%0 Journal Article
%@ 17265479
%A Pathmanathan, E.
%A Ibrahim, R.
%A Asirvadam, V.
%D 2012
%F scholars:2436
%J Sensors and Transducers
%K Analytical Instrumentation; Clean air regulations; Continuous emission monitoring system; Industrial pollution; Least squares support vector regression; Liquefied Natural Gas (LNG); Predictive algorithms; Predictive techniques, Algorithms; Gas emissions; Liquefied natural gas; Regulatory compliance; Simulated annealing; Support vector machines, Particle swarm optimization (PSO)
%N SPL 12
%P 98-109
%T Development of NOx emission model using particle swarm optimized least-squared SVR (PSO-LSSVR) hybrid algorithm
%U https://khub.utp.edu.my/scholars/2436/
%V 17
%X 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. © 2012 IFSA.
%Z cited By 0