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Predictive Modeling of Mercury Speciation in Combustion Flue Gases Using GMDH-Based Abductive Networks

Summary: Predictive Modeling of Mercury Speciation in Combustion Flue Gases
Using GMDH-Based Abductive Networks
R. E. Abdel-Aal
Department of Computer Engineering,
King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
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 modern 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 data set 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.


Source: Abdel-Aal, Radwan E. - Computer Engineering Department, King Fahd University of Petroleum and Minerals


Collections: Computer Technologies and Information Sciences; Power Transmission, Distribution and Plants