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Predictive Modeling Of Mercury Speciation In Combustion Flue

Gases

Using GMDH-Based Abductive Networks

Abdel-Aal, RE

ELSEVIER SCIENCE BV, FUEL PROCESSING TECHNOLOGY; pp: 483-491; Vol: 88

King Fahd University of Petroleum & Minerals

http://www.kfupm.edu.sa

Summary

Modeling mercury speciation is an important requirement for estimating harmful

emissions from coal-fired power plants and developing strategies to reduce them.

First-principle models based on chemical, kinetic, and thermodynamic aspects exist,

but these are complex and difficult to develop. The use of modem data-based machine

learning techniques has been recently introduced, including neural networks. Here we

propose an alternative approach using abductive networks based on the group method

of data handling (GMDH) algorithm, with the advantages of simplified and more

automated model synthesis, automatic selection of significant inputs, and more

transparent input-output model relationships. Models were developed for predicting

three types of mercury speciation (elemental, oxidized, and particulate) using a small

dataset containing six inputs parameters on the composition of the coal used and

boiler operating conditions. Prediction performance compares favourably with neural

network models developed using the same dataset, with correlation coefficients as

high as 0.97 for training data. Network committees (ensembles) are proposed as a

means of improving prediction accuracy, and suggestions are made for future work to

further improve performance. (c) 2006 Elsevier B.V. All rights reserved.

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Copyright: King Fahd University of Petroleum & Minerals;http://www.kfupm.edu.sa

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Copyright: King Fahd University of Petroleum & Minerals;http://www.kfupm.edu.sa