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Nutrient Removal Project Dissolved Oxygen Control Algorithms

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## PowerPoint Slideshow about ' Nutrient Removal Project Dissolved Oxygen Control Algorithms' - octavia-mccall

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Presentation Transcript

Outline

- Goals of oxygen control algorithms
- Explain the algorithms
- Constant Flow Rate Aeration
- On/Off Control Algorithm
- Linear Scalar Control Algorithm
- Exponential Control Algorithm
- Simulation Model Based Control Algorithm
- How we compared algorithms
- Analysis and Conclusions

Goals of Oxygen Control Algorithms

- To maintain DO level of wastewater to allow BOD degradation
- Optimize plant to save money on pumping air for oxygen transfer
- Increase oxygen transfer efficiency at lower DO levels

Complications

Oxygen Consumption rate decreases with a decrease in BOD concentration

Therefore, Constant flow rate aeration is not ideal with a CMFR wastewater treatment plant.

Constant Aeration Graph

Oxygen Sag Consumption greater than O2 transfer

On/Off Control Algorithm

- Uses same flow rate of constant algorithm
- Turns on flow rate below target DO (2 mg/L)
- Turns off flow rate above target DO
- Should work right???

On/Off Control Graph

Peaks spread w/ time

On/Off Deficiencies

- Slow DO recovery time (same flow rate as the constant model)
- Never constant DO, always varying about the target level.
- We attempt to fix these problems with the next algorithm

Linear Scalar Model

Changes the flow rate by a simple scalar

(Target DO – DO probe)

Flow rate approaches zero when DO approaches the target.

Linear Scalar

- Increases flow rate by 1.5 times when DO = 0.5 mg/L
- This should decrease oxygen sag time.

Linear Scalar Graph

Unexplainable Phenomena

Linear Scalar Deficiencies

- DO level never approaches target DO (consistently 0.2 mg/L below target)
- Lag time not significantly decreased

Exponential Model

- Should increase the flow rate by an exponential scalar to decrease lag time.
- Flow rate = C x e(TDO-DO) – C
- Flow rate still approaches zero when DO approaches target

Exponential: Success and Failures

- DO lag time significantly decreased.
- DO level never attains the target level.
- WHAT CAN WE DO???

Conclusions after much failure

- With scalar models the DO never reaches the target because the flow rate is too small too soon.
- To maintain a DO level:

oxygen consumption = oxygen transfer

Algorithm should incorporate O2 consumption rate

Simulation Model Based Air Flow Rate Control Algorithm

The basic idea:

input = consumption + storage

A 2-step iterative implementation:

Simulation Model Based Air Flow Rate Control Algorithm

The basic idea:

input = consumption + storage

A 2-step iterative implementation:

Lab View Lots of Vork

- Wrote Code for:
- Oxygen uptake
- Oxygen consumption
- Oxygen supply
- Since we know the required uptake rate we can determine the supply rate based on the current oxygen deficit and the oxygen transfer efficiency.

Precision

- What are we looking for?
- Inefficient fluctuations
- High Variation of DO levels
- How was precision measured?

STANDARD DEVIATION of the recorded DO levels for each algorithm

Accuracy

- …is measured by the root sum of squared errors! (A measure of the average distance from the target DO level)
- Accuracy is particularly important since we’re trying to maintain a specified DO level for optimal cellular respiration.

SMBC Model The Best

- The smallest RSSE: 0.08 mg/L .
- Maintains aeration at target DO.
- Yields the consumption rate!

Further study can determine the optimum time steps for when consumption and uptake rates are calculated.

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