Predicting and controlling moisture content to optimize forest biomass logistics

Transcription

1 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

2 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

3 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

4 Biomass supply chains in the study Supply chain III Supply chain I Supply chain II

5 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 roadside until period j for chipping and transport Y(i,j) (,j) = solid volume of stem wood harvested in period i and roadside until period j for chipping and the energy plant Z(i,j) = solid volume of logging residues harvested in period i and roadside until period j for chipping and transport

6 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

7 BIOPLAN Optimised planning tool

8 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 February 3, ,414 March 3, ,333 April 3, ,395 May 3, ,483 June 222 3, ,227 July 0 3, ,196 August 0 3, ,129 September 0 0 1,319 1, ,172 October 0 0 3, ,404 November 0 0 3, ,564 December 0 3, ,520 January 13, ,909 February 0 14, ,005 March , ,671 April , ,926 May ,741 5, ,065 14,288 June , ,467 13,239 July , ,565 2,940 13,110 August , ,837 September ,407 5, ,014 October , ,964 November , ,622 December ,441 14,441 Total 31, ,456 22,357 15,779 7, ,483 3,772 5,605 12,837 21,371 24,794 27, ,254254

9 BIOPLAN Drying curves YEAR 1 YEAR 2 YEAR 2 (current year) January February March April May June July August September October November December January February March April May June July August September October November December January February March April May June July August September October November December 51

10 BIOPLAN Optimised planning tool Parameters & conversion factors SCH I SCH II SCH III Energy content t at 0% MC (MJ/kg) 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) Truck volume (m 3 ) Round trip distance (km) Dry matter loss rate (%/month) 130.0* 47.0** 130.0* Interest rate (%/month) SCH I: Whole trees, SCH II: Delimbed stem wood, SCH III: Logging residues * m 3 loose, ** m 3 solid

11 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) * ** ** ** 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

12 BIOPLAN Optimised planning tool

13 BIOPLAN Optimised planning tool

14 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

15 Results Moisture content range (%) Moisture content 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%

16 Results Moisture content range Solid volume harvested (000's m3) WT SW LR WT SW LR WT SW LR Unconstrained 41 49% MC 41 43% MC Year 1 Year 2

17 Results Moisture content range 30.0 Harvesting Storage Chipping Transport 25.0 Supply chain cost ( /m3 / /MWh) Unconstrained 41 49% MC 41 43% MC Unconstrained 41 49% MC 41 43% MC Scenarios

18 'Vo olume harvested (00 00's m3) Months of storage a) 'Vo olume harvested (00 00's m3) Months of storage b) Whole trees Stem wood Logging residues Whole trees Stem wood Logging residues 'Vo olume harvested (00 00's m3) Months of storage c) Results Limits on the storage period Whole trees Stem wood Logging g residues

19 Results Effect of covering Unconstrained 10% LRR* 15% LRR** Activity / m 3 / MWh / m 3 / MWh / m 3 / MWh Harvesting Storage Covering Chippingi Transport Total *10% lower rewetting rate, **15% lower rewetting rate

20 Results Effect of interest rate solid) Supply ch hain cost ( /m chain cost ( /M MWh) Supply Annual interest rate (%) 12.80

21 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

22 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