Entropy and exergy analysis and optimization of the VVER nuclear power plant with a capacity of 1000 MW using the firefly optimization algorithm Article Swipe
Saeed Talebi
,
Nima Norouzi
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.net.2020.05.011
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.net.2020.05.011
A light water nuclear Reactor has been exergy analyzed, and the rate of irreversible exergy loss and exergy destruction is calculated for each of its components. The ratio of these losses compared to the total input exergy loss is calculated, which shows that most irreversible losses occur in the reactors, turbines, steam generators, respectively, as well as in the downstream operations. The main aim of this paper is to optimize the power plant using an innovative firefly algorithm and then to propose a novel strategy to improve the overall performance of the plant. As shown in the results, the exergy destruction rate of the plant decreased by 1.18% using the firefly method, and the exergy efficiency of the plant reached 29.3% comparing to the operational amount of 28.99%. Also, the results of the firefly optimization process compared to the Genetic algorithm and gravitational search algorithm to study the accuracy of the model for exergy analysis fitness problems in the power plants and the results of this comparison has shown that the results are nearly similar in the mentioned methods. However, the firefly is faster and more accurate in limited iterations.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.net.2020.05.011
- OA Status
- diamond
- Cited By
- 22
- References
- 29
- Related Works
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- OpenAlex ID
- https://openalex.org/W3035081462
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3035081462Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.net.2020.05.011Digital Object Identifier
- Title
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Entropy and exergy analysis and optimization of the VVER nuclear power plant with a capacity of 1000 MW using the firefly optimization algorithmWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-06-10Full publication date if available
- Authors
-
Saeed Talebi, Nima NorouziList of authors in order
- Landing page
-
https://doi.org/10.1016/j.net.2020.05.011Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.net.2020.05.011Direct OA link when available
- Concepts
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Exergy, Firefly algorithm, Exergy efficiency, Nuclear power plant, Process engineering, Environmental science, Power station, Multi-objective optimization, Computer science, Mathematical optimization, Mathematics, Engineering, Thermodynamics, Physics, Particle swarm optimization, Nuclear physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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22Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2023: 3, 2022: 3, 2021: 9Per-year citation counts (last 5 years)
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29Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 10, 33, 48, 58, 70, 87, 91, 96, 98, 103, 109, 113, 117, 123, 129, 132, 138, 147, 150, 158, 162, 170, 176, 180 |
| abstract_inverted_index.been | 6 |
| abstract_inverted_index.each | 22 |
| abstract_inverted_index.loss | 15, 37 |
| abstract_inverted_index.main | 62 |
| abstract_inverted_index.more | 185 |
| abstract_inverted_index.most | 43 |
| abstract_inverted_index.rate | 11, 101 |
| abstract_inverted_index.that | 42, 169 |
| abstract_inverted_index.then | 79 |
| abstract_inverted_index.this | 65, 165 |
| abstract_inverted_index.well | 55 |
| abstract_inverted_index.1.18% | 107 |
| abstract_inverted_index.29.3% | 120 |
| abstract_inverted_index.Also, | 128 |
| abstract_inverted_index.input | 35 |
| abstract_inverted_index.light | 1 |
| abstract_inverted_index.model | 151 |
| abstract_inverted_index.novel | 83 |
| abstract_inverted_index.occur | 46 |
| abstract_inverted_index.paper | 66 |
| abstract_inverted_index.plant | 72, 104, 118 |
| abstract_inverted_index.power | 71, 159 |
| abstract_inverted_index.ratio | 27 |
| abstract_inverted_index.shown | 94, 168 |
| abstract_inverted_index.shows | 41 |
| abstract_inverted_index.steam | 51 |
| abstract_inverted_index.study | 146 |
| abstract_inverted_index.these | 29 |
| abstract_inverted_index.total | 34 |
| abstract_inverted_index.using | 73, 108 |
| abstract_inverted_index.water | 2 |
| abstract_inverted_index.which | 40 |
| abstract_inverted_index.amount | 125 |
| abstract_inverted_index.exergy | 7, 14, 17, 36, 99, 114, 153 |
| abstract_inverted_index.faster | 183 |
| abstract_inverted_index.losses | 30, 45 |
| abstract_inverted_index.nearly | 173 |
| abstract_inverted_index.plant. | 92 |
| abstract_inverted_index.plants | 160 |
| abstract_inverted_index.search | 143 |
| abstract_inverted_index.28.99%. | 127 |
| abstract_inverted_index.Genetic | 139 |
| abstract_inverted_index.Reactor | 4 |
| abstract_inverted_index.firefly | 76, 110, 133, 181 |
| abstract_inverted_index.fitness | 155 |
| abstract_inverted_index.improve | 86 |
| abstract_inverted_index.limited | 188 |
| abstract_inverted_index.method, | 111 |
| abstract_inverted_index.nuclear | 3 |
| abstract_inverted_index.overall | 88 |
| abstract_inverted_index.process | 135 |
| abstract_inverted_index.propose | 81 |
| abstract_inverted_index.reached | 119 |
| abstract_inverted_index.results | 130, 163, 171 |
| abstract_inverted_index.similar | 174 |
| abstract_inverted_index.However, | 179 |
| abstract_inverted_index.accuracy | 148 |
| abstract_inverted_index.accurate | 186 |
| abstract_inverted_index.analysis | 154 |
| abstract_inverted_index.compared | 31, 136 |
| abstract_inverted_index.methods. | 178 |
| abstract_inverted_index.optimize | 69 |
| abstract_inverted_index.problems | 156 |
| abstract_inverted_index.results, | 97 |
| abstract_inverted_index.strategy | 84 |
| abstract_inverted_index.algorithm | 77, 140, 144 |
| abstract_inverted_index.analyzed, | 8 |
| abstract_inverted_index.comparing | 121 |
| abstract_inverted_index.decreased | 105 |
| abstract_inverted_index.mentioned | 177 |
| abstract_inverted_index.reactors, | 49 |
| abstract_inverted_index.turbines, | 50 |
| abstract_inverted_index.calculated | 20 |
| abstract_inverted_index.comparison | 166 |
| abstract_inverted_index.downstream | 59 |
| abstract_inverted_index.efficiency | 115 |
| abstract_inverted_index.innovative | 75 |
| abstract_inverted_index.calculated, | 39 |
| abstract_inverted_index.components. | 25 |
| abstract_inverted_index.destruction | 18, 100 |
| abstract_inverted_index.generators, | 52 |
| abstract_inverted_index.iterations. | 189 |
| abstract_inverted_index.operational | 124 |
| abstract_inverted_index.operations. | 60 |
| abstract_inverted_index.performance | 89 |
| abstract_inverted_index.irreversible | 13, 44 |
| abstract_inverted_index.optimization | 134 |
| abstract_inverted_index.gravitational | 142 |
| abstract_inverted_index.respectively, | 53 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5051547174 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I158248296 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.96445824 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |