Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization

Authors:
Hao Zhou;Jia Pei Zhao;Li Gang Zheng;Chun Lin Wang;Ke Fa Cen
Affiliations:
State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China;State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China;State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China and School of Safety Science and Engineering, Henan Polytechn ...;State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China;State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China
Venue:
Engineering Applications of Artificial Intelligence
Year:
2012

Citing 14
Cited 1

Learning experiments with genetic optimization of a generalized regression neural network

Decision Support Systems - Special double issue: unified programming
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Development of an Advisory System Based on a Neural Network for the Operation of a Coal Fired Power Plant

ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Practical selection of SVM parameters and noise estimation for SVM regression

Neural Networks
Predicting corporate financial distress based on integration of support vector machine and logistic regression

Expert Systems with Applications: An International Journal
Optimizing feedforward artificial neural network architecture

Engineering Applications of Artificial Intelligence
Modelling of electrostatic fluidized bed (EFB) coating process using artificial neural networks

Engineering Applications of Artificial Intelligence
Forecasting of the daily meteorological pollution using wavelets and support vector machine

Engineering Applications of Artificial Intelligence
NOx and CO Prediction in Fossil Fuel Plants by Time Delay Neural Networks

Integrated Computer-Aided Engineering
An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training

Neural Computing and Applications
A comparative study of optimization algorithms for low NOx combustion modification at a coal-fired utility boiler

Expert Systems with Applications: An International Journal
Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference

Engineering Applications of Artificial Intelligence
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
A general regression neural network

IEEE Transactions on Neural Networks

Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Modeling NO"x emissions from coal fired utility boiler is critical to develop a predictive emissions monitoring system (PEMS) and to implement combustion optimization software package for low NO"x combustion. This paper presents an efficient NO"x emissions model based on support vector regression (SVR), and compares its performance with traditional modeling techniques, i.e., back propagation (BPNN) and generalized regression (GRNN) neural networks. A large number of NO"x emissions data from an actual power plant, was employed to train and validate the SVR model as well as two neural networks models. Moreover, an ant colony optimization (ACO) based technique was proposed to select the generalization parameter C and Gaussian kernel parameter @c. The focus is on the predictive accuracy and time response characteristics of the SVR model. Results show that ACO optimization algorithm can automatically obtain the optimal parameters, C and @c, of the SVR model with very high predictive accuracy. The predicted NO"x emissions from the SVR model, by comparing with the BPNN model, were in good agreement with those measured, and were comparable to those estimated from the GRNN model. Time response of establishing the optimum SVR model was in scale of minutes, which is suitable for on-line and real-time modeling NO"x emissions from coal-fired utility boilers.