Predictive model for the analysis of industrial losses in a sugar process
Main Article Content
Abstract
The use of a predictive model can provide important knowledge about how goods are processed and obtained from the agro-industrial sugar process. For this work, 340 data were collected from the industrial processing of sugar cane, in three harvests of the agroindustry located at 22° 31' 55" N and 80° 52' 8" W, in the Calimete municipality, Matanzas Province (Cuba), to adjust a regression model. Predictive analytics was based on the calculation of the distance between the sugar potential of the sugarcane that is milled and the actual production obtained. From these differences between potential and actual, a daily scale index (Ip-DPRE) was determined. This index was used as a response variable for the adjustment of a predictive model, where a R2 of 0.82 was reached and the diagnostic and validation tests were met. In this way, a polynomial model was arrived, skilled of predicting damage, which evaluated case it was between 0 to 30 USD t-1 of milled cane. This result shows the importance of acting on the causes of industrial losses, in addition to using elements of artificial intelligence to obtain knowledge to the sustainability of the sugar agroindustry.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Andrade V. y Flores, P. (2018). Comparativa entre classification trees, random forest y gradient boosting, en la predicción de la satisfacción laboral en Ecuador. Ciencia Digital, 2, (4.1), 42-54. https://doi.org/10.33262/cienciadigital.v2i4.1..189
Bastian, O. y Grunewald, K. (2015). Properties, Potentials and Services of Ecosystems”. In: Grunewald, K., & Bastian, O. (Eds), Ecosystem Services – Concept, Methods and Case Studies (pp. 36-45). Berlin (Germany): Springer. doi:10.1007/978-3-662-44143-5_3
Bogunovic, I., Trevisani, S., Seput, M., Juzbasic, D., Durdevic, B. (2017). Short-range and regional spatial variability of soil chemical properties in an agro-ecosystem in eastern Croatia. Catena, 154, 50–62. DOI: http://dx.doi.org/10.1016/j.catena.2017.02.018
Cabrera, A. y Rodríguez, R. (2015). Perfeccionamiento de estimados fabriles en la Unidad Empresarial de Base central azucarero “Paquito Rosales”, de la provincia Santiago de Cuba. Anuario Facultad de Ciencias Económicas y Empresariales, Número Especial. 73-84. https://revistas.uo.edu.cu/index.php/aeco/article/viewFile/545/521
Cala, Y., Pacheco, U. y Sánchez, M. (2020). Análisis de indicadores de eficiencia productiva y perspectivas de la industria azucarera en Santiago de Cuba. AFCEE, Número especial. https://www.google.com/url?esrc=s&q=&rct=j&sa=U&url=https://anuarioeco.uo.edu.cu/index.php/aeco/article/view/5144/4617&ved=2ahUKEwiMl93V3fvxAhXqRTABHR9qBhYQFjADegQICBAB&usg=AOvVaw0TQglq602lyZw1ZZAYgaFZ
Carrasquilla, A., Chacón, A., Núñez, K., Gómez, O., Valverde, J., Guerrero, M. (2016). Regresión lineal simple y múltiple: aplicación en la predicción de variables naturales relacionadas con el crecimiento microalgal. Tecnología en Marcha, 29, 33-45. doi:10.18845/tm.v29i8.2983
Chiang, J., González, V., Reyes, Y., Miño, J. (2018). Influencia de las variedades de caña sobre la eficiencia industrial en la fábrica “14 de julio de Cienfuegos. Centro Azúcar, 45, 41-49. http://centroazucar.uclv.edu.cu/index.php/centro_azucar/article/download/52/45/73
Delgadillo, O., Ramírez, P., Leos, J., Salas, J., Valdez, R. (2016). Pronósticos y series de tiempo de rendimientos de granos básicos en México. Acta Universitaria, 26 (3). doi:10.15174/au.2016.882
Dietze, M., Fox, A., Beck, L., Betancourt, B., Jarnevich, S., Keitt, H., Kenney, A., Laney, M., Larsen, G., Loescher, W., Lunch, K., Pijanowski, C., Randerson, T., Read, K., Tredennick, T., Vargas, R., Weathers, C., White, E. (2018). Iterative near-term ecological forecasting: Needs, opportunities, and challenges. Proceedings of the National Academy of Sciences. 115 (7), 1424-1432. doi: 10.1073/pnas.1710231115
García, Y., González, L. y Cabrera, J. (2022). Aplicaciones de aprendizaje automático para el análisis industrial de la provisión azucarera en Matanzas, Cuba. Rev. U.D.C.A Act. & Div. Cient. 25(2):e2334. http://doi.org/10.31910/rudca.v25.n2.2022.2334
Granja, M. y Vidal, H. (2014). Pérdidas indeterminadas en la producción de azúcar. Imbabura (Ecuador): Derechos Reservados de Autor. https://www.academia.edu/29924196/P%C3%89RDIDAS_INDETERMINADAS_EN_LA_PRODOUCCI%C3%93N_DE_AZ%C3%9ACAR
Hammer, R., Sentelhas, P., y Mariano, J. (2020). Sugarcane yield prediction through data
mining and crop simulation models. Sugar Tech, 22, 216-225.
https://doi.org/10.1007/s12355-019-00776-z
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical with Applications in R. Second Edition. New York (USA): Springer. https://doi.org/10.1007/978-1-0716-1418-1
Kumar, A.V., Kumar, P.G., Hari, K.S.P., Kumar, V.D., Kumar, S.D., Kumar, A. (2021). Sugarcane Yield Forecasting Model Based on Weather Parameters. Sugar Tech, 23, 158-166. https://doi.org/10.1007/s12355-020-00900-4
Kustiyo, A., y Arkeman, Y. (2019). Design for improvement of sugar factory performance based on statistical thinking. Earth and Environmental Science, 335, 012033, 1-10. doi:10.1088/1755-1315/335/1/012033.
