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Open Science Index

Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29284

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Development of NOx Emission Model for a Tangentially Fired Acid Incinerator
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.
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[ACT 127]. s.l. : Department of Environment, Malaysia, 2000.
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[4] Developing Data Acquisition and Handling System for Continous Emission Monitoring System from Coal-Fired Power Plant. Haiming Zheng, Guiji Tang. Baoding, China : IEEE, 2008.
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[Cited: April 8, 2011.]
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[9] Combined Hybrid Clustering Techniques and Neural Fuzzy Networks to Predict Diesel Engine Emissions. Jiamei Deng, Richard Stobart. Brighton, UK : IEEE, 2007.
[10] 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.
[11] 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.
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[21] 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.
[22] 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.
[23] Using Analytic QP and Sparseness to Speed Training of Support VectorMachines. Platt, John C. 11, 1999, Advances in Neural Information Processing Systems.
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