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Desarrollo de un sistema de control inteligente basado en redes neuronales aplicado al proceso de pasteurización de lácteos

 

Development of an intelligent control system based on neural networks applied to the da= iry pasteurization process

 

Adrián Alejandro Aldaz Vargas. [1], = Lando Stephen Ocaña Pañora. [2], = Janeth Ileana Arias Guadalupe. [3= ] & Jonatahn Paúl Campos Castelo. [4]

 

Recibido: 04-04-2021 / Revisado: 12-04-2021 /Aceptado: 05-05-2021/ Publicado: 05-06-2021

 

 Abstract.                        =                   DOI: https://doi= .org/10.33262/concienciadigital.v4i2.2.1736

The pasteurization process requires the incorporat= ion of technology in its pasteurization methods to improve its production. The objective of this system is to implement a pasteurization process of a milk plant, for this purpose the Matlab 2014 tool wa= s used for the central control of the system considering the necessary conditions = of the process. The pasteurizer control has a sensor and its actuators whose function is constant throughout the process which send signals through a da= ta acquisition card and interpreted in Matlab 2014= and with the implementation of a human machine interface (HMI) through the Guide application. The pasteurization process initially uses raw milk at room temperature of 18°C according to NORMA NTE INEN 9:2012 (INEN, 2021), will be poured into the kettle to avoid waste, t= hen begins the pasteurization process from the HMI of Matl= ab 2014 where the operator will choose the parameters necessary for its operat= ion. For the pasteurization process, the slow and open pasteurization method (VA= T) is considered, which consists of heating large volumes of milk in 3 stages: heating, pasteurization, and cooling to finish the process.

Keywords: pasteurization, neural networks, intelligent control, VAT.

Resumen.

 

El proceso de pasteurizaciĂłn requiere la incorporaciĂłn de tecnologĂ­a en sus= mĂ©todos de pasteurizaciĂłn con el fin de mejorar su producciĂłn. El objetivo de este sistema es implantar un proceso de pasteurizaciĂłn de una planta de leche, = para esto se utilizĂł la herramienta Matlab 2014 utilizada para el control centr= al del sistema tomando en cuenta las condiciones necesarias del proceso. El control del pasteurizador cuenta con un sensor y sus actuadores cuya funciĂ= łn es constante en todo el proceso los cuales envĂ­an señales por una tarjeta de adquisiciĂłn de datos e interpretados en Matlab 2014 y con implementaciĂłn = de una interfaz hombre maquina (HMI) mediante la aplicaciĂłn = Guide. El proceso de pasteurizaciĂłn utiliza inicialmente la leche cruda a tempera= tura ambiente de 18°C segĂşn la norma NORMA NTE INE= N 9:2012 (INEN, 2021), será vertida= en la marmita evitando su desperdicio, posteriormente se da comienzo al proceso de pasteurizado desde el HMI de Matlab 2014 donde el operador escogerá los pa= rámetros necesarios para su funcionamiento. Para el proceso de pasteurizado se toma = en cuenta el mĂ©todo de pasteurizaciĂłn lento y abierto (VAT) el cual consiste= en calentar grandes volĂşmenes de leche en 3 etapas: calentamiento, pasteuriza= ciĂłn y enfriamiento para finalizar el proceso.

Palabras claves: pasteurizaciĂłn, redes neuronales, control inteligente, VAT.

 

IntroducciĂłn.

La pasteurización se ha establecido con éxito en el contexto mundial como el método más utilizado para erradicar virus, hongos y patógenos de la lech= e en los países desarrollados y en desarrollo, Con la pasteurización se logra reducir los microorganismos presentes en el producto lácteo sin que esto a= fecte a las características propias del alimento. (Interempresas, 2018).

PasteurizaciĂłn de la leche<= /p>

Desde su origen la pasteurización es relacionad= a con la leche, este proceso ayuda a la eliminación de microorganismos que crece= n en un rango de temperatura ambiente de 25°C a 37°C, razón por la cual se ev= ita su almacenamiento a esas temperaturas pasado el proceso de pasteurización. Con el paso de los años se han desarrollado varias técnicas para la eliminación de la mayor= ía de las agentes patógeno y evitar eliminar los nutrientes de la leche, varias técnicas generalmente son aplicadas bajo el punto de ebullición del agua = en su mayoría. Además, disminuye la flora asociada, lo cual prolonga la vida ú= til del producto sin alterar su composición química y sus características organolépticas. (Tortora, Funke, & Case, 2007).

