Thermodynamic assessment and optimization of a novel trigeneration energy system based on solar ener

Abstract

Keywords: Energy; Exergy; Gasification; Solar Energy; MSW; Optimization

Introduction

Nowadays, the use of non-conventional fuels and renewable energy instead of fossil fuels has been a significant challenge for the experts of the field throughout the world. Regarding this issue, it is established that biomass and wastes can be used as energy sources. Not only does MSW, which is used as a fuel in the gasification process, reduce CO2 blast, but also it can decrease the amount of waste and burial fission [1]. Lee et al.[2] presented the air gasification at the temperature ranging from 400℃ to 800℃ for syngas production. It should be stated that the problem that exists in this kind of gasification is the addition of nitrogen to the generated gas and dilution and reduction of the heating value. In an attempt, Vera et al.[3] solved this problem. They meticulously used steam gasification method in which nitrogen was removed. In the present article, biomass is used by steam gasification at a hypothetical temperature of 800℃, for which the equation of gasification is as follows [2]:

Table 1 illustrates MSW intended percentage composition with CH 1.46 O0.69 a formula whose percentage composition of O, H, C, coupled with high and low heat values. This paper was recommended for publication in revised form by Regional Editor Erman Aslan 1School of Engineering, Department of Environment, University of Tehran, Tehran, Iran 2 Department of Chemical Engineering, College of Engineering, University of Zanjan, Zanjan, Iran *E-mail address of corresponding author: amir_ghasemi@ut.ac.ir, amir.ghasemi.ut.ir@gmail.com Orcid id: 0000-0002-3910-6828, 0000-0002-6824-9457, 0000-0002-6137-1900, 0000-0003-1733-3365 Manuscript Received 17 September 2018, Accepted 20 December 2018

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Various researchers have investigated the power generation plants exergy output and cogeneration cycles. Yipping poi et al.[4] for instance, analyzed energy and exergy and did optimizations for power generation plants. In another study, energy analysis and power generation plants optimization through GA were carried out by Chomra and Mago [5]. In this research, exergy, energy, power generation plants' exergy destruction and single objective optimization through GA were carried out. In their study, Klein [6] utilized EES for the optimization option. The turbine that was used in the present research was a microturbine. In all the researches that Laurence and Asherafi [7] conducted, the percentage of different components in the gas mixture, generated from the gasifier, did not have any impact on the performance of the turbine and only the temperature and pressure of the gas, caused by combustion, had considerable effects on the turbine performance. Pablo et al. [8] used the combination of the gasifier and solar collector for the first time to produce the syngas required in the combustor and further generate the power seamlessly. They provided the required temperature for generating steam in the gasification process using solar power. Al-Sulaiman et al.[9] analyzed energy and exergy of a solar driven trigeneration system. The study revealed that the maximum electrical-exergy efficiency for the solar mode, solar and storage mode and the storage mode were 7%, 3.5%, and 3% respectively. Alternatively, when trigeneration is employed, the exergy efficiency increases noticeably. Jiang-Jiang Wang et al. [10] examined an integrated CCHP system with biomass air gasification in order to reconnaissance the energy and exergy performance of the system. Destruction analyses of energy and exergy indicated that the largest destruction occurs in the gasification system, which accounts for more than 70% of the total energy and exergy losses. The annual performance showed that the suggested biomassfueled CCHP system decreases biomass consumption by 4% compared with the non-utilization of a heat recovery system for the high-temperature gas product. Huicochea et al.[11] used a trigeneration system consisting of a micro gas turbine and a double effect absorption chiller to theoretically analyze the thermodynamic performance of the suggested system, formed by a micro-turbine and a double-effect water/LiBr absorption chiller. The results illustrated that the system represents an alluring innovative option to use the energy from the micro-turbine exhaust gases for power generation, cooling, and heating, produced at the same time. Nadezhda et al.[12] analyzed a cogeneration system with biogas-fired engines based on energy and exergy evaluation. The purpose plant was established in Varna to utilize the local wastewater treatment unit as a source of methane production. The percentage of exergy annihilation within the system component is estimated using the exergy balance formulas. Moreover, the details of the thermodynamic performance of the system were reported in that paper. Moharamian et al.[13] compared three suggested combined cycle include: biomass co-fired CHP system, biomass post-fired and outwardly fired CHP system based on the thermoeconomic investigation. Also, the various working fluid was utilized in the organic Rankine cycle part of the system to indicate the best efficient fluid. The study revealed that the highest rate of energy and exergy productivity were displayed by post-fired CHP plant and lowest by the outwardly fired CHP plant. The overall highest and lowest costs of products were 16 $/GJ and 5 $/GJ for biomass post-fired CHP plant and outwardly fired CHP plant, respectively Koroglu et al.[14] investigated a marine power plant with ORC. About energy and exergy analysis of that system the optimum range of operation for the suggested plant was determined. Furthermore, the thermodynamic assessment illustrated the exergy destruction rare in each element of the system and recommended the condition to improve the overall efficiency of that energy system. Ghasemi et al.[15] evaluated an integrated CCHP plant based on thermodynamic aspects. In that survey, cooling, heating, freshly desalinated water, power were produced by two integrated renewable energy sources. The thermodynamic assessment revealed the significant source of irreversibility of the suggested system and the biomass burner had the highest rate of exergy distraction. Final outputs showed after optimization that suggested system will able to generate 802.5 KW, 10391 KW,5658 KW and 9.328 kg/s power, heating, cooling, and desalinated water, respectively. Mehrpooya et al.[16] presented and analyzed a molten carbonate fuel cell (MCFC), Stirling engine and double effect LiBr/H2O absorption chiller integrated with CCHP plant. Thermodynamic performance of that system calculated and results showed that overall energy and electrical

