A dynamic model of the activated sludge process was used to analyze and optimize the operation of an SBR treating slaughterhouse wastewater. The existing treatment cycle (duration of fill, aeration, mix, decanting and wasting periods) was found to be inadequate for meeting effluent requirements under a number of different loading scenarios. Modelling analysis indicated that the aeration phase was too long and the settling phase too short. Simulation of a new SBR cycle operation, in which the superfluous time in the aeration phase was distributed to the settling phase and a new anoxic phase, confirmed that the unit could meet the stringent effluent requirements. Using an iterative approach, optimal cycle settings were determined for each of the loading and temperature scenarios investigated.
The Sequencing Batch Reactor (SBR) is an activated sludge process designed to accommodate both biological reactions and solid–liquid separation in a time sequence in the same tank. Currently, sequencing batch reactor (SBR) technology is a well-promoted and tested alternative, which has a relatively low cost and small footprint. The SBR process offers flexibility of operation, where the sequence of successive phases can be adjusted to create the required combination of the growth conditions for different groups of microorganisms to remove different contaminants from wastewater, i.e.:
- aerobic for COD removal only,
- aerobic/anoxic for COD/nitrogen removal and
- aerobic/anoxic/anaerobic for COD/nitrogen/phosphorus removal.
The number of biological processes and components, together with the complexity of SBR hydraulics, can make it very difficult to evaluate and optimize the performance solely based on experience and steady state analysis.
A dynamic mathematical model is an extremely useful tool for analyzing complex processes. Mathematical simulation models provide quantitative descriptions of the dynamic behavior of the system, providing predictions of the system response and performance under various operating conditions. From these predictions, design and operational parameters can be identified and optimized to maximize system performance (Hu 2001).
For this purpose, a number of mathematical activated sludge models have been developed over the last two decades. These models fall into two general categories:
- those that model COD removal/nitrification/denitrification (e.g. UCTOLD, Dold et al., 1991; ASM1, Henze et al., 1987), and
- those that model COD removal/nitrification/denitrification/biological P removal (e.g. UCTPHO, Wentzel et al., 1992; ASM2d, Henze et al., 1999).
These models have been used extensively over the past decade to analyze and optimize activated sludge plants of various types. This paper will focus on the case study of one SBR plant where model-based analysis was used to optimize process operation for optimal treatment.
A case study is presented in this paper to demonstrate how a simulation model can be used to evaluate and optimize the performance for COD and nitrogen removal of a SBR plant designed using a conventional experience-based approach. For this purpose, a simulation model for a specific SBR plant was first developed by using the IWA ASM1 model (Henze et al., 1987), and implemented in GPS-X™ simulator (Hydromantis, 2003). The developed simulation model was then used to evaluate and optimize the SBR plant by simulating the SBR performance under various operating conditions against the design criteria.