Energy Conversion and Management X,
Год журнала:
2023,
Номер
20, С. 100470 - 100470
Опубликована: Окт. 1, 2023
In
the
forthcoming
era
of
smart
energy
systems,
decentralised
solutions
are
gaining
increasing
prominence
due
to
their
superior
adaptability
for
interconnecting
sectors,
reduced
inefficiencies,
and
environmentally
friendly
operation.
This
study
introduces
a
new
medium-scale
biogas-based
power
plant
that
utilises
gas
turbine
meet
needs
specific
locality,
encompassing
electricity,
heating,
cooling,
water
supply,
all
whilst
considering
system's
environmental
impact.
To
optimise
plant's
performance,
three
different
multi-objective
optimisation
scenarios
employing
machine
learning
methodologies
Greywolf
algorithms
with
distinct
objective
functions
analysed.
Under
base
conditions,
proposed
showcases
impressive
capabilities,
delivering
1372
kW
246.2
293.3
4.1
kg/s
distilled
water.
It
operates
first
second
law
thermodynamics
efficiencies
72.3%
41.4%,
respectively,
while
maintaining
CO2
emission
index
0.778
kgCO2/kWh.
Furthermore,
net
present
value
investment
return
period
estimated
be
approximately
4.4
million
USD
4
years,
respectively.
Through
(scenario
1)
prioritises
maximising
efficiency
minimising
product
costs
impact,
following
parameters
achieved:
an
exergy
42.7%,
cost
products
at
28.8
$/GJ,
0.762
The
results
reveal
system
not
only
excels
in
but
also
proves
economically
viable
beneficial.
Case Studies in Thermal Engineering,
Год журнала:
2024,
Номер
56, С. 104240 - 104240
Опубликована: Март 18, 2024
Marine
transportation
is
a
significant
contributor
to
overall
energy
consumption
among
sectors
and
responsible
for
producing
considerable
amount
of
greenhouse
gases.
A
potential
solution
alternative
applications
involves
implementing
combined
heat
integration,
thereby
enabling
the
production
additional
utilities
mitigating
emissions.
The
current
work
introduces
an
innovative
integration
process
marine
engine,
focusing
on
optimal
thermal
matching
technique
minimize
irreversibility
in
liquefied
hydrogen
coolant.
incorporates
organic
flash-bi-evaporator
cooling
cycle,
humidification
dehumidification
desalination,
polymer
electrolyte
membrane
water
electrolysis
process,
Claude
cycle.
generated
freshwater
delivered
electrolyzer
produce
gaseous
hydrogen.
This
product
freezing
are
utilized
cycle
liquefaction.
study
utilizes
advanced
thermo-environmental
multi-criteria
investigation
optimization,
considering
sensitivity
analysis
optimization
based
artificial
intelligence
method.
training
testing
neural
networks,
NSGA-II
method,
TOPSIS
decision-making.
primary
objective
functions
include
exergetic
efficiency
carbon
dioxide
emission.
findings
demonstrate
that
specified
objectives
computed
be
0.121
2.67
kg/MWh,
correspondingly.
Besides,
this
condition
exhibits
flow
rate
6.44
L/h
output
43.61
kW,
showing
0.1145.
Also,
total
exergy
destruction
associated
with
arranged
structure
124.5
kW.
Furthermore,
optimum
state
reveals
exergoenvironmental
index
0.840
stability
factor
0.869.
