PNCWA Workshop Optimizing the Performance of Your Secondary Clarifier 1

Transcription

1 Optimizing the Performance and Capacity of Your Secondary Clarifier Ron Moeller, Kennedy Jenks (Moderator) Richard Finger, King County (retired) Randal Samstag, P.E., Carollo Engineers Edward Wicklein, P.E., Carollo Engineers Presented By PNCWA Plant Operations and Maintenance Committee Pre-Conference Workshop September 18, Workshop Agenda Time Speaker Topic 1:00 pm 1:10 pm Ron Moeller Introduction 1:10 pm 1:20 pm Randal Samstag Clarifier Performance and Capacity Overview 1:20 pm 1:45 pm Randal Samstag Introduction to Settleability Control 1:45 pm 2: 15 pm Dick Finger Settleability Control The Operations Perspective 2:15 pm 2:45 pm Randal / Dick Settleability Control Case Studies 2:45 pm - 3:00 pm Break 3:00 pm 3:15 pm Randal Samstag Introduction to Clarifier Models 3:15 pm 3:30 pm Randal Samstag Clarifier Field Tests 3:30 pm 4:00 pm Ed Wicklein Advanced Computational Models 4:00 pm 4:20 pm Randal / Ed Hydraulic Control Case Studies 4:20 pm 4:55 pm Group Use of Capacity Tools 4:55 pm 5:00 pm Ron Moeller Closure 2 Introduction Ron Moeller, Kennedy Jenks Performance and Capacity of Secondary Clarifiers Overview By Randal Samstag, P.E. Carollo Engineers 3 Presented By PNCWA Plant Operations and Maintenance Committee Pre-Conference Workshop September 18, Performance and Capacity of Secondary Clarifier Definitions Performance Issues Capacity Issues Summary of techniques for improvement of performance and capacity Definitions Secondary Clarifiers The gravity solids separator for the activated sludge process Performance The ability of the secondary clarifier to meet its effluent permit requirements Capacity The ability of a secondary clarifier to accommodate wastewater loading (while meeting permit requirements!) 5 6 Performance of Your Secondary Clarifier 1

2 Clarifiers are crucial for performance Clarifiers Are required for solids separation Can be designed to improve flocculation Clarifiers control capacity Clarifiers are critical to activated sludge capacity Activated sludge process capacity depends on both the aeration basin volume and the clarifier area 7 8 Clarifier Functions Separate solids (clarification function) Thicken solids (thickening function) Enhance flocculation Q IN X IN Activated Sludge Schematic (Conventional) S IN Sedimentation Tank Reactor Tank (Clarifier) Q MLSS X MLSS S Q OUT OUT X OUT Aerobic V AB V SC Q R X R Q W X W 9 10 Activated Sludge Schematic (Step Feed) Reactor Tank Sedimentation Tank Q IN X IN S IN (Clarifier) Activated Sludge Schematic (Selector) Reactor Tank Sedimentation Tank Q IN X IN S IN (Clarifier) Q MLSS S Q OUT OUT Q MLSS S Q OUT OUT X MLSS X OUT X MLSS X OUT V AB V SC V AB V SC Aerobic Selector Aerobic Q R X R Q W X W Q R X R Q W X W Performance of Your Secondary Clarifier 2

3 Activated Sludge Schematic (Contact Stabilization) Reactor Tank Sedimentation Tank Q IN X IN S IN (Clarifier) S Q MLSS Q OUT OUT Important Definitions For Secondary Clarifier Performance and Capacity Overflow Rate (OFR) Effluent( mgd)*1,000,000( gal / mgal) OFR( gpd / sf ) Area( sf ) X MLSS X OUT Solids Loading Rate (SLR) Stabilization Contact V AB V SC Feed Return ( mgd)* X MLSS ( mg / L)*8.34( lb / mgal / mg / L) SLR( ppd / sf ) Area( sf ) Return Sludge Ratio (RAS r ) Q R X R Q W X W 13 Return( mgd) RAS r (%) *100 Feed( mgd) Other Important Definitions For Secondary Clarifier Performance and Capacity Other Important Definitions For Secondary Clarifier Performance and Capacity Sludge Volume Index (SVI) SettledVolume( ml / L)*1000( mg / g) SVI( ml / g) MLSS( mg / L) Sludge Settling Velocity (V s ) X MLSS ( mg / L)* k ( L / mg ) Vs ( ft / hr) Vo ( ft / hr)* e V SettlingVelocity s V MaximumSettlingVelocity o X MLSS k Const. MLSSConcentration Sludge Volume Index (SVI) SettledVolume( ml / L)*1000( mg / g) SVI( ml / g) MLSS( mg / L) Sludge Settling Velocity (V s ) X MLSS ( mg / L)* k ( L / mg ) Vs ( ft / hr) Vo ( ft / hr)* e V SettlingVelocity s V MaximumSettlingVelocity o X MLSS k Const. MLSSConcentration Activated Sludge System Design Flow Diagram Activated Sludge System Design Alternate Flow Diagram Flow and Load Flow and Load Set SRT Set OFR Set SVI Calc WAS & Inventory Calc Clarifier Area Calc Allowable MLSS Calc Required AB Volume Set SRT Set MLSS Set SVI Calc WAS & Inventory Calc AB Volume Calc Clarifier Area Performance of Your Secondary Clarifier 3

