6E analyses of a new solar energy-driven polygeneration system integrating CO2 capture, organic Rankine cycle, and humidification-dehumidification desalination

Integrated solar-assisted polygeneration systems have emerged as an effective and sustainable alternative for meeting thermal, power and freshwater demands through decentralized generation. In this framework, this study introduces a new design and dynamic simulation approach to a solar energy-driven polygeneration system integrating gas and steam turbine cycles, organic Rankine cycle (ORC), CO2 capture, and humidification-dehumidification (HDH) desalination. The integrated system is designed to supply a greenhouse’s power, freshwater and carbon needs. The proposed system is modelled and dynamically simulated via MATLAB software, and the results are validated by literature data and THERMOFLEX software with high accuracy. A comparative study is conducted to evaluate the feasibility of integrating solar thermal energy, in which process simulations are carried out with and without the solar energy field composed of parabolic trough collectors. Sensitivity analysis is used to determine the optimal operating conditions of the HDH system and the ideal ORC working fluid. Furthermore, comprehensive Energy, Exergy, Exergoeconomic, Exergoenvironmental, Emergoeconomic, and Emergoenvironmental (6E) Jo urn l P repro of analyses are performed for scenarios with and without the solar energy field. The results reveal that solar energy integration boosts ORC’s power generation from 37.3% (winter) to 59.41% (summer), while the overall power production increases 18 kW compared to the base case scenario. Finally, the system revenues and the payback period are estimated at 50k US$/year and 4.67 years, respectively.

analyses are performed for scenarios with and without the solar energy field. The results reveal that solar energy integration boosts ORC's power generation from 37.3% (winter) to 59.41% (summer), while the overall power production increases 18 kW compared to the base case scenario. Finally, the system revenues and the payback period are estimated at 50k US$/year and 4.67 years, respectively.

