© Copyright 2015 OSIsoft, LLC EMEA USERS CONFERENCE 2015
Presented by Tibor Komróczki
PI System (and PI AF) – Enabling Business Transformation
The Journey from Refinery Business Unit to Group Downstream Level...and the Enterprise
© Copyright 2015 OSIsoft, LLC EMEA USERS CONFERENCE 2015
PI System
2
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
> 6 500 Elements
70 Element templates
30 Event Frames templates
11 Notifications templates
~ 105 000 Attributes
Extensive use of PI Asset Framework
© Copyright 2015 OSIsoft, LLC EMEA USERS CONFERENCE 2015
Exploration and Production
3
6 production units Oil and 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
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PI System history
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Individual
Team
Division
Enterprise
IoT
Machine learning
PI System Evolution in MOL
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MOL Downstream / History I.
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 Refinery (2004)
Unix / Windows server (2006)
SCM
Business Process Reengineering
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MOL Downstream / History II.
Slovnaft (2007) Rt Webparts
(2008) PI AF (2010)
Intensive PI ACE usage (2010)
Zala Refinery (2012)
PI Coresight (2012)
Industrial PI System
(2012)
Rijeka Refinery (2012)
TVK implementation
(2014)
MOL PI HA / PVS
(2014)
SN Upgrade
(2015)
Vision
Safety first (HSE,PSM) Lean
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MOL Asset Framework (AF) Based Applications
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
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
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Company goals
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MOL Values
Success &
Growth
Teamwork &
Partnership
Courage &
Decisiveness
Expertise &
Partnership
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Focus areas of Next Downstream Program (NDP)
Specific Energy Consumption
Base:3,13 GJ/t Plan:2,94 GJ/t
2%
Operational availability
Base:95,19% Plan:96,19%
1.1%
Loss
Base:0,53%
Plan:0,37% 30%
White Product (Yield)
Base:79,11%
Plan: 83,07% 5%
GOALS
Efficiency Trust Innovation Safety Proficiency
NDS is targeting $500M-550M EBITDA improvement by 2016
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Integration
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MES standardization in MOL group level
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ERP SCM CRM PLM
• Finance • Planning & Scheduling • Marketing & Sales • Human Resources • Materials & Inventory
MES
• Product/Resource Management • Production Data Collection • Product and Process Analysis • Plan Execution and Routing Mng. • Packaging and Distribution Mng.
Production & Operation Management based on PI
La
ye
r 3
La
ye
r 4
Planning & Logistics
SCADA/HMI • Process Control • Process Visualisation L
aye
r 2
PLCs/BES Sensors/Actors L
aye
r 1 Pro
du
ct C
on
tro
l
Pro
ce
ss C
on
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Ba
tch
Co
ntr
ol
Base Layer Production Process
Separate group and local level responsibility
Determine standardization goals
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
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Danube Refinery MES portfolio
14
Software components
PI System – (AF is key)
Sigmafine (Reconciliation)
LIMS (Laboratory)
Opralog (E-logbook)
SEMAFOR (KPI system )
NICE (Natural Info – center)
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REFIS system usage by sites
15
► PI System
► Sigmafine
► NICE
(ICA, Movement)
► PI System
► Opralog
► Sigmafine
► LIMS
► SEMAFOR
► NICE
(Movement, ICA, MOC)
► PI System
► SEMAFOR
► Opralog
► LIMS
► NICE
(MoC, ICA,ADAMS,
Movement
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MES integration efforts
16
Process
Structure IT
People
Main objectives
Shape the strategy via four domains
Merge Business and IS project goals
Share ideas and resources
Create long term plan
Involve the following areas:
Downstream, SCM, HSE, Logistic
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Production (Refinery & Petchem ) & Logistic standardization
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Pro
du
cti
on
Pro
ce
ss In
teg
rati
on
le
vel
AS
-IS
Production Process Standardization level AS-IS
Coordinated (low standardization, high integration)
Diversified (low standardization, low integration)
Replicated (high standardization, low integration)
Unificated (high standardization, high integration)
Operation
Quality mng.
Production acc.
Energy mng.
Prod. Mng. & Reporting &
Performance Mng.
