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Production of Ethanol from Sweet Sorghum Juice Using VHG Technology: A Simulation Case Study

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Abstract

The aims of this study were to develop the kinetic model and determine kinetic parameters describing ethanol production from sweet sorghum juice using very high gravity technology in the batch fermentation of Saccharomyces cerevisiae NP01. The obtained experimental data were tested with four different types of model, based on the experimental data, accounting for the substrate limitation, substrate inhibition, product inhibition, and the combination of those three effects, respectively. The optimization technique to find kinetic parameters was non-linear regression using Marquardt method performed through numerical procedure. The chosen model with its kinetic parameters obtained in the batch mode was validated and tested against the other independent experimental data in the small batch-scale and large-scale fermenter, in order to investigate the applicability and scale-up effect of the model, respectively. Then, the obtained model with its parameters was applied in the simulations of the continuous and fed-batch operations to examine the concentration profiles of fermentation components with the variations in operating parameters such as the dilution rate, feed-flow rate, start-up time, and feed concentration. The results indicated that the kinetic model (the substrate limitation with substrate and product inhibition effects) was suitable to describe ethanol fermentation. In the continuous mode, using the dilution rate of 0.01 h−1, the maximum ethanol concentration obtained was, approximately, 90 g/l whereas the simulated results from the fed-batch operation revealed that the maximum ethanol concentration at quasi-steady state condition was, approximately, 96 g/l. The start-up time of 21 h was the fastest time to reach the steady-state and quasi-steady state for both the continuous and fed-batch modes, respectively.

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Abbreviations

D :

Dilution rate (per hour)

F :

Substrate feed rate (in liter per hour)

K d :

Specific death constant (per hour)

K IP :

Substrate inhibition constant for ethanol formation (in grams per liter)

K IS :

Substrate inhibition constant for growth (in grams per liter)

K S :

Saturation constant for growth (in grams per liter)

K SP :

Saturation constant for ethanol formation (in grams per liter)

m :

Maintenance energy (per hour)

n p :

Ethanol inhibition constant for ethanol formation (unitless)

n x :

Ethanol inhibition constant for growth (unitless)

P :

Ethanol concentration (in grams per liter)

P p.max :

Maximum ethanol concentration for ethanol formation (in grams per liter)

P x.max :

Maximum ethanol concentration for growth (in grams per liter)

Q p :

Ethanol productivity (in grams ethanol per liter hour)

q p :

Specific production rate (in grams ethanol per grams cell hour)

q pm :

Maximum specific production rate (in grams ethanol per grams cell hour)

S :

Total sugar concentration (in grams per liter)

S i :

Total sugar concentration of inlet feed (in grams per liter)

S o :

Initial total sugar concentration (in grams per liter)

t :

Fermentation time (hour)

t c :

Start-up time of the continuous mode (hour)

t f :

Start-up time of the fed-batch mode (hour)

V :

Culture volume (liter)

V o :

Initial culture volume (liter)

X :

Viable biomass concentration (in grams per liter)

Y P/S :

Yield coefficient of ethanol (in grams ethanol per grams substrate)

Y X/S :

Yield coefficient of biomass (grams cell per grams substrate)

μ :

Specific growth rate (per hour)

μ m :

Maximum specific growth rate (per hour)

μ net :

Net specific growth rate or μ-k d (per hour)

References

  1. Mussatto, I. S., Dragone, G., Guimarães, P. M. R., et al. (2010). Biotechnology Advances, 28, 817–830.

    Article  CAS  Google Scholar 

  2. Staniszewski, M., Kujawski, W., & Lewandowska, M. (2007). Journal of Food Engineering, 82, 618–625.

    Article  CAS  Google Scholar 

  3. Ghally, A. E., & El-Taweel, A. A. (1997). Biomass and Bioenergy, 12, 461–472.

    Article  Google Scholar 

  4. Wu, X., Staggenborg, S., Propheter, J. L., Rooney, W. L., Yu, J., & Wang, D. (2010). Industrial Crops and Products, 31, 164–170.

    Article  CAS  Google Scholar 

  5. Laopaiboon, L., Nuanpeng, S., Srinophakun, P., Klanrit, P., & Laopaiboon, P. (2009). Bioresource Technology, 100, 4176–4182.

