Purpose of this course. Optimization of Water Treatment Plant Operations

Transcription

1 Optimization of Water Treatment Plant Operations Purpose of this course Discuss cause and effect relationships between unit processes and operations Alex Yavich, Ph.D., P.E. Optimization Solutions Environmental, LLC Demonstrate how to effectively control these relationships in order to Improve the quality of finished water Reduce chemical and other operational costs Make easier to operate the plant Content Section I Coagulation Coagulation mechanisms Coagulants commonly used in water treatment practice Raw water quality factors affecting coagulation Coagulation feed control methods Section II Treatment Train Flocculation Sedimentation Filtration Limesoftening Section III Summary Chemical feed rate optimization Water quality monitoring requirements Selection of optimal coagulant Flocculation mixing optimization Summary case Coagulation Mechanisms Al 3 Al(OH) 2 All(OH) 3 Charge neutralization Al 7(OH) Coagulant (e.g., Alum) (s) Sorption Alkalinity Sweep coagulation 1

2 Effect of Coagulant Dose on Effluent Turbidity Type of Coagulants Metallic salts Commonly used coagulants: Aluminum sulfate (alum: Al 2 (SO 4 ) 3 14H 2 O) Ferric sulfate (Fe 2 (SO 4 ) 3 ) Ferric chloride (FeCl 3 ) Polyaluminum chloride (PACl: Al n (OH) m Cl 3nm ) Undergo hydrolysis to form positively charged ions React with natural alkalinity to form metal hydroxide precipitate Goal Safe overfeed zone Cationic polymer Most widely used: polydiallyldimethyl ammonium chloride (polydadmac) Produces positive charges Enmeshment (sweep coagulation) is not a factor Chemical Feed Control Considerations Combination of Metal Coagulant and Cationic Polymer Separate feeding Premanufactured blend 2

3 How Metal Coagulant and Polymer Work Together How Metal Coagulant and Polymer Work Together Metal coagulant cationic polymer Metal coagulant cationic polymer Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) Low turbidity conditions High turbidity conditions How Metal Coagulant and Polymer Work Together Metal coagulant cationic polymer Saginaw Water Treatment Plant Saginaw, MI Case Analysis 1 Capacity: 52 MGD Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) Source water: Lake Huron Raw turbidity: NTU Al(OH)3 Al(OH)3 Saginaw WTP High turbidity conditions 3

4 Plant Schematics Chemical Feeds Ferric chloride Cationic polymer Cl FeCl3 CatFloc Cl Raw water PAC Filters To Clearwells Dose, mg/l Flocculation basins Clarifiers Sedimentation basins Sep97 Nov97 Jan98 Mar98 May98 Jul98 Sep98 Nov98 Jan99 Mar99 May99 Jul99 Sep99 Nov99 Jan00 Mar00 May00 Jul00 Sep00 Nov00 Jan01 Mar01 May01 Jul01 Sep01 Nov01 Jan02 Mar02 May02 Jul02 Sep02 Oct02 Dec02 Feb03 Apr03 Jun03 Saginaw WTP Note: polymer dose is given as mg/l as product (with 20% of active ingredient in the product) Saginaw WTP Computer Simulation Analysis Chemical Feed Optimization 10 FeCl 3, mg/l Chemical Cost Flow Avg. chemical usage (Fe & polym) 1 F. Turb Year MGD FeCl3 CatFloc NTU Total, $ $/MG mg/l mg/l , , , , Adjusted for 2009 chemical prices 3 2 Raw turbidity = 0.5 NTU Raw turbidity = 5 NTU CatFloc, mg/l Saginaw WTP Saginaw WTP 4

5 Case Analysis 2 Case Analysis 2 South Haven Water Treatment Plant South Haven, MI Capacity: Source water: Treatment: 4.2 MGD Lake Michigan Conventional treatment PACl/CatPolymer blend South Haven WTP South Haven WFP Separate Feeding vs. Premanufactured Blend Separate feeding Effective coagulation control Costeffective Additional feeding equipment may be required Premanufactured blend Simpler than separate feeding Do not always provide effective coagulation control More expensive Raw Water Quality Characteristics Turbidity ph Temperature Alkalinity Ion composition Natural organic matter What to choose Premanufactured blends could be feasible for smaller plants Larger plants would benefit from separate feeding 5

6 Raw Turbidity Low Turbidity Conditions The coagulant demand normally increases when the raw turbidity increases The relationship is not linear Al 7(OH) 17 4 Al 14(OH) Coagulant (e.g., alum) (s) High Turbidity Conditions Typical Relationship between Raw Turbidity and Coagulant Dose Coagulant (e.g., alum) Charge neutralization Al 7(OH) 17 4 Al 14(OH) (s) Sweep coagulation Al(OH) 3 Al(OH) 3 Al(OH) 3 Al(OH) 3 Transitional zone 6

