Fluorescence-Enhanced Treatment Process Optimization

Christopher Miller, PE

Associate Professor – Civil Engineering

AWWA Conference

October 29, 2015

Acknowledgements

2014 Headlines

2015 Headlines

2015 Headlines

(Water21, 2015)

2015 Headlines

Scope and Perspective

Efforts are driven by expressed concerns at WTPs regarding:

1. Taste-Odor Issues and Algal Toxins

2. Evaluating and Implementing New Technology and Options (i.e. chemical selection, AOP’s)

3. Data-Driven Management (a.k.a. Decision Support)

4. Intermittent elevated THM and HAA levels and more stringent compliance requirements

5. Emerging Contaminants and Unregulated DBPs

Background

• 2015 AWWA Guidance for Water Utilities

• 2015 WRF Report:

“…if the removal of DOC, UV, and color was optimized, this resulted in optimized cell removal as well, in most cases.”

8

Fluorescence as a surrogate for DOC• DOC is a complex mixture of compounds impacting multiple treatment

processes

• Fluorescence offers a rapid (< 10 minutes per sample) approach to characterizing the nature of DOC and monitoring DOC removal

• Fluorescence can also be used to monitor algal pigments and algal cells

Coagulation Performance

Pre-oxidantDemand of

DOC

PAC Effectiveness

Algal Activity –Risk

AWS Fluorescence Monitoring

2 sample locations (raw and settled water)

Sampled once per day or as needed – faster than a jar test (make adjustment and check!)

PAC added

AWS Fluorescence Monitoring• Fluorescence monitoring has been ongoing since 2010

• Three unique fluorescence components have been identified

• Fluorescence monitoring clearly identifies differential component removal

• PAC generally increases component removal

Humic Like Component

Fulvic Like Component

Protein Like Component

Frac

tio

n F

max

Rem

ove

d

AWS Fluorescence Monitoring

• Algal pigments fluoresce at higher excitation/emission wavelengths

• Fluorescence can also be used to monitor raw water algal activity and settled water cell removal (cell lysing and pigment analysis)

Raw Water

Project Objectives

1. Develop operational data-based models for the coagulation and oxidation processes

2. Identify optimal solutions (i.e. more efficient allocation of chemicals)

3. Evaluate optimal solutions under three scenarios:– Varying raw water DOC

– Varying raw water alkalinity

– Simulated harmful algal bloom conditions (High raw water DOC and oxidant restrictions)

4. Identify specific solutions to:– Reach treatment targets (TO1)

– Maintain current quality, but reduce cost (LC2)

– Maintain current cost, but improve quality (BQ3)

5. Assess optimum specific solutions using one year of AWS operational data

Modeling DOC and Turbidity Removal• Paper accepted for publication:

“Coagulation modeling using artificial neural networks to predict both turbidity and DOM-PARAFAC component removal”

• Settled Turbidity Model– Raw water turbidity, pH, alkalinity, hardness, temperature, chemical dose– 1,300 + data points over 4 years

• Fluorescence Component Models– Raw water component scores, alkalinity, pH, chemical doses– 215 data points over 10 months

• Models were assessed using ‘goodness of fit’, parametric analysis, and external validation criteria (comparison with line of ideal fit)

R = 0.93 R = 0.99 R = 0.95

Multiobjective Optimization

• Simultaneously optimize conflicting objectives

• i.e. Higher cost = improved water quality (lower turbidity, lower DOC)

• There is not one unique solution to optimize all objectives; the optimal solutions represent a tradeoff between objectives

• Goal: Identify the set of solutions that are not dominated by any other solution

Identifying Optimal Treatment Solutions

• There are many possible solutions (chemical dose combinations)

• The solutions can be evaluated dynamically as incoming water quality changes

Chemical Dose Ranges

Coagulant PAC PermChlorine Dioxide

Dose Interval

Number of Possible Combinations

50-100 mg/L 0-50 mg/L 0-3 mg/L 0-3 mg/L 0.5 499,849

50-100 mg/L 0-50 mg/L 0-3 mg/L 0-3 mg/L 0.2 16,128,256

50-100 mg/L 0-50 mg/L 0-3 mg/L 0-3 mg/L 0.1 241,211,961

50-100 mg/L 0-50 mg/L 0-3 mg/L 0-3 mg/L 0.05 3,728,445,721

• We define optimal solutions as Pareto efficient solutions

• Pareto efficient solutions are non-dominated:

– A solution (x*) dominates another solution if

1. fi(x*)≤fi(x) for all i

2. fj(x*)<fj(x) for at least 1 j

• Narrowing solution space to only those solutions that are efficient

Optimal Solutions for Different Scenarios

• Three different scenarios were evaluated and the resulting solution sets were assessed

• Scenarios:

• All other variables were held constant at their median value

• Constraints

Scenario FA Score HA Score Alkalinity (mg/L)

Varying Raw Water DOC Low 15 11 90

Moderate 22 17 90

High 40 25 90

Varying Raw Water Alkalinity Low 22 17 60

Moderate 22 17 90

High 22 17 130

HAB HAB 40 25 130

Non-HAB 40 25 130

Scenario Alum (mg/L) PAC (mg/L) Perm (mg/L) Chlorine Dioxide (mg/L)

Changing DOC 60-100 0-80 0-3 0-3

Changing Alkalinity 60-100 0-80 0-3 0-3

HAB 60-100 0-80 0-0.8 0

Solutions for Variable DOC and Alkalinity

• Results show that the Pareto efficient set of solutions capture expected trends

