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Clustering hybrid regression: a novel computational approach to study and model biohydrogen production through dark fermentation

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Abstract

Clustering hybrid regression (CHR) approach was developed and evaluated using data from H2-producing glucose-based, suspended-cell bioreactor operated for 5 months. The aim was to describe the relationship between metabolic end products and H2-production rate. Self-organizing maps (SOM) were used to better visualize the dataset and to detect main metabolic patterns in bioprocess data. SOM detected three distinct metabolic patterns with butyrate, acetate and ethanol as dominant metabolites, respectively. Butyrate dominated metabolism was related to high H2 production, while acetate and ethanol dominated metabolisms resulted in low H2 production. CHR models performed well [mean square error (MSE) 0.55 and 0.56] in modeling the H2-production rate. The results validate the suitability of the CHR approach in describing the bioprocess behavior and in the modeling of H2 production rate. The developed model can help in discovering key metabolic interactions and suitable process parameters from complex datasets, and increase the understanding of the bioprocesses occurring in engineered and natural environments.

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References

  1. Bockris JOM (2002) The origin of ideas on a hydrogen economy and its solution to the decay of the environment. Int J Hydrogen Energy 27:731–740

    Article  CAS  Google Scholar 

  2. Das D, Veziroğlu TN (2001) Hydrogen production by biological processes: a survey of literature. Int J Hydrogen Energy 26:13–28

    Article  CAS  Google Scholar 

  3. Benemann J (1996) Hydrogen biotechnology: progress and prospects. Nat Biotechnol 14:1101–1103

    Article  CAS  Google Scholar 

  4. Kapdan IK, Kargi F (2006) Bio-hydrogen production from waste materials. Enzyme Microb Tech 38:569–582

    Article  CAS  Google Scholar 

  5. Li C, Fang HHP (2007) Fermentative hydrogen production and wastewater and solid wastes by mixed cultures. Crit Rev Env Sci Technol 37:1–39

    Article  Google Scholar 

  6. Lin CY, Chang RC (2004) Fermentative hydrogen production at ambient temperature. Int J Hydrogen Energy 29:715–720

    Article  CAS  Google Scholar 

  7. Rodriguez J, Kleerebezem R, Lema JM, van Loosdrecht MC (2006) Modeling product formation in anaerobic mixed culture fermentations. Biotechnol Bioeng 93:592–606

    Article  CAS  Google Scholar 

  8. Bailey EJ (1998) Mathematical modeling and analysis in biochemical engineering: past accomplishments and future opportunities. Biotechnol Prog 14:8–20

    Article  CAS  Google Scholar 

  9. Bernard O, Bastin G (2005) On the estimation of the pseudo-stoichiometric matrix for macroscopic mass balance modelling of biotechnological processes. Math Biosci 193:51–77

    Article  Google Scholar 

  10. Husain A (1998) Mathematical models of the kinetics of anaerobic digestion—a selected review. Biomass Bioenerg 14:561–571

    Article  CAS  Google Scholar 

  11. McCarty PL, Mosey FE (1991) Modelling of anaerobic digestion processes (a discussion of concepts). Wat Sci Technol 24(8):123–129

    Google Scholar 

  12. Ramkrishna D, Amundson NR (2004) Mathematics in chemical engineering: a 50 year introspection. AIChE J 50:7–23

    Article  CAS  Google Scholar 

  13. Rani KY, Rao VSR (1999) Control of fermenters—a review. Bioprocess Eng 21:77–78

    Article  Google Scholar 

  14. Rapaport A, Dochain D (2005) Interval observers for biochemical processes with uncertain kinetics and inputs. Math Biosci 193:235–253

    Article  CAS  Google Scholar 

  15. Batstone DJ, Keller J, Angelidaki I, Kalyuzhnyi SV, Pavlostathis SG, Rozzi A, Sanders WTM, Siegrist H, Vavilin VA (2002) Anaerobic digestion model no. 1 (ADM1), IWA Task Group for mathematical modelling of anaerobic digestion processes. IWA Publishing, London

    Google Scholar 

  16. Blumensaat F, Keller J (2005) Modelling of two-stage anaerobic digestion using the IWA Anaerobic digestion model no. 1 (ADM1). Water Res 39:171–183

    Article  CAS  Google Scholar 

  17. Kalyuzhnyi SV (1997) Batch anaerobic digestion of glucose and its mathematical modeling. II. Description, verification and application of model. Bioresour Technol 59:249–258

