Determination of optimal operating conditions for a polymer electrolyte membrane fuel cell stack: optimal operating condition based on multiple criteria

A methodology for optimal control of the polymer electrolyte membrane fuel cell (PEMFC) with multiple criteria is presented here. In this regard, thermoelectric objectives and thermoeconomic objective are considered, simultaneously. The proposed fuel cell is a 1200 W Ballard PEMFC namely Nexa? power module. The net power density and exergetic efficiency of the PEMFC are maximized, and the unit cost of the generated power is minimized in a multi‐objective optimization procedure using the NSGA‐II (non‐dominated sorting genetic algorithm). Operating temperature and pressure, air stoichiometric coefficient at the cathode and the current density are considered as controlling parameters in order to acquire optimal performance of the PEMFC. A set of optimal solution namely the Pareto frontier is obtained, and a final optimal solution is selected from available solutions located on the Pareto frontier using the fuzzy decision‐making process based on the Bellman–Zadeh approach. Results are compared with corresponding results obtained previously in single objective optimization scenarios. It has been shown that the optimal operating condition obtained based on the multiple criteria approach has least deviation from the ideal features of the fuel cell in comparison to the corresponding optimal solution obtained in conventional single‐objective optimization approaches. Copyright © 2013 John Wiley & Sons, Ltd.     

Optimization of Output Power and Thermal Efficiency of Solar‐Dish Stirling Engine Using Finite Time Thermodynamic Analysis

This paper presents an investigation on finite time thermodynamic (FTT) evaluation of a solar‐dish Stirling heat engine. FTTs has been applied to determine the output power and the corresponding thermal efficiency, exergetic efficiency, and the rate of entropy generation of a solar Stirling system with a finite rate of heat transfer, regenerative heat loss, conductive thermal bridging loss, and finite regeneration process time. Further imperfect performance of the dish collector and convective/radiative heat transfer mechanisms in the hot end as well as the convective heat transfer in the heat sink of the engine are considered in the developed model. The output power of the engine is maximized while the highest temperature of the engine is considered as a design parameter. In addition, thermal efficiency, exergetic efficiency, and the rate of entropy generation corresponding to the optimum value of the output power is evaluated. Results imply that the optimized absorber temperature is some where between 850 K and 1000 K. Sensitivity of results against variations of the system parameters are studied in detail. The present analysis provides a good theoretical guidance for the designing of dish collectors and operating the Stirling heat engine system.     

Thermoeconomic optimization of multi effect distillation desalination systems

Thermoeconomic optimization of a multi effect distillation (MED) desalination system with thermo-vapor compressor (TVC) is performed. A model based on the 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. The proposed multi effect distillation system including six decision variables is considered for optimization. A stochastic/deterministic optimization approach known as genetic algorithm is utilized as an optimization method. This approach is applied to minimize the cost of the system product (fresh water).     

Exergoeconomic optimization of a 1000 MW light water reactor power generation system

A typical 1000 MW pressurized water reactor nuclear power plant is considered for optimization. The thermodynamic modeling is performed based on the energy and exergy analysis, while an economic model is developed according to the total revenue requirement method. The objective function based on the exergoeconomic analysis is obtained. The exergoeconomic optimization process with 10 decision variables is performed using a hybrid stochastic/deterministic search algorithm namely as genetic algorithm. The results that are obtained using optimization process are compared with the base case system and the discussion is presented. Copyright © 2009 John Wiley & Sons, Ltd.     

THERMO-HYDRAULIC BEHAVIOR MODELING OF PASSIVE HEAT TRANSFER ENHANCEMENT TECHNIQUES USING A SOFT COMPUTING APPROACH

The main aim of this research is to demonstrate effectiveness of soft computing techniques in thermo-hydraulic behavior modeling of passive heat transfer enhancement (HTE) techniques. An artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), two effective modeling methods, have been used to model Nusselt numbers and friction factors of wire coil and twisted tape inserts in various flow regimes. The experimental data sets were utilized for training and validation of these models, and their results were compared with the corresponding correlations. The mean relative error (MRE) between the predicted results and experimental data of ANN and ANFIS models were found to be less than 3% and 1.5% for thermo-hydraulic behavior modeling of wire coil and twisted tape inserts, respectively. Depending on model complexity, performance of both ANN and ANFIS models was found to be superior to that of the corresponding power-law regressions. Hence, application of the soft computing approach to predict the performance of thermal systems in engineering applications is recommended.     

Thermodynamic and thermoeconomic optimization of a cooling tower-assisted ground source heat pump

Thermodynamic and thermoeconomic optimization of a cooling tower-assisted ground source heat pump (GSHP) in a multi-objective optimization process is performed. A thermodynamic model based on energy and exergy analyses is presented, and an economic model of the hybrid GSHP (HGSHP) system is developed according to the total revenue requirement (TRR) method. The proposed hybrid cooling tower-assisted GSHP system, including 12 decision variables, is considered for optimization. Three optimization scenarios, including thermodynamic single objective, thermoeconomic single objective, and multi-objective optimizations, are performed. In multi-objective optimization, both thermodynamic and thermoeconomic objectives are simultaneously considered. An optimization process is performed using the genetic algorithm (GA). In the case of multi-objective optimization, an example of a decision-making process for selection of the final solution from the Pareto optimal frontier is presented. The results obtained using the various optimization approaches are compared and discussed. Further, the sensitivity of optimized systems to the interest rate, the annual number of operating hours in cooling mode, the electricity price, and the water price are studied in detail. It is shown that the thermodynamic optimization is focused on provision for the limited source of energy, whereas the thermoeconomic optimization only focuses on monetary resources. In contrast, the multi-objective optimization considers both energy and monetary. Further, it is found that thermodynamic optimization is economical when the operating time in cooling mode is long and/or the electricity price is high, and water prices variations have no marked impact on the total product cost.     

Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system

Multi-objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the exergetic, economic and environmental aspects have been considered, simultaneously. The thermodynamic modeling has been implemented comprehensively while economic analysis conducted in accordance with the total revenue requirement (TRR) method. The results for the single objective thermoeconomic optimization have been compared with the previous studies in optimization of CGAM problem. In multi-objective optimization of the CGAM problem, the three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact have been considered. The environmental impact objective function has been defined and expressed in cost terms. This objective has been integrated with the thermoeconomic objective to form a new unique objective function known as a thermoenvironomic objective function. The thermoenvironomic objective has been minimized while the exergetic objective has been maximized. One of the most suitable optimization techniques developed using a particular class of search algorithms known as multi-objective evolutionary algorithms (MOEAs) has been considered here. This approach which is developed based on the genetic algorithm has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of decision-making has been presented and a final optimal solution has been introduced. The sensitivity of the solutions to the interest rate and the fuel cost has been studied.     

Multi-objective optimization of a cooling tower assisted vapor compression refrigeration system

A cooling tower assisted vapor compression refrigeration machine has been considered for optimization with multiple criteria. Two objective functions including the total exergy destruction of the system (as a thermodynamic criterion) and the total product cost of the system (as an economic criterion), have been considered simultaneously. A thermodynamic model based on energy and exergy analyses and an economic model according to the Total Revenue Requirement (TRR) method have been developed. Three optimized systems including a single-objective thermodynamic optimized, a single-objective economic optimized and a multi-objective optimized are obtained. In the case of multi-objective optimization, an example of decision-making process for selection of the final solution from the Pareto frontier has been presented. The exergetic and economic results obtained for three optimized systems have been compared and discussed. The results have shown that the multi-objective design more acceptably satisfies generalized engineering criteria than other two single-objective optimized designs.     

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.