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Data Reconciliation Applied to Loss Identification in the Sugar Industry

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Abstract

Pursuit of the identification and reduction of losses, and consequently of increased industrial efficiency, is a constant challenge in sugarcane industries. However, little attention is given to the undetermined losses, despite the high impact they can have on industrial efficiency. Therefore, this work aimed to quantify the contribution of the sugar-manufacturing sector to the total undetermined losses of a sugar and ethanol plant, presenting a method that could be applied to any equipment or sector of the industry in order to help identify sugar losses. The method was based on data reconciliation for the process flow rates and concentrations and was applied to the juice concentration sector. A mass balance was then applied to the subsequent sectors (crystallization, centrifugation, and drying) with the purpose of calculating the sugar production and comparing it with the real production. Considering the entire period studied, the calculated sugar production was 1.0% higher than the real production, and 37.3% of undetermined losses were found to occur between juice concentration and sugar bagging.

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Abbreviations

F :

Mass flow rate (kg/h)

Q :

Volumetric flow rate (m3/h)

s :

Sucrose mass concentration (%)

ρ :

Density (kg/m3)

1:

Clarified juice

2:

Clarified syrup

3:

Final molasses

4:

Sugar

References

  • Beckman, James R. 1982. Data adjustment for non-reactive batch or steady-state processes. Chemical Engineering Communications 15: 357–365. https://doi.org/10.1080/00986448208911081.

    Article  CAS  Google Scholar 

  • Chen, James C.P., and Chung C. Chou. 1993. Cane sugar handbook: A manual for cane sugar manufacturers and their chemists, 12th ed. New York: Wiley.

    Google Scholar 

  • Chou, Chung C. 2000. Handbook of sugar refining: A manual for design and operation of sugar refining facilities. New York: Wiley.

    Google Scholar 

  • Copersucar. 1978. Controle Químico da Fabricação do Açúcar. São Paulo: Copersucar.

    Google Scholar 

  • Crowe, Cameron M. 1996. Data reconciliation—progress and challenges. Journal of Process Control 6: 89–98. https://doi.org/10.1016/0959-1524(96)00012-1.

    Article  CAS  Google Scholar 

  • Davis, S.B. 2001. The chemistry of colour removal: A processing perspective. Proceedings of the South African Sugar Technologists’ Association 75: 328–336.

    Google Scholar 

  • Fernandes, Antonio C. 2011. Cálculos na Agroindústria da Cana-de-açúcar, 3rd ed. Piracicaba, SP: STAB.

    Google Scholar 

  • Hubbard, G., and D. Love. 1998. Reconciliation of process flow rates for a steady state mass balance on a centrifugal. Proceedings of the South African Sugar Technologists’ Association 72: 290–299.

    Google Scholar 

  • Hugot, Émile. 1986. Handbook of cane sugar engineering, 3rd ed. Amsterdam: Elsevier.

    Google Scholar 

  • Jiang, Xiaolong, Pei Liu, and Zheng Li. 2014. Data reconciliation and gross error detection for operational data in power plants. Energy 75: 14–23. https://doi.org/10.1016/j.energy.2014.03.024.

    Article  Google Scholar 

  • Kneile, Robert. 1995. Wring more information out of plant data. Chemical Engineering (New York) 102: 110–116.

    CAS  Google Scholar 

  • Lopes, Claudio H., and Maria T.M.R. Borges. 2004. Manual de análise de açúcar: Açúcar VHP, VVHP, demerara, cristal, refinado e açúcar líquido. Araras, SP: Sucral.

    Google Scholar 

  • Martins, Márcio A.F., Carolina A. Amaro, Leonardo S. Souza, Ricardo A. Kalid, and Asher Kiperstok. 2010. New objective function for data reconciliation in water balance from industrial processes. Journal of Cleaner Production 18: 1184–1189. https://doi.org/10.1016/j.jclepro.2010.03.014.

    Article  Google Scholar 

  • Martins, Sara I.F.S., Wim M.F. Jongen, and Martinus A.J.S. van Boekel. 2001. A review of Maillard reaction in food and implications to kinetic modelling. Trends in Food Science & Technology 11: 364–373. https://doi.org/10.1016/S0924-2244(01)00022-X.

    Article  Google Scholar 

  • Mullin, John W. 2001. Crystallization, 4th ed. Woburn, MA: Butterworth Heinemann.

    Google Scholar 

  • Narasimhan, Shankar, and Cornelius Jordache. 2000. Data reconciliation & gross error detection: An intelligent use of process data. Houston: Gulf Publishing Company.

    Google Scholar 

  • Romagnoli, José A., and Mabel Cristina Sánchez. 2000. Data processing and reconciliation for chemical process operations. London: Academic Press.

    Google Scholar 

  • Schlumbach, Karl, A. Pautov, and Eckhard Flöter. 2017. Crystallization and analysis of beet and cane sugar blends. Journal of Food Engineering 196: 159–169. https://doi.org/10.1016/j.jfoodeng.2016.10.026.

    Article  CAS  Google Scholar 

  • Smaïli, F., D.K. Angadi, C.M. Hatch, O. Herbert, V.S. Vassiliadis, and D.I. Wilson. 1999. Optimization of scheduling of cleaning in heat exchanger networks subject to fouling. Food and Bioproducts Processing 77: 159–164. https://doi.org/10.1205/096030899532312.

    Article  Google Scholar 

  • Syed, Mohammed S., Kerry M. Dooley, Frantisek Madron, and F. Carl Knopf. 2016. Enhanced turbine monitoring using emissions measurements and data reconciliation. Applied Energy 173: 355–365. https://doi.org/10.1016/j.apenergy.2016.04.059.

    Article  CAS  Google Scholar 

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Acknowledgements

The authors obtained financial support from CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico).

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Authors and Affiliations

  1. Department of Chemical Engineering, Universidade Federal de São Carlos, Rodovia Washington Luís, s/n, São Carlos, 13565-905, Brazil

    Bruno José Chiaramonte de Castro & André Bernardo

Authors
  1. Bruno José Chiaramonte de Castro
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  2. André Bernardo
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Contributions

BJCC and AB conceived the work. BJCC developed the research and wrote the manuscript, with support from Bernardo. Both BJCC and AB contributed to the interpretation of the results and to the final version of the manuscript. AB supervised the project.

Corresponding author

Correspondence to André Bernardo.

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Conflict of interest

Castro reports grants from Conselho Nacional de Desenvolvimento Científico e Tecnológico, during the conduct of the study. Dr. Bernardo reports grants from Conselho Nacional de Desenvolvimento Científico e Tecnológico, during the conduct of the study.

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de Castro, B.J.C., Bernardo, A. Data Reconciliation Applied to Loss Identification in the Sugar Industry. Sugar Tech 21, 486–495 (2019). https://doi.org/10.1007/s12355-018-0649-4

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