Digital transformation (DX) is becoming the journey towards autonomous operations across devices, equipment, business and process systems. The application of advanced intelligence and cognitive technologies across operations, supply chain planning / scheduling / distribution, engineering, field operations, and maintenance is enabling adaptive and dynamic responses to market changes and asset disturbances. It also enables real time self-optimization of processes. High levels of industrial autonomy require a strong automation base layer; procedure automation; use of more intelligent sensors; remote surveillance and inspection through traditional approaches and with robotics and drones; digital twins; artificial intelligence (AI), and other analytics to monitor, predict, and mitigate process and equipment failures. This presentation will provide an overview of the levels of industrial autonomy and applications. Furthermore, it will show what companies can do today and in the future to achieve autonomous operations.

1. Tom Fiske
Principal Technology Strategist
Yokogawa
November 11, 2020
Plotting the Path to
Autonomous
Operations

2. Agenda:
1. Challenges
2. Digital Transformation & Autonomy
3. Requirements & Vision
4. Journey Towards Autonomous Operations
5. Summary

3. Challenges:
Efficiency
Human
Reliability
AvailabilitySafety
Agility
• Flawless Startup
• Optimized product slate
• Value Chain Opt.
Productivity
• Quality Improvements
• Operation Efficiency
• Performance Optimization
Continuity
• Predictive Maintenance
• Avoidable Downtime
• Knowledge Transfer
Sustainability
• Safe Operations
• Physical and Cyber
• Environmental

4. Challenges:
Changing business
environment
Sustainability and
circular economy
Changing
workforce
Profitable operation
with TOTEX
optimization
Supply chain visibility
and optimization

5. Resilient Supply Chains
• Local and regional sourcing and alliances
• Greater supply chain visibility
Manufacturing
• Greater agility and flexibility to respond to
market demand volatility, crisis, and shock
Worker safety
• Higher levels of automation, robotics, and
autonomous systems to protect workers
Remote operations
• Remote condition monitoring, operations,
control
• Connected remote worker,
• AR/VR/MR, Digital twins, MPA, AI,
etc.
Challenges:

6. Digitization
• The process of converting information into digital format
• Making the information available and accessible to humans,
computers, and digital devices
Digital Transformation
Digitization
• The process of using digitized information to simplify or
improve a specific operation
• It may involve workflow modifications
Digital
Transformation
• The process of creating new business applications that use
digitized data and digitalized applications (effects culture,
skills and customer relations)

7. Digital Transformation Drivers & Enablers
Safety and Security
Digital Twin
Smart Sensors
5G Wireless
VirtualizationDCS & SCADAIoT PlatformData AnalyticsCloud & Edge
Upskilling
Mobile Automation
Robots and Drones
3D Printing
Artificial IntelligenceCognitionBlockchainAR, VR, MR,
& Wearables
Modularization

8. A Journey Towards Autonomous Operations
It’s time to transform toward
Autonomous
Operations
How does process industry evolve?
What is the target through DX?

9. R & D,
ENGINEERING
LOGISTICS &
SUPPLY CHAIN
FINANCEHR
MARKETING,
SALES, SERVICE
THE
ENTERPRISE
PRODUCTION & MANUFACTURING
Digital Transformation

10. R & D,
ENGINEERING
LOGISTICS &
SUPPLY CHAIN
FINANCEHR
MARKETING,
SALES, SERVICE
THE
ENTERPRISE
PRODUCTION & MANUFACTURING
SMART
MANUFACTURING
DX Applied to
Production and
Manufacturing
Smart Manufacturing

11. AUTONOMOUS OPERATIONS
MANUAL / SEMI AUTOMATED
Through co-innovation,
Yokogawa creates new value with
our clients for a brighter future.
SMART
MANUFACTURIN
G
DX Applied to
Production and
Manufacturing
With its OpreX products, services,
and other solutions, Yokogawa
is helping its customers with a way
forward for the creation of value
that leads to industrial autonomy.
AUTOMATED
SEMI AUTONOMOUS
AUTONOMOUS ORCHESTRATION
INDUSTRIAL
AUTONOMY
IA2IA: The Transition from Industrial
Automation to Industrial Autonomy
Industrial Automation to
Industrial Autonomy (IA2IA)

12. Industrial Autonomy
Yokogawa’s Industrial
Autonomy Definition:
Plant assets and operations have
learning and adaptive capabilities
that allow response with minimal
human interaction, empowering
operators to perform higher-level
optimization tasks.

