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Estimación de la Demanda Bioquímica de Oxígeno (DBO5) en aguas residuales de las empresas de jeans de la ciudad de Pelileo utilizando redes neuronales artificiales

 

Estimatio= n of=  the Biochemic= al Oxygen Demand (DBO5), in=  wastewate= r of=  the jeans companies of=  the city<= i> of=  Pelileo using = artificia= l neural networks=

 


Yesenia Esthefa= nia Pillapa Vargas

 

https://orcid.o= rg/0000-0001-9357-7914

Universidad Técnica de Ambato, Facultad de Ciencia e Ingeniería en Alim= entos y Biotecnología. Ambato-Ecuador,&= nbsp;

ypillapa2034@uta.edu.ec  

Manolo Alexander Córdova Suárez

 

[1]  https://orcid.org/0000-0001-6786-7926

 

Universidad Nacional de Chimborazo, Facultad de Ingeniería, Riobamba-Ecuador <= o:p>

manolo.cordova@unach.edu.ec

Daniel Alfonso Cabrera Valle       &nbs= p;            &= nbsp;           

Universidad Técnica = de Ambato, Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Ambato-Ecuador

da.cabrera@uta.edu.ec

 

&nb= sp;

 

Artículo de Investigación Científica y Tecnológica

Enviado: 18/05/2022=

Revisado: 27<= span lang=3DES-TRAD style=3D'font-size:10.0pt;font-family:"Times New Roman",se= rif; mso-ansi-language:ES-TRAD;mso-fareast-language:EN-US'>/06/2022=

Aceptado: 01/07/2022

Publicado:30/07/2022

         &nbs= p;            &= nbsp;        DOI: https://doi.org/10.33262/c= oncienciadigital.v5i3.1.2320

 

 

 

Cítese:

 

 

Pillapa Vargas, Y. E., Córdova Suárez, M. A., & Cabrera Valle, D. A. (2022). Estimació= n de la Demanda Bioquímica de Oxígeno (DBO5) en aguas residuales= de las empresas de jeans de la ciudad de Pelileo utilizando redes neuronales artificiales. ConcienciaDigital, 5(3.1), 406-423. https://d= oi.org/10.33262/concienciadigital.v5i3.1.2320

 

3Deditorial1.png<= !--[if gte vml 1]>

 

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Esta revista está protegida bajo una licencia Creative Commons Attribution Non Commercial No Derivatives 4.0 International. Copia de la licencia: http://creativecommons.org/licenses/b= y-nc-nd/4.0/=

Palabras claves:

DBO5, aguas residuales, inteligencia artificia= l, redes neuronales artificiales, red feed-forward backpropagation, jeans.<= /p>

 

Resumen =

Introducción. Dentro de la inteligencia artificial, el uso de redes neuronales artificiales to= ma cada vez más importancia. Objetivo. Estimar el error de la Demanda Bioquímica de Oxígeno en aguas residuales de las empresas de jeans de la ciudad de Pelileo utilizando redes neuronales. Métodos.  En primer lugar, se desarroll&oa= cute; una base de datos conformada por 6 parámetros físicos, 9 parámetros químicos y una variable de salida. Estas variabl= es se seleccionaron mediante la Norma TUSLA y fueron recopiladas del catastro del GAD Pelileo de los años 2017-2018 y de un laboratorio de análisis de aguas certificado. A continuación, se utilizó el software Matlab con el diseño de la red neuronal artificial feed forward backpropagation con la capa de entrada de = 15 variables. La primera capa oculta tuvo diez neuronas, la segunda capa sumatoria con una neurona y la capa de salida con la variable de respuesta correspondiente a la estimación de la Demanda Bioquímica de Oxígeno con el algoritmo de aprendizaje de Levenberg-Marquardt. Resultados. Se obtuvo valores del coeficiente de correlación, del error estima= do de la red, y de la comparación de la Prueba Tukey, tales como:  0.98081; 0.8890; 0.9833. Estas c= ifras revelan una concordancia entre los valores estimados por la red y los val= ores reales.  Finalmente, los resultados demostraron que la Demanda Bioquímica de Oxígeno= se estimó numéricamente en aguas residuales a través de= los modelos neuronales. Este tipo de modelo de neuronas repre= senta sólo una parte de la función matemática que la red construye a partir del conjunto de observaciones. <= /span>

