eprintid: 2436
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/24/36
datestamp: 2023-11-09 15:50:40
lastmod: 2023-11-09 15:50:40
status_changed: 2023-11-09 15:43:31
type: article
metadata_visibility: show
creators_name: Pathmanathan, E.
creators_name: Ibrahim, R.
creators_name: Asirvadam, V.
title: Development of NOx emission model using particle swarm optimized least-squared SVR (PSO-LSSVR) hybrid algorithm
ispublished: pub
keywords: 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)
note: cited By 0
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. © 2012 IFSA.
date: 2012
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879751535&partnerID=40&md5=b58fe78177ce277035b4538dbe77ee2b
full_text_status: none
publication: Sensors and Transducers
volume: 17
number: SPL 12
pagerange: 98-109
refereed: TRUE
issn: 17265479
citation:   Pathmanathan, E. and Ibrahim, R. and Asirvadam, V.  (2012) Development of NOx emission model using particle swarm optimized least-squared SVR (PSO-LSSVR) hybrid algorithm.  Sensors and Transducers, 17 (SPL 12).  pp. 98-109.  ISSN 17265479