Development of an ANN based model to predict the CI engine performance and emissions fueled with Zinc Oxide Nanoparticle based Biodiesel

Abstract

Abstract Artificial Neural Network (ANN) is an approach in artificial intelligence that can be used to train and process the data using computers. Engineering, science, and pharmaceuticals are just a few of the many fields in which ANN is used. In the present work, ANN modeling has been used to forecast engine performance and emission characteristics. For network training, test data was gathered by running test rig using multiple fuel blends for a single-cylinder high-speed diesel engine. Data for the fuel input of the three distinct fuel used in the experiment - diesel, Mahua biodiesel (MME-20 and MME-50) and nano-blended fuels (50 ppm and 100 ppm) was taken from a framework. An artificial neural network based model was created to forecast performance and emissions using data from different fuels used in diesel engines. The simulation's findings showed that the developed diesel engine ANN 6-13-9 model could precisely forecast the engine performance and emission characteristics of a variety of alternative fuel blends. The Rtrain, Rval, Rtest, and Rall correction coefficients in the ANN 6-13-9 model were 0.99713, 0.99634, 0.99381, and 0.99617 respectively, indicating a stronger relationship between the expected and observed values.