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It is found that biodiesel has a great potential to reduce the nitrogen oxides (NOx) and soot emissions simultaneously in low temperature combustion (LTC) mode. The objective of this study is to investigate the combustion and emission characteristics of 20% biodiesel blend diesel fuel (B20) under several exhaust gas recirculation (EGR) conditions for LTC application. An experimental investigation of B20 was conducted on a four-stroke common rail direct injection diesel engine at 2000rpm and 25% load condition. The EGR ratio was adjusted from 10% to 66%, and the injection pressure was tuned from 100MPa to 140MPa. The result showed that B20 generated less soot emission than conventional diesel with increasing EGR ratio, especially when the EGR ratio was beyond 30%. Soot emission increased with increasing EGR ratio up to 50% EGR, after which there is a steep decrease in particular matter (PM).

Study and modeling of diesel combustion during transient operations is an important scientific objective. This is partially due to the fact that emissions under transient operations have aroused increasing attention by control groups during recent decades. The objective of this paper is to develop a combustion model to predict the peculiarities of transient combustion for developing and testing control strategies. To by-pass the complicated principles of transient combustion, the Neural Networks are applied to link the coefficients in an empirical combustion model with engine operating parameters. Finally, the Neural Networks combustion model would not only reflect the influence of turbocharge lag on combustion process during transient event, which cannot be predicted by its interpolation alternative, but also shown great potential for analyzing combustion characteristics during load increase transient event or other transient operations.