Evaluation of Techno-Economic Studies on the bioliq(R) Process for Synthetic Fuels Production from Biomass

Techno-economic studies by various research institutions on the costs for the production of biomass to liquid (BtL) fuels using the bioliq(R) process were analyzed and evaluated. The bioliq(R) process consists of decentralized pretreatment by fast pyrolysis plants for biomass energy densification, and of a central gasification and synthesis step for synthesis of gas and synthetic fuel production. For comparison, specific material and energy flows were worked out for both process steps, and conversion efficiencies were calculated for the conversion of straw to diesel fuel via the Fischer-Tropsch synthesis. A significant variation of the overall process efficiency in the range of 33-46% was mainly a result of the different assumptions made for electricity generation at the central location. After breaking down the individual cost items to either fixed or variable costs, it turned out that the largest cost items in the production of BtL fuels were attributable to feedstock and capital costs. Comparison of the specific investments showed that, in addition to economies of scale, other factors had a significant influence leading to values between 1000 and 5000 EUR/kW. This, particularly, included the origin of the equipment purchase costs and the factors applied to them. Fuel production costs were found to range between 0.8 and 2.6 EUR/L. Possible cost reduction by learning potential was investigated, leading to an improvement by a few percent of production costs. A sensitivity analysis of the individual cost items by up to 30%, for "investments" and "biomass and transport" cost increases, led to higher manufacturing costs of up to 17% in both cases. By harmonizing the depreciation period and the chosen interest rate, the production costs changed from -16% to +17%. Similarly, effects could be shown by adjusting the costs for maintenance and servicing, and the plant operation time. A superposition of these effects in a best-case scenario led to cost reductions of 21%. The most expensive variant in the opposing worst-case scenario raised costs by up to 27%. This uncertainty contributed already fifty percent to a preliminary cost estimate based on a conceptual design.