Bridging the Gap from Water Plant Data Collection and Data Analytics to Operational Decision Support for Harmful Algal Blooms

Christopher M. Miller, Ph.D., P.E.Department of Civil Engineering

One Water ConferenceColumbus, OH

August 28, 2014

Project Team and Resources

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PAC Suppliers

2013 – Heightened HAB Awareness

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2014 Headlines

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Scope and Perspective

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Our efforts are driven by expressed concerns at water treatment plants regarding:

1. Evaluating and Implementing New Technology and Options (i.e. new coagulants, new PAC materials)

2. Data-Driven Management (a.k.a. Decision Support)3. Taste-Odor Issues and Algal Toxins4. Intermittent elevated THM and HAA levels and

more stringent compliance requirements5. Emerging Contaminants and Unregulated DBPs

Systems Approach• Samples from

multiple water treatment plants (WTPs) source water and in the water plant

• Samples from distribution system

1. One of the largest fluorescence database in engineered system (multiple cities, multiple coagulants) for surface water sources

2. Large database of DBP measurements

AWS-HAB Project Tasks (March 2014)

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1. Enhanced water quality monitoring of AWS reservoirs (e.g. Lake Rockwell and Ladue) and watershed.

2. Data mining and knowledge extraction regarding HABs from watershed data.

3. Evaluate options for managing HABs in the watershed and the plant.

4. Develop HAB module for decision support.5. “Real-time” implementation and operation of

dashboard modules for coagulation, chlorine demand, and DBP formation.

Akron Water Supply Background

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1. Multiple Reservoirs

2. Agricultural Watershed

3. ~36 MGD

What is “The Gap” ?

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Water Plant Data Collection 

and Data Analytics

Operational Decision Support“The Gap”

Plenty of data and good science, but how do we convert it to operational improvements, particularly to respond to a Harmful Algal Bloom (HAB) ?

Bridging the Gap – UA Approach

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Water Plant Data Collection 

and Data Analytics

Operational Decision Support

1. Monitoring Program(s) – always evolving

2. Model Development –based on latest science and inputs

“The Gap”

1. Watershed2. Water Plant3. Distribution System

1. Require Monitoring Data Linked to Operations2. Test New Water Plant Response Alternatives3. Validated Models and Optimum Operational Response

Monitoring Data Linked to Operations

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1. Enhanced water quality monitoring of AWS reservoirs (e.g. Lake Rockwell and Ladue) and watershed. Watershed Monitoring and Data Platform Fluorescence Monitoring (Watershed and Plant)

650 700 750 800600700

8000

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Emission (nm)

WS02 May 2014

Excitation (nm)

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Reservoir Profiling - Chlorophyll

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Highest values of summer on 6/26/14

Can have range in Lake Rockwell of 10 ug/L or more (depth and distance to intake with ~ 2-4 week residence time)

Watershed Monitoring Platform

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Built on Google Fusion platform GIS and sampling data platform Ability to create custom tables and plots

Watershed Platform Data

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Data Table

Data Plot

Fluorescence Basics

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Basics:1) Small volume sample2) Minimal preparation3) Quick (< 10 Minutes)4) Produces EEM

EEM (Excitation‐Emission‐Matrix)1) Third dimension is Intensity2) Overall fingerprint of organic 

species in the water sample

Fluorescence Analysis

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Other measures:1) Peak Picking2) Fluorescence Index3) Chlorophyll‐Algal Pigments

C1 C2 C3PARAFAC Components

PARAFAC Analysis

Fluorescence Monitoring

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Estimate chlorophyll and phycocyanin and other pigments in raw and coagulated water

Monitor source water characteristics-nature Monitor coagulation dissolved organic carbon removal

Akron Water Supply – Lake Rockwell

Raw Water Algal Activity Monitoring

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Fluorescence-based approach Three different measurements from same EEM,

still working on pigment differentiation

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3/2/14 3/22/14 4/11/14 5/1/14 5/21/14 6/10/14 6/30/14 7/20/14 8/9/14

Method 1

Method 2

Method 3

Raw Water - Part 2

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Chlorophyll and other pigments plus phycocyanin and phycoerythrin

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3/2/14 3/22/14 4/11/14 5/1/14 5/21/14 6/10/14 6/30/14 7/20/14 8/9/14

Method 1

Method 2

Method 3

Phycocyanin

Phycoerythrin

Coagulated Water

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Can we monitor cell lysing via fluorescence?Recall fluorescence monitoring part of normal operations!