7 Effect of ph on Coagulation with Aluminum Salts Effect of ph on Coagulation with Ferric Salts Al 3 Al(OH) 2 Al 7(OH) Fe 3 Fe(OH) 2 Fe 2(OH) Increase Decrease Increase Decrease Decrease (s) Decrease Decrease Fe(OH) 3 (s) Decrease ph = 4 ph = 67 (lowest Al solubility) ph = 10 ph = 4 ph = 88.5 (lowest Fe solubility) ph = 10 Water Temperature May affect hydrolysis rates Metal hydroxide solubility decreases with decreasing temperature ph of minimum solubility of metal salts increases with decreasing temperature The rate of floc formation and sedimentation efficiency decrease with decreasing temperature Low Temperatures May Call for Increased Coagulant Dose Coagulant (e.g., Al 2(SO 4) 3 14H 2O) Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) 7

8 Typical Temperature Effect Alkalinity Metal coagulants consume alkalinity Al 2 (SO 4 ) 3 3Ca(HCO 3 ) 2 2 3CaSO 4 6CO 2 Alkalinity in water is important to maintain stable ph and for corrosion control purposes Increased raw alkalinity may be an indicator of deteriorating raw water quality Natural Organic Matter () is organic substances that are leached from soil and also produced within natural water and sediments by chemical and biological processes such as decomposition of vegetation. is present in all natural waters Major components of (humic substances) are anionic (negatively charged) polyelectrolytes of relatively high molecular weight. How to Measure the Concentration of? The concentration of in water is typically measured as concentration of total organic carbon (TOC) or dissolved organic carbon (DOC) Ultraviolet absorption measured at a wavelength of 254 nm (UV254) can be used as a simple surrogate measure for DOC. includes precursor compounds that form healthrelated byproducts when chlorine and other disinfectants are used for disinfection (e.g., THMs) 8

9 Effect of on Coagulation Coagulant (e.g., Al 2(SO 4) 3 14H 2O) Effect of UV254 on Coagulant Demand Lake Michigan Filtration Plant (LMFP), Grand Rapids, MI Design capacity 142 MGD Source Lake Michigan Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) Al(OH) 3 South Haven Case South Haven Case January 15, 2008 South Haven WFP South Haven WFP 9

10 South Haven Case January 15, 2008 South Haven Case PACl/cationic polymer blend Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) South Haven WFP South Haven WFP Effect of Coagulant Dose on Effluent Turbidity (conceptual) Coagulant Feed Control Methods Goal 10

11 Operators Experience Most important factor in plant operation Operators experience may vary Chemical feed rate changes made during one shift may affect effluent quality during the following shift Some parameters may affect chemical feed rates, but are not routinely monitored Jar Test Remains the predominant method of coagulation control Jar test s hydraulic characteristics may be different from that of actual plant conditions Not always easy to decide when to run the jar test Difficult to run effective jar test under rapidly changing raw water conditions Time consuming Streaming Current Detector (SCD) Chemical Feed Control Using SCD Introduced in early 90s Measures net colloidal surface charges of a water sample following chemical addition Source water Rapid Mix To flocculation/sedimentation Coagulant feed control (manual or automatic) SCD 11

12 SCD Set Point SCD Set Point Streaming Current Set point Determined by optimizing the plant turbidity removal (based on plant performance observations, jar tests, zeta potential etc.) and then noting the corresponding SCD value Filtered turbidity Operational goal The SCD set point changes seasonally; may change monthly or in some cases daily depending on raw water conditions Coagulant dose Charge Neutralization Coagulant (e.g., alum) Sweep Coagulation Coagulant (e.g., alum) Al 7(OH) 17 4 Al 14(OH) (s) Al 7(OH) 17 4 Al 14(OH) (s) Al(OH) 3 Al(OH) 3 Al(OH) 3 Al(OH) 3 Al(OH) 3 12

13 Temperature effect on SCD Effect of on SCD Coagulant (e.g., Al 2(SO 4) 3 14H 2O) Coagulant (e.g., Al 2(SO 4) 3 14H 2O) Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) Al(OH) 3 Chemical Feed Control Using Computer Models Lake Michigan Filtration Plant Grand Rapids, MI Capacity: 142 MGD Source water Rapid Mix To flocculation/sedimentation Source water: Lake Michigan Treatment train 1: Conventional basins Treatment train 2: Upflow clarifiers Coagulant: Alum Computer Model Coagulant feed control LMFP 13

14 BenchTop Computer Models Model Development and Implementation at LMFP Raw turbidity Alum Dose, mg/l Predicted alum dose Plant alum dose Plant settled turbidity Turbidity, NTU 10 Predicted settled turbidity at alum doses recommended by the model Time, hr 0 PostImplementation Auditing at LMFP Why is Predicting Effluent Quality Important? Modelcontrolled alum dose Raw turbidity 6 Alum Dose, mg/l Turbidity, NTU Plant settled turbidity Predicted settled turbidity Time, hr 0 14

15 Computer Model Can be Incorporated into SCADA Holland Water Treatment Plant Holland, MI Case Analysis Model s recommended feed rates Capacity: Source water: Turbidity: Treatment: Coagulants: 38.5 MGD Lake Michigan NTU Conventional FS Alum, PACl, Alumer SCD: Since 1989 Computer models: Since 2003 Holland WTP Lake Michigan Water Filtration Plant BenchTop Computer Models Case 1: Performance of SCD and Computer Models at HWTP 8 7 Raw water conditions: Temperature =11 O C; ph = 8.3; Alkalinity = mg/l as CaCO 3; Turbididity = NTU PACl dose, mg/l Streaming Current Signal 1 Plant PACl dose Model PACl dose Streaming current Time, hr

