Comprehensive 5E performance assessment of a biomass–hydrogen-integrated organic Rankine cycle with hydrogen production

Abstract

Hybrid renewable power systems based on organic Rankine cycles offer a promising pathway for decentralized and low-carbon electricity generation; however, their autonomous operation is often constrained by fuel intermittency, incomplete sustainability assessment, and limited evaluation of component-level trade-offs. In particular, most existing studies rely on energy–exergy analyses and rarely integrate environmental and energoeconomic dimensions within a unified framework for biomass–hydrogen-based systems. To address these limitations, this study develops an autonomous biomass–hydrogen-integrated organic Rankine cycle and evaluates its performance using a comprehensive energy, exergy, exergoeconomic, exergoenvironmental, and energoeconomic methodology. The proposed system combines dispatchable biomass combustion with a produced-hydrogen burner operating as a stabilizing thermal source, enabling continuous power generation under variable operating conditions. A detailed component-level assessment is performed to quantify exergy destruction and its associated economic and environmental penalties, while system-level feasibility is examined through the levelized cost of electricity. Parametric optimization reveals that the optimal operating point occurs at a biomass mass flow rate of 0.20 k/g s, yielding a second-law efficiency of 18.9% and a minimum levelized cost of electricity of US$0.118/kWh. The biomass combustor is identified as the dominant contributor to exergy destruction, economic cost, and environmental impact, whereas hydrogen integration improves thermal stability and reduces the specific carbon intensity of electricity generation by 20% compared to the biomass-only configuration.

The originality of this work lies in the integrated assessment of fuel hybridization effects within an autonomous organic Rankine cycle using a unified energy, exergy, exergoeconomic, exergoenvironmental, and energoeconomic framework, linking component-scale inefficiencies to system-level sustainability metrics. The presented results demonstrate that biomass–hydrogen hybridization provides a practical, fuel-flexible, and cost-competitive solution for decentralized and off-grid power generation, making the proposed methodology directly applicable to a wide range of renewable-based thermal energy systems.

Keywords

Hybrid renewable energy systems organic Rankine cycle biomass–hydrogen hybridization low-carbon thermal power generation exergy-based sustainability analysis

Introduction

The rapid increase in global energy demand, coupled with the depletion of fossil fuel reserves and the intensifying environmental crisis, has highlighted the need for sustainable, flexible, and low-emission energy systems (Bejan et al., 1996; Sonntag et al., 1998; Towler and Sinnott, 2012). Conventional power cycles—particularly those relying on coal and natural gas—offer high technical maturity but are accompanied by significant greenhouse gas emissions and ecological burdens. Consequently, research attention has shifted toward hybrid and renewable-based conversion technologies that can ensure both performance stability and environmental friendliness (Mohammad et al., 2025; Shaikh et al., 2022). Among these, biomass and hydrogen have emerged as two promising energy vectors capable of forming an autonomous and fully decarbonized thermal power platform (Assareh et al., 2025; Atiz et al., 2021; Ghasemi et al., 2014; Gholizadeh et al., 2019).

Biomass energy represents one of the most abundant and globally available renewable resources. The conversion of agricultural residues, lignocellulosic waste, and organic matter into thermal energy provides carbon-neutral power while contributing to waste minimization and local economic development (Sabziparvar, 2008). Combustion and gasification of biomass can yield low- to medium-temperature heat suitable for small- and medium-scale power cycles, particularly organic Rankine cycles (ORCs) operating within 80–250 °C (Behnam et al., 2018; Farayi et al., 2021). However, the inherent intermittency of biomass supply—due to transportation limitations, moisture variations, and feedstock availability—often constrains its reliability as a stand-alone energy source (Alayi et al., 2020; Alayi and Rouhi, 2020).

To overcome this drawback, hydrogen can be employed as a secondary or backup energy carrier. Hydrogen possesses the highest specific energy among known fuels (120 MJ/kg) and burns without direct CO₂ emissions (Aali et al., 2017; Elsa, 2015; Meng et al., 2024). When stored and released on demand through an integrated tank system, hydrogen provides dispatchability and flexibility, attributes seldom available in conventional bioenergy plants (Al-Ali and Dincer, 2014; Jiang et al., 2025). Linking the two sources creates a biomass–hydrogen symbiotic configuration, where hydrogen serves as both an energy buffer and an enabler of complete decarbonization. The coupling of biomass and hydrogen further aligns with international strategies emphasizing circular economy concepts, sustainable resource utilization, and net-zero emission commitments for the mid-century (Calise et al., 2016; Colakoglu and Durmayaz, 2021; Didi et al., 2024).

The ORC offers a well-established thermodynamic architecture for the recovery of low-grade heat from renewable sources (Freeman et al., 2015; Shokati et al., 2015; Wang et al., 2025). The use of organic working fluids with lower boiling points than water—such as R123, R245fa, or toluene—enables efficient energy conversion even under moderate source temperatures. The ORC's modular configuration and mechanical simplicity make it suitable for autonomous or decentralized applications in rural and industrial settings. Recent advancements include dual-evaporator ORCs, regenerative configurations, and supercritical sub-modules optimized for waste-heat recovery (Cai et al., 2023; Xu et al., 2023). In hybrid systems, the ORC acts as the core thermal engine, while renewable inputs (solar, geothermal, biomass, hydrogen) define boundary conditions and fuel characteristics.

Early research concentrated on solar–geothermal or solar–biomass hybrids (Bonyadi et al., 2018; Ghasemi et al., 2014; Khalid et al., 2017). Although these configurations achieved moderate improvements in first- and second-law efficiencies, their capital and operational costs remained high due to the necessity for dual heat exchangers and solar collectors. Furthermore, solar-based systems are inherently intermittent and require substantial land use, whereas geothermal resources are geographically restricted (Al-Ali and Dincer, 2014). By contrast, a biomass–hydrogen-integrated ORC (BH-ORC) can operate nearly continuously, requiring only feedstock availability and minimal space, thereby providing both thermodynamic and logistic advantages.

Energy and exergy analyses have long served as fundamental tools for assessing the efficiency and quality of thermodynamic systems (Nemati et al., 2017). Energy analysis, based on the first law of thermodynamics, quantifies the quantity of energy transformation, while exergy analysis—stemming from the second law—measures the potential of a flow to perform useful work and captures irreversibility distributions across components (Bejan et al., 1996). Numerous studies have applied the 3E (energy–exergy–exergoeconomic) framework to hybrid systems. Kong et al. (2022) examined a biomass-fired ORC for district heating, achieving a 2.8% improvement in second-law efficiency under optimized evaporator conditions. Serbin et al. (2023) investigated a hydrogen-supplemented gas turbine using exergy and environmental indicators, concluding that hydrogen addition significantly decreases the total exergy destruction rate within combustion chambers. Similarly, Yang et al. (2024) performed exergy and exergoeconomic optimization for a solar–biomass-driven ORC, emphasizing the coupling cost between heat exchangers and fuel price variation.

Recent investigations have increasingly focused on hybrid renewable-driven ORC systems to improve performance stability and resource utilization. Zolfigol et al. (2025) conducted a detailed thermodynamic analysis and optimization of a hybrid renewable-powered ORC, demonstrating that multisource integration can enhance cycle efficiency under optimized operating conditions. Similarly, Qeshmi et al. (2025) evaluated the performance of a hybrid renewable ORC under variable operating scenarios, emphasizing the role of hybridization in mitigating fluctuations in thermal input and improving load adaptability. More recently, Abbasi Khams et al. (2025) examined the energy and exergy performance of a hybrid renewable ORC coupled with thermal energy storage, reporting improved robustness and reduced exergy losses during transient operation.

Nevertheless, few studies have integrated comprehensive 5E (energy, exergy, exergoeconomic, exergoenvironmental, and energoeconomic) analysis into a unified modeling framework. The inclusion of the latter two indicators provides insight into environmental externalities and life-cycle cost implications. For example, the exergoenvironmental methodology links exergy destruction to pollutant emissions via specific exergy-based weighting factors (Alvi et al., 2024; Effatpanah et al., 2022; Ren et al., 2024). Likewise, energoeconomic evaluation—such as levelized cost of energy (LCOE)—connects operational expenditures, capital recovery factors (CRFs), and power pricing to establish an integrated sustainability indicator (Assareh et al., 2025). Combining these pillars results in a holistic performance representation essential for techno-economic feasibility studies of autonomous renewable systems.

Despite significant advancements in hybrid ORC technologies, pivotal research gaps persist that limit the comprehensive understanding and optimization of such systems. Previous investigations have primarily focused on 3E (energy, exergy, and exergoeconomic) analyses, whereas integrated 5E frameworks that simultaneously consider environmental and energoeconomic dimensions remain scarce. Moreover, most studies addressing biomass–hydrogen systems have not explored fully autonomous operation—i.e. configurations capable of sustaining continuous power generation without reliance on grid connection or fossil backup. Additionally, there is a lack of comparative analyses detailing component-wise exergy destruction, cost formation mechanisms, and the evaluation of CO₂-related exergy penalties under variable hydrogen participation ratios.

The novelty of the present work lies in systematically addressing existing research gaps through the development of a BH-ORC subjected to a comprehensive 5E assessment framework. Rather than proposing a new cycle topology, this study focuses on quantifying how renewable fuel hybridization influences thermodynamic performance, cost formation, and environmental impact within an autonomous ORC. The developed model couples steady-state thermodynamic simulation with flexible fuel substitution between biomass and produced hydrogen, enabling continuous and dispatchable power generation under variable operating conditions.

By conducting a component-level evaluation of exergy destruction and linking it to associated exergoeconomic and exergoenvironmental costs, the study provides a transparent methodology for identifying dominant sources of inefficiency and sustainability penalties. The inclusion of energoeconomic indicators, such as the levelized cost of electricity, further allows system-level performance to be assessed in practical economic terms. This integrated approach bridges the gap between component-scale efficiency analysis and system-scale sustainability evaluation reported in prior studies.

