Fuel sorption into polymers: Experimental and machine learning studies

• Published in: Fluid phase equilibria Vol. 556; p. 113403
• Main Authors: Creton, Benoit, Veyrat, Benjamin, Klopffer, Marie-Hélène
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2022; Elsevier
• Subjects: Chemical Sciences; Environmental Sciences; Fuel; Machine learning; Polymer; Polymers; Sorption; Sorption; Polymer; Fuel; Machine learning; Fuels; Machine Learning
• ISSN: 0378-3812; 1879-0224
• DOI: 10.1016/j.fluid.2022.113403

In the automotive industry, the introduction of alternative fuels in the market or even the consideration of new fluids such as lubricants requires continuous efforts in research and development to predict and evaluate impacts on materials (e.g., polymers) in contact with these fluids. We address here the compatibility between polymers and fluids by means of both experimental and modelling techniques. Three polymers were considered: a nitrile butadiene rubber (NBR), a fluorinated elastomer (FKM) and a fluorosilicon rubber (FVMQ), and a series of hydrocarbons mixtures were formulated to study the swelling of the polymers. The swelling of samples has been investigated in terms of weight and not volume variations as the measure of this former is assumed to be more accurate. Multi-gene genetic programming (MGGP) was applied to experimental data obtained in order to derive models to predict: (i) the maximum value of the mass gain (ΔM) and (ii) the sorption kinetics, i.e. the time evolution of ΔM. Predicted values are in excellent agreement with experimental data (with R2 greater than 0.99), and models have demonstrated their predictive capabilities when applied to external fluids (not considered during the training procedure). Combining experiments and modelling, as proposed in this work, leads to accurate models which drastically reduce the time necessary to quantify polymeric materials compatibility with a fluid candidates as compared to experiments.