16 Case 2: Performance of SCD and Computer Models at HWTP Low Turbidity Conditions Raw water conditions: Temperature =18 O C; ph = 8.4; Alkalinity = mg/l as CaCO 3;Turbididity = NTU 0.3 Coagulant 0.2 PACl dose, mg/l Streaming Current Signal Al 7(OH) 17 4 Al 14(OH) (s) Plant PACl dose Model PACl dose Streaming current Time, hr Case 3: Performance of SCD and Computer Models at HWTP Case 3: Performance of SCD and Computer Models at HWTP Raw turbidity, NTU Raw UV254, cm 1 Al dose, mg/l Filtered turbidity, NTU Raw turbidity Raw UV Plant Al dose Model Al dose Filtered turbidity Time, hr Time, hr 16

17 Case 3: Performance of SCD and Computer Models at HWTP Effect of on Streaming Current Monitoring Raw UV254 Streaming current signal Coagulant (e.g., Al 2(SO 4) 3 14H 2O) UV254, cm Streaming current signal Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) Al(OH) Time, hr Summary Treatment Train A number of tools available to help operators quickly and effectively respond to changing raw water quality Choosing the right tools depends on plant s specifics (raw water quality, treatment, operators experience etc.) Method(s) employed should help operators make better operational decisions, but not to substitute for operators judgment Rapid Mixing Flocculation Clarification Filtration Lime softening 17

18 Mixing Characteristics Conventional FlocculationSedimentation Basins Mixing Intensity (Gvalue) G = (P/μV) 1/2 Retention Time G velocity gradient, s 1 P power input, ft lb/s µ dynamic viscosity, ft s/ft 2 V volume, ft 3 Raw water Low lift pump Coagulant Mixing Chamber Flocculation Basin Settling basin G t Lower retention time requires greater mixing intensity and vice versa Type Detention Time G value, s 1 Rapid mixing 1 60 sec Flocculation min Sedimentation 3 4 hr Ideally 0 Coagulation and Rapid Mixing Coagulation and Flocculation Temperature Effect Coagulant (e.g., Al 2(SO 4) 3 14H 2O) Coagulant (e.g., Al 2(SO 4) 3 14H 2O) Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) G = (P/μV) 1/2 T μ G 18

19 Coagulation and Flocculation Low Raw Turbidity Coagulant (e.g., alum) Coagulation and Flocculation High Raw Turbidity Coagulant (e.g., alum) Al 7(OH) 17 4 Al 14(OH) (s) Al 7(OH) 17 4 Al 14(OH) (s) Al(OH) 3 Al(OH) 3 Al(OH) 3 Al(OH) 3 Al(OH) 3 Coagulation and Flocculation Optimization of flocculation mixing for various process conditions can significantly improve treatment effectiveness with respect to chemical usage, effluent quality etc. Flocculation mixing needs to be adjusted at least seasonally Sedimentation 19

20 Rectangular Clarifiers Circular Clarifiers Outlet zone Settling zone Outlet zone Settling zone Inlet zone Sludge zone Inlet zone Sludge zone Factors Affecting Sedimentation Size of the particles Particles density Temperature Water flow Size of the basin Adverse Effects of Poor Sedimentation Increased settled turbidity Reduced filter run Potential increase in filtered water turbidity Increased coagulant feed rates 20

21 Saginaw Water Treatment Plant Saginaw, MI Case Analysis Chemical Feed Rates Capacity: Source water: Raw turbidity: 52 MGD Lake Huron NTU Chemical Cost Flow Avg. chemical usage (Fe & polym) 1 F. Turb Year MGD FeCl3 CatFloc NTU Total, $ $/MG mg/l mg/l , , , , Adjusted for 2009 chemical prices Saginaw WTP Saginaw WTP Chemical Mixing at Saginaw Plant Chemical Feed Optimization Description Before chemical feed optimization Average dose, mg/l Avg. filter turbidity Annual cost 1 FeCl3 CatFloc NTU $135, After feed rate optimization $94, After rapid mixer installation $69, Adjusted for 2011 chemical prices Saginaw WTP 21

22 Case Analysis Plant Schematic St. Joseph Water Filtration Plant St. Joseph, MI Capacity: 12 MGD Raw water Cl NaOH Alum Source water: Lake Michigan Low lift pump Drain Sludge Raw turbidity: Treatment: 1 60 NTU Upflow clarifiers Alum No rapid mixer Accelator Clarifiers (3) Filters Clearwell Cl Treated water High lift pumps St. Joseph WFP Operational Challenges Rapid Mixing Analysis Difficult to control coagulant feed rate (often difficult to make sense of the effect of raw water quality changes on coagulant demand) Inconsistent filtered turbidity St. Joseph plant (hydraulic mixing in the pipe) Type Detention time, s G value, s Mechanical mixers (for comparison) Inline blenders (for comparison) St. Joseph WFP St. Joseph WFP 22

