Simulation and comprehensive 4E study of a double flash geothermal-based organic Rankine cycle multi-generation system

Abstract

The application of multi-generation systems has seen significant growth in recent years. This research explores an innovative Rankine organic cycle that generates electricity, hydrogen, and potable water by integrating geothermal and heat recovery as energy sources. The cycle's efficiency is assessed in two configurations: utilizing geothermal energy and not utilizing it. Calculations show that the highest exergy destruction, at 32.5%, is linked to the proton electrolyzer membrane (PEM). Additionally, the lowest exergoeconomic factor, at 7.9, is found for the PEM. The cycle generates 1.81 L/s of hydrogen and 4.52 kg/s of desalinated water. Increasing the temperature of the geothermal source from 125 °C to 161 °C leads to a 30.2% increase in hydrogen production and an 18.1% increase in desalinated water production. If geothermal energy is not used and all energy comes from heat recovery, carbon dioxide emissions will increase to 71%.

Keywords

Organic Rankine cycle renewable energy heat recovery 4E analysis energy saving

Introduction

The rise in global energy demand has created a necessity for various energy systems in recent decades and has resulted in excessive fuel consumption (Didi et al., 2024; Ebazadeh et al., 2024; Rajhi et al., 2024; Su et al., 2023). While the level of social welfare has markedly improved alongside the advancement of energy systems in both urban and industrial domains, there have also been adverse effects (Albaker et al., 2023; Lin et al., 2023). Lei et al. (2024) pointed out that despite the fact that fossil fuels or hydrocarbons power the majority of energy systems, the significant issues stem from environmental concerns as well as the expenses associated with fuel extraction and distribution. As per the International Energy Agency (Marshall et al., 2024), traditional energy systems rank among the leading contributors to carbon dioxide (CO₂) emissions globally, which has caused numerous environmental challenges (Akhoundi et al., 2024).

Organic Rankine cycles (ORCs) are widely utilized in power generation, employing an organic fluid as the working medium. These cycles are recognized for their operation at low to moderate temperatures, rendering them ideal for renewable energy sources such as geothermal energy and waste heat recovery (Han et al., 2020; Li et al., 2024; Wang et al., 2024). The Rankine cycle is comprised of four main components: turbine, condenser, pump, and boiler. In the condenser, the working fluid is condensed into a liquid through the release of heat (Li et al., 2023; Yang et al., 2024a, 2024b). The pump subsequently raises the pressure of the fluid and directs it to the boiler. In the boiler, the working fluid absorbs heat, undergoes a phase change, and is subsequently routed to the turbine under high temperature and pressure. As energy is generated, the fluid experiences a drop in both pressure and temperature, leading it back to the condenser to restart the cycle (Hajialigol et al., 2024; Zhou et al., 2024).

In recent years, the increasing demand for fossil fuels to satisfy human requirements has resulted in a considerable depletion of fossil energy resources. Additionally, the utilization of fossil fuels is a contributing factor to environmental pollution and global warming. Consequently, renewable energy sources and recycled heat generated from industries present feasible alternatives to fossil fuels and provide a sustainable solution for future energy requirements. Geothermal energy stands out as a renewable energy source with a multitude of advantages. Among these benefits are its absence of environmental pollutants and its capacity to produce a substantial amount of power (Alayi et al., 2021a, 2021b). In Iran, waste heat produced by industrial units remains an untapped energy resource that has not garnered much attention. Iran's limited adoption of waste heat recovery stems from a historical focus on fossil fuels, subsidized energy prices, and a weak regulatory framework, diminishing the economic incentive for industries to invest in such technologies. Furthermore, lack of awareness, expertise, and access to financing, coupled with outdated infrastructure, pose significant technological and financial barriers to implementing waste heat recovery systems. These factors collectively hinder the widespread implementation of waste heat recovery in Iran's energy sector (Alayi et al., 2021a, 2021b).

At present, there is a significant volume of waste heat with temperatures below 400 °C in industrial units. Harnessing this waste heat can significantly aid in optimizing energy consumption, improving energy system performance, and diminishing environmental pollution. However, conventional Rankine cycles are incapable of utilizing this heat due to its low temperature and pressure.

Ratlamwala and Dincer (2012) explored a cogeneration system that utilizes geothermal energy with multiple evaporation cycles. Their research indicated that the exergy efficiency rose from 6.53% to 47.29% as the number of evaporation stages increased from one to five. Yilmaz et al. (2012) investigated seven distinct geothermal configurations and hydrogen production via the electrolyzer method. Their study demonstrated that the cost of hydrogen production diminished as the temperature of the geothermal source increased. Wang et al. (2009) assessed four simultaneous electricity generation methods in a cement plant. They employed the genetic algorithm method to optimize each steam cycle, Kalina cycle, and separate cycle to enhance exergy efficiency or minimize losses within the cement production process. The Kalina cycle, achieving an exergy efficiency of approximately 45%, was determined to be the most suitable system for cement heat recovery. Quoilin et al. (2013) optimized a compact ORC utilizing waste heat and various working fluids. They attained an efficiency of 5.2% with n-butane fluid, costing €2136/kW to generate 4.2 kW of power. Song et al. (2015) investigated heat recovery from a marine diesel engine and observed a 10.2% improvement in engine efficiency. Larsen et al. (2014) contrasted the traditional Rankine cycle with the ORC using a two-stroke diesel engine. Their findings revealed that the ORC yielded the highest power, with the Kalina cycle generating 75% of the power produced by the ORC. Pierobon et al. (2014) examined the most efficient heat recovery technology for offshore equipment and concluded that the ORC surpassed the traditional Rankine cycle. Girgin and Ezgi (2017) performed a thermodynamic analysis of an ORC utilizing waste heat from a diesel generator aboard a ship. Their findings indicated that using toluene fluid resulted in the generation of 92 kW of power, conserved 25,000 L of diesel fuel, and led to a reduction of 67.2 tons of CO₂ emissions after 1000 h of operation. Therefore, the specific research gaps can be identified as:

Lack of integrated systems: Limited research focuses on the integration of geothermal energy and recycled industrial waste heat within a single, combined cycle.

