Predicting and controlling moisture content to optimize forest biomass logistics Mauricio Acuna 1, Perttu Anttila 2, Lauri Sikanen 3, Robert Prinz 2, Antti Asikainen 2 1 AFORA - University of the Sunshine Coast, Australia 2 METLA, Finland 3 University of Eastern Finland
Objectives Develop a tool that includes key fuel attributes (moisture content) t) to optimize i biomass supply chains Quantify the effect of moisture content limits on supply chain costs and volume Quantify the effect of the drying time on supply chain costs and volume Quantify the effect of covering on supply chains costs
Moisture content The most important single quality factor for biomass production Affects heating value, storage properties and transportation costs of the fuel It is a direct factor and affects the pricing of the fuel
Biomass supply chains in the study Supply chain III Supply chain I Supply chain II
Biomass supply chains in the study Whole trees - X(i,j) Stem wood Y(i,j) Logging residues Z(i,j) X(i,j) = solid volume of whole trees harvested in period i and stored @ roadside until period j for chipping and transport Y(i,j) (,j) = solid volume of stem wood harvested in period i and stored @ roadside until period j for chipping and transport @ the energy plant Z(i,j) = solid volume of logging residues harvested in period i and stored @ roadside until period j for chipping and transport
BIOPLAN Optimised planning tool Objective function: Minimize supply chain costs including harvesting & forwarding, storage, chipping, covering, and transportation costs Constraints: Meet the energy demand of the CHP plant Meet a specific MC of the biomass materials delivered to the plant Even production of logging crews throughout h the year Min and Max storage period
BIOPLAN Optimised planning tool
BIOPLAN Optimised planning tool Tonnes harvested YEAR 1 YEAR 2 YEAR 2 (current year) January February March April May June July August September October November December Total January 3,391 0 0 0 0 0 0 0 0 0 0 0 3,391 February 3,414 0 0 0 0 0 0 0 0 0 0 0 3,414 March 3,333 0 0 0 0 0 0 0 0 0 0 0 3,333 April 3,395 0 0 0 0 0 0 0 0 0 0 0 3,395 May 3,483 0 0 0 0 0 0 0 0 0 0 0 3,483 June 222 3,005 0 0 0 0 0 0 0 0 0 0 3,227 July 0 3,196 0 0 0 0 0 0 0 0 0 0 3,196 August 0 3,129 0 0 0 0 0 0 0 0 0 0 3,129 September 0 0 1,319 1,853 0 0 0 0 0 0 0 0 3,172 October 0 0 3,404 0 0 0 0 0 0 0 0 0 3,404 November 0 0 3,564 0 0 0 0 0 0 0 0 0 3,564 December 0 3,122 398 0 0 0 0 0 0 0 0 0 3,520 January 13,909 0 0 0 0 0 0 0 0 0 0 0 13,909 February 0 14,005 0 0 0 0 0 0 0 0 0 0 14,005 March 0 0 13,671 0 0 0 0 0 0 0 0 0 13,671 April 0 0 0 13,926 0 0 0 0 0 0 0 0 13,926 May 0 0 0 0 7,741 5,483 0 0 0 0 0 1,065 14,288 June 0 0 0 0 0 0 3,772 0 0 0 0 9,467 13,239 July 0 0 0 0 0 0 0 5,605 0 0 4,565 2,940 13,110 August 0 0 0 0 0 0 0 0 12,837 0 0 0 12,837 September 0 0 0 0 0 0 0 0 0 7,407 5,607 0 13,014 October 0 0 0 0 0 0 0 0 0 13,964 0 0 13,964 November 0 0 0 0 0 0 0 0 0 0 14,622 0 14,622 December 0 0 0 0 0 0 0 0 0 0 0 14,441 14,441 Total 31,146146 26,456 22,357 15,779 7,741741 5,483 3,772 5,605 12,837 21,371 24,794 27,912 205,254254
BIOPLAN Drying curves YEAR 1 YEAR 2 YEAR 2 (current year) January February March April May June July August September October November December January 40 40 40 37 32 27 23 19 18 22 26 30 February 40 40 40 37 32 27 23 19 18 22 26 30 March 39 39 39 36 31 26 22 18 17 21 25 29 April 43 43 43 40 35 30 26 22 21 25 29 33 May 49 49 49 46 41 36 32 28 27 31 35 39 June 50 50 50 47 42 37 33 29 28 32 36 40 July 53 53 53 50 46 41 36 33 32 36 40 44 August 56 56 56 53 48 43 39 35 34 38 42 46 September 58 58 58 55 50 45 41 38 36 40 44 48 October 58 58 58 55 50 45 41 37 36 40 44 48 November 56 56 56 53 48 43 39 35 34 38 42 46 December 51 51 51 48 43 38 34 31 29 33 37 41 January 49 49 49 46 42 37 32 29 27 31 36 40 February 50 50 47 42 37 33 29 28 32 36 40 March 48 45 41 36 31 28 27 31 35 39 April 49 45 40 35 32 30 34 39 43 May 51 46 42 38 37 41 45 49 June 47 43 39 38 42 46 50 July 46 43 41 45 49 53 August 45 44 48 52 56 September 46 50 54 58 October 50 54 58 November 52 56 December 51
