ArticlePDF Available

Abstract

In the present study we performed the measurement of the actual process capacity of the characteristic weight in the product 500 grams in a rice mill. The data were obtained by applying a sampling plan according to the production of the four packing machines. The ability of this process to meet specifications was determined by analyzing the actual capacity (AP), upper process capability (UPC), lower process capability (LPC), actual process capability and percentage (%) of nonconforming product (NCP). The study shows in economic terms the impact generated by the current scenario and finally shows statistical information of each machine to evidence trends that may be generating noise and variations in the packaging process.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 21 (2017) pp. 11588-11597
© Research India Publications. http://www.ripublication.com
11588
Real Process Characteristic Capacity Weight in the Product 500 Grams in a
Rice Mill
Nelson Corredor Sánchez *, Ruthber Rodriguez Serrezuela *,
Andrés Mauricio Navarrete Ramos * and Jorge Luis Aroca Trujillo *
* Industrial Engineer, University Corporation of Huila, CORHUILA
ORCID: 0000-0002-0405-0692
Abstract
In the present study we performed the measurement of the
actual process capacity of the characteristic weight in the
product 500 grams in a rice mill. The data were obtained by
applying a sampling plan according to the production of the
four packing machines.
The ability of this process to meet specifications was
determined by analyzing the actual capacity (AP), upper
process capability (UPC), lower process capability (LPC),
actual process capability and percentage (%) of nonconforming
product (NCP). The study shows in economic terms the impact
generated by the current scenario and finally shows statistical
information of each machine to evidence trends that may be
generating noise and variations in the packaging process.
Keywords: machines, mills, rice, packaging.
INTRODUCTION
The content of the packaged products must comply with
minimum requirements required by Colombian legal
regulations. Failure to comply entails major economic
sanctions that may threaten the stability of any business.
Weight is one of the most important quality features in a mass
consumer product. Packing quantities below or above the face
value has major drawbacks, in addition to the above, can also
affect the brand image and sometimes create scenarios where
operations are cost inefficient [1], [2].
For this reason, it is important to know the real situation of the
content of a product; For this purpose, this variable should be
measured and evaluated technically.
Statistical methods exist that allow to fulfill this purpose, they
are tools that were born in the middle of century XIX, in
essence identify the changes that occur in a process, due to its
nature, that is to say by the use of raw materials of different
suppliers, the skills of operators, the operating conditions of the
equipment and other factors involved [3], [4].
For variations to be acceptable, they must be within a range set
by a specification. This is defined by a technical standard, by
decision of the client or by necessity of the process itself.
In order to know the degree of compliance of a quality
characteristic with respect to its specification, its potential
processing capacity (PPC) index must be calculated, this
indicator compares the width of the specifications or the
variation tolerated for the process with the amplitude of the
actual variation of this, represented by its standard deviation;
lower process capability (LPC), Upper Process Capability
(UPC), Actual Process Capability (APC). These last indices
take into account the concept of process centering, as a
complement to these indices the probable percentage of product
that does not comply with the specifications must be
determined. (% PNC) [5], [6], [7]
The present study aims to measure the capacity of the
packaging process of a rice mill, to meet the specifications
established for the characteristic weight of the product of 500
grams, applying the statistical concept of process capacity and
nonconforming product with them is intended to provide
technical information for the company to scale the current
situation and undertake improvement activities [8], [9], [10].
In order to comply with the objective of the present study, data
collection was planned using sampling techniques and ensuring
randomization of the data.
Subsequently, the indices of potential processing capacity
(PPC), Upper processing capacity (UPC), lower processing
capacity (LPC), actual process capacity (APC) and
nonconforming product percent (NCP) were calculated [11],
[12], [13].
It was defined the degree to which the actual production of the
rice mill packaging process is able to meet the quality feature.
We also determined the probable percentage of product that
presented weights outside the established parameters. Finally,
the economic impact of the current situation was quantified in
economic terms in terms of non-compliance with the
characteristic weight of the product analyzed [14], [15].
The calculations and analysis were developed in the Minitab 16
statistical software.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 21 (2017) pp. 11588-11597
© Research India Publications. http://www.ripublication.com
11589
METHODOLOGY
The project began with the characterization of the packaging
process, a sample of 586 pounds of rice was selected [17, [18].
The measurements were carried out on an electronic scale,
OHAUS, Model CT600L, Series CK 05857, Identification
FC.CA-1003 Measuring intervals 2g to 600g, scale division
0.1g, calibration date 2014-09-17. Certificate Number 14279
ZC.
The sampling plan was based on the assumption of
homogeneity of the data. There are four (4) Tecnopack brand
packaging machines that work under similar conditions. The
type of sampling is the simple random. "a sampling design is
said to be random simple if all possible samples of size n are
equally likely to be selected" [19], [20].
To find the sample size, the daily production of the machines
was considered. Its nomenclature starts from number five (5) to
number eight (8), the production capacity of each machine is
60 units per minute. They work two shifts, each of 8 hours; the
daily production estimated by the four machines is 230400
units.
The standard deviation was calculated by a pilot sample of 200
units randomly extracted from production under normal
conditions for one day. (See Annex A) A maximum error of 0.2
grams with a reliability of 95% was estimated.
N = 230400 units
σ = 2.47 grams
B = 0.2 grams
K = 1.96
n = 584 units
Equation 1. Sample size
  
Source: Gutiérrez Pulido & De la Vara Salazar, 2009.
The units were collected in one working day. Each hour was
randomly selected 36 pounds, nine units per machine, starting
at 06:00 am and ending at 10:00 pm. In the last collection eight
samples were taken, two for each machine to complete the 584
units. (See Annex B).
Once this information was obtained we proceeded to calculate
the indices of capacity and nonconforming product applying
the properties of the normal distribution. The study was
stratified by the analysis of each machine, to determine
individual behavior.
The degree to which the actual production of the process is able
to meet the quality characteristic under study was established.
Finally, the current situation of the product with respect to
weight and its impact on the organization in economic terms
was analyzed. To fulfill this purpose it was necessary to project
a production condition in which the probability of finding
product below 500 grams was close to zero, this would ensure
compliance with the minimum weight recorded in the nominal
content. To achieve this new scenario the average of the current
process was theoretically increased, maintaining the same
dispersion, this is achieved in practice, mechanically modifying
the dosing system of the machines; the values that the variable
could take were calculated by increasing the weight of the
pounds and the costs that would incur the company considering
the amount of rice that would be packed above the established.
The above point estimate was made by applying the properties
of the normal distribution and based on daily yields and rice
prices to January 2017.
The company in which the research work was developed, is
licensed by Minitab 16, for that reason and for its kindness in
quality control issues, calculations and statistical analysis were
done in this software.
The Minitab 16 statistical program does not have the Shapiro-
Willk test for the normality test, but has a similar one, the Ryan-
Joiner test; to counter this condition, Anderson-Darling, and
kolmogorov-smirnov will also be tested.
RESULTS
Packaging Process
The rice mill referenced in the present study, packs its products
in different presentations, 250 grams, 1000 grams, 3000 grams,
5000 grams and 10000 grams. The most representative is the
one of 500 grams, constitutes 90% of the production. To obtain
the presentation of 500 grams multiple operations must be
performed.
