Mathematical modeling and through artificial neural networks of the Spirulina sp. growth in a photobioreactor with fluorescent light source and solid state lighting
Descripción del Articulo
The Gompertz and logistic mathematical models in the Spirulina sp. growth kinetics were evaluated and were compared with a modeling through Backpropagation Artificial Neural Networks (BP- ANN). Spirulina was cultivated in a (3 L/min) of 500 mL aerated laboratory photobioreactor with 40W fluorescent...
| Autores: | , , , |
|---|---|
| Formato: | artículo |
| Fecha de Publicación: | 2013 |
| Institución: | Universidad Nacional de Trujillo |
| Repositorio: | Revista UNITRU - Scientia Agropecuaria |
| Lenguaje: | español |
| OAI Identifier: | oai:ojs.revistas.unitru.edu.pe:article/333 |
| Enlace del recurso: | http://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/333 |
| Nivel de acceso: | acceso abierto |
| Materia: | Gompertz model logistic model Spirulina lighting on solid state-LED Modelo de Gompertz Modelo logístico iluminación en estado sólido-LED |
| id |
2411-1783_85665b979afac871d87086d636f566bb |
|---|---|
| oai_identifier_str |
oai:ojs.revistas.unitru.edu.pe:article/333 |
| network_acronym_str |
2411-1783 |
| repository_id_str |
. |
| network_name_str |
Revista UNITRU - Scientia Agropecuaria |
| spelling |
Mathematical modeling and through artificial neural networks of the Spirulina sp. growth in a photobioreactor with fluorescent light source and solid state lightingModelamiento matemático y por redes neuronales artificiales del crecimiento de Spirulina sp. en fotobiorreactor con fuente de luz fluorescente e iluminación en estado sólidoVásquez-Villalobos, VíctorArtega Gutiérrez, PaolaChanamé Acevedo, KattiaEsquivel Torres, AnaGompertz modellogistic modelSpirulinalighting on solid state-LEDModelo de GompertzModelo logísticoSpirulinailuminación en estado sólido-LEDThe Gompertz and logistic mathematical models in the Spirulina sp. growth kinetics were evaluated and were compared with a modeling through Backpropagation Artificial Neural Networks (BP- ANN). Spirulina was cultivated in a (3 L/min) of 500 mL aerated laboratory photobioreactor with 40W fluorescent lighting and 1W lighting Solid State (LED-Light Emitting Diode) obtaining 11.0 klx lighting with both systems. The LED lighting allowed to obtain a (ɑ) 0.90 high biomass value compared with that one obtained with fluorescent lighting of 0.82, as well as a greater growth rate μ=0.63 h-1 preceded by a shorter latency time λ = 0.34 h. The BP-ANN showed a good accuracy with respect to the Gompertz I corrected model for both the Spirulina sp cultivation case with fluorescent lighting and with LED displaying correlation coefficients (R) of the 0.993 and 0.994 order respectively, with regard to the experimental data. Spirulina modeling through the Gompertz I corrected model is advantageous because besides showing R 0.987 and 0.990 values in Spirulina sp. cultures with fluorescent lighting and with LED respectively, it allows to attain the growth parameters kinetics directly.Se evaluaron los modelos matemáticos de Gompertz y logístico en la cinética de crecimiento de Spirulina sp., los cuales fueron comparados con un modelamiento por Redes Neuronales Artificiales Backpropagation (RNA-BP). La Spirulina fue cultivada en un fotobiorreactor de laboratorio aireado (3 L/min) de 500 mL, con iluminación fluorescente de 40W y en Estado Sólido (LED-Light Emitting Diode ) de 1W; obteniendo con ambos sistemas 11,0 klx. La iluminación LED, permitió obtener un valor elevado de biomasa (ɑ) de 0,90 , en comparación con la obtenida con iluminación fluorescente de 0,82; así como una mayor velocidad de crecimiento μ=0,63 h-1 , precedida de un menor tiempo de latencia λ=0,34 h. La RNA-BP mostró buena precisión con respecto al modelo corregido de Gompertz I, tanto para el caso del cultivo de Spirulina sp. con iluminación fluorescente y con LED, mostrando coeficientes de correlación (R) del orden de 0,993 y 0,994 respectivamente, con respecto a los datos experimentales. Resulta ventajoso el modelamiento a través del modelo corregido de Gompertz I, porque además de valores de R de 0,987 y 0,990 en los cultivos de Spirulina sp. Con iluminación