Simulation Modeling of Logging Residue Supply for Bioenergy Production
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(2) Agenda • Forest-based bioenergy in BC • Logging residue logistics planning • Introducing ROLOS • Logging residue supply for bioenergy production case study • Inputs and methodology • Results.
(3) Forestry and Energy in British Columbia • 60% of BC’s 95 million ha is forest land • Mountain Pine Beetle o Affected 710 million m3 across more than 18 million ha o Increased timber harvest by up to 50% in hardest hit areas o Utilization has improved but much remains to be burnt. • BC Bioenergy Strategy 2008 o Empowered BC Hydro to issue Power Purchase Agreements. • BC Hydro: Call for Bioenergy o Many pulp mills were granted long term contracts o Generation capacity greatly increased.
(4) Sources of Forest Biomass • Sawmill Residue o Historically: Beehive burners o Fairly consistent and predictable quantity and quality. • Logging Residue o o o o o. Historically: Burn on site Quantity? Quality? Costs? Environmental impacts?. 1MacDonald,. A.J., Bernardo, J., Spencer, S. 2012 Assessment of Forest Feedstock for Campbell River. FPInnovations.
(5) Sources of Forest Biomass • Sawmill Residue o Historically: Beehive burners o Fairly consistent and predictable quantity and quality. • Logging Residue o o o o o. Historically: Burn on site Quantity? Quality? Costs? Environmental impacts?. 1MacDonald,. Logistics planning. A.J., Bernardo, J., Spencer, S. 2012 Assessment of Forest Feedstock for Campbell River. FPInnovations.
(6) Logistics Planning • Trial and error, experience • Spreadsheet models • Optimization models o Longer decision making horizon. • Simulation models • More powerful in capturing operational details • Off-the shelf simulation software.
(7) ROLOS Simulation Software • Specifications: o Java-based, using JaamSim simulation engine. • Features: o o o o o o o o. Close to emulation Highly integratable with Google Earth and GIS datasets 3D and interactive graphics Intuitive GUI Fast model development turnaround time Tailored for bio-based industry supply chains 40,000 lines of code so far 35 simulation objects so far.
(8) ROLOS Objects. • Transportation network o Road, rail, waterway. • Equipment. o Transporters (trucks, Tugs and Barges, Ship, Trains) o Bulk handling equipment (Loaders, Stackers/Reclaimers, Conveyors o Bulk material processors (e.g. grinders, reactors, dryers, pelletizers, ...). • Loading/Unloading/ Processing Areas o Nodes of supply chain (e.g. cutblocks, sawmills, bio-chemical plants). • Bulk Material • Decision Making Units, Delays, ... ..
(9) ROLOS Reports • Reports are collected during the simulation run • Every object automatically.
(10) Introducing ROLOS.
(11) Logging Residue Supply for Bioenergy Production Case Study.
(12) Howe Sound Pulp Mill • Located Near Vancouver, BC.
(13) Howe Sound Pulp Mill • Located Near Vancouver, BC • Requires 100,000 ODT/ Year at 35% MC w.b. • Fuel Sourced from BC Coast and Interior o Sawmills o Logging Residue.
(14) • Logging residue converted to hog fuel o Caterpiller 34D loader o Peterson 5710C horizontal grinder o 52’ Chain-out semitrailer truck.
(15) Barging to Howe Sound Pulp and Paper via Silverdale.
(16) Logging Residue Sources • Scope: BCTS Cutblocks in Chinook supply area that currently have slash piles available o 353,103m3 or 56,680 ODT of hog fuel. • Netdowns due to road accessibility o Eliminate 70% of cable blocks and 40% of ground based1 o Estimating yarding methods in Google Earth. o Left with 156,916m3 or 25,188 ODT 1MacDonald,. A.J., Bernardo, J., Spencer, S. 2012 Assessment of Forest Feedstock for Campbell River. FPInnovations.
(17) Cutblocks: Stockpiles. • Multiple areas per block. o To account for grinder/loader repositioning o Estimated based on survey of blocks for inblock road lengths and density o Related the number of spur roads to block area o Relocation time between stockpiles. • Volume from Biomass Ratios o Divided between stockpiles. • Moisture content schedule from literature o Assigned to entire operating area o Though local or regional variation would be possible.