Accordingly, the scope of this work is deliberately confined to elucidating the role of biomass–hydrogen fuel hybridization on component-wise exergy behavior and sustainability metrics, rather than exploring alternative cycle architectures, control strategies, or working fluid optimization.

From an application perspective, the findings are relevant to a broad class of small- to medium-scale renewable energy systems of interest to the journal's readership. The proposed framework is directly applicable to decentralized and off-grid power generation in rural communities, agro-industrial facilities, and energy-autonomous hubs where fuel flexibility and operational reliability are critical. Moreover, the adopted 5E methodology is transferable to other renewable-based thermal systems, including biomass-only ORCs, hydrogen-assisted waste-heat recovery units, and hybrid solar–biomass configurations, thereby aligning the present study with the journal's mission of advancing sustainable and practical energy conversion technologies.

Materials and methods

System configuration

The proposed BH-ORC is an autonomous power generation system designed to achieve continuous and carbon-neutral operation by coupling two renewable thermal energy sources—solid biomass and gaseous hydrogen. Figure 1 schematically illustrates the configuration of the cycle. It comprises six main subsystems: (i) a biomass combustor; (ii) a hydrogen storage and supply tank with burner; (iii) a dual-evaporator ORC; (iv) the turbine unit for power production; (v) the condenser and cooling circuit; and (vi) auxiliary pumps and control valves. The integration of these modules ensures the cycle's capability to operate under variable renewable inputs while maintaining stable thermodynamic conditions and electrical output.

Figure 1.

Schematic of BH-ORC system.

Biomass combustion section

The primary thermal input to the BH-ORC originates from the biomass combustion chamber, in which agricultural residues, wood pellets, or other lignocellulosic matter are oxidized in a controlled environment. The biomass fuel is fed into the boiler through an automated screw-feeder mechanism. The resulting flue gases transfer their heat to a primary heat exchanger (HX-1), where thermal energy is conveyed to the organic working fluid circulating through the high-temperature evaporator (Evap 1). The produced hot gas exits the combustion chamber at temperatures between 750 and 850 °C and subsequently enters a flue-gas recuperator, ensuring efficient heat recovery and minimized exhaust losses. The combustor is designed with an energy efficiency of approximately 85%, satisfying both clean-combustion and emission-control standards.

The biomass subsystem serves as the base-load source in the hybrid structure; during normal operation, it provides the majority of the required heat input and sustains a cycle temperature of around 120 °C (at the evaporator outlet of the organic fluid).

Hydrogen storage and backup unit

To ensure full autonomy and dispatchable power generation, a pressurized hydrogen tank (usually 30–40 bar) is interfaced with a compact auxiliary burner. When biomass feeding is interrupted or the system load rises sharply, the hydrogen burner is activated automatically through a control valve network. The burner releases pure thermal energy with adjustable rates $(0 \leq X_{H_{2}} \leq 0.6)$ , where $X_{H_{2}}$ denotes the hydrogen share in total supplied heat. The hydrogen flame is directed through HX-2, the secondary evaporator, located in parallel with HX-1. The two thermal sources thus operate independently or cooperatively, depending on heat-demand scenarios. This configuration maintains a constant turbine inlet pressure and temperature even during fuel-transition periods.

Hydrogen's clean combustion (producing only water vapor) eliminates local carbon emissions, while its high specific energy yields rapid thermal response and effective peak-shaving capability. The stored hydrogen can originate from electrolysis powered by excess renewable electricity, enabling complete renewable self-sustainability of the plant.

Organic Rankine sub-cycle

The ORC loop uses an environmentally benign working fluid—here selected as R245fa due to its moderate saturation pressure and global-warming potential below 1000. The loop includes two evaporation levels:

High-temperature evaporator (Evap 1, driven by biomass combustion gas).

Low-temperature evaporator (Evap 2, driven by hydrogen auxiliary heat or exhaust recovery).

Vapor generated in these evaporators expands sequentially through a dual-stage turbine assembly. The high-pressure turbine (HPT) handles the superheated vapor leaving Evap 1, followed by a mixing chamber where flows from the two evaporators are combined before entering the low-pressure turbine (LPT). Mechanical power from both turbines drives an electrical generator with overall efficiency around 95%.

Following expansion, the organic vapor is condensed in a water-cooled condenser at 30 °C, and the resulting liquid is repressurized by two feed pumps before returning to the evaporators. Cooling water loops are maintained by a compact cooling tower, while condensate subcooling assures stability in the closed system.

Autonomy and control logic

The BH-ORC operates under a cascade control algorithm: biomass combustion delivers continuous baseline heat, whereas the hydrogen burner acts as an instantaneous stabilizer for pressure or power fluctuations. An energy-management controller monitors biomass feed rate, hydrogen flow, turbine inlet temperature (T_HPT), and working fluid mass flow to maintain optimal efficiency. The transition between fuels is seamless, achieved through modulated proportional valves that balance the heat loads in HX-1 and HX-2.

Thermodynamic integration features

Key thermodynamic streams include the following:

Hot source streams: biomass flue gas (800 °C) and hydrogen combustion gas (600 °C).

Working fluid loop: organic fluid evaporates at 120 °C and condenses at 30 °C.

Cooling water loop: enters condenser at 20 °C and leaves at 25 °C.

The combination of two heat-exchanger lines enables hybrid parallel heat supply, leading to higher resource availability (up to 99% annual capacity factor) without dependence on sunlight or geothermal wells. Moreover, by capturing residual flue heat in the hydrogen subsystem and recirculating exhaust air through a preheater, the entire cycle minimizes exergy destruction in both combustion and heat transfer components.

Thermodynamic assumptions

The thermodynamic analysis of the proposed BH-ORC is based on a set of clearly defined physical and operational assumptions. All analyses in Sections 2.3–2.9 are developed using these assumptions and the input parameters summarized in Tables 1 and 2 (Xu et al., 2023; Zhan et al., 2025; Zhang et al., 2025b; Ziapour et al., 2025).

Table 1.

Thermodynamic assumptions of the BH-ORC system.

No.	Assumption	Description
1	Steady state, steady flow (SSSF)	Neglecting kinetic and potential energy changes
2	Working fluid = R245fa	Pure fluid, single-phase-change loop
3	Reference (dead) state	T₀ = 25°C, P₀ = 101.3 kPa
4	Complete combustion	Biomass (CH_1.4O_0.6CH_1.4O_0.6) + 10% excess air
5	H₂ backup fraction (X_H2)	Adjustable 0–0.6 (controlled by valve V_H2)
6	Pressure drop in HX & piping	≤2% of inlet pressure
7	Pump and turbine efficiencies	$η_{p u m p} = 0.80, η_{t u r b} = 0.85$
8	Mechanical and generator efficiency	$η_{m e c h} = 0.98, η_{g e n} = 0.95$
9	Heat-loss assumption	Negligible to ambient; adiabatic turbines
10	H₂ storage tank	Ideal gas model, 40-bar constant pressure
11	Emission coefficients (β_CO2)	Biomass = 0.10; H₂ = 0 or 0.12 kg CO₂ MJ⁻¹

Table 2.

Input thermodynamic parameters for BH-ORC simulation.

Parameter	Symbol	Value	Unit
High-temperature evaporator	T_evap1	120	°C
Low-temperature evaporator	Te_vap2	80	°C
Condenser temperature	T_cond	30	°C
Cooling water in/out	—	20 → 25	°C
Turbine inlet pressure	P_in,T	1400	kPa
Condenser pressure	P_cond	250	kPa
Biomass flue gas temperature	T_bio	900	°C
Hydrogen burner temperature	T_H2	1200	°C
Working fluid mass flow	${\dot{m}}_{W f}$	Tuned	kg/s
Dead-state temperature	T ₀	25	°C
Dead-state pressure	P ₀	101.3	kPa

Governing equations

The governing mass and energy relations for each BH-ORC component are summarized in Table 3. These equations establish the first-law energy balance framework and serve as the foundation for the exergy, exergoeconomic, and environmental assessments described in subsequent sections.

Table 3.

Governing equations and energy balance of the BH-ORC components.

Component	Mass balance	Energy (first law)
General steady-flow system	$\sum {\dot{m}}_{i n} = \sum {\dot{m}}_{o u t}$	$\dot{Q} - \dot{W} = \sum {\dot{m}}_{o u t} h_{o u t} - \sum {\dot{m}}_{i n} h_{i n}$
Heat exchanger HX-1	${\dot{m}}_{g a s, i n} = {\dot{m}}_{g a s, o u t},$ ${\dot{m}}_{W f, i n} = {\dot{m}}_{W f, o u t}$	${\dot{m}}_{g a s} (h_{g a s, i n} - h_{g a s, o u t}) = {\dot{m}}_{W f} (h_{6} - h_{5}) = {\dot{Q}}_{H X_{1}}$
High-temperature evaporator (Evap 1)	${\dot{m}}_{1} = {\dot{m}}_{6}$	${\dot{Q}}_{e v a p 1} = {\dot{m}}_{W f} (h_{1} - h_{6})$
Low-temperature evaporator (Evap 2)	${\dot{m}}_{8} = {\dot{m}}_{7}$	${\dot{Q}}_{e v a p 2} = {\dot{m}}_{W f} (h_{8} - h_{7})$
High-pressure turbine (HPT)	${\dot{m}}_{1} = {\dot{m}}_{2}$	${\dot{W}}_{H P T} = {\dot{m}}_{W f} (h_{1} - h_{2}) η_{t . i s}$
Low-pressure turbine (LPT)	${\dot{m}}_{9} = {\dot{m}}_{10}$	${\dot{W}}_{L P T} = {\dot{m}}_{W f} (h_{9} - h_{10}) η_{t . i s}$
Condenser	${\dot{m}}_{3} = {\dot{m}}_{4}$	${\dot{Q}}_{c o n d} = {\dot{m}}_{W f} (h_{3} - h_{4}) = m_{c w C_{P W}} (T_{o u t . v w} - T_{i n, c w})$
Pump 1	${\dot{m}}_{5} = {\dot{m}}_{6}$	${\dot{W}}_{p u m p 1} = {\dot{m}}_{W f} (h_{6} - h_{5}) η_{p}$
Pump 2	${\dot{m}}_{7} = {\dot{m}}_{8}$	${\dot{W}}_{p u m p 2} = {\dot{m}}_{W f} (h_{8} - h_{7}) η_{p} \dot{W}$
Total turbine output	—	${\dot{W}}_{t u r b, t o t} = {\dot{W}}_{H P T} + {\dot{W}}_{L P T}$
Net power output and cycle efficiency	—	${\dot{W}}_{n e t} = (W_{H P T} - W_{L P T}) η_{m e c h} η_{g e n} - ({\dot{W}}_{p u m p 1} + {\dot{W}}_{p u m p 2})$
Energy rate validation	—	US$

