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  • 标题:Thermoelectric generation with reduced pollutants made possible by bio-inspired computing
  • 本地全文:下载
  • 作者:Denis Carlos Lima Costa ; Lair Aguiar de Meneses ; Mara Líbia Viana de Lima
  • 期刊名称:Research, Society and Development
  • 电子版ISSN:2525-3409
  • 出版年度:2022
  • 卷号:11
  • 期号:1
  • 页码:1-13
  • DOI:10.33448/rsd-v11i1.24568
  • 语种:English
  • 出版社:Grupo de Pesquisa Metodologias em Ensino e Aprendizagem em Ciências
  • 摘要:The debate to establish a balance between the generation of electricity and the preservation of the environment is, extraordinarily, important. This article proposes, as a short-term solution, the replacement of diesel oil by natural gas in thermoelectric generation. Natural gas emits 75% less pollutants to the environment than diesel and has a similar energetic efficiency. As a strategy for this replacement to occur safely, the computational modeling was developed in a Bioinspired Computing methodology, called Genetic Algorithm (GA). The GA incorporated all the variables of the electricity and natural gas networks, presented in the mathematical modeling. The result was a significant reduction in the level of pollutants emitted, with high stability in the electrical power system.
  • 关键词:Power generation;Level of pollutants;Natural gas;Genetic Algorithm.
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