    Article  CAS  Google Scholar 

  18. Parker WJ (2005) Application of the ADM1 model to advanced anaerobic digestion. Bioresour Technol 96:832–1842

    Article  Google Scholar 

  19. Nikhil, Visa A, Yli-Harja O, Lin CY, Puhakka JA (2008) Application of CHR approach to model xylose-based fermentative hydrogen production. Energ Fuel 22(1):128–133

    Article  CAS  Google Scholar 

  20. Koskinen PEP, Kaksonen AH, Puhakka JA (2007) The relationship between instability of H2 production and compositions of bacterial communities within a dark fermentation fluidized-bed bioreactor. Biotechnol Bioeng 97(4):742–758

    Article  CAS  Google Scholar 

  21. Kohonen T (1995) Self-organizing maps. Springer series in information sciences, vol 30. Springer, Berlin

    Google Scholar 

  22. Simula O, Kangas J (1995) Process monitoring and visualization using self-organizing maps. Neural networks for chemical engineers. Computer-aided chemical engineering. Elsevier, Amsterdam, pp 377–390

    Google Scholar 

  23. Kaski S (1997) Data exploration using self-organizing maps. DTech Thesis. Helsinki University of Technology, Finland

  24. Kasslin M, Kangas J, Simula O (1992) Process state monitoring using self organizing maps. In: Aleksander I, Taylor J (eds) Artificial neural networks, vol 2. North Holland, Amsterdam, pp 1531–1534

    Google Scholar 

  25. Kohonen T, Hynninen J, Kangas J, Laaksonen J (1996) SOM_PAK: the self-organizing map program package. Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland: Technical Report A31

  26. Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (2000) SOM Toolbox for Matlab 5. Som Toolbox Team. Helsinki University of Technology, Finland. http://www.cis.hut.fi/projects/somtoolbox/

  27. MacQueen JB (1967) Some Methods for classification and Analysis of Multivariate Observations. Proc 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press 1:281–297

  28. Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice Hall, Englewood Cliffs

    Google Scholar 

  29. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31:264–323

    Article  Google Scholar 

  30. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley-Interscience, New York

    Google Scholar 

  31. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

  32. McGee VE, Carleton WT (1970) Piecewise Regression. J Am Stat Assoc 65:1109–1124

    Article  Google Scholar 

  33. Cleveland WS, Grosse EH, Shyu WM (1992) Local regression models. In: Chambers JM, Hastie TJ (eds) Chapman and Hall, London, pp 309–376

  34. Chen Y, Dong G, Han J, Wah BW, Wang J (2002) Multi-dimensional regression analysis of time-series data streams. In: Proceedings of 28th international conference very large data bases, Hongkong, China, pp 323–334

  35. Karim MN, Hodge D, Simon L (2003) Data-based modeling and analysis of bioprocesses, Some real experiences. Biotechnol Prog 19:1591–1605

    Article  CAS  Google Scholar 

  36. Massey FJ Jr (1951) The Kolmogorov-Smirnov test for goodness of fit. J Am Stat Assoc 46:68–78

    Article  Google Scholar 

  37. Darling DA (1957) The Kolmogorov-Smirnov, Cramer-von Mises Tests. Ann Math Stat 28:823–838

    Article  Google Scholar 

  38. Lilliefors HW (1967) On the Kolmogorov-Smirnov test for normality with mean and variance unknown. J Am Stat Assoc 62:399–402

    Article  Google Scholar 

  39. Hallenbeck PC (2005) Fundamentals of fermentative production of hydrogen. Wat Sci Technol 52(1–2):21–29

    CAS  Google Scholar 

  40. Nandi R, Sengupta S (1998) Microbial production of hydrogen: an overview. Crit Rev Microbiol 24:61–84

    Article  CAS  Google Scholar 

  41. Chang FY, Lin CY (2006) Calcium effect on fermentative hydrogen production in an anaerobic up-flow sludge blanket system. Wat Sci Technol 54(9):105–112

    Article  CAS  Google Scholar 

  42. Kim DH, Han SK, Kim SH, Shin HS (2006) Effect of gas sparging on continuous hydrogen production. Int J Hydrogen Energy 31:2158–2169

    Article  CAS  Google Scholar 

  43. Kim SH, Han SK, Shin HS (2006) Effect of substrate concentration on hydrogen production and 16S rDNA-based analysis of the microbial community in a continuous fermenter. Process Biochem 41:199–207

    Article  CAS  Google Scholar 

  44. Lin CN, Wu SY, Chang JS (2006) Fermentative hydrogen production with a draft tube fluidized bed reactor containing silicone-gel-immobilized anaerobic sludge. Int J Hydrogen Energy 31:2200–2210

