Component exergy analysis of solar powered transcritical CO<Subscript>2</Subscript> rankine cycle system

In this paper,exergy analysis method is developed to assess a Rankine cycle system,by using supercritical CO2 as working fluid and powered by solar energy.The proposed system consists of evacuated solar collectors,throttling valve,high-temperature heat exchanger,low-temperature heat exchanger,and feed pump.The system is designed for utilize evacuated solar collectors to convert solar energy into mechanical energy and hence electricity.In order to investigate and estimate exergy performance of this system,the energy,entropy,exergy balances are developed for the components.The exergy destructions and exergy efficiency values of the system components are also determined.The results indicate that solar collector and high temperature heat exchanger which have low exergy efficiencies contribute the largest share to system irreversibility and should be the optimization design focus to improve system exergy effectiveness.Further,exergy analysis is a useful tool in this regard as it permits the performance of each process to be assessed and losses to be quantified.Exergy analysis results can be used in design,optimization,and improvement efforts.     

Various criteria in optimization of a geothermal air conditioning system with a horizontal ground heat exchanger

Thermodynamic and thermoeconomic optimization of a horizontal geothermal air conditioning system has been performed. A model based on energy and exergy analysis is presented here. An economic model of the system is developed according to the Total Revenue Requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analysis are developed. An artificial intelligence technique known as evolutionary algorithm has been utilized for optimization. This approach has been applied to minimize either the total levelized cost of the system product or the exergy destruction of the system. Three levels of optimization including thermodynamic single objective optimization, thermoeconomic single objective optimization and multi‐objective optimization (with simultaneous optimization of thermodynamic and thermoeconomic objectives) are performed. In multi‐objective optimization, both thermodynamics and thermoeconomic objectives are considered, simultaneously. In the case of multi‐objective optimization, an example of decision‐making process for selection of the final solution from available optimal points on Pareto front is presented here. The results obtained using the various optimization approaches are compared and discussed. Copyright © 2009 John Wiley & Sons, Ltd.     

Performance assessment and optimization of an integrated solid oxide fuel cell-gas turbine cogeneration system

The combined solid oxide fuel cells and gas turbine (SOFC/GT) system is known to be a potential alternative for distributed power generation. In this paper, a novel SOFC/GT based cogeneration system, which integrated a transcritical carbon dioxide cycle (TRCC) with a LNG cold energy utilization system is proposed. A mathematical (zero-dimensional) model is developed to analyze the co-generation system performance from the perspective of thermodynamic (energy and exergy) and economic costs. The main parameters of the system are chosen to analyze their effects on thermodynamic performance. The results show that the current system can achieve 64.40% thermal efficiency and 62.13% exergy efficiency under given conditions, and can further improve efficiency through parameter optimization. Finally, the multi-objective optimization program using NSGA-II (Non-dominated Sorting Genetic Algorithm II) is used to obtain the optimal value of the system design parameters. In the multi-objective analysis, the thermodynamic efficiency and economic cost of the system are considered as objective functions. The optimization results show that the final optimized design selected from the Pareto front can achieve 63.08% thermal efficiency and 61.10% exergy efficiency, respectively.     

Exergy analysis and optimization of a HT-PEMFC using developed Manta Ray Foraging Optimization Algorithm

This paper analyzes the efficiency of a high-temperature proton exchange membrane fuel cell (HT-PEMFC) by calculating the output voltage of the cell in different working conditions, using the semi-experimental relationships. The irreversibility and the exergy efficiency of the fuel cell is calculated under different working conditions and the effect of temperature and pressure has been studied. To achieve optimal design for the PEMFC, its parameters are optimized based on irreversibility, exergy efficiency, and its work. The system optimization is applied by a modified version of the Manta Ray Foraging Optimization Algorithm. The suggested algorithm is then compared with other algorithms from the literature and also simulation results and showed a high agreement between the suggested algorithm and the simulation results.     

Exergy,economic, and optimization of a clean hydrogen production system using waste heat of a steel production factory

In this present research study a multi-generation energy system which is coupled with CO₂ capture unit which is based on Rankine cycle, organic Rankine cycle, ejector cooling system and absorption chiller has been analyzed via energy, exergy, exergy-economic aspects by developing MATLAB, also to achieve the optimum operating condition genetic algorithm has been applied for system optimization. The objective of this study is to propose an optimized efficient integrated energy system to recycle the energy waste of a typical industrial factory. The optimization has been illustrated on a Pareto frontier to achieve the optimum scheme of the multi-generation system regarding technical and economic viewpoints. Results indicate the optimal condition of this system has occurred at 0.37 exergy efficiency with 0.03 $/s. Furthermore, by surging the mass flow rate of waste gases up to 70 kg/s, net power output augmented up to 7500 kW. Besides, hydrogen production and produced desalinated water rise up to 8.5 g/s and 16 kg/s, respectively.     