• As DOC increases:– Treatment cost increases (approximately 38% from low to high DOC)

– Recommended PAC and coagulant doses increase with DOC

60

70

80

90

100

Low Mod HighDOC

Alu

m D

ose (

mg/L

)

0

20

40

60

80

Low Mod HighDOC

PA

C D

ose (

mg/L

)

0

1

2

3

Low Mod HighDOC

Perm

Dose (

mg/L

)

0

1

2

3

Low Mod HighDOC

Chlo

rine D

ioxid

e

0

5

10

15

Low Mod HighDOC

Sett

led H

A S

core

4

6

8

10

12

Low Mod HighDOC

Sett

led F

A S

core

1

2

3

4

Low Mod HighDOC

Sett

led T

urb

(N

TU

)

1

2

3

x 104

Low Mod HighDOC

Cost

($/d

)

• As Alkalinity increases:– Alum doses remain similar

– PAC doses increase

– Treatment costs increase

60

70

80

90

100

Low Mod HighAlkalinity

Alu

m D

ose (

mg/L

)

0

20

40

60

80

Low Mod HighAlkalinity

PA

C D

ose (

mg/L

)

0

1

2

3

Low Mod HighAlkalinity

Perm

Dose (

mg/L

)

0

1

2

3

Low Mod HighAlkalinity

Chlo

rine D

ioxid

e

0

5

10

15

Low Mod HighAlkalinity

Sett

led H

A S

core

4

6

8

Low Mod HighAlkalinity

Sett

led F

A S

core

1

2

3

4

Low Mod HighAlkalinity

Sett

led T

urb

(N

TU

)

1

2

3

x 104

Low Mod HighAlkalinity

Cost

($/d

)

Solutions for HAB Scenario• The HAB Scenario was run for High DOC, High Alkalinity Water with restricted

oxidant doses

• Under the HAB Scenario

– Pareto efficient solutions had higher PAC and coagulant doses

– Treatment cost was similar

– DOC removal was similar

• These results suggest that under HAB scenario, cost effective DOC removal can still be achieved by limiting oxidants and increasing PAC dose

60

70

80

90

100

HAB Non-HAB

Alu

m D

ose (

mg/L

)

0

20

40

60

80

HAB Non-HAB

PA

C D

ose (

mg/L

)

0

1

2

3

HAB Non-HAB

Perm

Dose (

mg/L

)

0

0.5

1

1.5

HAB Non-HAB

Chlo

rine D

ioxid

e

0

5

10

15

HAB Non-HABAlkalinity

Sett

led H

A S

core

6

7

8

9

HAB Non-HAB

Sett

led F

A S

core

0.5

1

1.5

HAB Non-HAB

Sett

led T

urb

(N

TU

)

1

2

3

x 104

HAB Non-HAB

Cost

($/d

)How do we select just one solution to implement?

Selecting Solutions for Implementation

• For implementation in real time, the set of solutions must be further constrained

• Approach: identify three solutions from the optimal set of solutions for use by a Decision Maker that meet the following criteria:

1. Reach targets for objectives as specified by Decision Maker (TO1)

2. Lower cost while maintaining same water quality (LC2)

3. Improve water quality for similar cost (BQ3)

• This approach was tested using 268 daily observations of water quality and chemical dose from Akron WTP from August 2014 through August 2015 BQ3

TO1

LC2

Current position

Reach Target Objectives (TO1)• Targets were set as follows (but

could be set at any value):

– HA score = 4;

– FA score = 5;

– Turbidity = 1NTU

• The algorithm routinely returned solutions with objective function values at or near these targets

• The ability to achieve a target on any given day is dependent on raw water quality

• Input from a decision maker would be needed to set these targets as they change over time and to decide if the cost of the recommended solution is worth it

BQ3

TO1

LC2

Current

Lower Cost for Similar Quality (LC2)

• Estimated annual savings of $350,000

• Recommended turbidities generally within 0.4 NTU of actual turbidities

• Recommended FA scores generally with 2 units of actual scores

• Recommended HA scores generally within 1 unit of actual HA scores

BQ3

TO1

LC2

Current

Improved Quality at Similar Cost (BQ3)

• In most cases, recommended solutions show cost differences of less than 1%

• Improved quality was defined as an improvement in all objective function values

• At these similar costs, calculation of reallocation of chemicals yielded:

– FA reductions as high as 4

– HA reductions as high as 10

– Settled turbidity reductions as high as 1.4

BQ3

TO1

LC2

Current

Fluorescence Processing Dashboard• Fluorescence analysis of raw and settled samples is conducted as needed

• The resulting files are uploaded to the cloud and immediate feedback on coagulation efficiency is provided

Decision Support

• Current water quality and chemical doses are input

• Recommended solutions are provided

Conclusions• The removal of fluorescence components and turbidity during the

coagulation/oxidation process was successfully modeled

• These models can be used to identify optimal chemical doses given current water quality conditions

• General solution evaluation of various scenarios, including HAB, yielded reasonable results and trends (i.e. as source water DOC increases, treatment costs increase)

• Fluorescence can be used to monitor and optimize DOC removal, which can help a water treatment plant optimize algal toxin removal (TO1)

• Optimized solutions for ‘regular’ operations yield:

– Cost savings (as much as 5-10% annually) while maintaining water quality (LC2)

– Improved water quality while maintaining similar cost (BQ3)

“…if the removal of DOC, UV, and color was optimized, this resulted in optimized cell removal as well, in most cases.”

Questions?