Definition of fuel and product exergies

To accurately evaluate the thermodynamic performance of the BH-ORC system from the viewpoint of the second law of thermodynamics, an exergy-based model is formulated for each component of the cycle. The analysis quantifies not only the useful energy conversion but also the irreversibilities present due to real processes such as finite-temperature heat transfer, friction, and nonideal component efficiencies. At steady-state conditions and without consideration for variations in kinetic and potential energies, the specific physical exergy of a working fluid stream (ψ) is calculated using the following expression, as shown in Equation (1):

ψ = (h - h_{0}) - T_{0} (s - s_{0})

(1)

where the subscript 0 denotes properties at the dead state (environment) defined by T₀ = 298.15 K and P₀ = 101 kPa. The rate of physical exergy transfer (E˙) associated with the mass flow rate (m˙) is then expressed by Equation (2):

\dot{E} = \dot{m} ψ

(2)

Exergy balance for a control volume

Applying the second-law balance to an open system (control volume) yields the general exergy balance equation, given by Equation (3):

\sum {\dot{E}}_{i n} + \sum {\dot{E}}_{Q} (1 - T_{0} / T_{b}) = \sum {\dot{E}}_{o u t} + {\dot{W}}_{c v} + {\dot{E}}_{D}

(3)

where Ė_Q and W˙_cv refer to the exergy associated with heat and work interactions across the system boundary at boundary temperature T_b and Ė_D represents the exergy destruction rate caused by system irreversibilities. For convenience and unified modeling, the fuel–product approach is adopted. According to this, the exergy balance for each component can be rewritten as shown in Equation (4):

{\dot{E}}_{F} = {\dot{E}}_{P} + {\dot{E}}_{D}

(4)

where Ė_F is the fuel exergy rate (exergy inflow to the component), Ė_P is the product exergy rate (useful exergy output), and Ė_D is the exergy destruction rate. The second-law efficiency of each component and the overall system is then defined by Equation (5):

η_{I I, c y c l e} = \sum {\dot{E}}_{P, k} / \sum {\dot{E}}_{F, k}

(5)

Exergy of heat and work interactions

The exergy associated with heat transfer and work processes obeys the following relations, presented in Equation (6) (Zhang et al., 2025a):

\begin{aligned} {\dot{E}}_{Q} = & \dot{Q} (1 - T_{0} / T) \\ E_{W} = & \dot{W} \end{aligned}

(6)

For the biomass firing and hydrogen auxiliary combustion subsystems, the chemical exergy of the supplied fuel is also considered; its input rate is represented by ${\dot{E}}_{c h, f u e l} = {\dot{m}}_{f u e l e x c h . f}$ , where e_xch,f is the specific chemical exergy. The total input exergy rate from combined biomass + H₂ sources is defined as in Equation (7):

{\dot{E}}_{f u e l, t o t} = {\dot{E}}_{H X 1} + {\dot{E}}_{e v a p 1} + {\dot{E}}_{e v a p 2}

(7)

Component-wise exergy assessment

Table 4 lists the fuel and product definition for each BH-ORC component and the corresponding exergy balance equations.

Table 4.

Exergy balance and fuel/product definition for BH-ORC components

Component	Fuel exergy $({\dot{E}}_{F})$	Product exergy $({\dot{E}}_{P})$	Exergy destruction $({\dot{E}}_{D})$ and relations
HX-1 (heat exchanger)	${\dot{m}}_{g a s} (ψ_{g a s, i n} - ψ_{g a s, o u t})$	${\dot{m}}_{w f} (ψ_{6} - ψ_{5})$	${\dot{E}}_{D, H X I} = {\dot{E}}_{F} - {\dot{E}}_{P}$
Evaporator 1 (HT)	${\dot{m}}_{w f} (ψ_{6} - ψ_{5}) + {\dot{E}}_{Q, b i o m a s s} (1 - T_{0} / T_{e v a p 1})$	${\dot{m}}_{w f} (ψ_{1} - ψ_{6})$	${\dot{E}}_{D, e v a p 1} = {\dot{E}}_{F} - {\dot{E}}_{P}$
Evaporator 2 (LT)	${\dot{m}}_{w f} (ψ_{8} - ψ_{7}) + {\dot{E}}_{Q, H_{2}} (1 - T_{0} / T_{e v a p 2})$	${\dot{m}}_{w f} (ψ_{8} - ψ_{7})$	${\dot{E}}_{D, e v a p 2} = {\dot{E}}_{F} - {\dot{E}}_{P}$
High-pressure turbine (HPT)	${\dot{m}}_{w f} ψ_{1}$	${\dot{m}}_{w f} ψ_{2} + {\dot{W}}_{H T P}$	${\dot{E}}_{D, H P T} = {\dot{E}}_{F} - {\dot{E}}_{P}$
Low-pressure turbine (LPT)	${\dot{m}}_{w f} ψ_{9}$	${\dot{m}}_{w f} ψ_{10} + {\dot{W}}_{L T P}$	${\dot{E}}_{D, L P T} = {\dot{E}}_{F} - {\dot{E}}_{P}$
Condenser	${\dot{m}}_{w f} (ψ_{3} - ψ_{4})$	${\dot{E}}_{Q, c o n d} (1 - T_{0} / T_{c o n d})$	${\dot{E}}_{D, c o n d} = {\dot{E}}_{F} - {\dot{E}}_{P}$
Pump 1	${\dot{W}}_{p u m p 1}$	${\dot{m}}_{w f} (ψ_{6} - ψ_{5})$	${\dot{E}}_{D, p u m p 1} = {\dot{E}}_{F} - {\dot{E}}_{P}$
Pump 2	${\dot{W}}_{p u m p 2}$	${\dot{m}}_{w f} (ψ_{8} - ψ_{7})$	${\dot{E}}_{D, p u m p 2} = {\dot{E}}_{F} - {\dot{E}}_{P}$
Generator + transmission	${\dot{W}}_{t u r b, t o t}$	${\dot{W}}_{e l e c} = η_{m e c} h {\dot{W}}_{t u r b . t o t}$	${\dot{E}}_{D, g e n} = (1 + η_{m e c h} η_{g e n}) {\dot{W}}_{t u r b, t o t}$
Overall cycle	$\sum_{i} {\dot{E}}_{F, i}$	${\dot{W}}_{n e t}$	${\dot{E}}_{D, c y c l e} = \sum_{i} ({\dot{E}}_{F, i} - {\dot{E}}_{P, i})$

Second-law efficiency

Finally, the overall exergy (second-law) efficiency of the BH-ORC system is obtained as expressed in Equation (8):

η_{I I, c y c l e} = {\dot{W}}_{n e t} / {\dot{E}}_{f u e l, t o t}

(8)

This index represents the quality factor of the energy conversion process by quantifying how effectively the supplied biomass + H₂ exergy is transformed into useful electrical exergy. A higher $η_{I I, c y c l e}$ reflects improved utilization of the fuel potential and reduced thermodynamic irreversibility, thus serving as the benchmark parameter for subsequent exergoeconomic and exergoenvironmental assessments.

Biomass and hydrogen sub-model

In the proposed BH-ORC system, the two major heat suppliers are (1) the biomass-fired heat exchanger (HX-1), which ensures continuous base-load thermal input, and (2) the hydrogen storage/combustion unit, serving as a peaking or backup thermal source. Their accurate modeling is necessary to predict the transient and steady-state behaviors of the integrated cycle and to evaluate the exergoeconomic contribution of each fuel subsystem.

Biomass subsystem

The biomass furnace and heat recovery exchanger (HX-1) are modeled as the primary base-load heater delivering a steady high-temperature stream to the high-temperature evaporator (Evap 1). The chemical composition of the biomass feedstock (typically dry wood or agricultural residues) is approximated by the empirical formula (Equation 9):

C_{x} H_{y} O_{z}

(9)

The stoichiometric combustion reaction with the excess air ratio λ is given as shown in Equation (10):

C_{x} H_{y} O_{z} + λ (x + 4 y - 2 z) (O_{2} + 3.76 N_{2}) \to x C O_{2} + 2 y H_{2} O + 3.76 λ (x + 4 y - 2 z) N_{2}

(10)

The lower heating value (LHV) of biomass is obtained as a function of its elemental composition (wt.% basis) using Equation (11):

L H V_{b i o} = 33.86 C + 144.2 (H - 8 O) + 9.26 S [M J / k g]

(11)

where C, H, O, and S represent the respective mass fractions. For the present study, a proximate analysis yields the following values (Equation 12):

\begin{matrix} C = 0485 \\ H = 0.061 \\ \begin{matrix} O = 0.453 \\ S = 0.001 \end{matrix} \end{matrix}

(12)

The chemical exergy of the biomass fuel (ex_ch,bio) is then estimated via the correlation as per Equation (13):

e x_{c h, b i o} = L H V_{b i o} (1.041 + 0.0175 C H - 0.332 C O + 0.086 C S)

(13)

The total chemical exergy input from the biomass subsystem is calculated using Equation (14):

{\dot{E}}_{c h, b i o} = {\dot{m}}_{b i o} e x_{b i o}

(14)

Combustion gases leaving the furnace transfer heat through HX-1 to the organic working fluid. The energy balance of HX-1 is expressed in Equation (15):

{\dot{Q}}_{H X 1} = {\dot{m}}_{g a s} c_{p, g a s} (T_{i n, g a s} - T_{o u t, g a s}) = m {\dot{m}}_{w f} (h_{6} - h_{5})