4 Capacity Diagram for Activated Sludge How to Improve Performance Flocculation / Settleability Control Biological Chemical Physical Hydraulic Performance Improvement Inlet energy dissipation Outlet configuration control How to Improve Capacity Settleability Improvement Biological Chemical Hydraulic Hd Improvement Feed variation Inlet energy dissipation Outlet configuration control Introduction to Settleability Control By Randal W. Samstag Presented By PNCWA Plant Operations and Maintenance Committee Pre-Conference Workshop September 18, Outline Why is settleability important? Causes of poor settleability What can be done about poor settleability? A word about foaming Conclusions Why is Settleabilty Important? Settleability of activated sludge dramatically affects both performance and capacity! Affects effluent quality and the ability to retain biomass in the system Performance of Your Secondary Clarifier 4

5 Primary Causes of Poor Settleability Conditions Typically Associated with Specific Filaments Filamentous organisms Slime Low floc density Poor flocculation Low DO Short SRT (High F/M) Long SRT (Low F/M) Elevated VFA Septicity Nutrient Deficiency Low ph Low DO Type 1701 Straight, smoothly curved, or bent 10 to 150 μm long / 1.0 μm width Gram-negative Neisser-negative Cell septa Encouraged by Complete mix Readily biodegradable substrates (rbs) Low DO! SRT 2 20 days Selectors reported effective Short SRT (High F/M) Type 1863 Oval cells μm long by 0.8 to 1.0 μm diameter No sheath Gram-negative and Neisser- negative with Neisser-positive granules SRT < 2.5 days Like oil and grease Selectors not effective Long SRT (Low F/M) Microthrix Parvicella Coiled growth 50 to 200 m m long / 0.8 m wide Gram-positive Neisser-positive granules Encouraged by Alternating aerobic / anoxic conditions Cold temperatures Grow in unaerated zones Controlled by PAX SRT 8-50 days Anoxic selectors don t work on them (Can denitrify?) Elevated VFA Nostacoida limicola II Bent and irregularly coiled filaments μm long / μm diameter Cell septa Gram and Neisser variable Anaerobic selectors effective Performance of Your Secondary Clarifier 5

6 Septicity Thiothrix II Sulfur-oxidizing aerobe 50 to 200 μm in length, μm diameter extending from floc surface Gram-negative, Neisser- negative Intracellular sulfur granules Anaerobic selectors can be counter-productive Nutrient Deficiency Type 021 N Thiothrix I and II N. limicola III H. hydrossis S. natans Slime Bulking Low ph Funghi 32 Floc Density A Word about Foaming Compact flocs settle faster than highly filamentous sludges Phosphorus accumulating organisms (PAO) have a higher density than typical zoogleal or filamentous organisms Typically caused by Nocardioforms SRT > 2 days Aerobic selectors can control Anaerobic and Anoxic selectors may help if no foam trapping Selective wasting Chlorine not effective Cationic polymer controls them Irregularly shaped true- branching 5 to 30 μm long and 1.0 μm wide Gram-positive and Neisser-negative with Neisser-positive granules Many genera Nocardia, Gordona, Skermania Nocardioforms Types of Control for Settleability Problems Short-term term Control Long-term Control Chemicals Selectors Chlorination Aerobic PAX Anoxic Polymers Anaerobic Nutrients SRT Control DO Control 36 Performance of Your Secondary Clarifier 6

7 Chemical Control Examples Chlorine for general filament control PAX for M. parvicella control Aluminum for general control Polymers for Nocardioform control Nutrient addition for slime bulking control Selector A Definition Influent Selector Main Basin Clarifier Effluent A tank upstream of the main aerobic portion of the activated sludge process that is designed to control sludge settleability by metabolic or kinetic means Metabolic control Due to the way the organisms get food and energy Kinetic control Due to the growth rate of the organism under different conditions (SRT) 37 Metabolic Control Designed to encourage a certain organism by providing the right metabolic conditions for its growth Examples PAO in anaerobic selectors Anoxic selectors for S. natans control Kinetic Theory of Selection (Chudoba, 1973; Jenkins, 1975) Filaments have a competitive advantage over floc forming organisms under conditions of low substrate (food) concentration gradient (change). Selectors work by exposing treatment organisms to a high substrate concentration gradient. 40 Comparative Growth Rates DO 2.0 mg/l, 15 Degrees C Comparative Growth Rates DO 2 mg/l, 25 Degrees C Spec Growth Rate, Substrate, mg/l Zooglea ramigera Sphaerotilus natans Type 021N Rate, 1 Spec Growth Zooglea ramigera Sphaerotilus natans 021N Substrate, mg/l Performance of Your Secondary Clarifier 7

8 Rate, 1 Spec Growth Comparative Growth Rates DO 0.1 mg/l, 20 Degrees C Zooglea ramigera Sphaerotilus natans 021N Substrate, mg/l Types of Selectors Aerobic Anoxic Anaerobic Aerobic Selector Aerobic Selector Influent Selector Main Basin Clarifier Effluent Selector BOD Energy Main Basin CO 2 + H 2 O O 2 Aerobic first stage Classic kinetic mechanism SRT 3 to 5 days Relies on higher substrate concentration in smaller first stage of treatment Storage BOD CO 2 + H 2 O O 2 Storage Synthesis Energy 46 Anoxic Selector Anoxic Selector Influent Selector Main Basin Clarifier Effluent Selector BOD Main Basin CO 2 + H 2 O Energy O 2 Anoxic first stage Internal recycle Denitrification flow scheme Must nitrify! (SRT 4 to 10 days) Most filaments don t denitrify May not control M. Parvicella,, which can denitrify Storage BOD NO 3 N 2 CO 2 + H 2 O Storage Synthesis Energy 48 Performance of Your Secondary Clarifier 8