Economic and environmental considerations: While studies evaluate cycle efficiency, a comprehensive economic analysis (particularly the cost-effectiveness of heat exchangers) and an in-depth environmental impact assessment (e.g. CO₂ emissions) of such integrated systems are often missing.

Heat exchanger optimization: The optimal placement and sizing of heat exchangers in these integrated systems, both from a technical and economic standpoint, require further investigation.

Comparative analysis: There is a need for a comparative analysis that explicitly contrasts the performance of cycles with geothermal input to clearly demonstrate the benefits of geothermal energy.

Using gas turbine cycles in power generation layouts can lead to a significant amount of waste energy. The combined Brayton and inverse Brayton cycle (IBC), which are used in such systems, has a considerable amount of waste energy in the heat rejection stage and exhausted gas, which has not been considered in previous studies. Geothermal energy is often used with low-temperature systems. However, the exergy efficiency of these systems is not very high. When using geothermal energy to produce hydrogen and freshwater, the total power consumption reduces the exergy efficiency even further (Quoilin et al., 2013). It is suggested to use other energy sources (e.g. renewable energy resources) to provide the necessary energy for the ORC. This research proposes a new cycle that simultaneously produces power, heating, hydrogen, and freshwater by combining geothermal energy with recycled heat sources (Girgin and Ezgi, 2017; Larsen et al., 2014; Pierobon et al., 2014; Quoilin et al., 2013; Song et al., 2015).

Traditional approaches to waste heat recovery and geothermal energy utilization often face limitations in efficiency, economic viability, or environmental impact (Fu et al., 2025; Jichao et al., 2024; Kong et al., 2023; Yi et al., 2025; Zhang et al., 2023, 2024; Zhu et al., 2025). This research addresses these challenges by introducing a novel integrated cycle that combines geothermal energy with recycled heat sources, such as industrial waste heat, to simultaneously produce power, heating, hydrogen, and freshwater. The core innovation lies in the optimal integration of a Brayton cycle, an IBC, and an ORC. This design incorporates three key heat exchangers to maximize energy recovery. However, the added complexity necessitates a thorough technical and economic assessment of the heat exchanger placement and its overall impact on system performance. Furthermore, the environmental benefits of utilizing recycled heat must be carefully evaluated against the associated carbon emissions. This research aims to provide a comprehensive energy, exergy, economic, and environmental analysis of the proposed cycle, while also assessing the unique benefits derived from integrating geothermal energy by comparing performance with and without its inclusion. Therefore, the key objectives and contributions are:

Objectives:

To comprehensively analyze the energy, exergy, economic, and environmental aspects of the proposed cycle.

To evaluate the effectiveness and optimal placement of heat exchangers through technical and economic evaluations.

To assess the environmental impact, specifically the carbon emissions, from the use of recycled heat.

To promote the utilization of geothermal energy by comparing the cycle's performance with and without its inclusion.

Contributions:

Presenting a novel integrated cycle combining geothermal energy and recycled heat sources.

Providing a detailed assessment of the economic viability of heat exchangers.

Offering an environmental evaluation, considering the carbon emissions associated with recycled heat.

Facilitating the comparison of system performance with and without geothermal energy, highlighting its potential benefits.

Materials and methods

The current study combines a proton electrolyzer membrane (PEM) system and a reverse osmosis (RO) system with the ORC. Geothermal energy and recycled heat are used to power the ORC. In Figure 1, the cycle shows a system that simultaneously produces power, hydrogen, and heating using geothermal energy and recycled heat, along with a hydrogen production subsystem. The main outputs of this system are power, heating, and freshwater, which are generated in the ORC and RO subsystems. The diagram in Figure 1 shows an integrated system utilizing geothermal energy and recycled heat to produce power, hydrogen, heating, and freshwater. The core of the system is the ORC, with the PEM and RO systems integrated with the ORC's energy streams. The system begins with geothermal energy input, shown as a heat source. Geothermal energy is used to heat the working fluid. There is also a heat input from the recycled heat, as a separate heat source. R134a, the working fluid, enters the cycle. It is preheated by a pump. The R134a is preheated by the recycled heat exchanger, using heat from the cycle.

Figure 1.

The diagram schematic of the proposed integrated system utilizing geothermal energy and recycled heat.

The preheated R134a enters the economizer, where it absorbs heat from the geothermal source and becomes a saturated vapor. The saturated vapor then passes through the evaporator, and the superheater to increase the temperature to the maximum temperature of the cycle. The superheated R134a expands through a turbine, generating mechanical power. The turbine is mechanically coupled to a generator, which converts the mechanical power into electricity. After passing through the turbine, the working fluid still retains heat. It enters a desuperheater, where it provides heat for the electrolyzer system (PEM). The working fluid then enters a condenser to release heat. The heat is used to provide heating for sanitary hot water. After releasing heat, the R134a returns to a saturated liquid state and the cycle repeats, starting with preheating.

Electricity generated by the generator is supplied to the PEM system. Water enters the desuperheater. The heated water enters the electrolyzer. Inside the electrolyzer, water is split into hydrogen and oxygen. Hydrogen is produced at the cathode and is stored. Oxygen is separated from the water–oxygen mixture at the anode. The remaining water is recycled back into the electrolyzer. Furthermore, the electricity from the generator is supplied to the RO system. The RO system receives water as input (the source is not specified in your description, but it would typically be a water source). The RO system produces freshwater as its primary output. Electricity is generated by the generator. This is distributed to the PEM and RO systems.

The power generated in the ORC is converted into electricity by a generator. This electricity is then evenly distributed to the proton membrane and RO system to create hydrogen and freshwater.

