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Determinación del potencial energético del pigüe (Piptocoma Discolor) en la amazonía ecuatoriana.

Determination of the pigüe (Piptocoma Discolor) energetic potential in the Ecuadorian Amazon.

 <= /p>

Juan Elías González.[1],C= ampo Morillo Robles.[2], Janneth García[3], Janeth Cárdenas[4], & Deny Oliva5=

 

Recibido: 29-10-2018 / Revisado: 1-12= -2018 /Aceptado: 20-12-2018/ Publicado: 05-01-2019

 =

= ABSTRACT                      = DOI: <= /span>https://doi.org/10.33262/cienciadigital.v3i1.287 <= /span>

Three methods = were used to know the real value of caloric power, considering the margins of er= rors that are committed at the moment of calculating it, either by the method of bromatological analysis, or proximal. For the bromatological analysis, and = as a standard the calorimetric pump, with results of 19.66% error, with a reliability margin of 80.34%. It was therefore important to apply the three methods and correct the errors using mathematical models. The analysis of t= he fiber was taken into account to readjust the calorimetric formula and the constant to obtain reliable values ​​of 98.7% and with a margin= of error of 1.3%. According to the theory of real errors, in relation to the data obtained from the calorimeter was 19.07 (MJ / kg) as energetic potential of= the Pigüe, the other method was the proximal analysis, obtaining a margin of er= ror superior of 5,19 % and with a reliability of 94.81%. By means of a mathemat= ical modeling for fixed and volatile carbon, where the constant was adjusted and adjusted through the integral formula, we obtained 99.9% reliable results for the calorific value in wood biomass for energetic purposes for the Pigüe (Pollalesta<= /i> discolor) in the Amazon region of Ecuador.

Keywords: Caloric power, biomass, Pigüe and energy.

RESUMEN

Se utilizaron tres métodos para conocer el valor real del poder calórico, considerando los márgenes de erro= res que se cometen al momento de calcularlo, ya sea mediante el método de análi= sis bromatológico, o proximal. Para el análisis bromatológico, y como patrón la bomba calorimétrica, con resultados del 19,66% de error, con un margen de confiabilidad del 80,34%.  Por ello= fue importante aplicar los tres métodos y corregir los errores mediante modelos matemáticos.  Se tomó en cuenta el análisis de la fibra para reajustar la fórmula calorimétrica y la constante para lograr valores confiables del 98,7% y con un margen de error del 1,3%.  Según la teoría de los error= es reales, en relación a los datos obtenidos del calorímetro fue de 19,07 (MJ/= kg) como potencial energético del Pigüe, el otro método fue el análisis proxima= l, obteniendo un margen de error superior de 5,19% y con una confiabilidad del 94,81%. Mediante modelación matemática planteado para carbonos fijos y volátiles, donde fue modificada la constante y ajustada a través de la fórm= ula de integrales, se obtuvieron resultados confiables del 99,9% para el poder calorífico en biomasas maderables con fines energéticos para el Pigüe (Pollalesta discolor) en la región Amazónica del Ecuador.

<= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",se= rif'>Palabras clave: Poder calórico, biomasa, Pigüe y energía.

 

INTRODUCCIÓN

La madera como combustible se remonta desde la prehistoria del hombre, donde descubrió el fuego que servía para usos domésticos. Sin embargo, en la actualidad los altos costos del petróleo han obligado a muchos países a buscar la sustitución de las fuentes energéticas= de materiales lignocelulósicos, del recurso maderable para la producción de energía (Manuel & Cas= tillo, 2015).