9 Anaerobic Selector Anaerobic Selector PAO Phosphorus Accumulating Organisms Influent Selector Main Basin Clarifier Effluent Selector BOD Main Basin CO 2 + H 2 O Storage Storage O 2 Anaerobic first stage Encourage PAO and/or GAO SRT days to 5 days No internal recycle required Can encourage Thiothrix if it produces H 2 S PAO need both anaerobic and fully aerobic conditions Glycogen Poly P Reducing Power Energy PO 4 Energy Glycogen Synthesis Poly P PO 4 50 Anaerobic Selector GAO Selector Glycogen Accumulating Organisms Storage Glycogen BOD Energy Main Basin Storage Glycogen Energy CO 2 + H 2 O Synthesis O 2 DO Control Activate sludge organisms need oxygen for growth Low DO can directly cause bulking Low DO can discourage PAO Low DO can suppress nitrifiers DO control is crucial SRT Control Low SRT can cause outbreaks of Type 1863 High SRT encourages Nocardioforms and M. parvicella Bio P organisms wash out below 2 days SRT Nitrifiers wash out below 3-4 days SRT This is the primary control for micro- organism growth 53 Conclusions Settleability problems result from many different and interacting conditions The solutions to these problems are as varied as the conditions that cause them No one solution will cure all problems Anaerobic selectors can can encourage PAO but also Thiothrix Anoxic selectors can control many filaments, but not M. parvicella or Type 0041 Chlorine doesn t correct nutrient deficiency SRT and DO control are crucial The first step in a cure is a proper diagnosis 54 Performance of Your Secondary Clarifier 9

10 Settleability Control The Operations Perspective By Richard Finger Presented By PNCWA Plant Operations and Maintenance Committee Pre-Conference Workshop September 18, 2011 Presentation Outline Long Term Approaches for Improving Settleability General Discussion of Activated Sludge Process Control Biological Approaches Control of Sludge Age Control of F/M Use of Selectors Short Term Approaches to High Flow Conditions and/or High SVI Switching from Plug Feed to Contact Stabilization Chemical Treatment Coagulant Addition Polymers Chlorination Questions? Long Term Approaches for Improving Settleability No matter how well your clarifier is designed, it s ultimate performance will be determined by the activated sludge settleability. Settleability is dependent upon a number of factors, many of which are within the control of the Operator. General Discussion of Activated Sludge Process Control Factors Outside of the Operator s Control Flow Rate Sewage Strength Aeration Tank Volume This assumes that additional tankage is not available Wastewater Temperature Number of Clarifiers General Discussion of Activated Sludge Process Control (continued) Factors Within the Operator s Control Aeration Rates RAS Return Rates Wasting Rates Dissolved Oxygen Concentration Aeration Tank Configuration Within the design limits of the tanks General Discussion of Activated Sludge Process Control (continued) By Adjusting the Controllable Parameters, the Operator Can Control: The total mass of bacteria in the system By Controlling the Mass of Bacteria, the Operator can Control: The Food to Microorganism (F/M) Ratio The Sludge Age (SRT) by adjusting the waste rate Performance of Your Secondary Clarifier 10

11 Biological Approaches for Controlling Settleability Settleability in Conventional Activated Sludge Systems is a Function of: Sludge Age or SRT F/M Ratio Aeration Time Dissolved Oxygen Concentration Biological Approaches for Controlling Settleability (continued) Sludge Age or SRT Young sludges tend to settle more slowly than older sludges Long SRT sludges tend to settle rapidly, but may leave fine particles in solution SRT does not change instantaneously with changes in wasting rates. Once a change is made, it takes up to 3 SRT s to see the full effect Effect of Sludge Age on Settleability Biological Approaches for Controlling Settleability (continued) F/M Ratio Biological Approaches for Controlling Settleability (continued) Low F/M can result in filament growth At low substrate concentrations, filaments are more effective at capturing BOD than the floc forming organisms and thus can grow faster Biological Approaches for Controlling Settleability (continued) Aeration Time Bacteria need sufficient time in an aerobic environment to metabolize what they have removed. The time required is a function of both the F/M ratio and the temperature. Higher F/M requires longer aeration times while higher temperatures allow faster metabolism and thus shorter aeration times Performance of Your Secondary Clarifier 11

12 Biological Approaches for Controlling Settleability (continued) Dissolved Oxygen DO should normally be maintained in the 2 mg/l range DO s below 2 mg/l can lead to filament growth and can limit the ability of microorganisms to metabolize organic material DO s significantly above 2 mg/l wastes energy Biological Approaches for Controlling Settleability (continued) Selectors Poor settling conditions resulting from filaments can be addressed by changing the operating configuration to create conditions that favor the growth of non-filamentous organisms Originally developed for biological nutrient removal Most common are either anoxic selectors or anaerobic selectors Biological Approaches for Controlling Settleability (continued) Basic Selector Configurations Anoxic Selector Anaerobic Selector Short Term Approaches Switching from Plug Flow to Contact Stabilization High flows can result in excessive solids loading on the clarifiers High flows can result in inadequate aeration detention time Plug Flow vs Contact Stabilization Plug Flow Plug Flow vs Contact Stabilization : Clarifier Solids Loading Contact Stabilization At the point the feed is switched to contact, mixed liquor entering the contact zone is diluted by the relocated feed. This results in a period of reduced d solids loading entering the clarifiers. Solids concentration in the reaeration zone increases to the RAS concentration as mixed liquor is displaced. Sludge blanket in the clarifier is reduced as the solids loading drops Performance of Your Secondary Clarifier 12