Maintenance
Pro
du
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on
Pro
ce
ss In
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leve
l T
O-B
E
Production Process Standardization level TO-BE
Coordinated (low standardization, high integration)
Diversified (low standardization, low integration)
Replicated (high standardization, low integration)
Unificated (high standardization, high integration)
Maintenance
Production acc.
Energy mng. Prod. Mng & Reporting & Performance
Mng.
Operation
Quality mng.
Performance Monitoring Mng.
Performance Monitoring Mng.
Road,Rail, Barge,Pipe Trans. Storage op.
Maintenance
Maintenance
Storage op. Road,Rail, Barge,Pipe Trans.
.
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MOL logistics - PI System implementation
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Visualizing and monitoring logistic technology processes
Supporting preventive maintenance
Ensuring KPI measurement
Notification technology events
Inventory calculation
Project initiations
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Innovations &
Trends
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Data aggregation in refining
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150.000 samples per minute
1.8 million samples per shift
80 billion
samples per year
Process field sensors
Plant Information System
Laboratory Information
System
Manual Data Input
NICE
DATABASE
AF
Opralog
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate.
MOS
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Advanced Analytic Possibilities
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Energy Demand Forecast
Predictive Maintenance
Operation Optimization
Asset Monitoring Technology Assessment
REFINERY DATABASE
Predictive modeling Data mining
Production Modeling
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Increase efficiency
Identifying development opportunities
Specific development proposal
Business impact
Azure Machine Learning
Better understanding operation & processes
Identifying efficiency gains /losses reduction opportunities
Understanding operation optimum
Searching possibilities
Accurate business reasons
Mindset changes
Business Issue(yield inc-dec;
new feed,equipment behavior,
Maintenance problem)
Data Collection
PI
LIMS
NICE
TrainScore
Evaluate
Reusable Web Service
RetrainScore
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Energy Demand Forecasting with Future Data
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Collect real-time data
Train energy consumption models
Predict energy consumption based on production plan
Monitor and update models
Improve energy trading by
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# 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
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The PI System And Sigmafine – Better Together
Integrated
Production & Yield Accounting
1st Level Material and Energy Balance
2nd Level Material & Energy Balance
Data Aggregation, Normalization, Validation, Security, Transformation, and Historization
Data Infrastructure & Asset Framework Installation
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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
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# 3 – White Product (Yield) Analytics & Reporting
MOLSZHBPIAF
White Product
MOLSZHBPI01 MOLSZHBPI01
CONFIGURATION
MOLSZHBPI
REAL-TIME DATA
NICE
ORION
LIMS
ANALYSERS
LABORATORY RESULTS
PI HAPIMS
White Product Report
Planning and Scheduling Systems
Movement Data
Future data from Business Plan
Future Scheduling data
Calculated movement data from AF Analyses Rules
Forecast data used in PvA analytics
Integrated $/loss from deviation from PvA summed over periods of time
Report content
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# 4 - Asset teams & Importance of information service
Asset team tasks
Future data from Business Plan Providing key quality and utility cut assurance for 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 Establishing continuous contact with Energy department and Supply Chain Management to share unit performance information
PIMS catalogue
Data validation
Daily reports
AF
PI Clients
Increased Mechanical and Operational availability
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• 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.
# 5 - Improving Asset Integrity with Advanced Corrosion Analytics
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Conclusion
© Copyright 2015 OSIsoft, LLC EMEA USERS CONFERENCE 2015
RESULTS CHALLENGES SOLUTION
Tibor Komróczki Head of Process Information & Automation
“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.”
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
Significant Improvement in the fuels value chain performance in all key areas – energy, reliability, safety, & compliance
$500M-550M EBITDA improvement
• Low cultural alignment, standardization, and use of best practices
• limited data based and proactive decision making
• Installed the PI System across the full value chain
• Developed new PI System based applications in critical areas including energy & reliability
MOL (Global Integrated O&G Company – Hungary)
• Increased Yield – 5%
• Decreased energy consumption – 2% YOY
• Reduced HC loss: -30%
• Utilization: 1.1%
© Copyright 2015 OSIsoft, LLC EMEA USERS CONFERENCE 2015
Contact Information
33
Tibor Komróczki [email protected]
Process information and Automation leader
MOL Plc.
33
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Questions
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