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20121010 energ ymaestro presentation


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20121010 energ ymaestro presentation

  1. 1. Slide | 1
  2. 2. WHO ARE WE?• Dedicated team  Project managers  Process engineers  Development team• Dedicated tools  PEPITo© data mining platform  Technological partnerships• Focus on industry  pulp and paper, steel, aluminium, cement, energy production, food and beverage, chemicals Slide | 2
  3. 3. OUR EXPERIENCE WITH EMIS IN PULP AND PAPER (OR MT&R, M&V, ISO50001…) Participation in 2 EMIS Energy Blitz at implementation in mills with different 15+ mills situation, motivation, and culture with significant and Energy audit in sustainable cost reductions 20+ mills High level with focus on data monitoring models availability & quality,monitoring capability and implemented in performance gap 5+ mills + Several ongoing projects in N-A and Europe Slide | 3
  4. 4. MANAGING CHANGE4 KEY DRIVERS FOR ENERGY PERFORMANCEWhat prevent us to takeaction and sustain the Best practice we have seen gain?Operation is Address impact on production and qualityproduction-oriented • Leverage process data to bring facts • Set flexible and gradual rulesProblem solving culture Adopt a continuous improvement visionis CAPEX-oriented • Optimization projects (OPEX) • Secure ROI with energy managementLots of data but lack of Cascade of KPI with adaptive targetsrelevant information • Different KPI for each level of decision • Multivariate analysis to set relevant targetOperators are not Top-down approach, bottom up implementationempowered • Give operators practical tools for decision support & troubleshooting tools • Involve them at every step of the projectSlide | 4
  5. 5. NO ACCOUNTABILITY  NO RESULTS NO ACTIONABLE PARAMETERS  NO ACCOUNTABILITY Mill manager Management – kWh/t total Pulp plant Utility Papermachin manager – manager – Staff e manager – kWh/t pulp kWh/t power kWh/t PM plant plant Classical Papermachin approaches Operation e surintendent – kWh/t PM do not bringdecision tools in control PM operator – PM operator – PM operator – room kWh/t kWh/t Press kWh/t Drying Forming section & finishing section Slide | 5
  6. 6. MISSING LINK BETWEEN ENERGY STUDY ANDENERGY MANAGEMENT ENERGY ENERGY OPTIMIZATION MANAGEMENT PROJECTS SYSTEMS Are How to sustain the recommendations gains and take really applied and actions to continue maintained? to improve Slide | 6
  7. 7. SUCCESSFUL IMPLEMENTATION IS A MIX OF PROCESSEXPERTISE, TECHNOLOGY AND PEOPLE ENGAGEMENTThe right tool to the right person at the right time Slide | 7
  8. 8. MORE THAN A TYPICAL OPTIMIZATION PROJECT Continuous improvement Integration in the performance system of the mill Performance management KPI and reporting structure, workshop with operators and management, communication plan (before, during, after) Optimization project Optimization on high potential area of the mill projectof the mill of the mill Slide | 8
  9. 9. SUCCESS FACTORS① You’re richer than you think Meters, historian, display, analysis capabilities…② Top-down approach, bottom-up implementation No accountability without actionable parameters③ Start implementation with an energy optimization project Pilot: people readiness, potential, data available Slide | 9
  10. 10. RULE #1: LEVERAGE EXISTING INFORMATION SYSTEMS AND CONTINUOUS IMPROVEMENT STRUCTUREImpact of decision on day-to-day energy cost Historia Level of Exce- Managers ERP Intranet n (e.g. DCS decision based PI) Management X X X Supervisors Staff X X X Operators Operators X X X Slide | 10