13 Plug Flow vs Contact Stabilization : Clarifier Solids Loading Once flows moderate, the feed should be returned to the original configuration To the extent possible, transfer of feed back to the head of the aeration tank should be done gradually to avoid short term clarifier solids overloading as the RAS enters the clarifiers. Plug Flow vs Contact Stabilization : Aeration Time Assumptions: Aeration Tank Volume = 2 MG Return Rate = 35% Contact Volume = 1 MG Reaeration Volume = 1 MG Aeration Detention Time at 10 MGD Flow Plug Flow: 2 MG x 24/10 mgd x 1.35 = 3.6 hr Aeration Detention Time at 30 MGD Flow Plug Flow: 2 MG x 24/30 mgd x 1.35 = 1.2 hr Contact Stabilization Aeration Time: 1 x 24/ 24/30 mgd x 1.35 = 0.6 hr Reaeration Time: 1 x 24/30 x.35 = 2.3 hr Total Time for aeration: = 2.9 hr Short Term Approaches (continued) Chemical Treatment Coagulant Addition May be used to improve poor settleability due to filamentous conditions or non-filamentous conditions Acts both as a bulking agent to increase floc mass and as a flocculating agent to help compact the floc and reduce settling resistance Most commonly used chemicals are iron and aluminum salts Not recommended for long term use both due to chemical costs and increased mass of inorganic floc 75 Short Term Approaches (Coagulant Addition) Chemical addition should be upstream of clarifiers in an area where there is good mixing and with sufficient detention time to facilitate flocculation The feed rate should be proportional to the mixed liquor flow rate Alum and iron salts can depress the ph, thus feed rates should be limited so as to maintain the ph at 6.5 or above 76 Short Term Approaches (Coagulant Addition) Poly aluminum chloride (PAX) formulations are available which significantly reduce ph depression. Thus, they can be fed at a higher rate so as to achieve the desired results more rapidly Feed rate adjustments should be made based on observation of actual performance Alum Addition and SVI Performance of Your Secondary Clarifier 13

14 Alum Addition and SVI 79 Short Term Approaches (Coagulant Addition) Coagulant feed should be closely monitored and stopped once the desired level of settling is achieved As shown in the previous slide, the settleability reduction lasts for a period of time after coagulant feed is stopped. Feed can be resumed when settleability starts to increase and/or when an increase in filaments is observed under the microscope 80 Short Term Approaches (Polymer addition) Polymer Addition Polymers perform similarly to coagulants by enhancing flocculation Unlike coagulants, polymers to not increase density by incorporating mass within the floc The use of polymers does not significantly increase the total mass of sludge Jar tests can be used to identify the most effective polymer additive and dose Short Term Approaches (Polymer addition) As with coagulants, the polymer should be added upstream of clarifiers in an area where there is good mixing and with sufficient detention time to facilitate flocculation The feed rate should be proportional to the mixed liquor flow rate based on the dosage determined in the jar tests Short Term Approaches (Continued) RAS Chlorination RAS chlorination is used to control poor settleability due to filaments Chlorine is applied to the RAS ahead of the aeration tank The normal dosage rate is 3 6 lb. chlorine per 1000 pounds of VSS Start at the lower rate and closely monitor the activated sludge under the microscope Short Term Approaches (RAS Chlorination) Chlorine feed should be controlled proportional to the RAS flow rate to achieve the desired dosage ratio Care must be taken not to overdose Continue chlorination until filaments are no longer prevalent and/or settling improves Performance of Your Secondary Clarifier 14

15 Effect of RAS Chlorination Before Chlorination After Chlorination Settleability Control Case Studies By Dick Finger / Randal Samstag Presented By PNCWA Plant Operations and Maintenance Committee Pre-Conference Workshop September 18, Settleability Control Case Studies Nationwide plant survey King County South Plant Anaerobic selector success West Point HPO Pilot Anaerobic selector failure Bellingham Anaerobic selector success for low DO bulking and high VFA Aberdeen Anoxic/anaerobic selector success 87 Performance of Activated Sludge Plants with Anoxic Selectors 88 Performance of Activated Sludge Plants with Anaerobic Selectors Impact of Anaerobic Selector at King County South Plant Performance of Your Secondary Clarifier 15

