eprintid: 3150
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/31/50
datestamp: 2023-11-09 15:51:24
lastmod: 2023-11-09 15:51:24
status_changed: 2023-11-09 15:45:06
type: article
metadata_visibility: show
creators_name: Pathmanathan, E.
creators_name: Ibrahim, R.
creators_name: Asirvadam, V.S.
title: Development of CO2 emission model of an acid gas incinerator using Nelder-Mead least squares support vector regression
ispublished: pub
keywords: Backpropagation; Carbon dioxide; Gas emissions; Linear programming; Liquefied natural gas; Neural networks; Regulatory compliance; Simulated annealing; Support vector machines, Analytical Instrumentation; Back propagation neural networks; Continuous emission monitoring system; Industrial pollution; Least squares support vector regression; Nelder-mead simplex algorithms; Predictive algorithms; Predictive techniques, Support vector regression
note: cited By 2
abstract: This paper aims to develop a CO2 emission model of an acid gas incinerator using Nelder-Mead least squares support vector regression (LS-SVR). The Malaysia Department of Environment is actively imposing the Clean Air Regulation to mandate heavy industries to comply with emission limits. One of the latest measures is to mandate the installation of analytical instrumentation known as a continuous emission monitoring system (CEMS) to report the emission level online to the Department of Environment office. As a hardware-based analyser, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive techniques are often preferred and considered 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 liquefied natural gas complex. Simulated annealing is first used to determine the initial hyper-parameters, which are further optimized based on the performance of the model using a Nelder-Mead simplex algorithm. The LS-SVR model is shown to outperform a benchmark model based on back-propagation neural networks in both training and testing data. © The Author(s) 2012.
date: 2012
publisher: SAGE Publications Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869054610&doi=10.1177%2f0142331211425404&partnerID=40&md5=5977e623a0455e68cc578d3bfb69fa60
id_number: 10.1177/0142331211425404
full_text_status: none
publication: Transactions of the Institute of Measurement and Control
volume: 34
number: 8
pagerange: 974-982
refereed: TRUE
issn: 01423312
citation:   Pathmanathan, E. and Ibrahim, R. and Asirvadam, V.S.  (2012) Development of CO2 emission model of an acid gas incinerator using Nelder-Mead least squares support vector regression.  Transactions of the Institute of Measurement and Control, 34 (8).  pp. 974-982.  ISSN 01423312