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Development of a Model to Predict Efficiency Parameters in Industrial Processes


Affiliations
1 Department of Mechanical Engineering, KAI Research Group, Universidad del Atlantico, Barranquilla, Colombia
2 Department of Chemical Engineering, Sustainable Chemical and Biochemical Processes Research Group, Universidaddel Atlantico, Barranquilla, Colombia
 

Background/Objectives: The implementation of regression and correlation analysis as a tool for the modeling of industrial- scale milling processes, as a base for optimization processes that allow establishing a production regime with the highest consumption rate and therefore, a better process performance. Methods: Using the multivariate regression methods, a mathematical model to establish a correlation between the consumption rate and the factors that influence it was developed; in this process, a series of statistical analysis was developed to select the factors that generate an effect on the behavior of the consumption rate, at the same time, to determinate the best fit for the experimental data measured, using a statistical tool for the operations required. Findings: From the obtained results, it is shown that from the set of initial variables studied, only two (the humidity and the production), have a degree of correlation enough to be taken in account into post-processing operations; besides, it was found that the behavior of the consumption rate can be approximated as a linear combination of the relevant factors, with a better accuracy when compared to more complex fits, which in turn generates a model easy to handle in optimization processes Application: To develop a tool for the prediction of the behavior of consumption rate in milling processes, which will be used for the establishment of a more efficient production regime, where a better planification yields a lower energy consumption, with the same production level.
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  • Development of a Model to Predict Efficiency Parameters in Industrial Processes

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Authors

Jorge Duarte Forero
Department of Mechanical Engineering, KAI Research Group, Universidad del Atlantico, Barranquilla, Colombia
Guillermo E. Valencia
Department of Mechanical Engineering, KAI Research Group, Universidad del Atlantico, Barranquilla, Colombia
Luis G. Obregon
Department of Chemical Engineering, Sustainable Chemical and Biochemical Processes Research Group, Universidaddel Atlantico, Barranquilla, Colombia

Abstract


Background/Objectives: The implementation of regression and correlation analysis as a tool for the modeling of industrial- scale milling processes, as a base for optimization processes that allow establishing a production regime with the highest consumption rate and therefore, a better process performance. Methods: Using the multivariate regression methods, a mathematical model to establish a correlation between the consumption rate and the factors that influence it was developed; in this process, a series of statistical analysis was developed to select the factors that generate an effect on the behavior of the consumption rate, at the same time, to determinate the best fit for the experimental data measured, using a statistical tool for the operations required. Findings: From the obtained results, it is shown that from the set of initial variables studied, only two (the humidity and the production), have a degree of correlation enough to be taken in account into post-processing operations; besides, it was found that the behavior of the consumption rate can be approximated as a linear combination of the relevant factors, with a better accuracy when compared to more complex fits, which in turn generates a model easy to handle in optimization processes Application: To develop a tool for the prediction of the behavior of consumption rate in milling processes, which will be used for the establishment of a more efficient production regime, where a better planification yields a lower energy consumption, with the same production level.

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DOI: https://doi.org/10.17485/ijst%2F2018%2Fv11i29%2F130615