16 2 1 0 Primary Influent HPO Cell 2 HPO Cell 4 Effluent 2.5 Primary Influent Selector HPO Cell HPO Cell HPO Cell HPO Cell ML Channel Secondary Clarifier Effluent Waste 9/18/2011 West Point HPO Test Facility 4-stage HPO pilot facility (Lotepro) gpm SRT: 1-2 days Mar - Dec modes evaluated: Plug Flow Contact/Reaeration Anaerobic Selector w/ Plug Flow HPO Test Facility Schematic 92, ml/g SVI, Settleability Data from HPO Test Facility Plug Flow Plug Flow Plug Flow Plug Flow Plug Flow Plug Flow Contact / Reaeration Contact / Reaeration Selector Contact / Reaeration Contact / Reaeration 93 Min Ave Max Microscopic evaluation of both the pilot test facility and the UW bench scale found Thiothrix II Type 021N Sulfur oxidizing aerobes The Culprits Predicted BioWin P04 Profile Predicted Biomass Distribution: No PAO Primary Influent CONCENTRATION (mg/l) Selector HPO Cell 1 HPO Cell 2 HPO Cell 3 HPO Cell 4 ML Channel Waste BioWin Chart Effluent CONCENTRATION N (mg/l) BioWin Chart ML Channel ML Channel ML Channel ML Channel ML Channel ML Channel ML Channel ML Channel TIME Zbh Zba Zbp Zbpa Zbam Zbhm Ze TIME Performance of Your Secondary Clarifier 16

17 The Bellingham Post Point Plant 20 mgd capacity high purity oxygen (HPO) activated sludge plant Average SVI of 170 ml/g over period from 1999 to 2004 Typical filaments causing settleability problems: Type 1701 (Low DO) and Type 1863 (Low SRT) Periodically high VFA feed from influent sewer and from solids dewatering operation 3-4 days per week leading to slime bulking Idea for Improvement Anaerobic Selector Provide zone for uptake of VFA Encourage growth of phosphorus accumulating organisms (PAO) Increasing population distribution of PAO increases floc density PAO have a compact morphology and higher density than other typical activated sludge bacteria (Schuler and Jenkins) Experience at three other HPO plants (SE Essex SD, Hyperion, SE San Francisco) 98 Simulation of Anaerobic First Stage Simulations of Population Distributions Primary Effluent Anaerobic Aerobic Aerobic Secondary Clarifiers Cl2 Contact Outfall WAS Impact of Anaerobic Selector SVI Probability Impact of Anaerobic Selector SVI Probability SRT > 2.3 Days Performance of Your Secondary Clarifier 17

18 Impact on Capacity Aberdeen Anoxic / Anaerobic Selector Mechanically mixed activated sludge aeration tanks Upgraded in 2002 for fine bubble aeration with anoxic/anaerobic selectors 103 Aberdeen WWTP Schematic Selector Operation Has Improved SVI From Influent Pumps Screens Primary Sed Anoxic Anaerobic Aerobic Secondary Sed Chlorine Con Influence of Selector Operation on SVI and SRT SRT (days) NH3 Removal (mg/l) SVI (ml/g) Grit Cyclone Screenings 12 Selector Implemented 350 Grit Return Flows Gravity Thickener Rotary Thickener Main Digester Secondary Digesters Screw Press Biosolids SRT (days) and NH3r (mg/l L) SVI (ml/g) Introduction to Clarifier Models By Randal Samstag, Carollo Engineers Presented By PNCWA Plant Operations and Maintenance Committee Pre-Conference Workshop September 18, 2011 Why do Modeling? No matter what you do you can t avoid using a model of some kind. Every system is different Increasing permit limit it requirements Increasing Need to reduce safety factors to reduce cost Increasing need to maximize capacity of existing facilities Performance of Your Secondary Clarifier 18

19 Types of Sedimentation Models Solids flux models (state point analysis) One-dimensional dynamic models (Biowin, Sedtank, Takacs, Vitasovic, Stenstrom) Two-dimensional i dynamic models (UNO, TANKXZ, Carollo Fluent UDF) Three-dimensional dynamic models (Zhou/McCorquodale, Carollo Fluent UDF) State Point Analysis (Clariflux Clariflux ) Developed by Vesilind. Implemented by Carollo Engineers (among others) Solves solids flux equations based on measured settling velocity coefficients (or SVI) Calculates state point for steady state operation SOR Line MLSS Line RAS line One-dimensional (1D) Dynamic Models Developed by Stenstrom, Tracy, Vitasovic, Takacs, Sedtank, Biowin Simulate average upward velocity versus downward settling velocity Solved dynamically Layered model Used for long-term dynamic simulations Why do CFD Modeling? CFD based on prediction of two or three dimensional velocity profiles Thirty years of development using computational fluid dynamics (CFD) for analysis of sedimentation has proven that CFD can 1) Capture the main features of clarifier behavior 2) Predict detailed features of hydraulic behavior 3) Efficiently predict performance of novel designs 4) Be more cost effective than full-scale prototypes Two-dimensional (2D) Models Incorporate 2D tank hydraulics Boundary effects Turbulence Density effects Used for geometric optimization of symmetrical elements Proprietary codes or public domain programs Three-dimensional (3D) Models Resolution and detail limited only by computing power Very detailed grids can be used to capture geometric features as small as several inches Crucial for modeling of non-symmetric features Implemented in proprietary code or commercial CFD packages with special add-ons Performance of Your Secondary Clarifier 19

20 Each Type of Model Has its Place State Point Analysis Steady State Capacity Analysis 1D Dynamic Models Long-term Dynamic simulations 2D Models Simple design evaluations 3D Models For design problems that are not simple Clarifier Field Tests By Randal Samstag, Carollo Engineers Presented By PNCWA Plant Operations and Maintenance Committee Pre-Conference Workshop September 18, Outline Limitations of field tests What are field tests good for? Model calibration and validation Input tests Output tests 117 Limitations of Field Tests Limited to one geometry We can only test an existing tank at full-scale Tests don t tell us how to improve performance or capacity Limited to one point in time Characteristics of feed sludge change Flocculation characteristics Particle size distributions Settling velocity Temperature Wind speeds 118 What are field tests good for? Calibration and Validation of Models! Calibration or Validation? Definitions Calibration: Initial trials to Validation: Tests to adjust model parameters confirm that a model is to reproduce field representing field conditions (either long conditions. For example by term data or field testing independent stress tests data). with different flow or settling conditions or operating data Performance of Your Secondary Clarifier 20

