The feasibility study of the production of Bitcoin with geothermal energy: Case study

Abstract

In this paper, a multigeneration cycle of electricity, cooling, and Bitcoin whose energy source is geothermal, has been subjected to energy, exergy, and economic analyses. The cycle under consideration includes the steam cycle (upstream cycle), the carbon dioxide cycle (downstream cycle), and the liquid–gas line to absorb the heat dissipated by the carbon dioxide cycle. In this cycle, the steam cycle condenser acts as the carbon dioxide cycle evaporator. Part of the electricity generated by this cycle is used to generate Bitcoins. Energy and exergy efficiencies at baseline (excluding Bitcoin production) are 45.8% and 38.1%, respectively. In this cycle, if more power is spent on producing Bitcoin as a product, the energy and exergy efficiencies of the cycle are reduced. Because Bitcoin itself is not valuable in terms of energy and exergy. Considering the average price of Bitcoin during the years 2015–2022 and if 100% of the electricity generated by the system is spent on Bitcoin production, the payback period in 2018, 2021, and 2022 when the price of Bitcoin is equal to 13,412.4, 21,398.8, and $47,743.0, respectively, are less than the baseline. Therefore, the production of Bitcoin with a variety of renewable energies can be considered as a solution. Of course, it should be noted that large changes in the price of Bitcoin can affect the issue of economic benefit.

Full text

Received: 22 June 2023
|
Revised: 24 November 2023
|
Accepted: 30 November 2023
DOI: 10.1002/ese3.1648
ORIGINAL ARTICLE
The feasibility study of the production of Bitcoin with
geothermal energy: Case study
M. A. Ehyaei
1
|F. Esmaeilion
2
|Moein Shamoushaki
3
|H. Afshari
4
|
Biplab Das
5
1
Department of Mechanical Engineering,
Pardis Branch, Islamic Azad University,
Pardis New City, Iran
2
Department of Mechanical Engineering,
K.N. Toosi University of Technology,
Tehran, Iran
3
Department of Industrial Engineering,
University of Florence, Florence, Italy
4
Food Science & Engineering
Department, Faculty of Civil & Earth
Resources Engineering, Islamic Azad
University Central Tehran Branch,
Tehran, Iran
5
Department of Mechanical Engineering,
National Institute of Technology Silchar,
Assam, India
Correspondence
M. A. Ehyaei, Department of Mechanical
Engineering, Pardis Branch, Islamic Azad
University, Pardis New City, Iran.
Email: aliehyaei@yahoo.com
H. Afshari, Food Science & Engineering
Department, Faculty of Civil & Earth
Resources Engineering, Islamic Azad
University Central Tehran Branch,
Tehran, Iran.
Email: afshari1@gmail.com
Abstract
In this paper, a multigeneration cycle of electricity, cooling, and Bitcoin whose
energy source is geothermal, has been subjected to energy, exergy, and
economic analyses. The cycle under consideration includes the steam cycle
(upstream cycle), the carbon dioxide cycle (downstream cycle), and the
liquid–gas line to absorb the heat dissipated by the carbon dioxide cycle. In
this cycle, the steam cycle condenser acts as the carbon dioxide cycle
evaporator. Part of the electricity generated by this cycle is used to generate
Bitcoins. Energy and exergy efficiencies at baseline (excluding Bitcoin
production) are 45.8% and 38.1%, respectively. In this cycle, if more power
is spent on producing Bitcoin as a product, the energy and exergy efficiencies
of the cycle are reduced. Because Bitcoin itself is not valuable in terms of
energy and exergy. Considering the average price of Bitcoin during the years
2015–2022 and if 100% of the electricity generated by the system is spent on
Bitcoin production, the payback period in 2018, 2021, and 2022 when the price
of Bitcoin is equal to $13,412.4, $21,398.8, and $47,743.0, respectively, are less
than the baseline. Therefore, the production of Bitcoin with a variety of
renewable energies can be considered as a solution. Of course, it should be
noted that large changes in the price of Bitcoin can affect the issue of economic
benefit.
KEYWORDS
bitcoin, electricity, exergy, geothermal
1|INTRODUCTION
The tendency for cryptocurrencies has been growing in
recent years by increasing their value in the worldwide
market. Also, day by day, more companies and
businesses accept payment methods by cryptocurren-
cies, leading to more attention to investing in them.
Nevertheless, there is a massive challenge in mining
these cryptocurrencies: the remarkable electricity
consumption of relevant equipment.
1–4
The process
of validating Bitcoin blocks demands a substantial
quantity of electricity, contributing to heightened
greenhouse gas emissions. Consequently, prominent
nations like China, Iran, Russia, Turkey, and Vietnam
Energy Sci. Eng. 2024;12:755–770. wileyonlinelibrary.com/journal/ese3
|
755
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2023 The Authors. Energy Science & Engineering published by Society of Chemical Industry and John Wiley & Sons Ltd.

