Now showing 1 - 5 of 5
  • Publication
    Improving Log Loading Efficiency for Improved Sustainable Transport within the Irish Forest and Biomass Sector
    In Ireland, timber and biomass haulage faces the challenge of transporting enough material within strict legal dimensions and gross vehicle weights restrictions for trucks and trailers. The objective of this study was to develop a method to control payload weight by knowing the moisture content of the wood. Weights, volumes, and moisture content were gathered from 100 truckloads of Sitka spruce pulpwood. Truck volume and weight utilization patterns were analyzed based on stacked volume, truck volume, and weights recorded from the weighbridge. Solid/bulk volume conversion factors for the truckloads were estimated indicating the truck’s solid volume capacity to be filled. Trucks were grouped into five conditions based on their configuration—volume capacity and legal maximum payload. A loaded volume fraction was estimated to assess the optimal volume capacity and stanchion height at which the trucks should be loaded. Results showed that 100% of the trucks presented volume underutilization, with a maximum of 27.5 m3 (only 39.85% volume capacity). In contrast, 67% of trucks were overweight while the remaining 33% were under the legal maximum weight. The average solid/bulk volume conversion factor was 0.66 ± 0.013 at 95% confidence level. Depending on the conditions, trucks can be filled to 100% of their volume capacity with wood at an MC from 29% to 55%. The minimum truck volume capacity utilization was 45%. This methodology can be used by truck hauliers, enabling them to determine in-forest the optimum volume and weight of wood to be transported by knowing the moisture content (MC), the wood specie, and using the height of the stanchions of the trailer as reference when loading the truck.
    Scopus© Citations 19  324
  • Publication
    Managing the moisture content of wood biomass for the optimisation of Ireland's transport supply strategy to bioenergy markets and competing industries
    The aim of this study was to analyse the supply of wood biomass (short wood) to the three peat power plants in Ireland and the impacts on the competing wood-based panel industries. The methodology includes the development of a spatial decision support tool based on LP (Linear Programming). It uses drying curves to assess the moisture content, weight and energy content of biomass during a two year period planning. Harvesting, chipping, storage and transportation costs are calculated based on the biomass moisture content. The model optimally allocates woodchips and logs from thinnings and clearfells. Results show that the planned maximum 30% co-firing rate at the three peat power station could be met with the forecasted short wood availability from both the private and public sector. The costs of supply increased not only with higher demands, but also with tighter constraints on the MC demanded by power plants. Spatial distribution and operational factors such as efficiency in transportation and truck loading showed to be sensitive to changes in MC. The analysis shows the benefits of managing the MC when optimising supply chains in order to deliver biomass to energy plants in a cost-effective manner.
      827Scopus© Citations 39
  • Publication
    Analysing Performance Characteristics of Biomass Haulage in Ireland with GPS, GIS and Fuel Diagnostic Tools
    In Ireland, truck transport by road dominates and will remain the main transportation mode of biomass. Cost efficiency and flexibility of forest transport can be typically improved by optimising routes. It is important to know every process and attributes within the workflow of roundwood transport. This study aimed to analyse characteristics of timber trucking in Ireland, and to estimate the least-cost route for the distribution of biomass with the use of geographic information systems (GIS). Firstly, a tracking system that recorded the truck’s movements and fuel consumption was installed. A total of 152 trips were recorded, routes were chosen by the truck driver. The recorded information was used to analyse the distances and times travelled loaded and unloaded per road class, breaks, loading and unloading times as well as fuel consumption. Secondly, the routes taken by the truck where compared with routes created using Network Analyst (NA), an extension of ArcGIS. Four scenarios based on route selection criteria were selected: shortest distance (S1), shorted time (S2), and prioritising high-class roads with shortest distance (S3) and time (S4). Results from the analysis of the tracking system data showed that driving both loaded and unloaded occupied on average 69% of the driver’s working shift; with an average time driving loaded of 49%. The travel distance per trip varied from 112 km and 197 km, with