Machine Learning for Physicochemical Property Prediction of Complex Hydrocarbon Mixtures

Abstract

Machine learning has proven effective for predicting properties of pure compounds from molecular structures, but properties of mixtures, in particular oil fractions, are rarely dealt with. At best, the bulk properties are estimated based on pure compound properties, linear mixing rules, and a reconstructed composition of the feedstock. As the detailed composition of such mixtures is rarely well determined and often approximated by lumps, the accuracy of the estimated bulk properties can be improved. In this work, we demonstrate for a naphtha case study our bulk property estimation method. First, a detailed PIONA composition is delumped into a molecule-level composition, and a machine learning-based approach is used to predict properties of those molecules, which are further combined in another deep neural network for the prediction of bulk properties. The latter machine learning models are trained on mixture properties using vectors that represent the mixture. The first vector is a linear combination of the molecular representation vectors and the representation of the molecular geometries that make up the mixture. The second vector applies linear mixing rules on boiling temperatures, critical temperatures, liquid densities, and vapor pressures that are predicted with machine learning. The last vector consists of a learned distillation curve. We show that an integrated machine learning approach that starts from the molecular structures in the mixture offers significant improvements in predicting mixture properties over existing approaches applied in industry and academia.

Characterizing Uncertainty in Machine Learning for Chemistry

“…Machine learning models for chemical applications such as predicting molecular and reaction properties are becoming not only increasingly popular, but also increasingly accurate, for example for quantummechanical properties, 1-3 biological effects, [4][5][6] physicochemical properties, [7][8][9][10][11] , reaction yields, [12][13][14] or reaction rates and barriers. [15][16][17][18][19] Also, promising developments in the field of retrosynthesis [20][21][22][23][24] and forward reaction prediction, [25][26][27][28] have been made.…”

Characterizing Uncertainty in Machine Learning for Chemistry

“…Machine learning models for chemical applications such as predicting molecular and reaction properties are becoming not only increasingly popular but also increasingly accurate, for example for quantum-mechanical properties, − biological effects, − physicochemical properties, − reaction yields, − or reaction rates and barriers. − Also, promising developments in the fields of retrosynthesis − and forward reaction prediction − have been made.…”

Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning

“…Therefore, knowledge of critical temperature (T c ), pressure (P c ), density (ρ c ), and the acentric factor (ω) is required for many widely used EOSs, such as Peng-Robinson 5 and Soave-Redlich-Kwong. 6 Other than EOSs, various models use critical properties as inputs to predict physiochemical properties, such as diffusion coefficients, 7−10 surface tension, 11,12 and solubilities. 13−16 In addition, they are used to predict Lennard-Jones parameters, which are required to model transport and collisions in a reaction rate calculation.…”