It starts by filling the hoppers with white rice, from the
threshing process. Subsequently the rice passes directly to the
dosing system of the machine by gravity through a duct, this
system is composed of 8 molds (glasses) arranged on a rotating
disk. The rice is dropped into the molds as the disk rotates and
at the same time moves the load to the hole in the forming tube
of the machine. This is covered by a sheet of polyethylene that
is displaced while the rice falls. The vertical and horizontal
jaws seal the bags with the contents. The bags with rice fall to
a small conveyor belt to be taken quickly where an operative
PROVEEDORES
-Proceso de trilla
-Almacen
-Departamento de
calidad
-Depatamento de
mante|nimient o
-Area de despachos
ENTRADAS
-Arroz blanco
-Polietileno
-procediminet os
-Especificaciones
-Servicio tecnic o
-solicitud de
pediido
PROCESO
-Empaque
presentacion 500
gr
SALIDAS
-Arroz empacado
CLIENTES
-Almacen producto
terminado
-Area de logistica
-Area de
despachos
SALIDAS
-Arroz empacado
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 21 (2017) pp. 11588-11597
© Research India Publications. http://www.ripublication.com
11590
who places 25 units in a repackaging bag. The package is
placed on another conveyor belt, the packages are moved to a
machine to be sealed. An operator receives the bag and
transports it in a wheelbarrow to the finished product area [20],
[21].
The packaging process has four machines (machines 5, 6, 7 and
8) each capable of packing 3600 units per hour.
Figure 1: Tecnopack machine
Source: MOLINO DE ARROZ, 2017. Packing Machine
The production plant regularly works throughout the year in
two shifts of 8 hours each for 6 days a week. (Monday to
Saturday) [22], [23].
The quality of the rice is based on several characteristics, one
of them is the weight, represents the actual content of the
product.
For the presentation of 500 grams, the company has defined a
specification of 502 grams ± 2 grams, which means that each
packaged unit must have a weight within this range, otherwise
it is considered nonconforming product.
In order to contextualize the present study and to identify the
potential sources of noise, which could affect a possible non-
compliance with the variable weight, the packaging process
was characterized, clearly identifying its main components.
Source: MOLINO DE ARROZ, 2017. Packing process
Figure 2: Characterization
As Figure 2 shows, many factors are involved in the packaging
process that can generate variation and permanently threaten
the quality of the product.
Suppliers are identified, who supply products and services to
be transformed. The main input element is the rice that comes
from the threshing process. This must meet specific
characteristics to ensure a good performance in terms of the
variable weight. There are also activities that generate value
and convert the entries into finished product. There is very
important technical support, a relevant factor in the proper
functioning of machines, equipment and the reduction of
variation caused by the dosing system. Finally, the customers
are the ones who receive the production of the packaging
process.
NORMALITY TEST
The characteristic weight in the pounds of rice, is a continuous
random variable, follows a normal type distribution. To accept
this assumption, three normal tests were performed on the
sample data. The Minitab 16 statistical program has the Ryan-
Joiner, Anderson-Darling, and Kolmogorov-Smirnov tests.
The results of this evaluation are shown in graphs 1, 2 and 3.
Graph 1. Ryan-Joiner Normal Test
Source: MOLINO DE ARROZ, 2017. Weights of 584
pounds
With a confidence interval of 95%, the Ryan-Joiner test shows
that with a p value equal to 0.100, the null hypothesis is
accepted. The weights of the pounds in the packaging process
are normally distributed.
This statement is validated with the graph, it shows the
behavior of each data with respect to the blue line, which
represents the normal distribution.
PROVEEDORES
-Proceso de trilla
-Almacen
-Departamento de
calidad
-Depatamento de
mante|nimient o
-Area de despachos
ENTRADAS
-Arroz blanco
-Polietileno
-procediminetos
-Especificaciones
-Servicio tecnic o
-solicitud de
pediido
PROCESO
-Empaque
presentacion 500
gr
SALIDAS
-Arroz empacado
CLIENTES
-Almacen producto
terminado
-Area de logistica
-Area de
despachos
SALIDAS
-Arroz empacado
512.5510.0507.5505.0502.5500.0497.5495.0
99.99
99
95
80
50
20
5
1
0.01
PESO
Porcentaje
Media 502.7
Desv.Est. 2.176
N584
RJ 0.999
Valor P >0.100
Gráfica de probabilidad de PESO
Normal
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 21 (2017) pp. 11588-11597
© Research India Publications. http://www.ripublication.com
11591
Graph 2. Anderson- Darling normality test
Source: MOLINO DE ARROZ, 2017. Weights of 584
pounds
Like the previous test, that of Anderson-Darling confirms that
the data analyzed are distributed in a normal way, the graph of
normality shows this clearly. With a confidence interval of
95%, the p value is equal to 0.201, which leads to accept the
null hypothesis.
Graph 3. Normal test Kolmogorov Smirnov
Source: MOLINO DE ARROZ, 2017. Weights of 584
pounds.
With the third test of normality, that of Kolmogorov - Smirnov,
it can be seen that, in fact, the 584 pesos fit into a normal
distribution, the graph and p value validate this assumption (p
value = 0.150), with a confidence interval of 95%.
However, for process capability studies it is not a condition that
the data follow a normal distribution. "6σ (six times the
standard deviation) is the actual variation, due to the properties
of the normal distribution, where it is stated that between μ ±
3σ is 99.7% of the values of a variable with normal distribution,
even if there is no normality, a large percentage of the
distribution is found in μ ± 3σ because of Chebyshev's
inequality and the empirical rule" [23], [24], [25].
The rice mill defines the specifications for the variables that
represent the characteristics of the product and the process,
based on technical standards, the nature of the process and the
customer's requirements.
For the case of weight, it was established that the specification
for the presentation of pound out of 502 ± 2 grams.
The calculation of the indices of the process capacity and the
nonconforming product were done with the help of the
statistical software Minitab 16, the results are shown
in figure 4.
Graph 4. Process capacity analysis
Source: MOLINO DE ARROZ, 2017. Weights of 584
pounds.
The units analyzed were 584 corresponding to the total of the
established sample. The result shows that the amplitude of the
process is greater with respect to the amplitude of the
specifications, the red lines indicate the theoretical dispersion
allowed by the process, the filling system of the rice mill
presents little ability to comply with the established parameters.
Its potential capacity (0.310) reflects the degree of dispersion
of its data, it is far from the 1.33 that is suggested for this type
of indicator. The average of the sample (502.72 grams)
indicates that the process is off-center to the right side, since
the theoretical average of the process is 502 grams. This fact is
512.5510.0507.5505.0502.5500.0497.5495.0
99.99
99
95
80
50
20
5
1
0.01
PESO
Porcentaje
Gráfica de probabilidad de PESO
Normal
512.5510.0507.5505.0502.5500.0497.5495.0
99.99
99
95
80
50
20
5
1
0.01
PESO
Porcentaje
Media 502.7
Desv.Est. 2.176
N584
KS 0.030
Valor P >0.150
Gráfica de probabilidad de PESO
Normal
508506504502500498496
LEI LES
LEI 500
O bjetiv o *
LES 504
Me dia de la m uestra 502.721
Núm ero de mue stra 584
Desv .Est. (General) 2.17619
Procesar da tos
Pp 0.31
PPL 0.42
PPU 0.20
Ppk 0. 20
C pm *
C apacidad general
% < LEI 10.56
% > LES 27.84
% Total 38.39
Exp. R endimiento gene ral
Capacidad de proceso de PESO
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 21 (2017) pp. 11588-11597
© Research India Publications. http://www.ripublication.com
11592
confirmed by the value of higher and real capacity (0.20). The
likely percentage of nonconforming product is out of
specification is 38.39%, 10.56% of the pounds would have
weight below 500 grams and 27.84% would have content above
504 grams. Only 61.61% of production meets the desired
weight.
