EVALUATION OF CANE SUGAR PRODUCTION USING MULTIVARIATE STATISTICAL METHODS

Bruno José Chiaramonte de Castro, Andre Bernardo

Resumo


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.


Palavras-chave


Sugar industry; Process monitoring; Sugar quality; Multivariate statistics

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DOI: https://doi.org/10.18540/jcecvl5iss3pp0228-0237

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Direitos autorais 2019 The Journal of Engineering and Exact Sciences

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Esta obra está licenciada sob uma licença Creative Commons Atribuição - Não comercial - Compartilhar igual 4.0 Internacional.

eISSN 2527-1075 - The Journal of Engineering and Exact Sciences - JCEC

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