 

Keywords:<= span lang=3DEN-US style=3D'font-size:12.0pt;line-height:115%;font-family:"Time= s New Roman",serif; mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-font-weight:b= old'>

 

Abstract

Introduction= . Within artificial intelligence, the use of artif= icial neural networks is becoming increasingly important. Objective. Estimate the error of the Biochemical Oxygen Demand in wastewater from laundries of jeans of the city of Pelileo using neural networks. Metho= ds. First, a database consisting of six physical parameters, nine chemical parameters and an output variable was developed. These variables were selected using the TUSLA Standard and were compiled from the cadastre of = the GAD Pelileo of the years 2017-2018 and a certified water analysis laborat= ory. Next, the Matlab software was used with the design of the Artificial Neur= al Network FeedForward Backpropagation with the input layer of fifteen variables. The first hidden layer had ten neurons, the second summatory l= ayer with one neuron and the output layer with the response variable correspon= ding to the estimation of the Biochemical Oxygen Demand with the Levenberg-Marquardt learning algorithm. Results. Values of the correlation coefficient, the estimated network error, and the comparison = of the Tukey Test were obtained, susch as: 0.98081; 0.8890; 0.9833. These figures reveal a concordance between the values estimated by the network = and the actual values. Finally, the results showed that Biochemical Oxygen De= mand was estimated numerically in wastewater through neural models. This type = of model of neurons represents only part of the mathematical function that t= he network builds from the set of observations.

 

 

 

