The use of alternative fuels is considered to be an effective measure to meet strict emissions regulations of particulate matter (PM) and oxides of nitrogen (NO x ). In response to these requirements, emissions data from inuse alternative fuel and diesel-powered heavy-duty vehicles have been measured and collected from 32 transit agencies in 17 states using the two West Virginia University (WVU) transportable heavy-duty vehicle emissions testing laboratories (THDVETLs). More than 600 tests have been performed on over 300 buses and heavy trucks operating on alternative fuels such as natural gas, methanol, and ethanol and also operating on conventional fuel diesel. Regulated emissions of PM, NO x , carbon monoxide (CO), and total hydrocarbon (HC) have been measured and analyzed. In this study, emissions data from alternative fuel buses and diesel control buses are carefully compared. The results show that natural gas, methanol, and ethanol have a strong potential to reduce PM and NO x emissions levels.

No abstract

In this paper, BP neural network (BPNN) is studied to model and predict NO X emission of direct injection diesel engine. The model selects four parameters as input, namely rotation speed, load, exhaust temperature and fuel-air ratio. Through testing, it is concluded that the prediction performance of BPNN model will be greatly affected by the initial weight and threshold, so that the prediction accuracy of the model is not high. In order to reduce the influence of initial weight and threshold on the prediction performance of BPNN model, this paper adopts Particle Swarm Optimization (PSO) algorithm to optimize the initial weight and threshold of BPNN, and establishes the corresponding prediction model of NO X emission of diesel engine. The results show that the prediction model of BPNN optimized by PSO algorithm can effectively reduce the influence of initial weight and threshold on BPNN and make the prediction results of the model more reliable. In particular, when PSO adopts non-linear dynamic weight strategy and synchronous learning factor strategy, the prediction performance of BPNN model is more provided with reliability.