Colin Eustace, Aurecon - BMH Simulation – A Cornerstone of Fit-for-Purpose Design
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Colin Eustace, Aurecon - BMH Simulation – A Cornerstone of Fit-for-Purpose Design

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Colin Eustace, Associate, Resources & Manufacturing Services, Aurecon delivered this presentation at the 10th Annual Bulk Materials Handling conference 2013. This conference is an expert led forum on ...

Colin Eustace, Associate, Resources & Manufacturing Services, Aurecon delivered this presentation at the 10th Annual Bulk Materials Handling conference 2013. This conference is an expert led forum on the engineering behind the latest expansions and upgrades of bulk materials facilities. It also evaluates the latest engineering feats that are creating record levels of throughput whilst minimising downtime.

For more information on this conference, please vist http://www.informa.com.au/bmh2014

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Colin Eustace, Aurecon - BMH Simulation – A Cornerstone of Fit-for-Purpose Design Presentation Transcript

  • 1. Bulk Materials Handling Week Simulation – A Cornerstone of Fit-for-Purpose Design Colin Eustace, Simulation Technical Lead, Aurecon June 2013
  • 2. Bulk Materials Handling Week 2013 Simulation – A Cornerstone of Fit-for-Purpose Design • • • 3 Topic: The case for simulation of BMH systems Focus: Do I need simulation? What does simulation actually achieve? Identification of situations where simulation analysis is important and possibly impacts on design. Context: Supply chains for bulk materials (coal and iron ore, aggregate)
  • 3. What is the benefit of simulating? • • Why would you consider simulation? What do I need to know about my problem to understand whether simulating is worthwhile? Availability of information Variability, Queuing behaviour 5 Interfaces with other operations Operational Complexity Worthless? Valuable?
  • 4. Delivery of specific value • • Value should be able to be identified Value is in refining assumptions used in a static model • • • 6 Assumptions may change significantly affecting throughput and performance estimates Assumptions may stay the same, reducing uncertainty in performance estimates One of the best ways of identifying the value that simulation provides is by structuring the simulation analysis around an extension of a static model
  • 5. Static vs. Dynamic Models • Our objective is to improve on the accuracy/ robustness of the static models that we already have Many simulation projects fail to do this Accuracy of output • Dynamic Model Dynamic Model Static Model 7 Level of effort
  • 6. Comparison with static calculations • A line-by-line comparison should always be possible if both the static model and simulation model are well structured • Static calculations include: • Parameters related to equipment specs • Abstracted/ averaged performance assumptions • Rules of thumb/ educated guesses (availability at interfaces, typical delays) 8
