This paper presents a study of engine performance using a mixture of palm oil methyl ester blends with diesel oil as biodiesel in a diesel engine, and optimizes the engine performance using artificial neural network (ANN) modeling. To acquire data for training and testing of the proposed ANN, a four-cylinder, four-stroke diesel engine was fuelled with different palm oil methyl ester blends as biodiesel, operated at different engine loads. The properties of biodiesel produced from waste vegetable oil were measured based on ASTM standards. The experimental results revealed that blends of palm oil methyl ester with diesel fuel provided better engine performance. An ANN model was developed based on the Levenberg-Marquardt algorithm for the engine. Logistic activation was used for mapping between the input and output parameters. It was observed that the ANN model could predict the engine performance quite well with correlation coefficients (R) of 0.996684, 0.999, 0.98964 and 0.998923 for the incylinder pressure, heat release, thermal efficiency, and volume, respectively. The predicted MSE (mean square error) error was between the desired outputs, as the measured and simulated values were obtained as 0.0001 by the model. Long-term effects on engine performance when running on biodiesel fuel can be further studied and improved.

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Diesel engines are most widely used as power plant for many applications, like automotive, agricultural purposes, portable machines and remote location power generation, because of their higher torque, power output, energy content per unit mass and cost of fuel. Because of the higher compression ratios, the diesel engines are able to produce greater cylinder pressures resulting in higher temperatures and thermal efficiency. On other hand, the diesel engines produce COx, NOx, Soot and sulphur emissions which are harmful and pollute the environment leads to acid rain, global warming and variety of human diseases. Also, the Present emission regulations are framed such a way to ensure the environmental sustainability in addition to the economic and social importance. These constraints make the researchers to find an alternate fuel for replacing the diesel fuel on the existing diesel engines for the reduction of environmental pollutions. Biodiesel is found to be a very good alternative fuel obtained from natural resources and having good energy with least possible emissions. Rubber seed methyl ester (ROME) is one kind of the biofuel can be used in the existing diesel without any engine modifications. The ROME is produced using trans esterification process and the biodiesel blends are prepared in the sequence of B20, B40, B60 and B80. The ROME is tested on the Variable Compression Ratio (VCR) engine to test the emission characteristic in line with the performance characteristics. To reduce the emissions, the prediction models are developed for CO and NOx using the Response Surface Methodology (RSM). The models are verified through the ANOVA and p-test for their adequacy to create the hypothesis of the experimentation. The NSGA II evolutionary multi-objective optimization is used to optimize the engine parameters to minimise the pollutions from the ROME fuelled engine. Finally, the optimized parameters are verified though the experimentation to verify the least possible emissions from the engine.