TY - CHAP
T1 - Production costs of advanced biofuels using a multi-component learning curve model
AU - Karka, Paraskevi
AU - Johnsson, Filip
AU - Papadokonstantakis, Stavros
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/1
Y1 - 2021/1
N2 - The production costs of advanced biofuel options are currently higher than those of their fossil fuel equivalents. Capital Expenditures (CAPEX) for the production of liquid biofuels for road, aviation and marine transport sectors have a significant contribution to the overall production cost, together with the feedstock cost. It is, therefore, important to estimate the potential for cost reduction through R&D and experience in assembling a growing number of respective plants (i.e., from first-of-a kind (FOAK) to the Nth plant (NOAK)), which comprise a mix of established and innovative technological components. This could provide valuable information to stakeholders for the expected investment costs to meet European Commission goals in 2050. This study adopts a methodological framework based on the “learning curve theory” to estimate cost reduction as a result from the experience of technology implementation, in terms of numbers or capacity of units implemented. This work applies the learning theory as a multicomponent analysis, which requires a systematic decomposition of the entire production process to identify established and innovative technological components that can be analysed in detail using the corresponding technoeconomic data. The analysis showed that CAPEX reduction in the range of 10-25% could be expected to reach capacities corresponding to NOAK plants in 2050. To reach further CAPEX reduction of 40%, for example, would require higher cumulative annual growth rates to achieve two orders of magnitude increase of cumulative installed capacity. This corresponds to hundreds of GWs or equivalently some hundreds or thousands of large-scale plants to meet the goal of 20-25% transportation fuels consumption to be covered by advanced biofuels in 2050.
AB - The production costs of advanced biofuel options are currently higher than those of their fossil fuel equivalents. Capital Expenditures (CAPEX) for the production of liquid biofuels for road, aviation and marine transport sectors have a significant contribution to the overall production cost, together with the feedstock cost. It is, therefore, important to estimate the potential for cost reduction through R&D and experience in assembling a growing number of respective plants (i.e., from first-of-a kind (FOAK) to the Nth plant (NOAK)), which comprise a mix of established and innovative technological components. This could provide valuable information to stakeholders for the expected investment costs to meet European Commission goals in 2050. This study adopts a methodological framework based on the “learning curve theory” to estimate cost reduction as a result from the experience of technology implementation, in terms of numbers or capacity of units implemented. This work applies the learning theory as a multicomponent analysis, which requires a systematic decomposition of the entire production process to identify established and innovative technological components that can be analysed in detail using the corresponding technoeconomic data. The analysis showed that CAPEX reduction in the range of 10-25% could be expected to reach capacities corresponding to NOAK plants in 2050. To reach further CAPEX reduction of 40%, for example, would require higher cumulative annual growth rates to achieve two orders of magnitude increase of cumulative installed capacity. This corresponds to hundreds of GWs or equivalently some hundreds or thousands of large-scale plants to meet the goal of 20-25% transportation fuels consumption to be covered by advanced biofuels in 2050.
KW - biofuels deployment
KW - CAPEX reduction
KW - learning curve
KW - TRL increase
UR - http://www.scopus.com/inward/record.url?scp=85110518387&partnerID=8YFLogxK
U2 - 10.1016/B978-0-323-88506-5.50300-4
DO - 10.1016/B978-0-323-88506-5.50300-4
M3 - Chapter
AN - SCOPUS:85110518387
T3 - Computer Aided Chemical Engineering
SP - 1937
EP - 1942
BT - Computer Aided Chemical Engineering
A2 - Türkay, Metin
A2 - Gani, Rafiqul
PB - Elsevier Bedrijfsinformatie b.v.
T2 - 31st European Symposium on Computer Aided Process Engineering
Y2 - 6 June 2021 through 9 June 2021
ER -