EVALUATION OF CANE SUGAR PRODUCTION USING MULTIVARIATE STATISTICAL METHODS
DOI:
https://doi.org/10.18540/jcecvl5iss3pp0228-0237Palabras clave:
Sugar industry, Process monitoring, Sugar quality, Multivariate statisticsResumen
In sugarcane industries, process monitoring has the main purpose of maximizing sugar and ethanol production, meeting the quality parameters demanded by customers. The aim of this work was to identify industrial process variables that presented the greatest impacts on the quantity and quality of the produced sugar, by applying principal component analysis (PCA) and partial least squares regression (PLS) to the process data of a sugar and ethanol industry. The PCA correlation matrix highlighted the correlation between the presence of alcoholic flocs in sugar and the concentrations of starch and dextran in it. Both PCA and PLS showed that the color of the sugar was highly correlated to its moisture content. The first three principal components accounted for 40.92% of the total data variability.
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CHEN, R.; KANG, S.; HAO, X.; LI, F.; DU, T.; QIU, R.; CHEN, J. Variations in tomato yield and quality in relation to soil properties and evapotranspiration under greenhouse condition. Sci. Hortic, v. 197, n. 3, p. 318–328, 2015.
FERNANDES, A. C. Cálculos na agroindústria da cana-de-açúcar. 3rd ed., Piracicaba: STAB, 2011.
GE, Z.; SONG, Z.; GAO, F. Review of recent research on data-based process monitoring. Ind. Eng. Chem. Res, v. 52, n. 10, p. 3543–3562, 2013.
ICUMSA. ICUMSA Methods Book (2015). England: ICUMSA, 2015.
JIANG, Q.; YAN, X.; ZHAO, W. Fault detection and diagnosis in chemical processes using sensitive principal component analysis. Ind. Eng. Chem. Res, v. 52, n. 4, p. 1635–1644, 2013.
JOHNSON, R. A.; WICHERN, D. W. Applied multivariate statistical analysis. 6th ed., Upper Saddle River: Pearson Prentice Hall, 2007.
KANO, M.; NAKAGAWA, Y. Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry. Comput. Chem. Eng, v. 32, n. 1–2, p. 12–24, 2008.
KU, W.; STORER, R. H.; GEORGAKIS, C. Disturbance detection and isolation by dynamic principal component analysis. Chemom. Intell. Lab. Syst, v. 30, n. 1, p. 179–196, 1995.
LEMOS, L. R.; NOGUEIRA, A.; WOSIACKI, G.; LACERDA, L. G.; DEMIATE, I. M. The influence of different amounts of dextran and starch in crystallized sugar in the formation of floc in acidic carbonated solutions and alcoholic solutions. Sugar Tech, v. 15, n. 1, p. 65–70, 2013.
LOPES, C. H.; BORGES, M. T. M. R. Manual de análise de açúcar: açúcar VHP, VVHP, demerara, cristal, refinado e açúcar líquido. Araras: Sucral, 2004.
MERHEB, G. A. Influência da contaminação combinada de dextrana e amido na cristalização do açúcar. 2014. 300 p. Tese (Doutorado em Engenharia Química) - Universidade Federal de São Carlos, São Carlos, SP, 2014.
MERHEB, G. A.; OLIVEIRA, N. de; GIULIETTI, M.; BERNARDO, A. Combined effect of starch and dextran in sucrose crystallization. Sugar Ind, v. 141, p. 697–704, 2016.
MULLIN, J. W. Crystallization. 4th ed., Woburn: Butterworth Heinemann, 2001.
OLIVEIRA, D. T.; ESQUIAVETO, M. M. M.; SILVA JÚNIOR, J. F. Impacto dos itens da especificação do açúcar na indústria alimentícia. Ciênc. Tecnol. Aliment, v. 27, p. 99–102, 2007.
