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Research Highlights: A set of 128 potential bioenergy facility locations is established and evaluated based on the transport cost to select optimal locations. Background and Objectives: The identification of optimal facility locations to process recovered forest biomass is an important decision in designing a bioenergy supply chain at the strategic planning level. The result of this analysis can affect supply chain costs and the overall efficiency of the network, due to the low density and dispersed nature of forest biomass and the high costs associated with its logistics operations. In this study, we develop a two-stage decision support system to identify the optimal site locations for forest biomass conversion based on biomass availability, transport distance and cost. Materials and Methods: In the first stage, a GIS-based analysis is designed to identify strategic locations of potential bioenergy sites. The second stage evaluates the most cost-effective locations individually using a transportation cost model, based on the results from stage one. The sensitivity of inputs, such as maximum allowable transport cost, the distance of transport and their relations to the profit balance, and changes in fuel price are tested. The method is applied to a real case study in the state of Queensland, Australia. Results and Conclusions: The GIS analysis resulted in 128 strategic candidate locations being suggested for bioenergy conversion sites. The logistics analysis estimated the optimal cost and transportation distance of each one of the locations and ranked them according to the overall performance between capacities of 5 and 100 MW.
Research Highlights: A set of 128 potential bioenergy facility locations is established and evaluated based on the transport cost to select optimal locations. Background and Objectives: The identification of optimal facility locations to process recovered forest biomass is an important decision in designing a bioenergy supply chain at the strategic planning level. The result of this analysis can affect supply chain costs and the overall efficiency of the network, due to the low density and dispersed nature of forest biomass and the high costs associated with its logistics operations. In this study, we develop a two-stage decision support system to identify the optimal site locations for forest biomass conversion based on biomass availability, transport distance and cost. Materials and Methods: In the first stage, a GIS-based analysis is designed to identify strategic locations of potential bioenergy sites. The second stage evaluates the most cost-effective locations individually using a transportation cost model, based on the results from stage one. The sensitivity of inputs, such as maximum allowable transport cost, the distance of transport and their relations to the profit balance, and changes in fuel price are tested. The method is applied to a real case study in the state of Queensland, Australia. Results and Conclusions: The GIS analysis resulted in 128 strategic candidate locations being suggested for bioenergy conversion sites. The logistics analysis estimated the optimal cost and transportation distance of each one of the locations and ranked them according to the overall performance between capacities of 5 and 100 MW.
In order to secure a supply of forest biomass, as well as promote further utilization following the completion of the Feed-in-Tariff Scheme for Renewable Energy (FIT), small-diameter trees such as cleanings from young planted forests and broad-leaved trees from coppice forests are prospective resources in Japan. The goal of this study was to discuss effective methods for harvesting the small-diameter trees that are unutilized forest biomass in Japan. This study assumed a simplified model forest and conducted experiments and time studies of the harvesting of small-diameter trees with a truck-mounted multi-tree felling head. As a result, the machine used in the experiment could fell a maximum of six trees inward in a row from a forest road. However, the harvesting cost (felling, accumulating and chipping) was cheapest when the machine felled five trees inward in a row. Lengthening the maximum reach of a felling head to fell trees deeper inward in a row appeared effective in increasing the number of harvested trees. From the perspective of minimizing the harvesting cost, however, there were upper limits to the number of trees felled inward as well as to the maximum reach of a felling head. The results of a sensitivity analysis suggested the following machine improvements could be considered in future policy: increasing the moving velocity of a felling head and the maximum number of trees that can be held at a time are effective if it is possible to lengthen the maximum reach of a felling head. Meanwhile, shortening the machine’s moving time among operation points is also effective if the maximum reach of a felling head cannot be lengthened.
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