23 Computer Simulation Analysis Coagulation Feed Optimization St. Joseph WFP St. Joseph WFP Coagulation Low Raw Turbidity Upflow Clarifiers Coagulant (e.g. alum) Coagulant (e.g., alum) Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) Al 7(OH) 17 4 Al 14(OH) (s) St. Joseph WFP Return sludge 23

24 INFILCO Accelator Clarifier Upflow Clarifiers (high coagulant dose) Coagulant (e.g. alum) Influent Draft tube Secondary mixing zone Rotorimpeller Clarified water Effluent Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) Return flow zone Hood Primary mixing and reaction zone Drain Sludge drawoff Al(OH) 3 Al(OH) 3 Al(OH) 3 Al(OH) 3 Al(OH) 3 Al(OH) Al(OH) 3 3 Al(OH) 3 Al(OH) 3 South Haven WFP Return sludge INFILCO Accelator Clarifier How to Rectify the Problem? Influent Draft tube Secondary mixing zone Rotorimpeller Clarified water Effluent Return flow zone Hood Primary mixing and reaction zone Sludge drawoff Drain South Haven WFP South Haven WFP 24

25 Case Summary Factors that contributed to the problems Lack of a rapid mixer Clarification effectiveness South Haven Water Treatment Plant South Haven, MI Old plant Capacity: Treatment: 4.2 MGD Conventional treatment Case Analysis Solutions Coagulant feed rate optimization Choice of coagulant New plant (2011) Capacity: 7 MGD Treatment: Plate separators South Haven WTP St. Joseph WFP Inclined Plate Clarifiers Settled Turbidity at SHWTP Settled turbidity, NTU /29/08 11/28/08 12/28/08 01/27/09 02/26/09 03/28/09 04/27/09 05/27/09 06/26/09 07/26/09 08/25/09 09/24/09 10/24/09 11/23/09 12/23/09 01/22/10 02/21/10 03/23/10 04/22/10 05/22/10 06/21/10 07/21/10 08/20/10 09/19/10 10/19/10 11/18/10 12/18/10 01/17/11 02/16/11 03/18/11 04/17/11 05/17/11 06/16/11 07/16/11 08/15/11 09/14/11 10/14/11 11/13/11 12/13/11 01/12/12 02/11/12 03/12/12 04/11/12 05/11/12 06/10/12 07/10/12 New Plant 25

26 Filter Run SHWTP Old plant hr New plant hr Filtration Coagulation and Filtration Filtration Why do we need coagulation? How does filter configuration affect coagulation effectiveness? Filter media 26

27 Filtration Filtration Applied turbidity Filtration Filtration w/o Coagulation 27

28 Filtration w/o Coagulation Filter Configuration Large applied particles Larger applied particles Small applied particles Smaller applied particles Case Analysis Plant Schematic Holland Water Treatment Plant Holland, MI Capacity: 38.5 MGD Raw water Cl Coagulant Source water: Lake Michigan Low lift pump Turbidity: Treatment: NTU Conventional FS 6 gravel support 4 IMS cap filters MIxing Chamber Flocculation Basins Settling basins Filters Clearwell Cl Treated water High lift pumps Holland WTP 28

29 Integral Media Support (IMS) Filters at Holland WTP 1 porous plates Replaces support gravel Installed on top of the underdrain blocks Anthracite 1216" Fine sand 12" Anthracite 24" Course sand 34" Gravel 10" Fine sand 12" IMS Holland WTP Gravel Support Filters IMS Cap Filters Holland WTP Plant s Coagulation History Alum was historically employed for coagulation Sludge production was problematic In 2004, the plant switched to PACl Problem Description Elevated turbidity levels on IMS cap filters during summer/fall operation Six conventional filters: less than 0.08 NTU Four IMS cap filters: up to 0.25 NTU Turbidity on IMS cap filters was very unstable Holland WTP Holland WTP 29

30 General Observations Elevated levels of turbidity on the IMS cap filters occurred only under warm water conditions (70F or higher) Summer/fall: water production (and filtration rates) normally was increased during night shifts The turbidity on IMS cap filters increased when the filtration rate was either increased or reduced Coagulation Analysis PACl feed rates were adequate and did not cause the problem When the filtration rate was reduced, the problem was more persistent The problem never occurred when alum was employed Holland WTP Holland WTP Filter Analysis 6000 Applied Particles Analysis 5000 Particles count, #/ml PACl Alum 1000 Gravel support filter IMS cap filter Holland WTP Applied turbidity, NTU Holland WTP 30

