PI System (and PI AF) Enabling Business Transformation · Title: Best Practices for the OSIsoft UC...

© 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


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


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

© Copyright 2015 OSIsoft, LLC EMEA USERS CONFERENCE 2015 4

PI System history


Individual

Team

Division

Enterprise

IoT

Machine learning

PI System Evolution in MOL


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

http://omnitechsupport-scam.com/wp-content/uploads/2013/03/Windows-Vista-command-prompt.jpg


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

© Copyright 2015 OSIsoft, LLC EMEA USERS CONFERENCE 2015 © C o p yr i g h t 2 0 1 4 OS I s o f t , L L C . 8

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


Company goals


MOL Values

Success &

Growth

Teamwork &

Partnership

Courage &

Decisiveness

Expertise &

Partnership


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


Integration


MES standardization in MOL group level

13

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

tro

l

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


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)


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


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


Production (Refinery & Petchem ) & Logistic standardization

17

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

cti

on

Pro

ce

ss In

teg

rati

on

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.

.


MOL logistics - PI System implementation

18

Visualizing and monitoring logistic technology processes

Supporting preventive maintenance

Ensuring KPI measurement

Notification technology events

Inventory calculation

Project initiations


Innovations &

Trends


Data aggregation in refining

20

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


Advanced Analytic Possibilities

21

Energy Demand Forecast

Predictive Maintenance

Operation Optimization

Asset Monitoring Technology Assessment

REFINERY DATABASE

Predictive modeling Data mining

Production Modeling


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


Energy Demand Forecasting with Future Data

23

Collect real-time data

Train energy consumption models

Predict energy consumption based on production plan

Monitor and update models

Improve energy trading by


# 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


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


Sigmafine – An Extension of AF


# 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


# 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


# 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


• 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


Conclusion


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%


Contact Information

33

Tibor Komróczki [email protected]

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

http://eventmobi.com/emeauc15

State your name & company

http://eventmobi.com/emeauc15


Thank You

Köszönöm! Děkuji

PI System (and PI AF) Enabling Business Transformation · Title: Best Practices for the OSIsoft UC...

Documents

Transcript of PI System (and PI AF) Enabling Business Transformation · Title: Best Practices for the OSIsoft UC...