The results evidence large problems in compliance with the
characteristic weight, generating a large quantity of product
with higher and lower content of rice to the stipulated. The
company must immediately carry out improvement projects if
it wishes to achieve the objectives set. For this it is necessary
to know in more detail the possible sources of variation. The
stratification of information facilitates the interpretation of
facts, gives clear guidance on the real causes, and provides tools
for making sound decisions.
The individual behavior of the machines is analyzed, with this
it is tried to identify the degree of incidence of each of them in
the lack of capacity to fulfill specifications of the packing
process with respect to the characteristic weight. The results are
shown in graphs 5, 6, 7 and 8.
Graph 5: Processing capacity of machine 5
Source: MOLINO DE ARROZ, 2017. Weights of 584
pounds.
In figure 5 it is observed that the amplitude of the process of
the machine 5 is much superior to the amplitude of the
specifications. The red lines that represent the specifications
dimension the situation. The production of this machine has
little processing capacity. Its potential capacity index (0.27) is
much lower than expected, it is also lower than that of the
overall process. The mean of the sample (502,557 grams)
indicates that the weight of the pounds that packs this machine
have a bias towards the right side of the distribution, this fact
ratifies the index of superior capacity with a 0.19 and also the
quantity of product does not as it exceeds the weight of 504
grams (28.38%).
Machine 5 is generating more nonconforming product than the
overall process.
Graph 6 Processing capacity of machine 6
ea
Source: MOLINO DE ARROZ, 2017. Weights of 584
pounds.
In graph 6 shows the non-compliance of the machine
specification 6, the process amplitude is higher than the
tolerance. This causes the potential capacity index to be 0.40.
The capacity study of this machine also shows that the largest
quantity of nonconforming product is on the upper side of the
specifications, the probability of producing product weighing
above 504 grams is 21.67%.
Despite non-compliance with specifications, it is noted that the
production of nonconforming product of this machine
(26.88%) is lower than that of the process (38.39%).
Graph 7 Process capacity machine 7
Source: MOLINO DE ARROZ, 2017. Weights of 584 pounds.
508506504502500498
LEI LES
LEI 500
O bjetiv o *
LES 504
Media de la muestra 502.557
Núm ero de muestra 146
Desv .Est. (G eneral) 2.4928
Procesar dato s
Pp 0.27
PPL 0.34
PPU 0.19
Ppk 0.1 9
C pm *
C apacidad general
% < LEI 15.25
% > LES 2 8.13
% T otal 43.38
Exp. Re ndimiento gene ral
Capacidad de proceso de PESO
MAQUINA 5
507.0505.5504.0502.5501.0499.5
LEI LES
LEI 500
O bjetiv o *
LES 504
Media de la muestra 502.699
Núme ro de mue stra 146
Desv .Est. (G eneral) 1. 66068
Procesar datos
Pp 0.40
PPL 0.54
PPU 0.26
Ppk 0.26
C pm *
C apacidad general
% < LEI 5.20
% > LES 21.67
% T otal 26.88
Exp. Re ndimiento general
Capacidad de proceso de PESO
MAQUINA 6
508506504502500498496
LEI LES
LEI 500
Obj etiv o *
LES 504
Media de la muestra 502.419
Número de muestra 146
Desv .Est. (G eneral) 2.41056
Procesar datos
Pp 0.28
PPL 0.33
PPU 0.22
Ppk 0.22
Cpm *
Ca pacidad general
% < LEI 15.78
% > LES 25.60
% T otal 41.38
Exp. Rendim iento general
Capacidad de proceso de PESO
MAQUINA 7
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 21 (2017) pp. 11588-11597
© Research India Publications. http://www.ripublication.com
11593
1Figure 7 clearly shows that the machine 7 is also unable to
meet the set weight. Their capacity and nonconforming product
indices are lower than the overall process. It presents major
problems towards the upper side of the specification.
Graph 8 Processing capacity of machine 8
Source: MOLINO DE ARROZ, 2017. Weights of 584
pounds.
It can be seen from Figure 8 that the production of the machine
8 presents a large bias towards the upper side of the
specification. Similarly, a significant deviation of the central
value from the theoretical average is observed, 34.44% of the
pounds packed in this machine have weights above 504 grams.
The reflection of this situation is its superior capacity index,
which is the lowest value among the four machines studied.
Machine 8 is the one with the largest amount of nonconforming
product producing above 504 grams, with 34.44% compared to
28.13%, 21.67% and 25.60% of machines 5, 6 and 7
respectively.
Another aspect that can be seen in these graphs is that machines
5 and 7 are the one with the largest product weighing less than
500 grams with 15.25% and 15.78% compared to 5.20% and
5.21% of machines 6 and 8.
The study determined that there are pounds with weight outside
the specifications, generating negative impacts for the company
in terms of over cost and customer perception.
Immediate correction would mean that at least the weight
recorded at the nominal content (500 g) must be met. In this
case, if this correction were made and taking into account the
current centering and dispersion tendencies of the process
(average 502.72g and standard deviation 2.17), it would have
to be overweight since the dosing system should be adjusted,
increasing approximately the total average to 507 grams and
thus guarantee a minimum of product below 500g (close to
0%).
In this new scenario, the lower limit would be fulfilled, but
production would be at levels of overweight as shown
in Table 1.
Table 1: Economic impact
Source: Own
Only 8.33% of the production would comply with the desired,
the rest of the pounds would have an excess of content, this
would force the company to incur a surcharge of $ 2.8 per
additional gram packed, which according to projected
production (5000.000.000 pounds / month) would be
approximately $ 49,000,000 / month.
The above point estimate was made by applying the properties
of the normal distribution and based on daily yields and rice
prices to January 2017.
DISCUTION
Para la aplicación de estos métodos de clasificación se deben
tener en cuenta los parámetros que rige la manipulación de
alimentos en Colombia, para así mismo hacer cumplir las leyes
a la hora de realizar los diferentes empaques de arroz,
evidenciamos falencias en varias maquina alguna de sobre peso
y otras por falta de peso lo cual puede ser sancionado por estas
entidades que rigen la manipulación y empacado de alimentos.
CONCLUSION
The present work evaluated the capacity of the packaging
process in a rice mill to produce units with contents that are in
the range of 500 grams and 504 grams.