31 What Was the Problem? When the filtration rate increased, turbidity on IMS cap filters increased. Warm water conditions Dissolved gas concentration reaches the saturation point Increase in filtration rate creates low pressure conditions in the media causing release of gas bubbles that block water s paths Rapid pressure drop across IMS vs. gradual pressure decrease across gravel support Gravel support filters: most bubbles below the active media IMS filters: air bubbles in the active media Anthracite Fine sand Course sand Gravel Filtration rate increases Anthracite Fine sand IMS What Was the Problem? After the filtration rate leveled off, turbidity on IMS cap filters decreased and returned to normal levels Air bubbles are forced to the bottom of the media Some air bubbles may redissolve (due to pressure equalization) Anthracite Fine sand Course sand Gravel High filtration rate Anthracite Fine sand IMS What Was the Problem? Why There Was No Problems with Alum? When the filtration rate was subsequently reduced, turbidity on IMS cap filters increased and became very unstable Drag force decreased The buoyant force caused the air bubbles to rise back into the filter media, again blocking the water s paths Anthracite Fine sand Course sand Gravel Anthracite Fine sand IMS Filtration rate decreases After alum coagulation After PACl coagulation Holland WTP 31

32 Solutions Filtration rate adjustment Different coagulant Case Summary Factors that contributed to the problem Filter configuration Filtration rates Temperature Choice of coagulant What was affected Effluent quality Coagulant feed control Coagulant feed rate Filter configuration may dictate the choice of coagulant Holland WTP Holland WTP Lime Softening Lime (Ca(OH) 2 ) Lime Softening CH: CO 2 Ca(OH) 2 CaCO 3 H 2 O Ca(HCO 3 ) 2 Ca(OH) 2 2CaCO 3 2H 2 0 ph 9.5 Mg(HCO 3 ) 2 Ca(OH) 2 CaCO 3 MgCO 3 2H 2 O ph 9.5 MgCO 3 Ca(OH) 2 CaCO 3 Mg(OH) 2 ph 11 NCH: MgSO 4 MgCl 3 CaSO 4 CaCl 3 Ca(OH) 2 CaSO 4 Mg(OH) 2 Ca(OH) 2 CaCl 3 Mg(OH) 2 32

33 Partial Turbidity Removal by Lime Lime Effect of Metal Salt Coagulation on Lime Softening Lime (Ca(OH) 2 ) Al 2 (SO 4 ) 3 CO 2 Ca(OH) 2 Ca(HCO 3 ) 2 Ca(OH) 2 CaCO 3 (s) CO 2 Ca(OH) 2 CaCO 3 H 2 O etc. CaCO 3 CaCO 3 CaCO 3 CH: Ca(HCO 3 ) 2 Ca(OH) 2 2CaCO 3 2H 2 0 ph 9.5 Mg(HCO 3 ) 2 Ca(OH) 2 CaCO 3 MgCO 3 2H 2 O ph 9.5 MgCO 3 Ca(OH) 2 CaCO 3 Mg(OH) 2 ph 11 CaCO 3 CaCO 3 Al 2 (SO 4 ) 3 3Ca(HCO 3 ) 2 2 3CaSO 4 6CO 2 ph CO 2 Ca(OH) 2 CaCO 3 H 2 O Additional lime is required to neutralize CO 2 and increase ph Summary Chemical Feed Rate Optimization Summary Water Quality Monitoring Requirements Selection of optimal coagulant Flocculation mixing optimization 33

34 Why is Chemical Feed Optimization Important? Improved effluent quality Reduced chemical costs Helps troubleshoot problems (e.g., Holland case; South Haven case) Helps optimize other processes and operations (e.g., Saginaw case, St. Joseph case) Chemical Feed Rate Optimization Ensure that coagulant feed rate are optimal under all plant conditions Chemical feed control methods Jar test SCD Computer model Raw Water Quality Monitoring Requirements Why is UV254 Problematic? Turbidity UV254 Other indicators Results in increased coagulant demand May result in increased filtered turbidity Feed control challenges DBP Control UV disinfection 34

35 Effect of Raw Turbidity and UV254 on Coagulant Demand UV254 and Coagulant Demand ( LMFP Case) UV254 and Coagulant Choice ( South Haven Case) UV254 Analysis is Fast and Simple Modelcontrolled PACl/polymer coagulation Alumer coagulation before model implementation Alumer coagulation after model implementation UV254 = cm /05/05 12/14/05 03/24/06 07/02/06 10/10/06 01/18/07 04/28/07 08/06/07 11/14/07 02/22/08 06/01/08 09/09/08 12/18/08 03/28/09 07/06/09 10/14/09 01/22/10 05/02/10 08/10/10 11/18/10 02/26/11 Filter turbidity, NTU UV254 = 0.21 cm 1 UV254 = 0.26 cm 1 35

36 UV254 Monitoring Benefits Improved chemical feed control Improved effluent turbidity Reduced chemical usage Helps troubleshoot problems Help select the most effective coagulant(s) Icing on the cake: Can help improve DBP control Can help increase the effectiveness of UV disinfection Raw Water Quality Monitoring Requirements Turbidity UV254 Other indicators ph Conductivity Chloride Alkalinity etc. Other Indicators (example) Holland Water Treatment Plant, Holland, MI Source Lake Michigan Selecting Optimal Coagulant 36

37 Me(OH) 3 6/18/2012 Major Considerations Optimal coagulant is the coagulant that best meets plant s operational goals Operational objective(s) Raw water quality Plant size Treatment train Filter configuration Hardware Low Turbidity Source Water High Turbidity Source Water Coagulant Alum, Fe 2 (SO 4 ) 3 Coagulant FeCl 3, PACl, cat. polymer Me(OH) 3 Me(OH) Me(OH)

