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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. Science Foundation Ireland

Ireland's transport sector consumes just slightly less than one third of all energy in Ireland and is heavily dependent on oil imports, especially diesel. The European Union has set targets that are to be met by 2020, in order to guarantee a sustainable future for Europe and assure security of energy supply. There is an increase of biofuel usage in the transport sector, to reduce greenhouse gas emissions and encourage indigenous production of renewable sources. Currently, Ireland has only two licensed suppliers of biodiesel. The Irish government has issued a number of policy support mechanisms, and while that has increased the use of biofuels in Ireland, it has not necessarily aided the domestic suppliers of biofuels. The aim of this paper is to detail the existing policies and support mechanisms in Ireland and to examine whether it is possible for Ireland to produce biofuels indigenously while meeting the 2020 targets and competing with the alternative conventional imports. Alternatives to the current supply scenario will be considered, including alternatives such as electric vehicles, recovered waste vegetable oils, and grass gasification for biomethane for natural gas vehicles. Science Foundation Ireland Charles Parsons Energy Research Program

Práve je Vám predkládán výkladový slovník zabývající se terminologií vztahující se ke zpracování lesní biomasy. Vysvetlované výrazy jsou rozdeleny do 5 kategorií a to: charakteristika porostu a zdroju, težba a soustredování, zpracování, odvoz a jednotky. V rámci každé z uvedených oblastí jsou výrazy trídeny podle abecedy. Tam, kde byla identifikována synonyma je uprednostnovaný termín vysvetlen a termín, který je dle názoru autoru zastaralý a nevhodný se odkazuje na termín prioritní. Nekteré výklady pojmu jsou podporeny obrazovou prílohou a je umístena ve stredu publikace. We have the pleasure of presenting you with this explanatory dictionary-glossary of in-forest biomass processing related terminology. The explained terms are divided into five categories as follows: description of stand and raw material, logging, processing, haulage and units. In each of these fi elds, the terms are presented in alphabetical order. Where synonyms are identified, an explanation is given of the preferred term and the term which, in the authors’ opinion, is obsolete and inadequate, refers to the priority term. The interpretation of some terms is supported with illustrations in the figure section. European Commission - Seventh Framework Programme (FP7)

Since the 1970‟s Decision Support Systems (DSS) have become popular following the development in computer technology. A DSS aims to support in the solving of specific problems with both human and computer techniques. The purpose of this research was to design and develop a DSS for application involved with the cost calculations and revenue calculations of contracted timber haulage in Ireland.

The threat of increasing fuel prices and climate change necessitates the need for clean, renewable and independent energy sources. A GIS (Geographical Information Systems) model was developed using ArcGIS 9.2 to analyze the availability of non-woody biomass (wheat, oat, barley and rape straw, willow and miscanthus) for pellet production in Ireland. Utilization within the heating and electricity sector would displace currently used fossil fuels with cleaner, carbon neutral non-woody residues. The aim of the analysis was to determine the total hectares of biomass within Ireland and compute the potential non-woody biomass yield. The greatest potential source of biomass for pelleting is cereal straw. Within the Republic of Ireland the South-East, South-West and Mid-East of Ireland have the greatest biomass yield for pellet production and likely to be most economically viable. Non-woody biomass has a realistic potential to displace fossil fuels within the heating and electricity sector resulting in CO2 mitigation

An economic analysis of a non-woody biomass pelleting process was performed for conditions in the Republic of Ireland. The analysis considered the establishment, harvest, storage and drying costs of the feedstock’s on-farm, together with transportation costs to the processing plant and the costs associated with the pelleting process. Capital and operating costs of the pelleting process were estimated at several plant capacities – 6t / h, 8t / h and 10t / h. The pellet production cost for the base case scenario of 8t / h was 102 / t of pellets produced. By decreasing production capacity to 6t / h an increase in the production cost of 10 / t was experienced while increasing capacity to 10t / h resulted in a decrease of 6 / t in the production cost of pellets. The cost of the raw material is the largest cost component (66%) of the total pellet production cost. Other major cost factors include pelleting and cooling plant (11%), straw grinding plant (10%) and personnel costs (9%). Considering current energy prices the equivalent energy cost of 7 / GJ (excluding profit margins) the potential production of non-woody biomass pellets competes favourably with oil and gas and was found to be economically viable. Department of Agriculture and Food Stimulus Fund

