended Citation

I. Olumegbon et al., "Development of machine learning models for the prediction of binary diffusion coefficients of gases," Engineering Applications of Artificial Intelligence, vol. 123, Aug 2023.

The definitive version is available at https://doi.org/10.1016/j.engappai.2023.106279

Additional Links

https://doi.org/10.1016/j.engappai.2023.106279