Through the characterization of the process, it was possible to
clearly visualize the input, output and main activities factors,
508.5507.0505.5504.0502.5501.0499.5
LEI LES
LEI 500
O bjetiv o *
LES 504
Me dia de la muestra 503.209
Núm ero de m uestra 146
Desv .Est. (General) 1.9747
Procesar da tos
Pp 0.34
PPL 0.54
PPU 0.13
Ppk 0. 13
C pm *
C apacidad ge neral
% < LEI 5.21
% > LES 34.44
% Total 39.64
Exp. R endimiento ge neral
Capacidad de proceso de PESO
MAQUINA 8
PROBABILIDAD PROD/MENSUAL GRAMOS DE MAS $ GRAMOS DE MAS
MENORES A 500g 0.06% 3000
500g-504g 8.33% 416500
504 g-505g 9.50% 475000 475000 1,330,000.00$
505g-506g 14.39% 719500 1439000 4,029,200.00$
506g-507g 17.70% 885000 2655000 7,434,000.00$
507g-508g 17.71% 885500 3542000 9,917,600.00$
508g-509g 14.39% 719500 3597500 10,073,000.00$
509g-510g 9.50% 475000 2850000 7,980,000.00$
510g-511g 5.10% 255000 1785000 4,998,000.00$
511g-512g 2.22% 111000 888000 2,486,400.00$
512g-513g 0.79% 39500 355500 995,400.00$
513g-514g 0.23% 11500 115000 322,000.00$
514g-515g 0.05% 2500 27500 77,000.00$
515g-516g 0.01% 500 6000 16,800.00$
TOTAL 49,659,400.00$
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 21 (2017) pp. 11588-11597
© Research India Publications. http://www.ripublication.com
11594
highlighting elements such as the quality of the rice that comes
from the threshing process and the technical support to the
dosing system of the machines. This identified the potential
generators of variation, a key concept to determine the quality
of a product.
Simple random sampling was designed; it defined a sample size
equal to 584 units and a collection methodology, ensuring that
the selected units represent the total production under normal
conditions in a day of work.
It was determined that the packing process of the rice mill does
not have the capacity to meet the specifications of the
characteristic weight in the reference of 500 grams. The result
of the analysis yielded a potential capacity index equal to 0.31,
a value that is far from the desired one (1.33). Consequently,
the amplitude of the process is much higher than that of the
specifications, and the actual capacity (0.2) shows a bias of the
pounds towards the upper limit. Machine number five (5) was
identified as the largest generator of nonconforming product
and number six (6) as the lowest percentage. It was also
established that machine number eight (8) is the one with the
largest amount of nonconforming product producing above 504
grams and the number seven (7) being greater than 500 grams.
The non-processing capacity implies that the rice mill, out of
its total production, 38.4% of the pounds are outside the
parameters established by the company (500gr-504gr), the
excess content being the of greater problem with 27.8%.
Producing less than 500 grams (10.56% probability) can
generate distrust in those customers who detect this situation,
and also creates a distorted perception of the product and the
company. To correct this problem is visualized an immediate
action, increase the average of the pounds produced by 4.5
grams, modifying the dosing system, this would involve
packing more content than the one referenced in the label
(91.6% of the production), incurring an envelope cost
approximately 49 million pesos per month.
RECOMMENDATIONS
It is suggested to the company to advance improvement
processes taking advantage of the valuable frame of reference
that leaves this study, since in addition to describing technically
the current situation of the characteristic weight, also shows
trends of each of the machines.
It is recommended to implement a training program in
statistical methods and quality control in the company,
especially the technical and operational personnel, so that
through the correct measurement, timely detection of
deviations that may occur in daily operations, also this
competition will allow to visualize more easily the options of
improvement.
REFERENCE
[1] Stark, J., Product lifecycle management. In Product
Lifecycle Management (Volume 1) (pp. 1-29),
Springer International Publishing, (2015)
[2] Azhmyakov, V., Rodriguez Serrezuela, R., Rios
Gallardo, A. M., and Gerardo Vargas, W., An
approximations based approach to optimal control of
switched dynamic systems, Mathematical Problems in
Engineering, (2014)
[3] Auger, P., Burke, P., Devinney, T. M., and Louviere,
J. J., What will consumers pay for social product
features?, Journal of business ethics, (2003), 42(3),
281-304.
[4] Serrezuela, R. R., and Chavarro, A. F. C..,
Multivariable Control Alternatives for the Prototype
Tower Distillation and Evaporation Plant.,
International Journal of Applied Engineering Research,
(2016), 11(8), 6039-6043.
[5] Ramanathan, U., Subramanian, N., Yu, W., &
Vijaygopal, R., Impact of customer loyalty and service
operations on customer behaviour and firm
performance: empirical evidence from UK retail
sector., Production Planning and Control, (2017), 28(6-
8), 478-488.
[6] Serrezuela, R. R., Chavarro, A. F. C., Cardozo, M. A.
T., & Zarta, J. B. R., An Optimal Control Based
Approach to Dynamics Autonomous Vehicle.,
International Journal of Applied Engineering Research,
(2016), 11(16), 8841-8847.
[7] Gao, H., Zhang, Y., and Mittal, V., How Does Local
Global Identity Affect Price Sensitivity?., Journal of
Marketing, (2017), 81(3), 62-79.
[8] Montgomery, D. C., Design and analysis of
experiments., John Wiley & Sons., (2017).
[9] Azhmyakov, V., Serrezuela, R. R., and Trujillo, L. G.,
Approximations based optimal control design for a
class of switched dynamic systems., In Industrial
Electronics Society, IECON 2014-40th Annual
Conference of the IEEE, (2014), (pp. 90-95). IEEE.
[10] [10] Hill, T., Lewicki, P., & Lewicki, P., Statistics:
methods and applications: a comprehensive reference
for science, industry, and data mining., StatSoft, Inc.,
(2006).
[11] Kane, V. E., Process capability indices,, Journal of
quality technology, (1986), 18(1), 41-52.
[12] Rodriguez Serrezuela, R., & Carvajal Pinilla, L. A.,
Ecological determinants of forest to the abundance of
Lutzomyia longiocosa in Tello, Colombia,
International Journal of Ecology, (2015).
[13] Mercer, A. W., Kreuter, F., Keeter, S., & Stuart, E. A.,
Theory and Practice in Nonprobability Surveys:
Parallels Between Causal Inference and Survey
Inference, Public Opinion Quarterly, (2017), 81(S1),
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 21 (2017) pp. 11588-11597
© Research India Publications. http://www.ripublication.com
11595
250-271.
[14] Rojas, J. H. C., Serrezuela, R. R., López, J. A. Q., &
Perdomo, K. L. R., LQR Hybrid Approach Control of
a Robotic Arm Two Degrees of Freedom, International
Journal of Applied Engineering Research, (2016),
11(17), 92219228.
[15] Kotz, S., & Johnson, N. L, Process capability indices,
CRC Press, (1993).
[16] Montiel, J. J. G., Serrezuela, R. R., & Aranda, E. A.,
Applied Mathematics and Demonstrations to the
Theory of Optimal Filters, Global Journal of Pure and
Applied Mathematics, (2017), 13(2), 475-492.
[17] Wu, C. W., Pearn, W. L., & Kotz, S., An overview of
theory and practice on process capability indices for
quality assurance, International journal of production
economics, (2009), 117(2), 338-359.
[18] Serrezuela, R. R., Cardozo, M. A. T., & Chavarro, A.
F. C., Design and Implementation of a PID Fuzzy
Control for the Speed of a DC Motor, ARPN Journal of
Engineering and Applied Sciences, (2017), 12 (8), pp.
26552660.
[19] Vivanco, M., Muestreo estadistico. Diseño y
aplicaciones., Editorial Universitaria., (2005).
[20] Serrezuela, R. R., Chavarro, A. F. C., Cardozo, M. A.