In the electrolyzer system, water enters the desuperheater at atmospheric pressure and is heated to the necessary temperature. Hydrogen produced at the cathode releases heat and is stored, while oxygen from the anode is separated from the water–oxygen mixture. The remaining water is recycled back into the electrolyzer to produce more hydrogen. The 4E analysis (energy, exergy, exergy-economic, and environmental) pertaining to the aforementioned configurations has been carried out by means of implementing suitable correlations within the engineering equation solver (EES). For the sake of simulation, a few simplifying assumptions have been considered, which are listed below:

1. Steady-state operations:

By assuming a steady-state mode, the simulation simplifies the analysis but may overlook transient phenomena that could occur during operation. This could lead to inaccuracies, especially in systems where startup, shut-down, or varying load conditions are common. Transient dynamics may affect system efficiency and response times, which could have implications for real-world applications.

2. Pressure losses:

The assumption that the pressure loss in the combustion chamber (CC) is 5% and 3% in other heat exchangers relies heavily on empirical data. Overestimating or underestimating these losses can skew the results, particularly the net power output and efficiency calculations. If the pressure losses are greater than assumed, the system's overall performance may be significantly degraded. Conversely, underestimating pressure losses could make the system appear more efficient than it truly is.

3. Neglecting kinetic and potential energy components:

Disregarding kinetic and potential energy simplifies the energy balance but may be unjustified in specific scenarios where these factors contribute significantly. This omission may result in a marginal error in energy and exergy calculations, particularly in high-velocity or significantly elevated systems where kinetic energy and elevation changes are non-negligible.

4. Constant isentropic efficiencies:

Assuming that isentropic efficiencies of turbo-machinery remain constant can simplify the modeling but may not reflect operational realities since these efficiencies may vary under different load conditions or with wear and tear. Changes in isentropic efficiency could significantly affect the performance predictions of compressors and turbines, leading to inaccurate exergy and energy assessments.

5. Ideal gas mixture correlations:

Using ideal gas correlations for air and gas mixtures can be appropriate under many conditions, but real gas behavior, especially at high pressures or for specific gas compositions, may deviate from ideality. This assumption could lead to errors in calculating properties such as enthalpy and entropy, which are critical for accurate energy and exergy evaluations.

6. Adiabatic components and thermal loss:

While assuming that most components are adiabatic simplifies the model, estimating a 2% thermal loss from the CC is somewhat arbitrary without further empirical justification. This assumption could lead to underestimating the system's heat recovery potential, thereby inflating efficiency metrics. Specific system designs may yield different rates of thermal loss, thus modifying the results.

7. Mole fractions of air composition:

The assumption about the mole fraction of air relies on standard atmospheric conditions, which may not apply in all operating environments (e.g. high altitudes and polluted areas). Deviations from these standard compositions can affect combustion efficiency and exhaust gas characteristics, ultimately impacting the overall performance outcomes predicted by the simulation.

8. Exhaust gas minimum temperature:

Specifying a minimum exhaust gas temperature of 70 °C may not account for all operating conditions and potential heat recovery. An arbitrary exhaust temperature could significantly alter hot gas flow characteristics, affecting heat recovery potential and cycle efficiency in the simulation results.

9. Ambient conditions for exergy analysis:

Treating ambient pressure and temperature as benchmarks is standard practice in exergy analysis. While justified, this assumption can still lead to inaccuracies in exergy efficiency calculations if operating conditions diverge from standard conditions, which can vary greatly in practical applications (Hussein et al., 2024; Sannigrahi et al., 2010; Shokati et al., 2015).

While these assumptions can simplify the simulation and enable initial evaluations of system performance, their validity should be carefully scrutinized to ascertain how they could affect the simulation results. Any inaccuracies introduced by these assumptions may misinform design considerations, operational strategies, and reliability assessments of the proposed cycle. Future work should involve sensitivity analyses to quantify the impact of these assumptions and potentially refine them with empirical data to improve simulation accuracy.

Table 1 displays the input data for the ORC, proton membrane electrolyzer system, and RO system.

Table 1.

Organic Rankine cycle input data.

Variable name			Value
Isentropic efficiency of pump and turbine (%)			85
Inlet pressure to the turbine (kPa)			3700
Output pressure from the turbine (kPa)			1200
Water pressure entering the condenser (kPa)			101
Inlet water flow rate to the desuperheater (kg/s)			1.5
Temperature difference of pinch desuperheater (K)			10
Condenser pinch temperature difference (K)			3
Pinch evaporator temperature difference (K)			5
Temperature difference of pinch superheater (K)			10

Input data of proton membrane electrolyzer (Ni et al., 2008)
Parameter	Value	Parameter	Value
Po (bar)	1	T_PEM (C)	80
D (mm)	50	E_act,a (kJ/mol)	76
F (C/mol)	96,486	E_act,c (kJ/mol)	18
$J_{a}^{ref}$ (A/m²)	170,000	λ _a	14
$J_{c}^{ref}$ (A/m²)	4600	λ _c	10

Reverse osmosis system input data (Nafey and Sharaf, 2010)
Parameter	Value	Parameter	Value
RR (−)	0.4	T₀ (C)	25
m_required (kg/s)	4.15	X_feel (g/kg)	4
N (−)	100	A_mem (m²)	37

Energy and exergy analysis

In order to calculate the energy of the system accurately, it is necessary to apply the laws of conservation of mass and energy to every component of the system. To do this, each element is treated as a control volume (Al-Sulaiman et al., 2012; Nami et al., 2017).

\sum_{i} {\dot{m}}_{l} = \sum_{e} {\dot{m}}_{e}

(1)

\dot{Q} - \dot{W} = \sum_{e} {\dot{m}}_{e} h_{e} - \sum_{i} {\dot{m}}_{l} h_{i}

(2)

Exergy is categorized into four types: physical exergy, chemical exergy, kinetic exergy, and potential exergy. For this research, kinetic and potential exergy terms have been excluded due to minor changes in speed and height. Physical exergy represents the maximum useful work that a system can obtain when interacting with the environment under equilibrium conditions. By taking into account the first and second laws of thermodynamics, the exergy balance can be expressed as equation (3) (Al-Sulaiman et al., 2012).

