Implementing Optimal Refinery Energy Efficiency

Transcription

1 Implementing Optimal Refinery Energy Efficiency Reduce Greenhouse Gas Emissions and Improve Profitability Rob Howard Senior Director Business Consulting MENA AspenTech Presented for MEPEC 2011 (Session 087) October Aspen Technology, Inc. All rights reserved

2 What is Energy Optimization? Energy Optimization Energy Efficiency GHG Mitigation Alternative Energy $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 CO 2 caps $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 *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 2011 Aspen Technology, Inc. All rights reserved 2

3 Energy Optimization Projects Energy Optimization Policies & Strategies: Global Survey of 53 Oil Companies % of companies with projects in each area 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 92% Energy Efficiency reduces costs & CO 2 emissions 59% 53% Energy Efficiency GHG Mitigation* Renewable Energy Source: Hydrocarbon Publishing, Refinery CO2 Management Strategies, Aspen Technology, Inc. All rights reserved 3

4 Energy Costs are Significant Typical Refinery Operating Costs Typical Olefins Plant Operating Costs 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. Note: Feedstock costs are excluded 2011 Aspen Technology, Inc. All rights reserved 4

5 Alternative Energy Optimisation Approaches Energy Management Excellence Sustains & Grows the Gains = Sustainability Opportunity $ Energy Management Excellence: Energy Management Programs Point Solutions: Time Consulting Report 2011 Aspen Technology, Inc. All rights reserved 5

6 Industry Response, Key Activities & Time Horizons Invest Capital Design Years Run Existing Plant as Efficient as Possible Planning & Scheduling Energy Performance Management Months Weeks Days Hours Lifecycle Revamps, redesigns & models to continuously increase energy efficiency Advanced Process Control Minutes 2011 Aspen Technology, Inc. All rights reserved 6

7 Integrated Solutions Deliver 10-30% Energy Savings Typical Energy Savings* 5-20% 3-5% 2-10% 2-10% Total energy savings: 10-30%** 100% 70-90% aspenone Energy Efficiency Solutions Current energy use Design Planning & Scheduling Performance Management Advanced Process Control Future energy use Sequencing varies based on priorities * Typical savings based on 26 energy efficiency case studies ** Total savings depends on overlap & synergies 2011 Aspen Technology, Inc. All rights reserved 7

10 Design Plants for Energy Efficiency Challenge Difficult to screen optimal design alternatives Hard to determine optimal balance between equipment, costs & energy usage Challenge Impact Takes longer to develop alternatives Make a suboptimal decision Retrofits can be very costly Higher Capital & Energy Costs Identify best design alternatives including equipment, capital costs & energy efficiency Solution Reduce capital & energy costs and improve asset ROI 2011 Aspen Technology, Inc. All rights reserved 10

11 aspenone Capability Design Plants for Energy Efficiency 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 2011 Aspen Technology, Inc. All rights reserved 11

12 Systematic approach for process design Utilities Model Relative Cost Estimation Detailed Equipment Design Plant Data Process Model Process Insights And Analysis Promising Energy Saving Ideas Cost v/s Benefit Analysis Energy Audit Project Selection Cost Estimation / Vendor Quotes Project Development Engineering Package 2011 Aspen Technology, Inc. All rights reserved 12

13 Case: PX1Simplified TEMPERATURE COMPOSITES (Real T, No Utils) Heat Balance ENTHALPY X10 3 kw DTMIN =10.00 HOT COLD Pum p 1 Pum p 2 Pum p 3 Pum p 4 Process and Utilities Modelling and Analysis Utilities Model Steam / fuel / Hydrogen / Water / Power System Modeling Aspen HYSYS Aspen Plus Process Model Pinch Technology Total Site Analysis Distillation Sequencing Column Targeting Process Insights And Analysis TEMPERATURE C 2011 Aspen Technology, Inc. All rights reserved 13

14 S-OIL Reduce Refinery Energy Consumption Challenge Solution Results Improve S-Oil ranking to 1 st 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, J.J.Kim, S-OIL; AspenTech Global Conference Washington, DC, May Aspen Technology, Inc. All rights reserved 14

