Linear multiple regression (LMR) and nonlinear polynomial network (NPN) models were developed from data collected from ISO 8178‐4 (1996) test cycle B‐type tests (ISO) and an expanded set of tests (EXP) to predict hydrocarbon (HC) emissions from a diesel engine. LMR using the ISO training data (R2 = 0.94) resulted in overfitting of the model as applied to the evaluation data (R2 = 0.49). LMR based on the expanded data (R2 = 0.68) was a better LMR model when applied to the evaluation data (R2 = 0.64). NPN using the expanded training data (R2 = 0.99) resulted in the best model when applied to the evaluation data (R2 = 0.98) and is preferred for predicting HC when the larger set of test mode data are available. NPN using the ISO training data (R2 = 0.99) resulted in a satisfactory fit for the evaluation data (R2 = 0.91), although with a higher average absolute error (0.52 vs. 0.42 g/kWh) than NPN using the EXP training data. This model was also considered suitable for predicting HC. Results of this initial study suggest that data could be collected during ISO 8178‐4 emission tests and modeled with NPN to predict HC emissions for a diesel engine operating at various constant speeds and loads.
Watson, Dennis G., Bostic, David R. and Harrison, Tony V. "Comparative Models of Hydrocarbon Emissions for a Diesel Engine Operating at Constant Loads and Speeds." Transactions of the ASABE 52, No. 4 (Jan 2009): 1079-1087. doi:10.13031/2013.27778.