Proceso VAT

Uno de los primeros métodos de pasteurización utilizado en la industria láctea es el proceso VAT que consiste en calentar volúmenes grandes de leche en un estanco a un rango de temperatura de 62°= C a 65°C por un lapso de 30 minutos y posteriormente se debe pasar mucho tiempo para continuar con el proceso de envasado del producto veces más de 24 hor= as. (Tipán & Flores, 2018)

Proceso HTST

El proceso High Temperature Short Time (HTST), consiste en someter un alimento a una temperatura próxima a los 79°C en un intervalo de tiempo d= e 15 segundos, la ventaja de este método es la rapidez en volúmenes grandes, p= ermite eliminar los microorganismos patógenos, mediante la aplicación de alta temperatura durante un corto período de tiempo. (INOXPA, 2021).

Proceso UHT

El proceso Ultra High Tempe= rature (UHT), consiste en someter un alimento a una temperatura próxima de 138°C= en un intervalo de por lo menos 2 segundos, esto permite una degradación mínima= del alimento para conservar los nutrientes y propiedades alimenticias, este pro= ceso está dedicado a fabricar productos de larga duración no refrigerados, se consigue una eliminación de todos los microrganismos del producto tratado = y sus formas de resistencia, consiguiendo así su esterilización. (IMPROLAC, 2021).

“La pasteurización es aquella operación para destruir por efecto del calor los microorganismos patógenos y en su mayoría de los gérmenes restantes, con= fines higiénicos o de conservación, conservando al máximo las características físicas, bioquímicas y organolépticas del alimento. Dicha pasteurización permite la conservación en un tiempo determinado, basándose en las leyes = de destrucción térmica de los microorganismos, estas leyes toman en consider= ación básicamente el número de microorganismos presentes, la temperatura en la = que tiene lugar el proceso y el tiempo durante el que se mantiene dicha temperatura” (Gösta, 2003).

La industria láctea de Ecuador ha implementado recientemente nuevas políticas destinadas a mejorar los medios de vida, como cambiar la matriz de producci= ón para permitir que los pequeños productores integren nuevas tecnologías pa= ra aumentar la producción. Esta política del estado permite el uso de nueva tecnología para ampliar el área de investigación para que el país se co= nvierta en un productor de productos lácteos refinados. (Ministerio de Agricult= ura y Ganadería, 2020).

Los procesos industriales modernos, requieren un funcionamiento eficiente de los sistemas de control en una amplia gama de condiciones de funcionamiento en varios entornos hostiles; Donde se originan los sistemas de control intelig= ente que son basados en control borroso o fuzzy, con= trol con redes neuronales, y control con algoritmos genéticos, con la finalidad= de comandar mediante inteligencia artificial donde se permite emular características de comportamiento humano como son: la fácil adaptación, aprendizaje y planeación con grandes datos.

Dado que es un sistema de control de temperatura, se decidiĂł desarrollar un sis= tema de control sensorial basado en la red neuronal para mejorar el sistema de control normal y apoyar la adquisiciĂłn de nuevos mĂ©todos de aprendizaje y tĂ©cnicas de investigaciĂłn. Esta tecnologĂ­a de control inteligente ha sido seleccionada para administrar una amplia variedad de datos para un mejor monitoreo. Una Red Neuronal Artificial (RNA) se basa en datos de aprendizaj= e, pruebas y entrenamiento, facilitando cambios en la práctica, es un modelo inspirado en como funciona el cerebro humano, c= onformado por nodos conocidos como neuronas artificiales conectadas entre sĂ­. Estas señales van conectadas desde las entradas hasta posteriormente generar seĂ= ±ales de salida. (ATRIA INNOVATION, 2021= ).

Un requisito para lograr generar redes neuronales es una etapa previa de entrenamiento en el cual se ingresa datos o informaciĂłn disponible del sis= tema en conjunto con una etapa de validaciĂłn para comparar el aprendizaje de la= red neuronal con otro grupo de datos disponibles.

Según Mendieta, Olarte, y González (20= 12), en un sistema real, la no linealidad reduce la eficiencia del sistema de fabricación. Por tanto, las estrategias de gestión técnica so= n muy eficaces para fortalecer el control. Los controles son más robustos y se p= ueden ajustar con diferentes valores. Las redes neuronales en los sistemas de con= trol han sido probadas y usadas de una manera satisfactoria en los procesos no lineales como inyectores de plástico, intercambiadores de calor, robótica= y visión artificial, etc.