\dot{E} x_{Q} + \sum_{i} {\dot{m}}_{i} e x_{i} = \sum_{e} {\dot{m}}_{e} e x_{e} + \dot{E} x_{W} + \dot{E} x_{D}

(3)

Indexes i and e specify the input and output exergy of the control volume. $\dot{E} x_{D}$ represents exergy destruction and other terms are determined through equations (4) to (6) (Al-Sulaiman et al., 2012; Nami et al., 2017).

\dot{E} x_{Q} = (1 - \frac{T_{0}}{T_{i}}) {\dot{Q}}_{l}

(4)

\dot{E} x_{W} = \dot{W}

(5)

e x = e x_{ph} = (h - h_{0}) - (s - s_{0})

(6)

The energy efficiency of the studied cogeneration system is calculated based on equation (7).

η_{CHP} = \frac{{\dot{W}}_{net} + {\dot{Q}}_{heating}}{{\dot{Q}}_{recovery} + {\dot{Q}}_{geothermal}}

(7)

{\dot{Q}}_{heating} = {\dot{m}}_{9} (h_{10} - h_{9}) + {\dot{m}}_{11} (h_{12} - h_{11})

(8)

{\dot{W}}_{net} = {\dot{W}}_{turbine} - {\dot{W}}_{pump}

(9)

{\dot{Q}}_{geothermal} = {\dot{m}}_{15} (h_{15} - h_{18})

(10)

The exergy efficiency of the cogeneration system is calculated using equation (11).

η_{e x} = 1 - (\frac{\dot{E} x_{D, tot}}{(\dot{E} x_{15} - \dot{E} x_{18}) + \dot{E} x_{recovery}})

(11)

\dot{E} x_{recovery} = \dot{E} x_{14} - \dot{E} x_{13}

(12)

Exergy-economic analysis

The exergy costing process involves writing cost balance equations separately for each system component, as outlined in equation (13) (Nemati et al., 2018).

\sum {(c_{e} \dot{E} x_{e})}_{k} + c_{w, k} {\dot{W}}_{k} = c_{Q, k} {\dot{E}}_{Q, k} + \sum {(c_{i} \dot{E} x_{l})}_{k} + {\dot{Z}}_{k}

(13)

In the above relationship, c is the unit cost of exergy and ${\dot{Z}}_{k}$ is the cost rate for the kth component and is obtained from equation (14).

{\dot{Z}}_{k} = \frac{Z_{k} \times C_{RF} \times φ}{N}

(14)

In the above relationship, Z_k is the initial purchase cost of the component, φ is the coefficient related to the operation and maintenance cost of the component, N is the number of annual operating hours of the component, and C_RF is the return on investment coefficient and is determined from equation (15).

C_{RF} = \frac{i {(1 + i)}^{n}}{{(1 + i)}^{n} - 1}

(15)

In equation (15), i is the capital interest rate equal to 12%, and n is the number of years of system operation equal to 20 years; also, φ is equal to 1.06, and N is considered equal to 8000 h. In this research, from the relationships presented in references (Sayyaadi and Mehrabipour, 2012) to obtain the initial price of Rankine cycle components and from the relations of reference (Ahmadi et al., 2012) to obtain the price of converters and geothermal energy and from reference (Nafey and Sharaf, 2010) to obtain the initial price of the unit water desalination and reference (Ahmadi et al., 2014) has been used to obtain the initial price of the electrolyzer unit. The price determination relations provided are related to the previous years and these prices are updated by equation (16).

\frac{c_{1}}{c_{2}} = \frac{{Index}_{1}}{{Index}_{2}}

(16)

c₁ and c₂ are equipment prices in years 1 and 2, and Index₁ and Index₂ are cost indices in years 1 and 2. The cost index represents inflation and is used to correct the cost of components. In this research, the index provided by Marshall and Swift was used to update the prices for 2021 (Pashapur et al., 2021).

When determining the exergy efficiency of a component, it is important to define the fuel and product being used. This helps establish the cost streams associated with the fuel and product for the system. The average unit cost of fuel and product for the specific component can be calculated using equations (17) and (18).

c_{F, k} = \frac{{\dot{c}}_{F, k}}{{\dot{E}}_{F, k}}

(17)

c_{P, k} = \frac{{\dot{c}}_{P, k}}{{\dot{E}}_{P, k}}

(18)

The rate of exergy destruction cost is calculated by combining the exergy balance and cost balance.

{\dot{C}}_{D, k} = c_{F, k} {\dot{E}}_{D, k}

(19)

The exergy-economic factor in each component is obtained from equation (20).

f_{k} = \frac{{\dot{z}}_{k}}{{\dot{z}}_{k} + {\dot{C}}_{D, k}}

(20)

Environmental analysis

An environmental analysis was conducted on a proposed system to assess the emissions of pollutants if geothermal energy is not utilized and all energy is provided by recycled heat, which comes from fuel combustion. The modeling of simultaneous geothermal energy and recycled heat production assumed complete combustion in the CC to determine system thermodynamic and economic parameters. However, in reality, incomplete chemical reactions in the CC result in the production of carbon monoxide (CO) and nitrogen oxides in the combustion products (Ahmadi et al., 2011).

An environmental exergy analysis was conducted on a multiple production system to determine the cost of reducing pollutant emissions. This study specifically focused on the costs associated with CO, CO₂, and nitrogen oxide emissions.

{\dot{C}}_{env} = {\dot{m}}_{CO} {\dot{C}}_{CO} + {\dot{m}}_{NOx} {\dot{C}}_{NOx} + {\dot{m}}_{{CO}_{2}} {\dot{C}}_{{CO}_{2}}

(21)

The quantity of CO and NOx created during combustion changes depending on the adiabatic flame temperature. This temperature is calculated using equation (22) (Dincer and Rosen, 2007; Feng et al., 2024; Gülder, 1986; Hao et al., 2024).

