Nutrient removal project dissolved oxygen control algorithms
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Nutrient Removal Project Dissolved Oxygen Control Algorithms. Dale Meck Roslyn Odum Nick Wobbrock. Outline. Goals of oxygen control algorithms Explain the algorithms Constant Flow Rate Aeration On/Off Control Algorithm Linear Scalar Control Algorithm Exponential Control Algorithm

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Nutrient removal project dissolved oxygen control algorithms

Nutrient Removal ProjectDissolved Oxygen Control Algorithms

Dale Meck

Roslyn Odum

Nick Wobbrock


Outline
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
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
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.


Our plant
OUR PLANT

WASTE

TRON


Constant aeration graph
Constant Aeration Graph

Oxygen Sag Consumption greater than O2 transfer


On off control algorithm
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
On/Off Control Graph

Peaks spread w/ time


On off deficiencies
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
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
Linear Scalar

  • Increases flow rate by 1.5 times when DO = 0.5 mg/L

    • This should decrease oxygen sag time.


Linear scalar graph
Linear Scalar Graph

Unexplainable Phenomena


Linear scalar deficiencies
Linear Scalar Deficiencies

  • DO level never approaches target DO (consistently 0.2 mg/L below target)

  • Lag time not significantly decreased


Exponential model
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
Exponential: Success and Failures

  • DO lag time significantly decreased.

  • DO level never attains the target level.

  • WHAT CAN WE DO???


  • Conclusions after much failure
    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
    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 algorithm1
    Simulation Model Based Air Flow Rate Control Algorithm

    The basic idea:

    input = consumption + storage

    A 2-step iterative implementation:


    L ab v iew l ots of v ork
    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.







    Statistical comparison of aeration models

    Statistical Comparison of Aeration Models

    Precision

    Accuracy

    Overall Performance


    Precision
    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
    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
    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.