Keywords: activated sludge, floc formation, flocculation, floc modelling.

Biological treatment is one of most important processes in the purification of wastewater. The activated sludge process is a biological suspended growth system, in which organisms that degrade the pollutants may aggregate to form floc or exist as dispersed cells. A good separation of biomass in the settling tanks is an essential component of the activated sludge treatment system. Poor separation of sludge in the sedimentation tank is the result of the microorganisms's failure to aggregate into floc. Prediction of these conditions is not possible with the present understanding of the nature of activated sludge and the limited scope of the available mathematical models.

The mathematical models are excellent tools for the development of conceptual knowledge about a process, providing a framework for incorporation of new ideas. An accurate model has ability to predict the process behaviour under different conditions, and can be use as a tool for educational purposes. The mathematical model allows us to investigate the static and dynamic behaviour of a system without doing or at least reducing the number of practical experiments. Mathematical modelling of the activated sludge process is useful for design, process control and trouble shooting [2]. The variables which are involved include kinetic parameters and composition of biomass.

The ability of cells to form floc depends on the physical, chemical and biological characteristics such as cell density, particle size, surface adsorption, and mixing regime and respiration rate. The floc formation of activated sludge however is a complex process due to the heterogeneity of the activated sludge floc composition. Any physical and chemical change in state such as pH and organic substrate can also affect the development of activated sludge floc. In addition, the natural fragile, highly porous and irregularly shaped flocs make them difficult to investigate and model. Although growth rates of individual microorganisms cannot easily be used to predict growth rate of floc [1], they may help to predict final floc form. However, the flocculation of activated sludge cannot be explained by a single mechanism. An understanding of the formation of floc may be helped by continuous improvement in modelling capability.

Flocculation is a complex procedure due to the large variety of bacteria, the large number of interactions and other components involved. The mechanisms responsible for flocculation of pure culture vary from different species and may not be the same as the mechanisms involved in flocculation for activated sludge [4]. It is widely held that there is no published information that clearly states the relationship between flocculation and organic matter removal kinetics [3], and there are only a few studies that can be found that deal with flocculation from activated sludge. By linking the mathematical model together with experimental data, the behaviour of natural phenomena can be described. Such models therefore can be used to predict the behaviour of systems which might be useful, in terms of time and cost, for design of new treatment systems. In addition, for the existing treatment plants, the models may be beneficial to improve the treatment efficiency and also reduce the operating costs.

Development of the system is based on using the Delphi (OOP) package. The main objectives of this paper are: (1) the development of an individual floc model starting from a single organism; (2) the development of a mathematical model to investigate the suspended floc growth properties such as substrate utilization and (increasing the number of microorganisms) microorganisms population growth, and growth rate; (3) the development of a multi-species model; (4) the development of a multi-substrate model.

The model is concerned with mechanisms involved in formation, substrate reduction, cell growth and cell death of an activated sludge floc. The model simulates two groups of ammonia oxidizers which are Nitrosomonas and Nitrobacters. While the biomass grows much slower than the rate of substrate diffusion, it is assumed that updating substrate concentration would be demonstrated for a group of cells instead of a single cell. The kinetic parameters of each group are obtained from the literatures. This will result in a prediction model for substrate reduction, substrate profile within the limited lattice, and also a screenshot of final floc shape. The model would be run; using default parameters that are totally changeable by the users. The substrate reduction, substrate profile cross section within the system, and growth curves show successful predictions from the mathematical model.

1

B.W. Brandt, A.L.M. Kooijman Sebastiaan, "Two Parameters Account for the Flocculated Growth of Microbes in Biodegradation assays", Biotechnology and Bioengineering, 70(6), 677-684, 2000. doi:10.1002/1097-0290(20001220)70:6<677::AID-BIT10>3.0.CO;2-3

2

K.J. Keesman, H. Spanjers, G. van Straten, 1998, "Analysis of Endogenous Process Behavior in Activated Sludge", Biotechnology and Bioengineering, 57(2), 155-163. doi:10.1002/(SICI)1097-0290(19980120)57:2<155::AID-BIT4>3.0.CO;2-M

3

E.J. La Motta, J.A. Jiménez, J.C. Josse, A. Manrique, "Role of Bioflocculation on Chemical Demand Removal in Solids Contact Chamber of Trickling Filter/Solids Contact Process", Journal of Environmental Engineering-ASCE, 130(7), 726-735, 2004. doi:10.1061/(ASCE)0733-9372(2004)130:7(726)

4

Wilen Britt-Marie, "Properties of Activated Sludge Flocs", PhD Thesis, Chalmers Tekniska Hogskola, Chalmers University of Technology, Sweden, 1999.

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