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efficiency of the purposed system were 71.77% and 42.28% respectively. Moreover, natural combustion of CH4 and H2 in the burners caused the primary sources of exergy annihilation in that power plant. Ghasemi et al.[17] suggested and investigated an integrated poly-generation energy system using solar and biomass as the renewable energy source for producing power, freshly desalinated water, biogas, cooling and heating. The thermodynamic and thermoeconomic rules applied to that system and resulted illustrated that the maintained cycle could generate 16.11kW, 28.94 kW, 23.41 kW, 8.8 kg/h, 0.02 m3/h, electric power, heating, cooling, desalinated water, and liquated natural gas, respectively. Initial performance of plant showed the energy and exergy efficiencies of 46.8 %, 11.2%, and product cost rate 15.16 $/h. After using the genetic algorithm as a multi-objective optimizer, overall exergy efficiency improved to 9.9% and the total product cost rate of the system decreased to 13.32 $/h.

System Description

Power generation cycle is depicted in Figure 1. This cycle is composed of a gaseous micro-turbine, a compressor to supply air for the gas combustion, a gasifier to generate syngas from MSW which is utilized as gaseous fuel in the combustor, a solar collector to generate superheated steam at the sutaible and accessible condition and finally two heat exchanger units, one to absorb the heat of the gas, generated from the turbine, and the other one for cooling the syngas, and a pump.

Figure 1. Trigeneration energy system This cycle is contained four subsystems as it clears in Figure 1.They were separated into absorption chiller, gasification, organic Rankine cycle and, solar collector subsystems. MSW with the optimized content with stream number 5 and hot steam with stream number 4, generated by the solar collector, enter the gasifier in gasification subsystem. They react based on equation 1, and the part of output syngas with stream number 7 enters the heat exchanger 1 to be cooled and made ready to enter the compressor with stream number 8. This output syngas uses its heat capacity to warm the water entering with stream number 3 the solar collector. After the syngas is cooled, the compressor increases its pressure. During this process, syngas is combined with the incoming air with stream number 9 from a compressor in the combustor, and consequently, the combustion takes place. Then, a hot gas which has high pressure with stream number 11 enters the turbine and generates power. The compressor consumes the amount of this power.

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Moreover, another part of syngas enters the absorption chiller with stream number 24. Hot syngas passes through the vapor generator and enters a single-effect absorption cycle which uses LiBr-H2O to provide cooling. At the next stage, low-pressure gas again enters the ORC heat exchanger with stream number 12, and its heat capacity is transferred to R134a for power generation with ORC microturbine with stream number 16. The heat exchanger 2 is the final element which uses the maximum heat capacity to feed water heating. Finally, the process is completed.