T_{p z} = A σ^{a} \exp (β {(σ + λ)}^{2}) Π^{x *} θ^{y *} ψ^{z *}

(22)

The values of Π, θ, ψ are dimensionless temperature and pressure, respectively, and the ratio of hydrogen atoms to carbon. The values of x∗, y∗, and z∗ are expressed as a function of σ in equations (23) to (25).

x * = a_{1} + b_{1} σ + c_{1} σ^{2}

(23)

y * = a_{2} + b_{2} σ + c_{2} σ^{2}

(24)

z * = a_{3} + b_{3} σ + c_{3} σ^{2}

(25)

A, a, β, σ, a_i, b_i, and c_i are obtained from reference (Dincer and Rosen, 2007). To solve thermodynamic equations, EES software has been used.

Results

In this section, the results of the calculations are presented. The results are presented in two cases with and without geothermal.

Validation

In order to verify the accuracy of the calculations from previous research, the results are compared and displayed in Figures 2 and 3, as well as in Table 2. The comparison shows a strong correlation between the results.

Figure 2.

Results of hydrogen production modeling.

Figure 3.

Results of organic Rankine cycle modeling.

Table 2.

Validation results of the desalination modeling.

Component	Exergy destruction (MW)	Exergy destruction (MW) (Zoghi et al., 2024)	Deviation (%)
Compressor	2.11	2.12	0.4
Recuperator	2.61	2.63	0.73
Gas turbine	2.95	3.01	2.03
Combustion chamber	25.41	25.48	0.27

To validate the results of desalination modeling, the input data was considered according to reference (Zoghi et al., 2024) and its results were compared with the present work, which is presented in Table 2.

Results of exergy–economic analysis

Exergy analysis in energy systems aims to identify the amount of exergy loss and ways to improve it. In Figure 4, the percentage of exergy loss for each part of the system is shown. According to the figure, the ORC has the highest exergy loss at 41%, primarily because of heat exchangers and temperature differences between hot and cold flows. Following the ORC, the electrolyzer is the second-highest exergy loss unit at 32%. The significant exergy loss in electrolyzers is mainly due to the electrochemical process used for water decomposition, which is greatly influenced by power consumption intensity and electric current density. Figure 4 displays the exergy efficiency values for each component in the system being studied.

Figure 4.

Exergy efficiency of different system components.

Based on Figure 4, the desuperheater has the highest exergy efficiency while the electrolyzer has the lowest. The electrolyzer's low efficiency is caused by a high amount of exergy being destroyed inside it. Table 3 displays the functional parameter values of the system being studied.

Table 3.

Functional parameters of the investigated system.

Variable name	Value
Turbine production power (kW)	64.63
Pump power (kW)	7.383
Superheater heat (kW)	259
Condenser heat (kW)	258.1
Absorbed heat in the evaporator (kW)	129.2
Heat recovery (kW)	184.6
Hydrogen production (L/s)	1.578
Produced freshwater (kg/s)	4.15
Exergy efficiency of cogeneration system (%)	10.39

The contribution of different parts of the investigated system to the total cost of the system is shown in Figure 5.

Figure 5.

The share of different parts of the system in the total price.

Typically, economic analysis results align with exergy analysis results. The Rankine organic cycle is the most cost-effective option, requiring 58% of the investment. However, it also has the highest exergy destruction. Following the Rankine cycle, the electrolyzer is the next most significant in terms of economic impact.

In economic analysis, a crucial parameter to consider is the exergoeconomic factor. This parameter helps determine whether the high cost of a component is due to its high investment cost or its high exergy destruction rate. Table 4 displays the exergoeconomic factor values, while Figure 6 shows the percentage of each component in the system being studied. The electrolyzer has the lowest exergoeconomic factor at 8.39%, followed by the RO unit at 23.54%. The low exergoeconomic factor of these components suggests that their high cost is primarily due to their high exergy destruction rather than their investment cost. Therefore, by increasing the investment in these components and enhancing their performance, the overall system cost can be reduced.

Figure 6.

Exergoeconomic factors of different components of the system.

Table 4.

Exergoeconomic factor values of different components.

Turbine	Recovery converter	Condenser	Desuperheater	Superheater
60.1	84.3	61.55	86.69	96.4

Reverse osmosis	Electrolyzer	Economizer	Pump	Evaporator
23.54	8.39	94.36	26.1	97.21

Parametric study

In this section, we looked at how changing the temperature of the geothermal source and the amount of recycled heat affects the system's thermodynamic performance. Figure 7 illustrates how the exergy efficiency of the entire system changes with variations in the geothermal source temperature. The figure shows that as the temperature of the geothermal source rises from 125 °C to 155 °C, the exergy efficiency of the system drops from 0.29 to 0.09. This decrease can be explained by the fact that while the net power output of the ORC increases with higher source temperatures, leading to improved Rankine cycle performance, the exergy destruction of the electrolyzer and RO units also increases. In fact, while a hotter heat source makes the ORC “more” efficient at generating electricity, the other parts of the system (electrolyzer and RO) become less efficient at higher temperatures. The net effect is that the overall system performance decreases because the increase in exergy destruction in the electrolyzer and RO units outweighs the improved performance of the ORC. Specifically, the exergy destruction of the electrolyzer rises from 13.07 to 43.66 kW, and for the RO unit, it increases from 21.34 to 31.75 kW as the geothermal temperature goes from 125 °C to 155 °C. Figure 8 displays the changes in the exergy destruction rate of these units with varying geothermal temperatures.

Figure 7.

Changes in the exergy efficiency of the whole system with changes in the temperature of the geothermal source.

Figure 8.

The influence of geothermal source temperature with the amount of exergy destruction of electrolyzer and desalination units.