21 Input and Output Parameters for Model Calibration and Validation Input parameters: Settling velocity test parameters Flow measurements MLSS tests Flocculation parameters Fractionation Simulation parameters Others like temperature, dry floc density, atmospheric parameters, etc. Output parameters: ESS Solids profile Velocity profile Dye behavior RAS SS Blanket depth Solids fraction distribution 121 Input Parameter Tests Settling velocity testing Flow measurement MLSS measurement Density measurement: lock exchange Dispersed solids / flocculation tests Particle size distributions Temperature 122 The McCorquodale Settling Model Discrete Settling Total Suspended Solids Concentration (TSS) Settling Domain Settling Model TSS < 5-15 mg/l Non-settleable V S = mg/l < TSS < 600 mg/l Discrete Settling V S1 < 1.5 m/hr ( small ) 1.5 m/hr < V S2 < 6 m/hr ( medium ) V S3 > 6 m/hr ( large ) V SD = f i V Si 600 mg/l < TSS < 1200 mg/l Flocculent Settling TSS > 1200 mg/l TSS > 6,000 mg/l Hindered Settling Compressive Settling Vs = f H *V O *e (-k1*tss) + (1-f H )*V SD V S = V O * exp (-k H * TSS) V S = V C * exp (-k C * TSS) 123 The settling velocities of large and medium flocs are found by direct measurement (visual inspection) in a column batch test using a light source, a scale and a stopwatch 124 Sludge Settling Velocity Tests Settling Velocity Data Fits Goal: Establish settling velocity at the time of field tests Sensitive to: Column shape (Dick 1975) Mixing intensity Temperature Performance of Your Secondary Clarifier 21

22 Hindered and compressive settling coefficients from the same data set. Vs m/h 10 1 Output Validation Tests Sludge blanket monitoring Solids profile testing Velocity profile testing Dye transport testing RTD Continuous dye snapshot MLSS mg/l Sludge Blanket Monitoring Dynamic monitoring of sludge blanket using a sludge judge Difficulties: What is the threshold concentration of the sludge blanket? Solids Profile Measurement Sampling Method Larsen: Larsen: Kemmerer Crosby: Solids Distribution Test - Sample pumps Current use: Portable optical probe Solids Profile Visualization Solids Profile Comparison to Simulation Field Test (Crosby SD test) Simulation (2DC) Performance of Your Secondary Clarifier 22

23 Velocity Profile Measurement Velocity Profile Visualizations Larsen built his own ultrasonic velocity probe Commercial probes: ADV Drogues Concerns: Low velocities Probe sensitivity Difficult to hold still! Dye Tests: Residence Time Distribution Tests Dye Tests: Flow Pattern Distribution Test (Crosby and Bender 1984) N = 2.3 West Side South Side East Side North Side Average C/Co Conclusions: Output validation tests Dynamic blanket monitoring Useful for rough monitoring of test conditions Not as quantitative as solids profiles Solids profiles Relatively easy to measure Directly comparable to model results Velocity profiles More difficult to measure directly Dye tests Useful for flow distribution issues Continuous test not commonly used Advanced Computational Models By Edward Wicklein, P.E. Presented By PNCWA Plant Operations and Maintenance Committee Pre-Conference Workshop September 18, Performance of Your Secondary Clarifier 23

24 CFD Modeling Computational Fluid Dynamics is the Numerical Solution of: Turbulent Fluid Motion, Energy, Reactions, Process Equations, etc. Solution Visualization Aided By Graphics Fundamental Fluid Equations u ( u ) ( uv) ( uw) 1 p t u u u t x y z x x y z Transport modeling treats solids concentration as a passive scalar quantity that is transported through a discrete grid User defined functions (UDF) to implement Solids transport Density coupling Solids settling velocity C VxC VzC sx t x z x C sz x z C C Vs z z Solids Settling Velocity is Empirical, Using Latest Research and Is Easily Calibrated to Field Conditions V s V 0 (Vesilind Equation) e kc 141 Model Selection Commercial Software Packages Extensive validation Current Ease of grid development Some customization capabilities Open Source and Proprietary/Custom Complete ability to customize Significant work to develop and maintain 143 Computational Grid Developed for Key Features of the Model Domain Cell shape/mesh type: Hexahedral Tetrahedral Hybrid Polyhedral Cell quality: Aspect ratio Length ratio Grid refinement 144 Performance of Your Secondary Clarifier 24

25 Flow Solver and Solution Convergence Fluid Flow Solver: Finite volume numerical methods Second order discratezation 2. Convergence = for global l residual and turbulent viscosity ratio 3. Underelaxation Results Show Details of Flow Field Density Current Recirculation Sedimentation Velocity Gradients Short Circuiting Poor Performance Existing inlet ports act as nozzles leading to stagnation areas within floc well Initial Inlet Spreadsheet Model Geometry Optimized with 3D Model Spreadsheet Inlet Optimized Inlet Optimized Inlet has Improved Energy Dissipation Spreadsheet Inlet Optimized Inlet Other Geometries Readily Modeled Square Peripheral Feed / Withdrawal Tank Overall Geometry and Grid Performance of Your Secondary Clarifier 25