38 Me(OH) 3 6/18/2012 Source Waters with Significant Turbidity Variations Source Waters with Significant Turbidity Variations Coagulant Coagulant Me cat. polymer Me(OH) 3 Me(OH) Me(OH) 3 3 High Low turbidity Source Waters with Significant Turbidity Variations Effect of Natural Organic Matter () Coagulant Me cat. polymer Coagulant Alum, Fe 2 (SO 4 ) 3, metal coag polymer Me(OH) 3 Me(OH) 3 Me(OH) 3 Me(OH) 3 Me(OH) 3 Me(OH) 3 Me(OH) 3 38

39 Effect of ph on Coagulation with Aluminum Salts Effect of ph on Coagulation with Ferric Salts Al 3 Al(OH) 2 Al 7(OH) Fe 3 Fe(OH) 2 Fe 2(OH) Increase Decrease Increase Decrease Decrease (s) Decrease Decrease Fe(OH) 3 (s) Decrease ph = 4 ph = 67 (lowest Al solubility) ph = 10 ph = 4 ph = 88.5 (lowest Fe solubility) ph = 10 Al coagulation is most effective Fe coagulation is most effective Alkalinity Coagulation and Lime Softening Coagulant Lime (Ca(OH) 2 ) Metal coagulants consume alkalinity Ca(OH) 2 Hardness ph 10 For waters with low alkalinity, the coagulants that consume less alkalinity may need to be considered (unless lime is added): Ferric chloride Polyaluminum chloride Metal coagulant/polymer FeCl 3 Fe/polymer Cat polymer CaCO 3(s) CaCO 3(s) CaCO 3(s) CaCO 3(s) CaCO 3(s) CaCO 3(s) CaCO 3(s) 39

40 Me(OH) 3 6/18/2012 Upflow Clarifiers Filter Configuration and Coagulation Coagulant FeCl 3, PACl, Metal salts/cat polymer Applied particles Filters Alum Fe 2 (SO 4 ) 3 FeCl 3 /PACl MePolymer Large applied particles Larger applied particles Small applied particles Smaller applied particles Return sludge Testing Considerations Testing Considerations What is (are) the goal(s)? Effluent quality Chemical cost Sludge production etc. Alternative coagulant considerations Identify possible choices (based on raw water quality, treatment train, etc.) Has the plant used other coagulants in the past? Design modification Jar test Preliminary evaluation only Does not always correctly reflect the plant coagulation situation Hydraulic characteristics may be different Does not cover all plant conditions Fullscale plant testing One year is recommended Analysis after one year Has the goal been achieved? What are the tradeoffs? Identify conditions under which could be problematic If does not meet plant operational objectives, comparative analysis of the old and tested coagulant could give very good idea what type of coagulants would work. 40

41 Saginaw WTP, Saginaw, MI Capacity: 52 MGD Source water: Lake Huron Raw turbidity: NTU Case Analysis 1 Case Analysis 2 South Haven WTP, South Haven, MI Capacity: 4.2 MGD Source water: Lake Michigan Treatment: Conventional treatment Old coagulation practice: No rapid mixer Coagulant: FeCl 3 and cationic polymer New coagulation practice: Rapid mixer installed Coagulant: FeCl 3 w/ reduced polymer Goals achieved: Improved effluent turbidity Reduced chemical usage Expected optimal coagulant: Fe 2 SO 4 Old coagulation practices: Coagulants: Alum, PACl, PACl/polymer New coagulation practice: Alumer Goals achieved: Increased filter run by 20% (compared to alum and PACl) Reduced chemical usage Future plans: New plant plate separators Alum and cationic polymer (fed separately) St. Joseph WFP, St. Joseph, MI Capacity: Source water: Raw turbidity: Treatment: Old coagulation practice: No rapid mixer Coagulant: Alum 12 MGD Lake Michigan 1 60 NTU Upflow clarifiers New coagulation practice tested: Alum feed directly to upflow clarifiers Goals achieved: Improved effluent turbidity Reduced chemical usage Tradeoff: individual control to clarifiers Expected best coagulation practice: Alum and polymer fed separately Case Analysis 3 Holland WTP, Holland, MI Capacity: Source water: Turbidity: Treatment: 38.5 MGD Lake Michigan NTU Conventional FS Original coagulation practice: Coagulants: Alum Case Analysis 4 Tested coagulation practices: PACl, alumer, seasonal alternation between alum and alumer Current coagulation practices: alumer cationic polymer Goal achieved: Reduced sludge production Reduced chemical cost 41

42 Low Turbidity Conditions Coagulant (e.g., alum) Flocculation Mixing Optimization Al 7(OH) 17 4 Al 14(OH) (s) High Turbidity Conditions Coagulant (e.g., alum) Low Temperatures May Call for Increased Coagulant Dose Coagulant (e.g., Al 2(SO 4) 3 14H 2O) Al 7(OH) 17 4 Al 14(OH) (s) Al(OH) 2 Al 7(OH) 17 4 Al 14(OH) (s) G = (P/μV) 1/2 T μ G Al(OH) 3 Al(OH) 3 Al(OH) 3 Al(OH) 3 42

