Implementing Optimal Refinery Energy Efficiency

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Rob Howard Senior Director Business Consulting MENA AspenTech Presented for MEPEC 2011 (Session 087)

23-26 October 2011

Implementing Optimal

Refinery Energy Efficiency

Reduce Greenhouse Gas Emissions

and Improve Profitability

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What is Energy Optimization?

$80 billion per year $680 billion per year $20 trillion total

• $80 bn/year global

capex through 2020, to capture energy

efficiency savings, with >10% IRR

• Refining & chemical

industry annually spends $50-100M on energy per plant

• 70% of oil companies

rank energy efficiency as the best method to

meet CO2 caps

*Intergovernmental Panel on Climate Change, whose reports drive initiatives like the Kyoto Protocol

Sources: McKinsey Investing In Energy Productivity, Global GHG abatement study; Daily Telegraph “A clean sweep for coal”; International Energy Agency reports; WRI 2005 report; Hydrocarbon Publishing 2010 report

• $680bn/year incremental investment by 2020, to achieve IPCC* target of 35% below 1990 emissions levels • $5-10bn/year estimated

market for CCS (carbon capture & storage) in 2030

• Process industries

account for 36% of global GHG emissions

• $20 trillion total global

investment through 2030 in alternatives

• Alternative energy

accounts for 13% of global energy supply, and is growing 2-3 times faster than traditional sources

• 53% of oil companies

currently involved in some type of renewable energy project

Energy Efficiency GHG Mitigation Alternative Energy

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Energy Optimization Projects

92%

59%

53%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Energy Efficiency GHG Mitigation* Renewable Energy

Energy Optimization Policies & Strategies: Global Survey of 53 Oil Companies % of companies with projects in each area

Source: Hydrocarbon Publishing, “Refinery CO2 Management Strategies”, 2010

Energy Efficiency reduces costs & CO

₂

emissions

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Energy Costs are Significant

Typical Refinery

Operating Costs

Typical Olefins Plant

Operating Costs

Note: Feedstock costs are excluded

Energy Costs

50 – 58%

Energy Costs

40 – 45%

Refinery energy costs: $75 -140M p.a.

Global spend on energy: $57 – 108B p.a.

Chemicals energy costs: $75 -125M p.a.

Global spend on energy: $20B p.a.

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Time ∆= Sustainability Opportunity $

►

Consulting Report

►

Point Solutions:

►

Energy Management Programs

►

Energy Management Excellence:

Energy Management Excellence “Sustains & Grows the Gains”

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Industry Response, Key Activities & Time

Horizons

Planning &

Scheduling

Energy

Performance

Management

Run Existing

Plant as

Efficient as

Possible

Design

Invest

Capital

Advanced

Process

Control

Revamps,

re-designs &

models to

continuously

increase energy

efficiency

Lif

ec

yc

le

Years Months Weeks Days Hours Minutes

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Integrated Solutions Deliver 10-30% Energy

Savings

Current

energy use Design Planning & Scheduling Performance Management Advanced Process Control Future energy use 3-5% 2-10% 5-20%

Typical Energy Savings*

100% 70-90%

Total energy savings: 10 - 30%**

* Typical savings based on 26 energy efficiency case studies ** Total savings depends on overlap & synergies

aspenONE Energy Efficiency Solutions

2-10%

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AspenTech in Energy Efficiency

Production Planning & Scheduling

Energy Performance Management

Advanced Process Control

Design Plant

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AspenTech in Energy Efficiency

Production Planning & Scheduling

Energy Performance Management

Advanced Process Control

Design Plant

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Challenge

Challenge ChallengeImpact

Design Plants for Energy Efficiency

 Difficult to screen optimal design alternatives  Hard to determine optimal balance between equipment, costs & energy

usage  Takes longer to develop alternatives  Make a sub-optimal decision  Retrofits can be very costly

Higher Capital & Energy Costs

Reduce capital & energy

costs and improve asset ROI

Solution

Identify best design alternatives including equipment, capital costs &

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Design Plants for Energy Efficiency

aspenONE Capability

Integrated

Economics

to reduced energy costs

Optimize

Tradeoffs

for energy use and environmental footprint

Reliable

Modeling

for screening energy alternatives

Process

Modeling

to minimize emissions

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Promising Energy

Saving Ideas

Relative Cost

Estimation

Equipment Design

Detailed

Systematic approach for process design

Process Model

Utilities Model

Process Insights

And Analysis

Cost v/s Benefit

Analysis

Project Selection

Project

Development

Cost Estimation /

Vendor Quotes

Engineering

Package

Pl

an

t D

ata

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Process and Utilities Modelling and Analysis

