Exergoeconomic and Environmental Modeling of Integrated Polygeneration Power Plant with Biomass-Based Syngas Supplemental Firing

There is a burden of adequate energy supply for meeting demand and reducing emission to avoid the average global temperature of above 2 °C of the pre-industrial era. Therefore, this study presents the exergoeconomic and environmental analysis of a proposed integrated multi-generation plant (IMP), with supplemental biomass-based syngas firing. An in-service gas turbine plant, fired by natural gas, was retrofitted with a gas turbine (GT), steam turbine (ST), organic Rankine cycle (ORC) for cooling and power production, a modified Kalina cycle (KC) for power production and cooling, and a vapour absorption system (VAB) for cooling. The overall network, energy efficiency, and exergy efficiency of the IMP were estimated at 183 MW, 61.50% and 44.22%, respectively. The specific emissions were estimated at 122.2, 0.222, and 3.0 × 10−7 kg/MWh for CO2, NOx, and CO, respectively. Similarly, the harmful fuel emission factor, and newly introduced sustainability indicators—exergo-thermal index (ETI) and exergetic utility exponent (EUE)—were obtained as 0.00067, 0.675, and 0.734, respectively. The LCC of $1.58 million was obtained, with a payback of 4 years, while the unit cost of energy was estimated at 0.0166 $/kWh. The exergoeconomic factor and the relative cost difference of the IMP were obtained as 50.37% and 162.38%, respectively. The optimum operating parameters obtained by a genetic algorithm gave the plant’s total cost rate of 125.83 $/hr and exergy efficiency of 39.50%. The proposed system had the potential to drive the current energy transition crisis caused by the COVID-19 pandemic shock in the energy sector.


Introduction
The demand for useful energy is at a record high, and it is expected to increase steadily to meet the growing energy demands for social, household, and productive uses [1]. The majority of the nations, especially the developing nations, is struggling to match demand with supply-a situation that could be termed an energy crisis. At the heart of the energy crisis, there is another perspective of energy-induced climate change and COVID-19 pandemic impeded energy transition [2,3]. The COVID-19 pandemic started at a time when energy transition and energy policies were settling in. The COVID-19 pandemic is causing an economic crisis in many nations, especially in the developing nations, with a severe negative impact on the energy access sector, due to logistical and economic challenges. Energy access could be used to quantify the impact of COVID-19 and evidence abound that COVID-19 has had a significant adverse impact on the energy access sector [3,4]. In this regard, the energy access sector needs stimulus beyond the COVID-19 era. The combined energy generation systems were adjudged as veritable means to drive the energy access beyond the COVID-19 pandemic crisis, for efficient energy solutions aimed at enhancing economic competitiveness, providing more affordable energy services, and reducing environmental impact [5].
There is a burden of adequate energy supply to meet demand and reduce emission, to avoid the average global temperature of above 2 • C of the pre-industrial era [6]. To meet the energy-supply-climate-change dilemma, there is a need to urgently explore opportunities that would optimally solve the dilemma. To this end, two possible spaces were given serious attention in the research community, namely efficient use of depleting and polluting energy sources and the application of renewable energy sources in the energy mix. Renewable energy was identified as the major enabler of the sixth innovation revolution, which is currently on course [7][8][9]. The integrated and multi-generation energy system was widely acknowledged as a technology that supports the efficient use of energy source at the supply side.
In recent times, multi-generation or polygeneration is becoming a more promising technology and an integration practice for continuous production of different products or outputs from a common or multi-energy source for efficient utilization of energy sources [10]. Multi-generation systems are powered by heat source from fossil-fuel-based plants or renewable energy-based plant. The latter was intended by design to increase the performance of an energy system [11]. However, apart from moderating environmental complications and cost, multi-generation systems also increase sustainability and efficiency [12]. Several studies exist in the application of multi-generation plants for energy production. For example, Ozturk and Dincer [13] estimated the potential and performance of a solar-biomass hybrid multi-generation plant for power, heating, cooling, hot water, and others. The study shows that the hybrid multi-generation plant is a promising technology for the efficient use of energy resources.
Mohan et al. [14] presented a thermodynamic and economic performance of a natural-gas-fired combined plant for tri-generation. The result indicated thermal efficiency of over 82%, with a

