WWTP Performance Metrics - IWEA - 2016 - v2
-
Upload
john-norton-jr-phd-pe -
Category
Documents
-
view
86 -
download
0
Transcript of WWTP Performance Metrics - IWEA - 2016 - v2
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 [email protected]