Predicting Kinematic Viscosity and Cetane Number of Diesel- Biodiesel Blend Using Neural Network and Empirical Models

Biodiesel, as a renewable and environmentally friendly fuel, which has gained great popularity in recent years, is a feasible alternative to fossil diesel. however, due to some undesirable properties such as higher viscosity, biodiesel must be blended with diesel in order to be utilizable in a diesel engine. therefore, a reasonable approach is required for predicting the diesel-biodiesel blend properties. this study tries to estimate two substantial properties of the blend, i.e. kinemattic viscosity (kv) and cetane number (cn), through neural network (nn) and empirical models which use the properties of pure biodiesel (kinematic viscosity, boiling point, evaporation point, flash point, pour point, heat of combustion, cloud point, and specific gravity) as independent variables. in this regard, a three-layer feed-forward network with varying input parameters, training algorithms, transfer functions, and hidden neurons has been examined to predict the kv and cn of the diesel-biodiesel blend. besides, the prediction capability of thirty empirical equations is investigated to determine the top equations describing the properties of the blend. the result reveals that an ann with three input parameters of the concentration (%),cn and cloud point of the biodiesel has the best prediction of cn with an r-value of 0.9961. moreover, nn estimates the kv of the blend with the highest correlation coefficient of 0.9985. the results corresponding to empirical equations also indicate that fractional-exponential equations are the best describer of the cn and kv of the blend with r-values of 0.9947 and 0.9980 respectively.