La biomasa vegetal es una alternativa para la producción de energía a nivel mundial. La conversión de los residuos en forma de energía requiere la aplicación tecnológica acorde a las fuentes renovables de energía a través = de procesos bioquímicos, térmicos y físico químicos para los usos de generación eléctrica (Patiño Martínez, 2014)

Las alternativas para calcular el potencial energético, como el método bromatológico,  para las fuentes renovables de energía, en la determinación del poder calorífico de las siguientes biomasas vegetales:  cas= cara, fibra, almendras, afrechos, cascarilla de café y bagazo de caña, en lo referente al poder calorífico utilizaron una bomba calorimétrica, según la Norma DIN 51900 (Gómez Alexander, Rincón, & Wiest, 2004),  

Para el análisis del poder calórico superior del carbón en once especies foresta= les según (Rivera & Uceda, 2004), utilizaron un calorímetro adiabático, este fue el método empleado con frecuencia para la determinación del calor en la combustión de un material orgánico de las especies forestales Peruanas.  El e= studio de comparación de la biomasa peletizada, en el cual destaca la relación ent= re la humedad y el poder calorífico, según la necesidad del sistema de pretratamiento de secado de acuerdo con la tecnología de transformación energética utilizada (Pantoja-Bucheli & Guerrero-Ordoñez, oct. 2= 016).

La evaluación del poder calorífico superior de la biomasa vegetal, mediante la bomba calorimétrica, presentó problemas de calibración, por este motivo se utilizó soluciones químicas.  Las pastillas de la muestra deben contener un peso menor a un gramo, se debe conocer las correcciones con azufre y ácido nítrico, este trabajo se basa e= n el estudio factorial, que provocan las variaciones y la determinación de valor exacto del poder calorífico superior en muestras vegetales para su valoraci= ón (Suare= z, Castro, & Maseda, 1999).

Otro método a través de la composición química del porcentaje del material volát= il y carbono fijo que aplicaron las ecuaciones para la determinación del valor calórico quesestá basado en valores del análisis próximo (Márquez-Montesino, Carbollo-Abreu, Álvarez-Olivera, & Castillo-Ramos, 2009).

El método que han utilizado otros investigadores en cuatro especies forestales= , Acacia mearnsii De Willd., Euc= alyptus grandis Hill, Mimosa scabrella<= /i> Benth. y Ateleia glazioviana Ba= ill; para el ajuste del poder calorífico superior (PCS) de la madera, utilizamos ecuaciones de regresión, en función de su contenido de material volátil (CM= V), cenizas (CC), carbono (CCF) y materia orgánica (CMO). Porque presenta coeficientes de determinación, de ajustes superiores al 82%, el error están= dar de estimación 1.1% y distribución adecuada de residuos. Las ecuaciones que implican CMV para A. mearnsii, CC para E. grandis y A. glazioviana, y los elementos del análisis proximal para M. scabrella, son tan adecuadas para estimar el PCS como las ecuaciones que incluyen más variables independiente= s (Silva1, y otros, 2014) .

En vista que en la actualidad pocas son las instituciones de educación superior que cuenta con equipos especializados para análisis calorimétricos de las b= iomasas forestales, fundamentalmente para la determinación del potencial energético= , esto conlleva a que se busque alternativas de métodos matemáticos que permitan la determinación del poder calórico de las especies forestales, sin embargo la mayor parte de estas instituciones cuenta con laboratorios de bromatología = para análisis de alimentos, y cometen errores aproximados entre el 15 y el 17,52= %, (Fonseca, = 2011).

Según lo expuesto anteriormente se consideró la investigación del Pigüe, a través= de la determinación de su potencial energético, aplicar los tres métodos experimentales. El primero con bromatología de alimentos, segundo con análi= sis calorimetría proximal y el tercero con la bomba calorimétrica para evaluar = las diferencias y llegar al valor real del calorímetro como equipo especializado para este propósito, en base a todos estos elementos se plantearán la teorí= a de errores propia y del cálculo diferencial para especies forestales.

Metodología.

