(197aw) ML-SAFT: A Machine Learning Framework for PCP-SAFT Parameter Prediction
AIChE Annual Meeting
2023
2023 AIChE Annual Meeting
Computational Molecular Science and Engineering Forum
Poster Session: Computational Molecular Science and Engineering Forum
Monday, November 6, 2023 - 3:30pm to 5:00pm
In this work, we develop ML-SAFT, a framework for predicting parameters of the PCP-SAFT EoS using machine learning.[11, 12] We were interested in the PCP-SAFT EoS because it can be used for a wide variety of thermodynamic prediction tasks including vapor liquid equilibrium,[11] solubility,[13] and surface tension,[14] yet each new molecule needs to be parametrized by regression to experimental data. To enable training of machine learning models, ML-SAFT includes the largest database of regressed PCP-SAFT parameters published in the literature (969 molecules) and a set of machine learning models trained on this dataset. We extract data from the Dortmund Databank[15] and develop a robust regression method to determine pure component PCP-SAFT parameters from experimental vapor pressure and liquid density data. Within ML-SAFT, we train random forests,[16] feed forward networks and message passing neural networks (MPNNs)[17] to predict the regressed PCP-SAFT parameters.
Our results show that random forests obtain the most accurate predictions of the regressed PCP-SAFT parameters. Furthermore, the best prediction of vapor pressure in terms of the average absolute deviation percentage (% AAD) on unseen molecules is obtained from the random forest. However, the best results on density predictions are obtained with parameters predicted by a MPNN. We attribute this difference to the increased representation capability of the MPNN for polar molecules, which we find to be important for density predictions. We also compare ML-SAFT models to two existing predictive PCP-SAFT models: SEPP[4] and group contribution PC-SAFT.[2] We find that ML-SAFT makes accurate predictions for a wider range of molecules than both methods while maintaining computational efficiency.
Overall, our work demonstrates that machine learning is a powerful tool for PCP-SAFT parameter prediction. We foresee that the results shown in this work can form a baseline for future work that explores multi-component mixture predictions using PCP-SAFT.
References
[1] A. Fredenslund, R. L. Jones, J. M. Prausnitz, Group-contribution estimation of activity coefficients in nonideal liquid mixtures, AiChE Journal 21 (6) (1975) 1086â1099. doi:10.1002/aic.690210607. URL https://doi.org/10.1002/aic.690210607
[2] E. Sauer, M. Stavrou, J. Gross, Comparison between a homo- and a heterosegmented group contribution approach based on the perturbedchain polar statistical associating fluid theory equation of state, Industrial and Engineering Chemistry Research 53 (38) (2014) 14854â14864. doi:10.1021/ie502203w.
URL https://doi.org/10.1021/ie502203w
[3] D. Constantinescu, J. Gmehling, Further development of modified UNIFAC (dortmund): Revision and extension 6, Journal of Chemical and Engineering Data 61 (8) (2016) 2738â2748. doi:10.1021/acs.jced.6b00136.
URL https://doi.org/10.1021/acs.jced.6b00136
[4] S. Kaminski, K. Leonhard, SEPP: Segment-based equation of state parameter prediction, Journal of Chemical and Engineering Data 65 (12) (2020) 5830â5843. doi:10.1021/acs.jced.0c00733.
URL https://doi.org/10.1021/acs.jced.0c00733
[5] J. Habicht, C. Brandenbusch, G. Sadowski, Predicting PC-SAFT purecomponent parameters by machine learning using a molecular fingerprint as key input, Fluid Phase Equilibria 565 (2023) 113657. doi:10.1016/j.fluid.2022.113657.
URL https://doi.org/10.1016/j.fluid.2022.113657
[6] F. Jirasek, R. A. S. Alves, J. Damay, R. A. Vandermeulen, R. Bamler, M. Bortz, S. Mandt, M. Kloft, H. Hasse, Machine learning in thermodynamics: Prediction of activity coefficients by matrix completion, The Journal of Physical Chemistry Letters 11 (3) (2020) 981â985. doi:10.1021/acs.jpclett.9b03657.
URL https://doi.org/10.1021/acs.jpclett.9b03657
[7] E. I. S. Medina, S. Linke, M. Stoll, K. Sundmacher, Graph neural networks for the prediction of infinite dilution activity coefficients, Digital Discovery 1 (3) (2022) 216â225. doi:10.1039/d1dd00037c. URL https://doi.org/10.1039/d1dd00037c
[8] J. G. Rittig, K. Ben Hicham, A. M. Schweidtmann, M. Dahmen, A. Mitsos, Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids, Computers & Chemical Engineering 171 (2023) 108153. doi:10.1016/j. compchemeng.2023.108153. URL https://doi.org/10.1016/j.compchemeng.2023.108153
[9] K. C. Felton, H. Ben-Safar, A. Lapkin, DeepGamma: A deep learning model for activity coefficient prediction (2022).
[10] B. Winter, C. Winter, T. Esper, J. Schilling, A. Bardow, SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients, Fluid Phase Equilibria 568 (2023) 113731. doi:10.1016/j.fluid.2023.113731.
URL https://doi.org/10.1016/j.fluid.2023.113731
[11] J. Gross, G. Sadowski, Perturbed-chain SAFT: an equation of state based on a perturbation theory for chain molecules, Industrial and Engineering Chemistry Research 40 (4) (2001) 1244â1260. doi:10.1021/ie0003887.
URL https://doi.org/10.1021/ie0003887
[12] J. Gross, J. Vrabec, An equation-of-state contribution for polar components: Dipolar molecules, AIChE Journal 52 (3) (2006) 1194â1204. doi:10.1002/aic.10683.
URL https://doi.org/10.1002/aic.10683
[13] M. Klajmon, Investigating various parametrization strategies for pharmaceuticals within the PC-SAFT equation of state, Journal of Chemical & Engineering Data 65 (12) (2020) 5753â5767. doi:10.1021/acs.jced.0c00707.
URL https://doi.org/10.1021/acs.jced.0c00707
[14] P. Rehner, J. Gross, Multiobjective optimization of PCP-SAFT parameters for water and alcohols using surface tension data, Journal of Chemical & Engineering Data 65 (12) (2020) 5698â5707. doi:10.1021/acs.jced.0c00684.
URL https://doi.org/10.1021/acs.jced.0c00684
[15] Dortmund databank (2022).
URL www.ddbst.com
[16] L. Breiman, Random forests, Machine Learning 45 (1) (2001) 5â32.
[17] J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, G. E. Dahl, Neural message passing for quantum chemistry, in: D. Precup, Y. W. Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning, Vol. 70 of Proceedings of Machine Learning Research, PMLR, 2017, pp. 1263â1272.
URL https://proceedings.mlr.press/v70/gilmer17a.html