An Artificial Intelligence Approach to Predict Physical Properties of Liquid Hydrocarbons

Accurate physical property prediction of newly developed compounds is vital across various industrial sectors, particularly for the customization of fuels and additives. Artificial intelligence (AI) has recently emerged as a best practice in numerous industrial fields because of its capacity for swift and precise calculations. While conventional methods such as group contribution models have been used to estimate physical properties from molecular structure, AI offers significant potential for improving the predictive accuracy. Thus, this work focuses on developing an AI model to predict key properties – boiling points, melting points, and flashpoints – of various hydrocarbons, to demonstrate the AI's superior predictive capabilities. A dataset consisting of 202 organic compounds was created and multilayer perceptron (MLP) neural networks were employed to estimate these properties using atomic numbers, functional groups, and molecular complexity as inputs. The model's performance was evaluated and compared against conventional group contribution methods on the same dataset. The AI model was further tested on new acetal compounds, revealing its broader applicability in both fuel and chemical sectors. Results show that the AI outperformed conventional methods, excelling in 5 out of 8 hydrocarbon types for boiling points, 7 for melting points, and all 8 for flashpoints.