Introducción

En los últimos años, el tema de la c=
ontaminación ambiental ha recibido más atención debido=
 al aumento de los riesgos de los agentes contaminantes en todo el mundo=
 (Maina et al., 2017). Sin embargo, la producción de muchas sustancias que no pued=
en ser asimiladas por el ecosistema, el daño al medio ambiente y a l=
a salud es cada vez más evidente, traduciéndose en un aumento=
 del consumo de energía y recursos, estos residuos peligrosos se gen=
eran con el desarrollo de agricultura, textiles, servicios e incluso activi=
dades domésticas. De hecho, debido al progreso de la economía=
 mundial, la cantidad de residuos va en aumento (Sigcha & Jordán, 2018; Programa Mundial de Evaluaci&oacut=
e;n de los Recursos Hídricos de las Naciones Unidas [WWAP], 2017).
Según la United Nations Climate Change News=
 (2018), el sector industrial textil genera el 20% del desperdicio total de=
 agua a nivel mundial a causa de los productos de teñido y acabado. =
El 10% por las emisiones globales de carbono, considerando que se requiere =
de un promedio de más de 7.500 litros de agua para producir unos jea=
ns, que es la misma cantidad de agua que la persona promedio bebe en siete =
años, por ende, la industria textil es el segundo contaminante del m=
undo (Conferencia de la ONU sobre Comercio y Desarrollo [UNCTAD], 2019).
La indust=
ria textil y de confecciones del país es una de las principales cons=
umidoras de agua, energía y productos químicos en la producci=
ón, lo que a su vez genera grandes cantidades de aguas residuales (Samanta et al., 2019).  La contaminaci&oacut=
e;n de aguas residuales de impresión y teñido de textiles es =
causada por impurezas o residuos en las materias primas, y muchos productos=
 químicos no se absorben completamente en la tela. Estas sustancias =
se liberan al agua junto con otros contaminantes. Estas aguas residuales, c=
ontienen altas concentraciones de pigmentos, contaminantes orgánicos=
, compuestos tóxicos, inhibidores, cloro y tensioactivos, siendo de =
esta manera parte de la degradación ambiental, generando impactos am=
bientales relacionados con las aguas residuales que produce y la carga qu&i=
acute;mica que contiene (Ministerio del Ambiente, 2015; Romero et al., 201=
6). 
El Cant&o=
acute;n Pelileo es el principal productor de jeans del país.  Con una producción del 70% a =
escala nacional y el 30% restante se vende en Cuenca, Quito, Guayaquil y ot=
ras ciudades (GAD Pelileo, 2019). Según la encuesta de producción en la provincia de T=
ungurahua, en la ciudad de Pelileo existe unas 1.100 fábricas textil=
es y 46 lavanderías de jeans. Por su parte, las industrias textiles =
y de jeans de Pelileo se han convertido en un sector económico repre=
sentativo de la sociedad. Sin embargo, está catalogada como una acti=
vidad muy contaminante por su altísimo consumo de agua, debido a que=
 cada prenda tratada requiere una media de 80 litros de agua y utiliza alre=
dedor de 8.000 productos químicos en diversos procesos (Kant, 2012; Luongo, 2015). Se ha verificado que las aguas residuales de salida provienen prin=
cipalmente de las siguientes etapas: desgasificación (15%), desengra=
sado (20%), blanqueo (45%) y lavado (30%). Durante este proceso, los produc=
tos químicos como: colorantes, ácidos, bases, sales se elimin=
an y pasan a formar parte de las aguas residuales finales (Choudhury, 2017; Kishor et al., 2021).
Para el a=
nálisis de la calidad del agua es necesario considerar aspectos f&ia=
cute;sicos, químicos y microbiológicos <=
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noteIndex":0},"schema":"https://github.com/citation-sty=
le-language/schema/raw/master/csl-citation.json"}(Quintero & Cardona, 2012). Los métodos de tratamiento ineficaces de las aguas residual=
es descargadas por las empresas de estampado y teñido de textiles ha=
n causado diversos grados de contaminación en los ecosistemas; por e=
sta razón la  norma amb=
iental del Texto Unificado de la Legislación Secundaria del Minister=
io del Ambiente en el Libro XI (TULSMA), establece los límites de de=
scarga para los sistemas de alcantarillado público, requiriendo  medir una variedad de pará=
metros que incluyen: demanda bioquímica de oxígeno (DBO5=
), DQO, pH, grasa, nitrógeno, fósforo, sulfato, s&oacut=
e;lidos suspendidos y más. Estas sustancias similares se encuentran =
en la composición de las aguas residuales de la industria textil y s=
e analizan mediante diferentes métodos físicos y quími=
cos (Norma de Calidad Ambiental y Descarga de Efluente=
s, 2011).
El desarr=
ollo industrial textil en la Ciudad de Pelileo ha generado un incremento en=
 el número de las lavadoras de jeans, causando problemas de contamin=
ación del agua del sector. Desde 2018, los organismos de control vie=
nen monitoreando la generación de residuos sólidos, lí=
quidos y gaseosos contaminantes, minimizándolos como lo exige la leg=
islación vigente, pero con un alto costo para las empresas (GAD Pelileo, 2019).
De esta m=
anera, se busca interactuar las Redes Neuronales Artificiales con los par&a=
acute;metros de calidad de agua relacionados. Estas redes neuronales son de=
finidas como un sistema que permite establecer una relación entre la=
s entradas y la salida, inspiradas en el sistema nervioso de un ser vivo, t=
ratando de imitar el comportamiento del cerebro, y diferenciándose d=
e la computación tradicional (Mcculloch & Pitts, 1990)=
. Estas redes neuronales artificiales son similares a un cerebro y, =
por lo tanto, exhiben algunas propiedades similares como: aprendizaje adapt=
ativo, autoorganización, tolerancia a fallos, operación en ti=
empo y la fácil inserción en la tecnología existente. =
Generalmente, la red neuronal artificial consta de 3 etapas: la primera inv=
olucra el diseño, donde se elige el tipo de red neuronal, la cantida=
d de neuronas que generara, la función de activación definida=
 y el algoritmo de aprendizaje. La fase de entrenamiento presenta una serie=
 de entradas y salidas a la red neuronal, de las cuales aprende mediante el=
 uso del algoritmo de entrenamiento (Barthakur et al., 2012).  Luego, la ent=
rada relevante se alimenta a la red, donde la red genera una salida basada =
en lo que aprendió durante la fase de entrenamiento. Por últi=
mo, se específica una serie de parámetros: el número d=
e capas, número de neuronas en la capa de entrada, número de =
neuronas de las capas intermedias y número de neuronas en la capa de=
 salida (Quiñones et al., 2020). 
El modelo=
 neuronal artificial es un diseño realizado partir de métodos=
 numéricos, permitiendo estimar el valor DBO5, en tiempo =
real, a partir de variables cualitativas y cuantitativas; demostrando grand=
es ventajas como: la reducción del tiempo y los costos económ=
icos. De acuerdo con la norma americana, este parámetro tarda cinco =
días y, por lo general, requiere la experiencia y las habilidades de=
l personal de laboratorio (INEN, 2013). La estimación de la Demanda Bioquímica de Oxigeno (D=
BO5) es uno de los factores más importantes para controla=
r la calidad del agua residual en empresas textiles. Este parámetro =
mide el contenido de carga orgánica en el paso de desgasificaci&oacu=
te;n, donde contribuye con alrededor del 50%. Especialmente por la adici&oa=
cute;n de químicos y colorantes naturales al agua de prueba, el agot=
amiento del oxígeno disuelto en el tanque receptor y la muerte de an=
imales acuáticos por falta de oxígeno (Raffo & Ruiz, 2014). 
La presen=
te investigación planea realizar una simulación a partir del =
modelo neuronal artificial basado en una red neuronal; correspondiente al t=
ipo de perceptrón multicapa de la red neuronal artificial supervisad=
a con el entrenamiento del algoritmo Levenberg-Marquardt para evaluar la es=
timación de error de la Demanda Bioquímica de Oxígeno =
con la recolección de datos de la salida del proceso productivo por =
parte de las empresas del Jeans de la Ciudad de Pelileo y hacer una compara=
ción con los valores estimados de la red neuronal con los valores re=
ales (valores de análisis experimentales) aplicando la prueba de Tuk=
ey.