Introduction
The development of advanced polygeneration plants has been fueled in recent decades by the need to address ever-increasing energy demands, water scarcity, and the environmental impacts related to greenhouse gas emissions. In this way, renewable energy-driven polygeneration systems integrating desalination and other subsystems have attracted increasing attention as an effective and sustainable alternative for meeting several thermal, power, and freshwater demands through decentralized energy generation. Nevertheless, the holistic design of integrated polygeneration systems is a demanding endeavor that requires thorough thermodynamic analyses and cutting-edge computational tools to enhance overall system energy, economic and environmental performances Khoshgoftar Manesh and Onishi, 2021).
Thermal and membrane desalination technologies have been considered in decentralized polygeneration systems to tackle rising water shortages worldwide. However, among the most J o u r n a l P r e -p r o o f promising alternatives, humidification-dehumidification (HDH) desalination technology is usually adopted in household-scale plants due to its lower running costs at low capacities, effectiveness in moderate operating conditions, and lower sensitivity to the quality of inlet saline water in comparison with membrane-based processes (Ayati et al., 2019). In this context, Ghiasirad et al. (2021) have evaluated the integration of heating, cooling, and power systems with an HDH desalination unit and an absorption heat transformer powered by a 100% geothermal resource.
Their results indicate energy and exergy efficiencies of 60.55% and 17.05%, respectively, for summer, and for winter, 70.58% and 43.59%, respectively.
The design and implementation of more cost-efficient and environmental-friendly solarassisted HDH desalination systems have gained significant traction over the last few years.  have performed the design and transient analysis of a solar energy-driven HDH desalination system for a greenhouse. The authors proposed using direct contact dehumidification instead of indirect condensers, together with a solar water heater to boost the freshwater production rate. They reported freshwater production rates ranging from 6 and 22 m 3 /day/ha. Deniz and Çınar (2016) have conducted energy, exergy, economic, and environmental analyses of a solar-assisted HDH desalination system using data acquired from the experimental results. Their results show peak values for daily energy efficiency and exergy efficiency of 31.54% and 1.87%, respectively.
They have also determined a maximum freshwater production rate of 1117.3 g/h. The cost of produced freshwater was estimated at 0.0981 US$/L and the enviro-economic factor at 2.4041 US$/year. Zubair et al. (2017) have assessed the energy and economic performance of a HDH desalination system integrated with solar evacuated tubes. The authors determined the rate of freshwater production and cost per liter for the HDH system operation in different geographical locations. Their results indicate that the productivity of freshwater ranged from 16,430 to 19,445 environmental impacts. In this regard, different investigations have been performed in the literature to analyze and design combined power cycles and polygeneration systems coupled to CO2 capture. Botero et al. (2009) economically evaluated a 400-MW natural gas combined cycle integrated with the post-combustion CO2 capture unit. The authors reported that using a postcombustion CO2 capture unit increases capital costs by 43% compared to a plant without a postcombustion system. Petrakopoulou et al. (2012a) have assessed a combined cycle integrated with post-combustion CO2 capture from exergy, economic and environmental points of view. Their results reveal that the implementation of the CO2 capture unit increases the overall cost difference, while the relative total environmental impact has a relatively low increase. Olaleye and Wang (2017) have studied the conventional and advanced exergy analyses of a post-combustion CO2 capture based on chemical adsorbents integrated with a coal-fired power plant. Their results showed that by reducing the energy required for absorption by 1%, the cost is reduced from 0.7 to 1%.
Integration of ORC as a bottoming cycle has also been proposed in the literature to recover the waste heat of power plants. Cao et al. (2016) have investigated the gas turbine and ORC (GT-ORC) integration with two recuperators. Based on the optimum design and thermodynamic assessment, the ORC's net power and thermal efficiency increased with the ORC turbine inlet pressure. Moreover, their research showed that Toluene is the most suitable working fluid for the GT-ORC combined cycle. Sun et al. (2017) have analyzed an ORC system based on industrial low-temperature waste heat as the energy source by using organic fluids to achieve low temperatures. The authors have evaluated the effects of evaporation and condensation temperatures and the degree of superheat on the thermodynamic system performance. Their results show that increasing the ORC's evaporation temperature reduces system exergy efficiency.
J o u r n a l P r e -p r o o f Nami et al. (2018) have conducted energy and exergy analyses of an ORC driven by GT exhaust heat recovery. The authors have compared four working fluids and identified MM and R124 as the best working fluids. Patiño and Rivera (2019) have studied the implementation of the ORC coupled to a natural gas combined cycle (NGCC) and post-combustion CO2 capture to increase the power output. They found that the reduction of CO2 emissions led to a 78% reduction in global warming potential. Liu et al. (2020) have carried out 4E analyses of carbon capture and storage (CCS), ORC, and an absorption refrigeration cycle in an integrated system using waste heat as the heat source. Their results indicate an exergy efficiency of 42.88%, while the total annual cost of the combination process is 72% lesser than that of the base CCS system. Khoshgoftar Manesh et al. (2021a) have investigated a polygeneration system composed of a GT, supercritical carbon dioxide (S-CO2) cycle, ORC, and RO desalination. The authors have evaluated the system from exergy, exergoeconomic, and exergoenvironmental perspectives. Their results show an increase of 10.9% in total efficiency by integrating the S-CO2 and GT cycles.
The pertaining literature shows a lack of research on the energy, exergy, exergoeconomic, exergoenvironmental, emergoeconomic, and exergoenvironmental (6E) analyses of integrated solar energy-driven polygeneration systems. This study addresses shortcomings in preceding research by introducing a new design and dynamic simulation approach to a solar-assisted polygeneration system for meeting 720 kW of power, freshwater and carbon demands of a greenhouse. To the best of knowledge, this is the first study proposing a solar energy-driven polygeneration system to simultaneously produce power, freshwater, and CO2 for increasing energy efficiency and productivity in greenhouse applications. The innovative polygeneration system integrates gas turbine (GT) and steam turbine (ST) power cycles, organic Rankine cycles (ORCs), a heat recovery steam generator (HRSG), and humidification-dehumidification (HDH) J o u r n a l P r e -p r o o f desalination and post-combustion CO2 capture units. Furthermore, the proposed integrated system takes advantage of the flue gas from the GT pack to drive the ORC for further enhancing process energy efficiency. The proposed integrated system is mathematically modelled and dynamically simulated via MATLAB, and the results are validated by literature data and via THERMOFLEX software. The feasibility of integrating solar energy to the polygeneration system is evaluated via comparative process simulations with and without a solar energy field composed of parabolic trough collectors. Sensitivity analysis is applied to identify the optimal operating conditions of the HDH desalination system and the ideal ORC working fluid. Finally, comprehensive 6E analyses are performed for scenarios with and without the solar field to further investigate the advantages of integrating solar energy resources.