fluorescente y con LED respectivamente, permite obtener los parámetros de la cinética de crecimiento de manera directa.Universidad Nacional de Trujillo2013-10-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/33310.17268/sci.agropecu.2013.03.06Scientia Agropecuaria; Vol. 4 No. 3 (2013): July - September; 199 - 209Scientia Agropecuaria; Vol. 4 Núm. 3 (2013): Julio - Setiembre; 199 - 2092306-67412077-9917reponame:Revista UNITRU - Scientia Agropecuariainstname:Universidad Nacional de Trujilloinstacron:UNITRUspahttp://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/333/311Derechos de autor 2013 Scientia Agropecuariainfo:eu-repo/semantics/openAccess2021-06-01T15:35:14Zmail@mail.com - |
| dc.title.none.fl_str_mv |
Mathematical modeling and through artificial neural networks of the Spirulina sp. growth in a photobioreactor with fluorescent light source and solid state lighting Modelamiento matemático y por redes neuronales artificiales del crecimiento de Spirulina sp. en fotobiorreactor con fuente de luz fluorescente e iluminación en estado sólido |
| title |
Mathematical modeling and through artificial neural networks of the Spirulina sp. growth in a photobioreactor with fluorescent light source and solid state lighting |
| spellingShingle |
Mathematical modeling and through artificial neural networks of the Spirulina sp. growth in a photobioreactor with fluorescent light source and solid state lighting Vásquez-Villalobos, Víctor Gompertz model logistic model Spirulina lighting on solid state-LED Modelo de Gompertz Modelo logístico Spirulina iluminación en estado sólido-LED |
| title_short |
Mathematical modeling and through artificial neural networks of the Spirulina sp. growth in a photobioreactor with fluorescent light source and solid state lighting |
| title_full |
Mathematical modeling and through artificial neural networks of the Spirulina sp. growth in a photobioreactor with fluorescent light source and solid state lighting |
| title_fullStr |
Mathematical modeling and through artificial neural networks of the Spirulina sp. growth in a photobioreactor with fluorescent light source and solid state lighting |
| title_full_unstemmed |
Mathematical modeling and through artificial neural networks of the Spirulina sp. growth in a photobioreactor with fluorescent light source and solid state lighting |
| title_sort |
Mathematical modeling and through artificial neural networks of the Spirulina sp. growth in a photobioreactor with fluorescent light source and solid state lighting |
| dc.creator.none.fl_str_mv |
Vásquez-Villalobos, Víctor Artega Gutiérrez, Paola Chanamé Acevedo, Kattia Esquivel Torres, Ana |
| author |
Vásquez-Villalobos, Víctor |
| author_facet |
Vásquez-Villalobos, Víctor Artega Gutiérrez, Paola Chanamé Acevedo, Kattia Esquivel Torres, Ana |
| author_role |
author |
| author2 |
Artega Gutiérrez, Paola Chanamé Acevedo, Kattia Esquivel Torres, Ana |
| author2_role |
author author author |
| dc.subject.none.fl_str_mv |
Gompertz model logistic model Spirulina lighting on solid state-LED Modelo de Gompertz Modelo logístico Spirulina iluminación en estado sólido-LED |
| topic |
Gompertz model logistic model Spirulina lighting on solid state-LED Modelo de Gompertz Modelo logístico Spirulina iluminación en estado sólido-LED |
| dc.description.none.fl_txt_mv |
The Gompertz and logistic mathematical models in the Spirulina sp. growth kinetics were evaluated and were compared with a modeling through Backpropagation Artificial Neural Networks (BP- ANN). Spirulina was cultivated in a (3 L/min) of 500 mL aerated laboratory photobioreactor with 40W fluorescent lighting and 1W lighting Solid State (LED-Light Emitting Diode) obtaining 11.0 klx lighting with both systems. The LED lighting allowed to obtain a (ɑ) 0.90 high biomass value compared with that one obtained with fluorescent lighting of 0.82, as well as a greater growth rate μ=0.63 h-1 preceded by a shorter latency time λ = 0.34 h. The BP-ANN showed a good accuracy with respect to the Gompertz I corrected model for both the Spirulina sp cultivation case with fluorescent lighting and with LED displaying correlation coefficients (R) of the 0.993 and 0.994 order respectively, with regard to the experimental