Air pollutant emissions prediction by process modelling - Application in the iron and steel industry in the case of a re-heating furnace
Monitoring air pollutant emissions of large industrial installations is necessary to ensure compliance with environmental legislation. Most of the available measurement techniques are expensive, and measurement conditions such as high-temperature emissions, difficulty of access, are often difficult. That is why legislation can not impose a permanent emission monitoring in many countries. The possibility to replace it with predictive models based on the routine measurements of the main control parameters of the installation is analysed in this paper. In order to identify these models, a special measurement campaign of emissions must be performed or, alternatively, a deterministic modelling of the process can be developed. This study was carried out in the case of a real installation in the steel industry i.e. a billet re-heating furnace. Physical phenomena involved in combustion within the furnace were complex enough to prefer an empirical black-box modelling of the furnace over a deterministic approach. A 3-week monitoring campaign of fume emissions at the stack was performed; furnace process parameters during the same period were available. The relationship between CO2 emissions and furnace process parameters could successfully be expressed linearly, while NO2 emission modelling required a non-linear model. Artificial neural networks modelling revealed a good ability to predict NO2 and CO2 emissions. (C) 2006 Elsevier Ltd. All rights reserved.
fume emissions co2 no2 artificial neural networks steelworks process modelling multiple linear regression correlation method