Using Data Analytics to Monitor and Reduce Energy Consumption

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Using Data Analytics to

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WWTP Case Study

2016 ISA Water / Wastewater and Automatic Controls Symposium

August 2-4, 2016 – Orlando, Florida, USA

Steve Wortendyke

2016 ISA WWAC Symposium

Aug 2-4, 2016 – Orlando, Florida, USA 2

Presenter

• Steve Wortendyke

[email protected]

480-842-1195

• Business Development, Smart

Integrated Infrastructure, Water

Solutions

• 17 years in Water/Wastewater Business

• 6 years in Semiconductor Business

• 6 years USAF, Captain, B-52s

• BS, Chemistry; MBA

Headshot

photo

mailto:[email protected]

http://www.bing.com/images/search?q=tcu+logo&id=F1A376A70B0BE1241A1D2B07F4FA4BCCD6D9ADB0&FORM=IQFRBA



Presentation Outline

• Rise of the Smart City

• The Path from Big Data to Analytics

• Developing the Smart Utility

• WWTP Energy Optimization Case Study



WHAT IS A SMART CITY?

A Smart City uses Information

and Communications

Technology (ICT) to manage

its critical functions –

infrastructure and social

systems -- in smarter and

more integrated ways.

Smart City is not one size fits all; each city has different

needs, priorities and flavors of Smart



Rise of the Smart City

• Wireless Coverage

• Integrated

Infrastructure - IOT

• Data Storage and

Processing

• Cloud Coverage

• Analytics

• Water Efficiency

• Power Efficiency

• Resiliency

• Sustainability

• Economic Growth

Smart cities enhance quality of life



Smart integrated infrastructure Two ends of the spectrum, working synergistically

Integrated Infrastructure

Improves system performance by leveraging synergies between multiple infrastructure systems

Smart Analytics

Improve asset and financial performance by transforming data into actionable intelligence



Cities benefit from synergies that

cultivate system-wide intelligence

Smart

Buildings

Intelligent

Lighting

Distributed

Power

Smart

Transportation

Connected

Kiosk

Smart

Water



How Many People Manage Your

Data Silos?



The Challenge is Building an

Integrated Approach



How Integrated Data Enhances

Actionable Intelligence with Pumps

Time-series • In-Flow

• Pressure

• Vibration data

Asset details • Name

• Model

• Manufacturer

External data • Performance curves

• Last maintenance date

• Design documents

• Best operating

procedures

Events • Downtime

• Startup

• Failure

Notifications • High speed

• Rotor failure

• Low pressure

Analyses • Efficiency analysis

• Key Performance

Indicators (KPI)

• Predictive analysis



What Might a Smart Utility Look

Like?

• Capitalize on intersections of data

– Asset health – CMMS and SCADA

– Current year plan – financial and SCADA

– Treatment optimization – SCADA and laboratory

• Systems working with systems

– AMI data for more than just a bill

– Leak detection, pumping energy, production

– Use IOT based savings to fund AMI conversion

– Balance your wastewater as a resource

– Collection systems, treatment, energy recovery, reuse

– Whole life cycle asset management

– CMMS plus condition and risk based

– Predictive-based, real-time modeling

– Using past to validate present to predict future



The Energy Use Dilemma –

Energy Use Case Study

• City of Lawrence, Kansas provides water and

wastewater services to its community

• Utility wanted to understand how is energy used

throughout its WWTP and major and sub systems

• Utility wanted to use this information to optimize energy

use throughout its plant by making evidence based

decisions



Goals

• Bring data together in a single point of truth

• Perform analytics

– See real-time efficiency

– Track financial metrics

– Understand true costs to run and start processes

– Ad Hoc visualization tools provide access to data to make

decisions



Challenges – Data Dilemmas

• The City of Lawrence faces several not so unique

challenges that prevented them from fully utilizing data

for decision making processes

• Institutional Assumptions and Biases

• Data in, Data inertial

• Data: Use vs Analysis



Institutional Assumptions and Biases

• Often utilities seem to “just know” certain things and

Lawrence recognized this as a problem data

management and visualization tools could solve

• How much does it cost to start Actiflow? $10,000?

• When should routine evening/morning Cl2 changes

occur? How large of a change?

• Still use trial and error plus institutional assumptions too

often and struggle to put wealth of data to answer these

questions



Data in, Data Inertial

• The City recognized that they were great at collecting

and storing data but the challenge was getting the data

out

• Death by excel – Volume

• Death by excel – Difficult to update so typically out of

date

• Processes to get the data out was slow and required

technical people to make it happen

• Need a more robust data tool

– For integration of data sources

– Easily visualize large quantities of data



Data: Use vs. Analysis

• Good proximate use

of SCADA Data

• Inconsistent use of

other data sources

with SCADA for

analysis

• Spotty analysis = lack

of proper optimization

• Quantity and

frequency of data is a

barrier to analysis



What was City’s real goal – In their

words “Make the Magic Happen”


Aug 2-4, 2016 – Orlando, Florida, USA

Potential Data Transfer Paths

19

Local

Data Hub

Visualization

Tools

Cloud

SCADA

Historian

Server

External Data

Center



Solutions

• Concise

actionable insights

delivered to

decision makers

• Performs standard

calculations using

SCADA and LIMS

data

• Display, trend,

and benchmark

Energy and

Chemical usage

KPIs in real-time.



So, How much did it cost to start

Actiflo?



Summary

• Ultimately the move to smart cities and big data

management is starting to impact how we manage data

in the water/wastewater space – but in a more practical

way

• The questions we ask:

– How can I view all my data quickly and easily?

– How can I use my data to help me optimize processes?

– How can I know what I don’t know?

• The Path from Big Data to Analytics is a journey that

starts with the question…I wish my data would tell me

________.

Using Data Analytics to Monitor and Reduce Energy Consumption

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