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 –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.

OUR PLANT WASTE TRON

Constant Aeration Graph Oxygen Sag Consumption greater than O 2 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 Graph

–DO lag time significantly decreased. –DO level never attains the target level. WHAT CAN WE DO??? Exponential: Success and Failures

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 O 2 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.

Supply

Uptake

Consumption

SMBC Dissolved Oxygen

SMBC Consumption Rate

Statistical Comparison of Aeration Models Precision Accuracy Overall Performance

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.

Questions?