System Description
The layout of the proposed integrated polygeneration system for power, CO2, and freshwater production in a greenhouse is depicted in Fig. 1. For enhancing power generation and overall system performance, two steam turbines (STs) cycles and two organic Rankine cycles (ORCs) are placed downstream of the gas turbine (GT)-pack. This configuration allows to recovery waste heat from the flue gases exhausted from the gas turbine cycle. In addition, the devised polygeneration system comprises a post-combustion CO2 capture unit, a heat recovery steam generator (HRSG), and a humidification-dehumidification (HDH) desalination unit. The system configuration without the solar energy field, as presented in Fig. 1, is used as the base case scenario.
The schematic diagram of the integrated solar energy-driven polygeneration system is displayed in Fig. 2 J o u r n a l P r e -p r o o f Table 1 Main technical specifications of the ET-100 parabolic-trough solar collectors (Lüpfert et al., 2003). A brief description of each system subset is given as follows. In this study, the P+W ST6L-721 GT-pack is used for generating power in the proposed polygeneration system. In the GT cycle, compressed air from the air compressor (AC) is mixed with natural gas (fuel) in the combustion chamber (CC). Subsequently, the high-temperature composition of gases is expanded in the GTpack for power generation. For improving heat recovery, the high temperature flue gases from the GT cycle are utilized as a heat source of the ORC to boost the power generation after passing through the HRSG unit. The latter comprises a superheater, evaporator and economizer units. The outlet flue gas and steam from the two ORC heat exchangers (ORCHXs) are used to supply the required thermal energy of the HDH desalination and CO2 capture units and address the freshwater and CO2 greenhouse demands.
The ORCs are composed of a heat exchanger, turbine, condenser, and pump. In the ORC, the working fluid passes through the pump to increase its pressure after being discharged from the condenser. The ORC working fluid then enters the heat exchanger, where it is heated until J o u r n a l P r e -p r o o f changing to a superheated vapor state. Afterwards, it is expanded in the turbine to reach the condenser pressure and restart the cycle. This study compares six different organic working fluids based on their performance to facilitate the selection of the best alternative for the proposed system. The HDH desalination system consists of open-air, open water, direct contact humidifier and dehumidifier units with packing bed structure to increase the contact surface area between water and air. The distinction between the humidification and dehumidification devices relies in the direction of heat and mass transfer processes. Air from the greenhouse is supplied to the humidifier, while the condensed steam is sprayed onto its structural packing after supplying the heat required for the ORC. As a result, heat and mass transfer occur from water to air direction, increasing the air stream's temperature and humidity. In the dehumidifier, heat and mass transfer are enabled between the heated humid air from the humidifier and the cold water stream, which is sprayed onto the dehumidifier packing bed. Hence, the moist air stream is cooled and condensed, while the water stream is heated due to the difference in the air/water interface humidity ratio.
Finally, freshwater is produced and stored in a freshwater tank.
In generating power or heat, CO2 and other polluting gases are constantly released, which can give rise to the greenhouse effect with adverse effects on the environment. Therefore, deriving electricity, heat, and CO2 from natural gas by purifying the flue gases will reduce pollution and protect the environment. In this study, a post-combustion CO2 capture unit is coupled to the system to recover carbon emissions for further reducing environmental impacts. The CO2 captured and stored is used to maintain the required greenhouse carbon levels and improve the plant productivity. In greenhouse applications, CO2 supplementation up to 1,000 ppm can increase photosynthesis and plant growth up to 61% (Bao et al., 2018). Additionally, increasing CO2 concentration reduces transpiration which diminishes water consumption of the crops.  The following assumptions are required to properly simulate the polygeneration system: i. Steady-state and steady flow conditions are maintained in equipment units.
ii. The fuel utilized for combustion in the CC consists of pure methane. iii.
The changes in potential and kinetic energy and exergy are negligible.
iv. The ambient temperature and pressure are 25°C and 1.013 bar, respectively, at the compressor inlet.
v. The outlet temperature of the superheater (SUP) is 516°C.
vi. Air and combustion products are operated based on the ideal gas behavior. vii.
The changes in humidity and temperature of air and vapor mix ( , ω) occur horizontally.
viii. Water temperature and flowrate ( , L) are only changed vertically.
ix. The height of packing in the humidifier and the dehumidifier units is equal.
J o u r n a l P r e -p r o o f x.
During daylight hours, the temperature (25°C) and humidity ratio (50%) inside the greenhouse will remain virtually stable.

xi.
A standard greenhouse with dimensions of 25 m length, 10 m width, and a maximum height of 5 m is assumed.
xii. Structural packing of HDH is made from polypropylene, and specific surface area (a) is 320 m 2 /m 3 .
xiii. Euro trough ET-100 solar collectors are used in the solar energy field.

xiv.
Therminol-VP1 is assumed as the heat transfer fluid (HTF) in the solar energy field.
xv. The percentage of water mass flow which passes through the solar field heat exchanger, known as solar fraction (SF), is equal to 0.8.
xvi. TRNSYS software is used to determine the average Direct Normal Irradiance (DNI) of each month for the specific location (Qom city, Iran).

Thermodynamic Analysis
Mass conservation and the first law of thermodynamics, known as thermodynamic analysis, are employed to determine process operating conditions and energy performance at each point of the devised system. The governing thermodynamic equations, input parameters and unknown variables for the different system components are presented in  The humidification and dehumidification desalination processes are simulated using the modelling equations presented in Table A.1. The finite difference method is applied to convert the partial differential governing equations into a system of linear equations. As shown in

J o u r n a l P r e -p r o o f
Integrating the solar energy field into the polygeneration system increases the flue gas temperature passing through the ORCHX. As a result, the organic fluid mass flowrate increases along with the ORC turbine power generation. The cost of adapting the solar energy field to the plant is determined by calculating the total solar collector area. The energy (in kWh) generated by solar energy field per month can be obtained by the difference in energy generation between the two modes with/without solar collectors and by multiplying it by the average hours of the day, number of days, and the power generated per month of the year. The retail electricity price per plant operating year is calculated by adding up the total power generated per month and multiplying it by operational years and the retail price for electricity per kWh. Finally, the difference between retail electricity price and the cost of collectors allows for estimating the system profit.
Sensitivity analysis is used to determine the optimal operating conditions of the HDH system and the ideal ORC working fluid. Thus, to estimate the impact of the input variables of the HDH unit, a for loop in MATLAB software is utilized, and results are extracted as input variables versus freshwater production rate plots. The latter allows determining the optimal values for the input variables that maximize the freshwater generation. Moreover, different ORC working fluids are considered, and their performance is compared based on the heat source temperature of the ORC heat exchanger.