13. Automation
Person responsible for safe operations, human intervention
between sequences of tasks
Automated
Task 1 Task 2 Task 3
Automated
Task 1 Task 2
Autonomy System responsible for safe operations with human oversight
Autonomy
Task 1 Task 2 Task 3 Task 1 Task 2
Autonomous Applications like startups, shutdowns, grade changes, etc.
Automation vs. Autonomy

14. • Prone to errors
• Knowledge loss
• Increased flexibility
• Improved safety
• Higher reliability
• Increased efficiency
• Lower costs
Machine Cognition
& Smart SensingMea
sure
Anal
yzeAct
Mea
sure
Anal
yzeAct
Automation with human
oversight and intervention
Autonomous system with
human oversight
Autonomous Applications
like startups, shutdowns,
grade changes, etc.
Benefits
Automation vs. Autonomy

15. Automation vs. Autonomy
Automation Resilient operations require anomaly, fault detection and mitigation
Automated
Task 1 Task 2 Task 3 Fault
Abnormal Situation
SIS/Trip Shutdown
Correct
Fix
Normal Ops Automated
Task 1 Task 2
Startup
Task 1
Autonomy System must detect and autocorrect anomaly and incipient faults
Autonomy
Task 1 Task 2 Task 3 Anomaly or Fault Task 1 Task 2

16. Autonomous Levels
AUTOMATION LEVELS OF AUTONOMOUS CARS
SOURCE: SAE International

17. Industrial Autonomy Levels
Industrial
automation
Industrial
autonomy Autonomy
Level
Stage Attribute
5
Autonomous
Operations
The facility is completely autonomous including process operations,
supply chain, etc
4
Autonomous
Orchestration
The facility operates autonomously, synchronized to optimize
manufacturing and safety under most circumstances.
3
Semi-Autonom
ous
A mixture of autonomous and automated assets with human
orchestration.
2 Automated
Humans are responsible for safe operations, assisted by traditional
automation systems
1
Semi-Automat
ed
Humans and automation systems share the workload, with humans
responsible for safe operations.
0 Manual Humans control the facility at all times.
Autonomous
System of
Systems
Autonomous
System
Autonomous
Components
Unattended
operations
Remote
operations

18. Requirements
Convert manual operations to fully
automated
Implement procedure automation
for manual ops
Use resilient and redundant
communication & controllers
Adopting intelligent sensor for
condition monitoring
Conduct remote monitoring and
inspection
Simplification of plant processes for
reliability
Apply predictive and prescriptive
maintenance
Use AI & advanced analytics to ID
faults and operate plant
Integrated data visualization,
analysis and KPI dashboards
Use digital twins to improve
decisions & asset utilization
Assist workers with AR/VR for ops
support & training
Adopt AOG to increase situation
awareness
Safety
Safety systems
Cybersecurity
Built-in
APC and loop tuning to improve
efficiency & stability

19. Autonomous Operations Vision
Vertical
end-to-end
integration
Horizontal end-to-end
integration
Digital TransformationDigitalizationDigitization
L4
L3
Traditional IA Autonomous Devices Autonomous Equipment Autonomous Units
Evolution of Traditional Process IA to Autonomous Equipment, Units and Plants
Industrial Stationary and AGV Robots Cobotics & Service Robotics Autonomous Robots
Stationary and AGV Robot Evolution to Robots that Perform non-IA process control Human Tasks, e.g. Maintenance, Inspection & Operator Rounds
L2
L1
Autonomous
Plants