Metodología

Se elaboró una base de datos clasificando las variables cualitativas y cuantitativas utilizadas para predecir el comportamiento de la variable dependiente (erro= r de medición de DBO5) y de las variables independientes relacionadas con el número de interacciones y observaciones, estimad= as mediante un modelo de Red Neuronal Artificial de RetroPropagación (RNARP) seguido del análisis correspondiente (Pascal, 2019).

Área de Estudio

El estudio se llevó a cabo en las aguas residuales de las empresas del jean de la ciudad de Pelileo, conocida como la “Ciudad Azul” por la presen= cia de la industria del Jean, ubicada en la Provincia de Tungurahua (Tungurahua Turismo, 2021). La información es recolectada de 30 empresas que fueron seleccionados del catastro de GAD Pel= ileo de los años 2017-2018 juntamente con el aporte de un laboratorio de análisis de agua certificado. Se mantiene la confidencialidad de la = información de las empresas.

Estimación de las variables de entrada y variable de salida

Para la matriz de dat= os se utilizaron 6 parámetros físicos (temperatura, humedad relativa, presión atmosférica, concentración de sólidos suspendidos, sólidos sediméntales, sóli= dos totales) y 9 parámetros químicos (potencial de hidróge= no, sulfuros, sulfatos, nitrógeno total, DQO, fenoles, tensioactivos, fosforo total, aceites y grasas). Un total de 15 parámetros fisicoqu= ímicos utilizados como variables de entrada y un parámetro químico c= omo variable de salida, para la elaboración del modelo. Los datos estima= dos se reportan utilizando la normativa sobre descarga de aguas residuales al sistema de alcantarillado público, obtenida en el Anexo VI de la Nor= ma TUSLA del Ministerio del Ambiente (Gilpavas et al., 2018; Norma de Calidad Ambiental y Descarga de Efluentes, 2011). 

Elaboración del modelo neuronal de DBO5

El modelo neuronal para la predicción de= la demanda bioquímica de oxígeno se elaboró a partir de u= na red Feed Forward Backpropagation. Se utilizó la = DBO5 como variable de salida y los parámetros físicos y químicos mencionados anteriormente como variables de entrada. La selección de estos parámetros se realizó con base en el efecto que causan sobre la DBO5 y teniendo en cuenta la frecuenc= ia de su uso en estudios previos. 

