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
Highlights
The anaerobic digestion of organic waste is the last stage of water depollution, in which organic matter is mineralised by microorgamisms in the absence of oxygen to safely disposable in the environment substances
The anaerobic digestion is preferred for the higher organic loads treated, the smaller amount of sludge produced, the energy recovery via utilization of the biogas, the reduced operating costs - no need of oxygen supply and control
The aim of the recent investigation is to build neuro-fuzzy prediction models of the anaerobic organic digestion process in wastewater treatment on the basis of laboratory and simulated experiments accounting for the variable organic load and process parameters in MATLAB environment and to show their application in control
Summary
The anaerobic digestion (methane fermentation) of organic waste is the last stage of water depollution, in which organic matter (animal litters, plant sludge, industrial and domestic waste) is mineralised by microorgamisms in the absence of oxygen to safely disposable in the environment substances. The anaerobic digestion is preferred for the higher organic loads treated, the smaller amount of sludge produced, the energy recovery via utilization of the biogas, the reduced operating costs - no need of oxygen supply and control. The models used are nonlinear both in terms of parameters and variables, and nonstationary. The parameter identification encounters various problems [2]-[6] due to the specific features of the microorganisms, low reproducibility of the experiments, limited number of time-consuming and expensive measurements and complex laboratory analyses, noisy experimental data, great number of model parameters, etc
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