Agwu, Okorie Ekwe, Alatefi, Saad, Agwu, Nwode and Agwu, Ogbonnaya
2026.
Explainable white-box machine learning framework for estimating fugacity coefficient of pure CO2: Key to accurate CO2 storage analysis.
Results in Engineering
30
, 110943.
10.1016/j.rineng.2026.110943
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Abstract
CO2 fugacity coefficient (ϕ) is an indispensable factor in phase equilibrium computation and process modelling. While this parameter is conventionally calculated using equation of state (EOS) models, the process is computationally intensive and numerically demanding. Contemporary studies have probed machine learning (ML) options; however, documented reports are scarce while extant models are mostly not transparent, non-replicable and not verified for their generalizability. In this investigation, a feed forward neural network that learns via the Levenberg-Marquardt paradigm was built to forecast ϕ at varying pressures and temperatures. To execute this, 640 data records aggregated from experimental trials encompassing an expansive range of thermodynamic conditions was used. The model diagnostics indicate that the model has a high prognostic utility given the following benchmarks: R2 of 0.997, MSE of 0.00054 and RMSE of 0.0232. To accentuate the interpretability of the model, an exact mathematical formula for ϕ is provided. To further illuminate the model for interpretation purposes, the relative influence of each input was appraised via sensitivity analysis using the relevancy factor. This investigation exposes temperature as the leading factor with a percentage contribution of 64% while pressure contributes 36%. Furthermore, trend analysis established the model to be in concordance with the thermodynamics of CO2 fugacity while the leverage plot diagnostics confirm that 99.5% of the entries in the database lie within the prognostication valid domain. Given that the model is interpretable, is presented in a closed equation form and it exhibits reasonable forecasting precision, then it can be used as a reliable substitute for the rigorous and cumbersome EOS models for ϕ estimation.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Engineering |
| Additional Information: | License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc-nd/4.0/, Start Date: 2026-05-07 |
| Publisher: | Elsevier |
| ISSN: | 2590-1230 |
| Date of First Compliant Deposit: | 11 May 2026 |
| Date of Acceptance: | 7 May 2026 |
| Last Modified: | 05 Jun 2026 11:31 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/186905 |
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