Preliminary analysis of the 250 largest WWTP in the US:

Initial findings, benchmarking results, and next steps

February 29 - March 2, 2016Dr. John Norton, PE

IWEA Annual Conference 2016

Description of the effort

• Selection of the largest 350 cities in the country by population, plus the largest 25 or so in each state.– Tried to capture WWTP in sanitary districts serving

multiple communities

• City by city examination of every wastewater treatment plant– Thousands of calls/emails to utilities, State EPAs,

DEPs, etc., reviews of NPDES permits, hundreds of masterplans read

Staggering challenges• Available treatment data

– Range from “non-existent” public data to full disclosure.– Numerous data sets contradict each other, e.g., average daily flow will

not match recent reporting, NPDES permitted levels, posted information, etc.

– Dozens of permits had errors, omissions, etc.– Inconsistent data sets, CSOs, plant excursions, often not available

• Social resistance– “Why do you want our data?” “Call the EPA if you want our data.”

“Send us a letter.” (Letter sent, no response despite numerous followups)

• Costing data– Goal: “simple” summary of all administrative, O&M, capital costs….– Occasionally available as part of comprehensive masterplans/CIP,

roughly 1/3 of the utilities post their masterplans

Five largest plants, by design flow

0 250 500 750 1000 1250 1500

DC WASA (Blue Plains)

Los Angeles (Hyperion)

Detroit

Boston (Deer Island)

Chicago (Stickney)

MGD (design flow)

Data (so far)

• Complete data for only about 20% of those WWTP examined (about 60/320)

• Basic load and flow data• O&M data, other costing data, very deficient• Plant treatment processes are almost unique on a plant

by plant basis, typically basic data available ~ 40% of the WWTP (135/320)

• BEST SYSTEM: Chicago! All treatment data posted online (limited financial data though)

• Worst system: New York City! (They require a FOIA request for EVERYTHING.)

Initial data (data set NOT COMPLETE)

NOTE: “Percent less than” is the percentage of values less than a particular value. For instance, this graph shows that the 50th percentile plant is roughly 30 MGD, meaning that 50% of the plants measured are smaller than 30 MGD.

Treatment capacity: actual to design

Stickney

Why benchmarking?• Search for innovative ideas

– Internal: year over year performance comparison– External: drill down into criteria to reveal success factors:

• e.g., energy reduction efforts, employee retention, health and safety practices, equipment performance, etc

• Establish best practices– Comprehensive and data-based comparison of efforts

• Gain broader, more accurate, organizational perspective– Since it is based on what the best are doing it takes the emotion

out of arguments about the need to change

Data set: materials and methods• Types of data

– Operational aspects such as flow and loading, treatment goals, and permit compliance

– Economic aspects such as treatment cost, energy use, and capital investment– Managerial aspects such as utility metrics, employee training, and

procurement systems

• Sources of data– Facility websites, operations reports, master plans, NPDES permits, posted

data, personally provided data.– This data is being collected into a comprehensive database. – All data is being confirmed via multiple methods, including

• review with facility personnel, • direct assessment of operational and other published data, and • discussion and review with regulatory officials

• Current data set is preliminary and is provided as an example

Everyone is unique? Yes!

Employees per MGD

Note the difference when accounting for economies of scale

Staff/MGD, as a function of MGD

Cost of treatment

Cost per MDG – accounting for economies of scale

Challenges of benchmarking

• Economies of scale – “artificially” outstandingperformance

• Hidden correlations – dry climates

• Using the results – gaining traction for positive change

Technology examples

• Inflow and infiltration reduction programs

• Energy use per unit treated

• Solids generation per unit treated

• Biogas utilization

Economics examples

• Cost per unit treated

• Employees per unit treated

• Capital investment over time

• Electrical energy rate agreements

Organizational examples

• Training expenditure per employee

• Organizational structure and type

– Integrated city service, independent political agency, privately run

• Internal versus external laboratory services

• Operator scheduling, shift rate, etc.

Successfully enabling change needs “The Whole Story”

REASONS

TARGETS

ACTIONS

Examples of data driving organizational performance

• School systems:

– Graduation/placement data informs parents

• The World Bank –

– http://www.doingbusiness.org/, motivated countries to initiate reforms, e.g., Namibia, Zambia, Singapore, etc.

• Toxics Release Inventory

– public information drove huge reductions in industrial pollution

Next steps

• “Stick with the effort” - >2,500 hours personally invested

• Focus on a specific area to get an initial “win” to share, motivate further collaboration

• Refine/streamline the approach based on lessons learned

• Standardized data request?

Personal note“I believe this type effort, these types of data and resulting analysis, are a critical missing link in the management and evolution of our country’s water infrastructure.

I feel that, so far, I have failed in my efforts to deliver even a fraction of the promise that may yet come to pass.

I will never give up.”

- Dr. John, W. Norton, Jr., PEMarch, 2016

Questions?

www.clarkdietz.com

John W. Norton, Jr., Ph.D., P.E.977 N. Oaklawn Avenue, Suite 106Elmhurst, IL 60126630.413.4130 - office312.550.1274 - cell john.norton@clarkdietz.com