Thermoynamic Modeling

Mass and energy conservation laws are used in order to simulate the system. Exergy is the maximum amount of work obtained from the given form of exergy employing environmental parameters as the reference state. Taking the first and second thermodynamic laws into consideration, some assumed input, energy, and exergy balance have been shown as follows: • • • • • • •

The gas produced through gasification does not require any treatment and contains no suspended solids. The gas behavior is assumed to be ideal. The changes in kinetic and potential energy and exergy terms are negligible. Expansion and density are assumed to be isotropic. The condition is steady-state. The reference-environment state's temperature and pressure are T0 = 298 K and P0 = 100 KPa, respectively. LHV for MSW is 10500 kJ/kg.

The exergy balance for a control volume includes the exergy of heat, mass, and workflows, transforming across the boundaries as well as the exergy destroyed within the control volume. Neglecting the kinetic and potential effects, the exergy of mass flow is divided into chemical and physical exergy parts. Exergy balance for a control volume is formulated as follow [18],[19]:

 T    ph ,ch  ph ,ch  E x D = ∑ 1 − 0 Q k − W +  ∑ E xk  −  ∑ E xk  Tk  k   k  in  k  out

For a better classification, three subsystems have been taken into account for the trigeneration energy system. Furthermore, thermodynamic modeling of the subsystems has been conducted as below:

Parabolic Trough Collector Sub-System

Parabolic Trough Collectors (PTCs) have been used to collect solar energy as one of the energy sources of the trigeneration energy system. Due to Therminol vp-1 oil is the heat transfer properties and it has the hightemperature stability, it is selected to be used as working fluid in the solar collector. Governing equation of PTC subsystem can be composed as follows [20]:

A E s = m 4C p (T4 − T3 ) = Aa FR ( S − r U L (T4 − T0 ) Aa

Here, FR is the heat removal factor, S is the heat absorbed by the receiver, Ar is the receiver area, Aa is the aperture area, and UL is the solar collector overall heat loss coefficient [21].

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Ex D , PTC = Ex s − m 4 (ex4 − ex3 )  1  T  4 4  T  Ex s = Acoll × Gt × 1 +  0  −  0  3  Ts   3  Ts  

Rankine Cycle Sub-System

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Ex D , HX ,1 = [m 7 (ex7 ) − m 8 (ex8 )] − [m 3 (ex3 ) − m 2 (ex2 )] Electric Generator:

m 24 (h24 − h31 ) + W pump 2 = m 39 (h39 − h40 ) + m 33 (h33 − h32 ) + m 38 (h38 − h37 )

Ex D , AC = m 24 (ex24 − ex31 ) + W pump 2 − m 38 (h38 − h37 )

Gasification Sub-System

HHVFuel = 0.3491zc + 1.1783 z H − 0.1034 zO − 0.0151z N + 0.1005 z S − 0.021z A

HHVFuel is the higher heating value of the fuel in MJ/kg and z A , weight

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HHVFuel is the higher heating value of fuel in MJ/kg, exCh,Fuel the

chemical exergy of fuel in MJ/kg, β the ratio of chemical exergy and higher heating value of fuel and C, H, O the mole fractions of carbon, hydrogen and oxygen in the fuel, respectively [22].

E Gasfier = m MSW ( HHVMSW ) + m OX (hOx ) + m 4 (h4 ) − (m 7 + m 24 )[(h7 + h24 )]

Ex D ,Gasfier = m MSW (exch , Fuel ) + m OX (exOx ) + m 4 (ex4 ) + (m 7 + m 24 )[(ex7 + ex24 )]

Performance Assessment

In order to assess the energetic and exergetic performance of the aimed energy system, energy efficiency and exergy efficiency are selected as the performance criteria and are defined as below: Energy efficiency [23]:

W Network = Wturb (ηGen ) + WORC ,turb (ηGen ) − (W pump1 + W pump 2 + WComp )

E x f = E xGasfier + E xs E x p = W Network + E x Heating + E xColling

Optimization

Optimization plays an undeniable role in many engineering designs. An optimization modeling can result in an optimum design for which it is not necessary to consider all possible cases and their impacts. In other words, ensuring an optimum case, optimization can decrease simulation time. From the thermodynamic point of view [24], the actual performance of a processor energy system can be assessed by the exergetic efficiency.

