Authors: Matt Ridens/CH2M, Jason Curl/CH2M

(FRIT05) OCTOBER 30, 2015

REAL WORLD APPLICATIONS OF USING DYNAMIC SIMULATION SOFTWARE TO OPTIMIZE WATER TREATMENT PLANT PROCESS OPERATIONS

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Overview

• Drivers for Use of Dynamic Simulation Models

• Project Examples and Applications of Models

– 30 mgd Greenfield Municipal Water Treatment Plant

– 770 mgd Existing Municipal Water Treatment Plant

• The Future of Predictive Models

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Drivers for Use of Dynamic Simulation Models

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Water Market Drivers for Optimization

Adapt to water scarcity events and increasingly challenging water qualities

Quantity & Quality

Regulations

Environmental

Diversify

New Facilities

Conservation

Comply with current and provide flexibility for future drinking water regulatory requirements

Minimize environmental impact by reducing energy, chemical usage and solids production

Diversify supply portfolio to improve supply reliability and respond to water shortages

Gain greater understanding of new facilities and refine design criteria during design

Integrate new recycle streams for direct/indirect potable re-use

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Big Data’s Influences Are Increasing as We Become More Connected

• Household security and HVAC are connected to the web.

• Stores like Home Depot and Costco are using store data to understand shopping trends nationally, regionally, and locally.

• Water and wastewater systems have been collecting mountains of data for years, storing for documentation purposes.

– How can we make sense of this data?

– How can we use this data to inform decision-making?

– How can we improve treatment efficiency and effectiveness?

– Are we taking advantage of the technology and data on hand or are improvements a worthwhile investment?

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Project Examples and Applications of Models

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Applications for Dynamic Simulation Models

• Design Phase

– What treatment processes are required?

– Can facilities be optimized to reduce capital or O&M costs?

• Startup

– What are the initial set points for each process?

– What alarms should be in place to alert operators of potential impacts to water quality?

• Operations

– How can plant performance and design criteria be optimized based on existing data?

– What changes are needed to increase existing plant capacity?

– How can plant performance be predicted for future events or changes to my system?

– How can my system be optimized for energy consumption?

NEW FACILITIES

EXISTING FACILITIES

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Using Dynamic Simulation as a Predictive Model

• Run a number of “What if?” scenarios or analyses quickly in a no-risk environment.

• Customizable range of time allows for both short and long events (Seconds/Minutes or Weeks/Months)

• Evaluate many alternatives or scenarios for a defensible solution

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Predictive Model Example

• CH2M’s dynamic simulation model makes use of the following process predictive models

– Readily Calculated Mathematical Results

• Water chemistry in a calcium carbonate dominated system

– Calibrated Models with Understood Kinetics

• Ozone demand and decay

– Empirical Models

• Clarifier performance in an existing treatment facility

– Hybrids

• Total Trihalomethane formation in distribution systems

We can only calibrate models and then predict performance if plenty of good data exists

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Project #1 Overview• 30 mgd greenfield Water Treatment Plant, located in West

• Project Drivers

– Design-Build-Operate project, including 15 years of operations

– WTP makes use of sand-ballasted clarification, ozone, biologically active carbon contactors and complete solids handling system

– Ability to treat highly fluctuating turbidity levels

– Multiple barrier approach

– Performance contract is more stringent than regulations

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Project Goals

Provide high-quality water at all times to customers

Achieve sustainable operations

– Energy

– Chemicals

– Solids handling

Operate plant proactively in terms of regulatory and contractual compliance

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DSGNCHK

DATE

REVCLIENTPROJECT S O U R C E

MANAGER

F

TSS

Riv er Supply

NaOH

AETSS

FeCl3 PolyO3

LOXAE

TSSAE

TSS

FSolids Hauling

AETSS

FTo Distribution

AETSS

AETSS

Poly

AETSS

NaOCl

AETSS

Poly

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Optimization of Treatment Process Design

• Ozone system sizing and type

– Lower ozone dose

– Smaller ozone contactor

– Direct injection vs. side-stream

• Sand Ballasted Clarification (SBC) selection

– Replaced pre-sedimentation ponds and conventional flocculation/sedimentation

• Drying beds

– Replaced mechanical dewatering

• Optimized plant hydraulics to maximize gravity flow

– Evaluated pumping vs. earthwork

– Optimized intermediate PS

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Optimization of Chemical Use

• Analysis of tradeoffs for chemical dose selection with varying water qualities

– Alkalinity augmentation (NaOH)

• Ozone dose

– Primary Disinfection: 0.5-log Giardia credit – injection cell ozone residual of 0.3 mg/L

– Evaluate using entire contactor at lower ozone dose

– Taste and odor as needed (with hydrogen peroxide)

• Continuous water quality monitoring and feedback loops

– Sand ballasted clarification

• Predictive modeling of finished water quality

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Operations Will Be Optimized by Performance Predictions with Live Data

Parameter Compliance Period Target Value Unit Allowed Variance Current Value Note

Turbidity - CFE Monthly 0.15 NTUMust comply for 95% of

measurements0.07 data direct from instrument

Turbidity - Filter 1 Monthly 0.15 NTUMust comply for 95% of

measurements0.06 data direct from instrument

Turbidity - Filter 1 15 Minutes 0.3 NTU15 min following out of

compliance value0.06 data direct from instrument

Turbidity - Filter 2 Monthly 0.15 NTUMust comply for 95% of

measurements0.08 data direct from instrument

Turbidity - Filter 2 15 Minutes 0.3 NTU15 min following out of

compliance value0.08 data direct from instrument

Total Chlorine Residual Daily 1.1 mg/L +/- 0.2 mg/L 1 data direct from instrument