La falta de artículos científicos en el Ecuador acerca del uso de redes neur= onales en la industria es evidente. Sin embargo, el tema de investigación se puede aplicar en distintas áreas de desarrollo del proyecto. Esta técnica puede resolver problemas comunes que afectan de manera directa a la calidad de la leche y a la salud de los consumidores. El análisis, diseño e implementac= ión de este sistema de control inteligente se basó en redes neuronales aplicado al proceso de pasteurización de leche con el método VAT con el objetivo de automatizar este proceso.

Se debe tomar en cuenta que la leche debe cumplir ciertas normas de higiene, p= ara esto previamente pasara por una preparación bajo normas de asepsia previo = al vaciado de un tanque pasteurizador. El método de control inteligente con b= ase en redes neuronales nos permite un mejor comportamiento en el sistema no lineal.

Una Red Neuronal Artificial (RNA), usa como conjunto de entrada una serie de da= tos muy significativos para identificar patrones específicos o cierta regulari= dad de datos para conseguir una construcción aproximada del problema, culminad= o el entrenamiento de la red se podrá usar daros distintos a los iniciales sin degradación del rendimiento. (Palma & Márin, 2011).

Se pretende mejorar el comportamiento de este sistema disminuyendo el tiempo de respuesta en comparaciĂłn de los sistemas de control tradicionales y mejora= ndo la estabilidad. De igual manera, se busca compensar las desventajas de la computaciĂłn, la falta adaptabilidad y existencia de imprecisiĂłn e incerti= dumbre del control tradicional.

MetodologĂ­a.<= /b>

Diseño e implementac= ión

Para el sistema de control inteligente con base = en redes neuronales para el proceso de pasteurización de leche se utilizó el método heurístico con base en procesos empíricos y experimentales para el diseño e implementación del tablero de control, elementos, etc. Se tomó = en cuenta las variables en cada etapa del proceso.

Las variables a medir son la temperatura, y velocidad del motor como se muestra en la tabla 1, se tomĂł una comparativa entre dos tratamientos distintos como son: el termĂłmetro de alcohol y valor arrojado por el sensor de temperatura DS18B20, para el arranque del motor c= on distintas cargas dentro de la marmita se toma en cuenta la velocidad.<= /o:p>

Tabla 1.  Variables de sistema

Variables del sistema

Consideraciones

Temperatura

·=          Rangos de temperatura de exposiciĂłn de la leche.=

·=          Valores medidos mediante el sensor de temperatura DS 18B20

·=          Registro de temperatura para el proceso

Velocidad

·=          ObtenciĂłn de una mejor homogenizaciĂłn

·=          EstabilizaciĂłn de la homogenizaciĂłn para evitar el corte de la pr= oteĂ­na de la leche

·=          Evitar la formaciĂłn de nata en leche

Tiempo<= /b>

·=          Velocidad de homogenizaciĂłn y temperatura dependen del tiempo=

·=          Por medio del tiempo se escoge los datos brindados por el sensor y velocidad de homogenizaciĂłn

Fuente: Elabora= ciĂłn propia de los autores.

Sistema de control

Se tomó en consideración la implementación de= un sistema de control en lazo cerrado, para lo cual se describirá cada una de= sus etapas.

Sistema de control en lazo cerrado

El sistema de control en lazo cerrado del proces= o de pasteurizaciĂłn de leche consta de 4 etapas principales:<= /p>

·         Primera etapa: ElecciĂłn del controlador.

·         Segunda etapa: Actuadores utilizados en el proceso.

·         Tercera etapa: Planta u objetos= a controlar como pasteurizador VAT o marmita.

·         Cuarta etapa: RetroalimentaciĂł= n del sistema entre temperatura real y la deseada.

Figura 1. Diagrama de bloque de la temperatura del sensor DS18B20 del proceso de pasteurizaciĂłn = de leche.

Fuente: ElaboraciĂłn propia de los autores.

El diagrama de bloques (fig. 1),  representa un sistema de control en lazo cerrado en el cual el valor de entrada Set Point el cual será la temperatu= ra deseada, se tiene un error que es la diferencia entre la temperatura real y= la deseada, la información pasa al controlador realizado en el toolbox RNA de Matlab 2014, siguiendo a los actuadores como son la válvula de calo= r, frio y el motor agitador para llegar a la planta de pasteurizado (marmita) y llegando a obtener una temperatura real sin antes realizar una retroaliment= ación.

En la figura 2 se aprecia el diagrama de bloques= o esquemático del sistema de control de temperatura en lazo cerrado del pasteurizador o marmita.