(18) Moisture Content of Logging Slash. • •. Highly Variable: Species, region, weather, location in cutblock, location in pile, time of timber harvest, seasoning period Pick schedule from literature which best represent limited BC data 60 50 40 30 20 10. • •. December. Novemeber. October. September. July. August. June. May. April. March. January. February. December. Novemeber. October. September. July. August. June. May. April. 0. Blue: Acuna et al 2012 Predicting and Controlling Moisture Content to Optimise Forest Biomass Logistics. Red: Dyson, Peter 2013 Effects of Season on the Moisture Content of Roadside Harvesting Debris in Coastal British Columbia..
(19) Transportation Network • Road network for the province from DataBC • Identify all roads required to access selected blocks • Assign speed classes o Freeway o Paved o Gravel* • Identify those gravel roads machines will use to walk between blocks. • Prepare the data in ArcGIS o Merge broken up segments o Flatten into Geometric Network o Export vertices to ROLOS *Region specific (Interior vs Coastal).
(20) Equipment Inputs Machine. Rates (road classes). Fuel Consumption (idle/ operating) (L/h). Emissions (idle/ operating) (Kg CO2e/h). Cost ($/h). Trucks. 90/70/15 km/h. 3.1/10.6. 8.3/28.4. 125. 8.9/ 107.3. 23.8/ 286.1. 1.75/17.5. 4.7/46.7. Grinder Loader. 4km/h 170m3/h. 750.
(21) Modeling Logging Residue Supply in ROLOS • The transportation network and operations were setup in the simulation model. • Need to specify: o o o o. Operating window: June through October, 10 hr days Fleet size and number of grinding operations: vary by scenario Moisture content schedule: from literature and vary by scenario Block volumes and schedule: closest first, clusters, optimize for MC, etc.. • Run multiple scenarios and analyze results.
(22) Modeling Logging Residue Supply in ROLOS. http://www.youtube.com/watch?v=nNfJx2pChF8.
(23) Scenario Analysis • Goal to complete in single operating season o 151 operating days. Fleet & Equipment: Days to Complete 200 190. Operating Days. 180 170. 1 Equipment Set. 160. 2 Equipment Sets. 150. 3 Equipment Sets. 140 130 120 110 100 3. 4. 5. 6. 7. 8 Fleet Size. 9. 10. 11. 12. 13.
(24) Scenario Analysis cont. • Would like to limit fuel consumption and emissions Fuel Consumption and Emissions 196 000. Fuel Consumption (L). 555. 192 000. 550. 190 000. 545. 188 000. 540. 186 000. 535. 184 000. 530. 182 000. 525. 180 000. 520 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. Fleet Size. 13. • Also want to limit pieces of equipment used o 7 Trucks with 2 Equipment sets selected for further analysis. Total Emissions (tonnes CO2e). 560. 194 000. 1 Equipment Set 2 Equipment Sets 3 Equipment Sets.
(25) Results – 7 trucks & 2 Grinders • Total operating days to complete: 131 • Total throughput: 46,405 Wet Tonnes. • Total truckloads: 1756 • Total operating emissions: 533 tonnes CO2e • Average fuel consumption: 3.98 L/tonne.
(26) Results – Equipment • 1756 loads equals roughly 251 cycles per truck • Average trucks cycle times: 3.85 h Truck Utilization per Cycle. Fuel Consumption. 100 %. 100 %. 90 %. 90 %. 80 %. 80 %. 70 % 60 % 50 % 40 % 30 %. Parked for Dispatch Delayed Queued Unloading Loading Travelling. 70 % 60 %. Grinding. 50 %. Loading. 40 %. Trucking. 30 %. 20 %. 20 %. 10 %. 10 %. 0%. 0%.
(27) Results – Moisture Content • Resulting average moisture content of 34.4% Moisture Content Input Schedules. Stockpile M.C. at Reload Facility 45%. 0.6. 40%. 0.4 0.3 0.2 0.1 0. Moisture Content (w.b.). Moisture Content (w.b.). 0.5. 35% First Year Second Year. 30%. Half Above 25%. Half Between Half Below. 20% 15% 0. 31. 60. 91. Operating Days. 121.
(28) Results – MC cont’d • Lower MC results fewer operating days Required Operating Window 138. Days to Complete. 136 134 132 130 128 126 124 122 0%. 10%. 20%. 30%. 40%. 50%. Moisture Content (w.b.). Information is Key! The more you know the better you can plan.
(29) Thank you! [email protected] [email protected].
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