P u mp 4 P u mp 3 P u mp 2 P u mp 1 Steam / fuel / Hydrogen /

Water / Power System Modeling

TEMPERATURE COMPOSITES (Real T, No Utils) Case: PX1Simplified HOT COLD ENTHALPY X10 3 kW TEM PER AT UR E C 0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0

400.0 Heat Balance DTMIN =10.00

Pinch Technology

Total Site Analysis _SequencingDistillation Column Targeting

Process Model

Utilities Model

Process Insights

And Analysis

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Challenge Solution Results

S-OIL

Reduce Refinery Energy Consumption

Challenge

 Improve S-Oil ranking to 1st quartile of Solomon EII in Asia-Pacific Region

 Improvement of Energy

Efficiency by 1.5% annually

 Reduction of Solomon EII by 2.0

(2.3%)

Ref: “S-OIL Onsan Refinery Energy Saving Study”,

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S-OIL

Reduce Refinery Energy Consumption

Challenge

 Use Aspen Plus® and Aspen Energy Analyzer® to

 Optimize column operating condition by

 Reduce column pressure.  Reduce the product

specification Give-Away.

 Maximize hot feed

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S-OIL

Reduce Refinery Energy Consumption

Challenge

 More than 100 ideas are generated.

 35 ideas have been implemented.

 Total Saving $39 million with

payback time < 1 yr.

 Reduction of EII by 3.2

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AspenTech in Energy Efficiency

Production Planning & Scheduling

Energy Performance Management

Advanced Process Control

Design Plant

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Challenge

Production Planning and Scheduling

 Current planning & scheduling models do not include

energy costs

 CO2 costs or

impacts are difficult to assess  Energy used inefficiently  CO2 impacts are not accounted  Inaccuracies resulting in sub-optimal decisions

Higher Energy Costs

& CO2 emissions

Reduce energy costs and

improve CO

₂

emissions

Solution

More accurate Planning & Scheduling models including energy costs &

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Production Planning and Scheduling

aspenONE Capability

Manage

Emissions

such as GHG & CO₂

Optimize

Schedules

with operations

Energy Costs

included as part of planning models

Superior

Feedstock /

Crude Slate

decisions

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Planning and Scheduling – Energy

Energy and Hydrocarbon Processes

Crude

_{Energy Demand}

Process Units –

Products

Utilities Energy Supply

Demand: How much energy for

•Crude distillation unit •Hydrocracking unit •Reformer

•Etc.

Supply: How much energy from

•Fired heater

•Purchased power / gas •Process units

•Etc.

Aspen PIMS & Aspen Petroleum Scheduler

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Typical LP Model Representation of Energy

Use

Typical LP model representation of energy use with the crude and vacuum units.

Simple, straight line correlations. Take no account of

• Real performance • State of fouling • Drive selection

• Rarely, if ever, updated • Base load often omitted • Loading on boilers,

turbines etc

Other unit representation could be even simpler

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Best Practice – Integrated Planning, Scheduling and

Energy Management in Refinery Operations

Production Planning & Scheduling

Performance Management

Utilities

Planning & Scheduling Utilities Demand Forecasting Utilities Real-time operation Demand Supply

Information Management System Utilities

Reconciliation

Plan v. Actual

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Rompetrol

Integrated Planning, Scheduling, and Utilities Management

Challenge

 Proactively manage energy costs at a large, integrated refinery

 Excluding crude purchase costs, energy consumption

represented 36% of operating expenses

 Complex interaction of energy

cost drivers across units

Ref: I. Lemnaru, D. Croitoru, O. Bradin,

Aspen PIMS™ & Aspen Orion™/MBO Users' Conference, July 2005

Utilities 36% Cost of energy represents 36% of non crude-purchase expenses

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ChallengeChallenge Solution Results

 Aspen Utilities for rigorous modeling and optimization of energy use to determine optimal configuration and load for

utilities

 Aspen PIMS for production planning and optimization

 Aspen ORION for refinery

scheduling

Rompetrol

Integrated Planning, Scheduling, and Utilities Management

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ChallengeChallenge Solution Results

 The Trinity of PIMS/ORION/ Utilities is a central to the daily decision making process within the refinery

 Due to synergies, the benefits from the integrated solution are greater than the sum of the parts

 Model maintenance is very

important and the integrated solution leads to a more

accurate PIMS model… …hence better economic decisions and higher profitability!

