Environmental Modeling & Assessment

, Volume 24, Issue 1, pp 87–94 | Cite as

BioTOOL—a Readily and Flexible Biogas Rate Prediction Tool for End-users

  • Sebastian HienEmail author
  • Joachim Hansen
  • Jörg E. Drewes
  • Konrad Koch


This study illustrates an approach to provide an easily manageable tool for end-users to predict the biogas production rate to ease the transition to a flexible and proactive operation of digesters at both biogas plants as well as at wastewater treatment plants. To provide a high flexibility to end-users but keep BioTOOL easily manageable, the approach uses a pre-trained artificial neural networks library consisting of different variable combinations. In BioTOOL’s workflow, end-user will be asked for current measurements disregarding the combination of those. BioTOOL uses a seek-and-implement method to find the correct stored network for the entered input. This approach results in predictions similar to the optimized pre-trained network. It has been demonstrated that the idea of a flexible prediction tool could be fully realized.


Biogas prediction tool Power generation prediction Artificial neural network Wastewater treatment 



This research was supported by the University of Luxembourg as Internal Research Project “BioVirt - Network controlled integration of Biological Wastewater Treatment Plants and Biogas Plants in the concept of Virtual Power Plants.”


  1. 1.
    Abu Qdais, H., Bani Hani, K., & Shatnawi, N. (2010). Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm. Resources, Conservation and Recycling, 54, 359–363.CrossRefGoogle Scholar
  2. 2.
    Beltramo, T., Ranzan, C., Hinrichs, J., & Hitzmann, B. (2016). Artificial neural network prediction of the biogas flow rate optimised with an ant colony algorithm. Biosystems Engineering, 143, 68–78.CrossRefGoogle Scholar
  3. 3.
    Bharat, A. J., & Barin, N. N. (1998). A neural network model to predict long-run operating performance of new ventures. Annals of Operations Research, 78, 89–110.Google Scholar
  4. 4.
    Eurostat (2015) Smarter, greener, more inclusive?—Indicators to support the Europe 2020 strategy, 2015th edn. Publications Office of the European Union.Google Scholar
  5. 5.
    Güçlü, D., Yılmaz, N., & Ozkan-Yucel, U. G. (2011). Application of neural network prediction model to full-scale anaerobic sludge digestion. Journal of Chemical Technology & Biotechnology, 86, 691–698.CrossRefGoogle Scholar
  6. 6.
    Gueguim Kana, E. B., Oloke, J. K., Lateef, A., & Adesiyan, M. O. (2012). Modeling and optimization of biogas production on saw dust and other co-substrates using artificial neural network and genetic algorithm. Renewable Energy, 46, 276–281.CrossRefGoogle Scholar
  7. 7.
    Holubar, P., Zani, L., Hager, M., Fröschl, W., Radak, Z., & Braun, R. (2002). Advanced controlling of anaerobic digestion by means of hierarchical neural networks. Water Research, 36(10), 2582–2588.CrossRefGoogle Scholar
  8. 8.
    Holubar, P., Zani, L., Hager, M., Fröschl, W., Radak, Z., & Braun, R. (2003). Start-up and recovery of a biogas-reactor using a hierarchical neural network-based control tool. Journal of Chemical Technology and Biotechnology, 78(8), 847–854.CrossRefGoogle Scholar
  9. 9.
    Kanat, G., & Saral, A. (2009). Estimation of biogas production rate in a thermophilic UASB reactor using artificial neural networks. Environmental Modeling and Assessment, 14(5), 607–614.CrossRefGoogle Scholar
  10. 10.
    Khataee, A. R., & Kasiri, M. B. (2011). Modeling of biological water and wastewater treatment processes using artificial neural networks. Clean: Soil, Air, Water, 39(8), 742–749.Google Scholar
  11. 11.
    Kianmehr, P., Mansoor, W., & Kfoury, F. A. (2014). Prediction of biogas generation profiles in wastewater treatment plants using neural networks. Journal of Clean Energy Technologies, 2(3), 201–205.CrossRefGoogle Scholar
  12. 12.
    Kusiak, A., & Wei, X. (2011). Prediction of methane production in wastewater treatment facility: a data-mining approach. Annals of Operations Research, 216(1), 71–81.CrossRefGoogle Scholar
  13. 13.
    Kusiak, A., & Wei, X. (2012). A data-driven model for maximization of methane production in a wastewater treatment plant. Water Science and Technology, 65(6), 1116–1122.CrossRefGoogle Scholar
  14. 14.
    Levstek, T., & Lakota, M. (2010). The use of artificial neural networks for compounds prediction in biogas from anaerobic digestion—a review. Agricultura, 7, 15–22.Google Scholar
  15. 15.
    Mathworks (2013). MATLAB ® : Neural Network Toolbox™ getting started guide R2013b. Natick: Mathworks, Inc.Google Scholar
  16. 16.
    Mauky, E., Naegele, H. J., Weinrich, S., Jacobi, H. F., Liebetrau, J., & Nelles, M. (2016). Model predictive control for demand-driven biogas production in full-scale. Chemical Engineering & Technology, 39(4), 652–664.CrossRefGoogle Scholar
  17. 17.
    McCulloch, W., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biology, 5(4), 115–133.Google Scholar
  18. 18.
    Ozkaya, B., Demir, A., & Sinan Bilgili, M. (2007). Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors. Environmental Modelling & Software, 22(6), 815–822.CrossRefGoogle Scholar
  19. 19.
    R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, URL
  20. 20.
    Schäfer, M., Gretzschel, O., Schmitt, T. G., & Knerr, H. (2015). Wastewater treatment plants as system service provider for renewable energy storage and control energy in virtual power plants—a potential analysis. Energy Procedia, 73, 87–93.CrossRefGoogle Scholar
  21. 21.
    Strik, D. P. B. T. B., Domnanovich, A. M., Zani, L., Braun, R., & Holubar, P. (2005). Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB Neural Network Toolbox. Environmental Modelling & Software, 20(6), 803–810.CrossRefGoogle Scholar
  22. 22.
    Thorin E., Nordlander E., Lindmark J., Dahlquist E., Yan J., Rebei B.F. (2012) Modeling of the biogas production process—a review. Proceedings of the International Conference on Applied Energy.Google Scholar
  23. 23.
    Wu, R. C. (1997). Neural network models: foundations and applications to an audit decision problem. Annals of Operations Research, 75, 291–301.CrossRefGoogle Scholar
  24. 24.
    Yetilmezsoy, K., Ozkaya, B., & Cakmakci, M. (2011). Artificial intelligence-based prediction models for environmental engineering. Neural Network World, 21, 193–218.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Chair of Urban Water Management and Hydraulic EngineeringUniversity of LuxembourgLuxembourgLuxembourg
  2. 2.Chair of Urban Water Systems EngineeringTechnical University of MunichGarchingGermany

Personalised recommendations