eprintid: 4045
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/40/45
datestamp: 2023-11-09 15:52:19
lastmod: 2023-11-09 15:52:19
status_changed: 2023-11-09 15:48:12
type: article
metadata_visibility: show
creators_name: Ganesan, T.
creators_name: Elamvazuthi, I.
creators_name: Ku Shaari, K.Z.
creators_name: Vasant, P.
title: Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production
ispublished: pub
keywords: Artificial intelligence; Carbon dioxide; Carbon monoxide; Chemical industry; Learning algorithms; Methane; Methanol; Particle swarm optimization (PSO); Steam reforming; Synthesis gas; Synthesis gas manufacture, Carbon dioxide reforming; Carbon monoxide selectivity; Comparative studies; Empirical model; Gravitational search algorithms; Higher hydrocarbons; Methane conversions; Multi objective; Multi objective optimizations (MOO); Normal boundary intersections; Performance metrics; Problem formulation; Swarm Intelligence; Syn gas; Synthesis gas production, Multiobjective optimization, algorithm; ammonia; carbon dioxide; carbon monoxide; chemical industry; empirical analysis; gas production; hydrogen; industrial technology; methane; methanol; multiobjective programming; optimization; oxidation; performance assessment
note: cited By 95
abstract: In the chemical industry, the production of methanol, ammonia, hydrogen and higher hydrocarbons require synthesis gas (or syn gas). The main three syn gas production methods are carbon dioxide reforming (CRM), steam reforming (SRM) and partial-oxidation of methane (POM). In this work, multi-objective (MO) optimization of the combined CRM and POM was carried out. The empirical model and the MO problem formulation for this combined process were obtained from previous works. The central objectives considered in this problem are methane conversion, carbon monoxide selectivity and the hydrogen to carbon monoxide ratio. The MO nature of the problem was tackled using the Normal Boundary Intersection (NBI) method. Two techniques (Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO)) were then applied in conjunction with the NBI method. The performance of the two algorithms and the quality of the solutions were gauged by using two performance metrics. Comparative studies and results analysis were then carried out on the optimization results. © 2012 Elsevier Ltd.
date: 2013
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84871717797&doi=10.1016%2fj.apenergy.2012.09.059&partnerID=40&md5=737e3f094bb35fa3fa3d41dd5bf13712
id_number: 10.1016/j.apenergy.2012.09.059
full_text_status: none
publication: Applied Energy
volume: 103
pagerange: 368-374
refereed: TRUE
issn: 03062619
citation:   Ganesan, T. and Elamvazuthi, I. and Ku Shaari, K.Z. and Vasant, P.  (2013) Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production.  Applied Energy, 103.  pp. 368-374.  ISSN 03062619