Rompetrol

Integrated Planning, Scheduling, and Utilities Management

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AspenTech in Energy Efficiency

Production Planning & Scheduling

Energy Performance Management

Advanced Process Control

Design Plant

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Challenge

Energy Performance Management

 Energy usage &

costs are not widely available

 Process & utility

systems are not optimized against operations

 Need to manage

energy and CO₂ emissions

 Use more energy

than necessary

 Overrun energy

contract costs

 Use the ‘wrong’ equipment

 Make sub-optimal decisions

Higher operating and energy costs

Reduce operating & energy

costs across plant/enterprise

Solution

Plant/enterprise wide energy cost visibility and

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Energy Performance Management

aspenONE Capability

Optimize

Equipment

selection & use

Integrate

Models and

Reporting

for decision support

Real-time Data

manage, validate, report

Optimize

Energy Sources

utility contracts & fuel

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Energy Performance Management

Utility System Challenges

PROCESS UNIT A PROCESS UNIT B PROCESS UNIT C PROCESS UNIT D PROCESS UNIT F PROCESS UNIT E PROCESS UNIT G

What drives should I use for the BFW pump?

Which boilers should I use and at what load should I run these boilers?

How is my equipment performing? When should I shut down for maintenance?

At what load should I run the GTG?

How much steam do I need to provide today, tomorrow, next week? How does Actual compare to Plan?

How much electricity should I purchase, how much could I sell and at what price?

What fuels should I use and how much should I purchase – what contract?

Is it economic to run my steam turbine generator?

Typical Objectives:

• Lowest cost operation

• Optimal reliability

• Security of supply

• Flexibility to cater for variations

• Highest profit from energy export

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Energy Management vs Energy Monitoring

Value Gained Current Practices Energy Management Shorter Delay  It’s all about taking timely

action on relevant information

 Better management of complex utility systems

 Improved make or buy decisions

 Improved startups & shutdowns

 Accurate what-if analyses

 What was optimum in the past may not be optimum in the future

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Energy Performance Management

Utilities System Modelling



Proven simulation

and optimization

environment



Many different

utilities models

available



New models can be

built easily



Physical properties

available for steam

analysis

Model library

Drag and drop modelling

_environment

Fuel header

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off-line Data Input Data Entry through standard Aspen Utilities Editors (or Excel) Linearized Optimization Model Rigorous Simulation Model Outputs Investment Evaluation Budgets/ Forecasts

View Results

Configured Excel Interface File Utility Consumption Planning Demand Forecasting Utilities Contract Data

Energy Performance Management

Aspen Utilities Workflow

Aspen Utilities Planner

Equipment Availability & Constraints

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off-line

Energy Performance Management

Aspen Utilities – Real-time Operations

on-line _{Raw Data}

Reconciled, Target &

Optimized Data Operations Optimization

Performance Monitoring (process units, demand side)

Cost Allocation

On Line Link

Aspen On-line GUI for Interactive use

InfoPlus.21

Web.21

DCS

off-line Data Input Equipment Availability & Constraints Data Entry through standard Aspen Utilities Editors (or Excel) Linearized Optimization Model Rigorous Simulation Model Outputs Investment Evaluation Budgets/

Forecasts

_{View Results}

Configured Excel Interface File Utility Consumption Planning Demand Forecasting Utilities Contract Data Demand forecasting not required for on-line model

Aspen Utilities Operations

Real-Time Database InfoPlus.21, PI

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Energy Performance Management

Aspen Utilities Optimize Results

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Texas City, TX Clearlake, TX Bishop, TX Pampa, TX Canregera, Mexico Singapore Burkville, Ala. Bergen Op Zoom, NL Geleen, NL Schwechat, Austria Bulwer Island, Queensland Hull, UK Lavera, France

Dunkerque, France _{Collie, Western Australia}

Constanta, Romania Paulsboro, NJ Houston, TX Corpus Christi, TX Yeosu, Korea Mizushima, Japan

Aspen Utilities Customer Base

Corpus Christi, TX Channelview, TX Botlek, NL Martinez, CA Florange, France BASF-YPC JV Nanjing, China Ferrera, Italy Livorno, Italy Ravenna, Italy Venice, Italy

/

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Utility Optimization in a Refinery

Challenge

 Complex refining system

 Utilities that support the plant and can export power

 Contracts to buy in power as

needed

 Need to manage costs to maintain profitability

AspenWorld 2004 Gary Faagau Director, Energy OptimizationValero Energy Corporation

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Utility Optimization in a Refinery

Challenge

 Capture and validate real-time data

 Build models of processes and equipment to simulate

performance

 Look for data items out of range

and unusual; take action to correct

 Validate equipment performance vs. optimized; address the ones not optimal

Validate

Data

Check Equipment

Performance

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Utility Optimization in a Refinery

