Prediction of Reformed Gas Composition for Diesel Engines with a Reformed EGR System Using an Artificial Neural Network

Abstract

Facing the reinforced emission regulations and moving toward a clean powertrain, hydrogen has become one of the alternative fuels for the internal combustion engine. In this study, the prediction methodology of hydrogen yield by on-board fuel reforming under a diesel engine is introduced. An engine dynamometer test was performed, resulting in reduced particulate matter (PM) and NOx emission with an on-board reformer. Based on test results, the reformed gas production rate from the on-board reformer was trained and predicted using an artificial neural network with a backpropagation process at various operating conditions. Additional test points were used to verify predicted results, and sensitivity analysis was performed to obtain dominant parameters. As a result, the temperature at the reformer outlet and oxygen concentration is the most dominant parameters to predict reformed gas owing to auto-thermal reforming driven by partial oxidation reforming process, dominantly.