Modelling of Manhattan K-nearest neighbor for exhaust emission analysis of CNG-diesel engine

Abstract

Hybrid fuel for the operation of diesel engine is the motivated research in this study. The diesel engine is modified to operate with the hybrid diesel and compressed natural gas (CNG). In this work a four stroke, single cylinder diesel engine is considered to operate at variable load and speed. At is operation condition the emission characteristics are measured to model the proposed Manhattan K-nearest neighbor (MKNN) technique. The MKNN is modelled to effectively analysis and predict the torque, brake power, exhaust emissions and break specific fuel consumption (BSFC). The MKNN is modelled with the constant K=3 and applied Manhattan distance formula for neighbor determination. From the result analysis it is evident that the proposed MKNN technique can effectively predict the engine performance and exhaust emission while the usage of hybrid fuel.