Evaluation Of Aeration Control

Transcription

1 Evaluation Of Aeration Control Randal Samstag - Carollo Engineers Carol Nelson and Curtis Steinke King County DNRP PNCWA ANNUAL CONFERENCE 24 Outline Introduction Aeration control Why is it important? Control system levels and strategies South Plant testing and modeling 2 Aeration Control - Simplified Process Diagram Why is Aeration Control Important? High operating cost Crucial to the process 4 1

2 Typical Energy Consumption Profile Aeration System Energy Consumption Aeration system: 5-7% All other systems: 3-5% 6 Aeration Energy Cost 2 mgd plant Annual energy cost: $75, Factors That Affect Aeration Power Cost Water depth Type of diffuser Type of blower Friction losses Degree of control 7 8 2

3 Aeration Control System Levels Auto Control of Common Air Header Manual Auto control of common air header Auto control of each aeration basin Auto control of each aeration zone 9 Auto Control of Each Basin Auto Control of Each Zone 3

4 General Summary and Conclusions South Plant Testing and Modeling Aeration systems are the biggest consumers of energy in a WWTP Energy consumption of aeration systems can be optimized with proper level of control Within a single basin, where is the best place to control? Performed dye test to determine hydraulic character of the aeration tank Used commercial simulation program to calibrate air flows to demand distribution Used customized model to test impact of control at different locations Tank Schematic Dye Test Results : End of Pass 4 Experimental Data Best Fit (N=53) N=3 N=7 MLSS 4. Air C/C Air 1. RAS Time (min) PE COMPARISON OF EXPERIMENTAL DATA AND TANKS IN SERIES MODEL PREDICTIONS

5 Dye Test Results : End of Pass 2 Schematic: 56-celled BioWin TM Model Experimental Data Best Fit (N=18) N=1 N=3 Secondary Influent Tank 2-4 Pass 1-1 Pass 1-2 Pass 1-3 Pass 1-4 Pass 1-5 Pass 1-6 Pass 1-7 Pass 1-8 Pass 1-9 Pass 1-1 Pass 1-11 Pass 1- Pass 1-13 Pass Pass 2-14 Pass 2-13 Pas 2- Pass 2-11 Pass 2-1 Pass 2-9 Pass 2-8 Pass 2-7 Pass 2-6 Pass 2-5 Pass 2-4 Pass 2-3 Pass 2-2 Pass Pass 3-1 Pass 3-2 Pass 3-3 Pass 3-4 Pass 3-5 Pass 3-6 Pass 3-7 Pass 3-8 Pass 3-9 Pass 3-1 Pass 3-11 Pass 3- Pass 3-13 Pass Pass 4-14 Pass 4-13 Pass 4- Pass 4-11 Pass 4-1 Pass 4-9 Pass 4-8 Pass 4-7 Pass 4-6 Pass 4-5 Pass 4-4 Pass 4-3 Pass 4-2 Pass 4-1 C/C 1. ML Channel Secondary Effluent.5 RAS Mixing Box WAS Time (min) COMPARISON OFEXPERIMENTALDATAAND TANKS IN SERIES MODEL PREDICTIONS Calibration: 56-celled BioWin TM Model Biowin TM Schematic -celled System 56-Celled BioWin Model Settled Sewage Measured Air Supply Rate Per Cell Measured Predicted Air Supply Per Tank Per Cell Filtrate Air Supply per cell (cfm) (mg/l) TO ML Channel RAS Mixing Box Effluent. WAS

6 Calibration: -celled BioWin TM Model Schematic: Custom Control Model -Celled BioWin Model MeasuredAir Supply Rate Per Cell Measured Predicted Air Supply Per Cell 8 4 PID PID PID Air Supply per Cell (cfm) (mg/l) Air Air Air Control Modeling Calibration to -celled Model Control Modeling: Air Flows with Single Control Point -Celled BioTank Model Measured Air Supply Rate Per Cell Measured Simple Carbon Model Predicted Predicted Air Supply, Simple Carbon Model (cfm) Total Air Flow (cfm) Air Supply per Cell (cfm) (mg/l) 1.5 Air Flow (cfm) 18, 16, 14,, 1, 8, 6, 4, 2, Each Zone and

7 Control Modeling: Distribution Single Point Control Control Modeling: Air Flows with Two Points of Control (mg/l) (mg/l) Cell 1 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 9 Cell 1 Cell 11 Cell Each Zone and Air Flow (cfm) 14,, 1, 8, 6, 4, 2, 6 and 6 and 1 5 and Total Air Flow (cfm) 5 and 1 4 and 4 and 1 3 and 3 and 1 1 and Control Modeling: with Two Points of Control Conclusions South Plant Modeling (mg/l) (mg/l) Cell 1 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 9 Cell 1 Cell 11 Cell and 6 and 1 5 and 5 and 1 4 and 4 and 1 3 and 3 and 1 1 and 27 Even though hydraulics indicate a 56-celled system, a -celled model is adequate for modeling control Optimal control is two point control 6 and s 5 and

8 Acknowledgements King County Department of Natural Resources and Parks Carollo Engineers Questions? 29 Most Common Control Strategies Buffered pressure control Most open valve control Incremental control Buffered Pressure Control Modify aeration air control valves to deliver setpoint This changes the aeration header pressure Modify blower output to keep a constant header pressure setpoint

9 Most Open Valve Control Incremental Control Designed to minimize pressure losses Abandon PI and/or pressure control Adjust control valves to maintain setpoints in the tanks Vary airflow in increments based on step changes in Poll the valves and adjust blower capacity to keep at least one valve in the desired open condition (8-95% open) Effect of Water Depth Factors that Affect Diffuser Transfer Efficiency Transfer efficiency goes up with increased depth But so does blower head Net wire to water difference usually small Type of diffuser Coarse bubble Tray or Bubble Cap Fine bubble Tube Disc Panels Air rate per diffuser Area Loading Rate MLSS Concentration

10 Aeration Air System Air Supply System Air Distribution System Air Supply System Filters Silencers Piping and Valves Blowers Flow/Pressure Table Flow/Pressure Graph CONDITION FLOW PRESSURE Minimum 1, 7.6 Average 2, 8.2 Maximum 3,

11 Blower Types Positive Displacement Positive Displacement (PD) Centrifugal Multi-stage Single-stage 41 Multi-Stage Centrifugal Single-Stage Centrifugal 11

12 Profile Aeration Air Demand Variation Minimum: 5-7 % of Average Maximum: % of Average 46 Air Distribution Control System Potential Problems with MOV Systems s Improperly sized valves (too big) Air Flow Meters Air Flow Control Valves Controller Pressure regulation time scale must be slower than change Integrator windup (K I e dt) Process dynamics 47 48

13 Potential Solutions to MOV Problems Carollo Experience with MOV Properly size control valves Problems at Orange County Dampen control loops Hunting of valves and blower Tracking Converted back to pressure control Keep track of integral error for all regulators Continuous tuning strategies Gain scheduling Self tuning Exact linearization Working well at Clark County Only two basins Controlling air flow to zones rather than