    Article  CAS  Google Scholar 

  6. Bennett, S. A., & Anex, R. P. (2009). Bioresource Technology, 100, 1595–1607.

    Article  CAS  Google Scholar 

  7. Khongsay, N., Laopaiboon, L., & Laopaiboon, P. (2010). Biotechnology, 9, 9–16.

    Article  CAS  Google Scholar 

  8. Sánchez, J. Ó., & Cardona, C. A. (2008). Bioresource Technology, 99, 5270–5295.

    Article  Google Scholar 

  9. Shuler, L., & Kargi, F. (2002). Bioprocess engineering basic concepts (2nd ed.). New York: Prentice Hall.

    Google Scholar 

  10. Birol, G., Doruker, P., Kirdar, B., Önsan, Z. İ., & Ülgen, K. (1998). Process Biochemistry, 33, 763–771.

    Article  CAS  Google Scholar 

  11. Bai, W. F., Chen, L. J., Zhang, Z., Anderson, W. A., & Moo-Young, M. (2004). Journal of Biotechnology, 110, 287–293.

    Article  CAS  Google Scholar 

  12. Chen, J. L., Xu, Y. L., Bai, F. W., Anderson, W. A., & Moo-Young, M. (2005). Biotechnology and Bioengineering, 10, 115–121.

    CAS  Google Scholar 

  13. Ge, M. X., & Bai, F. W. (2006). Journal of Biotechnology, 124, 363–372.

    Article  CAS  Google Scholar 

  14. Phisalaphong, M., Srirattana, N., & Tanthapanichakoon, W. (2006). Biochemical Engineering Journal, 28, 36–43.

    Article  CAS  Google Scholar 

  15. Rivera, C. E., Costa, A. C., Atala, D. I. P., Maugeri, F., Wolf Maciel, M. R., & Filho, R. M. (2006). Process Biochemistry, 41, 1682–1687.

    Article  CAS  Google Scholar 

  16. Staniszewski, M., Kujawski, W., & Lewandowska, M. (2009). Journal of Food Engineering, 91, 240–249.

    Article  CAS  Google Scholar 

  17. Shen, J., & Agblevor, F. A. (2010). Applied Biochemistry and Biotechnology, 160, 665–681.

    Article  CAS  Google Scholar 

  18. Constantinides, A., & Mostoufi, N. (1999). Numerical methods for chemical engineers with MATLAB applications. New Jersey: Prentice Hall.

    Google Scholar 

  19. Englezos, P., & Kalogerakus, N. (2001). Applied parameter estimation for chemical engineers. New York: Marcel Dekker.

    Google Scholar 

  20. Steven, C. C. (2005). Applied numerical methods with MATLAB for engineers and scientists. New York: Mcgraw hill.

    Google Scholar 

  21. Laopaiboon, L., Laopaiboon, P., Jaisil, P., & Thanonke, P. (2008). Development of ethanol production process from sweet sorghum stem juice. Khon Kaen, Thailand: Research Report Khon Kaen University.

    Google Scholar 

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Acknowledgments

We gratefully acknowledge Assistant. Prof. Dr. Nanthiya Hansupalak, faculty of engineering, Kasetsart University, for giving a useful advice on the optimization technique performed in this research. We would also like to thank the financial supports from Kasetsart University Research and Development Institute (KURDI), National Center of Excellence for Petroleum, Petrochemicals and Advanced Materials, S&T Postgraduate Education and Research Development Office (PERDO) and Center for advanced Studies in Industrial Technology (NRU), Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, and the Graduate School Kasetsart University, Bangkok, Thailand.

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Authors and Affiliations

  1. Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, 50 Ngamwongwan Road Chatuchak, Bangkok, 10900, Thailand

    Preuk Thangprompan, Anusith Thanapimmetha, Maythee Saisriyoot & Penjit Srinophakun

  2. Department of Biotechnology, Faculty of Technology, Khon Kaen University, Khon Kaen, 40002, Thailand

    Lakkana Laopaiboon

  3. National Center of Excellence for Petroleum, Petrochemicals and Advanced Materials, S&T Postgraduate Education and Research Development Office (PERDO), Bangkok, 10330, Thailand

    Anusith Thanapimmetha, Maythee Saisriyoot & Penjit Srinophakun

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  1. Preuk Thangprompan
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  2. Anusith Thanapimmetha
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  3. Maythee Saisriyoot
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  4. Lakkana Laopaiboon
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  5. Penjit Srinophakun
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Correspondence to Penjit Srinophakun.

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Thangprompan, P., Thanapimmetha, A., Saisriyoot, M. et al. Production of Ethanol from Sweet Sorghum Juice Using VHG Technology: A Simulation Case Study. Appl Biochem Biotechnol 171, 294–314 (2013). https://doi.org/10.1007/s12010-013-0365-1

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  • DOI: https://doi.org/10.1007/s12010-013-0365-1

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