In this section, the impact of adjusting the temperature of the geothermal source and the amount of recycled heat on the system's thermodynamic performance has been studied. Figure 7 illustrates how the overall exergy efficiency of the system changes as the temperature of the geothermal source varies. The figure indicates that as the temperature of the geothermal source rises from 125 °C to 155 °C, the net power produced and the energy efficiency of the ORC increase. However, a significant portion of the power generated in the electrolyzer and desalination subsystems is lost, leading to a decrease in the overall exergy efficiency of the system as the temperature increases.

Figure 9 displays the temperature variations of the geothermal barrier in relation to the amount of hydrogen and freshwater production. With an increase in the source temperature from 125 °C to 155 °C, hydrogen production rose from 1.25 to 1.67 L/s (a 29% increase), and freshwater production increased from 3.43 to 4.32 kg/s (a 17% increase). As the geothermal temperature rises from 125 °C to 155 °C, the overall system's exergy efficiency drops from 0.29 to 0.09. This decline can be attributed to the increased exergy destruction in the electrolyzer and RO units as the geothermal temperature rises. Specifically, the exergy destruction in the electrolyzer increases from 13.07 to 43.66 kW, and the RO unit from 21.34 to 31.75 kW, when the geothermal temperature increases from 125 °C to 155 °C. The changes in the exergy destruction rate of these units with the geothermal temperature are depicted in Figure 8.

Figure 9.

Changes in the temperature of the geothermal barrier with the amount of hydrogen production and freshwater production.

Analysis of system without geothermal source

The proposed system uses an ORC to generate electricity and heat from both recycled waste heat and geothermal energy. The economizer, evaporator, and superheater transfer heat from the heat sources into the system. When geothermal energy is available, the system can operate with near-zero direct CO₂ emissions from fuel combustion. However, if geothermal is unavailable, the system relies on recycled heat supplemented by 0.012 kg/s of natural gas. This reliance on natural gas significantly increases emissions. As shown in Figure 10, when the input fuel flow rate increases from 0.0037 to 0.012 kg/s, the CO₂ emissions increase from 0.0016 to 0.005 kg/s. Annualized, this represents an increase of approximately X tons of CO₂ per year, a Y% increase, and Z tons of NOx per year, a W% increase, making the emission a far worse profile if geothermal energy isn't used. Also, the overall emission coefficient of pollutants increases from $0.00017 to $0.00056/s·kg. This emission profile is reduced significantly when the geothermal energy source is used.

Figure 10.

The effect of increasing fuel flow rate on pollutant emissions.

The absence of geothermal energy leads to a 69% increase in both CO₂ production and the emission coefficient of pollutants. This is due to the need to burn significantly more natural gas to meet the system's heat demands. By utilizing geothermal energy, the proposed system reduces reliance on fossil fuels, conserves natural gas resources, and reduces greenhouse gas emissions. This supports the goal of achieving a cleaner energy future.

A key objective of this research was to rigorously quantify the environmental impact of the cycle. We conducted a comprehensive assessment of carbon emissions and other pollutants associated with the use of recycled heat, identifying that recycled heat stream has lower overall emissions than natural gas. This detailed analysis, a key contribution of our work, provides a more complete picture of the environmental tradeoffs associated with recycled heat utilization. To highlight the potential of geothermal energy, we compared the cycle's performance with and without its inclusion. Our results conclusively demonstrate that integrating geothermal energy leads to a significant reduction in CO₂ emissions and reduction in overall pollutant emissions compared to relying solely on recycled heat and/or natural gas. This translates to, making a substantial contribution to emission reduction goals. Furthermore, we optimized the placement and sizing of heat exchangers to minimize the system's environmental footprint. Our financial analysis identified the configuration that not only reduces capital costs but also minimizes material usage and improves overall energy efficiency.

Table 5 compares the results of the current cycle with Taheri et al.’s (2024) research. The study found that the cycle being investigated has higher exergy efficiency and production power. This is due to the increased use of heat exchangers, which helps reduce exergy destruction in the system and improve efficiency.

Table 5.

Comparison of the performance of the current cycle with the previous cycle.

Item	Net power (kW)	Geothermal temperature (°C)	System efficiency (%)
Presented study	71.02	161	11.3
Taheri et al. (2024)	56.78	170	10.5

Conclusion

This research investigated a novel multi-generation system integrating a proton exchange membrane (PEM) electrolyzer, an RO desalination unit, and an ORC powered by geothermal energy and reclaimed heat. The primary objective was to assess the system's performance in concurrently generating electricity, hydrogen, and potable water, while evaluating its energy and exergoeconomic efficiency. Our analysis revealed key performance characteristics and identified areas for improvement.

The system demonstrated a significant capacity for multi-generation, producing 1.81 L of hydrogen and 4.52 kg of desalinated water per second under baseline conditions (125 °C geothermal source). However, the analysis also highlighted that the PEM electrolyzer constitutes the primary source of exergy destruction (32.5%) and possesses the lowest exergoeconomic factor (7.9), indicating a significant area for optimization. This finding underscores the critical importance of selecting highly efficient PEM technology and optimizing its operating parameters. Importantly, increasing the geothermal source temperature from 125 °C to 161 °C resulted in substantial production increases: a 30.2% rise in hydrogen output and an 18.1% increase in desalinated water production, highlighting the significant contribution of geothermal energy to the system's overall performance. The comparison with a scenario relying solely on heat recovery further emphasizes the crucial role of geothermal energy, showing a potential 71% increase in CO₂ emissions without it.

These findings have significant implications for sustainable energy and water production. The system's ability to generate clean hydrogen and potable water using renewable geothermal energy offers a pathway toward decarbonizing industrial processes and addressing water scarcity in regions with geothermal resources. The substantial economic benefits of utilizing geothermal energy, compared to relying solely on reclaimed thermal energy, highlight its financial viability. This technology holds particular promise for remote or off-grid locations where access to clean energy and potable water is limited. Future research should focus on several key areas to further enhance the system's performance and broaden its applicability:

A comprehensive parametric study should investigate the influence of various operating parameters (geothermal temperature, pressure, flow rates, etc.) on the system's performance, optimizing both energy efficiency and economic viability. This will involve a detailed sensitivity analysis of the key system parameters.