Figura 2. Diagrama de bloque del sistema de control en lazo cerrado de temperatura del proceso de pasteurizaciĂłn de leche en la marmita (VAT).

Fuente: Elabora= ciĂłn propia de los autores.

En el diagrama de bloques del proceso de pasteurizaciĂłn de leche se tiene que en la primera etapa realiza la lectur= a por el sensor DS18B20, en la segunda etapa se realiza una adquisiciĂłn de datos= del sensor de temperatura en el Arduino Mega 2560. En la tercera y cuarta etapa= se tiene la interfaz de comunicaciĂłn por puerto serie USB entre el Arduino co= mo tarjeta de adquisiciĂłn de datos y un controlador, este controlador es real= izado en Toolbox RNA y HMI Guide= como herramientas del software MATLAB.

Realizado el HMI todas las secuencias de pasos p= ara el proceso de pasteurizaciĂłn los valores de salida de datos son enviadas p= or puerto serial a la tarjeta Arduino Mega 2560, estos datos de salida son val= ores lĂłgicos 1 o 0, el encendido o apagado de las electroválvulas y un valor d= e PWM con valores de 0 a 255 bits para activar un motor agitador. Los actuadores = son activados por estos valores los cuales se acoplan al sistema de control en = lazo cerrado por medio de interfaces de potencia usadas como acople de activaciĂ= łn de relĂ©s autoprotegidos.

Proceso de pasteurizaciĂłn de la leche<= /span>

El método VAT o abierto se utiliza para elabora= ción de diferentes productos lácteos, los procesos son de pasteurización, homogenización y enfriamiento. El operario tiene una Interfaz Hombre Maqui= na (HMI) en la cual puede escoger el tipo de producto lácteo para la pasteurización, la velocidad del agitador para la homogenización y el tie= mpo requerido en minutos. Al iniciar la HMI se acciona un breaker general en el tablero de control, el sistema abrirá una electroválvula de vapor de agua= donde conducirá vapor hacia la marmita, el proceso de calentamiento se realiza h= asta que alcance una temperatura máxima de pasteurización.

Para iniciar el motor agitador se debe activar u= n switch, el cual comienza a la homogenización mediant= e las aspas, una vez alcanzada la temperatura máxima de pasteurización la electroválvula de vapor es desconectada y comienza el tiempo de pasteuriza= ción y homogenización, el sistema hace una comparativa entre la temperatura act= ual es mayor que la temperatura mínima de pasteurización, en caso de ser cier= to el sistema abre la válvula de agua fría y puede visualizarse en la HMI. Una = vez alcanzado el valor mínimo de pasteurización se activa la electroválvula = en conjunto con una alarma piloto y se podrá monitorear desde el tablero de control como en la HMI. Se presentarán los valores de temperatura, velocid= ad de homogenización y cambios en el proceso.

Figura 3. Proces= o de pasteurizaciĂłn

Fuente: ElaboraciĂłn propia de los autores.

Control Inteligente

El controlador neuronal implementado en Matlab 2= 014 ejecuta las acciones completas del sistema el cual interpreta los datos recibidos de la tarjeta de adquisiciĂłn de datos, se tomĂł en cuenta la herramienta Toolbox de RNA incluida en Matlab 2= 014 para una red de backpropagation feedforward realizada con funciĂłn de rendimiento MSE y un algoritmo de entrenamiento L= -M.

Entrenamiento de las Redes Neuronales Artificiales=

Se tomó en cuenta la red f= eedforward backpropagation incluida en el Toolbox RNA de Matlab 2014, inicialmente en la creación de la RNA se ingresa los d= atos de entrada/salida en forma de matrices en el script de Matlab 2014, se toma= en cuenta dos RNA una de subida y otra de bajada por una repetición de datos = en el calentamiento y enfriamiento se debe separar para evitar una incongruencia.= El comando newff ayuda con la creación de la RNA = en el cual se debe especificar ciertos valores mínimos y máximos de entrada, ca= pas de entrada, capas ocultas, capas de salida y la función de activación.<= /o:p>

Para el entrenamiento de la red neuronal se implementĂł un algoritmo de entrenamiento trainlm para la formaciĂłn de red en funciĂłn a los valores de entrada y salida en funci= ones de creaciĂłn de red como newff entre otras, este comando actualiza los pesos y sesgo de la funciĂłn de activaciĂłn con el algoritmo L-M (Levenberg-Marquardt).=

El algoritmo L-M es recomendable para procesos de entrenamiento con supervisiĂłn, en esta red fueron probados en sistemas no lineales en la robĂłtica y procesos de rendimiento MSE. Se utilizĂł el coma= ndo sim para simular los valores de las RNA.