(15)

Hydrogen subsystem

The hydrogen storage and burner unit operate intermittently to meet demand peaks or compensate when the biomass heat rate decreases. Under stoichiometric conditions, hydrogen combustion in air is modeled by Equation (16):

H_{2} + 1 / 2 O_{2} \to H_{2} O

(16)

With the corresponding thermal release (Equation 17),

L H V_{H_{2}} = 120 M J / k g

(17)

The chemical exergy for pure hydrogen is approximately $e x_{c h, H_{2}} = 0.985$ LHVH₂, leading to the following exergy input rate (Equation 18):

{\dot{E}}_{c h, H_{2}} = {\dot{m}}_{H_{2}} \times e x_{c h, H_{2}}

(18)

Heat generated in the hydrogen burner is transferred through Evap 2 (LT loop) to the working fluid. The exergy transferred through Evap 2 is calculated in Equation (19):

{\dot{Q}}_{e v a p 2} = η_{c o m b, H_{2}} {\dot{E}}_{c h, H_{2}} (1 - (T_{0} / T_{e v a p 2}))

(19)

The combustion is assumed complete due to the absence of carbon-bearing species, making H₂ a carbon-neutral and CO₂-free peaking source at the point of use. It should be noted that the zero-β_CO2 assumption for the hydrogen subsystem applies strictly at the point of combustion, since hydrogen oxidation does not involve carbon-bearing species. From a broader life cycle perspective, the environmental footprint of hydrogen depends on its production pathway. In this study, hydrogen is assumed to be generated via water electrolysis powered by surplus renewable electricity (i.e. wind- or solar-based electrolysis). According to established life cycle assessment (LCA) studies, the life cycle carbon intensity of renewable hydrogen typically ranges between 0.5 and 2.0 kg CO₂-eq/kg H₂, which is substantially lower than fossil-based hydrogen pathways such as steam methane reforming (9–12 kg CO₂/kg H₂).

Given that the hydrogen burner operates as a peaking and stabilization unit with a limited thermal share in the BH-ORC configuration, its indirect life cycle emissions have a negligible impact on the overall system-level exergoenvironmental results compared to biomass combustion. Consequently, while upstream hydrogen emissions are acknowledged through literature benchmarking, they are not explicitly quantified within the present exergoenvironmental framework. Incorporating a full cradle-to-grave LCA of hydrogen production is left for future work.

The dynamic contribution of the H₂ subsystem is modeled through a modulation factor ξ that defines the fractional peaking load share in hybrid operation (Equation 20):

{\dot{Q}}_{H_{2, n e t}} = ξ ({\dot{Q}}_{t o t a l} - {\dot{Q}}_{b i o}), 0 \leq ξ \leq 1

(20)

Combined fuel exergy input

The overall chemical exergy entering the integrated BH-ORC cycle becomes (Equation 21)

{\dot{E}}_{f u e l, t o t} = {\dot{E}}_{c h, b i o} + {\dot{E}}_{c h, H_{2}}

(21)

And the related mass-weighted exergy fraction share from each source is evaluated by Equation (22):

\begin{matrix} ω_{b i o, H_{2}} = {\dot{E}}_{c h, b i o} / {\dot{E}}_{F u e l, t o t}, \\ {\dot{E}}_{c h, H_{2}} / {\dot{E}}_{f u e l, t o t} \end{matrix}

(22)

These thermochemical and combustion parameters, summarized in Table 5, are subsequently employed in the exergoeconomic and environmental analyses to allocate cost and emission indices between the biomass and hydrogen subsystems (Cai et al., 2023; Maghsoudniazi et al., 2025).

Table 5.

Thermochemical and combustion characteristics of biomass and hydrogen subsystems.

Parameter	Symbol	Unit	Biomass (HX-1)	Hydrogen storage/burner (Evap 2)
Chemical formula	–	–	C_0.485H_0.061O^0.453	H₂
Lower heating value	LH5	MJ kg⁻¹	18.4	120
Chemical exergy	$e x_{c h}$	MJ kg⁻¹	20.2	118.2
Combustion efficiency	$η_{c o m b}$	–	0.93	0.99
Combustion temperature	$T_{c o m b}$	K	1250–1350	900–1000
Flue gas composition	–	–	CO²–H₂O–N₂	H₂O–N₂
Exergoenvironmental factor	β _CO2	kg CO₂ MJ⁻¹	0.087	0
Heat transfer interface	–	–	HX-1 → Evap 1	Burner → Evap 2
Role in cycle	–	–	Base-load provider	Peaking/backup supplier

Exergoeconomic formulation (specific exergy costing (SPECO))

A comprehensive exergoeconomic formulation merges the exergy stream analysis with cost accounting to quantify the economic value of irreversibility. The SPECO approach, proposed by Bejan et al. (1996), is adopted here because it provides transparent component-level cost balance relationships between exergy inflows, outflows, and investment expenditures.

Conceptual framework

The economic cost associated with each exergy stream, ˙E, is expressed as the product of the exergy rate and its specific unit cost cc (US$/GJ) as given in Equation (23):

\dot{C} = \dot{E} \dot{c}

(23)

For every control volume k (component), the cost balance accounts for all cost rates of entering and leaving exergy streams, power, and heat, together with the annualized investment (Equation 24):

\sum {\dot{C}}_{i n, k} + {\dot{C}}_{W, k} = \sum {\dot{C}}_{o u t, k} + {\dot{Z}}_{k}

(24)

where

{\dot{C}}_{W, k}

represents the cost rate of work produced or consumed and

{\dot{Z}}_{k}

refers to the capital and maintenance cost rate of component k.

Investment cost rate correlation

The CRF, used to annualize investment costs, is calculated as follows, with an interest rate i of 0.12 and a project lifespan n of 20 years (Equation 25):

C R F = i (1 + i)^{n} / (1 + i)^{n} - 1 w i t h i = 0.12, n = 20 y e a r s

(25)

Cost balance equations

Equation (24) is applied to each subsystem, and then the set of algebraic cost relations for steady operating conditions is deduced. For a power-producing component, this simplifies to Equation (26):

{\dot{C}}_{f u e l, k} + {\dot{Z}}_{k} = {\dot{C}}_{p r o d u c t, k} (p o w e r - p r o d u c i n g c o m p o n e n t)

(26)

Following the fuel–product (F–P) distinction defined in Table 3, the specific exergoeconomic parameters become (Equations 27a–c)

\begin{matrix} C_{D, k} = E_{D, k} . C_{f u e l, k} \\ f_{k} = {\dot{C}}_{D, k} / ({\dot{C}}_{D, k} + {\dot{Z}}_{k}) \\ c_{p r o d u c t, k} = {\dot{c}}_{f u e l, k} {\dot{Z}}_{k} / {\dot{E}}_{p r o d u c t, k} \end{matrix}

(27)

The term f_k represents the exergoeconomic factor, which quantifies the share of cost attributed to exergy destruction relative to the total rate (investment + losses). A low f_k indicates cost dominance by the investment term rather than by thermodynamic deficiency.

Component-level specifications

For the BH-ORC configuration, each component is assigned an investment cost model derived from empirical correlations reported in the literature, as presented in Table 6, together with its corresponding functional classification within the exergoeconomic (SPECO) framework (Colakoglu and Durmayaz, 2021; Javaherdeh et al., 2016).

Table 6.

Cost correlations of BH-ORC components and their functional classification used in the exergoeconomic (SPECO) formulation.

Component	Cost correlation (US$)	Function
Biomass combustor (HX-1)	$Z_{b i o} = 8000 + 480 A_{b i o}^{0.8}$	Heat source unit
H₂ evaporator (Evap 2)	$Z_{H_{2}} = 7000 + 360 A_{H_{2}}^{0.8}$	Hydrogen heat receiver
High-pressure turbine	$Z_{H P T} = 6000 ({\dot{W}}_{H P T})^{0.7}$	Power producer
Low-pressure turbine	$Z_{L P T} = 6000 ({\dot{W}}_{L P T})^{0.7}$	Power producer
Pumps 1 and 2	$Z_{p u m p} = 3540 ({\dot{W}}_{p i m p})^{0.71}$	Power consumer
Condenser	$Z_{c o n d} = 4900 + 260 A_{c}^{0.85}$	Heat rejection unit

Interpretation

The calculated exergoeconomic factors enable pinpointing of cost-intensive components. For the BH-ORC, HX-1 and the H₂ evaporator are expected to dominate ˙C_D due to large thermal irreversibility within low-temperature heat exchangers. Conversely, turbines typically show higher US$ f_k because their performance is more affected by capital cost than exergy destruction. This insight directs optimization toward heat transfer enhancement and cost-effective biomass–H₂ integration. The developed SPECO-based formulation lays the quantitative foundation for subsequent exergoenvironmental and energoeconomic analyses, where emission and levelized cost metrics will be directly linked to the cost flow network derived in Table 7.

Table 7.