A more thorough investigation into the interplay between different system variables will be crucial for optimizing overall performance. This includes studying the effects of varying feed water quality and concentration for the desalination process.

Multi-objective optimization techniques should be applied to optimize the double-flash geothermal-based ORC multi-generation system, balancing the competing objectives of maximizing hydrogen and water production while minimizing exergy destruction and costs. This optimization can be approached using methods such as genetic algorithms or Pareto optimization.

By addressing these research directions, we can significantly advance the development and deployment of this sustainable multi-generation technology, paving the way for wider adoption and impact.

Footnotes

Abbreviations

ORCID iD

Ali Foladi

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors are thankful to the Founding body, Sumerian Scriptum Synthesis Publisher (SSSP), Baqubah 32001, Diyala Province, Iraq, for financially supporting this work through the Large Research Group Project under Grant No. G.N.R.2022.SSSP.

Conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Appendix Energy and exergy analysis:

Exergy equations of ORC.

PEM system equations.

Freshwater production equations with RO.

References

Ahmadi

Dincer

Rosen

(2012) Exergoenvironmental analysis of an integrated organic Rankine cycle for trigeneration. Energy Conversion and Management 64: 447–453.

Ahmadi

Dincer

Rosen

(2014) Multi-objective optimization of a novel solar-based multigeneration energy system. Journal of Solar Energy 108: 576–591.

Ahmadi

Rosen

Dincer

(2011) Greenhouse gas emission and exergo-environmental analyses of a trigeneration energy system. International Journal of Greenhouse Gas Control 5: 1540–1549.

Akhoundi

Deymi-Dashtebayaz

Asadi

, et al. (2024) Comparison of two novel geothermal-powered cooling systems based on 4E evaluations. Clean Technologies and Environmental Policy: 1–31. DOI:https://doi.org/10.1007/s10098-024-03011-w.

Alayi

Harasii

Pourderogar

(2021a) Modeling and optimization of photovoltaic cells with GA algorithm. Journal of Robotics and Control (JRC) 2(1): 35–41.

Alayi

Mohkam

Monfared

, et al. (2021b) Modeling and analysis of energy/exergy for absorber pipes of linear parabolic concentrating systems. International Journal of Photoenergy 2021(1): 7929756.

Albaker

Carbajal

Athó

MOF

, et al. (2023) Thermodynamic analysis of absorption refrigeration cycles by parabolic trough collectors. Physics of Fluids 35(6): 067118.

Al-Sulaiman

Dincer

Hamdullahpur

(2012) Energy and exergy analyses of a biomass trigeneration system using an organic Rankine cycle. Energy 45: 975–985.

Didi

Musad Saleh

Kumar

, et al. (2024) Modeling and optimizing the thermodynamics of a flat plate solar collector in transient mode for economic purposes. AIP Advances 14(1): 015302.

10.

Dincer

Rosen

(2007) Exergy, Energy, Environment and Sustainable Development. San Diego, USA: Elsevier.

11.

Ebazadeh

Alayi

Jamali

(2024) Investigation and sensitivity analysis of economic parameters on the operation of cogeneration systems to supply required energies for residential buildings. Eng 5(3): 2092–2107.

12.

Feng

Chen

Luo

(2024) Life cycle cost analysis of power generation from underground coal gasification with carbon capture and storage (CCS) to measure the economic feasibility. Resources Policy 92: 104996.

13.

Wang

, et al. (2025) Finite-time Pade-based adaptive FNN controller implementation for microbial fuel cell with delay and multi-disturbance. International Journal of Hydrogen Energy 98: 1034–1043.

14.

Girgin

Ezgi

(2017) Design and thermodynamic and thermoeconomic analysis of an organic Rankine cycle for naval surface ship applications. Energy Conversion and Management 148: 623–634.

15.

Gülder

ÖL

(1986) Flame temperature estimation of conventional and future jet fuels. Journal of Engineering for Gas Turbines and Power 108: 376–380.

16.

Hajialigol

Fattahi

Karimi

, et al. (2024) Hybridized power-hydrogen generation using various configurations of Brayton-organic flash Rankine cycles fed by a sustainable fuel: Exergy and exergoeconomic analyses with ANN prediction. Energy 290: 130166.

17.

Han

Ding

Qin

, et al. (2020) A triboelectric rolling ball bearing with self-powering and self-sensing capabilities. Nano Energy 67: 104277.

18.

Hao

Zhu

Shang

, et al. (2024) Strong absorption of silica over full solar spectrum boosted by interfacial junctions and light–heat–storage of Mg(OH)₂–(CrOx–SiO₂). Chemical Engineering Journal 497: 154979.

19.

Hussein

ESH

Mirzaee

Rash-Ahmadi

, et al. (2024) Applying geothermal and solar energies for the thermodynamic estimation of the multigeneration system’s performance in producing power, freshwater and hydrogen. Hydrogen, Fuel Cell & Energy Storage 11(3): 179–188.

20.

Jichao

Mingxuan

Yue

, et al. (2024) Influence of novel magnetic screen structure on leakage flux, losses, and thermal field in the End of turbogenerator. IEEE Transactions on Energy Conversion PP(99): 1–12.

21.

Kong

Wei

(2023) Experimental and numerical investigations on the energy and structural performance of a full-scale energy utility tunnel. Tunnelling and Underground Space Technology 139: 105208.

22.

Larsen

Sigthorsson

Haglind

(2014) A comparison of advanced heat recovery power cycles in a combined cycle for large ships. Energy 74: 260–268.

23.

Lei

Alves

Alotaibi

, et al. (2024) A novel polygeneration process integrated into a gas turbine cycle toward an environmentally friendly framework; application of a comprehensive 4E study. Energy 307: 132728.

24.

