Modelling of algae based wastewater treatment : Implementation of the River Water Quality Model no. 1

Sammanfattning: The conventional wastewater treatment of today was developed aiming to mitigate problems occurring in wastewater recipients such as oxygen depletion and eutrophication. The focus of wastewater management has however broadened and major concern is now focused on the sustainability of the wastewater treatment process itself. Algae based wastewater treatment is an alternative to conventional treatment. It has the potential to yield an acceptable effluent quality at a lower ecological cost. This Degree Project was conducted as part of MOBIT, a project at Mälardalen University. The MOBIT project was aimed at the development of an algae based wastewater treatment process in an activated sludge environment. The aim of this Degree Project was to propose a model describing the dynamics of such a system. The model was constructed in Simulink, based on the River Water Quality Model no. 1. The River Water Quality Model no. 1 was chosen as the basis for modelling because it included the state variables and processes necessary to describe the dynamics of bacteria, algae and pH. The River Water Quality Model no. 1 was, as the name suggests, developed to describe a river system. It was hence considered important to evaluate if the model was applicable to an activated sludge environment. A major obstacle was the fact that no algae based activated sludge system had been studied prior the start of the MOBIT project, the project was pioneering. The lack of system understanding and of measurement data aggravated the evaluation. However, the proposed model was compared to the Activated Sludge Model No. 1 which was known to describe an activated sludge system accurately. The model structure of the River Water Quality Model no. 1 was considered a good starting point for future modelling of the algae based activated sludge process. However, the model set-up proposed in this report does not describe the system sufficiently well. Better system understanding and measurement data is needed in order to develop and calibrate the model.