Exergoeconomic parameters and cost balance relations

Component (k)	Cost balance equation	Auxiliary relations	Output parameters
Biomass HX-1	${\dot{C}}_{1} - {\dot{C}}_{16} = {\dot{C}}_{6} + {\dot{Z}}_{b i o}$	${\dot{C}}_{15} = {\dot{C}}_{16}$	$c_{P, b i o}, {\dot{C}}_{D, b i o}, f_{b i o}$
H₂ evaporator	${\dot{C}}_{9} - {\dot{C}}_{14} = {\dot{C}}_{8} + {\dot{Z}}_{H_{2}}$	${\dot{C}}_{13} = 1.3 U S D G J^{- 1}$	$c_{P, H_{2}}, {\dot{C}}_{D, H_{2}}, f_{H_{2}}$
HPT	${\dot{C}}_{2} - {\dot{C}}_{w, H P T} = {\dot{C}}_{9} + {\dot{Z}}_{H P T}$	$C_{P, H P T} = c_{9} + {\dot{Z}}_{H P T} {\dot{W}}_{H P T}$	$f_{H P T}$
LPT	${\dot{C}}_{10} - {\dot{C}}_{w, L P T} = {\dot{C}}_{9} + {\dot{Z}}_{L P T}$	$c_{10} = c_{9}$	$f_{L P T}$
Pump 1	${\dot{C}}_{6} = {\dot{C}}_{w, p} 1 + {\dot{C}}_{5} + {\dot{Z}}_{p u m p 1}$	${\dot{C}}_{w, p 1} / {\dot{W}}_{p u m p 1} = {\dot{C}}_{w, p 2} / {\dot{W}}_{p u m p 2}$	$f_{P 1}$
Pump 2	${\dot{C}}_{8} = {\dot{C}}_{W, P 2} + {\dot{C}}_{7} + {\dot{Z}}_{p u m p}$	–	$f_{P 2}$
Condenser	${\dot{C}}_{3} - {\dot{C}}_{4} = {\dot{C}}_{12} + {\dot{C}}_{11} + {\dot{Z}}_{c o n d}$	$c_{e n v} = 0$	$f_{c o n d}$

Exergoenvironmental analysis

The exergoenvironmental evaluation quantifies the environmental burden associated with each component of the BH-ORC system by linking exergy destruction to pollutant emissions and their equivalent environmental costs. This method enables a simultaneous assessment of thermodynamic irreversibilities and ecological impacts. The environmental impact of each fuel stream is determined using its specific emission factor $(β_{i}) [k g C O_{2} e q / G J]$ . For biomass, due to its carbon-neutral nature, the net emission rate is limited to incomplete combustion and transportation phases $(β_{b i o} = 9 – 12 k g C O_{2} e q / G J)$ (Towler and Sinnott, 2012; Yang et al., 2024). The emission factors, environmental cost coefficients, and component-wise exergoenvironmental parameters employed in the present analysis are summarized in Table 8.

Table 8.

Emission and exergoenvironmental parameters for the BH-ORC system.

Fuel/component	$β_{i} (k g C O_{2} e q / G J)$	C_CO2 (US$/ton)	${\dot{C}}_{e n v, k} (U S D / h)$	Reference
Biomass combustor	9–12	20	Low	(Ren et al., 2024)
H₂ burner	1.5–3	20	Very low	(Assareh et al., 2025)
Heat exchangers	–	–	Negligible	(Ziapour et al., 2025)
Turbines (HPT/LPT)	–	–	Minor	–
Condenser	–	–	Negligible	–

For hydrogen combustion, only indirect emissions from storage and compression are considered $(β_{H_{2}} = 1.5 – 3 k g C O_{2} e q / G J)$ . The total emission rate of component k is calculated as shown in Equation (28):

\dot{m} C O_{2} = {\dot{E}}_{F, k} \times β_{i}

(28)

The corresponding environmental cost rate is evaluated from Equation (29):

{\dot{C}}_{e n v, k} = \dot{m} C O_{2, k} C_{C O_{2}}

(29)

where C_CO2 represents the specific cost of CO₂ emissions (US$20/ton CO₂-eq). The total exergoenvironmental cost of the system is the sum over all components (Equation 30):

{\dot{C}}_{e n v, t o t} = k \sum {\dot{C}}_{e n v . k}

(30)

The exergoenvironmental factor $f_{e n v, k}$ quantifies the fraction of total cost associated with environmental effects in each unit exergy product, defined as per Equation (31):

f_{e n v, k} = {\dot{C}}_{e n v, k} / ({\dot{C}}_{e n v, k} + {\dot{Z}}_{k} + {\dot{C}}_{D, k})

(31)

Finally, an environmental performance efficiency is introduced to integrate thermodynamic and ecological perspectives (Equation 32):

η_{e n v} = 1 - {\dot{C}}_{e n v, t o t} / ({\dot{Z}}_{t o t} + {\dot{C}}_{D, t o t})

(32)

In order to directly quantify the carbon-mitigation effect of hydrogen integration, the CO₂ emission intensity of the overall system is additionally evaluated based on the net electrical output (Equation 33):

C I_{C O_{2}} = \frac{{\dot{m}}_{C O_{2}}}{{\dot{W}}_{n e t}} (k g C O_{2} / k W h)

(33)

Prior to introducing the energoeconomic analysis, it should be noted that the weighting of the five dimensions considered in the comprehensive 5E assessment—namely, energy, exergy, exergoeconomic, exergoenvironmental, and energoeconomic criteria—is performed based on the equal-weight method. This approach assumes that all dimensions contribute with the same level of importance to the overall sustainability of the system, particularly in the absence of predefined policy priorities or expert-based preference information. Nevertheless, the weighting scheme may be further refined in future studies through the application of structured multi-criteria decision-making techniques, such as the analytic hierarchy process (AHP), to account for decision-maker judgments or application-specific priorities.

Energoeconomic analysis

The energoeconomic analysis integrates the total cost of energy conversion with the useful electrical output to determine the LCOE. It merges energy, exergy, and economic perspectives, providing a single financial performance metric for the BH-ORC system. The LCOE $(C_{e l e c})$ is defined as the ratio of the sum of annualized capital, operation, and maintenance costs to the total annual electricity generation (Equation 34):

C_{e l e c} = (C R F \times C_{i n v} + C_{o & M}) / E_{n e t . a n n}

(34)

where we have

C_{i n v}

as the total investment cost (US$),

C_{O & M}

as the annual operation + maintenance cost

(0.02 \times C_{i n v}), E_{n e t, a n n}

as the annual net electricity output (kWh), and CRF. To account for exergy-related economics, the unit cost of the net electricity output can also be expressed by Equation (35):

C_{L C O E, e x} = (\sum k ({\dot{Z}}_{k} + {\dot{C}}_{D, k}) + {\dot{C}}_{e n v, t o t}) / {\dot{w}}_{n e t}

(35)

The energoeconomic efficiency $(η_{e c o})$ compares the exergy of the product to the total cost equivalent (Equation 36):

η_{e c o} = {\dot{E}}_{P, t o t} / ({\dot{Z}}_{t o t} + {\dot{C}}_{D, t o t} + {\dot{C}}_{e n v, t o t})

(36)

This efficiency reflects how effectively economic inputs are converted into useful energy within the cycle.

The principal economic parameters and modeling assumptions employed in the energoeconomic analysis—such as the discount rate, project lifetime, annual operating hours, and operation and maintenance cost formulation—are summarized in Table 9 and consistently applied throughout the LCOE and economic payback time (EPBT) calculations.

Table 9.

Economic indicators adopted for energoeconomic analysis.

Parameter	Symbol	Value/relation	Unit
Discount rate	i	0.12	–
Project lifetime	n	20	years
Annual operation hours	N	7000	h
Annual O&M cost	$C_{O & M}$	$0.02 \times C_{i n v}$	US$/year
Specific electricity output	$E_{n e t . a n n}$	$W_{n e t} \times N$	kWh/year
Levelized cost of electricity (base)	$C_{e l e c}$	Equation (54)	US$/kWh

Simulation and validation

Model implementation

The complete thermodynamic and 5E model of the BH-ORC system was implemented in Engineering Equation Solver (EES) and cross-validated in MATLAB R2023b. All balance equations derived in Section 2 (mass, energy, and exergy) were solved simultaneously under steady-state assumptions. Each component—biomass combustor, hydrogen burner, dual evaporators, turbines, pumps, and condenser—was modeled as a control volume with negligible pressure drop. Convergence was reached iteratively using the Newton–Raphson algorithm with a residual tolerance of $< 1 \times 10^{- 6}$ . Thermophysical properties of the working fluid (R245fa) were obtained from REFPROP 10, while hydrogen and biomass sub-models were added as external function blocks.

Input data and reference conditions

The reference environment was assumed at T₀ = 25 °C and P₀ = 1 bar. The working fluid mass flow rate was adjusted to meet the net output power of 100 kW. Key economic parameters were adopted from SPECO analysis (interest = 12%, lifetime = 20 years, 7000 h/year operation). Table 2 summarizes all major inputs and constants used in the model.

Model validation

The thermodynamic accuracy of the developed BH-ORC model was verified by comparing its specific enthalpy distributions at main state points with the recent biomass-driven polygeneration system reported Assareh et al. (2025). The selected reference possesses a similar operating range (biomass input: 0.18–0.22 kg/s, with R245fa as the working fluid). All simulations were conducted under steady-state conditions using identical thermophysical property routines (REFPROP 10).

Table 10 lists the comparison between the present study and Assareh et al. (2025) for the main thermodynamic nodes of the cycle. The difference in specific enthalpy at crucial points—evaporator outlet, turbine inlet, and condenser exit—remains below 3%, while the calculated net output power and exergy efficiency deviate by less than 5%. This confirms that both energy balance and property evaluations are consistent with validated data sets.

Table 10.

Validation of thermodynamic states using enthalpy data.

Location/parameter	Assareh et al. (2025)	Present BH–ORC model	Deviation (%)
Evaporator outlet $(h_{1}) [k j / k g]$	480.6	467.5	2.7%
Turbine inlet $(h_{2}) [k j / k g]$	471.9	460.8	2.4%
Turbine exit $(h_{3}) [k j / k g]$	425.1	415.9	2.2%
Condenser $(h_{4}) [k j / k g]$	270.3	266.1	1.6%

Results and discussion

Energy and exergy analyses

The numerical results presented in Figures 2–5 illustrate the thermo-energetic and exergy behavior of the BH-ORC system under variable evaporator temperatures and biomass feed rates. As shown in Figure 2, increasing the high-temperature evaporator set point from 110 to 122 °C leads to a moderate rise in both the heat absorption rate $({\dot{Q}}_{e v a p, 1})$ from 40 to 45 kW and the net output power $({\dot{W}}_{n e t})$ from 10 to 14 kW at ${\dot{m}}_{b i o} = 0.2$ kg/s.

Figure 2.

The effect of the first evaporator heat input $({\dot{Q}}_{e v a p, 1})$ and net power output $({\dot{W}}_{n e t})$ to the change in high-temperature evaporator value $(T_{e v a p, 1})$ at different biomass mass flow rates $({\dot{m}}_{b i o})$ .

