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The crude oil depletion, as well as aspects related to environmental pollution and global warming has caused researchers to seek alternative fuels. Biogas is one of the most attractive available fuels. It is of great interest both economically and ecologically. However, it faces problems that may compromise its industrial use. The dual-fuel engines have been investigated as a technique for the recovery of these gases and finding solutions to these problems. In the present work, performance and emissions of a direct injection diesel engine were first evaluated in conventional mode and dual fuel mode. The effect of biogas composition, based on methane content, is then examined. Also, dual fuel operation with regard to knock is investigated. The results show that, up to 95% of engine full load, the brake thermal efficiency (BTE) is lower in dual fuel mode. In terms of the specific consumption, although at high load the gap is much less, it is more significant in case of dual fuel mode.

Numerical simulation is a useful and a cost-effective tool for engine cycle prediction. In the present study, a dual Wiebe function is used to approximate the heat release rate in a DI, naturally aspirated diesel engine fuelled with eucalyptus biodiesel/diesel fuel blends and operated at various engine loads. This correlation is fitted to the experimental heat release rate at various operating conditions (fuel nature and engine load) using a least squares regression to find the unknown parameters. The main objective of this study is to propose a model to predict the Wiebe function parameters for more general operating conditions, not only those experimentally tested. For this purpose, an artificial neural network (ANN) is developed on the basis of the experimental data. Engine load and eucalyptus biodiesel/diesel fuel blend are the input layer, while the six parameters of the dual Wiebe function are the output layer.