Control de modulaciĂłn por an= cho de pulso (PWM)

El control de modulación p= or ancho de pulso PWM se diseño para variar la ve= locidad de homogenización del motor agitador, este control describe los valores PW= M que entrega la tarjeta controladora Arduino Mega 2560 con el rango de 0 a 255 b= its PWM, se debe tomar en cuenta los porcentajes de ciclo de trabajo y voltaje.=

Tabla 2. Control PWM

PWM de Ard= uino

Amplitud (= VDC)

Ciclo de t= rabajo (%)

Numero de = bits de trabajo (bits)

Valores Ăł= ptimos para homogenizaciĂłn

1

0 – 1,25

0 – 25

0 – 64

No

2

1,25 – 2,5

25 – 50

64 – 127

Adecuado

3

2,5 – 3,75

50 – 75

127 – 191

Si

4

3,75 - 5

75 - 100

191 - 255

Si

Fuente: Elabora= ciĂłn propia de los autores.

Los valores del PWM son utilizados para el motor agitador acoplados con un IRFZ44N el cual esta diseñado para soportar altos amperajes en conjunto con un disipador ubicad= o en el tablero de control. (Hardware Libre, 2021).

Tabla 3. CaracterĂ­sticas IRFZ44N

CaracterĂ­sticas

Voltaje de ruptura (VDC)

Voltaje Umbral (VDC)

Resistencia de estado ON (m Ω)

Corriente (A)

Temperatura (°C)

IRFZ44N=

55

10

17,5

49

175

Fuente: Elabora= ciĂłn propia de los autores

Interfaz Hombre Maqui= na (HMI) del proceso

La HMI consta de 8 etapas o secciones importante= s, fue realizado en la herramienta Guide de Matlab= 2014, está conectada de manera directa a una pantalla con la tarjeta de adquisic= ión de datos Arduino lo cual nos ayuda con un entorno amigable.

Figura 4. Interf= az Hombre Máquina (HMI)

Fuente: Elabora= ciĂłn propia de los autores.

Secciones del program= a

1.&n= bsp;     Se utiliza una comunicaciĂłn po= r USB, para esta secciĂłn se habilitan dos botones, el botĂłn Conectar para el arr= anque de la HMI del sistema, para salir del HMI se tiene el botĂłn Salir, se puede visualizar el valor de la temperatura inicial.

2.&n= bsp;     El botĂłn entrenar permite real= izar un entrenamiento de las RNA de subida y bajada.

3.      Una slider permite variar la velocidad del motor= en conjunto con un checkbox, se podrá visualizar = la velocidad del motor y su valor numĂ©rico de bits en PWM.<= /p>

4.      El tiempo lo puede ingresar en un edit-text en unidad de minutos no fraccionario. Una v= ez alcanzado el tiempo el programa sigue con la ejecuciĂłn.<= /p>

5.      El botĂłn simular dará valores de entrada y sal= ida a las redes neuronales, previo a la simulaciĂłn se deben ingresar los valores= de PWM y tiempo de pasteurizaciĂłn.

6.      Para la visualizaciĂłn de datos se toma en cuent= a un correcto monitoreo de sistema, se muestra el valor de la temperatura despuĂ= ©s de la simulaciĂłn de la RNA.

7.      En la programaciĂłn del sistema se detalla las p= artes del sistema con sus respectivas lĂ­neas de cĂłdigo.

8.      Muestra los valores de los actuadores “0” o = “1” y una gráfica en tiempo real de la temperatura del sistema.

= ProgramaciĂłn de la tarjeta de adquisiciĂłn de datos Arduino Mega 2560<= /b>

Se utilizĂł como base el cĂ= łdigo de entradas digitales: Analog and Digital Input= and Output Server for MATLAB (HaHn, 2014), este cĂłdigo ayudo a la adaptaciĂłn del sensor a la tarjeta de adquisiciĂłn= de datos Arduino Mega 2560.

= Entorno de programaciĂłn.

El entorno de programación= de Arduino brinda funciones básicas, se utilizaron las siguientes:=

·         Void setup (): La funsion setu= p() contiene la declaraciĂłn de las variables, comunicaciĂłn  que se utilizara en= el programa.

·         Void loop (): la funsion loop= () se ejecuta posterior a la setu= p (), esta funciĂłn genera un bucle de lectura y escritura de datos de entrad= as y salidas de programa.

