Open AccessNEURO-FUZZY MODELLING IN ANAEROBIC WASTEWATER TREATMENT FOR PREDICTION AND CONTROL
Author(s) -
Snejana Yordanova,
R. Petrova,
Nelly Noykova,
Plamen Tzvetkov
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.5.1.381
Subject(s) - artificial neural network , matlab , feed forward , computer science , neuro fuzzy , fuzzy logic , backpropagation , sensitivity (control systems) , black box , anaerobic digestion , process (computing) , feedforward neural network , fuzzy control system , machine learning , control engineering , artificial intelligence , engineering , chemistry , organic chemistry , electronic engineering , methane , operating system
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