The Journey to Asset Performance Excellence in Petroleum

Refining & Petrochemicals

Presented by :

Dr. Martin A. Turk

Schneider Electric

Agenda

> Business and Technology Drivers for Asset Performance Optimization

> Digitizing the Value Chain for Asset Performance Optimization

> The Evolution of Maintenance Practices

> The Power of Asset Performance Optimization

> Applications of Predictive Analytics in Petroleum Refining & Petrochemicals

> Value Proposition and Case Studies

Business and Technology Drivers

Market Environment Technology TrendsCustomer Imperatives

Operational

efficiency to drive profitability

Performance visibility and

enhanced decision support

Asset reliability

and availability

Capital expenditure constraints

Workforce evolution

and capability

Commodity Prices

and Oversupply

Compensation

and Consolidation

Environment,

Quality Safety

Regulations

Geopolitical

Uncertainties

Generation

Shift

Cloud

IIoT

Industry Specialization

Mobility

Big Data /

Predictive

Analytics

Empower People by Digitizing the Value ChainClosing the gap between IT & OT to achieve maximum value for your assets

How do I design and commission my assets at the lowest possible cost?

Process Engineering• Process Design & Simulation

• Training Simulators

How can I produce safely and profitably and meet regulatory norms?

Operations Management• Real-time Optimization

• Advanced Process Control

How do I ensure availability and reliability of assets?

Asset Management

• Asset Health & Performance Management

• Mobile Workforce Management

How can I enable better decision making?

Information Management

• Enterprise Historian

• Intelligence & Predictive Analytics

How can I monitor and control operations better?

Operations Control

• HMI

• Supervisory Control

• DCS / PLC

Pla

tform

sA

dva

nce

d A

pp

lica

tions

Steps to Achieve Enterprise Asset Performance Management (EAPM)

> Connect: To Plant-Wide Systems and Sources (IIoT)

> Collect: Capture high-fidelity process, production and equipment information on premise or in the cloud

> Analyze: Apply machine learning, advanced pattern recognition and rules-based logic for asset monitoring

> Act: Enable workforce with full EAPM complement, mobility and enterprise collaboration of people, processes and equipment

CONNECT

EAPM ‒ Typical Systems and Applications Utilized

Workflow

Collaboration

Enterprise Asset

Management

DCS, PLC

SCADA

Sensors and Other

Smart Devices

Online Process Data

Non-Instrumented

Data

Production

Information

Machine Learning

Rules

Advanced Pattern

Recognition

ST

RA

TE

GIZ

E

OPTIMIZE

CONNECT

Organizations are Evolving Beyond Traditional Maintenance Practices to Become Predictive

It’s a Journey

ARC studies show that only 18% of asset failures are age-related. Based on

these data, preventive maintenance provides a benefit for just 18% of assets,

such that monitoring for predictive maintenance is a recommended option for

the rest. www.arcweb.com/Lists/Posts/Post.aspx?ID=260

18%

82%

Failure PatternsAge-related failure Random failure

Reactive and

Preventive Programs

Predictive Technology

for Early Warnings

Transform Data Into Actionable InsightsEAPM bridges the IT/OT gap and contextualizes the increasing amount of industrial Big Data

Mobile Workforce Enablement

> Consolidate disparate data sources

> Bridge the IT/OT gap

> Make data available throughout the enterprise with advanced visualization tools, web solutions and APIs

> Turn data into actionable insights

> Enable cultural change and enforcement of best business practices

> Improve plant reliability, safety and operational profitability

> Ensure that workers execute all field tasks required to achieve reliable operations and cost-effective maintenance

Consolidates high-fidelity operational and asset-health data to

improve decision making

Enterprise Data Historian

Mobile workforce enablement ensures best business practices

Analytics Engines are the Heart of the Predictive Maintenance ProcessWhat Technology is Best for Monitoring Petroleum Refinery and Petrochemical Assets?

Analytic Technologies

> Artificial neural networks

> Clustering

> Decision tree learning

> Deep learning

> Fuzzy logic

> Machine learning

> Advanced pattern recognition

> Others...