Área de estudio y diseño experimental

Se utilizó el muestreo por estratificación o sistemático, que permitió clasificar en unidades homogéne= as las áreas del bosque de Pigüe, y reducir la variabilidad en los árboles. Y = fue seleccionado las siguientes zonas de vegetación de Pigüe: el Cantón Pastaza= , el bosque secundario de la Parroquia Rural de Canelos; el Cantón Mera, el bosq= ue de la Parroquia Shell; en el Cantón Arajuno, el bosque de la zona norte de = este sector; y del Cantón Santa Clara, el bosque de la comunidad Shuar.

 

 

 

 

 

 

 

 

 

 

Figura 1.  Ubicación de las fincas donde se realiz= ó el levantamiento de los datos de campo y características meteorológicas de los Cantones estudiados Fuente: Instituto Nacional de Meteorología e Hidrología, Quito – Ecuador 2015.

 

El tamaño de las parcelas se real= izó de acuerdo con el principio de (Akca, 1993), en donde contaban con 15 y 20 árboles (Murillo, 2004). Y su densidad de producción, o la intensidad del muestreo, fue de cuatro parcelas por hectárea, con un área de 400 m2 (Spitler, 1995).

 

Los materiales y elementos utilizados, fue un mapa, para la ubicación de las áreas y linderos, mediant= e el GPS, para cartografía y cuantificación de los árboles en las parcelas. El estudio experimental, se realizó usando criterios de diámetros y categoriza= ción de la materia prima, empleando la siguiente clasificación 3 árboles por categoría: primera (10 -15 cm), segunda (16 – 21 cm), tercera (22 – 27 cm) y cuarta (28 – 33 cm) y con subtotal de 12 árboles por cantón dando un total = de 48 árboles con características morfológicas semejantes, comprendidas entre = esos diámetros. Esta selección se realizó según la Norma TAPPI – cm-85 de 1985. =

 

Para los análisis respectivos de las muestras fueron llevados a los siguientes Laboratorio de Bromatología, perteneciente a la Universidad Estatal Amazóni= ca UEA, en la Provincia de Pastaza. Km 2½, vía Napo (paso lateral).=

Determinación del potencial energético

 

Los materiales y métodos utilizados fueron de acuerdo a las siguientes métodos y Normas: para el determinar el potencial energético

1.1.      = Brom= atológico

 

Granulometría         =    UNE-CEN/TS. 15149-2 EX

Humedad                    NTE INEN 896=

Cenizas                       NTE INEN 520.<= /o:p>

Proteína bruta    =          AOAC 19th 954.01

Grasa             =              AOAC 19th 920.39

Fibra                           NTE INEN 542.

1.2.      = Calo= rimetría y proximal

 

Estos análisis se realizaron en los laboratorios de la Universidad Estatal de Bol= ívar localizada en el cantón Guaranda Provincia Bolívar-Ecuador.

Calorímetro Automático Isoperibólico 6400, que cuenta con un subsistema de enfriamiento= de circuito cerrado y la más alta automatización en colorimetría según (Parr, 201= 6). Fue según la = Norma 14918: 2009.  Y el análisis proximal para.

Cenizas                       Norma 14775: 2= 009

Volátiles                     Norma UNE-EN 15148

Carbono fijo               Por diferencia de cálculo.

2.      =        Análisis datos

 

Previo análisis estadístico se procedió a la utilización de las siguientes fórmulas matemáticas para:

2.1.      = El a= nálisis bromatológico

 

Carbohidratos totales             CT =3D 100 - (H = + C + P + G)                          (1)      

Dónde

CT =3D carbohidratos totales,

H   =3D humedad,

P   =3D proteínas,

G =3D grasa  

C =3D cenizas.

De igual manera se utilizó con el mismo procedimiento para lograr calcular

2.2.      = Poder calorífico

                =                   

      =                                                (2)=

Dónde

A =3D es (4 kcal) (% Carbohidratos Totales);

B =3D es (9 kcal) (% Grasa) y

C =3D es (4kcal) (% Proteína).

El objetivo del calorímetro fue contar con datos reales y directos según algun= os autores es incrementar la exactitud, a través de la calibración usando mate= rial de referencia (González D, Cort= és, & Sánchez Rodríguez, 2010).