T., Toquica, A. L., & Martinez, L. F. O., Kinematic
Modelling of a Robotic Arm Manipulator Using
MatLab, ARPN Journal of Engineering and Applied
Sciences., (2017), 12 (7), pp. 2037-2045
[21] Díaz, A., Diseñno estadistico de experimentos 2a, Ed.
Universidad de Antioquia., (2009).
[22] López, P. L., Población muestra y muestreo, Punto
cero, (2004), 9(08), 69-74.
[23] Serrezuela, R. R., Chavarro, A. F., Cardozo, M. A.,
Caicedo, A. G. R., & Cabrera, C. A., Audio signals
processing with digital filters implementation using
MyDSP., ARPN Journal of Engineering and Applied
Sciences., (2017), 12 (16), pp. 4848-4853.
[24] Romo, H. L., La metodología de la encuesta. Técnicas
de investigación en sociedad, cultura y comunicación,
Ciudad de México: Pearson, (1998).
[25] Serrezuela, R. R., Sánchez, N. C., Zarta, J. B. R.,
Ardila, D. L., & Salazar, A. L. P., Case Study of Energy
Management Model in the Threshing System for the
Production of White Rice, International Journal of
Applied Engineering Research, (2017), 12(19), 8245-
8251.
[26] Salazar, A. L. P., Ardila, D. L., & Peñate, T. C., Mejora
en el proceso de trilla para reducci ón del exceso de
arroz partido en la empresa Molino XYZ., El Hombre
y la M aquina No. 46, Enero - Junio de 2015.
ANNEXES
The author authorizes the use of information contained in the
annexes, for academic purposes
ANNEX A Pilot sample
Muestra Peso Muestra Peso Muestra Peso Muestra Peso Muestra Peso
1 500.0 41 504.7 81 502.9 121 501.0 161 501.2
2500 42 502.3 82 503.5 122 503.4 162 502.6
3 500.7 43 503.7 83 494.7 123 504.8 163 504.4
4 505.7 44 502.8 84 506.2 124 501.2 164 500.3
5 500.4 45 501.0 85 499.3 125 500.3 165 501.7
6 501.2 46 500.4 86 501.2 126 501.6 166 495.0
7 502.2 47 503.5 87 502.4 127 501.6 167 495.0
8 500.9 48 500.2 88 498.6 128 499.2 168 501.2
9 501.6 49 501.7 89 501.4 129 502.4 169 499.3
10 502.2 50 500.3 90 502.9 130 498.6 170 497.3
11 502.3 51 502.4 91 501.3 131 499.1 171 495.9
12 501.6 52 500.3 92 503.6 132 499.2 172 498.2
13 494 53 501.4 93 499.4 133 498.8 173 497.8
14 499 54 502.0 94 501.9 134 499.1 174 499.9
15 501.7 55 500.3 95 501.1 135 500.0 175 494.6
16 501.3 56 499.3 96 502.8 136 501.3 176 502.2
17 503.4 57 503.3 97 502.6 137 505.9 177 503.4
18 502.3 58 501.3 98 500.8 138 504.9 178 496.9
19 502.4 59 499.8 99 500.8 139 500.8 179 504.5
20 503.7 60 500.5 100 502.8 140 502.4 180 502.0
21 501.2 61 502.8 101 502.1 141 501.3 181 500.4
22 504.8 62 502.5 102 502.3 142 501.3 182 502.3
23 493.7 63 504 103 503.1 143 503.3 183 501.4
24 505.2 64 506.2 104 502.3 144 506.1 184 502.0
25 501.4 65 498.1 105 500.6 145 502.6 185 501.1
26 500.2 66 500.8 106 500.5 146 499.5 186 497.4
27 501.5 67 502.3 107 505.2 147 502.3 187 497.4
28 501.7 68 499.7 108 499.7 148 501.8 188 502.7
29 503.3 69 501.7 109 500.4 149 501.5 189 501.2
30 500.8 70 501.6 110 499.2 150 501.3 190 495.7
31 501.4 71 502.1 111 499.2 151 500.0 191 498.2
32 505.8 72 502.7 112 498.7 152 500.6 192 498.9
33 497 73 503.6 113 499.1 153 504.0 193 498.0
34 502.2 74 503.9 114 501 154 498.1 194 501
35 503 75 499.5 115 494.5 155 498.5 195 495.2
36 501.1 76 501.6 116 501.5 156 499.2 196 502.3
37 504.4 77 500.7 117 505.7 157 506.4 197 501.7
38 502.4 78 502.0 118 503.6 158 495.8 198 494.4
39 499.8 79 499.8 119 501.1 159 501.3 199 502.3
40 500.3 80 501.2 120 502.2 160 501.3 200 502.4
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 21 (2017) pp. 11588-11597
© Research India Publications. http://www.ripublication.com
11596
Source: Own
ANNEX B Simple random sampling
HORA
MAQUINA PESO HORA MAQUINA PESO HORA MAQUINA PESO HORA MAQUINA PESO
06:00 5 499.1 07:00 5 505.4 08:00 5 503.5 09:00 5 504.7
06:00 5 503.8 07:00 5 499.8 08:00 5 503.9 09:00 5 506.3
06:00 5 504.8 07:00 5 503.1 08:00 5 500.1 09:00 5 504.3
06:00 5 500.6 07:00 5 500.6 08:00 5 499.4 09:00 5 503.8
06:00 5 502.4 07:00 5 499.8 08:00 5 502.8 09:00 5 503.2
06:00 5 500.5 07:00 5 502.0 08:00 5 499.1 09:00 5 502.0
06:00 5 502.2 07:00 5 505.2 08:00 5 499.1 09:00 5 499.0
06:00 5 507.0 07:00 5 505.5 08:00 5 499.0 09:00 5 503.1
06:00 5 506.5 07:00 5 507.9 08:00 5 504.8 09:00 5 499.1
06:00 6 503.8 07:00 6 501.2 08:00 6 507.0 09:00 6 504.9
06:00 6 502.1 07:00 6 501.1 08:00 6 505.1 09:00 6 504.0
06:00 6 504.1 07:00 6 501.5 08:00 6 503.6 09:00 6 505.2
06:00 6 503.0 07:00 6 501.2 08:00 6 501.2 09:00 6 502.6
06:00 6 503.6 07:00 6 502.2 08:00 6 503.6 09:00 6 502.3
06:00 6 505.2 07:00 6 498.6 08:00 6 505.2 09:00 6 505.7
06:00 6 500.5 07:00 6 499.9 08:00 6 503.6 09:00 6 504.4
06:00 6 500.6 07:00 6 500.1 08:00 6 504.0 09:00 6 505.0
06:00 6 499.7 07:00 6 501.4 08:00 6 504.2 09:00 6 504.6
06:00 7 500.9 07:00 7 501.1 08:00 7 499.5 09:00 7 501.5
06:00 7 503.8 07:00 7 500.8 08:00 7 499.4 09:00 7 499.0
06:00 7 504.7 07:00 7 501.4 08:00 7 499.3 09:00 7 501.8
06:00 7 496.0 07:00 7 498.2 08:00 7 498.8 09:00 7 499.4
06:00 7 500.8 07:00 7 498.2 08:00 7 500.3 09:00 7 502.0
06:00 7 502.3 07:00 7 498.6 08:00 7 500.3 09:00 7 499.0
06:00 7 499.8 07:00 7 500.5 08:00 7 500.1 09:00 7 500.6
06:00 7 504.2 07:00 7 500.1 08:00 7 500.1 09:00 7 500.6
06:00 7 505.8 07:00 7 499.9 08:00 7 499.7 09:00 7 499.9
06:00 8 505.1 07:00 8 507.8 08:00 8 504.6 09:00 8 506.4
06:00 8 504.4 07:00 8 504.5 08:00 8 504.1 09:00 8 505.8