Thermodynamic Modeling
In modeling the integrated multi-generation plant (IMP); Figure 1; analytical models were developed to describe the different subsystems that constitute the IMP: GT, ST, ORC, Gasifier, HRSG, KC, and VAB. The following assumptions were made [29][30][31]-(i) the system operates in a steady-state condition; (ii) the working fluid behaviors are considered ideal; (iii) the gasification reactions are in the equilibrium state; (iv) the potential energy and kinetic energy are neglected; and (v) the rich refrigerant is a strong solution, whereas the weak solution is rich in LiBr. H2O.
The thermodynamic analysis is based on the following-first law of thermodynamics, the second law of thermodynamics, and the conservation of mass, as expressed in Equations (1)-(3), respectively [29][30][31].

ORC-Power Cooling System
The energy analysis in the vapor generator (ORCVG), rate of work transfer in the turbine (ORCT), capacity of the ORC evaporator (ORCEV), net power output of the ORC, thermal efficiency, exergy destruction rate in the ORC, and overall exergy efficiency of the ORC system are evaluated by the aid of Equations (4)-(10), respectively.

Thermodynamic Modeling
In modeling the integrated multi-generation plant (IMP); Figure 1; analytical models were developed to describe the different subsystems that constitute the IMP: GT, ST, ORC, Gasifier, HRSG, KC, and VAB. The following assumptions were made [29][30][31]-(i) the system operates in a steady-state condition; (ii) the working fluid behaviors are considered ideal; (iii) the gasification reactions are in the equilibrium state; (iv) the potential energy and kinetic energy are neglected; and (v) the rich refrigerant is a strong solution, whereas the weak solution is rich in LiBr.
The thermodynamic analysis is based on the following-first law of thermodynamics, the second law of thermodynamics, and the conservation of mass, as expressed in Equations (1)-(3), respectively [29][30][31]. (1)

ORC-Power Cooling System
The energy analysis in the vapor generator (ORCVG), rate of work transfer in the turbine (ORCT), capacity of the ORC evaporator (ORCEV), net power output of the ORC, thermal efficiency, exergy destruction rate in the ORC, and overall exergy efficiency of the ORC system are evaluated by the aid of Equations (4)-(10), respectively.

Gas Turbine Plant (GT)
The work transfer rate in LPC and HPC, heat transfer rate in the CC, heat transfer rate in the REH (supplemental firing combustor), work transfer rate in the HPT and LPT, net power output and heat addition of the GT are determined from Equations (11)-(16), respectively. . . . .

Vapor Absorption System (VAB)
The heat transfer rate in the VAB desorber (VABD), heat transfer rate in the VAB condenser, heat transfer rate in VAB evaporator (VABEV), heat transfer rate in the VAB absorber (VABAB), rate of pump work of the VAB (VABP), and coefficient of performance of the VAB are expressed, respectively, by Equations (28) The mass flow rates of the working fluid, strong ( . m ss ) and a weak solution ( . m ws ) could be computed, respectively, by Equations (34) and (35) . m ws = ξ ss ξ ws + ξ ss (34) . m ss = ξ ws ξ ws + ξ ss (35) where ξ ss and ξ ws are defined by [32] ξ ss = 49.04 + 1.125t a − t e 134.65 + 0.47t a (36) where ξ ws and ξ ss are weak and strong solutions of LiBr.H 2 O (lithium-bromide) concentration of the refrigerant in that order; t a , t e , t c , and t g are temperatures of absorber, evaporator, condenser, and desorber, respectively. Specifics of the correlations of entropy and enthalpy and LiBr-H 2 O are presented elsewhere [32]. The heat transfer rate across the HP evaporator, HP economizer, LP evaporator, LP economizer and HRSG could be calculated by Equation (38).
The work transfer rate in LP and HP steam turbines could be computed by Equation (39), thus, The heat rejection rate in the steam turbine condenser (SC) is computed as follows: .
Equation (41) can be used to compute the power of HP and LP water feed pump The net power output of the ST cycle is estimated by Equation (42) .
The exergy destruction rate in the ST is calculated by Equation (43) .

