Pouria Ahmadi

Cost and Entropy Generation Minimization of a Cross-Flow Plate Fin Heat Exchanger Using Multi-Objective Genetic Algorithm

In the present work, a thermal modeling is conducted for optimal design of compact heat exchangers in order to minimize cost and entropy generation. In this regard, an e -NTU method is applied for estimation of the heat exchanger pressure drop, as well as effectiveness. Fin pitch, fin height, fin offset length, cold stream flow length, no-flow length, and hot stream flow length are considered as six decision variables. Fast and elitist nondominated sorting genetic algorithm (i.e., nondominated sorting genetic algorithm II) is applied to minimize the entropy generation units and the total annual cost (sum of initial investment and operating and maintenance costs) simultaneously. The results for Pareto-optimal front clearly reveal the conflict between two objective functions, the number of entropy generation units and the total annual cost. It reveals that any geometrical changes, which decrease the number of entropy generation units, lead to an increase in the total annual cost and vice versa. Moreover, for prediction of the optimal design of the plate fin heat exchanger, an equation,for the number of entropy generation units versus the total annual cost is derived for the Pareto curve. In addition, optimization of heat exchangers based on considering exergy destruction revealed that irreversibilities, such as pressure drop and high temperature difference between cold and hot streams, play a key issue in exergy destruction. Thus, more efficient heat exchanger leads to have a heat exchanger with higher total cost rate. Finally, the sensitivity analysis of change in the optimum number of entropy generation units and the total annual cost with change in the decision variables of the plate fin heat exchanger is also performed, and the results are reported.

AN EXERGY-BASED MULTI-OBJECTIVE OPTIMIZATION OF A HEAT RECOVERY STEAM GENERATOR (HRSG) IN A COMBINED CYCLE POWER PLANT (CCPP) USING EVOLUTIONARY ALGORITHM

In the present study, a heat recovery steam generator (HRSG) with a typical geometry and a number of pressure levels used at combined cycle power plants (CCPPs) is modeled. In order to validate the model results, they are compared with data obtained from the actual running power plant located near the Caspian Sea in Iran. The results show a good agreement between the model results and the experimental data. Upon a comprehensive exergy analysis conducted for this HRSG, the results show that an increase in the high and low drum pressures results in an increase in the HRSG exergy efficiency, while an increase in the pinch temperature leads to a decrease in the HRSG exergy efficiency. Also, a fast and elitist non-dominated sorting genetic algorithm (NSGA-II) with continuous and discrete variables is applied to obtain maximum exergy efficiency with minimum total annual cost per produced steam exergy as a two objective functions. The decision variables (or design parameters) are high and low drum pressures, steam mass flow rates, pinch point temperature differences, and the duct burner fuel consumption flow rate. The first objective function included capital or investment cost and operational cost and is minimized while satisfying a group of constraints, and HRSG exergy efficiency is maximized simultaneously. In addition, a regression analysis for curve fitting is conducted to correlate the data to determine the optimal points from the multi-objective optimization to predict the trend of each objective function. The results show that an increase in high pressure and low pressure drum pressure results in increasing HRSG exergy efficiency and also a smaller pinch temperature corresponding to a larger heat transfer surface area and more costly system, as well as higher exergy efficiency and lower operating cost.

Exergetic Optimization of Shell-and-Tube Heat Exchangers Using NSGA-II

In this article, a multi-objective exergy-based optimization through a genetic algorithm method is conducted to study and improve the performance of shell-and-tube type heat recovery heat exchangers, by considering two key parameters, such as exergy efficiency and cost. The total cost includes the capital investment for equipment (heat exchanger surface area) and operating cost (energy expenditures related to pumping). The design parameters of this study are chosen as tube arrangement, tube diameters, tube pitch ratio, tube length, tube number, baffle spacing ratio, and baffle cut ratio. In addition, for optimal design of a shell-and-tube heat exchanger, the method and Bell–Delaware procedure are followed to estimate its pressure drop and heat transfer coefficient. A fast and elitist nondominated sorting genetic algorithm (NSGA-II) with continuous and discrete variables is applied to obtain maximum exergy efficiency with minimum exergy destruction and minimum total cost as two objective functions. The results of optimal designs are a set of multiple optimum solutions, called “Pareto optimal solutions.” The results clearly reveal the conflict between two objective functions and also any geometrical changes that increase the exergy efficiency (decrease the exergy destruction) lead to an increase in the total cost and vice versa. In addition, optimization of the heat exchanger based on exergy analysis revealed that irreversibility like pressure drop and high temperature differences between the hot and cold stream play a key role in exergy destruction. Therefore, increasing the component efficiency of a shell-and-tube heat exchanger increases the cost of heat exchanger. Finally, the sensitivity analysis of change in optimum exergy efficiency, exergy destruction, and total cost with change in decision variables of the shell-and-tube heat exchanger is also performed.

Optimization of Energy Systems

Optimization of Energy Systems comprehensively describes the thermodynamic modelling, analysis and optimization of numerous types of energy systems in various applications. It provides a new understanding of the system and the process of defining proper objective functions for determination of the most suitable design parameters for achieving enhanced efficiency, cost effectiveness and sustainability.

