Construction of a generalized model of the harmful substances biochemical destruction process kinetics under conditions of substrate inhibition using the methods of simulation modeling

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.166571

Keywords:

biodegradation, substrate inhibition, queuing system, numerical experiment, unified formula.

Abstract

For the purpose of obtaining the complete range of solutions for substrate inhibition of varying intensity, the mechanism of enzyme kinetics in a biocell was modeled by a multi-channel queuing system. A full range of solutions is required to make a well-grounded choice of a unified generalizing formula. The process of biodegradation with substrate inhibition was described mathematically using the method of dynamics of averages. For specific destruction rate, a full range of solutions Vn of the system from minimum n=2 to limiting n→∞ order was found. It was established that the parameters of the curve shape for the solution with minimum inhibition intensity V2 substantially stand out from the general series of the spectrum formulas. The value of the coordinate of function maximum (n=2) V2 is by 1.42 times higher than that of dependence (n=3) V3.

In the numerical experiment, the physical test was simulated by description with the help of the method of the least squares of the data, assigned by the calculation from the formulas of different structures, bearing in mind a sporadic random error. The series of numerical experiments demonstrated the capability of the formula of limiting order formula Ve to describe the dependences of the whole spectrum of solutions. During describing the intermediate ratio V3 with the help of formulas V2 and Ve, the benefit is the possible range of changing the concentrations, which is by 1.5‒2 times larger at the same relative error for dependence Vе. For critical minimal order, an average relative error is sure not to exceed five percent. An increase in random error always result in statistical equality, in accuracy of describing by formulas of minimal V2 and limiting orders Ve of the data, assigned by calculation of second-order dependences. Statistical equality is achieved at the ratio of a random error to the initial error equal to ≥2.4.

Collectively, the importance of the results of numerical modeling of a physical experiment involves proving the possibility of using the formula of limiting order Ve as unified when describing the biodegradation processes with different mechanisms of substrate inhibition. This conclusion is proved by the adequate (R2=0.9396‒0.9953) description with the help of the dependence of limiting order of experimental data on five harmful substances with varying inhibition degrees. A large amount of calculation allowed achieving a definite result – we obtained the unified formula that makes it possible to proceed to scientifically grounded design calculations for bio-treatment plants.

Author Biographies

Ganna Bakharievа, National Technical University "Kharkiv Polytechnic Institute" Kyrpychova str., 2, Kharkiv, Ukraine, 61002

PhD, Associate Professor

Department of Occupational Safety and Environmental

Tetiana Falalieieva, National Technical University "Kharkiv Polytechnic Institute" Kyrpychova str., 2, Kharkiv, Ukraine, 61002

PhD, Associate Professor

Department of Organic Synthesis and Nanotechnology

Serhii Petrov, National Technical University "Kharkiv Polytechnic Institute" Kyrpychova str., 2, Kharkiv, Ukraine, 61002

PhD, Associate Professor

Department of Organic Synthesis and Nanotechnology

Iryna Mezentseva, National Technical University "Kharkiv Polytechnic Institute" Kyrpychova str., 2, Kharkiv, Ukraine, 61002

PhD, Associate Professor

Department of Occupational Safety and Environmental

Borys Kobylianskyi, Teaching and Research Professional Pedagogical Institute of Ukrainian Engineering and Pedagogical Academy Nosakova str., 9-a, Bakhmut, Ukraine, 84500

PhD, Associate Professor

Department of Occupational Safety and Ecological Safety

Ihor Tolkunov, National University of Civil Defense of Ukraine Chernyshevska str., 94, Kharkiv, Ukraine, 61023

PhD, Associate Professor, Head of Department

Department of Pyrotechnic and Special Training

Oleksandr Bondarenko, National University of Civil Defense of Ukraine Chernyshevska str., 94, Kharkiv, Ukraine, 61023

