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Transcript of Neil Rothwell – Director [email protected] The opportunities for the use of R in real...
Neil Rothwell – [email protected]
The opportunities for the use of R in real time industrial analytics
Dr Neil Rothwell
C3global Limited
Neil Rothwell – [email protected]
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
Neil Rothwell – [email protected]
• 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’
Neil Rothwell – [email protected]
• 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
Neil Rothwell – [email protected]
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.
Neil Rothwell – [email protected]
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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
Neil Rothwell – [email protected]
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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
Neil Rothwell – [email protected]
• 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 – [email protected]
Neil Rothwell – Technical [email protected]
Industry doesn’t even figure in the research on maturity
Neil Rothwell – [email protected]
Industrial Analytics Maturity
Neil Rothwell – [email protected]
Privileged and Confidential – © 2014. C3global Ltd., All Rights Reserved 11
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
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Privileged and Confidential – © 2014. C3global Ltd., All Rights Reserved 12
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
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Privileged and Confidential – © 2014. C3global Ltd., All Rights Reserved 13
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
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Privileged and Confidential – © 2014. C3global Ltd., All Rights Reserved 14
Observed versus Predicted
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Forecast and predicted values compared in model
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Demand forecasting by sector, customer, zone, season etc.
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Summary and detailed analysis including scenarios
Neil Rothwell – [email protected]
Privileged and Confidential – © 2014. C3global Ltd., All Rights Reserved 18
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
Neil Rothwell – [email protected]
Water Quality
Neil Rothwell – [email protected]
Techniques used
• Multivariate k-Nearest Neighbour• Clustering Algorithm• Binomial Event Detection• Consecutive Sampling Probability• Binary Tree
Neil Rothwell – [email protected]
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
Neil Rothwell – [email protected]
Station Overview
Neil Rothwell – [email protected]
Privileged and Confidential – © 2014. C3global Ltd., All Rights Reserved 23
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
Neil Rothwell – [email protected]
• 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