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Definition Of Objective Function

In this paper, the optimization of the cycle was performed using exergy efficiency. Exergy efficiency which is to be maximized is regarded as the objective function of the process. In this analysis, the objective function can be demonstrated as follow [25]:

Concerning the optimization problem, some parameters were included in a way to maximize the exergy efficiency, and consequently, the output work rate of the product was chosen.

Sensitivity Analysis

Sensitivity analysis as a general concept attempting to quantify the variations of an output parameter of a system regarding changes imposed on some important input parameters [26]. A comprehensive sensitivity analysis is conducted to check the impact of the variation of significant factors on total exergy efficiency. The most important factors which influence total exergy efficiency include air compressor pressure ratio, gas turbine inlet temperature, gasifier inlet steam temperature, and combustor inlet temperature. In this study, the decision variables are the most critical factors which affect total exergy efficiency. Each decision variables usually are required to be within a reasonable range as listed in Table 2. Table 2. The design parameters and their range of variation for the optimization procedure Case studies Air compressor pressure ratio Gasifier inlet steam temperature (K) Combustor inlet temperature (K) Gas turbine inlet temperature (K)

GENETIC ALGORITHM (GA) The genetic algorithm (GA) is defined as a method that can be employed to solve constrained and unconstrained optimization problems based on natural selection, the process which drives biological evolution. Passing through the system constraints, exergy analysis, and exergoeconomic formulations were utilized to assess the objective function for each trial. Then, parent selection was conducted, bearing in mind that each had the same probability of being chosen [27]. Np was used for the size of the generated population. Therefore, NP numbers of parents entered the reproduction step which generates NP offspring through a crossover strategy in which the decision variable values of the offspring fall in a range defined by the decision variable values of the parents. Some of the off-springs were also produced by adding a Gaussian random variable (N) with zero mean and a standard deviation proportional to the scaled cost value of the parent trial solution, i.e.,

The standard deviation σ i demonstrates the range over which the offspring is produced around the parent trial solution and is calculated by:

where f ( Pmin ) is the minimum value of the objective function among the NP trial solution, f ( Pi ) is the objective function value associated with the trial vector ( Pi ) and ϕ is a scaling factor. These off-springs, i = 1, 2,…, NP and their parents, i = 1, 2,..., NP form a set of 2NP trial solutions and contend for survival within the competing pool. Having completed, the 2NP trial solutions, consisting of the parents and the offspring, are ranked in 356

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descending order of the score. The first NP trial solutions survive and are subsequently transcribed along with their objective functions

f ( Pi ) into the survivor set as the basis of the next generation. Eventually, the number of

generations elapsed and the established maximum number of generations were compared with each other. Should the termination condition be met, the process stopped. Otherwise, the surviving solutions became the starting population for the next generation. In this study, in the first generation, 100 vectors Pi = [T11 , R AC , T4 , Tg ] were randomly generated within the operating range. Performance of the system with each vector was evaluated. The vector having the best system performance was stored for future comparison. The algorithm selected a group of vectors in the current generation, called parents that had better objective function values for the next generation (second generation). These parents were modified using in the equation (50) to generate the off-springs. The performance of the off-springs and the parent vectors were compared in order to select the best vector in the generation. The process of selecting parents and then generating the off-springs was repeated until the specified number of generations was achieved [28].

Result And Discussion

Thermodynamic analyses are run through developing codes in Engineering Equation Solver Software [6] using input data is shown in Table 4. Input data is the initial parameters that used to model the thermodynamically cycles. All of the data in table 4 was extracted from other similar articles and technical reports of components like collectors, turbines, evaporators, etc. Table 4. Input data for modeling of the Trigeneration energy system Parameters

Table 5 illustrates the outputs of results of modeling for each state of the system (Figure 1). Specific enthalpy, entropy, and exergy of each state was utilized to evaluate exergy destructions of elements. Moreover, calculation of exergy and energy efficiencies of the proposed trigeneration energy system is based on this information.

