In the present work, biodiesel prepared from Tropical almond oil (Terminalia Catappa) was used as fuel in C. I engine. Performance studies were conducted on a single cylinder four-stroke water-cooled compression ignition engine. Experiments were conducted for different percentage of blends of Tropical almond ester with diesel at different injection timings. Experimental investigations on the performance parameters from the engine were done. Artificial neural network (ANN) of back-propagation feed-forward Levenberg-Marquardt algorithm was used to predict the performance characteristics of the engine. An ANN model was developed for the performance parameters. To train the network, blend percentage, percentage load and injection timings were used as the input variables whereas engine performance parameters (brake thermal efficiency, exhaust gas temperature, and brake specific fuel consumption) were used as the output variables. The obtained experimental results were used to train the network structure. Results showed very good correlation between the ANN predicted values and the desired values for various engine performance values. Mean relative error values were less than 10 percent which by many standards is acceptable. The results show that ANN is an accurately reliable tool for the prediction of engine performance.

Engine pollutants have been a significant source of concern in most countries around the world because they are one of the major contributors to air pollution, which causes cancer, lung disorders, and other severe illnesses. The need to reduce emissions and its consequences has prompted studies into the emission profile of internal combustion engines running on particular fuels. To this end, this study employed the power of Artificial Neural Networks (ANNs) to investigate the impact of injection timing on the emission profile of Compression Ignition Engines fuelled with blends of Tropical Almond Seed Oil based-biodiesel; by conducting a series of experimental tests on the engine rig and using the results to train the ANNs; to predict the emission profile to full scale. Blend percentages, load percentages, and injection timings were used as input variables, and engine emission parameters were used as output variables, to train the network. The results showed that injection timing affect emission output of CI engines fuelled with Tropical Almond Oil based biodiesel; and for the emission pattern to be friendly, injection timing must rather be retarded and not advanced. The results also showed that for different engine emission parameters, there is a strong association between the ANN output results and the actual experimental values; with mean relative error values less than 10%, which fall within the acceptable limits. For emission of CI engines fuelled with Tropical Almond Oil based biodiesel to be friendly in pattern, injection timing must be relatively retarded. The study also concluded that Artificial Neural Network (ANN) is a reliable tool for predicting Compression Ignition Engines emission profiles.