C Cenens

Application of artificial neural networks as a non-linear modular modeling technique to describe bacterial growth in chilled food products

In many chilled, prepared food products, the effects of temperature, pH and %NaCl on microbial activity interact and this should be taken into account. A grey box model for prediction of microbial growth is developed. The time dependence is modeled by a Gompertz model-based, non-linear differential equation. The influence of temperature, pH and %NaCl reflected in the model parameters is described by using low-complexity, black box artificial neural networks (ANNs). The use of this non-linear modeling technique makes it possible to describe more accurately interacting effects of environmental factors when compared with classical predictive microbiology models. When experimental results on the influence of other environmental factors become available, the ANN models can be extended simply by adding more neurons and/or layers.

Towards on-line quantification of flocs and filaments by image analysis

A fully automatic image analysis method for recognizing flocs and filaments in an activated sludge sample was developed. Five simple shape descriptors, namely the aspect ratio, the roundness, the form factor, the fractal dimension and the reduced radius of gyration, were assessed on their capability to identify flocs and filaments in a binary image.

Modeling the competition between floc-forming and filamentous bacteria in activated sludge waste water treatment systems-II. A prototype mathematical model based on kinetic selection and filamentous backbone theory

In this paper a prototype mathematical model is introduced which describes the growth of flocs and filaments in activated sludge waste water treatment systems. The model is based on both kinetic selection and filamentous backbone theory. A stability analysis of the model is performed and it is shown that this model, in contrast with mathematical models only based on kinetic selection theory, is able to describe coexistence of both flocs and filaments for a wide range of dilution rates. Afterwards the model is extended in order to describe a standard activated sludge waste water treatment system.

Modeling the competition between floc-forming and filamentous bacteria in activated sludge waste water treatment systems—I. Evaluation of mathematical models based on kinetic selection theory

One of the main reasons for failing of the sedimentation process in activated sludge waste water treatment systems is the phenomenon of filamentous bulking. This occurs when filamentous bacteria overgrow floc-forming bacteria. In this paper mathematical models are discussed which describe the competition of flocs and filaments based on the kinetic selection theory. It is proven with a stability analysis for a continuous reactor, that, in order to control filamentous bulking, the dilution rate plays a crucial role. Moreover, it is shown that coexistence of both organisms is generically not possible. Afterwards the continuous reactor model is extended in order to describe a standard waste water treatment system. Due to the complexity of the extended model it is difficult to perform the stability analysis analytically. It is proven that the model can be reduced without loss of stability properties. For the reduced model it is proven with a stability analysis that the transport terms play a crucial role in the survival of one or the other organism. As for the continuous reactor model it is shown that coexistence of both organisms is generically not possible.