International Peer-Reviewed Journal•Open Access•ISSN 2456-8880
irejournals@gmail.com•+91-7433024337

Home / Current Issue / Paper 1701708

1701708 Vol 3 · Issue 4 Download Paper

Predicting Microbial Growth In Anaerobic Digester Using Gompertz And Logistic Models

Sylvia Injete Murunga Festus Were

Subject area: Science,Engineering and Technology  ·  Area of research: Agricultural and Biosystems Engineering

Abstract

Modelsplays a vital role in understanding microbial growth in wastewater treatment and bioremediation processes, as is, in safe food production, microbe-mediated and mining among others. However, it is also gaining popularity inoptimization designs. A study was undertaken to predict microbial growth in anaerobic digestor using Gompertz and logistic models. The objective was to determine the growth parameters and compare the performance of these primary models in anaerobic digestion(AD). Three isolates from brewery waste water closely related to Bacillus subtilis, Bacillus methylotrophicus and Lysinibacillus species were used as inoculum and their growth monitored based on optical density(OD) at the same conditions but different initial cell concentration. Microbial population growth data were fitted to the modified logistic function and Gompertz function using Marquardt algorithm and the comparison was based on both the Alkaike Information Criterion value (AIC), Residual Sum of Squares and R2values. Allthe models had a high goodness of fit (R2> 0.93) for all growth curves for three isolates, in all the cases. However, Gompertz model was accepted in 66.67% of the cases based on the AIC values and also supported by the R2> 0.95values and small RSS values. The models providedknowledge to define the growth of the methanogenic community in a bio-digester as a function of time, which could beused for maximum utilization of the exponential phase of the microbial growth for production of biogas. This indicates the practicality of applying Gompertz model to actual anaerobic digestion of brewery waste water. Growth parameters like the rate of increase in the number of cells per unit time and lag time were determined from the models.

Keywords

Anaerobic digestion, Biogas, Gompertz, Logistic, Microbial growth Models

References

[1] Y. Chen, J. J. Cheng, and K. S. Creamer, „Inhibition of anaerobic digestion process: A review‟, Bioresour. Technol., vol. 99, no. 10, pp. 4044–4064, 2008.

[2] R. M. Jingura and R. Matengaifa, „Optimization of biogas production by anaerobic digestion for sustainable energy development in Zimbabwe‟, Renew. Sustain. Energy Rev., vol. 13, no. 5, pp. 1116–1120, 2009.

[3] K. F. Adekunle and J. A. Okolie, „A Review of Biochemical Process of Anaerobic Digestion‟, Adv. Biosci. Biotechnol., vol. 6, no. 3, pp. 205– 212, 2015.

[4] D. Karakashev, D. J. Batstone, and I. Angelidaki, „Influence of Environmental Conditions on Methanogenic Compositions in Anaerobic Biogas Reactors‟, Appl. Environ. Microbiol., vol. 71, no. 1, pp. 331–338, 2005.

[5] J. L. Garcia, B. K. Patel, and B. Ollivier, „Taxonomic, phylogenetic, and ecological diversity of methanogenic Archaea.‟, Anaerobe, vol. 6, no. 4, pp. 205–226, 2000.

[6] D. S. Esser, J. H. J. Leveau, and K. M. Meyer, „Modeling microbial growth and dynamics.‟, Appl. Microbiol. Biotechnol., vol. 99, no. 21, pp. 8831–46, Nov. 2015.

[7] D. A. Mitchell, O. F. Von Meien, N. Krieger, and F. D. H. Dalsenter, „A review of recent developments in modeling of microbial growth kinetics and intraparticle phenomena in solid-state fermentation‟, Biochem. Eng. J., vol. 17, no. 1, pp. 15–26, 2004.

[8] B. P. Marks, „Status of microbial modeling in food process models‟, Compr. Rev. Food Sci. Food Saf., vol. 7, no. 1, pp. 137–143, 2008.

[9] M.-L. Pla, S. Oltra, M. D. Esteban, S. Andreu, and A. Palop, „Comparison of Primary Models to Predict Microbial Growth by the Plate Count and Absorbance Methods‟, Biomed Res. Int., vol. 2015, 2015.

[10] D. A. Longhi, F. Dalcanton, G. M. F. de Arag??o, B. A. M. Carciofi, and J. B. Laurindo, „Assessing the prediction ability of different mathematical models for the growth of Lactobacillus plantarum under non-isothermal conditions‟, J. Theor. Biol., vol. 335, pp. 88–96, 2013.

[11] M. Zwietering, I. Jongenburger, F. Rombouts, and K. van ‟t Riet, „Modeling of the bacterial growth curve‟, Appl Env. Microb, vol. 56, no. 6, pp. 1875–1881, 1990.

[12] L. DaSilva, S. Parveen, A. DePaola, J. Bowers, K. Brohawn, and M. L. Tamplin, „Development and validation of a predictive model for the growth of Vibrio vulnificus in postharvest shellstock oysters‟, Appl. Environ. Microbiol., vol. 78, no. 6, pp. 1675–1681, 2012.