Por su parte, el código para el entramiento del Perceptrón de Multicapa (MLP) = se elaboró a partir del modelo desarrollado por Payal et al. (2015). Este modelo, es entrenado por m= edio del número de capas ocultas y el número de neuronas ocultas. Además, requiere definir un algoritmo para el entrenamiento de la re= d, este permite acelerar el proceso de aprendizaje del MLP, un número de épocas definidas como la cantidad de veces que se aplica el algoritm= o de entrenamiento al conjunto de datos y un indicador de desempeño por m= edio del cual la red ajusta las predicciones a las observaciones (Rumelhart & Hintont, 1986; Vališ et al., 2020). Finalmente, = se codifica el algoritmo en el lenguaje de programación M, propio de la herramienta de programación MATLAB y en él se utiliza las herramientas incluidas en el Neural Network Toolbox para el mismo programa.=  

En la figura 1 incluy= e el diseño del modelo neuronal de una red neuronal artificial conformada= por 1 capa de entrada, 1 capa oculta, 1 capa sumatoria y 1 capa de salida. En e= ste modelo, el número de neuronas de la capa de entrada (15 neuronas), correspondió al número de variables utilizadas como entradas.  Para el entrenamien= to, se usó un número de 1000 épocas y el coeficiente de correlación como indicador de desempeño de la red neuronal.  

El modelo neuronal fue evaluado por medio del algoritmo de Levenberg-Marquardt, para acelerar el proceso y diseñar el aprendizaje a través de segundas derivad= as, donde, el peso se ajusta rápidamente, evitando cálculos matriciales (Dongardive & Abraham, 2017).  Dicho código, la instrucción trainlm escrita, se encuentran conectadas a una arquitectura de Perceptrón de Multicapa (MLP) (Ebtehaj & Bonakdari, 2016).

Figura 1

Diseño de la red Neuronal Artificial Feed Forward Backpropagation

3D"Un

 

 

 

 

Nota: Descripción del diseño de la red empleada para la estimación de error de la DBO5.

Perfiles de entrada y salida

Se generó una matriz de entrada de entrenamiento (SampleIn=3D Matriz 15x30 double). Las 15 filas corresponden las variables responsables y las 30 columnas representan= las 30 empresas como se muestra en la tabla 1.

Tabla 1

Matriz de entrada de entrenamiento

Dimensión

 Código

Parámetro

FÍSICOS

H|

Potencial de Hidrógeno

A

Temperatura °C

B

Humedad relativa %

C

Presión atmosférica (Pascal)

 QUÍMICOS=

D

Sólidos Totales

F

sólidos sediméntales

G

Sólidos Suspendidos

I

Sulfuros

J

Sulfatos

Tabla 1

Matriz de entrada de entrenamiento (continuación)

Dimensión

 Código

Parámetro

QUÍMICOS

K

Nitrógeno Total

L

DQO

M

Fenoles

N

Tensioactivos

Ñ

Fosforo total

O

Aceites y Grasas

Nota: Matriz de entrenamiento con la codificación de variables y empresas. Obtenidas del catastro de GAD Pelileo de los años 2017-2018 juntamen= te con el aporte de un laboratorio de análisis de agua certificado. 

<= i>Análisis estadístico     =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =           

Para verificar la efectividad del modelo neuronal como método de estimación de = la DBO5, se aplicó la Prueba de Tukey con la ayuda del progr= ama estadístico InfoStat, para comparar los valores estimados de la red neuronal con los valores reales

Resultados

Las simulaciones se realizaron utilizando muestras de aguas residuales de la matriz de datos recopilados, consiguiendo entrenar la red y comprobar el error de medida de= DBO5, entre los valores estimados por la red neuronal y los valores reales. 

Tabla 2

Resultados de la simulación de la Red Neuronal Artificial=

Ítem

Detalle

Tipo de entrenamiento

Descenso de gradiente con última incorporación en la décima épocas

Entrenamiento óptimo

Valor de coeficiente de correlación

0.98081

Nota: Se considera que menos épocas generará errores en la red, mientras que más épocas llegarán al entrenamiento.

En la figura 2 se obs= erva el coeficiente de correlación obtenido en la capa de salida de la red neuronal y la estructura de la red neuronal artificial Feed Forward Backpropagation, entrenada con el algoritmo de Levenberg-Marquardt.

Figura 2

Valor de correlación obtenido

3D"Gráfico Descripción

Nota: Se observa la concordancia entre los valores reales y las estimaciones real= izadas por la red.

En la figura 3 se obs= erva el resultado de la Comparación entre los valores reales y calculados= de la Demanda Bioquímica de Oxígeno.

Figura 3

Comparación de Valores Reales vs Valores obtenidos por la Red Neuronal

Nota. Los puntos rojos representan los valores obtenid= os por la red neuronal y los puntos azules los valores medidos.