Multi-criteria optimization of an integrated energy system with thermoelectric generator,parabolic trough solar collector and electrolysis for hydrogen production

In this research paper, a newly energy system consisting of parabolic trough solar collectors (PTSC) field, a thermoelectric generator (TEG), a Rankine cycle and a proton exchange membrane (PEM) is proposed. The integration is performed by establishing a TEG instead of the condenser as power generation and cooling unit thereafter surplus power output of the TEG is transferred to the PEM electrolyzer for hydrogen production. The integrated renewable energy system is comprehensively modeled and influence of the effective parameters is investigated on exergy and economic indicators through the parametric study to better understand the system performance. Engineering equation solver (EES) as a potential engineering tool is used to simulate the system and obtain the desired results. In order to optimize the system, a developed multi-objective genetic algorithm MATLAB code is applied to determine the optimum operating conditions of the system. Obtained results demonstrate that at optimum working condition from exergy viewpoint, exergy efficiency and total cost are 12.76% and 61.69 $/GJ, respectively. Multi-objective optimization results further show that the final optimal point which is well-balanced between exergy efficiency and total cost, has the maximum exergy efficiency of 13.29% and total cost of 63.96 $/GJ, respectively. The corresponding values for exergy efficiency and total cost are 10.01% and 60.21 $/GJ for optimum working condition from economic standpoint. Furthermore, hydrogen production at well-balanced operating condition would be 2.28 kg/h. Eventually, the results indicate that establishing the TEG unit instead of the condenser is a promising method to optimize the performance of the system and reduce total cost.     

Exergetic optimization of a solar photovoltaic thermal (PV/T) air collector

In this paper, an exergetic optimization has been developed to determine the optimal performance and design parameters of a solar photovoltaic thermal (PV/T) air collector. A detailed energy and exergy analysis has been carried out to calculate the thermal and electrical parameters, exergy components, and exergy efficiency of a typical PV/T air collector. The thermal and electrical parameters of a PV/T air collector include solar cell temperature, back surface temperature, outlet air temperature, open‐circuit voltage, short‐circuit current, maximum power point voltage, maximum power point current, etc. An improved electrical model has been used to estimate the electrical parameters of a PV/T air collector. Furthermore, a new equation for the exergy efficiency of a PV/T air collector has been derived in terms of design and climatic parameters. A computer simulation program has been also developed to calculate the thermal and electrical parameters of a PV/T air collector. The results of numerical simulation are in good agreement with the experimental measurements noted in the previous literature. Moreover, the simulation results obtained in this paper are more precise than the one given by the previous literature, and the new exergy efficiency obtained in this paper is in good agreement with the one given by the previous literature. Finally, exergetic optimization has been carried out under given climatic, operating, and design parameters. The optimized values of inlet air velocity, duct length, and the maximum exergy efficiency have been found. Parametric studies have been also carried out. Copyright © 2010 John Wiley & Sons, Ltd.     

Exergy analysis and multiobjective optimization of a biomass gasification based multigeneration system

Biomass gasification is a process of converting biomass to a combustible gas suitable for use in boilers, engines and turbines to produce combined cooling, heat and power. This paper presents a detailed model of a biomass gasification system and designs a multigeneration energy system which uses the biomass gasification process for generating combined cooling, heat and electricity. Energy and exergy analyses are first applied to evaluate the performance of the designed system. Next, minimizing total cost rate and maximizing exergy efficiency of the system are considered as two objective functions and a multiobjective optimization approach based on differential evolution algorithm and local unimodal sampling technique is developed to calculate the optimal values of the multigeneration system parameters. A parametric study is then carried out and Pareto front curve is used to determine the trend of objective functions and assess the performance of the system. Furthermore, a sensitivity analysis is employed to evaluate effects of design parameters on the objective functions. Simulation results are compared with two other multiobjective optimization algorithms and effectiveness of the proposed method is verified using various performance indicators.     