Since the late 1980s, GIS (geographical information systems) have evolved to fully enable the range of capabilities needed in transportation routing, research and management. The objective of this paper was to analyse the designation of articulated haulage routes from one central depot to various destinations around the country of Ireland in terms of road class, distance, speed and travel time and compare the results with simulated routes generated within the GIS. The analysis incorporated a digitised road map of Ireland, where the GPS routes could be overlayed, together with ESRI’s (Environmental Systems Research Institute Inc., CA) ArcGIS software. The ArcInfo Network Analyst Tool (NAT) was used to compare routes generated by Dijkstra’s routing algorithm with the actual GPS routes in terms of road classifications, distance, speed and journey time of the route selected i.e. 'destination planning', a term used here to describe the shortest optimum route based on road class, road length, road speed and route journey time. Results showed that the shortest path (in terms of distance) determined by the NAT did not replicate the actual GPS routes. However, when the NAT was manipulated and used to determine the routes based on road classes i.e. routing to higher classes of roads and not distance (by applying a cost weighting within the geometric network), then the GPS routes were over 90% similar with what was modelled within the GIS. This may allow the GIS alone to be used in the network analysis of truck routing and in particular, timber truck routing from forest harvesting site to destination timber mill in Ireland and incorporate the use of GPS for other advantages such as real-time tracking and monitoring of timber movement.

This book helps the reader understand the power of informed decision making by covering a broad range of DSS (Decision Support Systems) applications in the fields of medical, environmental, transport and business. The expertise of the chapter writers spans an equally extensive spectrum of researchers from around the globe including universities in Canada, Mexico, Brazil and the United States, to institutes and universities in Italy, Germany, Poland, France, United Kingdom, Romania, Turkey and Ireland to as far east as Malaysia and Singapore and as far north as Finland. Decision Support Systems are not a new technology but they have evolved and developed with the ever demanding necessity to analyse a large number of options for decision makers (DM) for specific situations, where there is an increasing level of uncertainty about the problem at hand and where there is a high impact relative to the correct decisions to be made. DSS's offer decision makers a more stable solution to solving the semi-structured and unstructured problem.

Agile, fast and flexible production networks are a must in today’s global competition. The interrelations between manufacturing systems and processes are becoming more complex and the amount of data for decision making is growing. Manufacturing, engineering and production management decisions involve the consideration of multiple parameters. These often complex, interdependent factors and variables are too many for the human mind to cope with at one time. Agile production needs a management and evaluation tool for production changes, manufacturing system development, configuration and operations planning. A decision support system based on manufacturing simulation is one suitable solution. Discrete Event Simulation (DES) has mainly been used as a production system analysis tool to evaluate new production system concepts, layout and control logic. Recent development has enhanced DES models for use in the day-to-day operational production planning of manufacturing facilities. These "as built" models provide manufacturers with the ability to evaluate the capacity of the system for new orders, unforeseen events such as equipment downtime, and changes in operations. After a simulation model has been built, experiments are performed by changing the input parameters and predicting the response. Experimentation is normally carried out by asking "what-if" questions and using the model to predict the likely outcome. A simulation-based Decision Support System (DSS) can be used to augment the tasks of planners and schedulers to run production more efficiently (Figure 1). Some of the benefits of implementing an operational simulation scheduling system include: less effort required to plan day-to-day scheduling, customer order due date conformance, synchronisation of flow through the plant, minimisation of set-ups/changeovers, early warnings of potential problems, checks of critical resources and materials, and, naturally, “what-if” scenario analysis for capacity planning. Although dedicated software packages are currently available, there are limited examples of the use of simulation tools in the operational planning of manufacturing. This chapter also sheds light on development challenges and current development efforts to solve these challenges for this data and model-driven DSS. The major challenges are: 1) data integration, 2) automated simulation model creation and updates, and 3) visualisation of results for interactive and effective decision making.