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Improved Design of Aeration Systems by Using Dynamic Simulation

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Significance: In activated sludge processes the aeration system can consume over 60% of the total energy demand of the facility and is also a significant process variable to control nutrient removal. It is essential to properly design the aeration system as a whole including equipment (blowers, diffusers, control valves), range of operating conditions, and automated control and instrumentation. Dynamic process simulations can be used to assist in sizing aeration systems that are optimized for efficient energy utilization and good process control over a wide range of operating conditions that the facility experiences over the course of a day, week, through different seasons and over the useful project life of the plant. This approach is compared to other, more traditional, approaches.

Material and Methods: Data from a WWTP in California is used to highlight the additional benefits from using dynamic process models when taking into account control systems and equipment constraints including the allowable operating range of diffusers, minimum turndown for and maximum capacity of blowers. Figure 1 shows the plant layout as implemented in BioWin and Table 1 lists the plant loding and temperature scenarios analyzed in this case study.

Outcomes: Design of aeration systems and selection of aeration equipment is often done through spreadsheet calculations based on an estimation of the specific oxygen requirements. These estimations can be derived through empirical (e.g. USEPA 1989) or mechanistic steadystate or dynamic process models (e.g. BioWin's ASDM). USEPA suggests minimum and maximum month, annual average and peak day loads and temperatures be used as inputs to the models and to cover the operational ranges of the plant. In the design procedure, the DO is often set to 2 mg DO/L and the required airflow is calculated using an aeration model, performance graph or empirical equation.

The outcomes for an aeration design are the required blower capacity, the number and distribution of diffusers for diffuser tapering and some information on the performance of different diffuser types.

However, there is limited information on the dynamic behaviour of the system when DO is controlled (Figure 2). The resulting DO profile can impact the nitrification performance (when DO is low) or waste energy (when DO is too high). Dynamic simulation integrates all parts of the treatment process including controllers and aeration equipment and therefore delivers additional valuable information to the designing engineer. Constraints such as violation of minimum or maximum airflow per diffuser can be identified and the design adapted accordingly (Figure 3). Since the aeration equipment is modeled as well, limitations stemming from the blowers (e.g. minimal possible airflow, maximum capacity or steps during shut-down or start of additional blowers) can be analyzed in detail (Figure 4) which subsequently can lead to a more rigorous planning of equipment upgrades.

To correctly simulate the plant process, representative hydraulics (in terms of number of tanks in series) must be balanced with a practical aeration control system including consideration of the number of controllable aeration drop legs and DO probes to provide active control. Dynamic modeling can be used to identify the impacts of these control and design decisions.

With respect to the design of aeration control systems, dynamic simulations can mimic the real behaviour of the plant under control. This allows better and more cost-effective planning and leads to tailored solutions.

Conclusions:The results show that modeling the DO profiles resulting from diffuser density, blower capacity, DO probe location and DO control is essential to the design of robust and flexible aeration systems. A simplified aeration system design can result in failing to meet diffuser specifications, which could damage equipment or increase fouling. Over-design or a lack of flexibility results in wasted energy and therefore in an increase of the plant's carbon footprint. In the worst case, a design based on averages can lead to a violation of the effluent limits.

Better design decisions can be made if not only the oxygen uptake rate under average or standard conditions is used but also information on influent and temperature variations, sludge inventory and details of the aeration control system. With increasing application of automated aeration control, dynamic process models can be used to define the best DO probe location or to carry out detailed cost-benefit analyses on diffuser selections (Schauer et al., 2010). Dynamic modeling can also be used to find the best suited controller design (e.g. if two DO probes would save enough energy to give a sufficient return on investments).

Document Type: Research Article


Publication date: January 1, 2011

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