Pronó= stico de la red neuronal

En la tabla 3 se evidencia los valores reales obtenidos al medir la variable de la DBO5= en las empresas 1 y 2.  <= /o:p>

 

Tabla 3

Medición real y valor pronosticado para la variable en relación con Sólidos Totales

Parámetro

Empresa 1

Empresa 2

Empresa 3

Empresa 4

Temperatura °C

15

16

15

16

Humedad relativa %

86

82

86

82

Presión atmosférica (Pascal)

103,1

103

103,1

103

Sólidos Totales

1352

1420

1000

1000

sólidos sediméntales

0,2

1,5

0,2

1,5

Sólidos Suspendidos

50

50

50

50

Potencial de Hidrógeno

7,04

7,29

7,04

7,29

Sulfuros

0,13

0,19

0,13

0,19

sulfatos

110

120

110

120

Nitrógeno Total

8,51

9,26

8,51

9,26

DQO

131

165

131

165

Fenoles

0,022

0,026

0,022

0,026

Tensioactivos

0,120

0,110

0,120

0,110

Fosforo total

1,7

1,7

1,7

1,7

Aceites y Grasas

2,2

2

2,2

2

DBO5 =

65<= /p>

70<= /p>

60,135<= /p>

67,8202<= /span>

Nota: Descripción de valores reales y pronosticados de las variables influyentes en las empre= sas 1 y 2 del Jean. La Matriz de Prueba (Test1=3D15x1 Matriz double) predice un= valor de DBO5 de 60,135 mg/l; y la Matriz de Prueba (Test2=3DMatriz 15= x1 double) pronostica un valor de DBO5 de 67,8202 mg/ml.

Comparación de valores medidos por la Red Neuronal vs Valores Reales<= /i>

La figura 4 muestra l= os resultados del Análisis de Varianza y la Prueba de Tukey. 

 

 

 

 

 

 

 

Figura 4

Resultados del Análisis de Varianza y Prueba de Tukey

3D"Tabla Descripción

 

 

3D"Texto Descripción 

 

 

 

Nota: Datos de Análisis de Varianza y Prueba de Tukey obtenidos del progra= ma estadístico InfoStat. Se evidencia que la red neuronal es capaz de capturar la relación entre las variables de entrada y salida. 

La figura 5 se muestra los resultados de la prueba Tukey entre los valores de los datos de laborat= orio y los de la res neuronal artificial. 

Figura 5

Interacción con Tukey entre los valores de laboratorio vs la red neuronal artificial

3D"Gráfico,

Nota: la relación entre la respuesta esperada y la red neuronal. Encontrando = un grupo homogéneo (A), con un nivel de cumplimiento de 98,33%. Ideal p= ara diagnosticar la salida estimada de la Demanda Bioquímica de Oxígeno por el proceso de las empresas de Jean. 

En la figura 6 se obs= erva el resultado de la prueba t para una media entre el valor real y el valor calculado por la red. Donde el valor p es igual a 0,8890 siendo este el val= or del error estimado por la red neuronal.

Figura 6

Prueba t para una media

3D"Interfaz

Nota: entre el valor real y el valor calculado por la red. Durante este período de entrenamiento, la solución se optimiza varias veces para reducir los errores de la red en el conjunto de datos de entrada.  

Discusión

En la tabla 2 se mues= tran los resultados de la simulación de la Red Neuronal, que contiene dos capas ocultas con un entrenamiento por descenso de gradiente, con la última incorporación en la décima época o epoch. Seguidamente, en la figura 3 se evidencia la estructura de la red neuronal artificial Feed Forward Backpropagation, entrenada con el algoritmo de Levenberg-Marquardt. De acuerdo con Singh et al. (2009), es el algoritmo más apropiado para efectuar estimaciones de la DBO5. 

El valor de correlación obtenido en la simulación se asemeja a los datos = de Meza & González (2020) y Gonzá= ;lez & García (2020), lo que demuestra un alto rendimiento del algoritmo de Levenberg-Marquardt. Este algoritmo muestra concordancia entre los valores reales y las estimaciones realizadas por la red (coeficiente de correlación de 0.9808). <= /o:p>

En la figura 4 se mue= stra la comparación entre los valores reales y calculados por la red neuronal, evidenciando el menor cambio en sus estimaciones (Vijayashanthar et al., 2018). Asimismo, se describe mediante puntos de color rojo los valores obtenidos por la red neuronal y mediante puntos azules los valores medidos. Claramente se puede observar un buen seguimiento de datos de la red neuronal con un vector de error denomin= ado RNDBO5_errors (Matriz 30x1).