Analytics Classifications

> Operational/Process Analytics

> Can be empirical (data driven) and/or theoretical (physics

driven)

> Improve efficiency and output of plant

> Business Analytics

> Typically not real time

> Improve planning associated with financial investments for

the business

> Asset Analytics

> Can be periodic and near real time; mostly empirical in

nature

> Greater Return on Assets due to improvement in equipment

reliability and reduction in unplanned outages

Predictive Analytics for the Monitoring of Asset Health

Software based modeling of equipment using

advanced pattern recognition for conditions which are

not definable by known rules:

> Uses historical data to describe how a piece of

equipment normally operates and builds a model

> Continuously monitors behavior in real-time

> Alerts when the operation differs from the historical

norm

> Provides early warning detection of equipment

problems

> Uses advanced analysis capabilities including

problem identification

Traditional Approach to the Monitoring of Asset Performance

Plot-0

4/6/2008 4:39:21.791 PM 4/9/2008 4:39:21.791 PM3.00 days

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75> Look at 1,000s of trends and 10,000s of

data points

> Cannot identify every failure pattern

> How do you know if asset data trend is normal

for current conditions?

> Look at trends only when the DCS or Asset

Management System sends an alarm

> Causes include production rate changes, trips,

asset failures because equipment damage has

already occurred it’s too late!!!

Example of Early Warning Notification

Subtle changes are difficult to identify

Actual Value

- Predicted Value

Residual

Ou

tbo

ard

Be

arin

g T

em

p (

°F)

Date and Time

First Pattern Recognition Alarm

Another Example of an Early Warning NotificationLP Rotor ‒ L-0 Blade Issue

> Unit was started after an outage and there was a vibration

step change on one of the LP turbines (but, vibration levels

were well below the alarm level)

> Engineering and the plant were notified

> Vibration data was collected and unit was retired for an

inspection

> Bolts on lower half of flow sleeve had broke and flow sleeve

contacted L-0 blades

> Upper half of flow sleeve was no longer supported by lower

half

> Although there was minor damage to the LP blades, the plant

avoided damaging multiple stages of blades, packing, and

diaphragms if there had been a severe blade liberation

> Estimated avoided cost = $4.1M!!!

Outage

Condition-Based Monitoring

When is it Used

How is it Used

> When the condition is known and definable using rule based logic

> Maintenance rule is fixed and does not significantly change based

on equipment loading, ambient or operational conditions

> Intelligent integration of plant process data with EAM systems

> Industrial asset condition monitoring

> Monitoring of pumps, motors, generators and any other industrial

process equipment (runtime, bearing temperature, volume flow,

vibrations, etc.)

> Equipment health and performance problem discovery and actions

Results Achieved

> Improve labor utilization by 10-20%

> Reduce unplanned downtime by 15-25%

> Increase planned work by 20%

Automated Model-Based Performance Monitoring

Automated Model-Based Performance Monitoring

> Robust and accurate on-line inferred

energy efficiencies and “clean”

equipment performance

> Most accurate corrections of drifting

instruments

> Better inference for missing instruments

Opportunities Abound for Delivering Value from Predictive Analytics in Petroleum Refineries and Petrochemical Plants

Petroleum Refineries

> Rotating Equipment:> Hydrogen Compressors

> Wet Gas Compressors

> Air Blowers

> Steam Turbines

> Gas Turbines

> HP-MP Turboexpanders

> MP-LP Turboexpanders

> Boiler Feedwater Pumps

> Others…

> Fixed Assets:> Furnaces/Fired Heaters

> Heat Exchangers

> Reactors

> Electric Systems (Transformers, Breakers, Switches, etc.)

Ethylene Plants

> Rotating Equipment:> Cracked Gas Compressors

> Refrigeration Compressors

> Boiler Feedwater Pumps

> Others…

> Fixed Assets:> Cracking Furnaces

> Heat Exchangers

> Reactors

> Electric Systems (Transformers, Breakers, Switches, etc.)