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Table 5. Results of thermodynamic modeling of trigeneration energy system State no. 0

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The consequential step in improving the thermodynamic performance of an energy system is the recognition of exergy destruction sources and the attempt to eliminate them; therefore, exergy destruction rate of the subsystem of the trigeneration energy system is assessed and illustrated in Figure 2. Figure 2 illustrates the overall exergy destruction rate of all components. As depicted in Figure 2 and the exergy analysis, for the operating conditions considered, the essential exergy destruction is caused by the gasifier. This result can be dealt with the significant irreversibility associated with the chemical reaction and heat transfer. The heat transfer between two sources of energy has proven as one of enormous reason for energy and exergy irreversibility. Moreover, because of changing between molecular bonds in the component at chemical reaction such as combustion, gasification and decomposition reactions most of the exergy annihilates in this kinds of process. As discussed in the related literature, a significant source of the exergy destruction can be a chemical reaction leading to more irreversibility [29]. As it is evident in Figure 2 compressor has the second rank of exergy destruction in the proposed system in comparison to other parts of the systems. It occurs because the vast difference at pressure factor between input and output streams in the compressor. Generally, any sodden shocks that are occurred in streams can make exergy destruction, and as it is clear, there is a high difference (100 and 2000 kPa) between stream number 9 and 10 that is the reason of exergy destruction. Finally, the third most exergy destruction is happened in the combustor and based on previous explanation due to the chemical irreversible reaction between compressed air, syngas that produces exhausted hot gas create exergy destruction in that component. The exergy destruction in ORC condenser. The exergy destruction of ORC condenser, ORC pump, absorption chiller, and ORC turbine are negligible are also presented in Figure 2 [30].

Figure 2. Exergy destruction ratios According to input data and formulas presented earlier, the first and second laws of thermodynamics were applied to all components of the proposed system. The performance indices of the system such as energy and exergy efficiencies, required MSW mass flow rate, required collector area, heating and cooling loads, exergy of heating and cooling loads and power to the network be achieved and are showed in Table 6. This information is based on initial input data( Table4), and it indicates the inner potential of aimed trigeneration system. In order to improve the performance of the system, genetic algorithm is used to make a change in sensitive parameters of the system due to reach the best point of them. In those points, both of the exergy and the energy efficiencies of the trigeneration energy system are optimized.

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Table 6. Thermodynamic performance of Trigeneration energy system Parameters

Optimization Results

After detecting the sensitive parameters of the aimed energy system, the effect of compressor pressure ratio on exergy efficiency was found as one of the sensitive points of the system. Figure 3 shows the effect of the air compressor pressure ratio variation on the exergy efficiency. It is clear that as compressor pressure ratio increases, exergy efficiency decreases and this change in exergy efficiency are occurred because of the compressor consumed the part of the produced power to increasing pressure of the inlet air to improve the performance of combustion process at the combustor chamber. Considering the mentioned explanation for exergy efficiency, the trend attained for exergy efficiency variation is expected according to the efficiency definition. The optimum point for this case is RAc = 8, so exergy efficiency is equal to 52.08 %.

Figure 3. Effect of the air compressor pressure ratio variation on the exergy efficiency

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The second sensitive parameter of the proposed energy system is gas turbine inlet temperature. The exergy efficiency for different values of gas turbine inlet temperature is illustrated in Figure 4. As it is shown, they have a direct relationship with one another. There is a constant rise in the exergy efficiency when this temperature is increased. In the other word, as far as gas turbine inlet temperature increases, the amount of exergy efficiency increase. There is a logical explanation for this event. Whatever the temperature of the inlet gas turbine increase, the enthalpy, and entropy of the feed stream of the gas turbine is increased and more thermal energy is converted to the power. So, based on the basic description of exergy concept, efficiency of that element is improved. The optimum point for this case is T3 = 692 K, and exergy efficiency is equal to 52.51 %. 0,53 0,52

Figure 4. Effect of the gas turbine inlet temperature on the exergy efficiency The third sensitive parameter of the aimed energy system is combustor inlet temperature. The performance of cycle at different combustor inlet temperature is shown graphically in Figure 5. It can be figured out that when combustor inlet temperature increases from 630 K to 670 K, the exergy efficiency decreases from 52.7 % to 48.3 %. As it is mentioned before the difference between two sources of temperature is one of the main reasons of exergy destruction and increasing in exergy destruction leads the system to poor performance in exergy efficiency. This figure shows that to obtain the highest exergy efficiency of aimed energy system the combustor inlet temperature must set on the minimum range at acceptable temperature. 0,53