  11. 11. RULES #2: TOP DOWN APPROACH, BOTTOM UP IMPLEMENTATION Top management:Impact of decision on day-to-day energy cost global view on Management Gain in $$$ cost control Managers (month) GJ saved Operation: Maintenance: Staff (weekly) GJ saved HEX efficiency Supervisors Average GJ/T Screen uptime Operation GJ / ton Control room: vs. target focus on (daily– hour) Operators actionable parameters Pressure Fresh water Kraft pulp setpoint per valve to WW temperature grade chest Slide | 11
  12. 12. BOTTOM UP: GIVE DECISION TOOLS TO OPERATORSSO THEY CAN TAKE ACTIONS Predicted regimes based on 3+ process variables KPI>1.1 KPI<1.1 A: Performance is C: Performance is > 1.1 good and we know good “but we doActua why not know why”lvalue B: Performance is D: Performance isof KPI < 1.1 bad “but we do bad and we know not know why” why Previously unseen situation! Insight to solve the problem Operator alerts energy team 1. CO pre-heater > 15% for more investigations 2. Temp heating tower < 84,5°C Slide | 12
  13. 13. RULE #3: CHOOSE YOUR BATTLE Normandy, 6 June 1944 Slide | 13
  14. 14. CLASSICAL EMIS IMPLEMENTATION SCHEME…cashflow PRESSURE planned ON CASHFLOW HIGH RISK AND OF RESOURCES PUSHBACK reality “let’s implement, the system will do the rest…” implementation Upfront investment: measurements, IT, software, services + cost of internal resources Slide | 14
  15. 15. IMPLEMENTATION BY SUCCESSIVE PROJECTS PROVIDES MORE BUY-IN WHILE USING LESS RESOURCES cashflow Kickoff project sub-project #2 sub-project #3PROGRESSIVEAND PLANNED BETTER IMPLEMEN- CHANCE OF TATION OPERATOR BUY-IN implementation Upfront investment minimized: focus on area with high potential, local resources, integration in existing systems, gain controlled and monitored Slide | 15
  16. 16. NOT ONLY ENERGY PROJECT, IT’STYPICAL ENERGY MAESTRO PROJECT CHANGE MANAGEMENT!Kick off session Data analysis Implementation Implementation• KPI structure • Exploration preparation • Operators training• Workshops with • Rootcause • Test and • Stakeholder operators and analysis validation of the training stakeholders (multivariate data model off line • Closing session• Process analysis) • Programming of • Follow up plan understanding • Modeling equations and• Data collection dashboard • Reporting structureImmediate actionstaken based on • Better knowledge •Awarenessperformance gap of operation •Capability buildinganalysis • Optimization rules of plant people of the process •First decisions, $$$ $$$ first savings $$$ Slide | 16
  17. 17. ENERGYMAESTRO IN ACTION: Energy management at a papermill – $600,000 / yr • Implementation of a KPI monitoring structure • Implementation of rules for optimal heat recovery operation Paper machine energy optimization – $500,000 / yr • Fast identification of the top causes for energy use variability • Development of an action plan to close the gap TMP heat recovery optimization – $800,000 / yr • Multivariate analysis of reboiler low performance • Development of an action plan to close the gap Boiler optimization at a steel plant – $250,000 / yr • Identification of operation rules that ensure high efficiency • Implementation of preventive maintenance tool to reduce power use Slide | 17
  18. 18. USER CASE #1Chemicals – Steam network Slide | 18
  19. 19. STEAM NETWORK OPTIMIZATION AT APHOSPHORIC ACID PLANT• Culture change in the way steam network is managed• Expected gains: 1,2 M$• 3 month project, no CAPEX① Kickoff with high management② 5 workshops, 4 department, 60+ operators, 200+ ideas③ Model development and analysis of new setpoints④ Implementation of new DCS screen and Excel reports⑤ Training of operators & staff Slide | 19
  20. 20. USER CASE #2P&P - Heat recovery system Slide | 20