06:00 8 503.7 07:00 8 504.5 08:00 8 506.2 09:00 8 507.4
06:00 8 503.0 07:00 8 508.3 08:00 8 504.9 09:00 8 505.4
06:00 8 503.8 07:00 8 504.9 08:00 8 506.7 09:00 8 504.4
06:00 8 504.9 07:00 8 505.5 08:00 8 507.4 09:00 8 507.1
06:00 8 503.7 07:00 8 506.4 08:00 8 505.9 09:00 8 504.6
06:00 8 503.7 07:00 8 507.4 08:00 8 505.8 09:00 8 507.0
06:00 8 503.2 07:00 8 509.0 08:00 8 506.6 09:00 8 504.4
HORA
MAQUINA PESO HORA MAQUINA PESO HORA MAQUINA PESO HORA MAQUINA PESO
10:00 5 498.8 11:00 5 504.8 12:00 5 501.9 13:00 5 506.3
10:00 5 504.0 11:00 5 507.0 12:00 5 503.1 13:00 5 501.8
10:00 5 500.0 11:00 5 499.5 12:00 5 501.0 13:00 5 499.9
10:00 5 501.8 11:00 5 501.8 12:00 5 500.0 13:00 5 500.3
10:00 5 503.0 11:00 5 501.3 12:00 5 499.8 13:00 5 499.9
10:00 5 496.5 11:00 5 504.1 12:00 5 503.2 13:00 5 506.9
10:00 5 499.7 11:00 5 502.5 12:00 5 498.4 13:00 5 502.6
10:00 5 501.7 11:00 5 502.9 12:00 5 503.2 13:00 5 505.0
10:00 5 505.0 11:00 5 503.1 12:00 5 497.4 13:00 5 505.3
10:00 6 501.1 11:00 6 503.0 12:00 6 504.0 13:00 6 504.8
10:00 6 503.7 11:00 6 502.5 12:00 6 503.9 13:00 6 503.0
10:00 6 502.4 11:00 6 501.6 12:00 6 504.8 13:00 6 504.3
10:00 6 502.9 11:00 6 502.1 12:00 6 503.2 13:00 6 504.8
10:00 6 500.7 11:00 6 503.2 12:00 6 504.2 13:00 6 501.3
10:00 6 502.7 11:00 6 503.4 12:00 6 502.8 13:00 6 503.5
10:00 6 502.3 11:00 6 503.0 12:00 6 503.3 13:00 6 502.7
10:00 6 505.1 11:00 6 504.4 12:00 6 506.7 13:00 6 505.0
10:00 6 502.8 11:00 6 504.1 12:00 6 502.8 13:00 6 506.4
10:00 7 504.8 11:00 7 501.1 12:00 7 501.5 13:00 7 503.7
10:00 7 498.1 11:00 7 503.2 12:00 7 499.9 13:00 7 504.0
10:00 7 501.5 11:00 7 506.4 12:00 7 504.1 13:00 7 502.8
10:00 7 506.7 11:00 7 502.1 12:00 7 505.4 13:00 7 505.1
10:00 7 501.3 11:00 7 499.8 12:00 7 504.1 13:00 7 504.5
10:00 7 507.3 11:00 7 506.7 12:00 7 504.1 13:00 7 506.2
10:00 7 499.9 11:00 7 503.6 12:00 7 505.1 13:00 7 502.9
10:00 7 508.9 11:00 7 506.7 12:00 7 501.0 13:00 7 504.8
10:00 7 505.8 11:00 7 505.9 12:00 7 504.2 13:00 7 504.7
10:00 8 500.5 11:00 8 503.4 12:00 8 505.1 13:00 8 502.3
10:00 8 502.9 11:00 8 502.9 12:00 8 502.7 13:00 8 504.1
10:00 8 501.8 11:00 8 501.4 12:00 8 504.1 13:00 8 505.7
10:00 8 503.5 11:00 8 503.4 12:00 8 503.3 13:00 8 504.1
10:00 8 504.6 11:00 8 501.9 12:00 8 501.2 13:00 8 502.2
10:00 8 503.4 11:00 8 504.0 12:00 8 503.9 13:00 8 503.5
10:00 8 502.2 11:00 8 503.8 12:00 8 504.5 13:00 8 504.2
10:00 8 501.4 11:00 8 503.3 12:00 8 502.5 13:00 8 499.5
10:00 8 501.7 11:00 8 504.5 12:00 8 503.0 13:00 8 500.3
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 21 (2017) pp. 11588-11597
© Research India Publications. http://www.ripublication.com
11597
Source: Own
HORA
MAQUINA PESO HORA MAQUINA PESO HORA MAQUINA PESO HORA MAQUINA PESO
14:00 5 503.0 15:00 5 505.8 16:00 5 498.5 17:00 5 499.2
14:00 5 505.1 15:00 5 499.0 16:00 5 501.3 17:00 5 503.3
14:00 5 497.0 15:00 5 505.4 16:00 5 502.2 17:00 5 500.8
14:00 5 502.8 15:00 5 500.1 16:00 5 503.9 17:00 5 503.5
14:00 5 501.6 15:00 5 501.7 16:00 5 498.1 17:00 5 498.4
14:00 5 505.0 15:00 5 502.3 16:00 5 501.0 17:00 5 505.1
14:00 5 502.4 15:00 5 501.5 16:00 5 499.0 17:00 5 503.2
14:00 5 501.4 15:00 5 504.6 16:00 5 500.7 17:00 5 505.9
14:00 5 504.4 15:00 5 505.8 16:00 5 501.1 17:00 5 499.3
14:00 6 501.0 15:00 6 501.3 16:00 6 500.7 17:00 6 503.3
14:00 6 503.1 15:00 6 500.7 16:00 6 502.4 17:00 6 500.3
14:00 6 504.3 15:00 6 500.9 16:00 6 502.7 17:00 6 501.9
14:00 6 499.6 15:00 6 500.2 16:00 6 502.5 17:00 6 501.8
14:00 6 503.2 15:00 6 500.9 16:00 6 505.0 17:00 6 502.3
14:00 6 502.8 15:00 6 502.4 16:00 6 500.3 17:00 6 500.3
14:00 6 502.6 15:00 6 503.1 16:00 6 502.7 17:00 6 502.7
14:00 6 503.3 15:00 6 500.1 16:00 6 500.2 17:00 6 502.5
14:00 6 502.7 15:00 6 502.1 16:00 6 501.2 17:00 6 502.8
14:00 7 504.1 15:00 7 504.9 16:00 7 502.1 17:00 7 501.3
14:00 7 506.2 15:00 7 500.5 16:00 7 505.4 17:00 7 503.8
14:00 7 503.9 15:00 7 505.4 16:00 7 504.5 17:00 7 504.5
14:00 7 504.8 15:00 7 504.1 16:00 7 501.7 17:00 7 501.2
14:00 7 501.6 15:00 7 504.5 16:00 7 504.7 17:00 7 505.5
14:00 7 502.4 15:00 7 500.2 16:00 7 500.8 17:00 7 500.7
14:00 7 503.2 15:00 7 499.9 16:00 7 504.6 17:00 7 501.8
14:00 7 506.2 15:00 7 503.8 16:00 7 500.4 17:00 7 496.9
14:00 7 506.3 15:00 7 504.1 16:00 7 504.2 17:00 7 503.2
14:00 8 501.6 15:00 8 501.4 16:00 8 501.8 17:00 8 501.3
14:00 8 500.6 15:00 8 501.3 16:00 8 504.0 17:00 8 500.3
14:00 8 501.7 15:00 8 502.0 16:00 8 502.5 17:00 8 500.5
14:00 8 503.3 15:00 8 503.1 16:00 8 502.0 17:00 8 500.4
14:00 8 502.2 15:00 8 504.0 16:00 8 503.0 17:00 8 502.1
14:00 8 502.9 15:00 8 502.2 16:00 8 499.6 17:00 8 502.3
14:00 8 500.2 15:00 8 502.3 16:00 8 498.8 17:00 8 502.8
14:00 8 504.5 15:00 8 503.4 16:00 8 500.8 17:00 8 500.7
14:00 8 503.2 15:00 8 501.3 16:00 8 503.3 17:00 8 502.5
HORA
MAQUINA PESO HORA MAQUINA PESO HORA MAQUINA PESO
18:00 5 503.4 19:00 5 506.1 20:00 5 504.2
18:00 5 505.5 19:00 5 499.9 20:00 5 507.3
18:00 5 502.6 19:00 5 504.8 20:00 5 503.9
18:00 5 504.2 19:00 5 502.7 20:00 5 505.2
18:00 5 503.6 19:00 5 505.1 20:00 5 506.4
18:00 5 504.4 19:00 5 503.2 20:00 5 505.1
18:00 5 500.6 19:00 5 500.8 20:00 5 501.3
18:00 5 504.9 19:00 5 502.2 20:00 5 505.3
18:00 5 502.8 19:00 5 499.5 20:00 5 504.8
18:00 6 500.8 19:00 6 504.3 20:00 6 502.9
