Post on 04-Apr-2018
October, 18 2016, Rotterdam
Reliability Driven Asset Management
Plant of the Year 2015
- Gábor Bereznai -
Plant of the Year winners in 2010
2
Small in the world…
Size of the logos are correlating to companies’ revenue.
3
But strong in the region…
4
6 production units
23 mtpa refining capacity
2.1 mtpa petrochemicals capacity
>2000 filling stations
Source: Wood MacKinsey, Global Refinery View, Refining in Europe, Africa and FSU
Business drivers around the Economical crisis
5
6
Diverging world in Donwstream business
Global refining and petrochemical business outside of Europe is growing
New, large scale, high-tech refineries and petrochemical sites in Asia and the Middle East are increasing product export to Europe
The European downstream business is pressurised by worldwide trends and not supported by local politics and regulations
• Becoming a net exporter of oil products
• Huge investments targeting European markets
• Favourable legislation (incl. tax legislation)
• Cheap feedstock,
UNITED STATES
• Strong demand growth
• Favourable legislation for local companies
CENTRAL AND SOUTH AMERICA
• Large new refining sites
• Additional ~2 mbpd refining capacity in 3 years (5x MOL capacity)
• Targeting the European market
MIDDLE – EAST
• Strong governmental support
• New refineries in China, India, Vietnam, etc.
ASIA-OCEANIA
• Not growing with stagnating demand • Record low margins, crude runs at 23-year
low • Still significant overcapacity • Decreased competitiveness, EU regulations
put further pressure on European DS business
EUROPE
2470 kbpd, $95bn
2750 kbpd, $60bn
4070 kbpd, $125bn New and ongoing investments
New and ongoing investments
New and ongoing investments
7
Delivering Business Value: 500M$+500M$ in five years
8
Production loss due to UPDT
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
20092010
20112012
20132014 Q1
Pro
du
ctio
n lo
ss [
USD
]
UPDT: Unplanned Down Time due to
instrumentation causes
FIMS: Field Instrumentation Maintenance System
Source: MOL SAP-PM Maintenance System, 2014
[ 607 kEDC ]
[ 563 kEDC ]
9
Layers of Protection
DCS systems
Definition of
Group of
Assets to
Maintenance
Master Asset
Database
Maintenance
planning and
sheduling
Maintenance
execution
Evalution of
Operative
Maintenance
Field Instrumentations
Risk evaluation
and handling
(Asset Policy)
Asset Strategy
Evaluation
Detailed
Reliability
analysis
Se
ria
l
dig
ita
l
co
mm
.
Strategy
optimisation
Statical equipements
Corrosion
databaseVibration data
collection
Risk Based
InspectionVibration diagnostic,
Oil analysis, Thermo
Control Valve and
Instrumentation
diagn.,
Rotating machines
Computerised
Maintenance
Management
Systems
Maintenance
Strategy
Creation
Data
Collectors
Equipements
Condition
Monitoring
Systems
Reliability
analysis
(MTBF, MTTR)
Failure modes
On
-lin
e,
dia
gn
os
ti
c
Off
-lin
e,
dia
gn
os
tic
On
-lin
e,
dia
gn
os
tic
Off
-lin
e, d
iag
no
sti
c, C
om
mu
nic
ato
r
RF
ID / P
DA
Off
-lin
e,
dia
gn
os
tic
10
AMS DM + AMS
FIMS servers
FIMS (AMS/PRM) user/
expert
SAP-PM server
SAP-PM user
Refinery Information System
DC MUX
On-line FIMS
subsystem
(AMS)
Intelligent
instrumentations
of the DC units
Alarm filtering in the
SAP-FIMS interface
CFV-087
Predictive notifications can save 7k$-700k$
avoiding unit outages.
CMMS integration
11
DCS and Smart Instrumentation in MOL Refining
MOL has 58 units in the
Refining 95% equipped with
DCS and Safety PLC.
(19000)
The number of the non-smart
pneumatic transmitters are
decresing.