La tabla 3 describe l= os valores reales y sugeridos para evaluar sólidos totales en relación con la DBO5. Estos valores fueron simulados en u= na red neuronal artificial pre entrenada para estimar el error de la Demanda Bioquímica de Oxígeno en las empresas 1 y 2. De esta manera, = se puede hacer la misma estimación para cualquier empresa, o si es necesario, cambiar los valores de las variables de entrada al sistema. En b= ase a los valores pronosticados de las empresas 1 y 2 de la tabla 3, la Matriz = de Prueba (Test1=3D15x1 Matriz double) predice un valor de DBO5 de = 60,135 mg/l; y la Matriz de Prueba (Test2=3DMatriz 15x1 double) pronostica un valo= r de DBO5 de 67,8202 mg/ml; dichos datos obtenidos estipulan un pronóstico de decrecimiento de la DBO5. Con un error cerc= ano al vector de error de la red neuronal, en relación con el parámetro de Sólidos Totales. De acuerdo con la referencia bibliográfica, los valores estimados se encuentran dentro del límite máximo permisible (Norma de Calidad Ambiental y Descarga de Efluentes, 2011).

Con el análisis estadístico realizado, en la figura 5 muestra los resultados del Análisis de Varianza y la Prueba de Tukey. Se comparan los valores obtenidos en la simulación con valores de laboratorio con un índice de confianza del 95%. Afirmando que no existe una diferencia estadísticamente significativa; por lo tanto, se puede concluir que = la red neuronal es capaz de capturar la relación entre las variables de entrada y salida.

Además, se observó que el número de neuronas y la capa oculta son independientes de la DBO5. Por lo que el modelo neuronal no util= iza las ecuaciones que describen el proceso de la DBO5 en sus cálculos. En este tipo de modelo, las neuronas representan solo una parte de la función matemática que la red construye a partir = del conjunto de observaciones (Baldiris et al., 2017; Gulyani et al., 2015). <= /span>

Finalmente, el modelo neuronal tiene las ventajas de precisión, rentabilidad, velocidad de monitoreo, reconocimiento de patrones complejos, captura de comportamiento = no lineal. Lo que demuestra que la red neuronal es una herramienta efectiva pa= ra modelar la Demanda Bioquímica de Oxígeno. <= /p>

Conclus= iones

·= ;      =    Se demostró que el aprendizaje de un modelo neuronal artificial basado = en una red neuronal multicapa se puede utilizar para la estimación de e= rror de la Demanda Bioquímica de Oxígeno en aguas residuales de las empresas del Jean. La red neuronal feedforward-backpropagation alcanz&oacut= e; un alto desempeño al ser entrenada con el algoritmo de Levenberg-Marquardt (R> 0,98081). Usando una matriz de 27 neuronas que constó de cuatro capas: una de entrada, dos ocultas y una salida.

·= ;      =    Los modelos neuronales artificiales en este tipo de redes neuronales son suficientes para realizar estimaciones sugeridas en función de indicadores de desempeño y relación entre el valor estimado y real. Las similitudes son suficientes para estimar la propuesta, dando un v= alor de 0.8890 entre el error estimado de la red y los valores reales.

·= ;      =    Se comparó entre los valores estimados por la Red Neuronal y los valores reales. Esto se muestra, cuando los datos se recopilan mediante la prueba de Tukey con un 95% de confianza. El valor de confiabilidad es del 98,33% de l= as respuestas obtenidas de la red neuronal artificial y el valor real en el laboratorio. Las afirmaciones de que las medias son iguales, no estadísticamente diferentes; son ideales para el diagnóstico = de la Demanda Bioquímica de Oxígeno y el cumplimiento por parte = de las empresas para la descarga de aguas residuales en los sistemas de alcantarillado.

·         En la actualidad, las redes neuronales poseen un amplio campo de investigaci&o= acute;n, lo que se fomenta la innovación tecnológica continua con este trabajo, especialmente la aplicación de técnicas informáticas RNA a futuros trabajos que impliquen riesgo o incertidumbre, ya que pueden verse como una alternativa a los sistemas trad= icionales.

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