BIOPLAN Optimised planning tool Parameters & conversion factors SCH I SCH II SCH III Energy content t at 0% MC (MJ/kg) 19.5 19.0 20.00 Basic density (kg/solid m 3 ) Bulk density (kg/solid m 3 ) Solid content Ratio loose-m 3 to solid m 3 Truck payload (tonnes) 410 172.2 0.42 2.38 35.0 400 168.0 0.42 2.38 35.0 415 174.3 0.42 2.38 35.0 Truck volume (m 3 ) Round trip distance (km) Dry matter loss rate (%/month) 130.0* 47.0** 130.0* 128 150 185 1.0 1.0 2.0 Interest rate (%/month) 0.5 0.5 0.5 SCH I: Whole trees, SCH II: Delimbed stem wood, SCH III: Logging residues * m 3 loose, ** m 3 solid
BIOPLAN Optimised planning tool Parameters SCH I SCH II SCH III 1. Mechanised felling, bunching & forwarding ( /m 3 ) 2. Chipping i ( /m 3 ) - MC% <= 35-36=MC%<=50 - MC%>50 3. Transport ( /km) 13.0 14.6 10.0* 5.8 3.8** 7.4 5.1 3.1** 6.7 43 4.3 2.3** 59 5.9 2.4 1.8 2.5 SCH I: Whole trees SCH II: Delimbed stem wood SCH III: Logging residues SCH I: Whole trees, SCH II: Delimbed stem wood, SCH III: Logging residues * It includes only forwarding, ** Chipping at the energy plant
BIOPLAN Optimised planning tool
BIOPLAN Optimised planning tool
Methods - Analysis Effect of moisture content range for biomass materials delivered to the plant Effect of drying (storage) period Effect of covering Effect of interest rate on storage
Results Moisture content range 60.0 55.0 (%) Moisture content 50.0 45.0 40.00 35.0 Whole trees Unconstrained Stem wood Unconstrained Logging residues Unconstrained Whole trees 41 49% MC Stem wood 41 49% MC Logging residues 41 49% MC Whole trees 41 43% MC Logging residues 41 43%
Results Moisture content range 180 160 Solid volume harvested (000's m3) 140 120 100 80 60 40 20 0 WT SW LR WT SW LR WT SW LR Unconstrained 41 49% MC 41 43% MC Year 1 Year 2
Results Moisture content range 30.0 Harvesting Storage Chipping Transport 25.0 Supply chain cost ( /m3 / /MWh) 20.0 15.0 10.00 5.0 0.0 Unconstrained 41 49% MC 41 43% MC Unconstrained 41 49% MC 41 43% MC Scenarios
'Vo olume harvested (00 00's m3) 100 90 80 70 60 50 40 30 20 10 0 0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324 Months of storage a) 'Vo olume harvested (00 00's m3) 100 90 80 70 60 50 40 30 20 10 0 0 1 2 3 4 5 6 Months of storage b) Whole trees Stem wood Logging residues Whole trees Stem wood Logging residues 'Vo olume harvested (00 00's m3) 100 90 80 70 60 50 40 30 20 10 0 0 1 2 3 4 5 6 7 8 9 10 11 12 Months of storage c) Results Limits on the storage period Whole trees Stem wood Logging g residues
Results Effect of covering Unconstrained 10% LRR* 15% LRR** Activity / m 3 / MWh / m 3 / MWh / m 3 / MWh Harvesting 13.19 7.04 13.87 7.32 13.43 6.94 Storage 0.24 0.13 0.15 0.08 0.15 0.08 Covering 0.35 0.18 0.35 0.18 Chippingi 402 4.02 215 2.15 411 4.11 217 2.17 479 4.79 248 2.48 Transport 6.99 3.74 6.39 3.37 6.54 3.38 Total 24.44 13.06 24.87 13.13 25.25 13.06 *10% lower rewetting rate, **15% lower rewetting rate
Results Effect of interest rate 26.0 13.15 25.5 13.10 solid) Supply ch hain cost ( /m3 25.0 24.5 24.0 23.5 13.05 13.00 12.95 12.90 12.85 chain cost ( /M MWh) Supply 23.0 2 4 6 8 10 Annual interest rate (%) 12.80
Conclusions Understanding the tradeoffs associated with the storage of biomass is key to optimise biomass supply chains BIOPLAN allows planners & decision makers to assess key operational factors and fuel quality attributes that affect the economics of different biomass supply chains For the case study analysed, constraining the model favoured the production of logging residues Demand at the energy plant was met with 33% less biomass volume when covering was implemented
Predicting and controlling moisture content to optimize forest biomass logistics Mauricio Acuna 1, Perttu Anttila 2, Lauri Sikanen 3, Robert Prinz 2, Antti Asikainen 2 1 AFORA - University of the Sunshine Coast, Australia 2 METLA, Finland 3 University of Eastern Finland