Monitoring the convection coefficient in fermentative processes using numerical methods
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This work is based on the importance of monitoring the thermodynamic variables of sugarcane juice fermentation by Saccharomyces cerevisiae, using a numerical technique, and providing artifices that lead to the best performance of this bioprocess. Different combinations of yeast quantity were added to diverse dilutions of cane juice, allowing the evaluation of the fermentation performance. This was conducted by observing the temperature signal obtained from thermal probes inserted in the experimental set up. The best performances are utilized in the mathematical model evaluation. Thus, the signal reconstructed by the appropriate inverse problem and subsequently, regularized by the simplified method of least squares (the method used for adjusting the defined parameters) allows a common method to process the convection coefficient that can be monitored and controlled within an actuation range. This leads to an increased level of refinement in the technique. Results show that it is possible to determine the best parameters for this technique and observe the occurrence of fermentation by monitoring the temperature signal, thereby ensuring the realization of a high-quality and high-performance bioprocess.
KeywordsAlcoholic fermentation Convection coefficient Inverse problem Regularization Temperature
We sincerely thank Prof. Dr. Karina Alves de Toledo, for permitting us to use the Laboratory of Cellular and Molecular Immunology, UNESP, FCLA for performing the fermentation experiments, and Prof. Dr. Paulo Seleghim Júnior, from NETeF, USP, São Carlos for providing the thermocouples, used in the experiment. Finally, we thank the Scientific Initiation Program of UNESP—PIBIC/PROPe, for the funding provided in 2015 and their collaboration for this study.
- 2.Bayer FM, Kozakevicius AJ (2010) SPC-threshold: Uma proposta de limiarização para filtragem adaptativa de sinais. TEMA Tend Mat Apl Comput 11:121–132Google Scholar
- 3.Bazán FSV, Borges LS (2004) In: Barcelos CAZ, Andrade EXL, Boaventura M (eds) Notas em Matemática Aplicada. SBMAC, São CarlosGoogle Scholar
- 4.British Petroleum Global (2017) BP statistical review of World energy. http://www.bp.com/content/dam/bp/pdf/energy-economics/statistical-review-2016/bp-statistical-review-of-world-energy-2016-full-report.pdf. Accessed 3 Feb 2017
- 6.Corazza ML, Rodrigues DG, Nozaki J (2001) Preparation and characterization of orange wine. Quím Nova 15:449–452Google Scholar
- 8.Incropera FP, Dewit DP, Bergman DL, Lavine AS (2008) In: Queiroz EM, Pessoa FLP (eds) Fundamentos de transferência de calor e de massa, 6th edn. LTC, Rio de JaneiroGoogle Scholar
- 10.Kozakevicius AJ, Bayer FM (2014) Signal denoising via wavelet thresholding. Cien Nat 36:37–51Google Scholar
- 11.Kumar S, Dheeran P, Singh SP, Mishra IM, Adhikari DK (2013) Cooling system economy in ethanol production using thermotolerant yeast Kluyveromyces, sp. IIPE453. J Microbiol Res 1:39–44Google Scholar
- 12.Lima UA, Basso LC, Amorim HV (2001) In: Lima UA, Aquarone E, Borzani W, Schmidell W (eds) Biotecnologia industrial. Edgard Blücher, São PauloGoogle Scholar
- 17.Paz PM, Oliveira J (2013) Use of a numerical technique for monitoring of convection coefficient in industrial processes. In: 22nd international congress of mechanical engineering. http://www.abcm.org.br/anais/cobem/2013/PDF/322.pdf Accessed 20 Jan 2017
- 20.Tikhonov AN, Arsenin VY (1977) Solutions of ill-posed problems. Wiley, New YorkGoogle Scholar