Prediction of fuel properties through Artificial Neural Networks

The production of next-generation biofuels is being explored through a variety of chemical and biological approaches, all aiming at lowering costs and increasing yields while producing viable alternatives to gasoline or diesel fuel. Chemical synthesis can lead to a huge variety of different fuels and the guidelines from which molecules yield desirable properties as a fuel are largely based on intuition.  Research includes the improvement and extension of existing models to increase the model’s generalizability to the large variety of new potential biofuels currently of interest to researchers. This predictive model uses artificial neural networks (ANN’s) as a tool for quantitative structure property relationship (QSPR) analysis.

Energy Storage

As renewable sources of electricity become more prevalent, one hurdle to widespread adoption is a cost-effective and efficient method of storing energy produced during off-peak consumption hours.  The cyclic nature of wind, solar, and tidal generation cannot provide on-demand load matching – thus, our ability to store produced energy and generate electricity at appropriate times will enable these renewable sources to compete with more traditional production methods.  Our research focuses on the use of novel thermodynamic cycles to generate power from a variety of intermediate storage solutions, including compressed hydrogen and oxygen produced via the electrolysis of water.

Evaluation of Novel Alternative Fuels

A fuel’s suitability for use in a combustion engine is traditionally evaluated using specified procedures involving a transportation-scale engine.  Recent research has worked to reduce the volumetric requirements to enable rapid testing, thus increasing the rate of innovation in fuel development.  Our laboratory is developing methods to further reduce the volumetric requirements while increasing precision and accuracy.

Homogeneous Charge Compression Ignition (HCCI)

HCCI has proven to be an attractive internal combustion technique in attaining low emissions and high fuel efficiency.  However, remaining challenges to widespread adoption increasing the power density of HCCI engines and robust methods for controlling the autoignition event.  The combustion timing of HCCI engines has been controlled using a variety of approaches, many of which are in use in production engines today.  However, the increased computerization and complexity of modern engines may enable further optimization of the HCCI cycle.  We are investigating a variety of control approaches through simulations and experiments.