Modeling the effects of Carbon nanotubes added to diesel-biodiesel fuel blends on performance and emissions of a diesel engine using artificial neural network

Biodiesel and some nano-catalysts are an important additive to diesel fuel and can improve the engine performance and reduce emissions. In this study, biodiesel was added to pure diesel with ratios of 5 and 10 percent. Then, the carbon nanotubes were mixed as additive with these blends with concentrations of 30, 60, and 90 ppm to evaluate the performance, emissions, and vibration levels in a diesel engine. An ANN model, based on standard back-propagation learning algorithm for the engine, was developed. Multi-layer perception network (MLP) was used. The input or independent parameters were fuel blend, engine speed, fuel density, fuel viscosity, LHV, intake manifold pressure, fuel consumption, exhaust gas temperature, oxygen contained in exhaust gases, oil temperature, relative humidity and ambient air pressure. The target parameters were performance, emissions and RMS and Kurtosis of engine vibrations. The results showed that the specific fuel consumption and CO and UHC emissions decreased, while NOx emission increased. Also, the ANN model showed the training algorithm of back-propagation with 20-20 neurons in hidden layers (logsig-logsig) is able to predict different parameters with good performance and accuracy. The corresponding R-values for training, validation and testing were 0.9999, 0.9985 and 0.9994, respectively.