26 Square Peripheral Feed / Withdrawal Solids Profiles Square Peripheral Feed / Withdrawal Sludge Blanket Level Topography Rectangular Lamella Clarifier Detailed Grid Capability Carollo Fluent UDF Model 2D and 3D flow in and around the lamella plate modules Ati Activated td sludge ld clarifiers Two different settling models: Vesilind Vesilind with Boycott in lamella zone D Model Allows for Detailed Flow Investigation Flow goes different directions across width and vertically Baffling Easy to Evaluate Initial Design Baffled Design Performance of Your Secondary Clarifier 26

27 High Velocity at Mid Depth Better Distributed with Baffle Initial Design Baffled Design Vesilind Model with Moderate SVI Vesilind Model of Inlet Baffle Vesilind Model with No Lamellas Vesilind/Boycott Model of Moderate SVI Different 3D Improvements Easily Compared Current Configuration Potential Modifications Performance of Your Secondary Clarifier 27

28 Modifications Generally Increase Velocity Gradient Current Configuration Potential Modifications Modifications Have Little Impact on Sedimentation Current Configuration Potential Modifications Conclusions CFD models are well developed for evaluation of sedimentation tanks Some important problems can only be adequately evaluated using 3D models Inlet design Radial flow / square shape Non-symmetrical elements Commercial 3D CFD codes can be productively used with custom add-ons Secondary Clarifier Hydraulic Control Case Studies By Randal Samstag / Ed Wicklein Presented By PNCWA Plant Operations and Maintenance Committee Pre-Conference Workshop September 18, Hydraulic Control Case Studies Reno / Truckee Meadows RAS control Olympus Terrace Sewer District Outlet t control (Stamford Baffle) Denver Metro, Las Vegas, Daly City Inlet control (MEDIC) Dallas Inlet control (Modified Inlet) BASE SIMULATION TMWRF SST MODELING Base Simulation - Existing Conditions FLOW FIELD Flow = 7.2 mgd MLSS = 1,500 mg/l SVI = 125 ml/g RAS Rate = 40% SOLIDS FIELD ESS = 18.5 mg/l Performance of Your Secondary Clarifier 28

29 TMWRF SST MODELING Impact of RAS Flow Reduction TMWRF SST MODELING Impact of Influent Distribution Changes SIMULATION 1 SIMULATION 2 FLOW FIELD FLOW FIELD Flow = 7.2 mgd MLSS = 1,500 mg/l SVI = 125 ml/g RAS Rate = 20% ESS = 15.3 mg/l ( = 3.2 mg/l) Flow = 7.2 mgd MLSS = 1,500 mg/l SVI = 125 ml/g RAS Rate = 20% ESS = 11.5 mg/l ( = 3.8 mg/l) SOLIDS FIELD SOLIDS FIELD TMWRF SST MODELING Impact of Density Current Baffle TMWRF SST MODELING Impact of Floc Well Optimization SIMULATION 3 SIMULATION 4 FLOW FIELD FLOW FIELD Flow = 7.2 mgd Flow = 7.2 mgd MLSS = 1,500 mg/l SVI = 125 ml/g ESS = 10.5 mg/l ( = 1.0 mg/l) MLSS = 1,500 mg/l SVI = 125 ml/g ESS = 8.0 mg/l ( = 2.5 mg/l) RAS Rate = 20% RAS Rate = 20% SOLIDS FIELD SOLIDS FIELD TMWRF SST MODELING Impact of Improved Settling Characteristics TMWRF SST MODELING Summary Comparison SIMULATION 5 FLOW FIELD Flow = 7.2 mgd MLSS = 1,500 mg/l SVI = 125 ml/g RAS Rate = 40% SOLIDS FIELD ESS = 12.5 mg/l ( cm/s = 6.0 mg/l) Performance of Your Secondary Clarifier 29

30 TMWRF SST MODELING Impact of Process Change Olympus Terrace Sewer District TABLE 3. EFFLUENT CONCENTRATIONS (MG/L) FROM CLARIFIER NUMBER 2 FOR DIFFERENT OVERFLOW AND BAFFLE ARRANGEMENTS ESS, mg/l /1/99 3/1/99 BEFORE AFTER ESS = mg/l ESS = mg/l 5/1/99 7/1/99 9/1/99 11/1/99 1/1/00 3/1/00 5/1/00 7/1/00 9/1/00 11/1/00 1/1/01 3/1/01 Overflow Rate (gallons/square feet day) Configuration Large Feedwell w/o Stamford Baffle Large Feedwell with Stamford Baffle Small Feedwell w/o Stamford Baffle Small Feedwell with Stamford Baffle Olympus Terrace Sewer District Clarity 2D CFD Model Solids Profile Small Feed-well Stamford Baffle Solids Profile Large Feed-well Stamford Baffle Upgrade in Operation Daly City, California Center-feed, Radial-flow Square Clarifiers Case study for use of models State Point Analysis 2D Model 3D Model State Point Comparison 33% RAS 66% RAS Performance of Your Secondary Clarifier 30