Gasifier Model
The global gasification reaction for biomass (wood) considered in this study is expressed in [18,19].
where C n H x O y N x is the chemical formulation, n i denotes molar composition for the ith element of the syngas, which is estimated from atomic, equilibrium reactions [19,33]. Similarly, the energy balance in the gasifier is described by Equation (47).
where h 0 f biomas , h 0 f O 2 , and h 0 f N 2 are enthalpy of formations for biomass, oxygen, and nitrogen gas, respectively; m denotes the number of moles of air, T is the temperature of the product, and c ij represents the heat constant (specific) for all the syngas species. The overall energy efficiency and exergy efficiency of the IMP are expressed as follow: where

Environmental Modeling
The environmental impact is determined by computing the quantity of the pollutants produced according to the empirical relations presented in [19,34], which include nitrogen oxide, . m NOx (kg/s), carbon dioxide, . m CO 2 (kg/s), and carbon mono oxide, . m CO (kg/s). The amount of these harmful emissions produced and their rates is a function of the following parameters-retention time, τ (s), adiabatic flame temperature, T pz , combustion chamber pressure drop, ∆PCC (kPa), as presented in [19,34]. Therefore, the emission rates and the harmful fuel emission factor, F EF , are defined in Equations (52)-(56), which are well established and proven empirical relations. .
where CO 2,sp kg CO 2 /MWh depicts the quantity and specific CO 2 emissions, respectively. Equally, . m g , M, yCO 2 and M CO 2 are mass flow rate of flue gas, molar mass of flue gas, mass fraction, and the molar mass of CO 2 . The adiabatic flame temperature T pz could be defined by Equation (57). All parameters, constants and the terms x, y, and z in Equation (57) were estimated according to the procedure presented in [34,35].

Sustainability Modeling
The sustainability index (SI) is an index that described the capacity and effective utilization and preservation of the energy resources [36]. The SI was evaluated using Equation (58).
Furthermore, novel ideas considered for sustainability estimation in this study comprise exergetic utility exponent (EUE) and exergo-thermal index (ETI). The EUE defines the degree at which exergy of the resource input is used for work production according to Equation (59). The small values of EUE presage poor process conversion, especially for multi-generation systems. For constant . E xin , the value of EUE increases as . E xout → 0 , and the EUE approaches the system exergy efficiency. The implication is that EUE is bounded by 0 < EUE < 1.
E xin , and . E xout are the combustion chamber efficiency, total work output, exergy input from the fuel, and exergy output from the exhaust stream. Similarly, the ETI quantifies the thermal impact of the energy generation system on the environment, subject to a physical environment, as defined in Equation (60). The ETI increases with an increase in T exhuast ; the low values of ETI are desired and connote less environmental impact.
where , the thermal pollution factor, represents the ratio of the environmental temperature (T ev ) to that of the exhaust stream (T exh ), according to Equation (61).

