Abstract
PrOlor is the first commercial software (Cartelle et. al 2014) that is able to predict odour emission episodes two days beforehand, using prediction meteorological data instead of real time data. This way the plant operator has a time frame to take actions to control the odour emission and prevent odour incidents, before they actually happen. PrOlor is based on CALPUFF and WRF at very high resolutions. The system is run in a Linux cluster with supercomputing capabilities.
In this experiment, PrOlor was used in an animal by-product rendering plant with a previous record of odour complaints. In a village nearby to this plant, a register of complaints from the residents was set for a year. Each volunteer had an application in its mobile to register a complaint every time they detected odours from the animal by-product processing plant. The first results were not very promising and it was necessary to increase the resolution of the model. Later it was necessary to apply a peak to mean ratio to further improve the results. In addition, it was necessary to run the model several times a day, to better tune the prediction ofodour episodes. After 10 months, the results showed that the optimum level to consider a forecasted result as an odour incident is 2.1 ouE/m3. Also, PrOlor was able to forecast adequately in a 41.2% of the incidents and 99.1% of the hours where no incident was recorded. Finally, the results showed that there is a trend to over- predict odour incidents.