Figura 5. ProgramaciĂłn para la lectura del sensor DS18B20 en Arduino 2560

Fuente: Elabora= ciĂłn propia de los autores.

Se declara el pin 2 para la captaciĂłn de datos = del sensor DS18B20 en comunicaciĂłn OneWire. Para e= ste apartado se deben instalar las librerĂ­as OneWire y DallasTemperature para tener un valor de captado y de inicio del sensor.

El valor del sensor se almacena en una variable = temp que mostrará el valor en grados centígr= ados (°C) por puerto serial con un delay de 500 milisegundos en tiempo real. Se puede utilizar una velocidad de transmisió= n de datos de hasta 115200 entre el Arduino y Matlab 2014.=

Resultados.

Para medir los resultados de las pruebas realiza= das al sistema y detallar las adecuaciones para que el proceso culmine de forma exitosa se utilizó el método inductivo-deductivo, científico-heurístico= y comparativo. Las pruebas realizadas para un mejor rendimiento del proceso de pasteurización, pruebas de temperatura y mejora del tiempo de pasteurizaci= ón se rigen bajo normas NTE INEN.

Temperatura.

El sensor DS18B20, permite realizar optimas mediciones de temperatura en el rango de 0°C a 85°C suficiente para el pr= oceso cuya temperatura máxima llegará a 65°C, este sensor puede realizar medic= iones con mucha o poca leche en el pasteurizador, en un día de producción se to= mó 20 lecturas durante el tiempo de pasteurización.

Tabla = 4. ComparaciĂłn temperaturas HMI sensor DS18B20 vs TermĂłmetro de Alcohol=

Lectura

Temperatura (°C) tomado desde el HMI sensor ds18b20

Temperatura (°C) tomado desde el termómetro de Alcohol

1

10

11

2

12

12

3

13

12

4

13

13

5

20

21

6

22

22

7

24

25

8

25

27

9

30

32

10

33

33

11

60

61

12

70

69

13

64

63

14

52

54

15

47

45

16

43

44

17

40

40

18

35

36

19

31

31

20

20

20

Media aritmética (Temperatura)=

33,2

33,55

Fuente: Elabora= ciĂłn propia de los autores.

Tomando como base estos datos se comparĂł dos tratamientos mediante la prueba T-Student que p= ermite utilizar una muestra de 30 datos en una distribuciĂłn normal. Los valores d= el sensor DS18B20 y del termĂłmetro deben seguir una distribuciĂłn normal y se comprueba mediante una prueba de normalidad de Shapiro= wilk.

Como hipĂłtesis para la prueba de distribuciĂłn de T-Student son:

·&n= bsp;        Ho: Los datos siguen una distribuciĂłn normal (hipĂłtesis nula).

·&n= bsp;        Ha: Los datos no siguen u= na distribuciĂłn normal (hipĂłtesis alterna).

 

Tabla 5. Prueba de Normalidad Shapiro-Wilk

Dispositivos de mediciĂłn<= /o:p>

Kolmogorov-Smirnov= a

Shapiro-Wilk<= /o:p>

EstadĂ­stico

Gl

Signi.

EstadĂ­stico

Gl

Signi.

Valores de temperatura

Sensor DS18B20

0,126

20

0,935

20

0,192

TermĂłmetro de Alcohol

0,112

20

0,933

20

0,179

Fuente: ElaboraciĂłn propia de los autores.

La prueba de normalidad de Shapiro-Wilk puede brindar un valor de significancia (Signi) con v= alor de 0,192 con el sensor DS18B20 y 0,179 con el termĂłmetro de alcohol, ambos tienen un nivel de significancia del 0,05 con lo cual se concluye que tienen una distribuciĂłn normal.

Mediante la prueba de distribuciĂłn T-Student de los 20 datos del sensor DS18B20 y el termĂ= łmetro de alcohol se tiene un nivel de confianza del 95% con un valor de significa= ncia del 0,05 para las hipĂłtesis:

·         Ho: La media de los datos captados por el sensor es igual a la media de datos de temperatura captados por el termĂłmetro de alcohol (hipĂłtesis nula)

·         Ha: La media de los datos captados por el sensor no es igual a la media de los datos de temperatura captados por el termĂłmetro de alcohol (hipĂłtesis alt= erna)

Tabla 6. Prueba de Normalidad Shapiro-Wilk

Dispositivos de me= diciĂłn

N

Media

DesviaciĂłn TĂ­pic= a

Error tĂ­pico de l= a media

 

 

Valores de temperatura

Sensor DS18B20

20

33,20

17,999

20

 

TermĂłmetro de Alcohol

20

33,55=

17,83= 4

20

 

Fuente: Elabora= ciĂłn propia de los autores.

