An Artificial Neural Network Based Model for Predicting H2 Production Rates in a Sucrose-Based Bioreactor System
The performance of a sucrose-based H2 production in
a completely stirred tank reactor (CSTR) was modeled by neural
network back-propagation (BP) algorithm. The H2 production was
monitored over a period of 450 days at 35±1 ºC. The proposed model
predicts H2 production rates based on hydraulic retention time
(HRT), recycle ratio, sucrose concentration and degradation, biomass
concentrations, pH, alkalinity, oxidation-reduction potential (ORP),
acids and alcohols concentrations. Artificial neural networks (ANNs)
have an ability to capture non-linear information very efficiently. In
this study, a predictive controller was proposed for management and
operation of large scale H2-fermenting systems. The relevant control
strategies can be activated by this method. BP based ANNs modeling
results was very successful and an excellent match was obtained
between the measured and the predicted rates. The efficient H2
production and system control can be provided by predictive control
method combined with the robust BP based ANN modeling tool.
[1] J-O. M. Bockris, "The origin of ideas on a hydrogen economy and its
solution to the decay of the environment," Int J Hydrogen Energy, vol.
27, pp. 731-740, 2002.
[2] D. Das, and T. N. Veziro─ƒlu, "Hydrogen production by biological
processes: a survey of literature," Int J Hydrogen Energy, vol. 26, pp.
13-28, 2001.
[3] J. Benemann, "Hydrogen biotechnology: progress and prospects," Nat
Biotechnol, vol. 14, pp. 1101-1103, 1996.
[4] I. K. Kapdan, and F. Kargi, "Bio-hydrogen production from waste
materials," Enzyme Microb Tech, vol. 38, pp. 569-582, 2006.
[5] C. Li, and H. H. P. Fang, "Fermentative hydrogen production and
wastewater and solid wastes by mixed cultures," Crit Rev Env Sci
Technol, vol. 37, pp. 1-39, 2007.
[6] R. Nandi, and S. Sengupta, "Microbial production of hydrogen: An
overview," Crit Rev Microbiol, vol. 24, pp. 61-84, 1998.
[7] D. B. Levin, L. Pitt, and M. Love, "Biohydrogen production: prospects
and limitations to practical application," Int J Hydrogen Energy, vol. 29,
pp. 173-185, 2004.
[8] J. Rodriguez, R. Kleerebezem, J. M. Lema, and M. C. van Loosdrecht,
"Modeling product formation in anaerobic mixed culture fermentations,"
Biotechnol Bioeng, vol. 93, pp. 592-606, 2006.
[9] P. Holubar, L. Zani, M. Hager, W. Fröschl, Z. Radak, and R. Braun,
"Advanced controlling of anaerobic digestion by means of hierarchical
neural networks," Water Res, vol. 36, pp. 2582-2588, 2002.
[10] D. P. B. T. B. Strike, A. M. Domnanovich, L. Zani, R. Braun, and P.
Holubar, "Prediction of trace compounds in biogas from anaerobic
digestion using the MATLAB neural network toolbox," Environ Modell
Softw, vol. 20, pp. 803-810, 2005.
[11] Ö. Cinar, H. Hasar, and C. Kinaci, "Modeling of submerged membrane
bioreactor treating cheese whey wastewater by artificial neural network,"
J Biotechnol, vol. 123, pp. 204-209, 2006.
[12] B. Özkaya, A. Demir, and M. S. Bilgili, "Neural network prediction
model for the methane fraction in biogas from field-scale landfill
bioreactors," Environ Modell Softw, vol. 22(6), pp. 815-822, 2007.
[13] M. Cakmakci, "Adaptive neuron-fuzzy modeling of anaerobic digestion
of primary sedimentation sludge," Bioprocess Biosyst Eng, vol. 30, pp.
349-357, 2007.
[14] E. Sahinkaya, B. Özkaya, A. H. Kaksonen, and J. A. Puhakka, "Neural
network prediction of thermophilic (65┬░C) sulfidogenic fluidized-bed
reactor performance for the treatment of metal-containing wastewater,"
Biotechnol Bioeng, vol. 97(4), pp. 780-787, 2007.
[15] M. Azwar, M. A. Hussain, and K. B. Ramachandran, "The study of
neural network-based controller for controlling dissolved oxygen
concentration in a sequencing batch reactor," Bioprocess Biosyst Eng,
vol. 28, pp. 251-265, 2006.
[16] F. Karaca, and B. Özkaya, "NN-LEAP: A neural network-based model
for controlling leachate flow-rate in a municipal solid waste landfill
site," Environ Modell Softw, vol. 21(8), pp. 1190-1197, 2006.
[17] B. Özkaya, E. Sahinkaya, P. Nurmi, A. H. Kaksonen, and J. A. Puhakka,
"Biologically Fe2+ oxidizing fluidized bed reactor performance and
controlling of Fe3+ recycle during heap bioleaching: an artificial neural
network-based model," Bioprocess Biosyst Eng, vol. 31(2), pp. 111-117,
2008.