12
Integrated Maintenance Systems
219
2114
1347
0
413
0 0 42 0 0
500
1000
1500
2000
2500
Honeywell AM Emerson AMS Yokogawa PRM D
evi
ces
[PC
S]
AM - AMS - PRM protocols (4135 pcs devices)
HART FFB Wireless HART
0
2110
850
0
413
0 0 9 0 0
500
1000
1500
2000
2500
Honeywell AM Emerson AMS Yokogawa PRM
Dev
ice
s [P
CS]
AM - AMS - PRM protocols (3382 pcs devices)
HART FFB Wireless HART
2010 2015
15
Improved organisational model is needed to
achieve the Refinery objective
Business rational of organisational model change:
Align the organisation with refinery objectives
Improve cooperation and eliminate silo operation
Put more focus on key areas in order to improve our efficiency and performance based on Solomon benchmark (maintenance, operational availability, energy efficiency)
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Peolpe behind the systems
October, 16 2016, Rotterdam
- Csaba Molnár-Valkó -
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Man behind system
Asset management system (AMS, PRM, FDM)
Online equipment types
Adding equipment / configuration
ALERT setting / handling
Valve diagnosis
Pressure transmitter calibration
Analytical instrument calibration
Tranings / courses
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Asset Management System
AMS Asset Manager System
8 online unit:
- MSA
- REF4
- GOK1
- HGY1
- HGY2
- REF100
- KGÜ
- BK4
Tags:
~1450 online
6 online unit:
- CL4
- CL6
- BEK5
- FCC
- AV2
- PEM1
Tags:
~ 1600 Online
PRM Plant Resource Manager
FDM Field Device Manager
5 online plant:
- DCU
- GOK3
- CL5
- HDS
- KBI
Tags:
~ 2600 online
~ 5000 offline
2010 2015
19
On-line equipment types
- Transmitters
- Pressure
- Differential pressure
- Level
- Temperature - Flow transmitter
- Valves
- Control valave
- ON-OFF valve (with positioner)
20
Adding device / Configuring
Add new online device
- Connecting new device to the system
- Scanning and Denomination
- Placing it in Plant structure
- Setting all the 3-level alerts:
- configaurated in the device
- adjustable in the maintenance system
- setting interface filter
21
Setting Alerts / Handling
Device Alert handling DVC5000 (66 pcs parameters / 33 alerts)
DVC 6200 (150 pcs parameters / 50 alerts)
22
Setting Alerts in maintenance system
Setting Alerts / Handling
23
Setting Alerts / Handling
Setting interface
24
Valve diagnostic
― ValveLink Online/Offline
― DTM based (Metso, Flowserve)
― Flowscan
― ValVue
― OVD
25
Pressure Transmitter Calibration
Process
- Adding device, scanning, denomination
- Test-scheme assigning, placing in the Plant structure
- Check Out
- Calibration
- Check IN
- Report template assign
- Report preparation
26
Analyser equipment calibration
Process
- Offline TAGS download from AMS to PDA
- Device identification with RFID
- Calibration (manual)
- Check IN from PDA
- Report generation
- Notice posting to ERP
27
Skills / Competance
A high level knowledge of systems (AMS, PRM, FDM)
Knowledge of specific devices
Database handling at user level
Calibration knowledge
Valve diagnostic on a really high level
Delivering Business Value from Digital Transformation
October, 18 2016, Rotterdam
- Tibor Komróczki
Operational Availability
Maintenance Efficiency
Energy Efficiency
Yield improvement
Digital Transformation of New & Next Downstream program
• Interlock statuses • Integrity Operating
Windows • Corrosion control
(HTHA) • Alarm management • Preventing coke steam
eruption
• Product quality • Analyser reliability
(Argus) • Yield Accounting via
Sigmafine (PI AF based)
• Operating envelopes • NG (natural gas) and
fuel gas demand forecasting
• Normal mode of control loops
• APC control monitoring • Diesel sulphur
optimization • Coker yield optimization
Safety & Asset
integrity (PSM)
Yields Operational
optimization
• Energy monitoring and management • Energy KPI breakdown • Column energy efficiency dashboard • Hydrogen, utilities - energy balances • Flaring
Energy
Operational Availability
Maintenance Efficiency
Energy Efficiency
Yield improvement
Asset Reliability from
Proactive & Predictive
Advanced Analytics
• SAP PM Integration • Health Score in PI AF • CBM on all rotating
equipment • PSA – Pressure Swing
Adsorbers • Chillers • Heat Exchangers • Electrical Infrastructure
Deeper understanding of technological processes - Alternative crude oil usage as feed; yield optimization
Increase productivity and efficiency across all major business units through the best practices for data harmonization
Alarm Management System (alarm rationalization) – Mode Base alarm , new alarm logics
Analyze control loops behavior
Inferential and descriptive statistics
Energy modelling optimization
Condition based & predictive maintenance
Advanced Analytics and IoT Oil & Gas Downstream
An
alyt
ic A
sce
nd
ancy
Mo
de
l
VALUE
DIFFICULTY
What happened?