ManipulaciĂłn directa de operario.

Mediante el método de inducción se logró dete= rminar el evento en que se encuentra el operario y el efecto en el proceso de pasteurización de leche.

En la tabla se observa los pasos y eventos en los que incide el operario que en un inicio se nota su presencia en los 5 pasos mientras que, después de la automatización del proceso tiene una mejoría= de rendimiento de un 80% en vista del operario.

Tabla 7. ManipulaciĂłn del operario vs automatizaciĂłn

Secuencia del proceso

ManipulaciĂłn (Antes)

Manipulación (Después)

Ingreso de Materia Prima(leche)

Si

Si

Lectura de temperaturas

Si

No

HomogenizaciĂłn

Si

No

MediciĂłn de tiempo

Si

No

Apertura de Válvulas/llaves de paso<= /span>

Si

No

Total Pasos

5

1

Fuente: ElaboraciĂłn propia de los autores.

La prueba de Levene, determinó una igualdad en = la varianza con una significancia (Signi.) de 0,95= 5 y este valor es mayor al 0,05 que se utilizará en cálculos. La prueba T-Student (T)=3D-0,062 y el valor crítico de dos colas= (Sigin Bilateral) de =3D±= 0,951 lo cual da una aceptación de hipótesis nula d= el tratamiento, se acepta el sensor DS18B20 para la adquisición de datos de temperatura.

Figura 6.<= /b> Zona de AceptaciĂłn para la utilizaciĂłn del Sensor DS18B20.

Fuente: Elabor= aciĂłn propia de los autores.

HomogenizaciĂłn =

El proceso de homogenizaciĂłn tiene como element= os un conjunto moto-reductor de marca Motor King Rigth sf7152 con aspas acopladas al eje del motor.

Figura 7. Aspas del pasteurizador

<= /span>

Fuente: ElaboraciĂłn propia de los autores.<= /span>

Este compuesto por un eje entre dos aspas y la marmita genera perdida de torque del motor reductor lo que provocĂł que el = motor no arranque, se realiza un redondeo en el eje final de aspas para superar el factor de rozamiento entre las aspas, esto ayuda a un arranque similar con distintas cargas.

Mediante las pruebas 11 pruebas de arranque se d= eterminó que se necesita 87 bits PWM para que el motor y las aspas comiencen a girar= con carga dentro de la marmita, el motor trabaja en un ciclo de 34,5% del valor= máximo de PWM.

Pruebas de la RNA (Red Neuronal Artificial) en MATLAB 2014

= Plotperform

Esta herramienta representa las actuaciones, pruebas y validaciĂłn mediante el registro de entrenamiento optimizado MSE, se logrĂł la reducciĂłn del Ă­ndi= ce de error con relaciĂłn al nĂşmero de iteraciones o nĂşmero de Ă©pocas del sist= ema. Posterior a la determinaciĂłn de los valores Ăłptimos de respuesta para la RNA, surgi= eron 8 iteraciones haciendo uso del paquete RNA de MATLAB 2014.<= /p>

Figura 8. Error cuadrá= tico vs número de Iteraciones

Fuente: ElaboraciĂłn propia de los autores.

 

= Plottrainstate

Esta herramienta representa el estado de formaciĂłn de un registro de vuelto por= un tren de datos, indica la disminuciĂłn de gradiente hasta una convergencia d= e la RNA, valores perdidos en validaciĂłn y de no existencia en las 8 iteracione= s. Se determina que la RNA entrega una salida con todos estos valores deseados de datos.

Figura 9. Estados de en= trenamiento vs nĂşmero de iteraciones.

Fuente: ElaboraciĂłn propia de los autores.

 

= Plotregression

Esta herramienta permite visualizar una regresión lineal de los valores como en= trada con relación a objetivos de salida, mediante este entrenamiento y la implementación de algoritmos nos permite observar el sistema linealizado y= la determinación de no existencia de pérdidas de información durante el entrenamiento. Se logro entrenar todos los datos de una manera excelente.

Figura 10. RegresiĂłn de Datos de Entrenamiento

Fuente: ElaboraciĂłn propia de los autores.

 <= /span>

 <= /span>

Conclusiones.