18:00 6 499.2 19:00 6 503.3 20:00 6 503.4
18:00 6 500.3 19:00 6 503.5 20:00 6 505.2
18:00 6 501.6 19:00 6 501.2 20:00 6 501.7
18:00 6 501.9 19:00 6 501.8 20:00 6 504.0
18:00 6 500.8 19:00 6 501.4 20:00 6 503.4
18:00 6 499.7 19:00 6 503.8 20:00 6 503.2
18:00 6 502.7 19:00 6 502.4 20:00 6 504.0
18:00 6 499.6 19:00 6 502.1 20:00 6 503.0
18:00 7 502.0 19:00 7 500.8 20:00 7 499.0
18:00 7 500.8 19:00 7 501.4 20:00 7 504.6
18:00 7 503.9 19:00 7 503.1 20:00 7 502.4
18:00 7 500.6 19:00 7 499.7 20:00 7 499.9
18:00 7 501.3 19:00 7 505.0 20:00 7 501.3
18:00 7 502.1 19:00 7 504.4 20:00 7 502.4
18:00 7 502.6 19:00 7 506.5 20:00 7 501.1
18:00 7 501.4 19:00 7 501.8 20:00 7 502.0
18:00 7 502.2 19:00 7 498.4 20:00 7 504.0
18:00 8 505.0 19:00 8 504.2 20:00 8 502.3
18:00 8 504.1 19:00 8 499.3 20:00 8 501.3
18:00 8 502.8 19:00 8 503.4 20:00 8 501.8
18:00 8 500.8 19:00 8 503.9 20:00 8 503.1
18:00 8 499.7 19:00 8 505.2 20:00 8 504.1
18:00 8 499.9 19:00 8 503.1 20:00 8 501.2
18:00 8 499.9 19:00 8 502.1 20:00 8 504.0
18:00 8 502.2 19:00 8 502.8 20:00 8 503.4
18:00 8 501.4 19:00 8 501.0 20:00 8 501.8
... Session 3 is dedicated to showing the results and discussing the evaluation obtained from the optimal hybrid control problem used for the development of Space Vector Modulation (SVM). In section 4, we present the conclusions obtained from the computational approach used based on gradient for the initial problem of Space Vector Modulation (SVM) [28], [29], [30]. ...
... = + , that is, maintaining the amplitude of the fixed triangular wave, we can vary the pulse width by varying the positive and negative reference voltage [27], [28].The frequency of the triangular signal imposes the fundamental frequency of the output voltage. In Figure-3, we can observe the signal of the three-phase inverter system. ...
Article
Full-text available
In our paper, we investigate the problem of optimal hybrid control for space vector modulation (SVM), applying a new optimal hybrid control approach for pulse width modulation (PWM). It is used for the creation of alternating current (AC) waveforms, which would be applied to three-phase motors using class D amplifiers. Our contribution demonstrates a reduction of the total harmonic distortion (THD) created by the rapid change inherent in the implementation of our algorithm. The results can be observed in the simulations obtained through the Matlab/Simulink software.
... The principle of maximum is very important in the optimal control theory. It says that any optimal control along with state trajectory must satisfy the condition called Hamiltonian system [16], [17]. In addition to the mathematics of the maximum principle, it is easy to maximize the Hamiltonian system as the main topic. ...
Article
Full-text available
In this document, we investigate the problem of optimal hybrid control for a non-stationary hydraulic system with autonomous location transitions. Using the Lagrange approach and the reduced gradient technique, we derive the optimality conditions necessary for the class of problems considered. These conditions of optimality are closely relate to a variant of the Maximum Hybrid Principle, are simulated in Matlab and implemented in Labview. They can be used for constructive optimization algorithms.
Article
Full-text available
Educators in Colombia are compelled to show effectiveness, which is meassured with the results obtained from the Pruebas SABER Pro. However, it is possible that educational institutions are being assessed with innadequate criteria. The objective of this work is to perform a bibliographic search on assessment processes and analyze the subject area of mathematics at the Educational Institution "Los Negros", in Algeciras, Huila (Colombia), in order to generate teaching strategies and to make future comparisons with other educational institutions of Colombia.
Article
Full-text available
The Betania Dam is a strategic ecosystem of the department of Huila in Colombia, whose goods and services include the generation of hydroelectric energy and the production of red tilapia (Oreochromis mossambicus) and silver tilapia (Oreochromis niloticus) in intensive systems. These activities generate conditions in the flow of matter and energy through the introduction of nutrients and foreign species that cause responses in the hydrobiological communities. In this research, zooplankton samples collected every two months for eighteen months were analyzed qualitatively and quantitatively at three sampling stations of the reservoir corresponding to three hydro morphological zones to determine the species present in the reservoir and the relative abundance of each one in this period. Biological diversity indexes, Kruskal Wallis tests were applied and oxygen and pH profiles were obtained throughout the day. Seven new zooplankton records were found for the reservoir. In addition, temporal variations were observed in the abundance of the species and some relationships between nutrient abundance and climatic conditions were discussed with the diversity and abundance patterns of 2932 Paula Martínez-Silva et al. zooplankton where some of them reflect the typical behavior of a eutrophic system.
