Publicado

2022-04-26

Dynamic Modeling Of The Energy Returned On Invested

Modelado dinámico de la Tasa de Retorno Energético

DOI:

https://doi.org/10.15446/dyna.v89n221.97965

Palabras clave:

primary source of renewable generation; life cycle analysis; energy return on investment. (en)
fuentes primarias de generación renovable; análisis de ciclo de vida; tasa de retorno energético. (es)

Autores/as

This work was developed to present a conceptual and preliminary analysis of the concepts and criteria for estimating the Energy Return on Investment (EROI). In this work, methods based on monetary studies, Life Cycle Analysis (LCA) were discussed and a dynamical systems modeling was proposed. In this respect, we made a mathematical development, defining the state and auxiliary variables and the adjustment parameters necessary to study the problem. Some criteria and influencing factors were defined, in the medium and long term, the sustainability of the energy system and seek to incorporate them into relevant areas of discussion and education, encouraging their dissemination and reviews. It is sought to discuss the issues and considerations for a standardized methodology that allows comparisons and decision-making, in order to minimize environmental impact.

Este trabajo fue desarrollado para presentar un análisis conceptual y preliminar de los conceptos y criterios para estimar la Tasa de Retorno Energética (EROI). En este trabajo se discutieron métodos basados ​​en estudios monetarios, Análisis de Ciclo de Vida (LCA) y se propuso un modelado de sistemas dinámicos. Al respecto, realizamos un desarrollo matemático, definiendo el estado y las variables auxiliares y los parámetros de ajuste necesarios para estudiar el problema. Se definieron algunos criterios y factores que inciden, en el mediano y largo plazo, en la sustentabilidad del sistema energético y buscan incorporarlos en áreas relevantes de discusión y educación, incentivando su difusión y revisión. Se busca discutir los temas y consideraciones para una metodología estandarizada que permita comparaciones y toma de decisiones, con el fin de minimizar el impacto ambiental.

Referencias

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Camargo, F.G. y Schweickardt, G.A., Estimación de la tasa de retorno energético: análisis comparativo de las metodologías disponibles en la actualidad. Maskana, [en línea]. 5, pp. 65-73, 2014. Disponible en: https://publicaciones.ucuenca.edu.ec/ojs/index.php/maskana/article/view/575

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Cómo citar

IEEE

[1]
F. G. Camargo, «Dynamic Modeling Of The Energy Returned On Invested», DYNA, vol. 89, n.º 221, pp. 50–59, abr. 2022.

ACM

[1]
Camargo, F.G. 2022. Dynamic Modeling Of The Energy Returned On Invested. DYNA. 89, 221 (abr. 2022), 50–59. DOI:https://doi.org/10.15446/dyna.v89n221.97965.

ACS

(1)
Camargo, F. G. Dynamic Modeling Of The Energy Returned On Invested. DYNA 2022, 89, 50-59.

APA

Camargo, F. G. (2022). Dynamic Modeling Of The Energy Returned On Invested. DYNA, 89(221), 50–59. https://doi.org/10.15446/dyna.v89n221.97965

ABNT

CAMARGO, F. G. Dynamic Modeling Of The Energy Returned On Invested. DYNA, [S. l.], v. 89, n. 221, p. 50–59, 2022. DOI: 10.15446/dyna.v89n221.97965. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/97965. Acesso em: 15 mar. 2026.

Chicago

Camargo, Federico Gabriel. 2022. «Dynamic Modeling Of The Energy Returned On Invested». DYNA 89 (221):50-59. https://doi.org/10.15446/dyna.v89n221.97965.

Harvard

Camargo, F. G. (2022) «Dynamic Modeling Of The Energy Returned On Invested», DYNA, 89(221), pp. 50–59. doi: 10.15446/dyna.v89n221.97965.

MLA

Camargo, F. G. «Dynamic Modeling Of The Energy Returned On Invested». DYNA, vol. 89, n.º 221, abril de 2022, pp. 50-59, doi:10.15446/dyna.v89n221.97965.

Turabian

Camargo, Federico Gabriel. «Dynamic Modeling Of The Energy Returned On Invested». DYNA 89, no. 221 (abril 22, 2022): 50–59. Accedido marzo 15, 2026. https://revistas.unal.edu.co/index.php/dyna/article/view/97965.

Vancouver

1.
Camargo FG. Dynamic Modeling Of The Energy Returned On Invested. DYNA [Internet]. 22 de abril de 2022 [citado 15 de marzo de 2026];89(221):50-9. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/97965

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CrossRef citations2

1. Federico Gabriel Camargo, Francisco Guido Rossomando, Daniel Ceferino Gandolfo, Esteban Antonio Sarroca, Omar Roberto Faure, Eduardo Andrés Pérez. (2024). A novel methodology to obtain optimal economic indicators based on the Argentinean production chain under uncertainty. Production, 34 https://doi.org/10.1590/0103-6513.20230091.

2. Federico Gabriel Camargo. (2023). A hybrid novel method to economically evaluate the carbon dioxide emissions in the productive chain of Argentina. Production, 33 https://doi.org/10.1590/0103-6513.20220053.

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