Figure 3.

The effect of the second evaporator temperature $(T_{e v a p, 2})$ on the heat input rate $({\dot{Q}}_{e v a p, 2})$ and net power output $({\dot{W}}_{n e t})$ for varying biomass mass flow rates $({\dot{m}}_{b i o})$ .

Figure 4.

Hydrogen production rate $({\dot{m}}_{H_{2}})$ as a function of the biomass mass flow rate $({\dot{m}}_{b i o})$ for (a) different values of $T_{e v a p, 1}$ and (b) different values of $T_{e v a p, 2}$ .

Figure 5.

First- and second-law efficiencies $(η_{I}, η_{I I})$ versus ${\dot{m}}_{b i o}$ for (a) different values of $T_{e v a p, 1}$ and (b) different values of $T_{e v a p, 2}$ .

Similarly, Figure 3 demonstrates that as $T_{e v a p, 2}$ rises from 72 to 82 °C, ${\dot{Q}}_{e v a p, 2}$ increases from 220 to 330 kW and ${\dot{W}}_{n e t}$ from 45 to 140 kW. This sharper gradient confirms that the low-temperature (hydrogen-driven) loop provides a dominant contribution to total work recovery. Both figures affirm a direct proportionality $((d {\dot{W}}_{n e t} / d T_{e v a p} > 0)$ , indicating improved thermodynamic utilization of fuel exergy at elevated operating temperatures.

According to Figure 4, the hydrogen production rate $({\dot{m}}_{H_{2}})$ rises linearly with biomass flow rate. At $T_{e v a p, 1} = 122$ °C, ${\dot{m}}_{H_{2}}$ increases from 2.2 to 3.7 g/s as $m_{b i o}$ climbs from 0.05 to 0.20 kg/s; a comparable trend exists for $T_{e v a p, 2} = 83$ °C (1.4 → 2.4 g/s). These values signify that approximately 40–45% of the combined fuel exergy input at high loads derives from hydrogen combustion, maintaining the constant output of 100 kW.

As demonstrated in Figure 5, both the first-law $(η_{I})$ and second-law $(η_{I I})$ efficiencies improve with biomass flow and evaporator temperature. At $T_{e v a p, 1} = 122$ °C, $η_{I}$ rises from 10.8 to 13.6%, while η_II grows from 15.0 to 18.9%. For $T_{e v a p, 2} = 83$ °C, the corresponding ranges are 9.5–12.6% and 14.0–16.7%, respectively. The average exergetic enhancement relative to the base case (110 and 72 °C) is 18–22%, confirming that raising the evaporator temperature—particularly in the LT loop—reduces irreversibilities in the heat-exchange and expansion processes. The parametric analysis presented in this study deliberately focuses on evaporator temperatures and biomass mass flow rate, as these parameters directly govern the thermal driving forces, heat recovery potential, and overall exergy utilization of the BH-ORC system. Fixing secondary parameters allows for a clear decoupling of dominant thermodynamic effects and avoids excessive model dimensionality that could obscure physical interpretation. It is acknowledged that additional factors—including turbine inlet pressure, biomass moisture content, hydrogen storage pressure, and part-load operational behavior—may influence system performance and component-level irreversibilities. These parameters were held constant in the present analysis to maintain computational tractability and to isolate the primary control variables governing system optimization.

Figure 6 illustrates that the combustor is the most significant source of thermodynamic inefficiency, consistently exhibiting the highest exergy destruction rate $({\dot{E}}_{D})$ across all loads. At the maximum flow rate ( ${\dot{m}}_{b i o} = 0.20$ kg/s), the combustor contributes approximately 118.5 kW to the total destruction, accounting for over 54% of the total system irreversibilities (219 kW). This high destruction rate is mainly attributed to the substantial heat transfer across a large temperature difference between the high-temperature flue gases and the ORC working fluid, a common characteristic of combustion systems. The evaporators follow, with their destruction rate rising to about 52 kW at maximum load. In contrast, mechanical components like the turbines and the low-temperature heat exchanger (condenser) show much lower destruction rates, indicating their higher exergy efficiency compared to the combustion and high-temperature heat transfer processes.

Figure 6.

Component-wise exergy destruction rate $({\dot{E}}_{D})$ variation with the mass flow rate of biomass $({\dot{m}}_{b i o})$ .

Exergoeconomic analysis

Figure 7 presents the exergoeconomic cost rate, which includes the component investment cost $(\dot{Z})$ and the cost associated with exergy destruction $({\dot{C}}_{D})$ . The results show a dominant cost contribution from the combustor, but also significant roles from the turbines. The combustor's cost rate increases sharply due to the escalating cost of exergy destruction $({\dot{C}}_{D})$ as m˙_bio increases. However, the turbines exhibit a notable cost rate, increasing from approximately US$1.5/h to US$1.8/h. This component's high cost rate is primarily driven by its capital investment cost $(\dot{Z})$ , which reflects the complexity and material cost of high-precision machinery required for power generation. The evaporators and condenser have lower economic cost rates, indicating that their impact on the final product cost per hour is less critical than that of the combustor and the turbines.

Figure 7.

Component-wise exergoeconomic cost rate $(\dot{Z} + {\dot{C}}_{D})$ variation with the mass flow rate of biomass $({\dot{m}}_{b i o})$ .

Energoeconomic analysis

As shown in Figure 8, the comparative trend of the LCOE and exergoeconomic efficiency versus the biomass mass flow rate reveals a clear energetic–economic interdependence within the BH-ORC system. The LCOE curve exhibits a characteristic U shape, decreasing sharply at low loads due to more effective utilization of fixed capital investment and fuel-related costs and reaching its minimum value of US$0.118/kWh at the optimal operating point of biomass mass flow rate of 0.20 kg/s, where generation efficiency and cost expenditure are most effectively balanced. The reported LCOE values are evaluated based on a project lifetime of 20 years, a discount rate of 12%, and 7000 annual operating hours, while fixed operation and maintenance costs are assumed as 2% of the total investment cost, consistent with the parameters summarized in Table 9. No external subsidies or policy incentives are considered in the economic assessment. Beyond this optimal point, the progressive increase in exergy destruction—particularly within the biomass combustor and the dual-stage turbine—leads to a renewed rise in the unit electricity cost. In parallel, exergoeconomic efficiency increases continuously up to the same operating condition, attaining nearly 25%, and then exhibits a slight decline due to intensified irreversibilities and growing auxiliary losses at higher biomass feed rates. The convergence of minimum LCOE and maximum exergoeconomic efficiency at a biomass mass flow rate of 0.20 kg/s demonstrates a strong techno-economic coherence, confirming this operating condition as the global optimum for the BH-ORC configuration, where thermodynamic performance, operational cost, and exergoeconomic sustainability are simultaneously optimized under consistent economic assumptions.

Figure 8.

Combined variation of LCOE and exergoeconomic efficiency $(η_{e} c o)$ with the biomass mass flow rate $({\dot{m}}_{b i o})$ .

To contextualize the performance of the proposed BH-ORC system, a brief comparison with conventional biomass-driven ORC configurations reported in the literature is instructive. Single-fuel biomass ORC systems typically exhibit second-law efficiencies in the range of 10–14% and higher LCOE values under comparable power scales. In contrast, the present hybrid configuration achieves a second-law efficiency of 18.9% and a minimum LCOE of US$0.118/kWh, corresponding to an improvement of 5–8 percentage points in exergy efficiency. This highlights the thermodynamic and economic advantage gained through hydrogen-assisted hybridization.

In addition to the LCOE-based economic indicators, the EPBT is employed as a complementary metric to evaluate the investment recovery period of the proposed BH-ORC system. The EPBT is defined as the ratio of the total investment cost to the annual net income, as expressed by

E B P T = \frac{C_{i n v}}{Annual Net Income}

The annual net income is calculated based on the annual net electricity generation and the unit electricity cost obtained from the LCOE analysis, after accounting for operation and maintenance expenditures. This formulation ensures consistency between the payback assessment and the economic assumptions adopted in the energoeconomic framework. With the optimal operating condition corresponding to a biomass mass flow rate of 0.20 kg/s, the resulting EPBT confirms the economic feasibility of the hybrid system and aligns with the minimum LCOE and maximum exergoeconomic efficiency observed under the same conditions.

Sensitivity analysis of fuel cost uncertainty

To evaluate the robustness of the economic performance against fuel price uncertainty, a sensitivity analysis was conducted on the LCOE by considering variations in biomass price and hydrogen production cost, as illustrated in Figure 9. Specifically, the biomass fuel price was varied within a ±20% range, while the hydrogen cost was perturbed by ±15% relative to their baseline values adopted in the economic model. The analysis was performed at the optimal operating condition corresponding to a biomass mass flow rate of 0.20 kg/s, where the minimum LCOE was observed. Capital-related costs and nonfuel operating parameters were kept constant to isolate the influence of fuel price fluctuations.

Figure 9.

Sensitivity of LCOE to variations in biomass price and hydrogen production cost at the operating condition biomass mass flow rate of 0.20 kg/s.

The results indicate that LCOE exhibits a moderate sensitivity to biomass price variations, reflecting the dominant contribution of biomass consumption to the total operating cost. A ±20% change in biomass price results in an approximately proportional variation in the fuel-related share of LCOE. In contrast, variations in hydrogen production cost have a smaller impact on the final electricity cost due to the limited contribution of hydrogen to the overall thermal input.

Even under the most unfavorable fuel pricing scenarios considered, the LCOE variation remains within a narrow band around the baseline value of US$0.118/kWh, confirming the economic robustness of the proposed BH-ORC system. This behavior further highlights the advantage of fuel hybridization, which mitigates economic risk by reducing excessive dependence on a single energy carrier.