are prohibiting Bitcoin mining to avert grid imbal-
ances, power outages, and environmental concerns.
5
Many researchers are thinking about the environmen-
tal impacts and carbon footprints of cryptocurrency
mining. Besides, these days, mining cryptocurrencies
consumes a considerable amount of produced electri-
city in the world.
6–8
Certainly, utilizing electricity
generated from fossil fuels for Bitcoin production
presents several challenges. This energy could serve
more crucial purposes, and its application for Bitcoin
production exacerbates environmental pollution. How-
ever, in light of the imminent role of digital currencies
in trade, this aspect cannot be overlooked. Conse-
quently, employing renewable energy resources for the
production of Bitcoin and other digital currencies
emergesasapotentialsolution.
4,9
Providing decarbonized energy for different sectors
of society is one of the most challenging issues
researchers are trying to tackle. Nowadays, with a
fast‐growing technology trend, the energy demand has
increased in many countries. For example, the energy
demand in 2017 showed a vast increase compared with
1950 (around 5.5 times).
10
On the other hand, the
significant fluctuation in fossil fuel prices affected
the energy market and industry predictions. Besides,
the fossil fuels application has had negative impacts on
controlling the global warming rising trend. These
factors prove that the transition from pollutant fuels to
renewable resources is inevitable. In this case, the
introduction of novel energy resources in different
sectors can provide vital influences.
11,12
To elaborate
on this issue, different sectors such as transportation,
industrial, residential, entertainment, financial, and so
forth have severe dependency on energy sources for
sustainable performance. By taking the environmental
issues, charges for fuel, availability, and sustainability
into consideration, renewable energy can provide
promising salvation for the aforementioned applica-
tions. Solar, wind, and geothermal energy are the most
practical and renowned types of renewable energy.
While the operation of solar and wind energies can be
greatly erratic and suffer from a lack of consistency,
geothermal energy can present a promising approach
for the required sustainable operation. This can affect
different aspects of the operational parameters both
technical and economic features. This energy has been
considered a clean resource as it is weather‐
independent.
13
Besides, geothermal‐based power
cycles have excellent potential to provide energy for
different applications such as heating and cooling in
addition to electricity production.
14
In addition, using
the remaining heat of geothermal fluid before reinjec-
tion could decrease the thermal loss.
2|LITERATURE SURVEY
While numerous studies have concentrated on energy
consumption and associated demands linked to Bitcoin
mining, fewer have focused on examining practical
solutions for alternative and more sustainable energy
supply. Stoll et al.
15
predicted that the energy consump-
tion of the Bitcoin mining process by November 2018 is
around 45.8 TWh and its yearly carbon footprint is in the
range of 22–22.9 Mt CO
2
. Another issue would be the
fluctuation of the grid network. In some cases, it can
affect the stability of the electricity transmission network.
Another issue would be stealing the electricity to mine
cryptocurrencies. Küfeoğlu and Özkuran
16
addressed the
computational power requirements involved in the
proof‐of‐work process, neglecting to assess the overall
energy intensity associated with mining activities. They
determined the peak energy demand period by analyzing
the installed Bitcoin mining equipment in certain
European countries. Several researchers have investi-
gated the implementation of renewable energy to supply
the necessary electricity for Bitcoin mining as a potential
solution.
Govender
17
examined and elucidated the phenome-
non of cryptocurrency mining utilizing renewable energy
by exploring innovative business models. The findings
indicated that the utilization of renewable energy for
cryptocurrency mining is an expanding business sector
motivated by the goal of maximizing profits through the
utilization of the most cost‐effective renewable energy
sources. Malfuzi et al.
18
conducted a thermodynamic and
economic modeling of a solid oxide fuel cell system,
powered by either natural gas or biogas as a renewable
energy source, which is employed not only to fulfill the
necessary power for Bitcoin mining but also to meet the
electricity demand. Bastian‐Pinto et al.
19
created a
numerical application using the real options approach
to assess the financial consequences of investing in a
Bitcoin facility for a wind energy producer. They
mentioned it can enable the producer to strategically
adjust outputs based on the future price differentials
between electricity and Bitcoins. Kumar
20
reviewed the
feasibility of utilizing geothermal energy for the purpose
of Bitcoin mining. He concluded that geothermal energy
stands out as a highly cost‐effective and environmentally
friendly choice among various alternative energy sources
for Bitcoin miners engaged in mining activities.
Corbet et al.
21
investigated the impact of Bitcoin
prices on energy markets and utility companies. Their
findings reveal a consistent and substantial impact of
cryptocurrency energy consumption on the performance
of specific companies in the energy sector, distinguished
by jurisdiction. This underscores the need for additional
756
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EHYAEI ET AL.