Exergy Analysis
Exergy analysis, defined as the maximum possible reversible work, is based on the first and second laws of thermodynamics. Energy analysis alone is insufficient to evaluate the system efficiency since it does not provide an in-depth understanding of integrated power cycles. Therefore, exergy J o u r n a l P r e -p r o o f assessment is employed to accompany energy analysis in this study and provide further insights regarding system energy losses, inefficiencies, irreversibilities, and incurred costs. Exergy transfer to/from an open system at steady-flow conditions must account for mass, work, and heat transfer components as expressed by Equation (1) (Nourpour and Khoshgoftar Manesh, 2021).
By neglecting kinetic and potential energy changes and assuming steady-state conditions, the physical specific exergy is conducted using the following relation (Bejan et al., 1995;Manesh and Amidpour, 2020).
The definition of solar exergy differs from previous equation and is given by Equation (3) (Dincer and Rosen, 2012).
The fuel-product-waste (F-P-L) concept proposed by Lozano and Valero (1993) is used, in which an equipment unit (modelled as a control volume) supplies the required resources to generate the product, changes part of the input exergy (fuel exergy) into desired exergy (product exergy) and wastes a part of it to the environment (exergy destruction). Fuel and product exergy relations for different polygeneration system components are shown in Table 3. The exergy J o u r n a l P r e -p r o o f destruction for each component of the system is defined as follows (Bejan et al., 1995;Nourpour and Khoshgoftar Manesh, 2021).
The exergy efficiency for each system component is determined by the following equation (Bejan et al., 1995;Nourpour and Khoshgoftar Manesh, 2021).

Table 3
Fuel-product exergy relations for the different polygeneration system components.

Exergoeconomic Analysis
To evaluate tradeoffs between costs and thermodynamic irreversibilities, the system is evaluated using an exergoeconomic analysis. The exergoeconomic analysis combines the exergy analysis with a detailed economic assessment. Hence, exergoeconomic analysis allows estimating the cost rate of fuel, product, and exergy destruction. The cost balance equation for each system component is based on the Specific Exergy Costing (SPECO) method (Lazzaretto and Tsatsaronis, 2006) as follows.
The associated cost balance equations of different equipment units are presented in Table   A.2 in Appendix A. The current cost of the system in dollars per unit of time is estimated by Equation (7) (Jadidi et al., 2021).
Where is the maintenance factor (equal to 1.06) and N is the number of operating hours per year (8000 h) (Bejan et al., 1995;Dincer et al., 2017).
The relations of the Purchased Equipment Cost (PEC), , are given in Table A.3 (Appendix A). The Capital Recovery Factor, CRF, is a factor that converts the present capital cost of equipment to an annualized cost, which is determined by the following equation (Smith, 2005).

J o u r n a l P r e -p r o o f
The cost rate for exergy destruction is given by Equation (9) as a product of the specific cost . by the exergy rate ̇. (Jadidi et al., 2021).
The average cost of fuel and product per unit exergy and the relative cost difference, rk, are defined respectively as follows (Jadidi et al., 2021).
Where the fuel and product cost rates (̇. ,̇. ) definitions associated with each system component are presented in Table A.4.

Exergoenvironmental Analysis
Exergoenvironmental analysis is carried out to assess the environmental performance of the proposed integrated polygeneration system. In this study, the exergoenvironmental analysis is centered on the damage-oriented Eco-indicator 99 methodology which is grounded on life cycle assessment (LCA) principles. In the LCA, environmental impacts related to system components are estimated based on their construction and material weights. The exergoenvironmental analysis combining LCA and exergy analysis is aimed at reducing greenhouse gas emissions via identifying J o u r n a l P r e -p r o o f inefficiencies over the lifetime of components to optimally and ecologically design plants. The exergoenvironmental procedure is analogous to exergoeconomic analysis. The equations of environmental impact balances for various equipment are calculated as follows (Meyer et al., 2009).
Similar to exergoeconomic analysis, cost balance equations of each equipment unit and the rate of environmental effects on fuel and product of the proposed cycle are defined as presented in  Table 4. Table 4 Weight functions of the different system components. Still similar to exergoeconomic analysis, the governing equations for environmental analysis are as follows. The environmental impact rate of the equipment unit is defined by (Meyer et al., 2009).
The environmental impact per exergy unit of the fuel and product are given by Equation (16) and Equation (17), respectively (Meyer et al., 2009).
The rate of environmental impacts associated with exergy destruction is calculated as follows (Meyer et al., 2009).
The relative environmental impact difference of each system component is estimated by (Petrakopoulou et al., 2012b).
J o u r n a l P r e -p r o o f Finally, the environmental exergy factor is defined as follows (Petrakopoulou et al., 2012b).