OLIVEIRA, A. S.; RINALDI, D. A.; TAMANINI, C.; VOLL, C. E.; HAULY, M. C. O. Fatores que interferem na produção de dextrana por microrganismos contaminantes da cana-de-açúcar. Semin., Ciênc. Exatas Tecnol., v. 23, n. 1, p. 99–104, 2002.
QIN, S. J. Survey on data-driven industrial process monitoring and diagnosis. Annu. Rev. Control, v. 36, n. 2, p. 220–234, 2012.
RAMBURAN, S.; ZHOU, M.; LABUSCHAGNE, M. Interpretation of genotype×environment interactions of sugarcane: Identifying significant environmental factors. Field Crops Res, v. 124, n. 3, p. 392–399, 2011.
RODUSHKIN, I.; BAXTER, D. C.; ENGSTRÖM, E.; HOOGEWERFF, J.; HORN, P.; PAPESCH, W.; WATLING, J.; LATKOCZY, C.; VAN DER PEIJL, G.; BERENDS-MONTERO, S.; EHLERINGER, J.; ZDANOWICZ, V. Elemental and isotopic characterization of cane and beet sugars. J. Food Compos. Anal, v. 24, n. 1, p. 70–78, 2011.
ROY, P. P.; ROY, K. On some aspects of variable selection for partial least squares regression models. QSAR Comb. Sci, v. 27, n. 3, p. 302–313, 2008.
SANTCHURN, D.; RAMDOYAL, K.; BADALOO, M. G. H.; LABUSCHAGNE, M. From sugar industry to cane industry: investigations on multivariate data analysis techniques in the identification of different high biomass sugarcane varieties. Euphytica, v. 185, n. 3, p. 543–558, 2012.
SARANTÓPOULOS, C. I. G. L.; OLIVEIRA, L. M.; CANAVESI, É. Requisitos de conservação de alimentos em embalagens flexíveis. 2nd ed., Campinas: CETEA/ITAL, 2002.
SCHLUMBACH, K.; PAUTOV, A.; FLÖTER, E. Crystallization and analysis of beet and cane sugar blends. J. Food Eng, v. 196, p. 159–169, 2017.
SUN, X.; CHEN, T.; MARQUEZ, H. J. Detecting leaks and sensor biases by recursive identification with forgetting factors. In: CONFERENCE ON DECISION AND CONTROL, 2001, Edmonton. Anais... Edmonton: IEEE, 2001. p. 3716-3721.
UDOP. Determinação das impurezas minerais em carregamentos de cana-de-açúcar pelo método da incineração em forno mufla. UDOP, 2014a. Avaiable at: <http://www.udop.com.br/download/legislacao/bioenergia/institucional_site_juridico/impurezas_minerais_cana_objetivo_equipamentos_procedimentos.pdf>. Accessed on September 19, 2017.
UDOP. Determinação das impurezas vegetais e totais em carregamentos de cana-de-açúcar pelo método de limpeza manual e a seco. UDOP, 2014b. Avaiable at: <http://www.udop.com.br/download/legislacao/bioenergia/institucional_site_juridico/impurezas_vegetais_totais_objetivo_equipamentos_procedimentos.pdf>. Accessed on September 19, 2017.
YIN, S.; DING, S. X.; XIE, X.; LUO, H. A review on basic data-driven approaches for industrial process monitoring. IEEE Trans. Ind. Electron, v. 61, n. 11, p. 6418–6428, 2014.
YIN, S.; LI, X.; GAO, H.; KAYNAK, O. Data-based techniques focused on modern industry: an overview. IEEE Trans. Ind. Electron, v. 62, n. 1, p. 657–667, 2015.
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Publicado
2019-06-28
Cómo citar
Chiaramonte de Castro, B. J., & Bernardo, A. (2019). EVALUATION OF CANE SUGAR PRODUCTION USING MULTIVARIATE STATISTICAL METHODS. The Journal of Engineering and Exact Sciences, 5(3), 0228–0237. https://doi.org/10.18540/jcecvl5iss3pp0228-0237
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Chemical Engineering