[18] G. Endo, T. Noike, and J. Matsumoto, "Characteristics of cellulose and
glucose decomposition in acidogenic phase of anaerobic digestion,"
Proc. Soc. Civ. Engrs, vol. 325, pp. 61-68, 1982.
[19] N. Kataoka, A. Miya, and K. Kiriyama, "Studies on hydrogen
production by continuous culture system of hydrogen-producing
anaerobic bacteria," Water Sci Technol, vol. 36(6-7), pp. 41-47, 1997.
[20] C.-C. Chen, and C.-Y. Lin, "Using sucrose as a substrate in an anaerobic
hydrogen producing reactor," Adv Environ Res, vol. 7, pp. 695-699,
2003.
[21] C.-Y. Lin, and C.-H. Lay, "Carbon/nitrogen-ratio effect on fermentative
hydrogen production by mixed microflora," Int J Hydrogen Energy, vol.
29(1), pp. 41-45, 2004.
[22] C.-Y. Lin, and C.-H. Lay, "Effects of carbonate and phosphate
concentrations on hydrogen production using anaerobic sewage
microflora," Int J Hydrogen Energy, vol. 29(3), pp. 275-81, 2004.
[23] M. Dubois, K. A. Giles, J. K. Hamilton, P. A. Rebers, and F. Smith,
"Colorimetric method for determination of sugars and related
substances," Anal Chem, vol. 28, pp. 350-356, 1956.
[24] APHA, Standard methods. 19th edn. American Public Health
Association, Washington, DC, 1995.
[25] S. Haykin, Neural Networks - A Comprehensive Foundation. New York:
Macmillan, 1994.
[26] M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural network design.
Boston, MA: PWS Publishing, 1996.
[27] D. Nguyen, and B. Widrow, "Improving the learning speed of 2-layer
neural networks by choosing initial values of the adaptive weights," in
Proc Int Joint Conf Neural Networks, San Diego, CA, USA, 1990, pp.
321-326.
[28] H. Abdi, D. Valentin, B. Edelman, and A. J. O-Toole, "A Widrow-Hoff
learning rule for generalization of the linear auto-associator," J Math
Psychol, vol. 40(2), pp. 175-182, 1996.
[1] J-O. M. Bockris, "The origin of ideas on a hydrogen economy and its
solution to the decay of the environment," Int J Hydrogen Energy, vol.
27, pp. 731-740, 2002.
[2] D. Das, and T. N. Veziro─ƒlu, "Hydrogen production by biological
processes: a survey of literature," Int J Hydrogen Energy, vol. 26, pp.
13-28, 2001.
[3] J. Benemann, "Hydrogen biotechnology: progress and prospects," Nat
Biotechnol, vol. 14, pp. 1101-1103, 1996.
[4] I. K. Kapdan, and F. Kargi, "Bio-hydrogen production from waste
materials," Enzyme Microb Tech, vol. 38, pp. 569-582, 2006.
[5] C. Li, and H. H. P. Fang, "Fermentative hydrogen production and
wastewater and solid wastes by mixed cultures," Crit Rev Env Sci
Technol, vol. 37, pp. 1-39, 2007.
[6] R. Nandi, and S. Sengupta, "Microbial production of hydrogen: An
overview," Crit Rev Microbiol, vol. 24, pp. 61-84, 1998.
[7] D. B. Levin, L. Pitt, and M. Love, "Biohydrogen production: prospects
and limitations to practical application," Int J Hydrogen Energy, vol. 29,
pp. 173-185, 2004.
[8] J. Rodriguez, R. Kleerebezem, J. M. Lema, and M. C. van Loosdrecht,
"Modeling product formation in anaerobic mixed culture fermentations,"
Biotechnol Bioeng, vol. 93, pp. 592-606, 2006.
[9] P. Holubar, L. Zani, M. Hager, W. Fröschl, Z. Radak, and R. Braun,
"Advanced controlling of anaerobic digestion by means of hierarchical
neural networks," Water Res, vol. 36, pp. 2582-2588, 2002.
[10] D. P. B. T. B. Strike, A. M. Domnanovich, L. Zani, R. Braun, and P.
Holubar, "Prediction of trace compounds in biogas from anaerobic
digestion using the MATLAB neural network toolbox," Environ Modell
Softw, vol. 20, pp. 803-810, 2005.
[11] Ö. Cinar, H. Hasar, and C. Kinaci, "Modeling of submerged membrane
bioreactor treating cheese whey wastewater by artificial neural network,"
J Biotechnol, vol. 123, pp. 204-209, 2006.
[12] B. Özkaya, A. Demir, and M. S. Bilgili, "Neural network prediction
model for the methane fraction in biogas from field-scale landfill
bioreactors," Environ Modell Softw, vol. 22(6), pp. 815-822, 2007.