Emergy
Emergy is related to macroscopic and microscopic fields. Emergy analysis transfers all input variables of the cycle, energies, and resources into a single unit of solar emergy: Solar Emergy Joule (sej). Emergy analysis, unlike energy analysis, integrates economic and environmental concepts by combining exergoeconomic and exergoenvironmental analyses. In this approach, the scale factor coefficient, β (~0.93), is used to convert all input variables or energy to emergy as given by the following equation (Bastianoni et al., 2007).
Where T0 and TS designate the ambient temperature and the sun temperature, respectively.

Emergoeconomic analysis
The emergoeconomic analysis is grounded on the conventional exergoeconomic assessment.
Hence, the SPECO methodology (Lazzaretto and Tsatsaronis, 2006) is employed to each process stream of the proposed integrated system. The emergy cost balance equation for the different system components is given as follows (Aghbashlo and Rosen, 2018).

J o u r n a l P r e -p r o o f
Where ̇ is the component-related emergoeconomic rate defined as the summation of investment and operating and maintenance costs (O&M) as expressed by Equation (24) (Aghbashlo and Rosen, 2018).

̇=̇+̇ (24)
Emergoeconomic balance equations of each equipment unit are presented in Table A.5 (Appendix A). The emergoeconomic rate of the k-th equipment unit is determined based on the exergy destruction as follows (Aghbashlo and Rosen, 2018).
The relative emergy-based cost difference , and emergy-based exergoeconomic factor . are obtained as follows.

Emergoenvironmental analysis
The emergy-based exergoenvironmental balance for each system component is defined by Equation (31) (Aghbashlo and Rosen, 2018).

̇=̇+̇+̇
Where ̇, ̇, and ̇ indicate the environmental emergy rates in the construction, operation and maintenance, and disposal phases, respectively. The emergy-based environmental balance equations of various system components are presented in Table A.5.
The environmental impact rate associated with the exergy degradation is given by Equation (34) (Aghbashlo and Rosen, 2018).
Where , and , are the specific emergoenvironmental values for product and fuel of the k-th system component, respectively. The previous values are determined as follows (Aghbashlo and Rosen, 2018).
The total emergoenvironmental rate of each system component can be determined by Equation (37) (Aghbashlo and Rosen, 2018).

Energy Analysis
The thermodynamic properties of different process streams obtained from MATLAB and THERMOFLEX software simulations for case studies with/without solar energy are compared in (summer), when the solar energy production is higher.
The validation of the thermodynamic results of the HDH desalination unit is shown in Table 5. Sensitivity analysis is used to determine the optimal inputs of the HDH unit. The pressure J o u r n a l P r e -p r o o f of all streams has been assumed to be 1.014 bar. In this case, several references are used to verify the model's accuracy, and most of the results show high accuracy with literature data.
The thermodynamic block diagram of the proposed polygeneration system coupled with the solar energy field is depicted in Fig. 5. The figure shows the thermodynamic properties (temperature, pressure, and mass flowrate) of main process streams and power and heat production outputs of system components.  Water and air temperature profiles in the humidifier are depicted in Fig. 6(a) and Fig. 6(b), respectively. As shown in Fig. 6(a), the water temperature decreases along the humidifier height due to heat transfer from water to airflow. On the contrary, the air stream temperature increases as a result of the increase of its humidity levels while passing along the humidifier. The most significant drop in water temperature occurs in the air entrance section, where the temperature difference is the highest. Water and air temperature profiles in the dehumidifier are shown in Fig.   7(a) and Fig. 7(b), respectively. In this case, air and water temperature profiles within the dehumidifier show a more prominent drop and growth, respectively, for lower packing length

Air mass flowrate
As aforementioned, the greenhouse's desired temperature and humidity ratio are stable and set at 25°C and 50%, respectively. Hence, it is assumed that the airflow enters the humidifier at 25°C and 50% humidity ratio. To determine the optimal air mass flowrate in this unit, the packing size is considered to be constant. The freshwater production ratio in function of the air mass flowrate is displayed in Fig. B.1 (Appendix B). This figure shows that the optimal air mass flowrate that maximizes freshwater production is 0.32 kg/sm 2 . It should be noted that increasing the air mass flowrate boosts heat and mass transfer coefficients, but it reduces the temperature of the air leaving the humidifier. Therefore, freshwater production is decreased at higher air mass flowrates.