evaluation of the environmental implications associated
with the growth of cryptocurrencies. Gundaboina et al.
22
discussed the outcomes of overclocking and undervolting
in terms of power consumption and hash rate during the
mining of Dogecoin, utilizing solar energy as the
renewable power source. They mentioned the prospec-
tive direction of crypto mining using renewable energy
and its linked hardware configuration, aiming to dimin-
ish electronic waste and enhance sustainable develop-
ment. Niaz et al.
5
performed a technoeconomic evalua-
tion across 50 states and a federal district in the United
States, examining the viability of Bitcoin mining through
the integration of carbon capture and renewable energy.
They assessed the carbon footprint for each state from
both economic and environmental standpoints to ascer-
tain their competitive advantages.
Yüksel et al.
23
investigated the Bitcoin mining using
nuclear energy. It was suggested that opting for nuclear
energy in Bitcoin mining would be advantageous. They
reported that it eliminates the release of carbon gases
into the atmosphere, leading to a substantial reduction in
environmental pollution. Kang et al.
24
researched mon-
itoring the electricity stealing for mining purposes
according to the available data. They assessed the system
voltage, electricity consumption of users, and its effect on
electricity loss in the network. They reported that
renewable energy applications for cryptocurrency mining
could reduce environmental impacts and network
problems. Velický
25
addressed the obstacles associated
with the shift to renewable energy, examined the
characteristics of the Bitcoin network, and explored the
involvement of Bitcoin mining operations in the global
production and consumption of energy. Vega‐Marcos
et al.
26
proposed the development of a wind power plant
to generate energy for Bitcoin mining in Spain. They
suggested that if a wind power facility simultaneously
invests in cryptocurrency mining while generating
electrical energy for the grid, it has the flexibility to
choose when to participate in the electricity market pool
or focus on mining activities. However, based on the
literature review, it is evident that there is a notable
deficiency in the assessment of renewable energy‐based
systems for the purpose of cryptocurrency mining.
In our research, we have examined a power system
that relies on geothermal energy, integrated with
additional units to enhance electricity generation effi-
ciency. Drawing on studies conducted by other research-
ers, geothermal energy has demonstrated notable poten-
tial for offering low‐carbon energy and substantial
flexibility for integration with other cycles. Subsequently,
researchers have been focusing on modeling and analyz-
ing geothermal‐based power cycles as a multiproduction
system to supply low‐carbon energy for diverse
applications. In addition, geothermal fluid, before
reinjection, retains a significant amount of residual heat
that can be utilized in other units to enhance the
efficiency of the overall system. Wang et al.
27
evaluated a
geothermal‐based system linking with a transcritical CO
2
cycle according to the liquefied natural gas (LNG) cold
energy application. Their assessment showed that rising
exergy efficiency leads to growth in bigger areas of heat
exchangers per network. Ahmadi et al.
28
carried out the
thermodynamic evaluation of a geothermal‐based cycle
coupled with transcritical CO
2
to generate power. They
applied optimization tools, three decision‐making ap-
proaches, sensitivity assessment, and error testing to
reach the best solution. They finally compared their
results with other obtained ones based on previous
studies.
Li et al.
29
assessed a transcritical CO
2
cycle utilizing
geothermal energy from energetic and economic views.
An energy and parametric study of a geothermal system
integrated with an ejector‐assisted transcritical CO
2
cycle
has been performed by Zare and Rostamnejad Takleh.
30
They compared this system with studies done by other
researchers. They also investigated the design variables'
influence on the composed unit's thermodynamic behav-
ior. Ehyaei et al.
31
modeled a geothermal cycle integrated
with cooling, sodium hypochlorite, and reverse osmosis
(RO) units. Energetic assessment results showed that the
presented cycle can generate 1.751 electricity and 1.04
cooling capacity. Also, the investment return is predicted
at 2.7 years. A combined geothermal system linked with
LNG, Proton Exchange Membrane, and Organic Rankine
Cycle (ORC) units has been evaluated from thermo-
dynamic and exergoeconomic aspects by Mehdikhani
et al.
32
The thermal efficiency of their designed cycle has
increased by 3.18% compared to previous similar studies.
A geothermal cycle with district heating and total
reinjection of noncondensable gases has been evaluated
from energetic and economical, and environmental
points of view by Shamoushaki et al.
33
They assessed
this system in subcritical and supercritical cases, and the
Levelized Cost of Energy for subcritical is obtained at
5.52
c€/kWh
and at supercritical at 6.96
c€/kWh
.
Habibollahzade et al.
34
researched the difference in
heat recovery of a geothermal system using a CO
2
cycle
(in both transcritical and supercritical cases) and an
ORC energetically and economically. Their results
illustrated that the transcritical CO
2
system produces
more power than others. Li et al.
35
researched a
geothermal cycle combined with a thermal storage unit
from thermodynamic and economic aspects. They opti-
mized their proposed system, and at the optimum point,
thermal efficiency and unit cost of production were
calculated at 23.35% and 17.07 $/GJ, respectively. Afshari
EHYAEI ET AL.
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757

