Decanol proportional effect prediction model as additive in palm biodiesel using ANN and RSM technique for diesel engine

Abstract

The present examination explores the impact of utilizing palm biodiesel and decanol blends as a ternary blends in compression ignition engine. Artificial Neural network (ANN) and Response surface methodology (RSM) model is developed to predict and optimize decanol proportion in ternary blends. Test engine was operated with decanol mixing of 10%, 20%, and 30% by volume while the diesel percentage was kept 50% in all the samples at different load conditions. With the support of obtained data, regression equation for all the responses are generated based on RSM model with R2 > 0.95. Further to improve the regression model, feed-forward back propagation ANN model is developed to replica the output of brake thermal efficiency, brake specific fuel consumption, oxides of nitrogen, hydrocarbon, smoke opacity and ignition delay period, carbon monoxide, carbon dioxide and exhaust gas temperature and MFB@90%. Based on the results, ANN predicts all the responses with R > 0.99 and the maximum mean absolute average error is 1.879%. The RSM based optimization study prescribes 30% decanol, biodiesel 20% and diesel 50% sufficiently improve performance and decrease emission parameters. Even, the confirmation study with desirability level of 75.4% was carried at 3 trials for the purpose of authentication, maximum error of 5.27% and minimum of 1.33% error was observed.