 

 

 

 

Imagen 1. Del calorímetro 6400 Fuente: .

Sin embargo, nuestro propósito fue comparar, analizar y corregir los errores que cometieron, las Universidades como: La Escuela Superior Politécnica de Chim= borazo, Escuela Superior Politécnica Nacional del Ecuador y la Universidad Técnica = de Ambato, en sus investigaciones de las biomasas forestales o vegetales. utilizando como patrón el calorímetro donde se propone reajustar los modelos matemáticos y las constantes para llegar al valor real del potencial calóri= co de las especies forestales.

Resultados y Discusión.

Los resultados aplicados para el ajuste de los errores calorimétricos de las biomasas forestales, a través de los modelos matemáticos en relación a los = análisis bromatológicos, proximal y de la bomba calorimétrica para el cálculo real d= el potencial calórico del Pigüe, se valoran a continuación.<= /p>

Análisis bromatológico=

Tabla 1. Calculo de carbohidratos totales de las biomasas forestales en las Universi= dades

 

Universid= ades

%

% Humedad=

%Proteína=

% Grasa

% Cenizas=

Carbohidr= atos Totales

ESPOCH

100<= /o:p>

9,1<= /o:p>

0,79=

0,52=

0,51=

89,08

EPN<= /o:p>

100<= /o:p>

8,41=

2,59=

0,2<= /o:p>

14,83

73,97

UTA<= /o:p>

100<= /o:p>

9,68=

1,54=

2,13=

1,53=

85,12

UEA<= /o:p>

100<= /o:p>

12

1,43=

0,16=

0,96=

85,45

            Elaborado por: Grupo de Investigación.

Los datos porcentuales físico-químicos del análisis bromatológico tomados de las tres universidades y el estudio del aserrín de Pigüe realizados en la UEA. = Son semejantes a las biomasas con fines energéticos mostrados por (Echeverria= Cruz & López Mena, 2010). Igual caso se observa en otra investigación (Tirado Jijó= n, 2015).

 

Tabla 2. Composición química de la biomasa y reajuste en la misma de la biomasa.

Muestras

Humedad (%)

Cenizas (%)

Fibra (%)

Proteína (%)

Grasa (%)

Carbohidratos Totales (%)

Universidades

Aserrín

9,10

0,51

61,66

0,79

0,52

89,08

ESPOCH

Cascarilla de arroz

8,41

14,83

32,02

2,59

0,20

73,79

EPN

Briquetas

9,68

1,53

48,33

1,54

2,13

85,12

UTA

Pellets de Pigüe<= /p>

12,00

0,96

75,18

1,43

0,16

85,45

UEA

Elaborado por: Grupo de Investigación.

 

Tabla 3. Calculo del potencial calórico de las biomasas normales hasta la actualidad= .

CÓDIGO =3D

#. Constante<= /p>

Unidad

% composición=

ESPOCH

EPN

UTA

UEA

 

 

 

 

 

 

 

 

A =3D

4

kcal

Carbohidratos T

89,08

73,97

85,12

85,45

B =3D

9

kcal

Grasa

0,52

0,20

2,13

0,16

C =3D

4

kcal

Proteína

0,79

2,59

1,54

1,43

Poder calorífico (Kcal/100g)  =3D

364,16

308,04

365,81

348,96

Multiplica por 1000 g que tiene un = kg =3D Kcal/kg

3641,60

3080,4

3658,10

3489,60

Poder calorífico (MJ/kg)  =3D

15,24

12,89

15,310

14,60

Elaborado por: Grupo de Investigación.

Las técnicas de alimentos utiliza= dos en los laboratorios de las Universidades Ecuatorianas (Echeverria Cruz & = López Mena, 2010). Igual caso de (Tirado Jij= ón, 2015). Muestran deficientes valores energéticos para acercarse al valor real, al no utilizar toda la composición química.