Design and Optimization of an Integrated System to Recover Energy from a Gas Pressure Reduction Station

This chapter deals with thermodynamic modeling, parametric analysis, and optimization of an integrated system to recover energy from pressure reduction station in city gate station (CGS). This chapter aims to fully cover the thermodynamic modeling of an integrated system consisting of a turbo expander, an organic Rankine cycle (ORC) and a proton exchange membrane (PEM) electrolyzer to produce and store hydrogen. The pressure of natural gas in transmission pipeline in Iran gas system is high which sometimes go beyond 7 MPa. This pressure needs to be reduced near the cities pipeline pressure to 1.7 MPa. This pressure reduction results in ample potential to recover energy to generate electricity. In the proposed integrated system in this chapter, a comprehensive parametric analysis including the effect of main parameters such as natural gas preheat temperature, the natural gas pressure inlet to turbo expander, the heater mass fuel flow rate, and high temperature of ORC on the system performance is investigated.

Modelling and exergoeconomic optimisation of a gas turbine with absorption chiller using evolutionary algorithm

In this paper, thermodynamic modelling of a gas turbine cycle with absorption chiller is performed. A thermoeconomic approach is utilised to find the most optimal values of design parameters for practical applications. Some design parameters considered are compressor pressure ratio, compressor isentropic efficiency, gas turbine isentropic efficiency, gas turbine inlet temperature, HRSG pressure, pinch point temperature, absorption generator temperature, absorption evaporator temperature and absorption condenser temperature. The objective function considered for the optimisation purpose is the summation of the fuel cost, purchase cost of each component and the total cost rate of exergy destruction. A sensitivity analysis of the changes in the design parameters with respect to the unit cost of fuel and gas turbine output power is also performed. The results show that increase in the unit cost of fuel tends to decrease in other term of objective function. Moreover, increase of the unit cost of fuel leads to elect the equipment in the way their cost of exergy destruction reduces.

Exergy Analysis of a Hybrid System Including a Solar Panel, Fuel Cell, and Absorption Chiller

This research paper mainly deals with a thermodynamic modeling and exergy analysis of a hybrid energy system consisting of a solar PV/T panel, PEM electrolysis, a polymer fuel cell (PEMFC), and single-effect Li-Br absorption chiller. Hydrogen is produced in this cycle using the electricity generated by PV/T panel, and it is stored in storage for later use at night when there the sun is not available. Hence, this cycle can be used at all hours of day and night. Solar radiation intensity per year is obtained by climate data of the capital city of Iran, Tehran. The effects of fuel cell current density on system efficiency, work and heat, voltage of system, and exergy losses in each component are investigated. Also, the exergy efficiency and the total cost rate for the objective function were used in an optimization problem based on the genetic algorithm. The results show that efficiency of energy and exergy of the cycle are 36% and 29%, respectively.

1.28 Energy Optimization

Optimization is treated as a significant tool in engineering for determining the best, or optimal, value for the decision variable(s) of a system. For various reasons, it is important to optimize processes so that a chosen quantity, known as the objective function (OF), is maximized or minimized. For example, the output, profit, productivity, product quality, etc., may be maximized, or the cost per item, investment, energy input, etc., may be minimized. In this chapter we try to highlight the importance of optimization in energy systems and review some of the recent published work on energy systems optimization. Various optimization methods are explained in details in the following parts of this chapter which provide useful information for the readers to determine which kind of optimization tool has better match for their problem. OFs, constraints and design parameters are discussed and the optimal selection of those presented. Since all the energy systems are composed of small components, we initially tried apply optimization for some of the major components in most of the energy systems and determine the optimal design parameters when desired outputs are considered as our OFs. In order to better understand the application of optimization, several case studies are considered from simple to complex and proper optimization techniques are applied and the optimal design parameters are listed. To one step further, a comprehensive sensitivity analysis is conducted to see how optimal design parameters will vary with respect some major conditions such as ambient temperature, ambient pressure, different fuel price and interest rates, etc. By the end of this chapter, readers have the ability to first model the system and form the main OFs depending on their criteria of selection and apply the proper optimization methods satisfying some constraints.

5.9 Optimization in Energy Management

In this chapter, we aim to highlight the importance of energy management optimization in energy systems and review some of the recent published work on energy management optimization. Various optimization methods are explained in detail in the following parts of this chapter, which provide useful information for the readers to determine which kind of optimization tool has better match for their problem. It can be seen that energy management optimization will result in energy and cost saving of the system, greenhouse gas emission reduction, and loss reduction of the system. In order to better understand the application of energy management optimization, several case studies are considered from simple to complex and proper optimization techniques are applied and the optimal design parameters are listed. By the end of this chapter, readers have the ability to first model the system and form the main objective functions depending on their criteria of selection and apply the proper energy management optimization methods satisfying some constraints.

1.8 Exergoeconomics

Exergy has been a hot topic during the last several decades and several researchers around the world have been widely using exergy for system analysis and performance assessment. There are also studies where exergy efficiency and exergy destruction rate are considered as objective functions for the optimal design of various energy systems. Although exergy is a potential tool that can provide good insight of the system, the importance of its connection with economics has been always highlighted. Exergoeconomics is a branch of engineering that appropriately combines, at the level of system components, thermodynamic evaluations based on an exergy analysis and economic principles, in order to provide information that is useful to the design and operation of a cost-effective system, but not obtainable by conventional energy and exergy analyses and economic analysis. In this chapter, the principles of exergoeconomics are discussed and several case studies are considered, and exergoeconomics is applied in order to calculate the cost for exergy destruction and exergoeconomic factors. The results provide better understanding of energy systems from an economic point of view.