20. Digital Twin performs real-time predictions
• AI predicts output variables behavior
• Builds Confidence - allows operators to take actions
Real-time recommendations
• The AI model recommends set points in real time
• Operators validate set points before making changes and
track performance
Autonomous operation
• The AI model downloads set points in real time Operators
monitor AI operation to ensure it functions as expected
• Operator can turn on/off AI in case of abnormal conditions
Startups, Shutdowns, Crude Switches, Grade Changes, etc.
AI Works Alongside Humans
Autonomous Ops: Process Control

21. Autonomous Ops: Asset Mgt.
Field operators and
maintenance staff
Manual operation
Manual inspection
25% to 40% value-add
Activities
Autonomous systems identifies
problems and provides instruction

22. Source: Yokogawa proprietary research by Omdia Source: Yokogawa proprietary research by Omdia
Refining Industry Adoption

23. Key: regulatory controllers, OTS,
Shift Logs, Production Reports
Key: Auto ML, AI Algorithms, Combining Knowledge with ML (Numeric AI)
Past: Automated Operations
No use of AI, plant performance
relies heavily on individual skill,
slow-decision making process
Key: High Fidelity “What If” Scenario Simulator
AI/Machine Learning Advisory Dashboards
Present: Select Autonomy
Partial use of AI to realize
Profit-Driven Operations (PDO), AI
advisory dashboards support
decision making
Plant of the Future:
• More plant data
• More powerful computing resources
• Demand tighter compliance with management KPI’s
• Less available skilled human resources
• Less time available for decision-making
Future: AI-Driven Autonomous Optimization
AI optimizes plant, limited to no human intervention.
Humans may be in remote locations since their immediate
presence is not required
Maximize
Management
KPIs
Autonomous components & AI

24. Autonomous components & AI
• Product quality prediction and control
• Process anomaly root cause analysis
• Asset anomaly predictive maintenance
• Process control
Applications:

25. Advanced
Decision
Support
Alarm
Management
HMI
Design
Procedure/
Batch
Management
Control
Room
Environment
Improving Situation Awareness
Getting Ready for Industrial Autonomy
DESIRED BUSINESS OUTCOMES:
Increase productivity
•Speed up decision-making
Improve safety
•Early finding abnormal situation
•Blocking false recognition
Improve work environment
•Less stress
Ensure skill transfer
• Translating skills of operators and process engineers to explicit knowledge
OPERATIONS
KNOWLEDGE
ERGONOMICS
KNOWLEDGE

26. Improve Asset Reliability
Getting Ready for Industrial Autonomy
Condition monitoring of rotating equipment (e.g. pumps,
Compressors) by EN510C (ISA 100)
Condition monitoring identifies potential equipment failures that are difficult to
detect through routine inspection and operator patrols

27. Getting Ready for Industrial Autonomy
Improve Operation Efficiency

28. Gecko robot
Inspect outside
vessel
Carry robot
Transport supplies and
product
Drone
Inspect chimney
and infrastructure
Dog & crawler robot
Patrol & monitor
Crawler robot
Operate & manipulate
Drone
Inspect inside
vessel
Spider & crab robot
Inspect outside
pipeline
Honeybee & beetle robot
Inspect inside & outside
pipeline
Mouse & spider robot
Inspect inside vessel
Snake robot
Inspect inside pipeline
Security robots
Patrol
Getting Ready for Industrial Autonomy
Robotics

29. Industrial autonomy is inevitable
Industrial autonomy enhances industrial automation by:
• Adding layers of smart sensing and machine cognition
• Anticipating and adapting to both known and unforeseen circumstances
• Removing the need for human intervention for some functions
or activities
Industrial autonomy will penetrate all areas of operation:
•Manipulating and controlling the process
•Manufacturing operations management
•Planning and Scheduling
•Supply chain activities, etc.
Expectation is mix of people / automation / autonomy
• People will need to understand and work along side
autonomous systems
Barriers include difficulty in defining ROI, regulations,
technology maturity, trust, etc.
Summary
Companies are saying they need industrial
autonomy sooner rather than later

30. The names of corporations, organizations, products and logos herein are either registered trademarks or
trademarks of Yokogawa Electric Corporation and their respective holders.
Thank You
For Your Attention
Tom Fiske, Ph.D.
Principal Technology Strategist
Tom.fiske@yokogawa.com