An exergetically-sustainable operational condition of a photo-biohydrogen production system optimized using conventional and innovative fuzzy techniques

The aim of the present study was to perform an exergy-based multi-objective fuzzy optimization of a continuous photobioreactor applied for biohydrogen production from syngas via the water-gas shift reaction by Rhodospirillum rubrum. For this purpose, the conventional and innovative fuzzy optimization techniques coupled with multilayer perceptron (MLP) neural model to optimize the main exergetic performance parameters of the photobioreactor. The MLP neural model was applied to correlate three dependent variables (rational and process exergy efficiencies and normalized exergy destruction) with two independent variables (syngas flow rate and agitation speed). The developed MLP model was then interfaced with three different multi-objective fuzzy optimization systems with independent, interdependent, and locally modified interdependent objectives. The optimization process was aimed at maximizing the rational exergy and process efficiencies, while minimizing the normalized exergy destruction, simultaneously. Generally, the innovative locally modified interdependent objectives fuzzy system showed a better optimization capabilities compared with the other two fuzzy systems. Accordingly, the optimal syngas photo-fermentation for biohydrogen production in the continuous bioreactor corresponded to the agitation speed of 383.34 rpm and syngas flow rate of 13.35 mL/min in order to achieve the normalized exergy destruction of 1.56, rational exergy efficiency of 85.65%, and process exergy efficiency of 21.66%.     

Dynamic multi-objective optimization applied to a solar-geothermal multi-generation system for hydrogen production,desalination, and energy storage

The design of optimal energy systems is vital to achieving global environmental and economic targets. In the design of solar-geothermal multi-generation systems, most previous investigations have relied on the static multi-objective optimization approach (SMOA), which may leave considerable room for improvement under certain conditions. In this numerical study, the optimal condition at which to operate a solar-geothermal multi-generation system – which can simultaneously produce hydrogen, fresh water, electricity, and heat, along with storing energy ? is determined via a dynamic multi-objective optimization approach (DMOA). Optimization is performed using a combination of NSGA-II and TOPSIS, and the results are benchmarked against those of SMOA. The decision variables include the solar area, geothermal water extraction mass flow, and hydrogen storage pressure. The objective functions include the production of electricity, heat, hydrogen, and fresh water, along with the exergy and energy efficiencies and the payback period. It is found that when compared with SMOA, DMOA can significantly improve all the objective functions. The annual production of electricity, heat, hydrogen, and fresh water increases by 14.4, 16.1, 13.5, and 14.3%, respectively, while the average annual exergy and energy efficiencies increase by 5.2 and 3.0%, respectively. The use of DMOA also reduces the payback period from 5.56 to 4.43 years, with a 4.4% reduction in hydrogen storage pressure. This shows that compared with a static approach such as SMOA, DMOA can improve the exergy and energy efficiencies, economic viability, and safety of a solar-geothermal multi-generation system.     

Design,modeling and optimization of a renewable-based system for power generation and hydrogen production

Due to the environmental concerns caused by fossil fuels, renewable energy systems came into consideration. In this study, a renewable hybrid system based on ocean thermal, solar and wind energy sources were designed for power generation and hydrogen production. To analyze the system, a techno-economic model was exerted in order to calculate the exergy efficiency as well as the cost rate and the hydrogen production. The main parameters that affect the system performance were identified, and the impact of each parameter on the main outputs of the system was analyzed as well. The thermo-economic analysis showed that the most effective parameters on the exergy efficiency and total cost rate are the wind speed and solar collector area, respectively. To reach the optimum performance of the system, multi-objective optimization, by using genetic algorithm, was applied. The optimization was divided into two separate case studies; in case A, the cost rate and the exergy efficiency were considered as two objective functions; and in case B, the cost rate and the hydrogen production were assigned as two other objective functions. The optimization results of the case A showed that for the total cost rate of 30.5 $/h, the exergy efficiency could achieve 35.57%. While, the optimization of the case B showed that for the total cost rate of 28.06 $/h, the hydrogen production rate could reach 5.104 kg/h. Furthermore, after optimizing, an improvement in exergy efficiency was obtained, approximately 19%.     

Multi-objective exergy-based optimization of a continuous photobioreactor applied to produce hydrogen using a novel combination of soft computing techniques

This work was aimed at proposing a flexible and reliable framework based on combination of three soft computing techniques, i.e., artificial neural network, genetic algorithm, and fuzzy systems for multi-objective exergetic optimization of continuous photobiohydrogen production process from syngas by Rhodospirillum rubrum bacterium. To this end, artificial neural network (ANN) coupled with fuzzy clustering method (FCM) to model exergetic outputs on the basis of input variables. The outputs of modeling system were then fed into a novel optimization approach developed by hybridizing additive linear interdependent fuzzy multi-objective optimization (ALIFMO) and the elitist non-dominated sorting genetic algorithm (NSGA-II). The optimization was carried out to minimize the normalized exergy destruction and maximize the rational and process exergetic efficiencies, simultaneously. The solutions of the proposed approach were also compared with conventional fuzzy multi-objective optimization procedure with independent objectives. Overall, the modeling system predicted the exergetic parameters of photobioreactor with a coefficient of determination higher than 0.90. Furthermore, the optimization approach suggested syngas flow rate of 13.35 mL/min and agitation speed of 383.34 rpm as the best operational condition by considering the preferences of process exergy efficiency, rational exergy efficiency, and normalized exergy destruction, respectively. This condition could yield the normalized exergy destruction of 1.56, process exergetic efficiency of 21.66%, and rational exergetic efficiency of 85.65%. The obtained results showed the superiority of the proposed approach over the conventional fuzzy method in optimizing the complex biofuel production systems.     