[13] S. Perni, P. W. Andrew, and G. Shama, „Estimating the maximum growth rate from microbial growth curves: Definition is everything‟, Food Microbiol., vol. 22, no. 6, pp. 491–495, 2005.

[14] A. Tsoularis and J. Wallace, „Analysis of logistic growth models‟, Math. Biosci., vol. 179, no. 1, pp. 21–55, 2002.

[15] R. Lindqvist, „Estimation of Staphylococcus aureus growth parameters from turbidity data: Characterization of strain variation and comparison of methods‟, Appl. Environ. Microbiol., vol. 72, no. 7, pp. 4862–4870, 2006.

[16] W. Joanne, L. Sherwood, W. R, and J. Christophe, Prescott‟s Microbiology, 8th ed. McGrraw Hill Education, 2016.

[17] M. P. Edwards, U. Schumann, and R. S. Anderssen, „Modelling microbial growth in a closed environment‟, J. Math-for-Industry, vol. 5, pp. 33–40, 2013.

[18] L. A. Koch, Bacterial Growth and Form, 2nd ed. Springer Netherlands, 2001.

[19] M. H. Zwietering, I. Jongenburger, F. M. Rombouts, and K. van ‟t Riet, „Modeling of the bacterial growth curve.‟, Appl. Environ. Microbiol., vol. 56, no. 6, pp. 1875–81, Jun. 1990.

[20] S. I. Murunga, D. O. Mbuge, A. N. Gitau, U. N. Mutwiwa, and I. N. Wekesa, „Characterization of brewery waste water and evaluation of its potential for biogas production‟, CIGR J., vol. 18, no. 3, pp. 308–316, 2016.

[21] S. I. Murunga, D. O. Mbuge, A. N. Gitau, U. Ndungwa, and I. N. Wekesa, „Isolation and characterization of methanogenic bacteria from brewery wastewater in Kenya‟, African J. Biotechnol., vol. x, no. x, 2016.

[22] M. Kahm, „grofit: fitting biological growth curves with R‟, J. Stat. …, vol. 33, no. 7, 2010.

[23] S. N. Panikov, Microbial Growth Kinetics. Springer Science & Business Media, 1995.

How to cite this paper

Sylvia Injete Murunga, Festus Were "Predicting Microbial Growth In Anaerobic Digester Using Gompertz And Logistic Models" Iconic Research And Engineering Journals Volume 3 Issue 4 2019 Page 198-205
Sylvia Injete Murunga, Festus Were "Predicting Microbial Growth In Anaerobic Digester Using Gompertz And Logistic Models" Iconic Research And Engineering Journals, vol. 3, no. 4, Oct. 2019
Sylvia Injete Murunga, Festus Were (2019). Predicting Microbial Growth In Anaerobic Digester Using Gompertz And Logistic Models. Iconic Research And Engineering Journals, 3(4).
Sylvia Injete Murunga, Festus Were "Predicting Microbial Growth In Anaerobic Digester Using Gompertz And Logistic Models" Iconic Research And Engineering Journals, vol. 3, no. 4, Oct. 2019.
@article{1701708,
      author = {Sylvia Injete Murunga, Festus Were},
      title = {Predicting Microbial Growth In Anaerobic Digester Using Gompertz And Logistic Models},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {4},
      pages = {198-205},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1701708.pdf},
      abstract = {Modelsplays a vital role in understanding microbial growth in wastewater treatment and bioremediation processes, as is, in safe food production, microbe-mediated and mining among others. However, it is also gaining popularity inoptimization designs. A study was undertaken to predict microbial growth in anaerobic digestor using Gompertz and logistic models. The objective was to determine the growth parameters and compare the performance of these primary models in anaerobic digestion(AD). Three isolates from brewery waste water closely related to Bacillus subtilis, Bacillus methylotrophicus and Lysinibacillus species were used as inoculum and their growth monitored based on optical density(OD) at the same conditions but different initial cell concentration. Microbial population growth data were fitted to the modified logistic function and Gompertz function using Marquardt algorithm and the comparison was based on both the Alkaike Information Criterion value (AIC), Residual Sum of Squares and R2values. Allthe models had a high goodness of fit (R2> 0.93) for all growth curves for three isolates, in all the cases. However, Gompertz model was accepted in 66.67% of the cases based on the AIC values and also supported by the R2> 0.95values and small RSS values. The models providedknowledge to define the growth of the methanogenic community in a bio-digester as a function of time, which could beused for maximum utilization of the exponential phase of the microbial growth for production of biogas. This indicates the practicality of applying Gompertz model to actual anaerobic digestion of brewery waste water. Growth parameters like the rate of increase in the number of cells per unit time and lag time were determined from the models.},
      keywords = {Anaerobic digestion, Biogas, Gompertz, Logistic, Microbial growth Models},
      month = {October},
  }