31 2D Model UNO Model Developed by J. A. McCorquodale and associates at the University of New Orleans for EPA Two-dimensional model based on Vorticity / stream function model (2D only) Turbulent hydraulics Radial flow coordinates (axi-symmetric) Solids transport Composite settling model Flocculation 2D Model Results Test Calibration Results Field UNO Model D Model Results Summary of Model Runs Three-dimensional Modeling Effluent Launders Comparison Existing Versus Stamford Baffle 14 Effluent TSS (mg g/l) Existing Clarifier (Normal Flow of 2.5 MGD, SVI of 110 and RAS of 33.3%) Existing Clarifier (High Flow of 3.5 MGD, SVI of 110 and RAS of 33.3%) Effluent Weir (Normal Flow of 2.5 MGD, SVI of 110 and RAS of 33.3%) Effluent Weir (High Flow of 3.5 MGD, SVI of 110 and RAS of 33.3%) Integration Time (minute) Existing Inlet Comparison Multilayer Energy Dissipating Inlet Colum (MEDIC) 3D Model (Zhou CFD) Developed by Siping Zhou and J. A. McCorquodale Three-dimensional solution based on Control volume model Turbulent hydraulics Generalized coordinates Solids settling Solids transport No flocculation or compression modeling Performance of Your Secondary Clarifier 31

32 Three-dimensional Modeling Inlet Optimization + Higher RAS at SVI 126 Three-dimensional Modeling Inlet Optimization at SVI 190 Existing Inlet Optimized Inlet Existing Inlet Optimized Inlet MLSS = 3,250 mg/l OFR = 714 gpd/sf RAS = 100% MLSS = 3,250 mg/l OFR = 714 gpd/sf RAS = 33% Three-dimensional Modeling Inlet Optimization at SVI = 110 ml/g Upgraded Clarifier Inlets Existing Inlet Optimized Inlet MLSS = 3,250 mg/l OFR = 918 gpd/sf RAS = 33% Estimated increase in performance (lower ESS) by 25% Estimated increase in capacity (higher flow at same SVI) by 40% Center-feed Circular Radial Flow Tank Comparison of Tangential to Puzzled Inlets Denver Metro and Clark County (Las Vegas) Comparison of Tangential to Puzzled Inlets Inlet Velocities Tangential Inlet Puzzled Inlet Tangential Inlet Puzzled Inlet Performance of Your Secondary Clarifier 32

33 Comparison of Tangential to Puzzled Inlets (3D Model) Inlet Velocity Intensity Clark County (Las Vegas) Denver Metro Upgraded Clarifiers Tangential Inlet Puzzled Inlet Current Dallas Clarifiers had Poor Inlet Energy Dissapation Dallas Proposed Inlet Evaluated and Improved Proposed Inlet Optimized Inlet Dallas Inlet Evaluation Upgraded Units Initial Inlet Optimized Inlet New Inlet Installed New Inlet in Operation Performance of Your Secondary Clarifier 33

34 Dallas - Modified Inlet Improved Performance Use of Capacity Tools By Randal Samstag and All Presented By PNCWA Plant Operations and Maintenance Committee Pre-Conference Workshop September 18, Use of Capacity Tools State Point Analysis State Point Analysis History Based on theory by Coe and Clevenger (1916) and subsequently advanced by Dick (1967), Yoshioka (1957) and Vesilind (1968) First Proposed by McHarg (1973) Systematically developed by Keinath (1979) More recent paper by Narayanan (2000) Elements of State Point Analysis Flux Line Elements of State Point Analysis Overflow Rate Line G x = XV o e -X MLSS k OFR = G x / X MLSS Slope = Q / A Performance of Your Secondary Clarifier 34

35 Elements of State Point Analysis MLSS Line Elements of State Point Analysis RAS Line X MLSS RAS = -G R / X RAS Slope = -Q R / A X RAS Interpreting SPA Flux Failure Interpreting SPA RAS Failure State Point Analysis Example State Point Example 33% Return Rate Parameter Value Parameter Value Flow (mgd) 3.5 Type Rectangular MLSS (mg/l) 3,250 Length (ft) 70 SVI (mg/l) 190 Width (ft) 70 (Daigger) RASr (%) 33 Safety Factor 1.0 Vo (ft/hr) 21.3 k (L/g) Performance of Your Secondary Clarifier 35

36 State Point Example 100% Return Rate Results of 3D Modeling High SVI Condition Configuration OFR (gpd/sf) SVI (ml/g) MLSS (mg/l) RASr (%) ESS (mg/l) Existing , >100 Existing , >1,000 Optimized , <10 Optimized , < State Point Example 100% Return Rate w/ SF = 1.3 Existing Inlet Configurations Optimized State Point Analysis Example Increase Flow to 5 mgd State Point Example 5 mgd 3,250 mg/l MLSS Parameter Value Parameter Value Flow (mgd) 5.0 Type Rectangular MLSS (mg/l) 3,250 Length (ft) 70 SVI (mg/l) 190 Width (ft) 70 (Daigger) RASr (%) 100 Safety Factor 1.3 Vo (ft/hr) 21.3 k (L/g) Performance of Your Secondary Clarifier 36

37 State Point Example 5 mgd 1,500 mg/l MLSS Conclusions State Point Analysis is a valuable tool But it needs to be used with a generous safety factor in practice due to hydraulic inefficiencies To adequately evaluate hydraulic inefficiencies a two or three dimensional CFD model is required Closure Ron Moeller, Kennedy Jenks 219 Performance of Your Secondary Clarifier 37