NEURO-FUZZY MODELLING IN ANAEROBIC WASTEWATER TREATMENT FOR PREDICTION AND CONTROL

Authors

  • Snejana Yordanova
  • Rusanka Petrova
  • Nelly Noykova
  • Plamen Tzvetkov

DOI:

https://doi.org/10.47839/ijc.5.1.381

Keywords:

Anaerobic digestion of organic waste, neuro-fuzzy modelling, sensitivity, simulation, predictive control

Abstract

The aim of the present paper is to develop neuro-fuzzy prediction models in MATLAB environment of the anaerobic organic digestion process in wastewater treatment from laboratory and simulated experiments accounting for the variable organic load, ambient influence and microorganisms state. The main contributions are determination of significant model parameters via graphical sensitivity analysis, simulation experimentation, design and study of two “black-box” models for the biogas production rate, based on classical feedforward backpropagation and Sugeno fuzzy logic neural networks respectively. The models application is demonstrated in process predictive control

References

DT. Hill. CL. Barth. A dynamical model for simulation of animal digestion, J. Water Pollution Contr. Fed 10 (1977). p. 2129-2143.

D. Dochain. P. Vanrolleghem. Dynamical Modelling and Estimation in Wastewater Treatment Processes. IWA Publishing, UK, 2001. p. 312

T. G. Muller. N. Noykova. M. Gyllenberg. J. Timmer. Parameter identification in microbial population dynamics models of anaerobic wastewater treatment, J. Math. Biosci. 177-178 (2002). pp.147-160.

N. Noykova. T. G. Muller. M. Gyllenberg. J. Timmer. Quantitative analysis of anaerobic wastewater treatment processes: identifiability and parameter estimation, J. Biotech. Bioeng., 78 (1) (2002). pp. 89-103.

I. Simeonov. V. Momchev. D. Grancharov. Dynamic modeling of mesophilic anaerobic digestion of animal waste, J. Wat. Res. 30 (5) (1996). pp. 1087-1094.

I. Simeonov. Modeling and control of anaerobic digestion of organic waste, J. Chem. Biochem. Eng. Q. 8 (2) (1994). pp. 42-52.

O. Z. Bernard. D. Dochain. Advanced monitoring and control of anaerobic wastewater treatment plants: II - dynamic model development and identification. Proceedings of the 5th Int. Symp. WATERMATEX, Gent, Belgium, September 2000. pp. 3.57-3.64.

S. Yordanova. N. Noikova. Influence of perturbations on the waste water treatment process, J. Chem. Biochem. Eng. Q. 10 (1) (1996). pp. 9-14.

T. J. Ross. Fuzzy Logic With Engineering Applications. McGraw Hill, Inc., 1995.

J.-S.R. Jang. C.-T. Sun. E. Mizutani. Neuro-Fuzzy and Soft Computing. A Computational Approach to Learning and Machine Intelligence. MATLAB Curriculum Series. Prentice-Hall, Inc., N.J., 1997. ISBN 0-13-261066-3.

S. Yordanova. Fuzzy two-level control for anaerobic wastewater treatment. Proceedings of the 2-nd Int. IEEE Conf. “Intelligent Systems”, Varna, June 22-24, Sofia, Bulgaria 2004, pp. 348-352.

S. Yordanova. R. Petrova. V. Mladenov. Neuro-fuzzy control for anaerobic wastewater treatment, WSEAS Trans. on Systems (3) (2004). pp. 724-129, ISSN 1109-2777.

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Published

2014-08-01

How to Cite

Yordanova, S., Petrova, R., Noykova, N., & Tzvetkov, P. (2014). NEURO-FUZZY MODELLING IN ANAEROBIC WASTEWATER TREATMENT FOR PREDICTION AND CONTROL. International Journal of Computing, 5(1), 51-56. https://doi.org/10.47839/ijc.5.1.381

Issue

2006, Volume 5, Issue 1

Section

Articles

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