  • 7. Coal In-loading Example Parameter Symbol Units Rate A Rate B Rate C Formulas Inl oa di ng Ca pa ci ty Avera ge Si ngl e Tra i n Pa yl oa d C PT tph t 8,000 8,700 9,600 9,500 Avera ge Si ngl e Wa gon Pa yl oa d Wa gon Length Coupl i ng to Coupl i ng Unl oa di ng Tra i n Speed PW LW t m vT km/hr Pl a nned Sys tem Ava i l a bi l i ty - Port Pl a nned Sys tem Ava i l a bi l i ty - Ra i l ɳP ɳR % % 97.0 95.0 Proporti on of Ma i ntena nce Schedul ed to Coi nci de Tota l Pl a nned Sys tem Ava i l a bi l i ty ρRP ɳT % % 50.0 93.58 ɳT = mi n(ɳP,ɳR) * *ma x(ɳP,ɳR) + ρRP.(100 - ma x(ɳP,ɳR)] Ava i l a bl e Loa di ng Ti me i ncl . Ava i l a bi l i ty Da ys Los t to Pl a nned Ma i ntena nce a nd Wea ther tA tLA hrs da ys 8,197 23.5 tA = 8760.ɳT tLA = 365 - 365.ɳT Reliability - Unplanned Downtime Avera ge Number of Inl oa di ng Conveyors i no. 3 Conveyor Rel i a bi l i ty Sta cker Rel i a bi l i ty RC RS % % 99.5 98.0 Sti cky Coa l Los s Fa ctor Ra i l Rel i a bi l i ty R SC RR % % 97.0 99.0 Tota l Inl oa di ng Sys tem Rel i a bi l i ty RT % 92.7 Inloading Operation Cycle Parameters Avera ge Conveyi ng Di s ta nce to Stockya rd Mi d Poi nt Train Parameters 120 Wa gons 80.0 16.2 1.62 1.76 1.92 vT = C.L / 1000.PW Availability - Planned Maintenance and Weather R T = R Ci.R S.R SC.R R d1 m 2,000 Avera ge Stockya rd Runwa y Length Sta cker Long Tra vel Speed vC d2 vS m/s m m/mi n 1,400 40 Opera tor Setup Del a ys for Sta cker Avera ge Dri ver Rea cti on Del a y a t Sta rt of Tra i n tOD tDR mi n mi n 15 2 Length of Locomoti ves - Sta rt of Tra i n Length of Locomoti ves - Mi ddl e of Tra i n LLS LLM m m 45 45 Stop Di s ta nce Beyond Sta ti on d3 m 50 Inloading Operation Cycle Time Components Ti me to Di s cha rge Coa l i ncl . Rel i a bi l i ty Locomoti ve Del a y Mi d Tra i n t1 t2 hrs hrs 1.29 0.03 1.19 0.03 1.09 0.02 t1 = PT / C.R T t2 = LLM / 1000.vT Tota l Ti me to Empty Tra i n t3 hrs 1.32 1.22 1.11 Avera ge Ti me to Repos i ti on Sta cker t4 hrs 0.29 0.29 0.29 t3 = t1 + t2 t4 = 0.5.d 2 / 60.vS Ti me to Run Out Coa l Opera tor Setup Del a ys t5 t6 hrs hrs 0.07 0.25 0.07 0.25 0.07 0.25 t5 = (d 1 - 0.5.d 2) / 3600.vC t6 = tOD / 60 Tra i n Del a y a t Sta rt of Dumpi ng Mi ni mum Ga p Between Tra i ns t7 t8 hrs hrs 0.0920 0.70 0.09 0.70 0.09 0.70 t7 = (LLS + d 3) / vT + tDR / 60 t8 = s um(t4:t7) Mi ni mum Tra i n Turna round Ti me t9 hrs 1.93 1.83 1.74 t9 = PT / C + t2 + t8 Avera ge Tra i n Turna round Ti me t10 hrs 2.03 1.92 1.82 t10 = t3 + t8 Gros s Unl oa di ng Ra te G1 tph 4,737 4,999 5,281 G 1 = PT / t10 Maximum Annual Capacity C1 Mtpa 38.8 41.0 43.3 C1 = G 1.tA Trains Based on Realistic Availability Effecti ve Tra i n Ava i l a bi l i ty Avera ge Tra i n Turna round Ti me ɳE t11 % hrs 2.38 2.26 2.14 Gros s Unl oa di ng Ra te G2 tph 4,026 4,249 4,489 t11 = t10 / ɳE G 2 = PT / t11 Da ys Los t to Rel i a bi l i ty tLR da ys 18.7 18.3 17.6 tLR = tA .(t1 - t1.R T) / 24.t10.ɳe Avera ge Addi ti ona l Del a y - Tra i n Una va i l a bl e t12 mi n 21.5 20.3 19.2 t12 = (t11 - t10)/60 Proporti on of Mi ni mum Tra i n Turna round Ti me p1 % 17.6 18.6 19.7 p 1 = t12 / t9 Proporti on of Avera ge Tra i n Turna round Ti me p2 % 18.5 19.5 20.6 p 2 = t12 / t10 Realistic Annual Capacity C2 Mtpa 33.0 34.8 36.8 C2 = G 2.tA Conveyor Bel t Speed 5.1 Trains Always Waiting - System Choke Fed 9 85.0
  • 8. Cases where Simulation is not required • • • • • No significant variability Sufficiently buffered from external influences No interactions between concurrent processes or shared equipment When visualisation is of little benefit Availability of information When there is insufficient information available Variability, Queuing behaviour 10 Interfaces with other operations Operational Complexity