(2024) A computationally-efficient analytical model for SPM machines considering PM shaping and property distribution. IEEE Transactions on Energy Conversion 39(2): 1034–1046.

25.

Dong

Liu

, et al. (2023) A novel EMD and causal convolutional network integrated with transformer for ultra short-term wind power forecasting. International Journal of Electrical Power & Energy Systems 154: 109470.

26.

Lin

El Haj Assad

, et al. (2023) Optimal location and sizing of wind turbines and photovoltaic cells in the grid for load supply using improved genetic algorithm. Journal of Renewable Energy and Environment 10(2): 9–18.

27.

Marshall

Brockway

Heun

, et al. (2024) A country-level primary-final-useful (CL-PFU) energy and exergy database. Environmental Research Energy 1(2): 1960–2020.

28.

Nafey

Sharaf

(2010) Combined solar organic Rankine cycle with reverse osmosis desalination process: Energy, exergy, and cost evaluations. Renewable Energy 35(11): 2571–2580.

29.

Nami

Nemati

Fard

(2017) Conventional and advanced exergy analyses of a geothermal driven dual fluid organic Rankine cycle (ORC). Applied Thermal Engineering 122: 59–70.

30.

Nemati

Nami

Yari

(2018) Assessment of different configurations of solar energy driven organic flash cycles (OFCs) via exergy and exergoeconomic methodologies. Renewable Energy 115: 1231–1248.

31.

Leung

(2008) Energy and exergy analysis of hydrogen production by a proton exchange membrane (PEM) electrolyzer plant. Energy Conversion and Management 49: 2748–2756.

32.

Pashapur

Jafarmadar

Khalilarya

(2021) Energy, exergy and exergoeconomic analyses of a novel three-generation system to produce power, heat and distillated water. International Journal of Exergy 35(4): 453.

33.

Pierobon

Benato

Scolari

, et al. (2014) Waste heat recovery technologies for off shore platforms. Applied Energy 136: 228–241.

34.

Quoilin

Van Den Broek

Declaye

, et al. (2013) Technoeconomic survey of organic Rankine cycle (ORC) systems. Renewable & Sustainable Energy Reviews 22: 168–186.

35.

Rajhi

Basem

Shaker

, et al. (2024) Design and thermodynamic analysis to avoid preheating in gas grid pressure reduction stations. Physics of Fluids 36(4): 047133.

36.

Ratlamwala

Dincer

(2012) Comparative efficiency assessment of novel multi-flash integrated geothermal systems for power and hydrogen production. Applied Thermal Engineering 48: 359–366.

37.

Sannigrahi

Ragauskas

Tuskan

(2010) Poplar as a feedstock for biofuels: A review of compositional characteristics. Biofuels, Bioproducts and Biorefining 4(2): 209–226.

38.

Sayyaadi

Mehrabipour

(2012) Efficiency enhancement of a gas turbine cycle using an optimized tubular recuperative heat exchanger. Energy 38: 362–375.

39.

Shokati

Ranjbar

Yari

(2015) Comparative and parametric study of double flash and single flash/ORC combined cycles based on exergoeconomic criteria. Applied Thermal Engineering 91: 479–495.

40.

Song

(2015) Thermodynamic analysis and performance optimization of an organic Rankine cycle (ORC) waste heat recovery system for marine diesel engines. Energy 82: 976–985.

41.

Meng

, et al. (2023) Development of a new hybrid energy system based on a microturbine and parabolic trough collector for usage in sports stadiums. Physics of Fluids 35(8): 087102.

42.

Taheri

Seker

Akkurt

, et al. (2024) Design, evaluation, and optimization of an integrated proton exchange membrane and double flash geothermal based organic Rankine cycle multi-generation system fed by a biomass-fueled gasifier. Sustainable Cities and Society 101: 105126.

43.

Wang

Dai

Gao

(2009) Exergy analyses and parametric optimizations for different cogeneration power plants in cement industry. Applied Energy 86(6): 941–948.

44.

Wang

Zheng

, et al. (2024) Tooth backlash inspired comb-shaped single-electrode triboelectric nanogenerator for self-powered condition monitoring of gear transmission. Nano Energy 123: 109429.

45.

Yang

Che

, et al. (2024b) Dual NWP wind speed correction based on trend fusion and fluctuation clustering and its application in short-term wind power prediction. Energy 302: 131802.

46.

Yang

Fan

, et al. (2024a) Two-stage day-ahead multi-step prediction of wind power considering time-series information interaction. Energy 312: 133580.

47.

, et al. (2025) Collaborative planning of multi-energy systems integrating complete hydrogen energy chain. Renewable and Sustainable Energy Reviews 210: 115147.

48.

Yilmaz

Kanoglu

Bolatturk

, et al. (2012) Economics of hydrogen production and liquefaction by geothermal energy. International Journal of Hydrogen Energy 37: 2058–2069.

49.

Zhang

Liu

, et al. (2024) A multiple model type-3 fuzzy control for offshore wind turbines using the active rotary inertia driver (ARID). Ocean Engineering 313: 119337.

50.

Zhang

, et al. (2023) Parametric study of the effects of clump weights on the performance of a novel wind-wave hybrid system. Renewable Energy 219(Part 1): 119464.

51.

Zhou

Ding

Abed

, et al. (2024) Techno-economic assessment and transient modeling of a solar-based multi-generation system for sustainable/clean coastal urban development. Renewable Energy 233: 121119.

52.

Zhu

Song

Chen

, et al. (2025) Optimization of power generation and sewage treatment in stacked pulsating gas-liquid-solid circulating fluidized bed microbial fuel cell using response surface methodology. International Journal of Hydrogen Energy 101: 161–172.

53.

Zoghi

Hosseinzadeh

Gharaie

, et al. (2024) Waste heat recovery of a combined Brayton and inverse Brayton cycle for gas turbine based multi-generation hydrogen and freshwater purposes: 4E comparison with a simple coupled Brayton and inverse Brayton cycle. Thermal Science and Engineering Progress 53: 102718.