et al.
36
examined an integrated geothermal system
connected to a CO
2
cycle, an RO system, an electro-
dialysis unit, a lithium bromide absorption chiller, and a
liquefaction unit for natural gas. The objective was to
generate electricity, cooling, desalinated water, sodium
hydroxide, and hydrogen.
Beyond the inherent capabilities and advantages of an
integrated geothermal power cycle, incorporating addi-
tional components such as a CO
2
cycle and LNG tank can
further enhance the overall benefits of the system. The
combination facilitates the utilization of cold energy
derived from LNG, thereby augmenting the overall
efficiency of the power generation process. This coupling
allows for the recovery of both CO
2
and LNG cold energy,
optimizing the utilization of resources within the power
generation system. The inclusion of a CO
2
cycle has the
potential to contribute to carbon capture and storage,
potentially lessening the environmental impact of the
power plant by mitigating CO
2
emissions. The synergistic
interaction among the geothermal system, CO
2
cycle, and
LNG tank unit can result in enhanced performance
parameters, including thermal efficiency and exergy
efficiency, when compared with conventional alterna-
tives. This integrated system may also yield economic
advantages by optimizing resource utilization and boost-
ing the overall efficiency of the power plant, potentially
leading to reduced operational costs. These benefits have
motivated researchers to incorporate these integrations
with other systems. A new definition of the ORC system
has been designed by Choi et al.
37
to harness the cold
energy from LNG for power generation. They analyzed
and optimized this system from energy, economic, and
environmental views.
The assessment showed that their novel system had
more efficient performance than previous conventional
ones. An ORC cycle to apply LNG cold energy has been
analyzed and optimized by Sun et al.
38
Their assessments
showed that a decline in LNG inlet temperature has an
effective role in lessening the cycle irreversibilities. Xia
et al.
39
designed a novel CO
2
cycle recovering LNG cold
energy working via ambient temperature. Their analysis
illustrated that thermal efficiency was obtained at 6.75%
and network at 108.7 kW. The evaluations showed that a
lower ambient mass flow rate had less influence on the
cycle operation. Cao et al.
40
assessed a biomass‐based
cycle linked with a CO
2
cycle, LNG tank, electrodialysis,
and multieffect distillation. Thermal and exergy efficien-
cies are obtained at 75.1% and 88.4%, respectively.
Gawusu et al.
41
a careful consideration of blockchain
and its relevance in applying renewable energy to
provide decarbonized electricity. Their assessment proves
that many researchers in recent years have concentrated
on integrating blockchain technology with renewable
resources considering the challenges and possible
solutions.
This study aims to address several research gaps that
have received little or no attention thus far. From the
examination of existing literature, there is a limited
number of studies that have explored the power
generation for cryptocurrencies specifically with a focus
on renewable energy sources. In addition, reviewing the
existing research literature, it can be inferred that there is
a notable absence of articles investigating the utilization
of geothermal energy specifically for Bitcoin production.
While numerous studies have explored the application of
geothermal energy for various purposes such as elec-
tricity generation, hydrogen production, and heating and
cooling systems, there is a distinct lack of research
elucidating the use of geothermal energy in the produc-
tion of digital currencies for added benefits. Moreover,
this study introduces a new configuration based on
geothermal energy designed for the efficient generation
of multiple products. This configuration is capable of
generating the necessary electricity for Bitcoin mining, in
addition to conventional power and cooling outputs. The
findings of this study have the potential to guide
policymakers, investors, and stakeholders in recognizing
this system as a promising solution for commercializa-
tion. This can contribute to reducing the volatility and
dependence of Bitcoin mining sites on fossil‐based fuel
power production and grid networks. This proves
especially advantageous in areas with unreliable or
insufficient grid infrastructure, providing miners with
increased authority over their energy provision and
diminishing susceptibility to power interruptions. Fur-
thermore, policymakers must navigate a delicate balance
between fostering innovation and economic development
linked to cryptocurrency technologies and the pressing
need to shift towards more sustainable and cleaner
energy sources which the results of this research would
be an effective solution.
A notable policy‐level challenge linked to Bitcoin
mining relying on fossil‐based power production is its
environmental impact and carbon footprint. Mining
operations for Bitcoin, especially those powered by
fossil fuels, contribute to heightened greenhouse gas
emissions, intensifying apprehensions regarding cli-
mate change and environmental sustainability. Given
the growing importance of digital currencies and their
market growth, especially in recent years, the lack of
this research is observed. As the focus on environ-
mental issues intensifies, authorities might enforce
more rigorous standards on energy‐intensive sectors,
like, Bitcoin mining. The adoption of renewable energy
can position mining operations to adhere to current
and forthcoming regulations, preventing potential
758
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EHYAEI ET AL.

legal and financial consequences. The scope of this
study is aligned with all these aspects. This article
proposes a new geothermal arrangement cycle to
analyze its energy, exergy, and economics. The
products of this proposed system are electricity and
cooling. Some of the electricity generated is used to
generate Bitcoins. Then, based on the percentage of
electricity that is spent on Bitcoin production and
changes in Bitcoin prices during the years 2015–2021,
sensitivity analysis is performed. The contributions of
this paper are as follows:
•A case study of a power generation system with a
geothermal energy resource to generate electricity and
Bitcoin.
•Investigating the energy, exergy, and economic effects
of Bitcoin production on the electricity production
system.
3|SYSTEM DESCRIPTION
The designed system is intended to produce electric
power from geothermal resources. The part of electricity
is used by Bitcoin miners. Figure 1demonstrates the
configuration of the designed system consisting geo-
thermal cycle, CO
2
cycle, and LNG line. This system has
the proper potential for electric energy production. The
system operation can be divided into two parts: (1)
geothermal cycle and (2) CO
2
—LNG cycle. In the
geothermal cycle, hot water is extracted from the
production well (1), and after pressure regulation in
the expansion valve; the obtained fluid is divided into
vapor and liquid phases. High‐grade energy vapor is
expanded in the turbine for electrical energy production
(3) while the liquid phase is sent to the second expansion
valve to be modified for the combination process in the
mixing chamber (4). In the mixing chamber, the obtained
FIGURE 1 Configuration of the proposed system for Bitcoin production. LNG, liquefied natural gas.
EHYAEI ET AL.
|
759

streams from the turbine outlet and expansion valve II
are combined to be employed as an energy source for the
CO
2
cycle in the evaporator (7). By losing the energy
content, the low‐grade fluid is returned to the production
well (8).
Through the CO
2
cycle, CO
2
is heated by the
geothermal working fluid in the evaporator (10). High
pressure and temperature fluid are sent to the expander I
for auxiliary power production (11). In the next stage
(12), the working fluid is cooled down by the LNG stream
in the condenser (9). After that, the designed pump
increases the working fluid's pressure (10).
In the LNG stream, after increasing the associated
temperature in the condenser (13), LNG turns to its
vapor phase as NG (natural gas) and expands in the
expander II (14). At that point, the working fluid is used
for cooling purposes in the cooler unit (15). The obtained
fluid from the cooler is sent for specific applications. One
important concept belongs to the produced electric
power. A specific share is sent for household applications
while the remaining part is delivered to the Bitcoin
miners for Bitcoin production.
4|MATHEMATICAL MODELING
In the first stage, several initial considerations are
established to simplify the modeling process
31,42–44
:
•All included processes are steady‐state.
•The associated polytropic efficiency of the involved
expanders, pumps, and turbine is equal to 85%.
•All heat exchangers have an effectiveness equal to 85%.
Also, their type is shell and tube.
•The working fluid of the geothermal cycle is hydro-
thermal type. The considered environmental condi-
tions are 15°C temperature and 1 bar for pressure.
•Related values to the potential and kinetic energies are
ignored.
•Presented connections and pipelines do not have any
type of pressure loss.
In a standard and simple method, balances equations
of the mass and energy rates are presented as
43,45


mm
=
,
in out (1)