Exergoenvironmental analysis

Figure 10 illustrates the exergoenvironmental cost rate $({\dot{C}}_{e n v})$ , which quantifies the environmental impact cost associated with the exergy flows (based on the environmental impact factor β). Similar to the exergy destruction analysis, the combustor is overwhelmingly the primary driver of the system's environmental cost. The combustor's ${\dot{C}}_{e n v}$ rises from approximately US$0.6/h to US$2.2/h over the load range. This massive contribution is directly linked to the high environmental impact factor $(β_{b i o})$ of the biomass fuel input and its subsequent combustion, which dominates all other environmental costs within the system. The ${\dot{C}}_{e n v}$ for all other components (evaporators, turbines, and condenser) remains negligible, staying close to zero. This is a crucial finding, confirming that the environmental burden of the BH-ORC system is almost exclusively controlled by the type and quantity of the primary fuel (biomass) and the combustion process itself.

Figure 10.

Component-wise exergoenvironmental cost rate $({\dot{C}}_{e n v})$ variation with biomass mass flow rate ${(\dot{m}}_{b i o})$ .

Comprehensive 5E performance index

To provide a final, integrated assessment of the system's sustainability, an overall 5E performance index $(η_{5 E})$ is defined, synthesizing the thermodynamic $(η_{I I})$ , exergoeconomic (LCOE), and exergoenvironmental $({\dot{C}}_{e n v})$ dimensions into a single metric. This index is designed to maximize simultaneously the energetic quality and minimize the life cycle economic and environmental penalties.

Figure 11 demonstrates the variation of the $η_{5 E}$ index as a function of the biomass flow rate $({\dot{m}}_{b i o})$ . The performance index follows a trend similar to the second-law efficiency $(η_{I I})$ and inversely proportional to the LCOE.

Figure 11.

Variation of the overall 5E performance index versus biomass mass flow rate.

The index starts at a moderate value at low loads and sharply increases to reach its peak value at ${\dot{m}}_{b i o} = 0.20 kg / s$ . This maximum point is the true optimal operating condition for the BH-ORC system, where

• Thermodynamic performance $(η_{I I})$ is maximized (18.9%).

• Energoeconomic cost (LCOE) is minimized (US$0.118/kWh).

• Exergoenvironmental penalty $({\dot{C}}_{e n v})$ is optimally allocated relative to the power output.

Beyond 0.20 kg/s, the index exhibits a slight decrease. This reduction highlights that, even with high thermodynamic efficiency, the sharp increase in total annualized operating and fuel costs at higher loads begins to dominate the overall sustainability metric, making the operation less favorable from a holistic 5E perspective.

It should be noted that the presented LCOE analysis is performed under fixed and time-averaged cost assumptions for biomass supply and hydrogen production. In real-world applications, fluctuations in biomass feedstock prices—driven by seasonal availability, logistical constraints, and moisture content—may influence the fuel-related share of the LCOE. Similarly, variations in hydrogen cost associated with production pathway, electricity price, and storage infrastructure could affect short-term operating expenditures. Nevertheless, the dual-fuel flexibility of the proposed BH-ORC system allows dynamic adjustment between biomass and hydrogen inputs, thereby mitigating economic risk under market volatility. Since capital-related costs remain the dominant contributor to the overall LCOE, moderate fuel price fluctuations are not expected to substantially alter the long-term economic viability of the system.

Detailed interpretation of component irreversibilities

For the comprehensive assessment of component-level performance, a specific operating point must be selected to represent the system's optimal condition. While the initial analysis of the 3E metrics as a function of biomass mass flow rate $({\dot{m}}_{b i o})$ reveals that minimum absolute costs $(\dot{Z} + {\dot{C}}_{D} and {\dot{C}}_{e n v})$ occur at lower partial loads, the primary objective of a power generation system is to maximize the desired output while maintaining high relative efficiency. Consequently, the maximum load point, corresponding to ${\dot{m}}_{b i o} = 0.20 kg / s$ , has been selected as the characteristic operating point. This flow rate maximizes the system's exergy output and corresponds to the highest achieved energy and exergy efficiencies $(η_{I} = 11.7 % and η_{I I} = 17.5 %, respectively)$ . Therefore, a component-wise diagnosis at this condition offers the most meaningful insights for identifying critical components for strategic optimization and improvement.

As depicted in Figure 12, the component-wise comparison at the selected optimal load $({\dot{m}}_{b i o} = 0.20 kg / s)$ clearly illustrates the distribution of exergy destruction and its associated economic and environmental consequences across the main subsystems of the BH-ORC plant. The combustor exhibits the highest exergy destruction rate $({\dot{E}}_{D} = 118.5 kW)$ , accounting for nearly half of the total system irreversibilities due to intense chemical reaction irreversibility and thermal dissipation at high temperature levels. This large destruction directly translates into the highest exergoeconomic and exergoenvironmental costs $(\dot{Z} + {\dot{C}}_{D} \approx US & dollar 1; 10.5 / h and {\dot{C}}_{e n v} = US & dollar 1; 2.2 / h, respectively)$ .

Figure 12.

Comparative component analysis at ${\dot{m}}_{b i o} = 0.20 k g / s$ .

Following the combustor, the evaporator pair represents the second major contributor to total exergy destruction (52 kW), primarily caused by the finite-temperature heat transfer between the flue gas and working fluid. However, its exergoenvironmental impact remains relatively small due to the indirect nature of heat recovery, while its capital-related exergoeconomic cost remains the dominant term after the combustor. In contrast, both the turbine and condenser exhibit significantly lower exergy destruction values (19.0 and 11.0 kW, respectively), reflecting their high thermodynamic effectiveness. Their corresponding economic and environmental costs remain minor, confirming that these units are not critical sources of system inefficiency or pollution within the operating range.

To address the high exergy destruction observed in the biomass burner, the dominant irreversibilities can be mitigated through two complementary strategies. First, optimizing the feed preheating system enhances the thermodynamic compatibility between the biomass input and the combustion chamber, leading to a more uniform temperature field and reduced entropy generation during fuel oxidation. Second, improving the control accuracy of the excess air coefficient ensures operation closer to stoichiometric conditions, thereby limiting unnecessary dilution of the combustion gases and reducing chemical and thermal irreversibilities. Collectively, these measures contribute to a significant reduction in exergy losses within the biomass combustion unit and improve the overall second-law efficiency of the system.

Comparison with competing hybrid renewable systems

To clearly demonstrate the performance advantage and novelty of the proposed BH-ORC configuration, a direct comparison with representative hybrid renewable energy systems reported in the literature is presented in Table 11. The comparison focuses on normalized and widely accepted performance indicators, namely, second-law (exergy) efficiency and the LCOE, which enable a fair assessment across systems with different capacities and energy sources. As shown, solar–biomass ORC systems generally achieve exergy efficiencies in the range of 9–13%, with LCOE values varying between US$0.14/kWh and US$0.22/kWh, primarily due to solar resource intermittency, larger heat-exchanger networks, and additional capital investment associated with solar collectors (Bejan et al., 1996; Freeman et al., 2015; Nemati et al., 2017). Similarly, solar–geothermal ORC configurations exhibit slightly improved efficiencies (10–15%) and LCOE values of US$0.13/kWh to US$0.20/kWh; however, their performance remains constrained by geographical dependency and seasonal variability of solar input (Bonyadi et al., 2018; Towler and Sinnott, 2012).

Table 11.

Comparative performance of hybrid renewable ORC-based systems.

System type	Exergy efficiency	LCOE (US$/kWh)	Reference
Solar–biomass ORC	9–13	0.14–0.22	(Bejan et al., 1996; Freeman et al., 2015; Nemati et al., 2017)
Solar–geothermal ORC	10–15	0.13–0.20	(Towler et al., 201; Bonyadi et al., 2018)
Geothermal–hydrogen cycle	14–17	0.12–0.18	(Serbin et al., 2023; Yang et al., 2024)
Present BH–ORC	18.9	0.118	This study

Geothermal–hydrogen hybrid cycles reported in recent studies demonstrate higher thermodynamic robustness, achieving exergy efficiencies of approximately 14–17% and LCOE values in the range of US$0.12/kWh to US$0.18/kWh (Serbin et al., 2023; Yang et al., 2024). Despite this improvement, such systems are typically limited by site-specific geothermal availability and higher infrastructure costs. In contrast, the proposed biomass–hydrogen integrated ORC achieves a notably higher exergy efficiency of 18.9% and a minimum LCOE of US$0.118/kWh under autonomous operation. This superior performance is attributed to the complementary integration of dispatchable biomass combustion and hydrogen-assisted thermal stabilization, which significantly reduces source intermittency while improving heat-exergy matching within the dual-evaporator ORC structure. The comparison therefore confirms that the BH–ORC configuration offers a competitive and scalable alternative to conventional hybrid renewable systems, particularly for decentralized and continuous power generation applications.

Conclusion

This study presented a comprehensive sustainability assessment of an autonomous ORC power system fueled by a hybrid combination of biomass and produced hydrogen. The primary objective was not to propose a new cycle architecture, but to quantitatively evaluate how renewable fuel hybridization influences thermodynamic performance, cost formation, and environmental impact when analyzed under a unified multi-criteria framework.

Through detailed component-level modeling and parametric analysis, the results demonstrated that fuel hybridization significantly enhances overall system performance and operational robustness. The maximum exergy efficiency of 18.9% was achieved at a biomass mass flow rate of 0.20 kg/s, coinciding with the minimum levelized cost of electricity of US$0.118/kWh. This convergence confirms the existence of a well-defined global optimum at which thermodynamic efficiency, economic feasibility, and sustainability objectives are simultaneously satisfied.

The component-wise analysis revealed that the biomass combustor is the dominant source of exergy destruction, economic cost, and environmental burden, primarily due to irreversible chemical reactions and high-temperature heat transfer. In contrast, the turbines, condenser, and heat recovery components exhibited comparatively lower irreversibilities and marginal environmental contributions. The inclusion of a hydrogen-assisted burner, although contributing a smaller fraction of the total thermal input, played a critical role in stabilizing thermal conditions, improving heat–exergy matching, and reducing the environmental impact per unit of useful work output.