Economic Analysis
The exergoeconomic assessment was performed to determine the competitiveness of the proposed plant with the existing power plants, and also, to suggest the system components that need to be redesigned to reduce the exergy destruction rate. The SPecific Exergy Cost (SPECO) method was applied in the current analysis. The SPECO method is derived from exergy, system cost for product and resource utilization per unit of exergy, coupled with closure equations [37]. The equations defining the component cost of the IMP are presented in Table 1. The cost relations ranged between the years 2008 and 2016. The range might not cover the present reality of the cost estimate. Nonetheless, they could well be considered an initial approximation for this assessment, without much deviation in the accuracy of the results. The general exergoeconomic balance for an exergy conversion system was related as follows: . Z k is the capital investment cost rate of the kth component, and c connote specific cost. The average exergoeconomic cost parameters, namely unit cost of fuel, c F,k , unit cost of product, c P,k , and cost rate of exergy destruction, C D,k , are defined in Equations (63)-(65), respectively; whereas the exergoeconomic factor, f k , and relative cost difference, r k , for the k th component are defined by Equations (66) and (67) [38].
The exergoeconomic factor and the relative cost difference are important parameters in the prioritization of the system's components for improvement. The exergoeconomic factor weighs the investment cost against irreversibility caused by a specific system's component, whereas the relative cost difference weighs relative increase in cost per exergy, against the unit cost of fuel input.  [39].
The annual levelized capital investment for the kth component is evaluated according to [39]: where Z k , N, φ k , and CRF represent the cost of purchasing the kth equipment, the yearly number of operating hours of the component functions, the maintenance factor, and CRF, represents the capital recovery factor, respectively. The capital recovery factor could be estimated by Equation (70).
where i and n represent the interest rate and the expected operational life of the system. The values of 0.15 and 20 years were adopted for the interest rate and the system operational life, according to [39]. The economic figures of merit of the proposed IMP and other economic parameters are presented according to [40]. Therefore, the unit cost of electricity, UCE ($/kWh), could be estimated by Equation (71).
where E DP is the daily energy produced by IMP 24 × . W net and the Z ALCC is defined by [38].
where Cq ($) represents the plant components cost, q The break-even point (BEP) or payback time (PBT) is determined with the aid of Equation (74), as expressed in [40] , C Tarri f ($/kWh), A EP (kWh/y) are the life cycle cost, the selling price per unit of electricity, and yearly energy production, respectively.

Results and Discussion
All the equations were programmed in the Engineering Equation Solver software platform to facilitate the ease of computation and simulation of the system.

Thermodynamic Performance of the IMP
The input parameters used for thermodynamic modeling are shown in Table 2. The outcomes of the thermodynamic and environmental performance of the IMP are depicted in Tables 3 and 4, respectively. The parameters of the state points of the proposed system are presented in Tables A1-A5 in the Appendix A. Further results are displayed in graphical plots to show the simulated results. From Table 3, the thermal efficiency (energy efficiency) for the system units varies from 16.96 to 35.91%, while the exergy efficiency ranges between 20.43 and 35.15%. The energy and exergy efficiencies of the IMP stood at 61.5 and 44.22%, respectively, with a net power output of 183.91 MW.
From Table 4, the harmful fuel emission factor, F EF , and the exergetic utility exponent, EUE, were estimated at 0.00066 and 0.7335, respectively, which are indicative of better plant performance. The specific CO 2 emission was estimated at 122.1 kgCO 2 /MWh, see Table 4. The specific CO 2 emission obtained from this study was improved when compared to the 408.78 kgCO 2 /MWh and 518.80 kgCO 2 /MWh values obtained from the studies of [35] and [42]. The discrepancies in these results are ascribed to plant capacity, plant configuration, and type of fuel. Furthermore, ETI and SI were estimated at 0.675 and 1.43 for the IMP in that order.

Validation and Comparison of Results
The IMP performance was validated and compared with results of biomass integrated multigeneration energy systems in the open domain, as shown in Table 5. The validation was based on the overall system's energy efficiency and exergy efficiency. The energy and exergy efficiencies presented in the current study compared favorably with the energy and exergy efficiencies presented in the literature. There was an observed variation between the current study and the literature, which could be attributed to the different plant configurations and the heating values of the fuel sources used in the literature. However, the exergy efficiency of the present study performed better than the Energies 2020, 13, 6018 14 of 27 majority of the literature presented in the table. The implication is that the present study has minimal irreversibilities and better use of resources, which corroborate the observed specific emission reduction, as compared to the previous study.  Figure 2 depicts the performance of the subsystems and the IMP with variations in ambient temperature (AT). For an increase in AT between 290 ≤ AT ≤ 300 K, the exergy efficiency, η ex , decreased by 6.8% for GT, 1.1% for the ORC, 1.2% for the KC, 1.9% for ST, and 2.97% for the IMP. The ORC and the Kalina cycles had the least decrease in η ex , indicating small exergy destruction in these cycles, which is expected for low-grade temperature energy conversion systems. The variation in AT had no substantial effect in the exergetic COP of the VAB system.

