A robust framework to predict mercury speciation in combustion flue gases
Jonathan L Ticknor1, Heileen Hsu-Kim1, Marc A Deshusses1
1Duke University, Department of Civil and Environmental Engineering, 121 Hudson Hall, Box 90287, Durham, NC 27708, USA.
Journal of Hazardous Materials
|December 10, 2013
Summary
A new Bayesian regularized artificial neural network (BRANN) model accurately predicts mercury speciation in coal combustion flue gas. This model identifies key factors like coal chlorine content influencing mercury emissions, aiding pollution control.
Area of Science:
- Environmental Science
- Chemical Engineering
- Artificial Intelligence
Background:
- Coal combustion is a major source of global mercury emissions.
- Understanding mercury speciation (elemental, oxidized, particulate) in flue gas is crucial for effective emission control technologies.
Purpose of the Study:
- To develop a predictive model for mercury speciation in flue gas from coal combustion.
- To identify key coal properties and combustion parameters influencing mercury speciation.
Main Methods:
- A Bayesian regularized artificial neural network (BRANN) model was developed.
- The model utilized five coal properties and combustion temperature as inputs.
- Parametric sensitivity analysis was performed on the BRANN model.
Main Results:
- The BRANN model captured up to 97% of the variation in mercury species concentration.
- Coal chlorine content and calorific value were the most sensitive parameters influencing mercury speciation.
- Combustion temperature was also significant, while coal sulfur content was least important.
Conclusions:
- BRANNs are effective for predicting mercury concentration and speciation in combustion flue gas.
- This approach offers a more efficient and effective alternative to other advanced modeling strategies for mercury emission control.
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