Name
Title | ||
|---|---|---|
Air pollutant emissions prediction by process modelling - Application in the iron and steel industry in the case of a re-heating furnace |
Abstract | ||
|---|---|---|
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 CO"2 emissions and furnace process parameters could successfully be expressed linearly, while NO"2 emission modelling required a non-linear model. Artificial neural networks modelling revealed a good ability to predict NO"2 and CO"2 emissions. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.envsoft.2006.09.008 | Environmental Modelling and Software |
Keywords | Field | DocType |
multiple linear regression,no 2,steelworks process modelling,fume emissions,3-week monitoring campaign,artificial neural networks,deterministic modelling,air pollutant emissions prediction,steel industry,process modelling,co 2,routine measurement,special measurement campaign,furnace process parameter,available measurement technique,re-heating furnace,measurement condition,deterministic approach,correlation method,monitoring air pollutant emission,artificial neural network,prediction model,iron,co2 | Process engineering,Combustion,Correlation method,Pollutant emissions,Hydrology,Computer science,Process modeling,Waste management,Deterministic system (philosophy),Physical phenomena | Journal |
Volume | Issue | ISSN |
22 | 9 | Environmental Modelling and Software |
Citations | PageRank | References |
3 | 0.55 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anda Ionescu | 1 | 24 | 3.41 |
| Yves Candau | 2 | 134 | 10.83 |