43 Effect of flocculation mixing on applied particles at Holland WTP Effect of flocculation mixing on applied particles at Holland WTP 3000 RPM = 1 RPM = RPM = 1 RPM = RPM = 1.4 Particles count, #/ml Particles count, #/ml Applied turbidity, NTU Applied turbidity, NTU Effect of flocculation mixing on applied particles at Holland WTP Flocculation Mixing Optimization RPM = 1 RPM = 1 RPM = 1.4 Lower and more consistent filtered turbidity Particles count, #/ml Increased filter run Reduced coagulant usage Applied turbidity, NTU 43

44 Three Rivers WFP, Fort Wayne, IN Summary Case Constructed in 1933 Initial capacity 24 MGD 24MGD expansion (Plant 2) in MGD expansion (Plant 3) in 1981 Total capacity today 72 MGD Source water St. Joseph River Fort Wayne Raw Water Quality Variations Raw Water Quality Variations Raw turbidity, NTU /24/02 09/01/02 12/10/02 03/20/03 06/28/03 10/06/03 01/14/04 04/23/04 08/01/04 11/09/04 02/17/05 05/28/05 09/05/05 12/14/05 03/24/06 07/02/06 10/10/06 01/18/07 04/28/07 08/06/07 11/14/07 02/22/08 06/01/08 09/09/08 12/18/08 03/28/09 07/06/09 10/14/09 01/22/10 05/02/10 08/10/10 11/18/10 02/26/11 06/06/11 09/14/11 12/23/11 04/01/12 07/10/12 10/18/12 Tota lraw hardness, mg/l as CaCO /24/02 09/01/02 12/10/02 03/20/03 06/28/03 10/06/03 01/14/04 04/23/04 08/01/04 11/09/04 02/17/05 05/28/05 09/05/05 12/14/05 03/24/06 07/02/06 10/10/06 01/18/07 04/28/07 08/06/07 11/14/07 02/22/08 06/01/08 09/09/08 12/18/08 03/28/09 07/06/09 10/14/09 01/22/10 05/02/10 08/10/10 11/18/10 02/26/11 06/06/11 09/14/11 12/23/11 04/01/12 07/10/12 10/18/12 44

45 Raw Water Quality Variations Treatment train Fe 2(SO 4) 3 Lime PAC Fe 2(SO 4) 3 Raw UV254, cm Influent Primary Coagulation / Lime Softening Stage CO 2 Second Coagulation Stage Filtration Effluent Lake Michigan Lake Huron Lake Superior 10/10/06 11/29/06 01/18/07 03/09/07 04/28/07 06/17/07 08/06/07 09/25/07 11/14/07 01/03/08 02/22/08 04/12/08 06/01/08 07/21/08 09/09/08 10/29/08 12/18/08 02/06/09 03/28/09 05/17/09 07/06/09 08/25/09 10/14/09 12/03/09 01/22/10 03/13/10 05/02/10 06/21/10 08/10/10 09/29/10 11/18/10 01/07/11 02/26/11 04/17/11 06/06/11 07/26/11 09/14/11 11/03/11 12/23/11 02/11/12 04/01/12 05/21/12 07/10/12 Major Goals Improve effluent turbidity Reduce chemical costs Optimization Project Streamline chemical feed operation 45

46 /18/2012 Phase I Feed Rate Optimization Phase I Results Coagulation computer model was developed and implemented in 2005 to provide real time advisement to the operators of the recommended coagulant feed rates at the primary and second coagulation stages A computer model to control lime feed rates was developed and integrated with coagulation model in 2006 Lime (Ca(OH) 2) Fe 2(SO 4) 3 Lime The computer models helped produce more consistent filtered water turbidity and reduce coagulant usage The plant still experienced elevated filtered water turbidities during major runoffs Additional computer simulation analysis showed that the plant s primary stage coagulation was less effective than the second stage coagulation CO 2 Ca(OH) 2 CaCO 3 H 2O CO 2 Ca(OH) 2 CaCO 3(s) CH: Ca(HCO 3) 2 Ca(OH) 2 2CaCO 3 2H 20 ph 9.5 Mg(HCO 3) 2 Ca(OH) 2 CaCO 3 MgCO 3 2H 2O ph 9.5 MgCO 3 Ca(OH) 2 CaCO 3 Mg(OH) 2 ph 11 Ca(HCO 3) 2 Ca(OH) 2 etc. Fe 2(SO 4) 3 3Ca(HCO 3) 2 2Fe(OH) 3 3CaSO 4 6CO 2 ph CO 2 Ca(OH) 2 CaCO 3 H 2O CaCO CaCO 3 Col loi d CaCO Col loi d CaCO CaCO Effect of ph on Coagulation with Ferric Salts Coagulation at Fort Wayne Plant Fe 3 Fe(OH) 2 Fe 2(OH) Increase Decrease 2 nd stage coagulation ph = st stage coagulation Decrease Fe(OH) 3 (s) Decrease ph = 4 ph = 88.5 (lowest Fe solubility) ph = 10 ph = 6 ph = (lowest Fe solubility) ph = 11 Fe coagulation is most effective 46

