ANN Prediction of Performance and Emissions of CI Engine Using Biogas Flow Variation

Abstract

Compression ignition (CI) engines are popular in the transport sector because of their high compression ratio. However, in recent years, it has become a major concern from an environmental point of view because of the emission and depleting fossil fuel. The advanced combustion concept has been a popular research topic in the CI engine. Low-temperature combustion with alternate fuel has helped in reducing the oxides of nitrogen (NOx) and soot emission of the engine. Biogas is a popular substitute of energy especially deduced from biomass because of its clean combustion properties, as well it being a renewable energy source compared to non-renewable diesel resources. In experiments with dual fuel, i.e., conventional diesel and alternate fuel (biogas) were carried out through them. In the present study, an artificial neural network model was used to estimate emissions and check the attributes of performance. Different algorithms and training functions were used to train the models. However, the best training algorithm was Levenberge Marquardt and the training function was Tansig (Hyperbolic tangent sigmoid) and Logsig (logarithmic sigmoid), which showed the best result with regression coefficient (R > 0.98) and Mean square error (MSE < 0.001). The best model was trained by evaluating MSE and regression coefficient. Experimental results and artificial neural network (ANN) prediction showed that the experimental results were similar to each other and lie at the same intervals. The ANN model helped in predicting experimental data that were earlier difficult to experimentally perform using interpolation and extrapolations. It was observed that there was an increase in Brake Specific Energy Consumption (BSEC) and a decrease in Brake thermal efficiency (BTE) with improved biogas flow rate and reduced NOx emission in the combustion chamber. Carbon monoxide (CO) and hydrocarbon (HC) emissions increase linearly with the increase in biogas flow rate, whereas smoke opacity decreases. It could be concluded that this study helps in understanding the effect of dual fuel (diesel-biogas) combustion under different load conditions of the engine with the help of ANN, which could be a substitute fuel and help to protect the environment.

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