Lee, J. (2020). Industrial AI: Applications with Sustainable Performance. Singapore: Springer. https://doi.org/10.1007/978-981-15-2144-7
Martínez, C. y De Leon, J. (2012). Influencia de la calidad de la materia prima en el proceso tecnológico, calidad del producto final, y el rendimiento industrial en una fábrica de azúcar. Centro Azúcar, 39(3), 28-34. http://centroazucar.uclv.edu.cu/index.php/centro_azucar/article/download/344/336/373
McDonald, J. (2014). Handbook of biological statistics. Third edition. Maryland (USA): Sparky House Publishing. https://www.researchgate.net/publication/267210763_The_Provision_of_Forest_Ecosystem_Services_-What_Science_Can_Tell_Us_5_2014_Volume_I_Quantifying_and_valuing_non-marketed_ecosystem_services_and_Volume_II_Assessing_cost_of_provision_and_designing_eco/download
Müller, A. y Sukhdev, P. (2018). Midiendo lo que importa en la agricultura y los sistemas alimentarios: síntesis de los resultados y recomendaciones del Informe sobre los Fundamentos Científicos y Económicos de la iniciativa TEEB para la Agricultura y la Alimentación. Ginebra (Suiza): TEEBAgriFood. http://digital.csic.es/bitstream/10261/156293/1/Maranon_Mat_org_octubre2017.pdf
Nashiruddin, I., Fadziyana, A., Rahman, A., Ilias, M., Wan, H. (2020). Process parameter optimization of pretreated pineapple leaves fiber for enhancement of sugar recovery. Industrial Crops and Products, 152, 112514. https://doi.org/10.1016/j.indcrop.2020.112514
Natarajan, R., Subramanian, J., y Papageorgiou, E. (2016). Hybrid learning of fuzzy cognitive maps for sugarcane yield classification. Computers and Electronics in Agriculture, 127, 147–157. http://dx.doi.org/10.1016/j.compag.2016.05.016
Navarro, H. y Rostgaard, L. (2014). Impacto de la materia extraña en la calidad de los jugos de caña y en los indicadores de eficiencia de un central azucarero. Centro Azúcar, 41. http://centroazucar.uclv.edu.cu/index.php/centro_azucar/article/download/245/236/272
R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, (Austria). https://www.R-project.org/
Rahman, M., y Robson, A. (2016). A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery: A Case Study on Bundaberg Region. Advances in Remote Sensing, 5, 93-102. http://dx.doi.org/10.4236/ars.2016.52008
Ramasubramanian, K., y Singh, A. (2019). Machine Learning Using R: With Time Series and Industry-Based Use Cases in R. Second Edition. New York (USA): Springer. https://doi.org/10.1007/978-1-4842-4215-5
Ribas, M., Consuegra, R. y Alfonso, M. (2016). Análisis de los factores que más inciden sobre el rendimiento industrial azucarero. Centro Azúcar, 43, 51-60. http://scielo.sld.cu/pdf/caz/v43n1/caz06116.pdf
Rico, O., Peñalver, Y., Leiva, J., González, V. (2013). Efecto del tiro directo de la caña en el rendimiento y la eficiencia energética. Centro Azúcar, 40, 77-82. http://centroazucar.uclv.edu.cu/index.php/centro_azucar/article/download/320/312/349.
Shahzad, S., Shokat, S., Fiaz, N., Hameed, A. (2017). Impact of Yield and Quality-Related Traits of Sugarcane on Sugar Recovery. J. Crop Sci. Biotech, 20 (1), 1 – 7. doi: No. 10.1007/s12892-016-0048-2
Tarafdar, A., Kaur, B., Nema, P., Babar, O., Kumar, D. (2020). Using a combined neural network ─ genetic algorithm approach for predicting the complex rheological characteristics of microfluidized sugarcane juice. Food Science and Technology, 123, 109058. https://doi.org/10.1016/j.lwt.2020.109058
Willcock, S., Martínez, J., Hooftman, D., Bagstad, K., Balbi, S., Marzo, A., Prato, C., Sciandrello, S., Signorello, G., Voigt, B., Villa, F., Bullock, J., Athanasiadis, I. (2018). Machine learning for ecosystem services. Ecosystem Services, 33, 165-174. https://doi.org/10.1016/j.ecoser.2018.04.004
Zeki, E. (2020). A Framework for Characterizing and Regulating Ecosystem Services in a Management Planning Context. Forests, 11, 102. doi:10.3390/f11010102.