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Vol. 17, Special Issue, December 2012, pp. 98-109

 

Selected papers from the 4th International Conference on Intelligent & Advanced Systems (ICIAS' 2012),

12-14 June 2012, Malaysia, Kuala Lumpur

 

Bullet

 

Development of NOx Emission Model Using Particle Swarm Optimized Least-Squared SVR (PSO-LSSVR) Hybrid Algorithm
 
Elangeshwaran PATHMANATHAN, Rosdiazli IBRAHIM, Vijanth ASIRVADAM

Electrical and Electronic Engineering Department,

Universiti Teknologi PETRONAS,

Bandar Seri Iskandar, 31720 Tronoh, Perak, Malaysia

Tel.: +605 368 7821, fax: +605 364 7443

E-mail: elanz117@gmail.com, rosdiazli@petronas.com.my, vijanth_sagayan@petronas.com.my

 

Received: 29 September 2012   /Accepted: 29 October 2012   /Published: 18 December 2012

Digital Sensors and Sensor Sysstems

 

Abstract: This paper aims to develop a NOx emission model of acid gas incinerator using a hybrid of particle swarm optimization (PSO) and least squares support vector regression (LSSVR). 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 office. As hardware based analyzer, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive techniques are often preferred and considered as a feasible alternative to replace the CEMS for regulatory compliance. The LSSVR model is built based on the emissions from an acid gas incinerator that operates in a Liquefied Natural Gas (LNG) Complex. PSO is used to optimize the hyperparameters used in training of the LSSVR model. The model is shown to outperform previously developed LSSVR models that were optimized using a combination of Nelder-Mead (NM) simplex and Coupled Simulated Annealing (CSA) algorithms.

 

Keywords: Industrial pollution, Particle swarm optimization, Predictive algorithms, Support vector machines

 

 

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