Why did it happen?
What will happen?
How can we make it happen?
Gartner, March 2012
Descriptive
Analytics
Diagnostic
Analytics
Predictive
Analytics
Prescriptive
Analytics Data
Cloud
Intelligence
Advanced Analytics
Enablement of Contextual Data Based Decision Making & Management
Improving skill knowledge capture changing paradigms about data - leading by example
Reengineering the workflow around enhanced & consistent data
Digital transformation
People
Process Technology
Success
Azure ML predictive
model building and scoring
Evaluation of results and data visualization
Data analysis and feature selection for
modelling
Find the optimal mixture of different feeds into the Delayed Coker process
Achieve minimal level of coke yield
Diesel Hydrotreater unit product sulfur content estimation based on available data
Azure ML technology adaptation
compare laboratory, online analyzer, APC soft sensor and ML data
Machine learning
Rapid development and scalability of applications
Reinforce the use of data and analytics based decision making
Support cultural change and normalization
Leverage advanced technologies including advanced analytics and IOT to accelerate business value
Enable sustainable business value in the 21st century
The Importance of Having an OT Data Infrastructure
35
35
PI System Opralog NICE
LIMS Laboratory data
Natural Info Center
E-Logbook
Real-time
data
&
Meta data
PI Integrator for BA
DCS SCADA
Machine Learning Architecture – Current & Future
Field
Business issues in Delayed Coker Unit
Steam Eruptions
Increasing coke yields from 25.39% to 27.43% (+2.04 %) from 2012.01.01 and 2016.03.01.
Average monthly steam eruptions in 2012-2015 period was 3.85, in the first month of 2016 it was 15.5
(4X increase)
1 % Coke yield decreasing
~ $6M/year benefit in Danube Refinery!
Coke Yield & Explosion
Blue: coke yield (output value)
Red: steam eruption ~In case of > 3100 t
Furnace feed input the coke explosion likelihood is increasing
Blue histogram: Row count coke cycle Between the 2550 -2800 t
intervallic the coke yield could be decreased without coke explosion
Improve Azure ML and Data Analyst competences
Participants: Process technologist, Developer technologist, Automation, operation and energy management; IT specialist
(with superlative PI and statistics knowledge)
Overview of tools (Azure ML environment , R, Python)
Regression methods, interpretation of models, regression tree, linear regression, evaluation methods
Steps of real data mining projects
Deeper analyses of Delayed Coker unit
Support Delayed Coker Feed Blender project
Training series Competence improvement
• OSISoft PI – SAP PM Connection
• Support Condition Base maintenance
• Ongoing Project
Challenge – Critical Availability Problems
• Hydrogen Production Plants (HPP) are critical units in the refinery
• Pressure Swing Adsorbers (PSA) are critical equipments in unit operation
• Cyclic operation – Heavy load on valves (9-10 open-close hourly)
• 1.2 MUSD loss in three years due to PSA valve failures
• UPTIME program: 97 % Operational availability
Architecture – Roles of components
PI Server
• Process database
• Online analysis of process information
• Calculation of asset health – Asset condition – Running hours – Performance
• User Interface – PI Coresight – PI DataLink
SAP PM
• Maintenance database
• Management of
maintenance processes
• Creation of work orders or
notifications
• Trigger maintenance
strategies based on asset
health
Connection
(WebLogic)
Calculated asset
health
Maintenance
related information
Future Project
PI Connector for HART-IP
PI Connector for HART-IP