 

Tabla 4. Calculo calórico de biomasas reajustadas para un futuro análisis de biomasa= s.

CÓDIGO

Constante

Unidad

% composición=

ESPOCH

EPN

UTA

UEA

A =3D

4

kcal

Carbohidratos T

89,08

73,97

85,12

85,45

B =3D

9

kcal

Grasa

0,52

0,20

2,13

0,16

C =3D

4

kcal

Proteína

0,79

2,59

1,54

1,43

D=3D

1,5

Kcal

Fibra

61,66

32,02

51,33

75,18

Poder calorífico (Kcal/100g)  =3D

456,65

356,07

442,805

461,73

Multiplica por 1000 g que tiene un = kg =3D Kcal/kg

4566,50

3560,70

4428,05

4617,3

Poder calorífico (MJ/kg)  =3D

19,11

14,90

18,53

19,32

Elaborado por: Grupo de Investigación.

 

En esta tabla se logró incorporar= la constante y el porcentaje de fibra en su composición, propuesto por la FAO (Food and Nutrition paper 77: Food energy methods of analysis and conversion factors, 2003) como se encuentra en él (Foroantiguo, 2015), para el caso = de la combustión como manifiestan  (Martínez M, 2017), el cual se to= mó en consideración una constante 1,5 kcal/100gr, para llegar al valor aproximado= del calorímetro (Parr, 201= 6). 

 

Análisis proximal

Tabla de datos de las diferentes biomasas y productos forestales de las Universid= ades Ecuatorianas, que permitirá calcular el valor calorífico, para realizar la corrección de los errores de este tipo de análisis.

Tabla 5. Promedios de análisis proximal de las biomasas y productos forestales

PARAMETRO= S

ESPOCH

EPN<= /o:p>

UTA<= /o:p>

UEA<= /o:p>

Briquetas= de Aserrín

Cascarill= a de arroz

Briquetas= de Nuez

Pellets de Pigüe

% Volátil= es

90,03

57,09

Ninguno

84,98

% Carbono=

0,36=

16,11

14,46

% Cenizas=

0,51=

19,39

0,96=

 

Elaborado por: Grupo de Investigación.

 

Para este cálculo se consideró la ecuación del contenido de volátiles y carbono fijo planteadas por (Márquez-Montesino, Cordero Alcántara, Rodríguez-Mirasol, & Rdríguez Jiménez , 2001). Como se demue= stra en la siguiente tabla del análisis realizado en el caso de nuestro estudio.=

 

Tabla 6. Promedio del potencial calórico de las biomasas y productos forestales 

Muestras

Análisis Bromatológico

Análisis Bomba Calorimétrica

Análisis último o Próximo

Universid= ades

Briquetas de aserrín de madera

15,24

Ninguno

17,52

ESPOCH

Cascarilla de arroz

12,04

12,70

15,58

EPN<= /o:p>

Briquetas de nuez

15,31

Ninguno

Ninguno

UTA<= /o:p>

Pellets de Pigüe

19,32

19,07

20,06

UEA<= /o:p>

         =    Elaborado por: Grupo de Investigación.

Determinación del error experimen= tal en el potencial calórico de la biomasa

En esta tabla todavía persiste % de confiabilidad y del margen de error, del análisis bromatológico y proximal.   Sin embargo, aún persiste el error, por todas estas razones descritas para el análisis del potencial energético en biomasa o productos forestales. <= /o:p>

Tabla 7. Porciento de confiabilidad y margen de error que se cometen

Muestras<= o:p>

Análisis Bromatológico

Análisis Bomba Calorimétrica

Análisis = último o Próximo

Universid= ades

Briquetas= de aserrín de madera

15,24

Ninguno

15,98

ESPOCH

% de confiabilidad

100<= /o:p>

95,369211= 51

% de marg= en de error

100<= /o:p>

4,6307884= 86

Cascarill= a de arroz

12,04

12,7=

15,58

EPN<= /o:p>

% de confiabilidad

100<= /o:p>

94,803149= 61

122,67716= 54

% de marg= en de error

100<= /o:p>

5,1968503= 94

22,677165= 35

Pellets de Pigüe

19,32

19,07

20,06

UEA<= /o:p>

% de confiabilidad

100<= /o:p>

101,31095= 96

94,808599= 9

% de marg= en de error

100<= /o:p>

-1,310959= 622

5,1914001= 05

           

Elaborado por: Grupo de Investigación.