data. Spirulina modeling through the Gompertz I corrected model is advantageous because besides showing R 0.987 and 0.990 values in Spirulina sp. cultures with fluorescent lighting and with LED respectively, it allows to attain the growth parameters kinetics directly. Se evaluaron los modelos matemáticos de Gompertz y logístico en la cinética de crecimiento de Spirulina sp., los cuales fueron comparados con un modelamiento por Redes Neuronales Artificiales Backpropagation (RNA-BP). La Spirulina fue cultivada en un fotobiorreactor de laboratorio aireado (3 L/min) de 500 mL, con iluminación fluorescente de 40W y en Estado Sólido (LED-Light Emitting Diode ) de 1W; obteniendo con ambos sistemas 11,0 klx. La iluminación LED, permitió obtener un valor elevado de biomasa (ɑ) de 0,90 , en comparación con la obtenida con iluminación fluorescente de 0,82; así como una mayor velocidad de crecimiento μ=0,63 h-1 , precedida de un menor tiempo de latencia λ=0,34 h. La RNA-BP mostró buena precisión con respecto al modelo corregido de Gompertz I, tanto para el caso del cultivo de Spirulina sp. con iluminación fluorescente y con LED, mostrando coeficientes de correlación (R) del orden de 0,993 y 0,994 respectivamente, con respecto a los datos experimentales. Resulta ventajoso el modelamiento a través del modelo corregido de Gompertz I, porque además de valores de R de 0,987 y 0,990 en los cultivos de Spirulina sp. Con iluminación fluorescente y con LED respectivamente, permite obtener los parámetros de la cinética de crecimiento de manera directa. |
| description |
The Gompertz and logistic mathematical models in the Spirulina sp. growth kinetics were evaluated and were compared with a modeling through Backpropagation Artificial Neural Networks (BP- ANN). Spirulina was cultivated in a (3 L/min) of 500 mL aerated laboratory photobioreactor with 40W fluorescent lighting and 1W lighting Solid State (LED-Light Emitting Diode) obtaining 11.0 klx lighting with both systems. The LED lighting allowed to obtain a (ɑ) 0.90 high biomass value compared with that one obtained with fluorescent lighting of 0.82, as well as a greater growth rate μ=0.63 h-1 preceded by a shorter latency time λ = 0.34 h. The BP-ANN showed a good accuracy with respect to the Gompertz I corrected model for both the Spirulina sp cultivation case with fluorescent lighting and with LED displaying correlation coefficients (R) of the 0.993 and 0.994 order respectively, with regard to the experimental data. Spirulina modeling through the Gompertz I corrected model is advantageous because besides showing R 0.987 and 0.990 values in Spirulina sp. cultures with fluorescent lighting and with LED respectively, it allows to attain the growth parameters kinetics directly. |
| publishDate |
2013 |
| dc.date.none.fl_str_mv |
2013-10-09 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/333 10.17268/sci.agropecu.2013.03.06 |
| url |
http://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/333 |
| identifier_str_mv |
10.17268/sci.agropecu.2013.03.06 |
| dc.language.none.fl_str_mv |
spa |
| language |
spa |
| dc.relation.none.fl_str_mv |
http://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/333/311 |
| dc.rights.none.fl_str_mv |
Derechos de autor 2013 Scientia Agropecuaria info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Derechos de autor 2013 Scientia Agropecuaria |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Universidad Nacional de Trujillo |
| publisher.none.fl_str_mv |
Universidad Nacional de Trujillo |
| dc.source.none.fl_str_mv |
Scientia Agropecuaria; Vol. 4 No. 3 (2013): July - September; 199 - 209 Scientia Agropecuaria; Vol. 4 Núm. 3 (2013): Julio - Setiembre; 199 - 209 2306-6741 2077-9917 reponame:Revista UNITRU - Scientia Agropecuaria instname:Universidad Nacional de Trujillo instacron:UNITRU |
| reponame_str |
Revista UNITRU - Scientia Agropecuaria |
| collection |
Revista UNITRU - Scientia Agropecuaria |
| instname_str |
Universidad Nacional de Trujillo |
| instacron_str |
UNITRU |
| institution |
UNITRU |
| repository.name.fl_str_mv |
-
|
| repository.mail.fl_str_mv |
mail@mail.com |
| _version_ |
1701379321299468288 |
| score |
13.945474 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).