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Method 1

Method 2

Method 3

Phycocyanin

Phycoerythrin

HAB Alert System

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Multiple Alert Systems Response (DSS) – Oxidant, PAC, Coagulant

Test New Water Plant Response Alternatives

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Evaluate options for managing HABs in the watershed and the plant. PAC Testing – Target Dissolved Compounds Coagulant Jar Tests – Target Dissolved Compounds

and Particulates (e.g. algal cells) – not presented today due to time limitations

Nutrient Management

Test New Water Plant Response Alternatives

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PAC Testing July RAW sample (second round) No pre-oxidation or coagulant, 15

and 30 mg/L dose One hour contact time UV and fluorescence removal

Company PAC Name Iodine # Origin of MaterialStandard Purification (Standard Carbon) Watercarb 500 WoodStandard Purification (Standard Carbon) Watercarb‐L 500 LigniteStandard Purification (Standard Carbon) Watercarb 800 800 Bituminous Coal

Carbochem LQ‐325 800 Bituminous CoalCarbochem P‐1000 1000 Ancient Chinese Secret ‐ Blended

Cabot (Norit) Hydrodarco B 500 min LigniteCabot (Norit) Hydrodarco M 550 min Secret BlendCabot (Norit) PAC 20BF 800 min Bituminous Coal

Biogenic Reagents, LLC Carbon Substrate 300 (UAC‐H2O 300 IN) 300 Biomass

Biogenic Reagents, LLC Carbon Substrate 500 (UAC‐H2O 500 IN) 500 Biomass

Biogenic Reagents, LLC Carbon Substrate 700 (UAC‐H2O 700 IN) 700 BiomassBiogenic Reagents, LLC UAC‐H2OW (Ultra Adsorptive Carbon) 500 Wood

Calgon Carbon Corporation WPC 800 (min) CoconutCalgon Carbon Corporation WPH 1000 1000 (min) Bituminous Coal

Jacobi Carbons, Inc. Aquasorb CB1‐MW PAC‐F 950 (min) Coconut & (lignite‐proprietary secret blend)Jacobi Carbons, Inc. Aquasorb CP1‐F PAC‐F 1000 (min) Coconut

Fluorescence and UV Removal

24Note: Ranking 1 indicates HIGHEST removal

Sample ID C1 C2 C3 UV

C11 1 1 1

C22 4 8 2

C33 2 2 4

C44 6 9 7

C55 5 4 5

C66 3 3 2

C77 7 7 6

C88 8 5 8

C99 9 6 9

Sample ID %C1 %C2 %C3 %UV

C1 56.5% 57.3% 57.9% 43.4%

C2 42.7% 37.4% < 2% 27.7%

C3 40.1% 33.1% < 2% 18.3%

C4 39.2% 39.5% 47.3% 27.7%

C5 40.0% 36.5% 37.9% 24.3%

C6 31.4% 27.6% 17.0% 18.7%

C7 42.5% 39.7% 48.3% 26.4%

C8 17.7% 16.7% 29.3% 13.6%

C9 23.9% 21.3% 34.4% 14.5%

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Validated Models and Operational Response

Most of the focus on this part of system

New data sources initiate new modeling efforts

New chemicals (e.g. coagulant, PAC, oxidant) initiate new modeling efforts

Difficult but where measurable change happens

Expertise required

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Water Plant Decision Support SystemWe started with a focus on coagulation and THM formation because:

Algal toxin management involves (a) intact cell removal (>99.5% by coag.) and (b) extracellular removal

Want to integrate daily operations approach into HAB response

We are also applying this approach to other operations

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Settled Turbidity (ST) Modeling

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Training: R=0.92677

DataFitY = T

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Validation: R=0.90839

DataFitY = T

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Test: R=0.84621

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All: R=0.91334

DataFitY = T

Last 4 years of daily values at Akron

Significant variation in water quality and weather

Multiple model functions tested including ANN, SVM, MFLR, etc.

Ongoing work to improve the models

ST = f(coagulant,KMnO4,ClO2,Raw Turbidity, Temp, others)

Multi-Objective Visualization Example

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Normal operations – can see the cost of lower settled turbidity and/or reduced THM (conflicting objectives)

In “real-time” can calculate distance from optimum

Decision Support Interface

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Operator can adjust the target and dashboard will make recommended dose of different chemicals

Review of historical data shows chemical savings opportunities

Still working on:(a) Built in constraints based

on chemical control flexibility and reasonable dose ranges

(b) Connection with other objectives (e.g. filter run rules)

Bridging the Gap – Moving Forward

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Water Plant Data Collection 

and Data Analytics

Operational Decision Support

1. Monitoring Program(s) – always evolving

2. Model Development –based on latest science and inputs

“The Gap”

1. Watershed2. Water Plant3. Distribution System

1. Require Monitoring Data Linked to Operations2. Test New Water Plant Response Alternatives3. Validated Models and Optimum Operational Response

1. Toxin Testing2. Many Objectives

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Thanks for your time.

Christopher M. Miller, Ph.D., P.E.cmmiller@uakron.edu

(330) 972-5915