4
gasification plant integrated with Brayton cycle and solid oxide steam electrolyzer for compressed hydrogen production.
[  Figure 2 depicts the performance of the subsystems and the IMP with variations in ambient temperature (AT). For an increase in AT between 290 ≤ ≤ 300 , the exergy efficiency, , decreased by 6.8% for GT, 1.1% for the ORC, 1.2% for the KC, 1.9% for ST, and 2.97% for the IMP. The ORC and the Kalina cycles had the least decrease in , indicating small exergy destruction in these cycles, which is expected for low-grade temperature energy conversion systems. The variation in AT had no substantial effect in the exergetic COP of the VAB system.   Figure 4 presents the effect of adiabatic flame temperature, Tpz, on the specific CO2 emission, and harmful fuel emission factor (FEF). At 2000 K adiabatic flame temperature, the specific CO2 emission was estimated at 140 kg/MWh, with about 60% increase in FEF. Increase in Tpz favored FEF and CO2 yield, and was consequently harmful to the environment. The optimal value of the adiabatic flame temperature is, therefore, required to reduce environmental pollution. 16 Figure 4 presents the effect of adiabatic flame temperature, T pz , on the specific CO 2 emission, and harmful fuel emission factor (F EF ). At 2000 K adiabatic flame temperature, the specific CO 2 emission was estimated at 140 kg/MWh, with about 60% increase in F EF . Increase in Tpz favored F EF and CO 2 yield, and was consequently harmful to the environment. The optimal value of the adiabatic flame temperature is, therefore, required to reduce environmental pollution. Figure 4 presents the effect of adiabatic flame temperature, Tpz, on the specific CO2 emission, and harmful fuel emission factor (FEF). At 2000 K adiabatic flame temperature, the specific CO2 emission was estimated at 140 kg/MWh, with about 60% increase in FEF. Increase in Tpz favored FEF and CO2 yield, and was consequently harmful to the environment. The optimal value of the adiabatic flame temperature is, therefore, required to reduce environmental pollution.     Figure 5 shows the effect of the ratio of adiabatic flame temperature to ambient temperature (T pz /T 0 ) on NOx and CO emissions. The measure of NOx and CO produced throughout the combustion process increases steadily with increasing T pz /T 0 , which is expected, as high combustion temperature favors NOx and CO formation through a dissociation process.

Economic Evaluation
The results of the total exergoeconomic parameters for the subsystems are depicted in Tables Figure 6. Change in exergy efficiency on sustainability indicators.
Energies 2020, 13, 6018 Figure 6 shows the variation in exergy efficiency, η ex , on the sustainability indicators. An increase in η ex results in corresponding increases in EUE and SI. Additionally, ETI decreases for all values of η ex . The trend of the indicators is analogous to other sustainability indicators found in the work of Owebor et al. [19].