  • 9. Borderline cases • Even when the system exhibits queuing behaviour (e.g. coal terminal) the value of simulation may be questionable in some cases • No shared resources • Concept level analysis • Little detailed information • No complex constraints Variability, Availability of information Interfaces with other operations Queuing behaviour 11 Operational Complexity
  • 10. Cases where industry rules of thumb are likely to be the best indicator • • • 12 No quantitative description of the operation is available Events are infrequent/ unusual Complicated to implement and difficult to develop a reliable representation
  • 11. Cases where simulation is essential • 13 Always application specific • Variability in rates or availability • Influenced by external operations (train availability) • Interactions between concurrent processes or shared equipment
  • 12. Example: Large Aggregate Import Terminal
  • 13. Aggregate Import Terminal Example: Layout 15
  • 14. Static Capacity Analysis • • • • • 16 Investigate various aspects of the terminal Assume average digging rate across the ship of 50% of peak digging rate Assume crane utilisation of 80% at capacity Capacity targets appear to be achievable Award contract for detailed design and construction
  • 15. Dynamic Capacity Analysis • • 17 Considers unloading sequences and constraints for concurrent events Uses historical data as a guide for likely ship Hatch Number load plans DWT 1 2 3 4 62902 42400 60046 37350 93534 37350 41000 42400 64778 54656 53676 81004 53074 37150 62944 54446 37000 52906 4 1 2 1 2 1 1 2 2 1 1 1 2 2 2 4 1 2 4 1 3 1 1 1 3 1 1 1 3 1 2 2 2 2 1 2 3 2 3 1 3 1 3 2 1 1 2 2 3 2 1 2 2 1 5 2 2 2 3 2 3 1 2 1 3 1 3 1 2 1 3 2 5 1 2 2 1 1 1 2 2 1 1 2 1 4 2 2 1 3 2 6 1 7 2 3 1 2 2 2 1 3 1 1 3 1 2 1 1 1 3 2 3 1 2
  • 16. Dynamic Capacity Analysis 18
  • 17. Capacity Analysis Outcomes • • • 19 Average crane utilisation more than ~50% is difficult to achieve with the current configuration Capacity is about two-thirds of the target Re-think the design?
  • 18. Not all simulation models are the same • • 20 Each simulation model is targeted at a different level of abstraction Different models are capable of doing a different range of analysis
  • 19. Example: Coal Export Terminal Simulation Model Approaches
  • 20. Different Approaches – Coal Terminal Example R S/R S 22 S S S S/R S/R S/R S/R S/R S/R
  • 21. Approaches to Simulating Bulk Export Terminals 101 Material Flow Transactions 10,000t S/R S/R S/R 10,000t S/R 80,000t 70,000t 23 S/R S/R
  • 22. Approaches to Simulating Bulk Export Terminals 101 • Stockpile “Bins” and equipment pools are often used to approximate stockpile geometry S/R S/R S/R S/R S/R S/R 24
  • 23. Examples – Cargo Assembly 25
  • 24. Concluding Remarks Benefitting from Thorough Analysis For many more complex BMH operations, simulation really is important to ensure the system works 26 • Consider using simulation early to improve design rather than checking at detailed design to see where the established problems areas are • Think about whether there is anything that warrants simulation (queuing, variation, shared equipment…) • Tie detailed simulation closely to preceding static analysis • Simulation must ADD VALUE to static analysis without diminishing any of the detail • Tailor the approach to the desired outcomes (what assumptions are we uncertain about)