Packing height
An optimal packing height is required because as the height of the packing increases, the mass flux on the packing decreases. As a result, the heat and mass transfer coefficients will also be reduced at higher packing heights. However, by increasing the packing height, the total heat transfer surface also increases. The effect of packing height on the freshwater production ratio is depicted in Fig.   B.2 (Appendix B). Because of the temperature and humidity difference between air and water streams at lower packing heights, an increase in height results in higher production rates. The total output will gradually decrease with further height increases due to reduced heat and mass transfer coefficient and temperature difference. According to Fig. B.2, the optimal packing height is determined at 0.7 m.
J o u r n a l P r e -p r o o f Fig. 8(a) and Fig.8(b) portray the effect of cooling water mass flowrate on freshwater production and energy consumption, respectively. It is observed that the freshwater production ratio decreases with increasing the cooling water flowrate. However, this effect is less pronounced at higher mass flowrate values. As shown in Fig.8(b), the increased cooling water mass flowrate leads to increased energy consumption. The effect of increasing the cooling water mass flowrate decreases at higher values.

Cooling water mass flowrate
A standard greenhouse with 25 meters length, 10 meters width, and 5 meters height is assumed for this design. The average amount of water required for the greenhouse is about 1 L/m 2 per day. Thus, based on the area of the greenhouse, the average volume of water required is 250 liters per day . Therefore, considering the freshwater production rate ( , − , ) of 0.003 kg/s, about 0.1 kg/sm 2 inlet cooling water to the dehumidifier is required in the greenhouse.

Packing length
The effect of the HDH packing length on the freshwater production ratio is depicted in Fig. B.3 in Appendix B. According to this figure, a value of 0.6 m is chosen due to the low impact of increased packing length on the freshwater production ratio and packing length standard.

ORC Working Fluid Selection
In taking advantage of low-grade heat sources, ORCs can be used to enhance power production.
The effect of working fluids on the system energy conversion efficiency, and economic and environmental performance indicators makes its selection a critical process. Likewise, the selected working fluid should be stable in the heat source temperature range. Regarding the operational temperature, five candidate working fluids are compared from different perspectives and the results as presented in Table 6. According to these results, R601 and R365mfc are appropriate fluids from several viewpoints apart from mass flowrate and total environmental impacts.
Therefore, R11 is the optimal selection for all parameters considered for this application.

J o u r n a l P r e -p r o o f
The monthly net power generation of the integrated system is displayed in Fig. 9. The highest monthly net power generation occurs in September and the lowest one in April. The monthly net power generation of the power plant varies from 728.3 kW to 737.5 kW.

Fig. 9.
Monthly net power generation (in kW) of the integrated polygeneration system.
The monthly power generation of the ORCs is shown in Fig. 10. The results reveal that the highest ORC power generation occurs in September. The latter is due to Qom's high DNI amount for this month. Therefore, SF increases in this month, and since water is mainly heated by the solar field and the temperature at the outlet flue gas of the economizer increases, the organic fluid mass flow is increased. Consequently, the power generation of ORC is enhanced. When the solar energy field is integrated into the polygeneration system, the lowest and the highest ORC power generation values are 59.3 kW in April and 68.85 kW in September, respectively. By contrast, ORCs produce 43.19 kW power in the base case scenario (polygeneration system without solar energy field). Consequently, the solar field unit increases ORC's power production by approximately 37.3% (winter) up to 59.41% (summer) compared to the base case study. The kWh net energy generation difference between the two cases is illustrated in Fig. 11. The maximum profit occurs in September due to the highest net energy generation difference.

J o u r n a l P r e -p r o o f
J o u r n a l P r e -p r o o f Fig. 11. Net energy generation difference (in kWh) between two case studies (i.e., polygeneration system with and without the solar energy field). Table B.3 and Table B.4 (see Appendix B) present the exergy analysis results of each system component for case scenarios with and without the solar energy integration, respectively. In these tables, the integrated solar system results are reported for June. Results reveal that, for both case scenarios, the related cost of exergy destruction of the CC is expressively higher than the other components. Although the solar system integration increases the plant exergy degradation by 18%, it decreases the exergy destruction of the economizer by 76%, and the total power generation of the polygeneration system is increased by approximately 18 kW compared to the base case scenario. The Sankey diagram corresponding to the exergy analysis of the polygeneration plant combined with the solar energy field unit is illustrated in Fig. 12.

Exergy Analysis
J o u r n a l P r e -p r o o f Fig. 12. Sankey diagram of exergy analysis for the polygeneration system integrated with the solar energy field (Fig. 2).

