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Methanol has recently emerged as a promising fuel for internal combustion engines due to its multiple carbon-neutral production routes and advantageous properties when combusting. Methanol is intrinsically more suitable for spark-ignition (SI) operation thanks to its high octane number, but its potential in heavy-duty applications also encourages engine manufacturers in this field to retrofit their existing compression-ignition products into methanol/diesel dual-fuel (DF) operation. For both SI operation and DF operation, injecting methanol into the engine’s intake path at low pressure is a relatively simple and robust method to introduce methanol into the cylinders. However, the much higher heat of vaporization (HoV) of methanol compared to conventional SI fuels like gasoline can be a double-edged sword.

Despite the increasing number of electrified vehicles the transportation system still largely depends on the use of fossil fuels. One way to more rapidly reduce the dependency on fossil fuels in transport is to replace them with biofuels. Evaluating the potential of different biofuels in different applications requires knowledge of their physicochemical properties. In chemistry, message passing neural networks (MPNNs) correlating the atoms and bonds of a molecule to properties have shown promising results in predicting the properties of individual chemical components. In this article a machine learning approach, developed from the message passing neural network called Chemprop, is evaluated for the prediction of multiple properties of organic molecules (containing carbon, nitrogen, oxygen and hydrogen). A novel approach using transfer learning based on estimated property values from theoretical estimation methods is applied.

In the search for sustainable transportation fuels that are not in competition with food production, considerable efforts are made in the development of so-called second-generation (2G) biofuels. This paper looks into the results of a novel 2G biofuel production technique that is based on a catalytic process that operates at low temperature and that converts woody biomass feedstock into a stable light naphtha. The process development is integrated in the Belgian federal government funded Ad-Libio project and the process outcome is mainly consisting of hydrocarbons containing five to six carbon atoms. Their composition can be altered, resulting in a large amount of different possible fuel blends. The ultimate goal is to produce a drop-in fuel that can be fully interchanged with the gasoline fuels in use today. This is a challenge, since the Ad-Libio fuel components differ significantly from gasoline fuel components.