QW mh hh
mh h h
−=(+(−))
−(+(−))
.
P
f0
R
f0
(2)
While Q
and Ẇsignify the rates of heat transfer and
shaft work (kW), each, ṁstands for the mass flow rate
(kg/s), and hrepresents specific enthalpy (kJ/kg).
Presented subscripts for the product, formation, reactant,
and ambient condition are shown by P, f, R, and 0,
respectively.
Table 1tabulates the related equations for each
component based on Equations (1) and (2). It should be
noted that ηsymbolizes the effectiveness of related
components.
The net value of generated power by the proposed
system can be calculated as follows
46
:
TABLE 1 Fundamental balance equations for the designed configuration.
Component Mass balance Energy balance
Separator
mmm
=+
23
4
mh mh mh
=+
22 33 4
4
Turbine
mm
=
3
6
W
mh h
=(−
)
T336
Evaporator
mm
=
7
8
mm
=
10 1
1
mh mh mh mhη
−=( −)
11 11 10 10 7 7 8 8 Evaporato
r
Expansion valve I
mm
=
12
h
h=
1
2
Expansion valve II
mm
=
45
h
h=
4
5
Mixing chamber
mmm
+=
56
7
mh mh m h
+=
55 66 7
7
Expander I
mm
=
11 1
2
W
mh h
=(−)
EXPI 11 11 12
Expander II mm
=
14 1
5
W
mh h
=(−
)
EXPII 14 14 15
Pump
mm
=
91
0
W
mh h
=(−)
P9109
Condenser
mm
=
91
2
mm
=
13 1
4
mh mh mh mhη
−=( −)
14 14 13 13 12 12 9 9 Condenser
Cooler
mm
=
15 1
6
Q
mh mh
=−
Cooler 15 15 16 1
6
760
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EHYAEI ET AL.

W
WW W W
=++−.
net,sys T EXPI EXPII P (3)
To calculate the effectiveness of the energy perform-
ance of the CO
2
cycle and total system, the following
equations are presented
47
:
η
WW
mh mh
=−
−
,
CO cycle
EXPI P
77 88
2(4)
η
W
mh mh
=
−
.
sys
net,sys
11 88
(5)
On the basis of the definition, specific exergy
comprises physical, chemical, kinetic, and potential
sections as follows
48,49
:


exexVe gz h h T s s
TxmR y
=+
2++(−)−(−)
+ln,
i
ii i
chi
2
00 0
0
(6)
while x
i
and esymbolize mass fractions and specific
exergy. Ve,g, and zrepresent velocity, gravity accelera-
tion, and height. Also s and y symbolize specific entropy
and molar fraction, respectively. Subscripts ch, i, and 0
characterize the chemical, component number, and
environmental conditions, respectively.
To calculate the exergy efficiency of the CO
2
cycle
and the total cycle, the following expressions are applied:
Ɛ
WW
me me
=−
−
,
CO cycle EXPI P
77 88
2(7)
Ɛ
W
me
=

.
sys
net,sys
11
(8)
To study the economic behavior of the proposed
system, the annual income (CF) is calculated by
Equation (9).
50
CF Y k=
,
Power Power (9)
here
Y
Power
stands for the produced power per annum and
k
Power
represents the specific cost of generated power
which is equal to 0.22 US$/kWh.
51
The investment charge (C
0
) for the total system is
written as
52,53
CC C C
C
=++
+,
0 I,Geothermal loop I,Miner I,CO cycl
e
I,LNG line
2(10)
here C
I
represents the charge of investment and setting
up for involved subsystems. Table 2provides the
associated equations for the investment and commission-
ing of each subsystem. Besides, calculating the opera-
tional charges, a 3% surplus of the initial capital costs is
measured.
52,53
In Table 2,zand Adesignate the well's
depth and area, respectively.
The surface area of heat exchangers is a crucial
parameter in economic analysis. In this case, the logarithmic
method is employed to calculate this parameter
59
:
Q
UAF T
=
,
tln
∆(11)
TABLE 2 Related equations of investment cost for the subsystem.
Component Cost function Reference
Geothermal loop
Geothermal well z
1
6.5 ×
1.60
7
[54]
LNG loop
LNG expander
()
()
TT
4
79.34 ln (1 −exp (0.036 −exp(0.036 −54.4))
)
m
η
P
P