Lecturer

Department of Pyrotechnic and Special Training

References

  1. Andrews, J. F. (1968). A mathematical model for the continuous culture of microorganisms utilizing inhibitory substrates. Biotechnology and Bioengineering, 10 (6), 707–723. doi: https://doi.org/10.1002/bit.260100602
  2. Yano, T., Nakahara, T., Kamiyama, S., Yamada, K. (1966). Kinetic Studies on Microbial Activities in Concentrated Solutions. Agricultural and Biological Chemistry, 30 (1), 42–48. doi: https://doi.org/10.1080/00021369.1966.10858549
  3. Yano, T., Koga, S. (1969). Dynamic behavior of the chemostat subject to substrate inhibition. Biotechnology and Bioengineering, 11 (2), 139–153. doi: https://doi.org/10.1002/bit.260110204
  4. Webb, J. L. (1963). Enzyme and Metabolic Inhibitors. New York: Academic Press. doi: https://doi.org/10.5962/bhl.title.7320
  5. Aiba, S., Shoda, M., Nagatani, M. (1968). Kinetics of product inhibition in alcohol fermentation. Biotechnology and Bioengineering, 10 (6), 845–864. doi: https://doi.org/10.1002/bit.260100610
  6. Tseng, M. M.,Wayman, M. (1975). Kinetics of yeast growth: inhibition-threshold substrate concentrations. Canadian Journal of Microbiology, 21 (7), 994–1003. doi: https://doi.org/10.1139/m75-147
  7. Luong, J. H. T. (1987). Generalization of monod kinetics for analysis of growth data with substrate inhibition. Biotechnology and Bioengineering, 29 (2), 242–248. doi: https://doi.org/10.1002/bit.260290215
  8. Tsuji, S., Shimizu, K. (1987). Performance evaluation of ethanol fermentor systems using a vector-valued objective function. Biotechnology and Bioengineering, 30 (3), 420–426. doi: https://doi.org/10.1002/bit.260300313
  9. Han, K., Levenspiel, O. (1988). Extended monod kinetics for substrate, product, and cell inhibition. Biotechnology and Bioengineering, 32 (4), 430–447. doi: https://doi.org/10.1002/bit.260320404
  10. Edwards, V. H. (1970). The influence of high substrate concentrations on microbial kinetics. Biotechnology and Bioengineering, 12 (5), 679–712. doi: https://doi.org/10.1002/bit.260120504
  11. Bakharevа, A., Shestopalov, O., Filenko, O., Kobilyansky, B. (2016). Development of universal model of kinetics of bioremediation stationary process with substrate inhibition. Eastern-European Journal of Enterprise Technologies, 2 (10 (80)), 19–26. doi: https://doi.org/10.15587/1729-4061.2016.65036
  12. Nweke, C. O., Okpokwasili, G. C. (2014). Kinetics of growth and phenol degradation by Pseudomonas Species isolated from petroleum refinery wastewater. International Journal of Biosciences, 4 (7), 28–37. doi: https://doi.org/10.12692/ijb/4.7.28-37
  13. Prifti, T., Pinguli, L., Malollari, I. (2017). A comparative study of kinetic immobilized yeast parameters in batch fermentation processes. Europen Journal of Advanced Research in Biological and Life Sciences, 5 (3), 1–8.
  14. Ahmad, F. (2011). Study of growth kinetic and modeling of ethanol production by Saccharomyces cerevisa. AFRICAN JOURNAL OF BIOTECHNOLOGY, 10 (81). doi: https://doi.org/10.5897/ajb11.2763
  15. Olivera, S. C., Stremel, D. P., Dechechi, E. C., Pereira, F. M. (2017). Kinetic Modeling of 1-G Ethanol Fermentations. Fermentation Processes, 93–117. doi: https://doi.org/10.5772/65460
  16. Dutta, K. (2015). Substrate Inhibition Growth Kinetics for Cutinase Producing Pseudomonas cepacia Using Tomato-peel Extracted Cutin. Chemical and Biochemical Engineering Quarterly, 29 (3), 437–445. doi: https://doi.org/10.15255/cabeq.2014.2022
  17. Tazdaït, D., Abdi, N., Grib, H., Lounici, H., Pauss, A., Mameri, N. (2013). Comparison of different models of substrate inhibition in aerobic batch biodegradation of malathion. Turkish Journal of Engineering and Environmental Sciences, 37, 221–230. doi: https://doi.org/10.3906/muh-1211-7
  18. De Prá, M. C., Kunz, A., Bortoli, M., Scussiato, L. A., Coldebella, A., Vanotti, M., Soares, H. M. (2016). Kinetic models for nitrogen inhibition in ANAMMOX and nitrification process on deammonification system at room temperature. Bioresource Technology, 202, 33–41. doi: https://doi.org/10.1016/j.biortech.2015.11.048