TTHM Monthly 0.04 mg/L Maximum value 0.02 f(UV254, Cl, Temp)

HAA5 Monthly 0.03 mg/L Maximum value 0.01 f(UV254, Cl, Temp)

Total Manganese Monthly 0.02 mg/L Maximum value 0.01 f(RWMn, TSS removals)

Total Iron Monthly sample 0.24 mg/L Maximum value 0.05 f(RWFe, TSS removals)

Total Aluminum Monthly sample 0.16 mg/L Maximum value 0.06 f(RWAl, TSS removals)

Thiobencarb Monthly sample 0.001 mg/L Maximum value 0.00001 f(RWThio, TSS removals)

MIB and Geosmin Agency request 5 ng/L Maximum value 2, 2 f(RWMIB, RWGeosmin, TSS removals)

pH Daily 7.4 Units +/- 0.2 7.5 data direct from instrument

LSI Monthly 0.1 Units +/- 0.1 0.15 f(Ca, Alk, pH)

Phosphate Weekly 0.3 mg/L +/- 0.2 mg/L 0.45 f(chem feed)

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Project #2 Overview

• 770 mgd municipal WTP, located in the South

• Project Drivers

– Zebra mussels found in one water supply, resulting in adjustment to how raw water was blended and brought into WTP

– Droughts have stressed water supplies, reducing water quality (increasing taste and odor events)

– New blending of raw water sources

– New ozone addition with concern to bromate formation

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Project Goals

• Evaluate impact of a sudden loss of one of the water supplies on the finished water quality. Identify what can be done to minimize water quality impacts.

• Investigate plant operation with the new water supply to comply with regulations while limiting operational costs.

• Evaluate performance of the new ozone system under the different raw water quality conditions associated with the new water supply, before ozone installation.

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Model Background/Goals

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Model Evaluations

1. Review water quality from each water source to understand variability in water quality for differing blend ratios of raw water entering WTP.

2. Model how changes in raw water quality (including different blend ratios) affect coagulation chemistry

3. Assess possible impact of raw water quality and chemical doses on TSS removal from the process.

4. Evaluate impact of the lower quality water supply at various blend ratios on bromate formation through the ozone system

5. Determine finished water quality and chemical doses entering the distribution system under varying raw water conditions

6. Determine the impact of raw water quality and various chemical dosages on water quality entering the distribution system, including finished water stability.

7. Evaluate impacts of raw water induced changes to coagulation and settling chemistry on solids production.

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Blend Tank Scenario Results• Impact of blend tank on settled/finished water TDS

• 75 min through Floc/Sed

• 10 min through Ozone

• 10 min through Filters

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No Blend Tank Scenario Results

• Impact on TDS without blend tank

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Blend Tank Analysis Conclusions

• Inclusion of the Blend Tank provides up to 15 minutes of buffer time for operations staff to react to changes in water quality prior to the Flocculation/Sedimentation treatment facilities.

• Inclusion of Blend Tank reduces overall magnitude (example: from ~900 mg/L to ~500 mg/L TDS) of water quality impacts witnessed in the influent Flocculation/Sedimentation facilities due to a sudden raw water quality change.

• Blend Tank has no impact on overall finished water quality due to large residence times at Flocculation/Sedimentation and Filtration facilities.

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Chemical Dose for TOC Removal Scenario Input Data

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Chemical Dose for TOC Removal Scenario Results

Clarification Inlet TOC

(mg/L)

Ferric Sulfate Dose (mg/L)

TOC Removal (%)

Ferric Sulfate Usage (gal/wk)

Chemical Deliveries per

Week3.0 10 41.3 17,380 5

3.465 12 43.0 20,857 64.0 13 40.3 22,595 64.5 15 41.3 26,072 75.0 17 42.2 29,548 85.5 18 40.6 31,286 96.0 20 41.3 34,762 10

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Project Conclusions

• At historic averages for TOC values, 10 mg/L of ferric sulfate (as ferric sulfate) would provide 35% TOC removal. If higher TOC values are seen (TOC of 6 mg/L) 40% TOC removal would require a dose of approximately 20 mg/L.

• Plant operational decision-making will benefit from a more robust dataset

• With more high-quality data, we can correlate raw water characteristics with process response (e.g., TSS and TOC removal).

• When we understand process response more clearly, we can aid operational response (e.g., chemical dosages, raw water source selection).

• Operations staff can gain more confidence in operating the new ozone system and responding to raw water quality events by testing what-if scenarios.

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The Future of Predictive Models and Dynamic Simulation in the Water Industry

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Wrap Up and Conclusions• As a whole, our industry is moving to optimize systems from a complete

sustainability perspective.

• Using sound data in an intelligent way provides a means to move toward more sustainable designs and operations of our water treatment facilities and distribution systems.

Economic EnvironmentalSocietal

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Wrap Up and Conclusions• How will we handle the need to share data across platforms so that we can make

more informed decisions while still ensuring security of our infrastructure?

• These data analysis and modeling methods are applicable to all types (simple to complex) and sizes (small to large) of systems.

• Dynamic simulations provide a means to investigate many what-if scenarios in a safe environment while considering variable raw water qualities over time, different responses to events, new infrastructure, and various operational responses

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Thank you!

Questions? Matt.Ridens@ch2m.com