Commenced in January 2007
Paper Count: 30184
Development of NOx Emission Model for a Tangentially Fired Acid Incinerator
Abstract:This paper aims to develop a NOx emission model of an acid gas incinerator using Nelder-Mead least squares support vector regression (LS-SVR). Malaysia DOE is actively imposing the Clean Air Regulation to mandate the installation of analytical instrumentation known as Continuous Emission Monitoring System (CEMS) to report emission level online to DOE . As a hardware based analyzer, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive technique is often preferred and considered as a feasible alternative to replace the CEMS for regulatory compliance. The LS-SVR model is built based on the emissions from an acid gas incinerator that operates in a LNG Complex. Simulated Annealing (SA) is first used to determine the initial hyperparameters which are then further optimized based on the performance of the model using Nelder-Mead simplex algorithm. The LS-SVR model is shown to outperform a benchmark model based on backpropagation neural networks (BPNN) in both training and testing data.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334077Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1614
 U.S Environmental Protection Agency. Clean Air Act as of 2008. Chapter 85: Air Pollution Prevention and Control. s.l. : U.S Government Printing Office, 2008.
 ENVIRONMENTAL QUALITY (CLEAN AIR) REGULATIONS 1978. FEDERAL SUBSIDIARY LEGISLATION ENVIRONMENTAL QUALITY ACT 1974
[ACT 127]. s.l. : Department of Environment, Malaysia, 2000.
 Design CEMS For Flue Gas From Thermal Power Plant. Fan Xiaoliang, Zheng Haiming. Baoding, China : IEEE, 2009.
 Developing Data Acquisition and Handling System for Continous Emission Monitoring System from Coal-Fired Power Plant. Haiming Zheng, Guiji Tang. Baoding, China : IEEE, 2008.
 US Environmental Protection Agency. Climate Change - Health and Environmental Effects. US EPA Home Page.
[Cited: April 8, 2011.] http://www.epa.gov/climatechange/effects/index.html.
 Emission Monitoring Using Multivariate Soft Sensors. Dong, Dong and McAvoy, Thomas J. Seattle, Washington : American Control Conference, , 1995.
 Neural Networks for Boiler Emission Prediction. Baines, Glenn. s.l. : Performance Consultant Fisher-Rosemount Solutions, 1999, pp. 435- 439.
 Predictive emission monitors (PEMS) for NOx generation in process heaters. S. S. S. Chakravarthy, A. K. Vohra, B. S. Gill. Gurgaon, India : Elsevier, 2000, Computers and Chemical Engineering 23 (2000), pp. 1649-1659.
 Combined Hybrid Clustering Techniques and Neural Fuzzy Networks to Predict Diesel Engine Emissions. Jiamei Deng, Richard Stobart. Brighton, UK : IEEE, 2007.
 Monitoring NOx Emissions from Coal-Fired Boilers using Generalized Regression Neural Network. Ligang Zheng, Shuijun Yu, Minggao Yu. Jiaozuo Henan, China : IEEE, 2008, pp. 1916-1919.
 Prediction of NOx Concentration from Coal Combustion Using LS-SVR. Ligang Zheng, Hailin Jia, Shuijun Yu, Minggao Yu. Jiaozuo Henan, China : IEEE, 2010, pp. 1-4.
 Modeling and optimization of the NOx emission characteristics of a tangentially firedboiler with artificial neural networks. Hao Zhou, Kefa Cen, Jianren Fan. Hangzhou, PR China : Science Direct, 2001, Energy 29 (2004) 167-183, pp. 167-183.
 S., Haykins.Neural networks: a comprehensive foundation, 2nd ed. Englewood Cliffs, NJ : Prentice-Hall, 1999.
 Monitoring pollutant emissions in a 4.8 MW power plant through neural network. Tronci, S., Baratti, R. and Servida, A. 2002, Neurocomputing, Vol. 43, pp. 3 - 15.
 J.A.K Suykens, T.V. Gestel, J.D Brabenter, B.D Moor, J. Vandewelle.Least Squares Support Vector Machines. NJ 07661 : World Scientific Publishing , 2002.
 N, Cristianini and J, Shawe-Taylor.An introduction to support vector machine and other kernel-based learning method. Cambridge : Cambridge University Press, 2000.
 Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin.A Practical Guide to Support Vector Classi. Department of Computer Science, National Taiwan University, Taipei 106, Taiwan. 2010.
 Teukolsky, Vetterling, Flannery.Numerical Recipes in C. 2nd Edition. Cambridge University Press : s.n., 1998.
 Optimization by Simulated Annealing. S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi. 4598, s.l. : American Association for the Advancement of Science, May 13, 1983, Science, New Series, Vol. 220, pp. 671-680.
 S.Jamaludin, S.Manaf, P.Oortwin.World Largest Tangentially Fired Acid Gas Incinerator in LNG Plant: Startup and Operational Experiences. Malaysia LNG Sdn. Bhd & Shell Global Solutions International . 2005. Technical Report.
 Adaptive and Iterative Least Squares Support Vector Regression Based on Quadratic Renyi Entropy. Jingqing Jiang, Chuyi Song,Haiyan Zhao,Chunguo Wu,Yanchun Liang. Beijing, China : s.n.
 Polani, Tobias Jung and Daniel.Sequential Learning with LS-SVM for Large-Scale Data Sets. Dept. of Computer Science, Univ. of Mainz. Mainz, Germany : s.n. Technical Report.
 Using Analytic QP and Sparseness to Speed Training of Support VectorMachines. Platt, John C. 11, 1999, Advances in Neural Information Processing Systems.