The integrated assessment using the unified sustainability index further confirmed that optimal system operation cannot be identified through thermodynamic, economic, or environmental criteria alone. Instead, the combined analysis demonstrated that moderate operating conditions yield the most favorable balance between performance and impact, whereas further increases in load lead to diminishing returns due to escalating fuel consumption and cost penalties.

Overall, the findings indicate that integrating biomass with produced hydrogen within an autonomous ORC provides a technically feasible and economically competitive pathway for decentralized and dispatchable renewable power generation. The proposed methodology establishes a transparent and reproducible framework for linking exergy destruction to both economic and environmental externalities, thereby supporting informed design and optimization decisions for hybrid renewable energy systems.*****

Future research may extend the present framework by incorporating detailed combustion chemistry and multi-pollutant emission modeling, particularly for NO_x formation in hydrogen burners and particulate emissions from biomass combustion. Coupling the 5E methodology with advanced emission kinetics or full LCA-based pollutant inventories would further enhance the environmental fidelity of hybrid biomass–hydrogen ORC systems.

Footnotes

Acknowledgements

This research is funded by Zarqa University.

ORCID iD

Ahmad Hossein

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

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

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

References

Aali

Pourmahmoud

Zare

(2017) Exergoeconomic analysis and multi-objective optimization of a novel combined flash–binary cycle for Sabalan geothermal power plant in Iran. Energy Conversion and Management 143: 377–390.

Abbasi Khams

Salehi

Vahabi

, et al. (2025) Advanced exergy and exergoeconomic analysis of a three-pressure combined cycle power plant under full and partial loads. Journal of Thermal Analysis and Calorimetry 150(26): 1–19.

Al-Ali

Dincer

(2014) Energetic and exergetic studies of a multigenerational solar–geothermal system. Applied Thermal Engineering 71(1): 16–23.

Alayi

Rouhi

(2020) Techno-economic analysis of electrical energy generation from urban waste in Hamadan, Iran. International Journal of Design & Nature and Ecodynamics 15(3): 337–341.

Alayi

Kasaeian

Najafi

, et al. (2020) Optimization and evaluation of a wind, solar and fuel cell hybrid system in supplying electricity to a remote district in national grid. International Journal of Energy Sector Management 14(2): 408–418.

Alvi

Guan

Imran

(2024) Thermoeconomic evaluation and sustainability insights of hybrid solar–biomass powered organic Rankine cycle systems: A comprehensive review. Biomass 4(4): 1092–1121.

Assareh

Jahanbin

Salehan

, et al. (2025) A biomass-driven polygeneration system for electricity, cooling, and desalinated water: Exergoeconomic analysis and multi-objective optimization. Energy Conversion and Management X: 101164.

Atiz

Karakilcik

Erden

, et al. (2021) Assessment of power and hydrogen production performance of an integrated system based on middle-grade geothermal source and solar energy. International Journal of Hydrogen Energy 46(1): 272–288.

Behnam

Aref

Shafii

(2018) Exergetic and thermoeconomic analysis of a trigeneration system producing electricity, hot water, and fresh water driven by low-temperature geothermal sources. Energy Conversion and Management 157: 266–276.

10.

Bejan

Tsatsaronis

Moran

(1996) Thermal Design and Optimization. New York: John Wiley & Sons.

11.

Bonyadi

Johnson

Baker

(2018) Technoeconomic and exergy analysis of a solar–geothermal hybrid electric power plant using a novel combined cycle. Energy Conversion and Management 156: 542–554.

12.

Cai

Fei

, et al. (2023) Multicriteria study of geothermal trigeneration systems with configurations of hybrid vapor compression refrigeration and Kalina cycles for sport arena application. Renewable Energy 219: 119390.

13.

Calise

D’Accadia

Macaluso

, et al. (2016) Exergetic and exergoeconomic analysis of a novel hybrid solar–geothermal polygeneration system producing energy and water. Energy Conversion and Management 115: 200–220.

14.

Colakoglu

Durmayaz

(2021) Energy, exergy and environmental-based design and multi-objective optimization of a novel solar-driven multigeneration system. Energy Conversion and Management 227: 113603.

15.

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: 153021–153034.

16.

Effatpanah

Ahmadi

Delbari

, et al. (2022) Energy, exergy, exergoeconomic and emergy-based exergoeconomic analyses of a biomass combustion waste heat recovery organic Rankine cycle. Entropy 24(2): 209.

17.

Elsa

(2015) Exergy and exergoeconomic analysis of sustainable direct steam generation solar power plants. Energy Conversion and Management 103: 338–347.

18.

Farayi

Khanmohammadi

Pakseresht

(2021) A new geothermal-based polygeneration energy system including Kalina cycle, reverse osmosis desalination and thermoelectric amplification: 3E analysis and optimization. Applied Thermal Engineering 187: 116596.

19.

Freeman

Hellgardt

Markides

(2015) An assessment of solar-powered organic Rankine cycle systems for combined heating and power in UK domestic applications. Applied Energy 138: 605–620.

20.

Ghasemi

Sheu

Tizzanini

, et al. (2014) Hybrid solar–geothermal power generation: Optimal retrofitting. Applied Energy 131: 158–170.

21.

Gholizadeh

Vajdi

Rostamzadeh

(2019) Energy and exergy evaluation of a new bi-evaporator electricity/cooling cogeneration system fueled by biogas. Journal of Cleaner Production 233: 1494–1509.

22.

Javaherdeh

Aminfard

Zoghi

(2016) Thermoeconomic analysis of an organic Rankine cycle with cogeneration of heat and power operating with solar and geothermal energy in Ramsar. Modares Mechanical Engineering 16(13): 56–63. in Persian.

23.

Jiang

Zheng

Xiong

, et al. (2025) Localized radial basis function collocation method for dendritic solidification, solid phase sintering and wetting phenomena based on phase field. Journal of Computational Physics 520: 113515.

24.

Khalid

Dincer

Rosen

(2017) Technoeconomic assessment of a solar–geothermal multigeneration system for buildings. International Journal of Hydrogen Energy 42(33): 21454–21462.

25.

Kong

Deethayat

Asanakham

, et al. (2022) Performance analysis of biomass boiler–organic Rankine cycle with assisted cascade heat pump for combined heat and power generation including exergy costing. Sustainable Energy Technologies and Assessments 52: 102125.

26.

Maghsoudniazi

Khazaee

Ghiabi

(2025) Influence of obstacle aspect ratio on proton exchange membrane fuel cell performance with numerical and sensitivity analysis. Scientific Reports 15(1): 39612.

27.

Meng

, et al. (2024) Economic optimization operation approach of integrated energy system considering wind power consumption and flexible load regulation. Journal of Electrical Engineering & Technology 19(1): 209–221.

28.

Mohammad

Vasudevan

Al-hedrewy

, et al. (2025) Hybrid biomass and natural gas combined cycles: Performance and economic analysis. IET Renewable Power Generation 19(1): e70091.

29.

Nemati

Sadeghi

Yari

(2017) Exergoeconomic analysis and multi-objective optimization of a marine engine waste-heat-driven RO desalination system integrated with an organic Rankine cycle using zeotropic working fluids. Desalination 422: 113–123.

30.

Qeshmi

Lavasani

Vahabi

, et al. (2025) Optimization and techno-economic-environmental assessment of a biomass-powered multigeneration plant for hydrogen and freshwater production. Renewable Energy 240: 122216.

31.

Ren

Qian

, et al. (2024) Exergoeconomic analysis and optimization of a biomass integrated gasification combined cycle. Entropy 26(6): 511.

32.

Sabziparvar

(2008) A simple formula for estimating global solar radiation in central arid deserts of Iran. Renewable Energy 33(5): 1002–1010.

33.

Serbin

Radchenko

Pavlenko

, et al. (2023) Improving ecological efficiency of gas turbine power systems by combusting hydrogen and hydrogen–natural gas mixtures. Energies 16(9): 3618.

34.

Shaikh

Kumar

, et al. (2022) Design and modeling of a grid-connected PV–WT hybrid microgrid system using net metering facility. Iranian Journal of Science and Technology, Transactions of Electrical Engineering 46(4): 1189–1205.

35.

Shokati

Ranjbar

Yari

(2015) Exergoeconomic analysis and optimization of basic, dual-pressure and dual-fluid ORCs and Kalina geothermal power plants: A comparative study. Renewable Energy 83: 527–542.

36.

Sonntag

Borgnakke

Van Wylen

(1998) Fundamentals of Thermodynamics. New York: Wiley.

37.

Towler

Sinnott

(2012) Chemical Engineering Design. Oxford: Elsevier.

38.

Wang

Niu

Waktole

, et al. (2025) A self-powered wireless temperature sensing system using flexible thermoelectric generators. Measurement 253: 117637.

39.

Meng

, et al. (2023) Thermodynamic, exergoeconomic and exergoenvironmental analysis of a novel geothermal trigeneration system. Process Safety and Environmental Protection 177: 278–298.

40.

Yang

, et al. (2024) Exergy, exergoeconomic and exergoenvironmental analyses of a novel solar–biomass trigeneration system integrated with an ORC. Energy 301: 131605.

41.

Zhan

Lin

Zhang

, et al. (2025) Effect of in situ polymerization on thermal stability and enthalpy properties of graphene oxide-modified phase change microcapsules. Polymer Composites 46: 15342–15356.

42.

Zhang

Meng

, et al. (2025a) Techno-economic and sensitivity analysis of a hybrid concentrated photovoltaic/thermal system and an organic Rankine cycle to supply energy to sports stadiums. IET Renewable Power Generation 19(1): e12790.

43.

Zhang

, et al. (2025b) Design and performance analysis of a 15 MWth sCO₂-cooled microreactor. Nuclear Engineering and Technology 57(12): 103863.

44.

Ziapour

Adib

Salavat

(2025) Enhancing cellulose industry sustainability using a gasification–ORC system: Thermodynamic, exergo-economic and environmental analyses. Energy Reports 14: 5698–5717.

45.

Zolfigol

Salehi

Nimafar

, et al. (2025) Simulation and 4E analysis of a combined cycle integrated with ORC and solar energy. Results in Engineering 29: 1–21.