Tabla 8. Errores corregidos mediante los modelos matemáticos

 

Tipo de muestras

Análisis Bromatológico

Análisis Bomba Calorimétrica

Análisis último o Próximo

Universid= ades

Briquetas= de aserrín de madera

19,11

Ninguno

17,52

ESPOCH

Cascarill= a de arroz

12,70

12,7=

12,7=

EPN<= /o:p>

Pellets de Pigüe Corregido el error

19,07

19,07

19,07

UEA<= /o:p>

 

Elaborado por: Grupo de Investigación.

En la tabla 8, se puede observar las correcciones demostradas con los modelos matemáticos propuestos para las correcciones de los errores y que se plante= ados a partir de toda la información donde fue demostrado los errores que se com= eten a la hora del cálculo del valor energético de las biomasas forestales.

Modelos Matemáticos propuestos pa= ra la corrección de errores

Aplicando la teoría de ecuaciones para la composición Bromatológica

Donde se incorpora el % fibra y su constante 1,5 (Kcal/100g), en la fórmula del cálculo para el valor calórico.  + F                       (3)

Aplicando la teoría de errores pa= ra bromatología

Error absoluto<= /b>

Los valores de las magnitudes y las diferencias que existe, entre el valor real medido, con el obtenido en el valor calórico en MJ/kg, la diferencia de los valores de calorimetría medidos con el error absoluto expresados, las unida= des calóricas. Así X es el valor medido, en los análisis bromatológicos, elemen= tal o según sea el caso X* el valor real de la bomba calorimétrica y ΔX el error instrumental o sensibilidad del aparato medido satisface la relación = en los análisis bromatológicos, frente a la bomba calorimétrica entalpica, que arroja valores exactos por sus calibración y patrones utilizados para la obtención de los resultados de los valores reales.  

<= ![if !msEquation]>       =                                        (4)

Que representa en forma                   

      =                                              (5)

Si bien es cierto el error instrumental conocido, como parámetro básico para la efectividad o aproximación de la medida, fue importante llegar al.

Error relativo<= /b>

Este viene definido como el cociente entre el error absoluto y valor real X* de = la magnitud como se describe a continuación.

      =                                            (6)

Donde x es el valor medido u obtenido utilizando la segunda expresión, como es habitual no conocemos el valor real de la magnitud, la costumbre de expresa= r el error relativo porcentualmente.

      =                                       (7)

Mediante la expresión de la teoría del error real, nos permitió llevar a cabo la corrección del error experimental, en el caso del análisis del poder calóri= co de las biomasas y productos forestales, que están descritas en la tabla 7. = Con un margen de error del más o menos de 1,9%, que sobrepasa a la bomba calorimétrica, el margen de confiabilidad equivale al 98,7% para el caso del estudio de los pellets de Pigüe.

Tomando en cuenta esta información permitió plante= ar la siguiente ecuación.

)     =                              (8)

Donde:

PCR =3D Poder calórico real.

PCB =3D poder calórico bromatológico

ER =3D Promedio del error real experimental

En base al potencial calórico bromatológico, y proximal de los resultados ayudaron ha validar la información obtenida del análisis demostrados por (Echeverria Cruz & = López Mena, 2010). Y (Tirado Jijón, 2015), por esta razó= n fue importante realizar este estudio empleando diferentes métodos, aparte del bromatológico, el método de análisis próximo y el calorimetría por la exact= itud y confiabilidad como manifiestan CITATION Com16 \l 12298 (Parr, 201= 6). 

