Bridging the Gap from Water Plant Data Collection

Bridging the Gap from Water Plant Data Collection

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 Conference
Columbus, OH
August 28, 2014
Project Team and Resources
PAC Suppliers
2
2013 – Heightened HAB Awareness
3
2014 Headlines
4
Scope and Perspective
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 Toxins
4. Intermittent elevated THM and HAA levels and
more stringent compliance requirements
5. Emerging Contaminants and Unregulated DBPs
5
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)
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.
7
Akron Water Supply Background
1. Multiple
Reservoirs
2. Agricultural
Watershed
3. ~36 MGD
8
What is “The Gap” ?
Water Plant Data Collection and Data “The Gap”
Analytics
Operational Decision Support
Plenty of data and good science, but how do we convert it to operational improvements, particularly to respond to a Harmful Algal Bloom (HAB) ?
9
Bridging the Gap – UA Approach
Water Plant Data Collection and Data Analytics
“The Gap”
1. Monitoring Program(s) – always evolving
2. Model Development –
based on latest science and inputs
Operational Decision Support
1. Watershed
2. Water Plant
3. Distribution System
1. Require Monitoring Data Linked to Operations
2. Test New Water Plant Response Alternatives
3. Validated Models and Optimum Operational Response
10
Monitoring Data Linked to Operations
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)
WS02 May 2014
0.3
0.25
0.25
0.2
0.2
0.15
0.15
0.1
0.1
0.05
0.05
0
800
700
600
Excitation (nm)
650
700
750
Emission (nm)
800
0
11
Reservoir Profiling - Chlorophyll


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)
12
Watershed Monitoring Platform
 Built on Google Fusion platform
 GIS and sampling data platform
 Ability to create custom tables and plots
13
Watershed Platform Data
Data Plot
Data Table
14
Fluorescence Basics
Basics:
1) Small volume sample
2) Minimal preparation
3) Quick (< 10 Minutes)
4) Produces EEM
EEM (Excitation‐Emission‐Matrix)
1) Third dimension is Intensity
2) Overall fingerprint of organic species in the water sample
15
Fluorescence Analysis
Other measures:
1) Peak Picking
2) Fluorescence Index
3) Chlorophyll‐Algal Pigments
PARAFAC Analysis
C1
C2
PARAFAC Components
C3
16
Fluorescence Monitoring
Akron Water Supply – Lake Rockwell
 Estimate chlorophyll and phycocyanin and other
pigments in raw and coagulated water
 Monitor source water characteristics-nature
 Monitor coagulation dissolved organic carbon removal
17
Raw Water Algal Activity Monitoring
 Fluorescence-based approach
 Three different measurements from same EEM,
still working on pigment differentiation
0.014
0.012
0.010
Method 1
0.008
Method 2
0.006
Method 3
0.004
0.002
0.000
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
18
Raw Water - Part 2
 Chlorophyll and other pigments plus phycocyanin
and phycoerythrin
0.014
0.05
0.012
0.04
Method 1
0.010
Method 2
0.03
0.008
Method 3
Phycocyanin
0.006
0.02
Phycoerythrin
0.004
0.01
0.002
0.000
3/2/14
3/22/14
4/11/14
5/1/14
5/21/14
6/10/14
6/30/14
7/20/14
0
8/9/14
19
Coagulated Water
 Can we monitor cell lysing via fluorescence?
Recall fluorescence monitoring part of normal operations!
0.014
0.05
Method 1
0.012
Method 2
0.04
Method 3
0.010
Phycocyanin
0.03
Phycoerythrin
0.008
0.006
0.02
0.004
0.01
0.002
0.000
3/2/14
3/22/14
4/11/14
5/1/14
5/21/14
6/10/14
6/30/14
7/20/14
0
8/9/14
20
HAB Alert System
 Multiple Alert Systems
 Response (DSS) – Oxidant, PAC, Coagulant
21
Test New Water Plant Response Alternatives
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
22
Test New Water Plant Response Alternatives




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
Standard Purification (Standard Carbon)
Standard Purification (Standard Carbon)
Standard Purification (Standard Carbon)
Watercarb
Watercarb‐L
Watercarb 800
PAC Name
Iodine #
Origin of Material
500
Wood
500
Lignite
800
Bituminous Coal
Carbochem
Carbochem
LQ‐325
P‐1000
Cabot (Norit)
Cabot (Norit)
Cabot (Norit)
Hydrodarco B
Hydrodarco M
PAC 20BF
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
Biogenic Reagents, LLC
Carbon Substrate 700 (UAC‐H2O 700 IN)
UAC‐H2OW (Ultra Adsorptive Carbon)
700
500
Biomass
Wood
Calgon Carbon Corporation
Calgon Carbon Corporation
WPC
WPH 1000
800 (min) Coconut
1000 (min) Bituminous Coal
Jacobi Carbons, Inc.
Jacobi Carbons, Inc.
Aquasorb CB1‐MW PAC‐F
Aquasorb CP1‐F PAC‐F
950 (min) Coconut & (lignite‐proprietary secret blend)
1000 (min) Coconut
800
1000
500 min
550 min
800 min
Bituminous Coal
Ancient Chinese Secret ‐ Blended
Lignite
Secret Blend
Bituminous Coal
23
Fluorescence and UV Removal
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%
Sample ID
C1
C2
C3
C4
C5
C6
C7
C8
C9
C1
C2
C3
UV
1
1
1
1
2
4
8
2
3
2
2
4
4
6
9
7
5
5
4
5
6
3
3
2
7
7
7
6
8
8
5
8
9
9
6
9
Note: Ranking 1 indicates HIGHEST removal
24
Validated Models and Operational Response
 Difficult but where
measurable change
happens
  Expertise required
 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
25
Water Plant Decision Support System
We 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
26
Settled Turbidity (ST) Modeling
3
2.5
2
1.5
1
0.5
1
Output ~= 0.83*Target + 0.16
Output ~= 0.82*Target + 0.2
Data
Fit
Y= T
3.5
2
2
Data
Fit
Y= T
2
1.5
1
0.5
0.5
1
1.5
2
Target
Target
Test: R=0.84621
All: R=0.91334
1.5
1
0.5
0
-0.5
0
2.5
3
Data
Fit
Y= T
2.5
Validation: R=0.90839
Output ~= 0.85*Target + 0.16
Output ~= 0.86*Target + 0.15
Training: R=0.92677
1
Target
2
2.5
Data
Fit
Y= T
3.5
3
2.5
2
1.5
1
0.5
0
-0.5
0
1
2
 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
3
Target
ST = f(coagulant,KMnO4,ClO2,Raw Turbidity, Temp, others)
27
Multi-Objective Visualization Example
 Normal operations – can see the cost of lower settled
turbidity and/or reduced THM (conflicting objectives)
 In “real-time” can calculate distance from optimum
28
Decision Support Interface
 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)
29
Bridging the Gap – Moving Forward
Water Plant Data Collection and Data Analytics
“The Gap”
1. Monitoring Program(s) – always evolving
2. Model Development –
based on latest science and inputs
Operational Decision Support
1. Watershed
2. Water Plant
3. Distribution System
1. Toxin Testing
2. Many Objectives
1. Require Monitoring Data Linked to Operations
2. Test New Water Plant Response Alternatives
3. Validated Models and Optimum Operational Response
30
Thanks for your time.
Christopher M. Miller, Ph.D., P.E.
[email protected]
(330) 972-5915
31