Challenge

 Total operating cost down 12%  Natural Gas Import reduced by

~ 0.7 MMSCFD Saving: $ 2,900 /day

 Non intuitive result because it advised running smaller turbine to more effectively sell power

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Operational Excellence in Manufacturing

Optimize Energy use by

minimizing heat exchanger

fouling



An automated performance

monitoring application using

aspenONE Engineering tools



Daily automated monitoring

and reporting of heat

exchanger fouling and overall

performance



Significant benefit achieved:

Savings of $3-4 MM/year on

one VDU

Preheat Train Outlet Temperature

Heat exchangers train rinsing/cleaning

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AspenTech in Energy Efficiency

Production Planning & Scheduling

Energy Performance Management

Advanced Process Control

Design Plant

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Challenge

Advanced Process Control

 Slow human response

 Too many factors to consider

 Energy costs are not considered or are dated  Lower yields  Higher energy costs  Inconsistent product quality  Lower capacity Lost Margin

Increase margin

and improve quality & safety

Solution

Improve energy efficiency through inclusion of energy costs in APC objective function

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aspenONE Capability

Advanced Process Control

Automated

Functions

to

build, test,

deploy, monitor

Guided

Workflow

to

reduce resource

burden

Optimize

Performance

while minimizing

energy use and

emissions

Unified

Environment

for inferential &

fundamental

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Energy & Advanced Process Control

How APC Reduces Energy Consumption?

Reduces variability and avoids over purification

Motor Optimal constrained operation Speed Temperature Compressor Column DP Amps Pressure Typical Operating Region, no APC Qualities

2. Maximizes profit by moving steam and reflux to minimum values while honoring specifications.

• Increase operational stability, reliability and safety

• Reduces Composition variability 50%

• Reduce energy consumption 2-5%

Specification

Average

Current

Operation Reduced with Variations Advanced Control

Move Average Closer to Specification or Limit

1. Minimize process variances enabling the “pushing” of constraints

Quality/Target

XD XL

XC

∆X

Off Control Distribution

On Control Distribution

F Z( C)

F Z( D)

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Energy & Advanced Process Control

How APC Reduces Energy Consumption?



Avoiding over purification in distillation

– Composition control



Improved separation tray efficiency

– Pressure minimization against ambient constraints



Optimization of heat recovery

– Full utilization of the lowest cost heat sources first



Boiler and furnace efficiency

– O

₂

minimization and stack gas optimization

– Fuel gas optimization



Compressor speed control

– Minimize energy use vs. pressure control valves

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PC AI LC dPI AI TC TC FC FC VI LC FC DMCplus Limits cost factors FC

• Continuously maximizes use of lower cost heat medium

•“Best Operator” optimizing the process 24 x 7

• Energy cost reductions of 1-5%

Low cost

heat

maximized

High cost

heat

minimized

Energy & Advanced Process Control

How APC Reduces Energy Consumption?

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Energy & Advanced Process Control

How APC Reduces Energy Consumption?

 Reducing compressor speed will reduce electricity or steam usage – APC will monitor loop and reactor pressure control valves.

– Compressor speed and pressure will be reduced to minimize energy loss from unnecessarily closed control valves

Compressor speed control

PC 7,500 RPM 80% Open PC 7,000 RPM 95% Open

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Energy & Advanced Process Control

How APC Reduces Energy Consumption?

Fuel Gas Optimizer

• Non-Linear Multivariable Control and Optimization Technology - DMCplus

• Rigorous, Dynamic modeling for prediction • Inferential quality predictions

• Furnace monitoring and advisory system • Fuel Gas Manipulation controllers (FMCs) • Operational view of the entire fuel gas system

• Remote monitoring of fuel gas and fuel optimization energy systems

Benefits

• Optimizes fuel gas - pressures

and qualities

• Utilizes the most cost effective

fuel sources

• Stabilizing furnace performance

• Maximizing production capacity

within “no flare” operating limits.

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Advanced Process Control

Gas processing facilities for

removing CO

₂

and H

₂

S

optimizing feed rate and lower

energy use:



Reduced steam & power usage



Optimized feed management across

2 plants and 7 process units



Reduced variability in fractionation

process



Energy savings over $1.4MM per

year over 7 units

Energy

Savings and Capacity Increase

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Chevron

Process Improvements & Energy Savings in GHT

Challenge

Gasoline Hydrotreater Unit

 Minimizing fuel gas usage

 Minimizing steam usage

 This plant only processes the feed

that it receives, so no opportunity to increase throughput

 No yield improvement possibilities

since the only goal is to eliminate sulfur & diolefins

 Savings primarily in utilities reduction

Ref: Process Improvements & Energy Savings in GHT As A Result of DMC Implementation