the truck driver using mostly national and regional roads. An average 2% of the total distance and 11% of the total time was spent driving on forest roads. In general, the truck’s speed recorded on the different road classes was on average 30% lower than the legal maximum speed. The average fuel consumption was 0.64 L/km. In terms of the route comparison, the driving directions from the truck routes coincided with 77% of the directions of the routes based on shortest driving time (S2 and S4). All the routes chosen by the driver had 22% longer distance than the routes in S1 (shortest distance). The routes selected based on shortest distance (S1 and S3) had the longest travelling time, approximately 19% more than the ones taken by the truck and 30% more than S2 and S4. The average running cost for the truck was 0.83 €/km. Choosing the shortest distance routes (S1 and S3) not only implies reducing travelling costs but also a reduction of CO2 emissions by 12% in comparison to routes in S2 and S4. However, when selecting the routes, travel time can be a much more crucial parameter to analyse rather than distance in terms of transportation costs. Choosing the routes generated in scenario S2 over S1 implied an increase in distance by 12% but a decrease in time of 30%. Less driving time translates into better driving conditions across higher classes or roads; less wear and tear of trucks; and lesser fuel used. It also complies with local authorities preferences of having timber trucks move on higher road types in order to minimise the expenses associated with road maintenance.
      657Scopus© Citations 9
  • Publication
    Controlling moisture content and truck configurations to model and optimise biomass supply chain logistics in Ireland
    In the coming years, Ireland will continue to face an increasing demand for wood biomass as a renewable source of energy. This will result in strained supply/demand scenarios, which will call for new planning and logistics systems capable of optimizing the efficient use of the biomass resources. In this study, a linear programming tool was developed which includes moisture content (MC) as a driving factor for the cost optimisation of two supply chains that use short wood and whole trees from thinnings as material feedstock. The tool was designed and implemented to analyse the impact of moisture content and truck configurations (5-axle and 6-axle trucks) on supply chain costs and spatial distribution of the supply materials. The results indicate that the inclusion of wood chips from whole trees reduces the costs of wood energy supply in comparison with only producing wood chips from short wood to satisfy the demand, with 9.8% and 10.2% cost reduction when transported with 5-axle and 6-axle trucks respectively. Constraining the MC of the wood chips delivered to the power plant increases both transport and overall supply chain costs, due, firstly to an increase in the haulage distance and secondly, to the number of counties providing the biomass material. In terms of truck configuration, the use of 6-axle trucks resulted in a 14.8% reduction in the number of truckloads and a 12.3% reduction in haulage costs in comparison to the use of 5-axle trucks across the MC scenarios analysed.
      957Scopus© Citations 73
  • Publication
    Life cycle assessment of biomass-to-energy systems in Ireland modelled with biomass supply chain optimisation based on greenhouse gas emission reduction
    The energy sector is the major contributor to GHG (greenhouse gas emissions) in Ireland. Under EU Renewable energy targets, Ireland must achieve contributions of 40%, 12% and 10% from renewables to electricity, heat and transport respectively by 2020, in addition to a 20% reduction in GHG emissions. Life cycle assessment methodology was used to carry out a comprehensive, holistic evaluation of biomass-to-energy systems in 2020 based on indigenous biomass supply chains optimised to reduce production and transportation GHG emissions. Impact categories assessed include; global warming, acidification, eutrophication potentials, and energy demand. Two biomass energy conversion technologies are considered; co-firing with peat, and biomass CHP (combined heat and power) systems. Biomass is allocated to each plant according to a supply optimisation model which ensures minimal GHG emissions. The study shows that while CHP systems produce lower environmental impacts than co-firing systems in isolation, determining overall environmental impacts requires analysis of the reference energy systems which are displaced. In addition, if the aims of these systems are to increase renewable energy penetration in line with the renewable electricity and renewable heat targets, the optimal scenario may not be the one which achieves the greatest environmental impact reductions.
      794Scopus© Citations 46