Calculo Integr= al para análisis próximo.

Los propósitos fueron llegar a una confiabilidad del 99,9%, para los valores calóricos de las biomasas, permit= an diseñar apropiadamente los equipos industriales y de acuerdo a las necesida= des reales de la información se plantea el siguiente cálculo.=

 

      =                           (9)

 

      =                                 (11)

 

      =                                 (12)

 

      =                                              (13)

Donde:

Pc=3D potencial calórico experimental de la bomba entálpica

X=3D constante para carbones fijos de la biomasa según  (Márquez-Montesino, Cordero Alcántara, Rodríguez-Mirasol, & Rdríguez Jiménez , 2001).

Cf=3D % Carbones fijos de la biomasa

V=3D % volátiles

Y=3D factor de corrección para volátiles experimentales 

=3D0,3563 Cf + Y*V                      (14= )

Como se puede observar en la fórmula 14, solo se puede trabajar en los compuestos volátiles de la ecuación y aplicar el factor de corrección respectivo de estos. 

 

Conclusiones.

 

·      =           Los tres métodos utilizados para la determinación del potencial energético del Pigüe, en comparación con otras biomasa y productos de recursos forestales, estudiadas en tres Universidades del Ecuador, permitió determinar el potenc= ial calórico real de la biomasa forestal.

·      =           La selección de los tres métodos: análisis bromatológico, proximal y el calorímetro, ha permitido diseñar modelos matemáticos planteados para obten= er el valor calórico real.

·      =           La aplicación de las normas y parámetros fueron otros elementos para el diseño= de los modelos matemáticos y la aproximación real, obteniendo una confiabilidad del 99,9 %. según el poder calorífico y la norma ASTM D-240, para combustib= les (referida al peso seco). y lograr una combustión eficiente en la caldera.

·      =           La teoría de errores y calculo diferencial, con estos modelos matemáticos ajustados permitió desarrollar una herramienta matemática para el desarroll= o de otros estudios similares para la determinación del potencial calórico.

 

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=  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

PARA CITAR EL ARTÍCULO INDEXADO.

 

 

González J., Morillo = C., García. J., Cárdenas J., & Oliva. D= . (2019). Determinaci= ón del potencial energético del pigüe (Piptocoma Discolor) en la amazonía ecuatori= ana., = Revista electrónica Ciencia Digital 3(1), 78-97. Recuperado desde: http://cienciadigital.org/revistacienciadigital2/index.php/CienciaDigi= tal/article/view/287/688

 


 

 

 

El artículo que se publica es de exclusiva responsabilidad de los autores y no necesariamente reflejan el pensamiento de la Revista Ciencia Digital.

 

El artículo queda en propiedad de la revista y, por tanto, su publicación parc= ial y/o total en otro medio tiene que ser autorizado por el director de la Revista Ciencia Digital.

 

 

 

 

 



[1] Universidad Estatal Amazónica. Carrera de Agroindustrias, Past= aza, Ecuador, jgonzalez@uea.edu.ec

[2] Universidad Estatal Amazónica. Carrera de Agroindustrial. Pastaza, Ecuador,= cmorillo@uea.edu.ec 

[3] Universidad Estatal Amazónica. Carrera de Agroindustrias. Past= aza, Ecuador, jgarcia@uea.edu.ec

[4] Universidad Estatal Amazónica. Carrera de Agroindustrias. Past= aza, Ecuador, janedur2009@gmail.com

5 Universidad Tecnológica de la Habana CUJAE, La Habana, Cuba. deny@ceter.cujae.edu.cu

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www.cienciadigital.org

                                                 =                                                                       ISSN: 2602-8085

                                                 =                               Vol. 3, N°2, p. 78-97, enero - marzo, 201 9

 

CONOCIMIENTO GLOBAL                                                =                                                 =                         Página 17<= /span> de 18<= /span>

 

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