Productividad y Competitividad – GIPYC
https://repositorio.ufps.edu.co/handle/ufps/65
2024-03-29T04:40:20ZAnalysis of variables related to the mass flow of gaseous emissions in beehive ovens
https://repositorio.ufps.edu.co/handle/ufps/6612
Analysis of variables related to the mass flow of gaseous emissions in beehive ovens
mendoza lizcano, sonia maritza; Palacios Alvarado, W; Medina Delgado, Byron
In the combustion processes of the ceramic industry, large amounts of pollutant gas concentration are generated, so it is necessary to measure the system and the physical phenomenon, as well as the variables inherent to the process, for subsequent estimation in terms of physical magnitudes, and a dimensional analysis, this allows to generate approximations in the causal relationships of the variables. The objective of the research is to analyze the behavior of the variables involved in the process and the influence of the mass flows of polluting gases. An exploratory and experimental methodology was used, for this, the data were taken with the technique of direct measurement in the sources of gas emissions and subsequently subjected to simulation in software such as AMOS V24.0 and LISREL V8.8., on the other hand, the data were subjected to an exploratory and confirmatory factorial analysis, using SPSS software version 24.0. As a result, the variables capacity and production are practically identical, which leads to a high correlation and, consequently, an undesired multicollinearity between the variables, so the suggestion is to omit one of them. On the other hand, the sulfur percentage variable is inversely correlated with most of the variables and its saturation is not clear, so it is theoretically sustained as a latent variable of the SO2 indicator.
2021-05-22T00:00:00ZPredictive model of mass flows of gaseous emissions from beehive ovens
https://repositorio.ufps.edu.co/handle/ufps/6561
Predictive model of mass flows of gaseous emissions from beehive ovens
mendoza lizcano, sonia maritza; palacios alvarado, wlamyr; Medina Delgado, Byron
One of the techniques used in the industry for the control of variables is, from their
magnitudes, such as fuel flow, air volume, amount of material mass, among others. The ceramic
industry needs to measure and control the polluting gases of its fixed sources in a less costly
way, based on tools that allow agility in decision making to mitigate the adverse effects, not only
to comply with a legal standard, but also for environmental and management commitment. The
objective of the research is to design a predictive model of the concentration of polluting gases
in the beehive ovens based on the results of the balance of matter and energy in the beehive
ovens. An exploratory descriptive methodology was used, where data on beehive ovens and
fourteen (14) continuous quantitative variables were considered through the statistical technique
of multiple regression to analyze the predictive behavior of the pollutant concentration variables.
As a result, the predictive capacity of the resulting model was high, explaining 79% of the total
variation of the variable. The multiple correlation coefficient of the complete model was 0.79.
During the analysis of the model assumptions, the Durbin Watson score reached a value of 1.971,
evidencing compliance with the assumption of independence of the errors.
2021-08-09T00:00:00ZDiagnosis of physical conditions for the implementation of a reverse logistics management model in a supply chain
https://repositorio.ufps.edu.co/handle/ufps/6542
Diagnosis of physical conditions for the implementation of a reverse logistics management model in a supply chain
GARZÓN AGUDELO, PEDRO; palacios alvarado, wlamyr; Medina Delgado, Byron
The objective of this research was to structure a Reverse Logistics Management
model applied to the furniture sector in the city of San José de Cúcuta, Colombia, based on the
identification and analysis of the current business practices adopted by the companies that are
part of this sector. For this purpose, engineering methods and techniques were used to propose
alternatives that contribute to achieve a competitive growth, a better use of resources and
environmental impact through strategies for the reuse of products or materials that, based on
their physical properties, represent the best feasibility conditions. The type of research used
was descriptive, with a qualitative approach. The methods used for data collection were mainly
surveys, interviews and direct inspection of the companies studied, which made it possible to
evaluate the physical variables of the object of study. It was possible to demonstrate that
furniture manufacturing companies can adopt better reverse logistics practices in the
manufacturing processes through referencing and articulation with strategic allies in the
distribution channels. It was possible to establish the importance of implementing this model as
a strategy to diagnose physical conditions and improve production systems to achieve a more
efficient use of physical manufacturing resources.
2021-06-09T00:00:00ZAutoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosis
https://repositorio.ufps.edu.co/handle/ufps/6539
Autoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosis
Soto Vergel, Angelo Joseph; Medina Delgado, Byron; palacios alvarado, wlamyr
This article evaluates autoregressive modeling as a feature extraction method in a
database of chromatographic signals from urine samples for non-invasive diagnostic support of
prostate cancer in response to the research question: Can chromatographic signals from urine
be characterized and used as a non-invasive method for cancer diagnosis? For this purpose, a
database of 18 patients, 9 diagnosed with prostate cancer and 9 control patients, is
consolidated, statistical methods are implemented to generate autoregressive coefficients from
the data signals, and finally, the principal component analysis technique is applied for crossclass classification. As a result, a correct classification was obtained in the total number of
samples validating the autoregressive modelling as a feature extraction method in contrast to
the conventional methodology usually followed in chromatographic signal processing.
2021-06-09T00:00:00Z