47 Phase II Chemical Feed Optimization Corrosion Control Issue Proposed chemical feed scheme: 1 st Stage: Lime softening* 2 nd Stage: Fe sulfate coagulation *Small dose of ferric sulfate can still be added at the primary stage The new chemical feed method was implemented in 2007 Meeting plant s hardness and lead/copper corrosion control requirements (ph, alkalinity, Ca) all at the same time was extremely difficult Efforts to meet these requirements adversely affected the effectiveness of chemical feed control Lime (Ca(OH) 2) Fe 2(SO 4) 3 The computer models were updated for the new feed method CO 2 Ca(OH) 2 CaCO 3 H 2O This new chemical feed practice helped produce consistent filtered water turbidity and further reduce chemical usage CH: Ca(HCO 3) 2 Ca(OH) 2 2CaCO 3 2H 20 ph 9.5 Mg(HCO 3) 2 Ca(OH) 2 CaCO 3 MgCO 3 2H 2O ph 9.5 MgCO 3 Ca(OH) 2 CaCO 3 Mg(OH) 2 ph 11 Fe 2(SO 4) 3 3Ca(HCO 3) 2 2Fe(OH) 3 3CaSO 4 6CO 2 ph CO 2 Ca(OH) 2 CaCO 3 H 2O Control of Calcium Carbonate Scaling Effects of Plant s Hardness and Lead & Copper Requirements on Chemical Feed Control Fe 2 SO 4 Lime CO 2 Treatment Fe 2 (SO 4 ) 3 Lime CO 2 Effluent Quality ph Alkalinity Hardness Ca Corrosion Indices LSI CCPP Buffer Intensity Formation of CaCO 3 scaling Filter Turbidity HSD Hardness T. Alkalinity = 2[CO 2 3 ] [HCO 3 ] [OH ] [H ] CO 2 H 20 = H 2CO 3* HCO 3 H HCO 3 CO 2 3 H HSD Alkalinity Langelier Saturation Index (LSI): LSI = ph ph s, where ph s is the ph at which water is saturated with CaCO 3 LSI < 0: unsaturated water; tends to dissolve CaCO 3 protective scale LSI > 0: water supersaturated; tends to form protective scale of CaCO 3 Calcium Carbonate Precipitation Potential (CCPP): CCPP is theoretical mass of calcium carbonate that could precipitate on a pipe surface Buffer Intensity Buffer intensity is the capacity of water to resist changes in ph HSD Ca HSD ph Ca(HCO 3) 2 Ca(OH) 2 2CaCO 3 2H

48 Effects of Plant s Hardness and Lead & Copper Requirements on Chemical Feed Control Fe 2 SO 4 Lime CO 2 Phase III Corrosion Control Optimization Corrosion control computer model was developed in 2010 and integrated into the chemical feed control program. Filter Turbidity HSD Hardness HSD Alkalinity The new chemical feed/corrosion control computer program helped improve corrosion control conditions and further reduce chemical usage HSD Ca HSD ph 189 ChemFeed/Corrosion Control Computer Program Chemical feed optimization 1 All costs in 2011 chemical prices 48

49 Results Summary By optimizing the chemical feed operation, the plant was able to Consistently reduce filtered water turbidity, which is currently maintained below 0.1 NTU Reduce ferric sulfate usage by 4045% and lime usage by 10 15%, Streamline chemical feed operation Improve corrosion control conditions in the distribution system Current Steps Implementation of cationic polymer coagulation In place of ferric sulfate at the 1 st coagulation stage In combination with ferric sulfate at the 2 nd coagulation stage Expected benefits: Reduced settled turbidity Further improvement of corrosion control conditions Further streamlining chemical feed operation Further reduction in chemical costs Optimization of flocculation mixing Expected benefits: Meeting Phase IV partnership goals for settled turbidity Improved filter operation (filter run, backwash usage etc.) Further reduction in chemical costs Optimization Scheme at Three Rivers WFP Optimization Scheme at the Three Rivers WFP Flocculation Coagulation Filtration Sedimentation 49

50 Optimization Scheme at Three Rivers WFP Optimization Scheme at Three Rivers WFP Flocculation Flocculation Coagulation Filtration Coagulation Filtration Sedimentation Sedimentation Lime Softening Corrosion control Lime Softening Optimization Scheme at Three Rivers WFP Optimization Scheme at Three Rivers WFP Flocculation Flocculation Cationic polymer Coagulation Filtration Cationic polymer Coagulation Filtration Sedimentation Sedimentation Corrosion control Lime Softening Corrosion control Lime Softening 50

51 Optimization Scheme at Three Rivers WFP This plant has been able to UV Disinfection Flocculation Establish cause and effect relationships between unit processes and operations Cationic polymer Coagulation Sedimentation Filtration Effectively control these relationships resulting in Improved quality of finished water Reduce chemical and other operational costs Streamlined chemical feed operation Corrosion control Lime Softening Q&A Alex Yavich Ph. (616) Web: 51