By Lasse Rosendahl
This booklet offers a variety of thermo-chemical applied sciences for conversion of biomass, not just to provide warmth and tool, but additionally intermediate items reminiscent of syngas and bio-oils. Part one offers an creation to biomass offer chains and feedstocks, and descriptions the rules of biomass combustion for energy iteration. Chapters additionally describe the categorization and training of biomass feedstocks for combustion and gasification. half explores biomass combustion and co-firing, together with direct combustion of biomass, biomass co-firing and gasification, quick pyrolysis of biomass for the creation of beverages and intermediate pyrolysis applied sciences. Later sections specialize in largescale biomass combustion and biorefineries and large-scale biomass combustion crops, key engineering matters and plant operation. The concluding bankruptcy seems to be on the position of biorefineries in expanding the price of the end-products of biomass conversion.
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Tatsiopoulos and Tolis (2003) presented a detailed cotton-stalk supply chain model, which employs LP optimization for biomass delivery scheduling. This model was used for centralized (electricity) and decentralized combined heat and power (CHP) power plant scenarios. In a similar vein, Papadopoulos and Katsigiannis (2002) performed a technoeconomic assessment of a biomass power plant, using a mixture of many biomass types. The authors focused primarily on reducing biomass logistics costs, and more specifically on eliminating biomass warehousing needs by performing a twostage optimization: firstly, the CHP power-plant location is determined to minimize the transportation distance and secondly, dynamic programing optimization is employed to identify the optimum biomass fuel mix.
However, a significant amount of research has used optimization models to focus on the location problem. Voivontas et al. (2001) proposed a GIS-based model to locate a bioenergy conversion facility based on the optimum exploitation of available biomass potential. Papadopoulos and Katsigiannis (2002) focused primarily on siting the bioenergy facility to reduce the biomass logistics costs, and more specifically, on eliminating biomass warehousing needs. In Nilsson’s model (1999) the bioenergy facility location was defined by the model user; however, the intermediate storage locations are defined by the simulation model.
It should be noted though that these two options lead to a significantly different supply chain structure: for heavy goods vehicles, the common approach is to form a contract with a third-party logistics (3PL) provider, and therefore the supply chain operator would have a single point of reference and alternative contractor choices. When using agricultural equipment, the operator would normally have to coordinate a significant number of farmers and equipment owners, and therefore the supply chain management workload would be increased.