Exergoeconomic Analysis
The cost rate (̇), exergy destruction cost rate (̇, ), fuel and product cost per exergy unit (̇, ,̇, ), relative cost difference ( ) and exergoeconomic factor ( ) for various system components are presented in Table B.5 and Table B.6 (Appendix B) for both configurations (polygeneration system with and without solar energy field). The results for the integration of the solar system are reported for June.
According to Table B.5, the highest cost rate and exergy destruction cost rate given by ̇+̇ is associated with the CO2 capture unit, followed by the CC and ORC heat exchanger 1.
The exergoeconomic evaluation presented in Table B.6 depicts that, similarly to the base case scenario, the CO2 capture unit, CC, and ORC heat exchanger 1 present higher exergy costs to convert fuel into products. This difference in exergoeconomic values between the two cases is due to the temperature difference between the hot and cold fluids in the ORC heat exchanger 1 and ORC mass flowrate increase due to solar energy integration. Thus, the investment cost of system components with lower exergoeconomic factors needs to be increased to improve the exergy efficiency. However, this enhancement is limited to avoid negative impacts on the exergy destruction and the efficiency of other components.
The maximum relative cost difference is allocated to CO2 capture unit and ORC heat exchanger 1 in both scenarios (with/without the solar energy field). Therefore, the first candidate for improvement is the ORC heat exchanger 1 with the highest amount of ̇+̇ and . Based on the exergoeconomic analysis, it is concluded that integration of solar field increases ̇+̇ by 6.6%. The Sankey diagram of exergoeconomic analysis of the solar-assisted polygeneration system is illustrated in Fig. 14. J o u r n a l P r e -p r o o f Fig. 14. Sankey diagram of the exergoeconomic analysis for the polygeneration system integrated with the solar energy field (Fig. 2).  Given the difference between the revenues earned by selling the generated power and the annualized investment cost of purchasing solar collectors, the profit of employing the solar field is calculated at 50k US$/year, and the payback period is estimated at 4.76 years.

Exergoenvironmental Analysis
The exergoenvironmental analysis is performed for both scenarios (with/without solar energy field). The values of environmental impacts (̇), environmental impact rate associated with the exergy destruction (̇, ), environmental impacts per exergy unit for product and fuel (̇, ,̇, ), exergoenvironmental factor ( ) , and the difference between the relative environmental destructive impacts are listed in Table B.7 and Table B.8 (Appendix B). The results are reported for June.
According to Table B.7, the highest environmental impact rate and exergy destruction expressed by ̇+̇ belongs to the CC, followed by the CO2 capture unit and steam turbine 2.
The exergoeconomic evaluation in Table B.8 reveals that, similar to the base case, the highest ̇+ is also attributable to the same units. Thus, system components with lower exergoenvironmental factors, such as condensers 1 and 2 and HDH desalination units, require an increase in their exergy efficiency. However, this enhancement is limited to avoid negative impacts on other system components' exergy destruction and efficiency. Still, the CO2 capture unit and condensers, as components with the highest amount of , are considered to increase their exergy efficiency and reduce the total environmental impact. In the scenario with solar energy integration into the polygeneration system, the solar field heat exchanger stands at the top of the list of the units to be improved with the maximum . Based on the exergoenvironmental analysis, it is concluded that integration of solar field increases ̇+̇ by 3.9%.
The Sankey diagram of exergoenvironmental analysis of the polygeneration plant integrated with the solar energy field is illustrated in Fig. 16. The total environmental impact rate of system components (̇) -corresponding to the summation of environmental impacts ̇ and environmental impact rate associated with the exergy destruction ̇,is presented in Fig. 17 J o u r n a l P r e -p r o o f over the different months of the year for the solar-assisted scenario. The lowest value of ̇ occurs in April, and the highest in September. The latter is due to the increase in the contribution of the solar field to supply the required heating demands in this month. Therefore, the temperature of the outlet flue gas from the economizer increases, which leads to a rise of the organic fluid mass flowrate of the ORC1.
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Emergoeconomic Analysis
The results of emergoeconomic analysis for both proposed system configurations are presented in Table B.9 (without solar field) and Table B.10 (with solar field). The results are reported for June.
For both scenarios, the maximum summation of economic emergy rate and component-related economic emergy rate given by ̇+̇ is attributable to the CO2 capture unit, followed by the CC and the heat exchanger 1. For exergy analysis of the polygeneration plant without the solar energy system, condensers 1 and 2 as components with lower emergoeconomic factors, and the heat exchanger 1 and CO2 capture unit as the components with the highest amount of relative monetary emergy difference should be considered to increase their exergy efficiency. On the J o u r n a l P r e -p r o o f other hand, when solar integration is considered, condensers 1 and 2 as components with lower emergoenvironmental factors and solar system pump, CO2 capture unit, and heat exchanger 1 as the elements with the highest amount of should be considered to increase their exergy efficiency. Based on the emergoeconomic analysis, the integration of solar energy field increases ̇+̇ by 2.12%.
The total economic emergy rate ̇ for the integrated solar-assisted polygeneration system throughout the year is portrayed in Fig. 18. In this case, the maximum total economic emergy rate occurs in September and the minimum rate in April. The monthly cost rate based on the emergy associated with the exergy degradation of different system components is shown in Fig. B.7 (Appendix B). As explained previously, the economizer operates in standby mode in September. Consequently, the monthly cost rate based on the emergy associated with the exergy degradation of this device is decreased. On the other hand, J o u r n a l P r e -p r o o f the monthly cost rate based on the emergy associated with the exergy degradation of the ORC components is increased in September due to the organic fluid mass flowrate increase.

