530
D. Hara et al.
where ε entire is the exergy efficiency of the entire hydrogen path, ε p is the exergy
efficiency of hydrogen production, ε s is the exergy efficiency of hydrogen storage,
and ε f is the exergy efficiency of the FC used for mobility.
35.2.4 Data Envelope Analysis
DEA is a nonparametric assessment methodology used in operations and economics
research to estimate production frontiers. Specifically, DEA can assess comprehensive efficiency using both output parameters (the results of a certain organization’s
business or products) and input parameters (the resources used to obtain the results)
(Sato et al. 2018). That is, the smaller the input parameter’s values are and the
larger the output parameter’s values are, the more efficient a certain process is. In
this manner, DEA does not compare merely how small or large the input or output
parameters are but uses the relative efficiency calculated from both the input and
output parameters. Thus, DEA can be used to compare the different types of functional mobility. Another advantage of DEA is that it allows the integration of multiple
indicators into a single indicator as a relative efficiency value. For example, previous
research integrates the performance and environmental impacts of a household FC
cogeneration system into one efficiency value using DEA (Sato et al. 2018).
In this study, DEA was used to compare 12 paths with different functional units.
Using DEA, the hydrogen paths were assessed via the integrated indicator of exergy
efficiency and LCA results.
The input parameters are the amount of biomass, ADP, and ADP (fossil fuels).
The amount of biomass can represent the heating value difference between woody
biomass (13.23 MJ (Kondo et al. 2018)) and sewage sludge (12.75 MJ, Dry-basis).
In addition, ADP (fossil fuels) can represent the differences between plant-scale
hydrogen production (woody biomass) and on-site hydrogen production (sewage
sludge) because the latter does not include transportation of feedstock and hydrogen.
In this scenario, fossil fuels need not be used for transportation. The output parameters
are the total exergy efficiency and the GWP value of the hydrogen path.
The efficiency DEA was calculated using linear programming problems as follows
(Sato et al. 2018):
maxθ = u 1 y 1k + u 2 y 2k · · · + u n y nk
(35.6)
v 1 x 1k + v 2 x 2k · · · + v m x mk = 1
(35.7)
u 1 y 1k + u 2 y 2k · · · + u n y nk
≤ v 1 x 1k + v 2 x 2k · · · + v m x mk
(35.8)
D. Hara et al.
where ε entire is the exergy efficiency of the entire hydrogen path, ε p is the exergy
efficiency of hydrogen production, ε s is the exergy efficiency of hydrogen storage,
and ε f is the exergy efficiency of the FC used for mobility.
35.2.4 Data Envelope Analysis
DEA is a nonparametric assessment methodology used in operations and economics
research to estimate production frontiers. Specifically, DEA can assess comprehensive efficiency using both output parameters (the results of a certain organization’s
business or products) and input parameters (the resources used to obtain the results)
(Sato et al. 2018). That is, the smaller the input parameter’s values are and the
larger the output parameter’s values are, the more efficient a certain process is. In
this manner, DEA does not compare merely how small or large the input or output
parameters are but uses the relative efficiency calculated from both the input and
output parameters. Thus, DEA can be used to compare the different types of functional mobility. Another advantage of DEA is that it allows the integration of multiple
indicators into a single indicator as a relative efficiency value. For example, previous
research integrates the performance and environmental impacts of a household FC
cogeneration system into one efficiency value using DEA (Sato et al. 2018).
In this study, DEA was used to compare 12 paths with different functional units.
Using DEA, the hydrogen paths were assessed via the integrated indicator of exergy
efficiency and LCA results.
The input parameters are the amount of biomass, ADP, and ADP (fossil fuels).
The amount of biomass can represent the heating value difference between woody
biomass (13.23 MJ (Kondo et al. 2018)) and sewage sludge (12.75 MJ, Dry-basis).
In addition, ADP (fossil fuels) can represent the differences between plant-scale
hydrogen production (woody biomass) and on-site hydrogen production (sewage
sludge) because the latter does not include transportation of feedstock and hydrogen.
In this scenario, fossil fuels need not be used for transportation. The output parameters
are the total exergy efficiency and the GWP value of the hydrogen path.
The efficiency DEA was calculated using linear programming problems as follows
(Sato et al. 2018):
maxθ = u 1 y 1k + u 2 y 2k · · · + u n y nk
(35.6)
v 1 x 1k + v 2 x 2k · · · + v m x mk = 1
(35.7)
u 1 y 1k + u 2 y 2k · · · + u n y nk
≤ v 1 x 1k + v 2 x 2k · · · + v m x mk
(35.8)