Economic Evaluation
The results of the total exergoeconomic parameters for the subsystems are depicted in Tables 6-11. The exergoeconomic factors ( f k ) were estimated at 53.01%, 45.08%, 41.29%, 22.79%, 47.41%, and 20.11% for GT, HRSG, KC, ORC, ST, and VAB, respectively. Low f k values for a component signifies a high exergy destruction cost and equally connotes a high potential for improvement, whereas high r k denotes the potential for subsystem optimization. The combined effects of f k and r k showed that the gas turbine (GT) had the highest potentials for improvement, followed by the HRSG, ST, and KC, while the VAB and ORC had limited space for optimization.            Table 11. Summary of the exergoeconomic parameters for the vapor absorption system (VAB).    Figure 7 shows the overall exergoeconomic data for the proposed integrated multi-generation plant. The figure shows that the overall exergoeconomic factor, , and the relative cost difference, , were estimated at 50.37% and 162.38%, respectively. However, the study indicates that only 49.37% of the total cost was connected with the exergy destruction, following the 50.37% overall for the IMP.  Table 12 presents other economic data of the IMP. The proposed IMP had $1.575 million as the life cycle cost, whereas $9.43 million was obtained for the cost of the IMP equipment, with a breakeven point (BEP) estimated at 4 years. The value of 0.0166 $/kWh was obtained for the unit cost of electricity (UCOE) of the proposed IMP, which was far less than the average cost of electricity (0.067 $/kWh) from the  Figure 7 shows the overall exergoeconomic data for the proposed integrated multi-generation plant. The figure shows that the overall exergoeconomic factor, f k , and the relative cost difference, r k , were estimated at 50.37% and 162.38%, respectively. However, the study indicates that only 49.37% of the total cost was connected with the exergy destruction, following the 50.37% overall f k for the IMP. Table 12 presents other economic data of the IMP. The proposed IMP had $1.575 million as the life cycle cost, whereas $9.43 million was obtained for the cost of the IMP equipment, with a breakeven point (BEP) estimated at 4 years. The value of 0.0166 $/kWh was obtained for the unit cost of electricity (UCOE) of the proposed IMP, which was far less than the average cost of electricity (0.067 $/kWh) from the national grid of Nigeria. The low value of the UCOE of the proposed IMP was an indication that the proposed plant would have an economic advantage over the existing power plants in the country. The cost of emissions was related to the mass of emissions per annum and is shown in Figure 8. The unit cost of emissions was taken as 0.02086, 0.024, and 6.853 $/kg for NOx, CO, and CO 2 , respectively [48]. The emission cost of CO was remarkably low, since the operation of the combustion chambers was at values above the air-fuel stoichiometric requirements. Furthermore, the choice of the turbine inlet temperature of 1300 K in the initial data consideration, assisted in severely cutting down the emissions as well as its associated cost. The cost associated with CO and NOx emissions were directly governed by the adiabatic flame temperature (AFT) and appeared nearly constant between the AFTs of 1200 and 2200 K. However, the data points showed that the cost from CO emissions were negligible when the AFT was between 1200 and 2295 K. For the same AFT range, NOx emissions cost was high, about 588.2$ at 2295 K. Therefore, for small emissions, cost from NOx and CO, AFTs between 1200 and 1400 are recommended. Additionally, the emissions cost of CO 2 was 338.9 $ per annum and remained constant and independent of the AFT, from its defining relationship. The cost of emissions was related to the mass of emissions per annum and is shown in Figure 8. The unit cost of emissions was taken as 0.02086, 0.024, and 6.853 $/kg for NOx, CO, and CO2, respectively [48]. The emission cost of CO was remarkably low, since the operation of the combustion chambers was at values above the air-fuel stoichiometric requirements. Furthermore, the choice of the turbine inlet temperature of 1300 K in the initial data consideration, assisted in severely cutting down the emissions as well as its associated cost. The cost associated with CO and NOx emissions were directly governed by the adiabatic flame temperature (AFT) and appeared nearly constant between the AFTs of 1200 and 2200 K. However, the data points showed that the cost from CO emissions were negligible when the AFT was between 1200 and 2295 K. For the same AFT range, NOx emissions cost was high, about 588.2$ at 2295 K. Therefore, for small emissions, cost from NOx and CO, AFTs between 1200 and 1400 are recommended. Additionally, the emissions cost of CO2 was 338.9 $ per annum and remained constant and independent of the AFT, from its defining relationship.

