Application of ANN, RSM on engine response prediction using lemongrass biomaterial blends

By framing the design of experimental combinations in RSM CCD technique with specified influencing variable ranges,
the viability of employing biodiesel generated from lemongrass in a CRDI engine was investigated. To check the accuracy, observed experimental data is supplied into prediction tools such as ANN and RSM. It is concluded that sophisticated neural networks and response surface methodologies can be successfully used for forecasting output responses within the ranges of experimentation based on experimental data. Grey analysis was used to find the best engine settings. In terms of emissions and performance in emergency scenarios, lemongrass fuel can be utilised as a partial replacement for diesel fuel in engines.