Predictive model of mass flows of gaseous emissions from beehive ovens

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.

• Autores: mendoza lizcano, sonia maritza; palacios alvarado, wlamyr; Medina Delgado, Byron
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2021
• Institución: Universidad Francisco de Paula Santander
• Repositorio: Repositorio Digital UFPS
• Idioma: eng
• OAI Identifier: oai:repositorio.ufps.edu.co:ufps/6561
• Acceso en línea: https://repositorio.ufps.edu.co/handle/ufps/6561; https://doi.org/10.1088/1742-6596/1981/1/012014
• Palabra clave: Ceramics industry; Decision making; Environmental regulations; Ovens; Measure and controls; Multiple correlation coefficients; Multiple regressions; Pollutant concentration; Predictive behaviors; Predictive modeling; Quantitative variables; Statistical techniques; Predictive analytics
• Rights: openAccess
• License: Content from this work may be used under the terms of theCreative Commons Attribution 3.0 licence