Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.     

Improved land cover and emission factors for modeling biogenic volatile organic compounds emissions from Hong Kong

This paper describes a study of local biogenic volatile organic compounds (BVOC) emissions from the Hong Kong Special Administrative Region (HKSAR). An improved land cover and emission factor database was developed to estimate Hong Kong emissions using MEGAN, a BVOC emission model developed by Guenther et al. (2006). Field surveys of plant species composition and laboratory measurements of emission factors were combined with other data to improve existing land cover and emission factor data. The BVOC emissions from Hong Kong were calculated for 12 consecutive years from 1995 to 2006. For the year 2006, the total annual BVOC emissions were determined to be 12,400 metric tons or 9.82 × 10⁹ g C (BVOC carbon). Isoprene emission accounts for 72%, monoterpene emissions account for 8%, and other VOCs emissions account for the remaining 20%. As expected, seasonal variation results in a higher emission in the summer and a lower emission in the winter, with emission predominantly in day time. A high emission of isoprene occurs for regions, such as Lowest Forest-NT North, dominated by broadleaf trees. The spatial variation of total BVOC is similar to the isoprene spatial variation due to its high contribution. The year to year variability in emissions due to weather was small over the twelve-year period (?1.4%, 2006 to 1995 trendline), but an increasing trend in the annual variation due to an increase in forest land cover can be observed (+7%, 2006 to 1995 trendline). The results of this study demonstrate the importance of accurate land cover inputs for biogenic emission models and indicate that land cover change should be considered for these models.     

Characterizing Driving Patterns for Zhuhai for Traffic Emissions Estimation

Abstract

Zhuhai, a relatively less developed city on the western coast of the Pearl River Delta (PRD) of China, is planning to undergo major development in coming years. A Hong Kong-Zhuhai-Macao Bridge project has been approved by the Central Government of China. The project will have great impact on the driving pattern and vehicular emissions to the city. This baseline study collected speed-time data of two instrumented private cars in morning and evening periods, as well as a daytime nonpeak period of >10 consecutive days in the spring and winter of 2003. The authors used the microwave speed sensor and global positioning system installed in the instrumented cars and used car-chasing technique to perform the data collection. They used the statistical package SPSS to assess the consistency, as well as to evaluate the variability of the data. Nine parameters, namely, average speed, average running speed, average acceleration rate, average deceleration rate, mean length of a driving period, time proportions of driving modes, average number of acceleration-deceleration changes, root mean square acceleration, and positive acceleration kinetic energy are calculated to represent the driving characteristics. A driving cycle for private cars was developed. If emission tests were conducted using the Zhuhai driving cycle, the level of vehicle emissions measured is likely to be in between that of the Federal Test Procedure (FTP) cycle and the Melbourne Peak cycle.