15 S-OIL Reduce Refinery Energy Consumption Challenge Solution Results 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 Ref: S-OIL Onsan Refinery Energy Saving Study, J.J.Kim, S-OIL; AspenTech Global Conference Washington, DC, May Aspen Technology, Inc. All rights reserved 15

16 S-OIL Reduce Refinery Energy Consumption Challenge Solution Results 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 Ref: S-OIL Onsan Refinery Energy Saving Study, J.J.Kim, S-OIL; AspenTech Global Conference Washington, DC, May Aspen Technology, Inc. All rights reserved 16

18 Production Planning and Scheduling Challenge Current planning & scheduling models do not include energy costs CO 2 costs or impacts are difficult to assess Challenge Impact Energy used inefficiently CO 2 impacts are not accounted Inaccuracies resulting in suboptimal decisions Higher Energy Costs & CO 2 emissions More accurate Planning & Scheduling models including energy costs & CO 2 emissions impacts Solution Reduce energy costs and improve CO 2 emissions 2011 Aspen Technology, Inc. All rights reserved 18

19 aspenone Capability Production Planning and Scheduling Manage Emissions such as GHG & CO 2 Optimize Schedules with operations Energy Costs included as part of planning models Superior Feedstock / Crude Slate decisions 2011 Aspen Technology, Inc. All rights reserved 19

20 Planning and Scheduling Energy Energy and Hydrocarbon Processes Demand: How much energy for Crude distillation unit Hydrocracking unit Reformer Etc. Aspen PIMS & Aspen Petroleum Scheduler Crude Process Units Energy Demand Products Utilities Energy Supply Aspen Utilities Supply: How much energy from Fired heater Purchased power / gas Process units Etc Aspen Technology, Inc. All rights reserved 20

21 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 2011 Aspen Technology, Inc. All rights reserved 21

22 Best Practice Integrated Planning, Scheduling and Energy Management in Refinery Operations Production Planning & Scheduling Performance Management Utilities Demand Forecasting Utilities Planning & Scheduling Utilities Real-time operation Utilities Reconciliation Demand Supply Plan v. Actual Information Management System 2011 Aspen Technology, Inc. All rights reserved 22

23 Rompetrol Integrated Planning, Scheduling, and Utilities Management Challenge Solution Results 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 Refining Cost Structure Utilities 36% Cost of energy represents 36% of non crude-purchase expenses Ref: I. Lemnaru, D. Croitoru, O. Bradin, Aspen PIMS & Aspen Orion /MBO Users' Conference, July Aspen Technology, Inc. All rights reserved 23

24 Rompetrol Integrated Planning, Scheduling, and Utilities Management Challenge 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 Ref: I. Lemnaru, D. Croitoru, O. Bradin, Aspen PIMS & Aspen Orion /MBO Users' Conference, July Aspen Technology, Inc. All rights reserved 24

25 Rompetrol Integrated Planning, Scheduling, and Utilities Management Challenge 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! Ref: I. Lemnaru, D. Croitoru, O. Bradin, Aspen PIMS & Aspen Orion /MBO Users' Conference, July Aspen Technology, Inc. All rights reserved 25

27 Energy Performance Management Challenge Energy usage & costs are not widely available Process & utility systems are not optimized against operations Need to manage energy and CO 2 emissions Challenge Impact Use more energy than necessary Overrun energy contract costs Use the wrong equipment Make sub-optimal decisions Higher operating and energy costs Plant/enterprise wide energy cost visibility and management Solution Reduce operating & energy costs across plant/enterprise 2011 Aspen Technology, Inc. All rights reserved 27

28 aspenone Capability Energy Performance Management Optimize Equipment selection & use Integrate Models and Reporting for decision support Real-time Data manage, validate, report Optimize Energy Sources utility contracts & fuel 2011 Aspen Technology, Inc. All rights reserved 28

