After fully exploiting digital computing technology to enable safe and efficient operation since the 1970s, the tank farm and terminal industry is in the midst of a major step change as organizations apply advanced analytics, artificial intelligence and machine learning to the massive operational data they have collected. Applications of digital transformation technologies are ultimately leading to the autonomous terminal. An autonomous terminal possesses comprehensive knowledge of its capabilities and limitations; it works with operators to provide maximum operational safety and efficiency. To realize the autonomous terminal, digital transformation is inevitable and includes fully digitalized execution, digitalized information exchange with all internal and external stakeholders, digitalized asset optimization and fully automated operations. Participants in this session will learn how to realize broad-based benefits and how operations should continuously improve in a sustainable manner.
4. Industrial
Terminals
LNG, LPG and
Chemical Gases
Terminals
Chemical
Terminals
Oil
Terminals
Different Type of Terminal
Energy Manufacturing Food and Agriculture
End Markets
6. Autonomous Terminals Should Be Silent and Boring
Operator
intervention
Optimize execution
of customer orders
without operator
intervention, including
coordination with other
terminals with
overlapping coverage
to maximize
distribution efficiency
Information
Exchange
Automatically
exchange information
with business partners
such as port
authorities, transport
agents, ship captain,
customers, etc.
Self
Diagnostics
Perform
self-diagnostics
of terminal assets by
analyzing data
provided by smart
sensors and
instruments in
real-time, in
coordination with
digitized historical
maintenance records
Adaptive
Adapt to and learn
from terminal
dynamics to provide
operational advice
for improving safety
and efficiency
7. Digitalized Operations
Master Contract
Order Handling
Movement Planning
and Scheduling
Inventory Book Stocks
Invoice Consolidation
Inventory Physical
Stock
Information Mgmt
Order Mgmt
Transport Preparation
Blend Property Control
(BPC)
Scheduling Activity
Equipment
Reports
Departure Handling
Actcivity Mgmt
Arrival Handling
Transfer Blending
HMI
Master Database
Role and Security
Audit Trail
Business Mgmt Workflow Mgmt
Distributed Control System/Programable Logic Controller
Ll LI SI SI SI SI SI MC SI FI SI SI SI
8. Integration with Business Domain
Terminal B
Inventory
Invoice
Terminal Orders and
Movement Planning
Terminal Orders and
Movement Scheduling
Invoice Calculations
Book and Physiscal
Stock Managementt
BOLConsolidate BOL
Orders
Breakdown Orders
into Jobs and Tasks
Reesources Scheduling
(Tank, Jetty, Pump
and Line)
Task Exexcution
Consolidate BOL BOL
Terminal A
9. Software Design Architecture for scalability and
flexibility
Core
System
Plug-in
Component
Plug-in
Component
Plug-in
Component
Plug-in
Component
Microservices with Event-Driven Architecture
(EDA) design for scalability and flexible
real-time deployment
Event Producer
Event Ingestion
Event
Consumer
Event
Consumer
Event
Consumer
14. Current Material Compatible Material Material Group
BGE BTG OG BULK BGE BTG BULK BTG
EOA DEA BULK EOA DEA BULK OG DEA
GLYCERINE GLYCERINE
PU CPP HL 809 BULK OG PU CPP HL806 BULK Polyol Blend
Contamination Check
Path Optimizing Criterias Check Heel Contents Line Clearing
■ No line cleaning
■ Minimum of segment
■ Check line heel contents recorded
by last previous activity
■ Check destination heel
■ Create line clearing sub-task
to clear the line before line-up
Compatibility Table
15. Jetty Scheduling
Laycan
01
Ship A/Product A/Amt
Demurrage Ship B/Product B/Amt
Ship C/Product A/Amt
Ship D/Poduct
C/Amt
02
Maintenanc
e
Ship 1/Poduct C/Amt
Ship 2/Product A/Amt
Ship 3/Product B/Amt
NOA
NOR
NOR
ETA
ETA
ETA
Shipping Schedule
Jetty Schedule Optimizer
Optimizing Criteria
First come first serve
Minimum waiting time
Minimum operation time
Minimum demurrage
Jetty Constraint
Product/Ullage Availability
Harbor Information
Shipping Agents
17. Digitalized Maintenance
Digital Maintenance System
Terminal
Assets
Customer Operations and Maintenance (KPI) Dashboard
Rule Based Engine and Auto Discovery (CMCDB) Ticket System and Run Books
Third party “Plug-ins”
“ComerComercial off the shelfs”
Yokogawa “Plug-ins” Others “Plug-ins”
Symantec
McAfee
What’sUpGold
WSUS
DELL
CentrumVP
ProSafeRS
ControlValve
Diagnostic
HEXFouling
Monitoring
Pump
Diagnostic
Symantec
McAfee
WUG
Network
WSUS
DELL
Admin
FCS
SCS
Control
Valve
Heat
Exchanger
Pump
19. Machine Learning to Reproduce Operator Actions
Detect Deviation
from SOP
Capture Implicit
Knowledge
Detect Abnormal
Conditions
Machine
Learning to
Construct
Operating
Procedures
Realtime and
Historized Data
20. ■ Minimum site operation and maintenance crew as the Terminal is fully automated
■ Average terminal operator salary in U.S is c. $68k pa
■ No contamination; all heel and residues tracked and monitored
■ Minimum product losses; all movements monitored, and long-term data analyzed
to detect small leaks
■ Minimum demurrages; close collaboration between the Logistics, Terminal Operation,
Shipping Agent, Ship Captain, Port Authority, etc.
■ Demurrage bill is typically $ millions pa
■ Improve collaboration between Terminals with overlapping market coverage to improve
the Level of services
Value of Autonomous Operation
21. Many aspects of terminal operations
are often directed by operators based
on experience and judgment. This often
results in inconsistent and non-optimal
operations, with greater risk
of incidents.
Automating actions—and advising
operators regarding their best course
of action—by digitalized systems and
making better use of data results in
improved and consistent operator
actions. These digitalized systems can
also be used to shift maintenance from
reactive to proactive, reducing
downtime and maintenance costs.
1 2
Conclusions
23. 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!