·      =    El proceso de pasteurizaciĂłn realiz= ado con un sistema de control basado en una red neuronal permite la pasteurizac= iĂłn con reducciĂłn de manipulaciĂłn y el riesgo en un 80% del operario.<= /span>

·&n= bsp;        La Red Neuronal Artificial (RNA) de = tipo Feedforward contiene un algoritmo de backpropagation L-M y mitigaciĂłn de error MSE, permi= te una convergencia de datos tanto de entrada como salida a su 100% con la aplicac= iĂłn de dos redes neuronales artificiales aplicadas a la subida y bajada asignad= as al calentamiento y enfriamiento de la leche final.

·&n= bsp;        Mediante el uso del software Matlab = 2014 y el hardware Arduino Mega 2560 se logrĂł obtener un sistema neuro-controla= dor para temperatura con una respuesta optima y buena respuesta al proceso de pasteurizaciĂłn con buena tolerancia a fallas tĂ©cnicas.<= /p>

·&n= bsp;        La tarjeta Arduino Mega 2560 logrĂł = una lectura y escritura de gran cantidad de datos con una velocidad máxima de 115200 baudios.

Recomendaciones.

·&n= bsp;        Realizar una revisiĂłn previa de compatibilidad entre hardware y software de los componentes, asĂ­ como sus = hojas de datos para verificar voltajes máximos admitidos y evitar daños.

·&n= bsp;        Separar los circuitos de mando y potencia como protecciĂłn ante un corto circuito, sobretensiĂłn o ruido en = la etapa de mando y sistema neuro-controlador.

·&n= bsp;        Tomar en cuenta que los switches deben estar en modo activo, de igual manera = los dispositivos de protecciĂłn y alimentaciĂłn.

 <= /p>

Referencias bibliográficas.=

ATRIA INNOVATION. (26 de 05 de 2021). ATRIAINNOVATIO= N. Obtenido de https://www.atriainnovation.com/que-son-las-redes-neuronales-= y-sus-funciones/

Gösta, B. (20= 03). Manual de industrias lácteas TetraPak Processing. Madrid, España: Madrid Vicente.

HaHn, N. (26 d= e 05 de 2014). GitHub. Obtenido de https://github.com/nhahn/gripitgood/= blob/master/arduino_serial_control/pde/adioes/adioes.pde<= /p>

Hardware Libre. (26 de 05 de 2021). Hardwarelibre. Obtenido de https://www.hwlibre.com/irfz44n/

IMPROLAC. (26 = de 05 de 2021). IMPROLAC. Obtenido de https://www.improlac.com/es/17/procesos-y-maquinas-tratamientos-termicos-= procesos-uht.html

INEN. (26 de 0= 5 de 2021). INEN INSTITUTO ECUATORIANO DE NORMALIZACIĂ“N Norma TĂ©cnica Ecuatoriana NTE INEN 9:2010 Quinta RevisiĂłn. Obtenido de https://www.normalizacion.gob.ec/buzon/normas/9-5.pdf

INOXPA. (25 de= 05 de 2021). INOXPA S.A.U. Obtenido de https://www.inoxpa.es/productos/equipos/tratamiento-termico/pasteurizador= -htst

Interempresas.= (15 de 10 de 2018). Canales sectoriales Interempresas. Obtenido de https://www.interempresas.net/Alimentaria/Articulos/227016-Pasteurizar-pa= ra-garantizar-la-seguridad-alimentaria.html

Mendieta Villamizar, J. C., Olarte Daza, M. A., & González Acevedo, H. (2012). Diseño de un sistema de control inteligente para un pasteurizador tipo t= únel. Fundación Dialnet, 81-88.

Ministerio de Agricultura y GanaderĂ­a. (12 de 11 de 2020). Ministerio de Agricultur= a y GanaderĂ­a. Obtenido de https://www.agricultura.gob.ec/ecuador-se-nutre-de-leche-y-el-sector-lact= eo-se-fortalece-con-apoyo-del-gobierno-nacional/

Palma MĂ©ndez,= J., & Márin Morales, R. (2011). Inteligencia Artificial: MĂ©todos, tĂ= ©cnicas y aplicaciones. Barcelona, España: McGraw-Hill.

Tipán, M., &a= mp; Flores, D. (2018). Diseño y construcción de un prototipo de pasteurizad= ora para el procesamiento de 50 litros de Leche/Hora. Quito, Pichincha, Ecuad= or: Universidad Politécnica Salesiana Sede Quito.

Tortora, G., Funke, B., & Case, C. (2007). Introducción a la microbiología. Madrid: EDITORIAL MÉDICA PANAMERICANA. S.A.





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