29 Energy Performance Management Utility System Challenges 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? SUPPLY Typical Objectives: What fuels should I use and how much should I purchase what contract? Lowest cost operation Optimal reliability Security of supply Is it economic to run my steam turbine generator? How much steam do I need to provide today, tomorrow, next week? How does Actual compare to Plan? DEMAND How much electricity should I purchase, how much could I sell and at what price? At what load should I run the GTG? Flexibility to cater for variations PROCESS UNIT D PROCESS UNIT A Highest profit from energy export PROCESS UNIT G PROCESS UNIT E PROCESS UNIT B PROCESS UNIT C PROCESS UNIT F What drives should I use for the BFW pump? 2011 Aspen Technology, Inc. All rights reserved 29

30 Energy Management vs Energy Monitoring 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 Value Gained Energy Management Shorter Delay Current Practices 2011 Aspen Technology, Inc. All rights reserved 30

31 Energy Performance Management Utilities System Modelling Fuel header Proven simulation and optimization environment Many different utilities models available New models can be built easily Physical properties available for steam analysis Steam header Model library Drag and drop modelling environment 2011 Aspen Technology, Inc. All rights reserved 31

32 Energy Performance Management Aspen Utilities Workflow Aspen Utilities Planner Data Input Outputs Data Entry through standard Aspen Utilities Editors (or Excel) Utilities Contract Data Equipment Availability & Constraints Demand Forecasting Linearized Optimization Model Rigorous Simulation Model Budgets/ Forecasts Investment Evaluation Utility Consumption Planning View Results Configured Excel Interface File off-line 2011 Aspen Technology, Inc. All rights reserved 32

33 Energy Performance Management Aspen Utilities Real-time Operations Data Input Aspen Utilities Operations Outputs Data Entry through standard Aspen Utilities Editors (or Excel) Utilities Contract Data Equipment Availability & Constraints Demand Forecasting Linearized Optimization Model Rigorous Simulation Model Budgets/ Forecasts Investment Evaluation Utility Consumption Planning View Results Configured Excel Interface File off-line Demand forecasting not required for online model DCS on-line Raw Data Reconciled, Target & Optimized Data Real-Time Database InfoPlus.21, PI On Line Link Operations Optimization Performance Monitoring (process units, demand side) Cost Allocation Aspen On-line GUI for Interactive use InfoPlus.21 Web Aspen Technology, Inc. All rights reserved 33

35 Aspen Utilities Customer Base Paulsboro, NJ Houston, TX Florange, France Hull, UK Lavera, France Bergen Op Zoom, NL Geleen, NL Schwechat, Austria Constanta, Romania Burkville, Ala. Ferrera, Italy Livorno, Italy Ravenna, Italy Venice, Italy Martinez, CA Mizushima, Japan Clearlake, TX Bishop, TX Pampa, TX Canregera, Mexico Singapore Yeosu, Korea / BASF-YPC JV Nanjing, China Bulwer Island, Queensland Corpus Christi, TX Channelview, TX Botlek, NL Texas City, TX Corpus Christi, TX Dunkerque, France Collie, Western Australia 2011 Aspen Technology, Inc. All rights reserved 35

36 Utility Optimization in a Refinery Challenge Solution Results 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 2011 Aspen Technology, Inc. All rights reserved 36

37 Utility Optimization in a Refinery Challenge Solution Results 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 AspenWorld 2004 Gary Faagau Director, Energy OptimizationValero Energy Corporation 2011 Aspen Technology, Inc. All rights reserved 37

38 Utility Optimization in a Refinery Challenge Solution Results 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 AspenWorld 2004 Gary Faagau Director, Energy OptimizationValero Energy Corporation 2011 Aspen Technology, Inc. All rights reserved 38

39 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 2011 Aspen Technology, Inc. All rights reserved 39

41 Advanced Process Control Challenge Slow human response Too many factors to consider Energy costs are not considered or are dated Challenge Impact Lower yields Higher energy costs Inconsistent product quality Lower capacity Lost Margin Improve energy efficiency through inclusion of energy costs in APC objective function Solution Increase margin and improve quality & safety 2011 Aspen Technology, Inc. All rights reserved 41

