The aluminium producers Alcoa and Hydro are working towards zero emissions and thereby increasing sustainability by 2050. At the same time, production volumes are expected to increase while governmental regulations of emissions to air and water, for example of heavy metals, are getting more and more strict. This represents a challenge, as these elements often are present at very low levels, both in incoming and outgoing material streams. There is an increasing focus on emissions and environmental pollution. Currently the industry is obligated to report yearly, averaged values based on measurements and models tailored for this purpose. This approach is considered not sufficient going forward. To control and reduce emissions, the industry needs systems for online emissions monitoring and predictions. The industry must move from a reporting-after methodology towards an online temporal and spatial representation of the emissions, both inside and outside the process areas and plants.
The goal of the project is to develop and demonstrate methods for robust online sampling, modelling and prediction of heavy metal emissions associated with dust, from source to environment. This will be done by developing a distributed measurement system that is coupled with a faster than real-time modelling/predictive system for pot room ventilation and emission dispersion. The concept makes use of heavy metals found in particulate matter as proxy for the overall emissions. The correlation between condensed and total (i.e. including gaseous phase) heavy metal emissions will be established through direct comparison of analysis of particular matter and standardized sampling. Through fingerprinting of heavy metal content in particular matter as function of size fractions the system can predict the heavy metal dispersion to the environment.