    Article  CAS  Google Scholar 

  45. Lin CY, Hung CH, Chen CH, Chung WT, Cheng LH (2006) Effects of initial cultivation pH on fermentative hydrogen production from xylose using natural mixed cultures. Process Biochem 41:1383–1390

    Article  CAS  Google Scholar 

  46. Wu SY, Hung CH, Lin CN, Chen HW, Lee AS, Chang JS (2006) Fermentative hydrogen production and bacterial community structure in high-rate anaerobic bioreactors containing silicone-immobilized and self-flocculated sludge. Biotechnol Bioeng 93:934–946

    Article  CAS  Google Scholar 

  47. Ren NQ, Gong ML (2006) Acclimation strategy of a biohydrogen producing population in a continuous-flow reactor with carbohydrate fermentation. Eng Life Sci 6:403–409

    Article  CAS  Google Scholar 

  48. Blackwood AC, Neish AC, Ledingham GA (1956) Dissimilation of glucose at controlled pH values by pigmented and non-pigmented strains of Escherichia coli. J Bacteriol 72:497–499

    Article  CAS  Google Scholar 

  49. Cheong DY, Hansen CL (2006) Acidogenesis characteristics of natural, mixed anaerobes converting carbohydrate-rich synthetic waste water to hydrogen. Process Biochem 41:1736–1745

    Article  CAS  Google Scholar 

  50. Hussy I, Hawkes FR, Dinsdale R, Hawkes DL (2003) Continuous fermentative hydrogen production from a wheat starch co-product by mixed microflora. Biotechnol Bioeng 84:619–626

    Article  CAS  Google Scholar 

  51. Hawkes FR, Dinsdale R, Hawkes DL, Hussy I (2002) Sustainable fermentative hydrogen production: challenges for process optimisation. Int J Hydrogen Energy 27:1339–1347

    Article  CAS  Google Scholar 

  52. Mizuno O, Dinsdale R, Hawkes FR, Hawkes DL, Noike T (2000) Enhancement of hydrogen production from glucose by nitrogen gas sparging. Bioresour Technol 73:59–65

    Article  CAS  Google Scholar 

  53. Liang TM, Cheng SS, Wu KL (2002) Behavioral study on hydrogen fermentation reactor installed with silicone rubber membrane. Int J Hydrogen Energy 27:1157–1165

    Article  CAS  Google Scholar 

  54. Teplyakov VV, Gassanova LG, Sostina EG, Slepova EV, Modigell M, Netrusov AI (2002) Lab-scale bioreactor integrated with active membrane system for hydrogen production: experience and prospects. Int J Hydrogen Energy 27:1149–1155

    Article  CAS  Google Scholar 

  55. Kataoka N, Miya A, Kiriyama K (1997) Studies on hydrogen production by continuous culture system of hydrogen-producing anaerobic bacteria. Water Sci Technol 36(6–7):41–47

    Article  CAS  Google Scholar 

  56. Chen CC, Lin CY (2003) Using sucrose as a substrate in an anaerobic hydrogen producing reactor. Adv Environ Res 7:695–699

    Article  CAS  Google Scholar 

  57. Lin CY, Lee CY, Tseng IC, Shiao IZ (2006) Biohydrogen production from sucrose using base-enriched anaerobic mixed microflora. Process Biochem 41:915–919

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This research was funded by the Academy of Finland (HYDROGENE project, no. 107425), Nordic Energy Research (BIOHYDROGEN project, no. 28–02) and Tampere University of Technology Graduate School (P. E. P. Koskinen). The work was also supported by the Academy of Finland (application number 213462, Finnish Programme for Centers of Excellence in Research 2006–2011). The technical assistance of Soong Linhao and Aino-Maija Lakaniemi is highly acknowledged.

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Authors and Affiliations

  1. Department of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101, Tampere, Finland

    Nikhil, Ari Visa & Olli Yli-Harja

  2. Department of Chemistry and Bioengineering, Tampere University of Technology, P.O. Box 541, 33101, Tampere, Finland

    Nikhil, Perttu E. P. Koskinen, Anna H. Kaksonen & Jaakko A. Puhakka

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  1. Nikhil
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  2. Perttu E. P. Koskinen
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  3. Ari Visa
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Correspondence to Nikhil or Perttu E. P. Koskinen.

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Nikhil, Koskinen, P.E.P., Visa, A. et al. Clustering hybrid regression: a novel computational approach to study and model biohydrogen production through dark fermentation. Bioprocess Biosyst Eng 31, 631–640 (2008). https://doi.org/10.1007/s00449-008-0213-9

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  • DOI: https://doi.org/10.1007/s00449-008-0213-9

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