A Bee Colony Neuro-Fuzzy Controller to Improve Well Premixed Combustion
In order to actively control combustion reaction, this study proposes an adaptive neuro-fuzzy (ANFIS) control scheme of interaction between premixed combustion reaction and acoustic flame perturbation where the flame pressure movement will be considered as model perturbation. Using the Cantera datab...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2023-05-01
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| Series: | Journal of Mechanical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/scjme-2023-0003 |
| _version_ | 1797814178399911936 |
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| author | Debbah Abdesselam Kelaiaia Ridha Kerboua Adlen |
| author_facet | Debbah Abdesselam Kelaiaia Ridha Kerboua Adlen |
| author_sort | Debbah Abdesselam |
| collection | DOAJ |
| description | In order to actively control combustion reaction, this study proposes an adaptive neuro-fuzzy (ANFIS) control scheme of interaction between premixed combustion reaction and acoustic flame perturbation where the flame pressure movement will be considered as model perturbation. Using the Cantera database, it is possible to investigate the mechanisms by which the combustion process interacts with acoustic, vorticity, and entropy waves. A well-stirred reactor (WSR) has been extensively used to model combustion processes in three different reaction zone regimes. We designed the control architecture to achieve an intelligent representation of the system for various operating scenarios, which was motivated by the complexity of the mathematical model that was being used. This goal is accomplished by an artificial bee colony (ABC), which uses simulated data from a mathematical model to optimize a neuro-fuzzy with less computational expense. The optimized neuro-fuzzy identifier is converted to an adaptive neural-based (ANFIS) controller optimized to control the outputs of the system. In keeping with the combustion temperature set point, the results demonstrate a remarkable attenuation of flame perturbation and acceptable combustion reaction quality (NOx emission). |
| first_indexed | 2024-03-13T08:03:44Z |
| format | Article |
| id | doaj.art-f1a03ad127ee45bba5f859fd5daa0012 |
| institution | Directory Open Access Journal |
| issn | 2450-5471 |
| language | English |
| last_indexed | 2024-03-13T08:03:44Z |
| publishDate | 2023-05-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Journal of Mechanical Engineering |
| spelling | doaj.art-f1a03ad127ee45bba5f859fd5daa00122023-06-01T09:44:44ZengSciendoJournal of Mechanical Engineering2450-54712023-05-01731254210.2478/scjme-2023-0003A Bee Colony Neuro-Fuzzy Controller to Improve Well Premixed CombustionDebbah Abdesselam0Kelaiaia Ridha1Kerboua Adlen21L2RCS Laboratory, University of Badji Mokhtar-Annaba, Annaba, Algeria2GMM Laboratory, Department of mechanical engineering, University of 20 August 1955-Skikda, PB N° 26 Route Elhadaik, Skikda, 21000, Algeria3Department of Petrochemical engineering, University of 20 August 1955-Skikda, PB N° 26 Route Elhadaik, Skikda, 21000, AlgeriaIn order to actively control combustion reaction, this study proposes an adaptive neuro-fuzzy (ANFIS) control scheme of interaction between premixed combustion reaction and acoustic flame perturbation where the flame pressure movement will be considered as model perturbation. Using the Cantera database, it is possible to investigate the mechanisms by which the combustion process interacts with acoustic, vorticity, and entropy waves. A well-stirred reactor (WSR) has been extensively used to model combustion processes in three different reaction zone regimes. We designed the control architecture to achieve an intelligent representation of the system for various operating scenarios, which was motivated by the complexity of the mathematical model that was being used. This goal is accomplished by an artificial bee colony (ABC), which uses simulated data from a mathematical model to optimize a neuro-fuzzy with less computational expense. The optimized neuro-fuzzy identifier is converted to an adaptive neural-based (ANFIS) controller optimized to control the outputs of the system. In keeping with the combustion temperature set point, the results demonstrate a remarkable attenuation of flame perturbation and acceptable combustion reaction quality (NOx emission).https://doi.org/10.2478/scjme-2023-0003combustionflame perturbationwell stirred reactorneuro-fuzzy adaptive controlartificial bee colony optimization |
| spellingShingle | Debbah Abdesselam Kelaiaia Ridha Kerboua Adlen A Bee Colony Neuro-Fuzzy Controller to Improve Well Premixed Combustion Journal of Mechanical Engineering combustion flame perturbation well stirred reactor neuro-fuzzy adaptive control artificial bee colony optimization |
| title | A Bee Colony Neuro-Fuzzy Controller to Improve Well Premixed Combustion |
| title_full | A Bee Colony Neuro-Fuzzy Controller to Improve Well Premixed Combustion |
| title_fullStr | A Bee Colony Neuro-Fuzzy Controller to Improve Well Premixed Combustion |
| title_full_unstemmed | A Bee Colony Neuro-Fuzzy Controller to Improve Well Premixed Combustion |
| title_short | A Bee Colony Neuro-Fuzzy Controller to Improve Well Premixed Combustion |
| title_sort | bee colony neuro fuzzy controller to improve well premixed combustion |
| topic | combustion flame perturbation well stirred reactor neuro-fuzzy adaptive control artificial bee colony optimization |
| url | https://doi.org/10.2478/scjme-2023-0003 |
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