Figure 5. Effect of the combustor inlet temperature on the exergy efficiency

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In Figure 6, the effect of gasifier inlet steam temperature on the exergy efficiency is examined. This parameter was detected as another sensitive parameter of the suggested cycle. As it is extracted from this figure, there is two section of the figure. In the one part of that, the slope of the figure is sharp, and exergy efficiency of the aimed system is decreased. In another part of the figure the slope of the figure is slowed down and in exergy efficiency of the system is decreased with the more average slope. Also one of the common ways of improving the performance of a thermal energy system is increasment in the input temperatures, in some cases, thermal irreversibility is increased and it leads the system to the worse efficiency condition. So based on the clear trend of the Fig6, at the minimum acceptable gasifier inlet temperature, the system shows the highest exergy efficiency. As can be seen on the figure, the slope of changes between 600 K to 891 K is much more than 891 K to 1200 K. So, the optimum point is 600 K.

Figure 6. Effect of the gasifier inlet temperature on the exergy efficiency As presented in Table 7, exergy efficiency increases 4.4% in comparison to base case value. This optimize point extracted from single objective optimization by genetic algorithm in the EES software. The decision variables based on sensitive analyses are changed to reach the best overall exergy efficiency. In fact, improving exergy efficiency is the final goal of the optimization process. The thing that needs to be highlighted here is that the weight of parameters in this optimization was consumed equally. Moreover, the ideal case for decision variables is extracted after optimization of the trigeneration system. Table 7. Optimum values of decision variables and objective functions for single objective optimization Parameters Exergy Efficiency (%) Gas turbine inlet temperature(K) air compressor pressure ratio variation Combustor inlet temperature(K) gasifier inlet temperature(K)

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Conclusion

In this study, energetic evaluation and optimization have been carried out for an 11.2 kW output work. The exergy of all components has been defined, and the energetic efficiency of each component has been evaluated. Based on the results of the current study, it can be concluded that the most significant percentage of the exergy destructions is associated with the gasifier, compressor, and combustor respectively. Furthermore, the amount of these percentages are 47 %, 26.3 %, and 14 %. These destructions are caused by internal and external irreversible processes including combustion, chemical reactions, phase change and high temperature difference between two parts in the gasifier, compressor, and combustor, so it causes total incoming exergy destruction by syngas energy and absorbed radiation from the solar collector. The net generating electric power, cooling load, heating load are reported to be 11.2 kW, 15.3 kW, and 17.4 kW respectively. The values of optimum exergy efficiency as the output of running the Genetic Algorithm for single-objective optimization problem reached 4 %. The optimization code developed shows that exergy efficiency improved significantly for optimum operation and we can save a significant amount of energies through a dynamic optimization of the system. The following remarks can be extracted from this study: • • •

Exegetic efficiency has a linear and remarkable increase via gas turbine inlet temperature. By increasing gasifier inlet steam temperature, compressor pressure ratio, and combustor inlet temperature, energetic efficiency of the cycle are increased. Increasing the gasifier inlet temperature, compressor pressure ratio, and combustor inlet temperature will increase exergy destruction significantly.

Nomenclature

Temperature Pressure Entropy Entropy Enthalpy of reference state Hour

Mas flow rate Kilowatt Megawatt The temperature of reference state The pressure of reference state

Solar collector overall heat loss efficiency Heat absorbed by the receiver

Solar radiation flux on the plane of the solar collector Organic Rankine cycle Low heating value Litume bromide Parabolic Trough solar collector Engineering equation solver Exergy destruction 363

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Chemical exergy Chemical exergy rate Exergy destruction rate

Exergy rate of parabolic trough solar collector The temperature of the sun surface

The work rate of the turbine The isentropic work rate of turbine Exergy destruction rate of the turbine The work rate of pump1 Energy of evaporator Exergy rate of evaporator Exergy destruction rate of heater Exergy destruction rate of evaporator1 Exergy rate of sun radiation Work flow rate of the compressor

Output energy rate Input energy rate Energy rate of evaporator Exergy rate of product

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Collector Compressor Electric Electric generation Combined heating and power plant Combined cooling heating and power plant Molten carbonate fuel cell

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