Optimum Parameters
The objective functions considered include the overall exergy efficiency and the overall cost rate of the product. The exergy efficiency is to be maximized, whereas the overall cost rate is to be minimized. Added to the overall cost rate equation of the plant is the cost of pollution damage. The objective functions are presented as follows:

Optimum Parameters
The objective functions considered include the overall exergy efficiency and the overall cost rate of the product. The exergy efficiency is to be maximized, whereas the overall cost rate is to be minimized. Added to the overall cost rate equation of the plant is the cost of pollution damage. The objective functions are presented as follows: where ψ is the overall exergy efficiency, and the denominator (A d ) of Equation (75) is expressed in Equation (76) where . Q 6 and . Q 9 are heat inputs into the combustion chamber and the supplemental firing process (reheater)-see Figure 1. The total cost rate of the system and the cost due to environmental impact are presented in Equations (77) and (78) [48]. The components of optimization function parameters, objective functions, constraints, and performance index are presented in Table 13. .
where the unit damage costs C NO x , C CO 2 , and C CO were taken as 0.02086 $/kg, 6.853 $/kg, and 0.024 $/kg, respectively, according to [48]. The genetic algorithm (GA) was used because of its ability to handle multi-objective and multi-variable problems. Ninety sets of Pareto-frontiers from the genetic algorithm (GA) were established and compiled, based on the objective functions and the corresponding constraints. The 19th Pareto-front corresponded to the maximum exergy efficiency and least-cost rate of 45.32% and 125.84 $/hr, respectively. The equivalent parameters of the system at this point existed at-compression ratio (8), LPC (0.88), HPC (0.88), LPT (0.88), and HPT (0.88) isentropic efficiencies. Similarly, the intercooler outlet temperature, CC inlet temperature, supplemental firing (reheater) temperature were estimated at 376.7 K, 1150 K, and 1250.3 K, respectively. The optimum inlet pressures of the ORC and KC were obtained as 16.27 kPa and 20 kPa, respectively, corresponding to the obtained optimum operating enthalpies. The specific emissions at the optimal operating parameters were estimated at 120.34, 2.9 × 10 −7 , and 0.213 kg/MWh for CO 2 , CO, and NOx, respectively, with enhanced environmental emissions of 1.55% for CO 2 , 4.23% for NOx, and 3.45% for CO.

Conclusions
The present study presents integrated multi-generation system by retrofitting an in-service FRAME 9E gas turbine power plant located in Calabar, Nigeria. The in-service gas turbine plant, fired by natural gas, was retrofitted with a gas turbine (GT), steam turbine (ST), organic Rankine cycle (ORC) for cooling and power production, a modified Kalina cycle (KC) for power production and cooling, and a vapor absorption system (VAB) for cooling. The retrofitted plant was fired by natural gas, with a biomass-based syngas supplemental firing. The proposed system was prompted to solve the energy-supply-climate-change dilemma, by reducing emission and increasing the useful energy output of the existing FRAME 9E gas turbine plant. The operational data of the gas turbine were used as a basis to conduct the thermo-enviroeconomic analysis. The results obtained are summarized as follows: • • The novel sustainability indicators applied to this study-exergetic utility exponent (EUE) and exergo-thermal index (ETI) for the IMP were calculated at 0.7335 and 0.675. These values were found to be improved, as compared to the stand-alone GT plant, which connotes good conversion efficiency.

•
The cost of CO emissions was found negligible at an adiabatic flame temperature of 1200 and 2295 K, while for the same temperature range NOx and CO 2 emission cost were maximum, approximated at 588.2 and 338.9$ per annum, respectively. • Life cycle cost of $1.58 million was achieved, with a BEP (or payback period) of four years. The UCOE of 0.0166 $/kWh was obtained with exergoeconomic factor of 50.37% for the IMP. Additionally, 125.83 $/hr cost and 45.32% exergy efficiency were achieved at the optimum operating condition.

•
The present study will support policymakers and decision-makers to drive sustainable energy access to meet both the Paris Agreement and Sustainable Development Goals agenda in the context of Nigeria's energy landscape and the global south. • The proposed system is very important to drive the current energy transition crisis caused by the COVID-19 pandemic shock in the energy sector. Funding: This research received no external funding.

Conflicts of Interest:
The authors declared no conflict of interest.