  19. Agarry, S. E., Audu, T. O. K., Solomon, B. O. (2009). Substrate inhibition kinetics of phenol degradation by Pseudomonas fluorescence from steady state and wash-out data. International Journal of Environmental Science & Technology, 6 (3), 443–450. doi: https://doi.org/10.1007/bf03326083
  20. Mounira, K. A., Serge, H., Nawel, O., Radia, C., Noreddine, K. C. (2017). Kinetic models and parameters estimation study of biomass and ethanol production from inulin by Pichia caribbica (KC977491). African Journal of Biotechnology, 16 (3), 124–131. doi: https://doi.org/10.5897/ajb2016.15747
  21. Wei, Y.-H., Chen, W.-C., Chang, S.-M., Chen, B.-Y. (2010). Exploring Kinetics of Phenol Biodegradation by Cupriavidus taiwanesis 187. International Journal of Molecular Sciences, 11 (12), 5065–5076. doi: https://doi.org/10.3390/ijms11125065
  22. Halmi, M. I. E., Shukor, M. S., Shukor, M. Y. (2014). Evoluation of several mathematical models for fitting the growth and kinetics of the Catechol-degrading Candida parapsilopsis: part 2. Journal of Environmental Bioremediation and Toxicology, 2 (2), 53–57.
  23. Day, S., Mukherjee, S. (2014). A study of the kinetic coefficients and the rate of biodegradation of phenol by indigenous mixed microbial system. African Journal of Water Conservation and Sustainability, 2 (1), 099–107.
  24. Chakraborty, B., Ray, L., Basu, S. (2015). Study of phenol biodegradation by an indigenous mixed consortium of bacteria. Indian Journal of Chemical Technology, 22 (5), 227–233.
  25. Nsoe, M. N., Kofa, G. P., Ndi, K. S., Mohammadou, B., Heran, M., Kayem, G. J. (2018). Biodegradation of Ammonium Ions and Formate During Ammonium Formate Metabolism by Yarrowia lipolytica and Pichia guilliermondii in a Batch Reactor. Water, Air, & Soil Pollution, 229 (5). doi: https://doi.org/10.1007/s11270-018-3795-0
  26. Deriase, S. F., Younis, S. A., El-Gendy, N. S. (2013). Kinetic evaluation and modeling for batch degradation of 2-hydroxybiphenyl and 2,2′-dihydroxybiphenyl byCorynebacterium variabilisSh42. Desalination and Water Treatment, 51 (22-24), 4719–4728. doi: https://doi.org/10.1080/19443994.2012.744950
  27. Raghuvanshi, S., Gupta, S. (2012). Growth kinetics of acclimated mixed culture for degradation of Isopropyl Alcohol (IPA). Journal of Biotechnology and Biomaterials, s13. doi: https://doi.org/10.4172/2155-952x.s13-002
  28. Dey, S., Mukherjee, S. (2012). Kinetic modelling for removal of m -cresol from wastewater using mixed microbial culture in batch reactor. Journal of Water Reuse and Desalination, 2 (3), 149–156. doi: https://doi.org/10.2166/wrd.2012.055
  29. Taho Hemdi, A. (2001). Vvedenie v issledovanie operaciy. Moscow, Sankt-Peterburg, Kyiv: Izdatel'skiy dom "Vil'yams", 912.
  30. Keleti, T. (1990). Osnovy fermentativnoy kinetiki. Moscow: Mir, 350.
  31. Tikhonov, A. N. (1952). Systems of differential equations containing small parameters in the derivatives. Matematicheskiy sbornik, 31 (3), 575–586. Available at: http://www.mathnet.ru/links/672ece88e3cd9c1c4e2f0a144ca1951e/sm5548.pdf
  32. Romanovskiy, Yu. M., Stepanova, N. V., Chernavskiy, D. S. (2003). Matematicheskoe modelirovanie v biofizike. Moscow, Izhevsk: Institut komp'yuternyh issledovaniy, 402.

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Published

2019-05-13

How to Cite

Bakharievа G., Falalieieva, T., Petrov, S., Mezentseva, I., Kobylianskyi, B., Tolkunov, I., & Bondarenko, O. (2019). Construction of a generalized model of the harmful substances biochemical destruction process kinetics under conditions of substrate inhibition using the methods of simulation modeling. Eastern-European Journal of Enterprise Technologies, 3(10 (99), 6–16. https://doi.org/10.15587/1729-4061.2019.166571

Issue

Vol. 3 No. 10 (99) (2019): Ecology

Section

Ecology

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Copyright (c) 2019 Ganna Bakharievа, Tetiana Falalieieva, Serhii Petrov, Iryna Mezentseva, Borys Kobylianskyi, Ihor Tolkunov, Oleksandr Bondarenko

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