Objective function proposed for optimization of convective heat transfer devices

In this work, a general method using exergy analysis has been proposed to achieve a compromise between heat transfer effectiveness and pressure loss in heat transfer optimization problems involving internal channels. The proposed method is applied to the design optimization of a channel roughened by staggered arrays of dimples for heat transfer augmentation. Optimization is performed using surrogate-based optimization techniques and three-dimensional Reynolds-averaged Navier–Stokes analysis. Three nondimensional design variables are defined using the dimpled channel height, dimple print diameter, dimple spacing, and dimple depth. The objective function is defined as the net exergy gain considering the exergy gain by heat transfer, and exergy losses generated by friction and heat transfer. Twenty design points are generated using Latin hypercube sampling, and the Kriging model is used as a surrogate model to approximate the objective function values in the design space. Through optimization, the objective function is successfully improved with respect to the reference geometry.     

Thermodynamic,exergoeconomic and exergoenvironmental analyses and optimization of a solid oxide fuel cell-based trigeneration system

This study proposes a trigeneration system based on solid oxide fuel cell (SOFC) for generating power, cooling and heating simultaneously. The system mainly contains a SOFC, a gas turbine (GT), an organic Rankine cycle (ORC), a steam ejector refrigerator (SER) and a heat exchanger. The thermodynamic, exergoeconomic and exergoenvironmental models of proposed trigeneration system are developed, and the effects of design parameters on system performances are analyzed. The results indicate that the system average product cost and environmental impact per unit of exergy increase with SOFC inlet temperature and working pressure, the pinch point temperature difference and evaporating pressure of Generator, while decrease with the current density of fuel cell. Finally, optimization is performed to achieve the optimal exergy-based performance. It is revealed that though the system exergy efficiency is decreased by 7.64% after optimization, the system average product cost and environmental impact per unit of exergy are significantly reduced.     

Sustainability improvement in combined electricity and freshwater generation systems via biomass: A comparative emergy analysis and multi-objective optimization

For energy systems, sustainability is a major concern that must be carefully considered when designed and established. Emergy analysis is an effective technique to scrutinize the sustainability of these systems. On the other hand, water shortage is seen to become a big problem in the close future; however, this problem can be effectively alleviated by combined electricity/water production plants, where waste heat is recovered to generate freshwater. This study applies emergy analysis to evaluate and improve the sustainability, renewability, environmental impacts, and economic aspect of such a plant, in which a multi-stage desalination (MSF) system is employed to recover the waste heat from a gas turbine (GT). The plant is fueled by biomass/natural gas (system I), natural gas (system II), and biomass (system III), and the above-mentioned features are compared for the different fuel types. To estimate chemical equilibrium state inside the gasifier, Lagrange's method of undetermined multipliers is applied. Also, considering exergy efficiency and emergy sustainability index as objective functions, biomass/natural gas-fueled system is optimized by adopting a multi-objective optimization approach based on the non-dominated sorting genetic algorithm II (NSGA II). To predict the optimized points' behavior, the Pareto optimal frontier of the system is utilized. The results reveal that using biomass as inlet fuel remarkably improves the sustainability index and reduces environmental impacts. The optimization results show that as sustainability index increases, exergy efficiency decreases. Also, the two optimized points of the system are found to have exergy efficiencies of 20.14% and 25.09% and sustainability indices of 24.67% and 13.60%.     

基于RSM和NSGA-Ⅱ法的风电系统经济优化研究

针对风电储能系统复杂多变运行工况引发系统效率低和经济性差问题,提出一种基于响应面法(RSM)和非支配遗传算法(NSGA-Ⅱ)相结合的多目标优化方法。首先建立系统平衡及成本守恒模型,并以效率和成本差作为系统评价指标;其次,以变量转速、蓄电池类型、蓄电池充放电状态及功率波动量组合为设计工况,通过中心复合设计实验(CCD)获得16组运行工况样本数据,根据RSM获得效率和成本差的回归方程;然后经方差分析验证该模型的可靠性;最后,以效率极大和成本差极小为优化双目标,借助RSM和NSGA-Ⅱ相结合法对模型进行求解,根据结果选择最佳运行工况,从而提高系统效率和经济。     