42 aspenone Capability Advanced Process Control Automated Functions to build, test, deploy, monitor Guided Workflow to reduce resource burden Unified Environment for inferential & fundamental models Optimize Performance while minimizing energy use and emissions 2011 Aspen Technology, Inc. All rights reserved 42

43 Energy & Advanced Process Control How APC Reduces Energy Consumption? Reduces variability and avoids over purification Specification 1. Minimize process variances enabling the pushing of constraints X Average Average Number Of Samples On Control Distribution Off Control Distribution F( Z C ) F( Z D ) Current Operation Variations Reduced with Advanced Control 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% Move Average Closer to Specification or Limit Pressure Temperature Quality/Target Qualities X D X C X L Typical Operating Region, no APC Motor Compressor Speed Optimal constrained operation Column DP Amps 2011 Aspen Technology, Inc. All rights reserved 43

44 Energy & Advanced Process Control How APC Reduces Energy Consumption? APC reduces energy in process units by... 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 O₂ minimization and stack gas optimization Fuel gas optimization Compressor speed control Minimize energy use vs. pressure control valves 2011 Aspen Technology, Inc. All rights reserved 44

45 Energy & Advanced Process Control How APC Reduces Energy Consumption? Exploiting columns with dual heat sources VI Low cost heat maximized FC TC dpi LC PC FC AI DMCplus Limits cost factors TC FC LC FC AI Continuously maximizes use of lower cost heat medium Best Operator optimizing the process 24 x 7 Energy cost reductions of 1-5% High cost heat minimized 2011 Aspen Technology, Inc. All rights reserved 45

46 Energy & Advanced Process Control How APC Reduces Energy Consumption? Compressor speed control PC PC 80% Open 95% Open 7,500 RPM 7,000 RPM 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 2011 Aspen Technology, Inc. All rights reserved 46

47 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 Aspen Technology, Inc. All rights reserved 47

48 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 and Capacity Increase Energy savings over $1.4MM per year over 7 units 2011 Aspen Technology, Inc. All rights reserved 48

49 Chevron Process Improvements & Energy Savings in GHT Challenge Solution Results 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 Adam Beerman, C hevron 2010 Aspen Global Conference, Boston, May Aspen Technology, Inc. All rights reserved 49

50 Chevron Process Improvements & Energy Savings in GHT Challenge Solution Results Instrument improvements Steam Flow meter # 1 corrected to monitor energy use Steam Flow meter # 2 corrected for column steam control Control strategy improvements Corrected temperature-toflow steam cascade Updated makeup H2 & system pressure control scheme Ref: Process Improvements & Energy Savings in GHT As A Result of DMC Implementation Adam Beerman, Chevron 2010 Aspen Global Conference, Boston, May Aspen Technology, Inc. All rights reserved 50

51 Chevron Process Improvements & Energy Savings in GHT Challenge Solution Results Stable operation Improved constraint handling Project benefits Furnace Fuel Gas -18% High Pressure Steam to Column -17% High Pressure Steam to Turbine -31% Ref: Process Improvements & Energy Savings in GHT As A Result of DMC Implementation Adam Beerman, Chevron 2010 Aspen Global Conference, Boston, May Aspen Technology, Inc. All rights reserved 51

52 Industry Response, Key Activities & Time Horizons Invest Capital Design Years Run Existing Plant as Efficient as Possible Planning & Scheduling Energy Performance Management Months Weeks Days Hours Lifecycle Revamps, redesigns & models to continuously increase energy efficiency Advanced Process Control Minutes 2011 Aspen Technology, Inc. All rights reserved 52

53 Best Practices for Energy Efficiency Asset lifecycle approach design through operations Improve performance know the constraints Identify and prevent problems before they occur predictive organization Respond to problems faster improve safety Optimize Energy & Emissions lower costs, meet environmental requirements & reduce GHG emissions 2011 Aspen Technology, Inc. All rights reserved 53