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These last years, much of researches were carried out to find the appropriate substitution fuel to the fossil fuels. The use of biofuel prepared from non-edible vegetable oils are becoming a promising source to produce a fuel for diesel engine, commonly referred to as “biodiesel”. Considering the high oil extraction yield (around 40%) and the great quantity of pistacia lentiscus (PL) trees available in arid and semi-arid areas of Mediterranean countries, it is selected in the present work to study the biodiesel prepared from PL oil. PL biodiesel is obtained by converting PL seed oil with a single-step homogenous alkali catalyzed transesterification process. PL biodiesel characterization, according to the standard methods, shows that the physicochemical properties are comparable to those of conventional diesel fuel. In a second part, a single cylinder air-cooled, DI diesel engine is used to test PL biodiesel at 1500 rpm under various engine load conditions.

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.