Optimization of the coupling of pressurized water nuclear reactors and multistage flash desalination plant by evolutionary algorithms and thermoeconomic method

Thermodynamic simulation programs are widely used for designing complex thermal systems, but most of them do not incorporate second law optimization techniques. In this study, an efficient optimization strategy is presented, which integrates three optimization techniques with a professional power plant and a cogeneration simulator so as to perform exergoeconomic optimization of complex thermal systems and generate combined pinch and exergy representations. This paper deals with the application of an evolutionary algorithm based on NSGA‐II to multi‐objective thermoeconomic optimization of coupling desalination plant with pressurized water reactor (PWR). In addition, one‐objective thermoeconomic optimization through genetic algorithm and mixed integer non‐linear mathematical programming methods has been applied for evaluation of multi‐objective optimization. The thermodynamic simulation of this plant has been performed in the THERMOFLEX simulator. An Excel Add‐in called THERMOFLEX link has been developed to calculate the exergy of each stream from THERMOFLEX simulation results. In addition, a computer code has been developed for thermoeconomic and improved combined pinch–exergy analysis in the MATLAB environment. Also, multi‐objective and one‐objective evolutionary algorithm optimization has been performed in MATLAB and one‐objective mathematical programming has been performed in LINGO software. Both the design configuration and the process variables are optimized simultaneously. The optimization algorithm can choose among several design options included in a superstructure of the feed water heaters and multistage flash desalination in a dual‐purpose plant. For the assumptions and simplifications made in this study, a 3000 MWh PWR power plant similar to Bushehr power plant has been considered. Copyright © 2008 John Wiley & Sons, Ltd.     

An analysis of SOFC/GT CHP system based on exergetic performance criteria

In this study, we first consider developing a thermodynamic model of solid oxide fuel cell/gas turbine combined heat and power (SOFC/GT CHP) system under steady-state operation using zero-dimensional approach. Additionally, energetic performance results of the developed model are compared with the literature concerning SOFC/GT hybrid systems for its reliability. Moreover, exergy analysis is carried out based on the developed model to obtain a more efficient system by the determination of irreversibilities. For exergetic performance evaluation, exergy efficiency, exergy output and exergy loss rate of the system are considered as classical criteria. Alternatively, exergetic performance coefficient (EPC) as a new criterion is investigated with regard to main design parameters such as fuel utilization, current density, recuperator effectiveness, compressor pressure ratio and pinch point temperature, aiming at achieving higher exergy output with lower exergy loss in the system. The simulation results of the SOFC/GT CHP system investigated, working at maximum EPC conditions, show that a design based on EPC criterion has considerable advantage in terms of entropy-generation rate.     

Multi-objective optimization of a multi water-to-water heat pump system using evolutionary algorithm

This paper deals with the energy recovery in the dairy industry. Thermodynamic, economic and environmental optimization of three water-to-water heat pumps has been studied in order to replace totally or partially a fuel boiler used to produce heat at different temperature levels in a cheese factory. These heat pumps have their evaporators connected to one effluents source and two of them are equipped by storage tanks at the condenser side. Multi-objective optimization permits optimal repartition of mass flow rates of effluents and optimal choice of electrical power of the compressors and volumes of storage tanks. The thermodynamic objective is based on the exergy destruction in the whole system. The economic objective is based on the investment cost and the operating cost obtained with the heat pump system. The environmental impact objective has been defined and expressed in cost terms by considering a CO₂ taxation (carbon tax) on the GHG emissions. This objective has been integrated with the economic objective. Multi-objective genetic algorithms are used for Pareto approach optimization.     

Optimal design of distillation column using three dimensional exergy analysis curves

This paper presents two main contributions. Firstly, a new exergy graphical method is proposed for optimal design of distillation column with minimum exergy lost. The method is applicable to both grass-root and retrofit cases, respectively. The effect of design and operating parameters of a distillation column on the exergy lost is graphically visualized by three dimensional exergy analysis curves. The curve shows the correlations between exergy lost, design and operating parameters of a distillation column. This technique can be used as an effective method to reduce the simulation effort to search for the optimum design and operating parameters of a distillation column at minimum exergy lost. Besides, visualization also enhances the engineers’ understanding of the column performance. The other contribution is a four-level idealization concept, which is based on three dimensional graphical exergy analysis curves. The concept defines the effect of transport rate and configuration on exergy lost of distillation column. The effectiveness of the method has been demonstrated on a xylene column, which suggested that an implementation of feed pre-heater yields a significant reduction in exergy lost by up to 15.5%.