The impact of driving styles on fuel consumption: a data-warehouse-and-data-mining-based discovery process

This paper discusses the results of applied research on the eco-driving domain based on a huge data set produced from a fleet of Lisbon's public transportation buses for a three-year period. This data set is based on events automatically extracted from the control area network bus and enriched with GPS coordinates, weather conditions, and road information. We apply online analytical processing (OLAP) and knowledge discovery (KD) techniques to deal with the high volume of this data set and to determine the major factors that influence the average fuel consumption, and then classify the drivers involved according to their driving efficiency. Consequently, we identify the most appropriate driving practices and styles. Our findings show that introducing simple practices, such as optimal clutch, engine rotation, and engine running in idle, can reduce fuel consumption on average from 3 to 5l/100 km, meaning a saving of 30 l per bus on one day. These findings have been strongly considered in the drivers' training sessions.

Assessment and control of nanoparticles exposure in welding operations by use of a control banding tool

This paper describes the use of a Control Banding Tool to assess and further control of exposure of nanoparticles emitted during welding operations. The tool was applied to Metal Active Gas (MAG) arc welding of mild and stainless steel, providing semi-quantitative data on the process, so that protection measures could be derived, e.g. exhaust gas ventilation by hoods, local ventilation devices and containment measures. This tool is quite useful to compare and evaluate the characteristics of arc welding procedures so that more eco-friendly processes could be preferred over the more potentially noxious ones.