0.93 −31 31
31
T
31
32
[49]
LNG cooler QW
1
.218 × exp(0.4692 + 0.1203 ln( ) + 0.0931(ln( )
)
)
2[55]
CO
2
cycle
Pump
1
0
WW3.3892+0.05361 lo g +0.1538 (log )
2
[56]
Turbine
1
0WW2.6259 +1.43981 log −0.1776 (log )
2[57]
Heat exchanger A
(
/0.093)
0.7
8
[57]
Miner [58]
Miner 12,000
Abbreviation: LNG, liquefied natural gas.
EHYAEI ET AL.
|
761

where Ustands as the coefficient of the overall heat
transfer, Arepresents the surface area (m
2
),
F
t
is a
correction factor, and
T
ln
∆
is the logarithmic mean
temperature difference. Table 3presents the values of the
overall heat transfer for each component.
60,61
The inflation rate is an important parameter that
influences the cost functions. On the basis of the operational
ears, the related impacts can be considered by
62
CC i=(1+)
,
nn
0(12)
while nsignifies the number of years, and istands for the
inflation rate and is 3.1% for all operating years.
31
To calculate the simple payback period (SPP), a ratio
of expenses and revenues is provided as
52,53
S
PP C
CF
=
.
n(13)
Also, the payback period (PP), which calculates the
payback time of a paid investment, is defined as
52,53
()
PP r
=
ln
ln(1 + )
,
CF
CF r C−·n(14)
where rrepresents the discount rate and is equal to 3%
for all operating years.
To calculate the net present value (NPV), Equation
(15) is employed. In this equation, a trade‐off between
income and investment costs is established
52,53
:
N
PV CF r
rr C=(1 + ) −1
(1 + ) −
,
N
Nn(15)
where Nstands for the system's lifetime and is expected
to be 25 years.
One of the main parameters in the economic study is
the internal rate of return (IRR), presented as
52,53,63






















IRR CF
CIRR
=1−1
(1 + )
.
nN(16)
5|ELECTRICAL POWER
CONSUMPTION TO PRODUCE ONE
BITCOIN
On the basis of ASIC models and the manufacturer's
market share, the power consumption of miners to
produce each Bitcoin can be calculated. For this purpose,
many ASIC models are allocated in proportion to the
market share of each manufacturer.
64–67
According to the mining pool data, it takes about
122,000 TH/s (hash rate) over 24 h to generate 1 Bitcoin
based on BTC price, difficulty level, and network
size.
64–67
The hash rate (hash per second, h/s) is a unit derived
from SI that represents the number of dual calculations
in the Bitcoin network per second.
64
On the basis of the data provided for the 25 miners
who have the major market share and their information,
which includes the model name, number of calculations
per second, and their efficiency, we reach 142,498 kW per
Bitcoin production.
66
6|RESULTS AND DISCUSSION
6.1 |Results validation
The actual case of the Kamojang Geothermal Power
Plant in Indonesia has been used for validation.
68
The
TABLE 3 Values of Ufor designated components.
Component
U
(W/m K)
2
SEP (separator) 300
COND (condenser) 800
Heat exchanger 700
Evaporator 700
TABLE 4 Computer code input data.
No. Parameter Unit Value
1P
1
Bar 9
2ƞ
T
–0.85
3T
1
°C 170
4ƞ
P
–d.85
5M
1
kg/s 111.1
6P
2
Bar 1.2
7P
6
Bar 1
8T
8
°C 60
9P
10
Bar 14
10 P
9
Bar 5.5
11 Ƞ
HX
–0.8
12 P
13
Bar 6.6
13 T
14
°C 48
14 P
15
Bar 1
15 T
6
°C 20
762
|
EHYAEI ET AL.

capacity of the power plant is 55 MW and its geothermal
source is steam with a temperature of 245°C Which is
extracted from 10 geothermal wells. On the basis of the
data collected from the operating power plant, the energy
efficiency of the considering power plant is 35.86%.
This efficiency means that 111,138.92 kW of electrical
energy is extracted from 309,000 kW of geothermal energy.
To validate the data of the considered power plant, input
data have been collected from Tables 1to 3of Rudiyanto
et al.
68
and have been considered as input to the EES code
written for this article. It is worth noting that changes
have been made to the EES code to match the layout of the
reference geothermal power plant.
68
On the basis of the
input data, the geothermal efficiency of the cycle is
calculated to be about 37.1%. The error rate is about 3.4%.
To confirm the system simulation codes, the CO
2
cycle
and LNG line are selected as the design parameters for
model validation. In this prospect, research conducted by
Naseri et al.
69
is designated to validate this subsystem
performance. Three important outputs are compared: power
generation by the CO
2
cycleandtheLNGexpanderand
power consumption by the CO
2
cycle pump. Table 4presents
the results of the current model obtained results by Naseri
et al.
69
From the main perspective, the results are in good
agreement. The errors present are due to the calculation of
the thermodynamic properties with different software
libraries (Table 5).
6.2 |Results discussion
A computer code has been developed in EES software for
the studied system and modeling of energy, exergy, and
economy. The same software has been used to calculate
the thermodynamic properties of fluids. Computer code
input data are shown in Table 4.
Table 6shows the thermodynamic properties of each
point in the system, including mass flow rate, pressure,
temperature, specific enthalpy, entropy, and exergy.
Table 7shows the specifications of the system
products in their basic state. In the basic state, no
Bitcoin is produced. The whole system generates
4202 kW of electricity, which is 3838 kW of the CO
2
cycle. The amount of cooling produced by the cooler
located in the LNG line is equal to 19,613 kW. The energy
TABLE 5 Comparison of main parameters from the current
model and Naseri et al.
69
Parameter Current research Naseri et al.
69
Error (%)
W

