Production

Transcription

Production

PI System (and PI AF) – Enabling
Business Transformation
The Journey from Refinery Business Unit to
Group Downstream Level...and the Enterprise
Presented by Tibor Komróczki
EMEA USERS CONFERENCE 2015
© Copyright 2015 OSIsoft, LLC
MOL Group
Downstream
6 production units
23.5 mtpa refining capacity
2.1 mtpa petrochemicals capacity
>1,900 filling stations
under 8 brands in 11 CEE
370 000 PI Tag capacity in 4 PI Servers
Extensive use of PI Asset Framework
> 6 500 Elements
70 Element templates
30 Event Frames templates
11 Notifications templates
~ 105 000 Attributes
PI System
2
Exploration and Production
6 production
Oil and units
Gas
exploration and production assets
in 13 countries
Valuable production activity in 8 countries
MOL Group is determined to continue organic
and inorganic growth through acquisition.
70 000 PI Tag capacity in Hungary
Vision to Expand across all E&P
3
PI System history
4
PI System Evolution in MOL
IoT
Enterprise
Machine learning
Division
Team
Individual
5
MOL Downstream / History I.
SCM
Business Process Reengineering
FIR
Project team
(1997)
FIR
Department
establishment
(1998)
PI System
Implementation
(1998)
PI API
Development
(2002)
Complex
Natural Account
(2002)
Alpha Deck
OPC
(2003)
Tisza
Unix / Windows
Refinery
server
(2004)
(2006)
6
MOL Downstream / History II.
Safety first (HSE,PSM)
Vision
Slovnaft
(2007) Rt Webparts PI AF
(2008)
(2010)
Intensive
PI ACE
usage
(2010)
Industrial
PI System
Zala
PI Coresight
(2012)
Refinery
(2012)
(2012)
Lean
TVK
Rijeka
Refinery implementation
(2014)
(2012)
SN
MOL
PI HA / PVS Upgrade
(2015)
(2014)
7
MOL Asset Framework (AF) Based Applications
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Interlock statuses
Operating envelopes
Alarm management
Energy KPI breakdown (6 tiers)
Column Dashboards
Normal mode of control loops
APC monitoring
1st & 2nd Level material balancing
Sigmafine (PI AF) used for yield
accounting & Material Movement
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Energy Monitoring
CH, Utilities and Energy balances
Flare activities
Corrosion control (HTHA)
Crude Blending Control
Natural Gas and Fuel gas forecasting
Control rooms’ temperature
Yield Optimization/Reporting
Plan vs Actual (PvA) Analytics with
Future Data
© C o p©
y r Copyright
i g h t 2 0 1 42015
O S I sOSIsoft,
o f t , L L LLC
C.
8
Company goals
9
MOL Values
Success
&
Growth
Courage
&
Decisiveness
Teamwork
&
Partnership
Expertise
&
Partnership
10
Focus areas of Next Downstream Program (NDP)
NDS is targeting $500M-550M EBITDA improvement by 2016
GOALS
Specific Energy
Consumption
Loss
Operational
availability
White
Product (Yield)
Base:3,13 GJ/t
Plan:2,94 GJ/t
2%
Base:0,53%
Plan:0,37%
30%
Base:95,19%
Plan:96,19%
1.1%
Base:79,11%
Plan: 83,07%
Efficiency
Trust
Innovation
5%
Safety
Proficiency
11
Integration
12
MES standardization in MOL group level
ERP
SCM
CRM
PLM
Layer 4
Planning &
Logistics
Finance
Planning & Scheduling
Marketing & Sales
Human Resources
Materials & Inventory
•
•
•
•
•
Separate group and local level responsibility
Base Layer
Batch Control
Process Control
Production & Operation
Management
based on PI
Product Control
Layer
1
Layer
2
Layer
3
Determine standardization goals
MES
•
•
•
•
•
Product/Resource Management
Production Data Collection
Product and Process Analysis
Plan Execution and Routing Mng.
Packaging and Distribution Mng.
SCADA/HMI
•
•
Process Control
Process Visualisation
PLCs/BES
Sensors/Actors
according to common principles
Establish unified software architecture based
on PI System
Ensure equivalent PI System structure in MOL
Group refineries