  21. 21. HEAT RECOVERY SYSTEM OPTIMIZATION0. BUILT KPI STRUCTRE AND CHOOSE PROJECTS Tactical level 1 Total GJ/day consumed – Total energy cost in $/month Tactical level 2 GJ/day recovered Operational level T dirty steam/MWH - % reboiler efficiency Heat recovery EACs Users EACs EAC # 1 EAC # 2 EAC # 3 EAC # 4 dirty steam TMP reboiler TMP P-machine t stm/MWh, GJ/GJ Specific KPIs: Specific KPIs: % valve opening WW make-up GJ/t, reject GJ/t, exhaust to preheater, Preheater exhaust recov., heat recovery, heating tower efficiency kWh/t kWh/t temp, … Pressure diff. Slide | 21
  22. 22. 1. DEFINE THE KPI AND SET THE TARGET KPI: Ton of dirty Steam/MWH of refining energy Slide | 22
  23. 23. 2. IDENTIFY POSSIBLE ROOTCAUSES THROUGHBRAINSTORMING SESSION WITH OPERATORS losses and vent of dirty data steam circuit Operation data temperature fouling data header data pressure HRS Usersperformance types of user data capacity Design safety valves refiners connected Slide | 23
  24. 24. 3. BUILD MODELS TO EXPLAIN AND TO IDENTIFY OPTIMAL RULES OF OPERATION 1 Best performance when dirty steam 2 Most of the bad valve is open <15% performance and heating tower occurs when dirty outlet temp is >85 °C steam valve is open more than 15%3 Even when those conditions are not met, 1 2 there’s alternatives 1 3 Slide | 24
  25. 25. 4. ADAPT AND IMPLEMENT THE MODELS AND RULESIN OPERATORS ENVIRONMENT Predicted regimes based on 3+ process variables KPI>1.1 KPI<1.1 A: Performance is C: Performance is > 1.1 good and we know good “but we doActua why not know why”lvalue B: Performance is D: Performance isof KPI < 1.1 bad “but we do bad and we know not know why” why Previously unseen situation! Insight to solve the problem Operator alerts energy team 1. CO pre-heater > 15% for more investigations 2. Temp heating tower < 84,5°C Slide | 25
  26. 26. IMMEDIATE AND SUSTAINABLE BENEFITS$600,000/YR OF RECURRENT ENERGY COST SAVINGS Sustainable gain Unexpected end of the drift data analysis Period of “unexpected” higher performance Immediate results of data analysis: new operation rules for higher process Cumulative efficiency gain Beginning of unexpected drift Project duration = 3-4 months Slide | 26
  27. 27. USER CASE #3P&P - Papermachine Slide | 27
  28. 28. Paper machine – Consumption of steam per ton of paper PAPER MACHINE ENERGY OPTIMIZATION The causes for variability in steam usage is not clear Slide | 28
  29. 29. Paper machine – Consumption IMPACT OF THIS ON MY COSTS? WHAT IS THE of steam per ton of paper Step 1: Quantifying variability Peaks of consumption Medium consumption ≈ + 3.6 $/t ≈ + 3 $/t Low consumption Slide | 29
  30. 30. ISSUE TREE FOR PM VARIABILITY Kraft Step 2: Brainstorm rootacauses temperature Groundwood temperature Furnish mix temperature Broke Temperature temperature setpoint Steam consumption at PM3 silo Furnish ratio PM circuit temperatureSteam consumption Make-up flows at PM6 FW make-up Make-up temperature Make-up flows water make-up temperature WW make-up Make-up temperature Shower water Preheating flows Paper Showers FW temperature production Shower water temperature Paper Recirculation of production Basis weight used water to showers Moisture target at reel Water to Stock evaporate temperature Drainage Stock freeness Steam consumption at PM6 dryers Press load Pressing Steam box Dryer pressure setpoints Dryer pressure differencials Drying efficiency Dryer temperature Number of can in operation Slide | 30
  31. 31. SO WHAT… WHAT CAN WE DO ABOUT IT?Step 3: Rootcause data analysisPareto chart % 30 25 20 15 10 5 0 A B C D E F G H I J K L M N O P Parameters Slide | 31
  32. 32. SteamCO siloSpeed Slide | 32
  33. 33. Paper machine – Consumption of steam per ton of paper NOW WE CAN TAKE CLEAR ACTIONS + stock temp <140 °C Speed < 2400 fpm Step 4: Take actions WW heating valve opening > 44% $500,000 recurrent savings Slide | 33
  34. 34. THANK YOU! Visit: www.myenergymaestro.comSebastien Lafourcade I I +1-5124-571-9118 Slide | 34