Article
Full-text available
In this research we studied the community of Betania dam for three years, in order to establish some changes regarding an initial study in the 80´s and a research from 2017 and the relation among theses changes and the physic chemical conditions and the fish farming activities developed in the reservoir.
Article
Full-text available
El documento CONPES 3756 de 2013 establece los sistemas de transporte público masivo en Colombia, estos han presentado una mejoría y un ordenamiento en la cultura de las grandes ciudades del país, así mismo el Departamento Nacional de Planeación establece reordenar el transporte público en las ciudades intermedias a través de la implementación de sistemas estratégicos de transporte público SETP. El sistema estratégico de transporte público (SETP) es un servicio de transporte colectivo integrado y accesible para la población, cuya operación es planeada, gestionada y controlada mediante el sistema de gestión y control de flota por la autoridad de transporte o por quien esta delegue y se estructurarán con base en los resultados de los estudios técnicos desarrollados por cada ente territorial y validados por la Nación a través del departamento nacional de planeación DNP (Decreto 3422, Artículo 2, 2009). [1] Estos sistemas son implementados en ciudades intermedias con poblaciones entre 250.000 habitantes y 600.000 habitantes con el fin, de reducir el número y tiempo de viajes mejorando así la movilidad de la ciudad, en el país ya hay ciudades en el proceso de implementación del SETP como son Pasto, Popayán, Armenia, Montería, Sincelejo, Valledupar, Santa Martha y en este momento en la ciudad de Neiva, al mismo tiempo hay otras ciudades en el proceso de estructuración como Manizales, Buenaventura, Ibagué y Villavicencio. (Departamento Nacional de Planeacion, 2013) [2] La implementación del SETP junto con el reordenamiento territorial que tendrá la ciudad, impactaran directamente la cultura ciudadana, debido a que el sistema establece grandes cambios físicos en la infraestructura urbana como paraderos, intercambiadores, buses con alta tecnología, lo que exige un cambio de comportamiento de los ciudadanos
Article
Full-text available
In this paper, we performed the acquisition of the temperature generated in the rice drying process "Paddy" to later carry out its identification, simulation and validation of the obtained model. The data generated by the model are compared with the data emanating from the process from which the analysis is performed. The drying process of paddy rice consists of generating a moisture loss of 12% rice and is done through a line towers system in which air is injected at a specific temperature and a constant flow of air over the rice which in each tower is developing a programmed movement. The drying of the rice in the tower No 1 takes approximately 1 hour and 20 minutes and is very important for the drying process because it must reach a moisture loss of at least 5% and the rice must be homogenized with moisture minimizing the dispersion of that moisture that is mainly caused by the diversity of paddy rice suppliers. This article presents results on the recommendations that the operator tower No 1 must carry out in relation to the air temperature during the drying process to reach a greater loss of humidity without increasing the grain temperature above 32 °C because you can try the quality of the rice. This paper does not reach the controller calculation process; simply remains in the collection and model validation.
Article
Full-text available
The metrological tools have significantly helped in the production processes of each sector in all industries of the country. In the areas of analysis this feature becomes important because of the diversity of equipment and measuring instruments for evaluating and calibrating aspects that the project can be identified through market studies, technical and financial resources in accordance with the specific needs of a metrology laboratory that serves the hydrocarbon industry and agribusiness in the department of Huila. The impact of creating a metrology laboratory for these sectors is reflected in the provision of calibration services to potentially 762 companies in which they also significantly reduce the costs of sending their teams to other cities, the corresponding time management and mismatches because of the distances they have to travel.
Article
Full-text available
This work is a contribution in mathematical modeling through Matlab in the process of threshing for the production of white rice, a mode of energy management to achieve energy savings must be implemented with the purpose of reducing costs and CO2 emissions. The sample of production is taken again power consumption and modeled by a regression line called the base line. With the higher points of production (threshing) at lower consumption second line regression line reference is created and on the basis of the reference line a goal line is established as an objective for improvement with 95% confidence. Once the objective of saving power is established, E.F.M.A. is one of the most important of the methods used to determine whether or not the process is effective. The results of the research clearly show the importance of incorporating a good system of systematic control for the management of energy management in the mill industry, seeking an annual savings of approximately 60 million C.O.P.
Article
Full-text available
In this document, a software application for Digital Signal Processing is implemented with a MyDAQ device; in the designed application, audio signals from MP3 Files are used as input data. A Labview based software tool GUI is developed for this porpoise to visualize frequency spectrum response. Two specific filters as the Finite Impulse Response (FIR) or (IIR) Infinite Impulse Response were implemented and compared. The procedure and simulation are designed in Matlab to understand the process carried out by the Digital Signal Processor (MyDSP) from National Instruments as a study case in educational activities.
Article
Full-text available
This paper presents deferent alternatives for multivariable control for two industrial processes: distillation column and evaporator. Four control strategies are presented, including multiloop control, a decoupling multivariable control, both with proportional integral structure, a decoupling control through Inverse Nyquist Array, an internal model controller (IMC) and model predictive controller (MPC). Simulations results are obtained using Matlab, and show the potentialities for each one of those strategies to handle reference changes and disturbance rejections. .The advantages of predictive control in regulation of multivariable processes are evident
Article
Full-text available
This paper presents the design of a speed control for a DC motor using fuzzy logic by software LabView, is also a literature review the design and implementation environment is presented by fuzzy logic describing the materials and methods used. Various processes on the subject highlighting the idea, creation, development and implementation of intelligent control and finally the results considering the application and development for this purpose are presented exposed.
Article
Full-text available
In this paper the design and implementation of a kinematic model for a manipulator robot arm type with four degrees of freedom is developed, model robot performance can be checked mathematically using results from coordinate's frames, which set the proposed matrices by Denavit-Hartemberg method to determine the robot joins angle vector. This procedure describes the direct and inverse kinematics. The goal is to determine the final robot´s position and orientation according to the joint angles related to a coordinate system, the final effector position, where joint angles are located. The results were implementedin a MATLAB application that performs fast calculations, it allows the verification of the theory and at the same time becomes as a tool to simplify the analysis and learning for its friendly interface which displays virtually the movements of the robotic arm AL5A.
Article
The capability indices Cp, CPU, CPL, k and Cpk are presented and related to process parameters. These indices are shown to form a complementary system of measures of process performance, and can be used with bilateral and unilateral tolerances, with or without target values. A number of Japanese industries currently use the five indices and the U.S. automotive industry has started using these measures in a number of areas. Various applications of the indices are discussed along with statistical sampling considerations.
Article
This second volume moves beyond a general introduction to product lifecycle management (PLM) and its principal elements to provide a more in-depth analysis of the subjects introduced in Volume 1 (21st Century Paradigm for Product Realisation). Providing insights into the emergence of PLM and the opportunities it offers, key concepts such as the PLM Grid and the PLM Paradigm are introduced along with the main components of PLM and the associated characteristics, issues and approaches. Detailing the 10 components of PLM: objectives and metrics; management and organisation; business processes; people; product data; PDM systems; other PLM applications; facilities and equipment; methods; and products, it provides examples and best practices. The book concludes with instructions to help readers implement and use PLM successfully, including outlining the phases of a PLM Initiative: development of PLM vision and strategy; documentation of the current situation; description of future scenarios; development of implementation strategies and plans; implementation and use. The main activities, tasks, methods, timing and tools of the different phases are also described.