US Department of Energy: The Value of Predictive Maintenance

> Reduction in maintenance costs (25% to 30%)

> Elimination of breakdowns (70% to 75%)

> Reduction in downtime (35% to 45%)

> Increase in production (20% to 25%)

On average, predictive maintenance can help

industrial process companies in the following ways:

Value Delivered with Enterprise Asset Performance Management

Maximize return on assets by:

> Improving asset performance

> Increasing reliability and reducing

unscheduled downtime

> Increasing asset utilization and

extending equipment life

> Reducing operations and

maintenance costs

Case Study: International oil & gas company deployed a standardized mobile Decision Support System

Closing the Loop

After a successful pilot, the company

selected a mobile operator rounds and

decision support system to collect non-

instrumented data and standardize work

processes. The mobile workforce

enablement tool allows the company to

achieve significant savings and reduce costs

year on year.

Results:

> Standardized work processes

and data collection to improve

productivity

> Proactively analysis and

reaction to potential problems

to increase efficiency and

better plan maintenance

> Increased equipment

availability and reliability

> Enabled field workforce to

improve decision making

By preventing a motor failure,

early detection led to

$70K Saved

Identifying a blocked plug on

a floating roof tank averted a

loss of

$120K

Estimated annual

maintenance savings of

$3-5Million$

Case Study: US refinery implemented a standards-based CMMS to better respond to the changing needs of the company

Results:

> Scalable asset management

to support company growth

> Familiar Microsoft®

Windows® work environment

> Improved management of

maintenance and operations

> Full accountability of work

orders, labor and materials

> Streamlined purchasing of

materials and services

Enabled the refinery to

manage its maintenance

activities and business more

efficiently than before

The software also enforces

data integrity, keeping all

data current and in check

The new software has been

widely accepted as a daily

utility and is used extensively

by employees

Closing the Loop

After installation of the new CMMS, the

refinery now has full accountability from the

moment that a person issues a work order,

requisitions materials, or contracts labor.

Asset management is a key application

used every day. They manage maintenance

activity and business more efficiently than

before. The software also enforces integrity,

keeping all data current and in check.

Case Study: Industrial gas company uses Predictive Analytics to detect compressor problems & avoid forced outages

Results:

> Detected equipment failures

before they occur

> Prevented unscheduled

system downtime

> Harnessed real-time

information that brings plant

operators and engineering

experts together to

collaborate

Predicting a

Compressorfailure

Using

Real-time Datato detect unforeseen events

Saving

+500K by preventing reactive

maintenance and unplanned

downtime

$

Closing the Loop

The company used a scheduled opportunity

outage to repair a damaged turbine engine.

Prior to a scheduled outage, the plant

notified the operations center of a vibration

sensor anomaly. As a result, the company

was able to use a planned outage to

investigate the compressor and found a

cracked impeller.

Case Study: US Refinery ‒ Equipment Performance Monitoring

Problem:

> Compressor performance degrades over time

> Operations doesn’t know impact of performance

degradation

> “How is the compressor operating currently?” vs.

“How it is supposed to operate?”

Solution:

> Model-based performance monitoring

> Track actual efficiency vs. design

> Track impact of cleaning on energy usage and

throughput

Results:

> Know when it is economical to service compressors

> Prevent damage to multi-million dollar machinery300Time (Days)0 100 200

DE

0

Design Eff(best)

Effi

cien

cy

Flow

Day 0

Day 200Day 100

Design

Case Study: Indian Refinery ‒ Crude Preheat Train Monitoring

Situation:

> Complex network of heat exchangers in crude preheat

train

Objective:

> Minimize crude furnace firing (i.e., fuel consumption

by maximizing preheat efficiency

Results:

> Increase in crude furnace inlet temperature

> Improvement in maintenance efficiency

> Identification of instruments needing recalibration

Published daily reports - Bad flow measurements

Benefit: Decreased energy usage by improving heat integration and

optimization of exchanger cleaning, resulting in annual

savings of more than $1 million/year

Thank you for your time and attention!

martin.turk@schneider-electric.com