[13] M. Cakmakci, "Adaptive neuron-fuzzy modeling of anaerobic digestion
of primary sedimentation sludge," Bioprocess Biosyst Eng, vol. 30, pp.
349-357, 2007.
[14] E. Sahinkaya, B. Özkaya, A. H. Kaksonen, and J. A. Puhakka, "Neural
network prediction of thermophilic (65┬░C) sulfidogenic fluidized-bed
reactor performance for the treatment of metal-containing wastewater,"
Biotechnol Bioeng, vol. 97(4), pp. 780-787, 2007.
[15] M. Azwar, M. A. Hussain, and K. B. Ramachandran, "The study of
neural network-based controller for controlling dissolved oxygen
concentration in a sequencing batch reactor," Bioprocess Biosyst Eng,
vol. 28, pp. 251-265, 2006.
[16] F. Karaca, and B. Özkaya, "NN-LEAP: A neural network-based model
for controlling leachate flow-rate in a municipal solid waste landfill
site," Environ Modell Softw, vol. 21(8), pp. 1190-1197, 2006.
[17] B. Özkaya, E. Sahinkaya, P. Nurmi, A. H. Kaksonen, and J. A. Puhakka,
"Biologically Fe2+ oxidizing fluidized bed reactor performance and
controlling of Fe3+ recycle during heap bioleaching: an artificial neural
network-based model," Bioprocess Biosyst Eng, vol. 31(2), pp. 111-117,
2008.
[18] G. Endo, T. Noike, and J. Matsumoto, "Characteristics of cellulose and
glucose decomposition in acidogenic phase of anaerobic digestion,"
Proc. Soc. Civ. Engrs, vol. 325, pp. 61-68, 1982.
[19] N. Kataoka, A. Miya, and K. Kiriyama, "Studies on hydrogen
production by continuous culture system of hydrogen-producing
anaerobic bacteria," Water Sci Technol, vol. 36(6-7), pp. 41-47, 1997.
[20] C.-C. Chen, and C.-Y. Lin, "Using sucrose as a substrate in an anaerobic
hydrogen producing reactor," Adv Environ Res, vol. 7, pp. 695-699,
2003.
[21] C.-Y. Lin, and C.-H. Lay, "Carbon/nitrogen-ratio effect on fermentative
hydrogen production by mixed microflora," Int J Hydrogen Energy, vol.
29(1), pp. 41-45, 2004.
[22] C.-Y. Lin, and C.-H. Lay, "Effects of carbonate and phosphate
concentrations on hydrogen production using anaerobic sewage
microflora," Int J Hydrogen Energy, vol. 29(3), pp. 275-81, 2004.
[23] M. Dubois, K. A. Giles, J. K. Hamilton, P. A. Rebers, and F. Smith,
"Colorimetric method for determination of sugars and related
substances," Anal Chem, vol. 28, pp. 350-356, 1956.
[24] APHA, Standard methods. 19th edn. American Public Health
Association, Washington, DC, 1995.
[25] S. Haykin, Neural Networks - A Comprehensive Foundation. New York:
Macmillan, 1994.
[26] M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural network design.
Boston, MA: PWS Publishing, 1996.
[27] D. Nguyen, and B. Widrow, "Improving the learning speed of 2-layer
neural networks by choosing initial values of the adaptive weights," in
Proc Int Joint Conf Neural Networks, San Diego, CA, USA, 1990, pp.
321-326.
[28] H. Abdi, D. Valentin, B. Edelman, and A. J. O-Toole, "A Widrow-Hoff
learning rule for generalization of the linear auto-associator," J Math
Psychol, vol. 40(2), pp. 175-182, 1996.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:54578", author = "Nikhil and Bestamin Özkaya and Ari Visa and Chiu-Yue Lin and Jaakko A. Puhakka and Olli Yli-Harja", title = "An Artificial Neural Network Based Model for Predicting H2 Production Rates in a Sucrose-Based Bioreactor System", abstract = "The performance of a sucrose-based H2 production in
a completely stirred tank reactor (CSTR) was modeled by neural
network back-propagation (BP) algorithm. The H2 production was
monitored over a period of 450 days at 35±1 ºC. The proposed model
predicts H2 production rates based on hydraulic retention time
(HRT), recycle ratio, sucrose concentration and degradation, biomass
concentrations, pH, alkalinity, oxidation-reduction potential (ORP),
acids and alcohols concentrations. Artificial neural networks (ANNs)
have an ability to capture non-linear information very efficiently. In
this study, a predictive controller was proposed for management and
operation of large scale H2-fermenting systems. The relevant control
strategies can be activated by this method. BP based ANNs modeling
results was very successful and an excellent match was obtained
between the measured and the predicted rates. The efficient H2
production and system control can be provided by predictive control
method combined with the robust BP based ANN modeling tool.", keywords = "Back-propagation, biohydrogen, bioprocessmodeling, neural networks.", volume = "2", number = "1", pages = "16-6", }