Emergoenvironmental Analysis
The results of emergoenvironmental analysis for both proposed system configurations are shown in Table B.11 (without solar field) and Table B.12 (with solar field). The results are reported for June. In this case, the maximum summation of the environmental emergy rate and componentrelated environmental emergy rate ̇+̇ is attributable to the CC, followed by the CO2 capture unit and the heat exchanger 1 when both scenarios are considered. According to the exergy analysis, for the polygeneration system without solar energy integration, the HDH desalination unit as the component with the lowest emergoenvironmental factor and the CO2 capture unit and condensers 1 and 2 as the components with the highest amount of should be considered to increase their exergy efficiency. While solar integration is studied, the HDH desalination unit, solar field pump, condensers 1 and 2 as components with the lowest emergoenvironmental factors and the solar collectors, solar field heat exchanger, and solar field pump as components with the highest amount of should be considered to increase their exergy efficiency. Based on the emergoenvironmental analysis, the integration of solar energy field increases ̇+̇ by 1.22%.
The total environmental emergy rate ̇ for the integrated solar-assisted polygeneration system throughout the year is depicted in Fig. 19. In this case, the maximum total economic emergy rate occurs in September and the minimum amount in April. Finally, the monthly environmental emergy rate related to the exergy degradation of different system components is depicted in Fig. B.8 (Appendix B). Once again, the environmental emergy rate related to the exergy destruction of the economizer is minimal in September due to its standby operation.
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Conclusions
This study introduces a design and dynamic simulation approach of a new integrated solar-assisted polygeneration system to meet the power, freshwater, and CO2 demands of greenhouse applications. The innovative polygeneration system integrates gas and steam turbine cycles, organic Rankine cycles (ORCs), humidification-dehumidification (HDH) desalination, postcombustion CO2 capture unit, and parabolic trough collectors. Sensitivity analysis is applied to determine the optimal HDH operating conditions and identify the ideal ORC working fluid. capture and HDH technologies to simultaneously meet several demands of greenhouses, which can easily be adapted to other low and large-scale buildings and industrial applications.
ii. Use of detailed 6E analyses allied to a more precise dynamic simulation to further investigate the advantages of using solar energy by evaluating several thermodynamic, economic and environmental performance indicators of the integrated system.
iii. Development of new weight functions and cost relations for several system components to enhance environmental and economic assessments.
Energy analysis results reveal that due to the solar incident angle, the highest monthly net power generation of the solar-assisted polygeneration system occurs in September (737.5 kW), while the lowest amount is in April (728.3 kW). Despite an increase of 18% in the overall plant exergy degradation, the solar energy integration boosts ORCs power generation from 37.3% (winter) to 59.41% (summer) and the overall plant power generation by 18 kW, compared to the base scenario (without solar energy). Exergoeconomic analysis results indicate that for both case scenarios, the CO2 capture unit, combustion chamber, and ORC heat exchanger 1 present higher exergy costs to convert fuel into products, requiring exergy efficiency enhancement. In addition, solar energy integration increases the sum of cost rate and exergy destruction cost rate by 6.6%.
Furthermore, the revenues of using solar energy and the system payback period are estimated at 50k US$/year and 4.67 years, correspondingly.
Exergoenvironmental analysis results show that both scenarios' highest environmental impact and exergy destruction rate is attributable to the combustion chamber, followed by the CO2 capture unit and steam turbine 2. Moreover, a 3.9% increase is observed in the total environmental impact and exergy rate when the solar energy field is integrated into the polygeneration system.

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The emergoeconomic analysis emphasizes that both case scenarios' maximum economic and component-related economic emergy rates are related to CO2 capture unit, followed by the combustion chamber and heat exchanger 1. The solar energy integration increases the total emergy rates by 2.12%. The emergoenvironmental analysis shows that the maximum sum of environmental emergy and component-related environmental emergy rates is attributable to the combustion chamber, followed by the CO2 capture unit and the heat exchanger 1 when both case studies are considered. In this case, the emergoenvironmental indicator increases by 1.22% when the solar energy field is integrated into the polygeneration system.
The previous 6E analyses also highlights that the listed system components should receive special attention in reducing irreversibilities and energy degradation to improve the plant's overall economic and environmental performances. Moreover, the price of these equipment units must be reduced as much as possible while their life cycle should be less harmful to the environment.
Although additional costs and environmental impacts are related to integrating the solar energy field, it is still a cost-effective and environment-friendly solution for greenhouse applications due to the increase in the overall exergy and energy efficiencies and power generation. Finally, future research will focus on a more effective humidity supply with the aid of the cycle. For example, by designing a fogging system that uses generated freshwater and brings the greenhouse temperature to the optimum in winter using piping supplied from the plant waste heat. Additionally, optimization can be applied to the polygeneration system to further increase the energy efficiency.