Create efficient contract management system
with vendors
Production Process
13
Danube Refinery MES portfolio
Software components
PI System – (AF is key)
Sigmafine (Reconciliation)
LIMS (Laboratory)
Opralog (E-logbook)
SEMAFOR (KPI system )
NICE (Natural Info – center)
14
REFIS system usage by sites
►
►
►
►
►
►
►
►
►
PI System
Opralog
Sigmafine
LIMS
SEMAFOR
NICE
(Movement, ICA, MOC)
PI System
► SEMAFOR
► Opralog
► LIMS
► NICE
(MoC, ICA,ADAMS,
Movement
►
PI System
Sigmafine
NICE
(ICA, Movement)
15
MES integration efforts
Main objectives
Shape the strategy via four domains
People
Process
Merge Business and IS project goals
Share ideas and resources
IT
Structure
Create long term plan
Involve the following areas:
Downstream, SCM, HSE, Logistic
16
Production (Refinery & Petchem )
Unificated
(high standardization, high
integration)
(low standardization, high
integration)
Prod. Mng. &
Reporting &
Performance Mng.
Diversified
(low standardization, low integration)
Quality mng.
Performance
Monitoring Mng.
Road,Rail,
Barge,Pipe
Trans.
Replicated
(high standardization, low
integration)
Maintenance
Energy mng.
Production
acc.
Maintenance
Operation
Coordinated
Production Process Integration level
TO-BE
Coordinated
Production Process Integration
level
AS-IS
& Logistic standardization
(low standardization, high
integration)
Prod. Mng &
Reporting &
Performance
Mng.
.
Unificated
(high standardization, high
integration)
Energy mng.
Road,Rail,
Barge,Pipe
Trans.
Storage op.
Performance
Monitoring Mng.
Diversified
(low standardization, low
integration)
Quality mng.
Replicated
(high standardization, low
integration)
Production
acc.
Maintenance
Maintenance
Storage op.
Production Process Standardization level
AS-IS
Operation
Production Process Standardization level
TO-BE
17
MOL logistics - PI System implementation
Project initiations
Visualizing and monitoring logistic
technology processes
Supporting preventive
maintenance
Ensuring KPI measurement
Notification technology events
Inventory calculation
18
Innovations
&
Trends
19
Data aggregation in refining
DATABASE
NICE
Opralog
MOS
Manual
Data Input
80 billion
Laboratory
Information
System
Process
field sensors
1.8 million
samples
per shift
samples
per year
150.000
samples
per minute
Plant
Information
System
AF
Big data is a broad term for data sets so large or complex that traditional data
processing applications are inadequate.
20
Advanced Analytic Possibilities
REFINERY
DATABASE
Predictive modeling
Asset Monitoring
Predictive
Maintenance
Production
Modeling
Data mining
Energy Demand
Forecast
Operation
Optimization
Technology
Assessment
21
Azure Machine Learning
Business Issue
(yield inc-dec;
new feed,
equipment behavior,
Maintenance problem)
Business impact
Better understanding
operation & processes
Increase efficiency
PI
Identifying development
opportunities
Identifying efficiency gains /losses
reduction opportunities
Data Collection
Understanding operation optimum
LIMS
Specific development proposal
Retrain
Score
Train
Score
NICE
Evaluate
Mindset changes
Searching possibilities
Accurate business reasons
Reusable Web Service
22
Energy Demand Forecasting with Future Data
Improve energy trading by
Collect real-time data
Train energy consumption models
Predict energy consumption based
on production plan
Monitor and update models
23
# 1- Energy Monitoring & Reporting
Benefits
Continuous monitoring of the
parameters
Normalized, unified, and quality
information leveraging AF
Easy maintenance and governance of
the AF templates
Structured format
Increased production as a result of
improved energy and environmental
constraint visibility and forecasting
24
