Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends

This paper reports an artificial neural networks (ANN) modelling programme for a light-duty diesel engine powered using blends of various biodiesel fuels with conventional fossil diesel. ANN was used here to predict nine different engine-out responses, namely carbon monoxide (CO), carbon dioxide (CO2), nitrogen monoxide (NO), unburned hydrocarbon (UHC), maximum pressure (Pmax), location of maximum pressure (CAD Pmax), maximum heat release rate (HRRmax), location of maximum HRR (CAD HRRmax) and cumulative HRR (CuHRR). Four pertinent engine operating parameters, engine speed, output torque, fuel mass flow rate and biodiesel fuel types and blends, were used as the input parameters for this modelling work. The feasibility of using ANN in predicting the relationships between these inputs and outputs were assessed. Simulated results were first validated against data from parallel engine test-bed study. Key effects of ANN “model” and “model parameter” such as type of transfer function, training algorithm and number of neurons, along with the methods of optimising the network settings were also presented in this paper.

► An ANN model was developed for rapid prediction of engine-out responses.
► Type of transfer function, training algorithm and number of neurons were appraised.
► The optimised network accurately predicted seven out of the nine engine-out parameters.
► Proportionality value and absolute average tolerance used to measure ANN predictive capability.
► The developed approach was able to determine any generic engine input–output relationships.