EXP
I
(kW) 14.2 14.66 3.13
W

EXPI
I
(kW) 7.19 7.46 3.61
W
Pum
p
(kW) 4.98 4.78 4.01
TABLE 6 Thermodynamic properties of each point in the system, including mass flow rate, pressure, temperature, specific enthalpy,
entropy, and exergy.
No. ṁ(kg/s) P(bar) T(°C) h(kJ/kg) s(kJ/kg K) e(kJ/kg)
1 111.1 9 170 719.3 2.042 115.1
2 111.1 1.2 105.8 719.3 7.303 −1454
3 13.86 1.2 105.8 2683 7.298 512
4 97.24 1.2 105.8 439.4 1.361 38.18
5 97.24 1 99.63 439.4 7.359 −1750
6 13.86 1 99.63 2657 7.359 467.5
7 111.1 1 99.63 716.1 2.104 93.43
8 111.1 1 60 251.2 0.8311 7.973
9 96.43 5.5 −55.17 −424 −2.205 234.5
10 96.43 14 −54.85 −423.2 −2.205 235.2
11 96.43 14 45 5.305 −0.4669 145.5
12 96.43 5.5 −9.398 −35.35 −0.4392 96.63
13 84.95 658.5 −65.17 −502.4 −5.095 1019
14 84.95 658.5 48 −149.4 −3.741 968.1
15 84.95 101.3 −39.87 −348.9 −3.588 723.1
16 84.95 101.3 20 −118 −2.697 688.4
EHYAEI ET AL.
|
763

and exergy efficiencies of the system are equal to 45.8%
and 38.1%, respectively. Exergy efficiency is about 16.8%
lower than energy efficiency due to a lower exergy
cooling value compared with energy.
Figure 2shows the changes in power output along
with the ratio of the amount of power consumed by the
miners to the total power output. In other words, the
ratio Xrepresents the amount of electrical consumption
of the miners to the total electricity generated. The trend
of this chart is linear due to the electrical consumption of
miners.
Figure 3shows the changes in system energy and
exergy efficiencies along with the Xratio. The trend in
both charts is downward. That is, by increasing xfrom
0% to 100%, the energy and exergy efficiencies of the
system decrease from 45% and 34.5% to 37.7% and 2.8%.
The reason for this decline is that the production of
Bitcoin cryptocurrency is not valuable from the point of
view of energy and exergy and is only of economic value.
For example, if the electricity generated by this system
was used to generate hydrogen by decomposing water in
the electrolysis machine instead of producing Bitcoins
for the miners, the process of energy efficiency and
exergy of the system would not be so declining.
It should be noted that the negative slope of the
exergy efficiency curve is greater than the energy
efficiency, which is more sensitive to the reduction of
generated electricity due to the lower value of cooling
produced from the exergy perspective (note the exergy
efficiency equation).
Table 8shows the economic indicators of the system
in its basic state. As previously explained, the basic state
is one in which no Bitcoin is produced.
TABLE 7 Specifications of the system products in the basic
state.
Parameter Unit Value
W

net,CO
2
kW 3838
Ẇ
net,sys
kW 4202
Q
cooleṙ
kW 19,613
ƞ
en,sys
–45.8
ƞ
ex,sys
–38.1
FIGURE 2 Changes in power output along with the Xratio.
FIGURE 3 Changes in system energy and exergy efficiencies
along with the Xratio.
TABLE 8 Economic indicators of the system in the basic state.
Parameter Unit Value
PP Year 2.22
SPP Year 2.11
NPV Million US$ 113.2
IRR –0.473
Abbreviations: IRR, internal rate of return; NPV, net present value; PP,
payback period; SPP, simple payback period.
FIGURE 4 PP and SPP changes of the system with Xratio. PP,
payback period; SPP, simple payback period.
764
|
EHYAEI ET AL.

In this table, the price of Bitcoin is estimated at US
$47,743 which is the average price in 2022. Due to the
production of Bitcoin by the studied system, compared
with other power‐generating systems with geothermal
sources, the economic indicators of the system have
improved significantly.
Figure 4shows the PP and SPP changes in the system
with the Xratio. In Figure 4, the price of Bitcoin is
estimated at $40,000. By increasing the Xratio, both PP
and SPP economic parameters decrease. This means that
the higher the percentage of generated electricity to
produce Bitcoin, it is the more economically viable it.
This economic benefit depends on the price of Bitcoin in
the cryptocurrency market. Figure 5shows the changes
in NPV and IRR with the Xratio, the process, and logic of
which are similar to Figure 4.
In the next part of the article, the changes in the
economic parameters of the system based on the average
price of Bitcoin from 2015 to 2022 are examined. Figure 6
shows the final average price of Bitcoin from 2015 to
2022. From 2015 to 2022, the average price of Bitcoin has
always been up, except in 2019 and 2020.
Figure 7shows the changes in PP and SPP from 2015
to 2022. In this case, it is assumed that 100% of the power
generated by the system is used to generate Bitcoins.
Comparing the data in Figure 7with the values in
Table 2, it can be concluded that in 2018, 2021, and 2022,
when the price of Bitcoin was US$13,412.4, US$21,398.8,
and US$47,743, respectively, PP and SPP values were
lower than the baseline. So it can be concluded that the
price of Bitcoin has a direct effect on PP and SPP.
Similar to Figure 7,NPV and IRR in different years
are shown in Figure 8. The price effects of Bitcoin on
NPV and IRR are similar to PP and SPP.
Figure 9shows the changes in PP based on the
percentage of electricity spent on Bitcoin production
(X%) based on the average price of Bitcoin during the years
2018–2022. During the years 2018–2020, when the price of
Bitcoin was US$13,412.40, US$3869.4, and US$7188.46,
respectively, the system PP increased by a percentage of X.
FIGURE 5 NPV and IRR changes with the Xratio. IRR,
internal rate of return; NPV, net present value
FIGURE 6 Final average price of Bitcoin from 2015 to 2022.
FIGURE 7 The changes in PP and SPP
over different years, assuming that 100% of
the system's electricity is used to generate
Bitcoins. PP, payback period; SPP, simple
payback period.
EHYAEI ET AL.
|
765

