Neil Rothwell – Director [email protected] The opportunities for the use of R in real time industrial analytics Dr Neil Rothwell C3global Limited

Neil Rothwell – [email protected]

The opportunities for the use of R in real time industrial analytics

Dr Neil Rothwell

C3global Limited


What is this all about

• Operational/Industrial analytics• Why now• Is there a market?• Barriers to adoption• Is it really that simple?• Where does R fit


• IT and OT have evolved from totally different start points and have been based on different data types and user needs

• IT has developed from the top down and focussed on business and corporate requirements. Thus the needs of the engineer are rarely met.

• OT has developed from the bottom up with control of proprietary equipment being the prime goal. These systems have low tolerance to failure so are not deigned to be flexible.

• Hence the barrier to integration that has led to low takeup

Why Now? - ‘IT OT Convergence’


• ERP, CRM, Financial, Scheduling, Resource planning have all been delivered

• If we are to deliver further savings in industrial applications need to address the operation

• In the North Sea effectiveness/efficiency is ~60% with ambition to reach 80%

• Common need across all industries• CIOs are changing

Why Now? – Done everything else


5

Neil Rothwell – Technical [email protected]

Growth of Industrial Operational Technologies

Source: Defining and Sizing the Industrial Internet, Wikibon 2013

Projected Spend & Value Delivered by the Industrial Internet (USD Billions)

Value Delivered by Industrial Internet Technology Investments

Total Industrial Internet Technology Spend

$1,400

$1,200

$1,000

$800

$600

$400

$200

2012 2020

$514 Bn

$1,279 Bn

$20 Bn $23 Bn

Industrial Internet spend will grow

at a 50% CAGR to reach $514Bn

Analytics focus for Fortune 500 moving to industrial operations: “Analytics have been transformative in wide areas of customer & product service…industrial analytic applications are the next frontier.”

Operational technologies include: • Core systems that deal with running

plant and industrial equipment.

• Devices and sensors to ensure system integrity and to meet technical constraints.

• Event-driven and frequently real-time software applications or devices with embedded software.

• Systems used to manage and control mission-critical production or delivery processes.


6

Need to bridge the gap Between IT and OT

• Operational Technologies: “Exist in all industries & are a major source of big data with massive data volumes, disparate sources & variable latency.”

• “There are few companies capable of providing applications with embedded predictive analytics” to predict asset performance and optimize operational production.

Costs

Orders

Constraints

Specifications

State

Location

Conditions

Events

Context

Visualization

Prediction

Monitoring

ERP

CRM

Demand Management

FinancialPlanning

NetworkOptimization

ControlSystems

SmartMachines

LocationSystems

EnvironmentSensors

Barcodes / RFID

Operational TechnologyInformation Technology


7

Need Both Predictive & Prescriptive Analytics

Need to Monitor Assets & Events in Real-time to Anticipate Operational Needs

Anticipate events

Identify event correlations and leading indicators

Create models for propensity and risk

Forecast performance

Descriptive: What happened?

Diagnostic: Why did it happen?

Predictive: What will happen?

Prescriptive: What should I do?

Decision Support

Decision Automation

Data Decision Action

A need to anticipate future events that are often missed by traditional BI tools and gives operators forward looking action plans to mitigate operational risk

Process Flow -> From Raw Data to Actionable Operational Intelligence


• Multiple data sources• Time series, metadata, batch

• Structured data needs to be used in an unstructured manner

• Traditional systems, SCADA, DCS, historians not ideal toolsets for data manipulation

• Solutions tend to be domain and vertical specific so niche

Barriers to adoption


Neil Rothwell – Technical [email protected]

Industry doesn’t even figure in the research on maturity


Industrial Analytics Maturity


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Case Study– from regression to total asset management

• South Australia is the driest State in the driest continent on earth

• The main source of water is the Murray River which is dependent on inflows from the North and East of Australia

• The last period of drought prompted the building of a high capacity desalination plant and a North/South interconnecting pipeline which, for the first time, allowed SA Water to move water almost anywhere in their network

• This new infrastructure and the ongoing investment in telemetry equipment lead to SA Water looking for smarter ways to manage their network and optimize water usage



Demand Forecasting• Water demand varies due to a number of factors including:

• Population• Residential development• Industrial activity• Temperature • Rainfall• Water stock levels• Water usage restrictions• Weather patterns and weather events• Tourism, major entertainment and sporting events (in the hot

months of February and March the population of Adelaide can increase by hundreds of thousands)

• Maintenance activity• Large scale infrastructure projects



The Solution

• System uses data from both the IT and OT systems to perform complex calculations providing demand forecasts on an hourly, daily, weekly, monthly and annual basis. Long term prediction of demand over a 10 year time frame

• The major systems to which we interface include:• Production Data• Customer Billing• Bureau of Meteorology• Water Quality• Maintenance Activity• Water Availability • Geospatial Information• Demand Areas and Usage Patterns



Observed versus Predicted



Forecast and predicted values compared in model



Demand forecasting by sector, customer, zone, season etc.



Summary and detailed analysis including scenarios



The Outcome

• Weather forecast leads to demand forecast• From demand forecast can do production planning• From production planning can determine where to

produce the water• Transportation costs then come into play• Energy tariff then determine when you make it• This then determines the energy requirements• Energy requirement can be determined in advance• Energy can be bought on spot market• December 2013 temperatures reached 47C, produced

minimal requirement in water and saved over $1.3M in a week


Water Quality


Techniques used

• Multivariate k-Nearest Neighbour• Clustering Algorithm• Binomial Event Detection• Consecutive Sampling Probability• Binary Tree


Event Detection - From raw data to event probabilities

0.0

0.2

0.4

0.6

0.8

Ch

lori

ne

(m

g/L

)

Dec 07 Dec 12 Dec 17 Dec 22

Raw data Filtered Data

-4-2

02

4

No

rma

lize

d R

esi

du

al

Dec 07 Dec 12 Dec 17 Dec 22

Chlorine Ammonia Pressure Turbidity

Gather Data

AlarmAggregate

Predict

Filter


Station Overview



Solution Examples by Industry

• Predict corrosion rates in pipelines, downholes, etc.

• Predict scale and well intervention requirements

• Predict chemical consumption

• Predict production flow rates and usage per year

• Integrity operating window & dashboard

• Predict cable and hot joint condition

• Anticipate transformer and compressor failure through real-time health monitoring

• Provide tower refurbishment & maintenance plan based on corrosion rate modeling

• Predict tree maintenance near overhead lines

• Predict water production levels and consumer demand

• Forecast effects on demand based on temperature patterns

• Alarm suppression based on predicted weather patterns across network

• Prescribe optimal water movements based on cheaper tariffs

• Energy consumption modeling and targeting

• Predict and manage carbon footprint & output

• Predict fuel usage to optimize fleet management

• Anticipate failure of refrigeration equipment

• Prescriptive load shedding for optimal power reduction

• Prescriptive set point remapping when overriding settings

• Identify and prescribe field service maintenance needs

Oil and Gas Power Transmission Water & Utilities Sustainability Refrigeration


• We have just started to understand the power of analytics in operational environments

• There will be a rapid growth in the use of R in real time industrial environments

Documents

Neil Rothwell – Director [email protected] The opportunities for the use of R in real time industrial analytics Dr Neil Rothwell C3global Limited