The PI System And Sigmafine – Better Together
Integrated
Production &
Yield Accounting
2nd Level Material
& Energy Balance
1st Level Material and Energy Balance
Data Aggregation, Normalization, Validation,
Security, Transformation, and Historization
Data Infrastructure & Asset Framework Installation
25
Sigmafine – An Extension of AF
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# 2 – Energy Intensity Index (EII)
New AF structure & workflow
to calculate key SOLOMON
indicators:
• Energy Intensity Index (EII)
• Utilized Equivalent Distillation
Capacity (UEDC)
• Complexity-Weighted Barrel
27
# 3 – White Product (Yield) Analytics & Reporting
Planning and Scheduling Systems
Report content
REAL-TIME DATA
PIMS
MOLSZHBPIAF
ORION
PI HA
White Product
Future Scheduling data
CONFIGURATION
MOLSZHBPI01
Future data from Business Plan
MOLSZHBPI01
White Product Report
MOLSZHBPI
Calculated movement data from AF
Analyses Rules
LABORATORY RESULTS
Movement Data
Forecast data used in PvA analytics
ANALYSERS
NICE
LIMS
Integrated $/loss from deviation from
PvA summed over periods of time
28
# 4 - Asset teams & Importance of information service
Asset team tasks
PIMS
catalogue
AF
Data
validation
Daily
reports
Providing
key quality and utility cut assurance for
Future data from Business Plan
the units
Investigating efficiency improvement
opportunities:
• DMHCK conversion reserve 9,5 MUSD
• Increased LCO rate effects 17 MUSD
• Further unit load possibilities 8 MUSD
• CDU3 replacing all structured packing
bed
9 MUSD
PI Clients Establishing continuous contact with Energy
department and Supply Chain Management to
share unit performance information
Increased Mechanical and Operational availability
29
# 5 - Improving Asset Integrity with Advanced Corrosion Analytics
•
High Temperature Hydrogen Attack (HTHA)
•
fx (metallurgy, temperature, hydrogen partial
pressure(PP), length of exposure)
Developed AF template that:
•
Determine partial pressure
•
Attribute of pipe class
•
Temperature and length of exposure limits
•
Total time above Temp and PP
•
Alerts/notification/event frame
•
•
Tested and rolled out in 6 units < 1 week
Expanding to all plants in 2015.
30
Conclusion
31
$500M-550M EBITDA improvement
MOL (Global Integrated O&G Company – Hungary)
“Installing the PI
System infrastructure across our fuels
value chain was fundamental to our New Downstream Program and
the significant performance and sustainability
improvement we have seen.”
Tibor Komróczki Head of Process Information & Automation
CHALLENGES
SOLUTION
Need to significantly improve
performance of a portfolio of 6
refineries & related value chain
Implemented a “New Downstream
Program – NDSP” based on a new
data and information
PI System centric strategy
• Low cultural alignment,
standardization, and use of best
practices
• Installed the PI System across the
full value chain
RESULTS
Significant Improvement in the
fuels value chain performance
in all key areas – energy,
reliability, safety, & compliance
• Increased Yield – 5%
• limited data based and proactive
decision making
• Developed new PI System based
applications in critical areas
including energy & reliability
• Decreased energy consumption –
2% YOY
• Reduced HC loss: -30%
• Utilization: 1.1%
Contact Information
Tibor Komróczki
tkomroczki@mol.hu
Process information and
Automation leader
MOL Plc.
33
Questions
Please wait for the microphone
before asking your questions
Please don’t forget to…
Complete the Online Survey
for this session
State your
name & company
http://eventmobi.com/emeauc15
34
Thank You
Köszönöm!
Děkuji

Production

Transcription

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