Of course, the slope of this increase in 2019 will reach
its maximum. In 2021 and 2022, when the average price
of Bitcoin reached US$21,398.8 and US$47,743, the slope
of the chart became negative.
It should be noted that the price of Bitcoin depends
on the price of electricity in addition to market
conditions. In other words, the base price of Bitcoin
depends on the price of electricity in that
area. For example, in Qatar, where the price of electricity
is 0.03 US$/kWh, the base price of Bitcoin is US$3610.8.
Also, in Denmark, where the price of electricity is
0.36 US$/kWh, the base price of Bitcoin is US$43,329.
So, in the country or geographical region, first of all,
you should calculate the basic price of Bitcoin based on
the price of electricity, and then based on the market
price of Bitcoin, you should conclude whether Bitcoin
production is cost‐effective or not.
7|CONCLUSION AND
RECOMMENDATION
The combination of renewable energy resources with
financial concepts can provide a sustainable approach to
policy‐making decisions. Limited controls over the
currency supplies is one aspect that springs from the
decentralized features of Bitcoin and the absence of a
fundamental supervisory entity, standard financial polic-
ies tools have the potent effects on sustainable financial
methods. Moreover, original sources of data, improved
FIGURE 8 Changes in PP and SPP over
different years, assuming that 100% of the
system's electricity is used to generate
Bitcoins. IRR, internal rate of return; NPV,
net present value; PP, payback period; SPP,
simple payback period.
FIGURE 9 Changes in PP based on the
percentage of electricity spent on Bitcoin
production (X%) based on the average price
of Bitcoin during the years 2018–2022. PP,
payback period.
766
|
EHYAEI ET AL.

financial inclusions, and enhanced financial competi-
tions can be extracted from such a consideration.
This study introduces a new way to use geothermal
energy to make electricity for Bitcoin mining. This new
system has three parts: the steam cycle, the carbon
dioxide cycle, and the liquid gas line. The steam cycle's
condenser is used as the evaporator for the carbon
dioxide cycle, and the liquid gas line absorbs heat from
the carbon dioxide cycle. The system also uses LNG to
make natural gas, which powers a turbine and produces
cooling. The electricity generated by this system is used
to mine Bitcoin. In summary, the system produces
electricity, cooling, and Bitcoin. The key findings from
this research are as follows:
•The system generated 4202 kW of electricity, mostly from
the CO
2
cycle without Bitcoin production. The cooling
system provided 19,613 kW of cooling. The system's
energy efficiency and exergy were calculated to be 45.8%
and 38.1%, respectively. The difference in cooling value
resulted in exergy efficiency being about 16.8% lower.
•Bitcoin production had a positive impact on the
system's economic indicators. With Bitcoin produc-
tion, the PP was estimated to be 2.22 years, the
sensitivity PP was 2.11 years, the NPV was $113.2
million, and the IRR was 0.473.
•These economic parameters were sensitive to Bitcoin
price fluctuations. As the percentage of electricity
allocated to Bitcoin production (X%) increased, and
especially with higher Bitcoin prices, the economic
outcomes became more favorable.
The study highlights the interdependence of geo-
thermal power plant performance and Bitcoin mining
profitability, with the economic attractiveness of the
system closely tied to Bitcoin price fluctuations. Policies
regarding the integration of geothermal power plants
with Bitcoin mining should consider the dynamic nature
of the cryptocurrency market, and incentives may be
more favorable when Bitcoin prices are high. This
approach not only contributes to the growth of the
cryptocurrency industry but also promotes the utilization
of renewable energy sources, fostering a more sustainable
and economically viable future.
NOMENCLATURE
Aarea (m
2
)
C
0
investment cost (US$)
C
ei
exergoenvironmental impact coefficient
CF annual income (US$)
C
I
investment and installations charge (US$)
C
n
investment cost in the nth year (US$)
especific exergy (kJ/kg)
Ėexergy rate (kW)
f
ei
exergoenvironmental factor
f
es
stability factor
F
t
correction factor
gacceleration due to gravity (m/s
2
)
hspecific enthalpy (kJ/kg)
IRR internal rate of return
kspecific cost of the product (US$/kWh)
ṁmass flow rate (kg/s)
Nlifetime of system (year)
NPV net present value (US$)
Ppressure (kPa)
PP payback period (year)
Q
heat transfer rate (kW)
rdiscount factor
Runiversal gas constant (kJ/kmol K)
sspecific entropy (kJ/kg K)
SPP simple payback period (year)
Ttemperature (°C, K)
Ucoefficient of overall heat transfer (W/m
2
K)
Vvolume (m
3
)
Ve velocity (m/s)
Ẇwork rate (kW)
Yannual capacity of system production (kWh/year)
zdepth of geothermal well (m)
GREEK SYMBOLS
ηpolytropic efficiency
SUBSCRIPTS
0 dead state
ch chemical
D destruction
en energy
ex exergy
f formation
ispecies i
in inlet
out outlet
P product
R reactant
Sep separator
T turbine
ORCID
M. A. Ehyaei http://orcid.org/0000-0002-4721-9427
F. Esmaeilion http://orcid.org/0000-0001-9674-2153
Biplab Das http://orcid.org/0000-0002-0246-7312
EHYAEI ET AL.
|
767

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How to cite this article: Ehyaei MA, Esmaeilion
F, Shamoushaki M, Afshari H, Das B. The
feasibility study of the production of Bitcoin with
geothermal energy: case study. Energy Sci Eng.
2024;12:755‐770. doi:10.1002/ese3.1648
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