Thermodynamic optimization of cross flow plate-fin heat exchanger using a particle swarm optimization algorithm

This study explores the use of particle swarm optimization (PSO) algorithm for thermodynamic optimization of a cross flow plate-fin heat exchanger. Minimization of total number of entropy generation units for specific heat duty requirement under given space restrictions, minimization of total volume, and minimization of total annual cost are considered as objective functions and are treated individually. Based on the applications, heat exchanger length, fin frequency, numbers of fin layers, lance length of fin, fin height and fin thickness or different flow length of the heat exchanger are considered for optimization. Heat duty requirement constraint is included in the procedure. Two application examples are also presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using PSO are validated by comparing with those obtained by using genetic algorithm (GA). Parametric analysis is also carried out to demonstrate the effect of heat exchanger dimensions on the optimum solution. The effect of variation of PSO parameters on convergence and optimum value of the objective has also been presented.     

Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique

Shell-and-tube heat exchangers (STHEs) are the most common type of heat exchangers that find widespread use in numerous industrial applications. Cost minimization of these heat exchangers is a key objective for both designer and users. Heat exchanger design involves complex processes, including selection of geometrical parameters and operating parameters. The traditional design approach for shell-and-tube heat exchangers involves rating a large number of different exchanger geometries to identify those that satisfy a given heat duty and a set of geometric and operational constraints. However, this approach is time-consuming and does not assure an optimal solution. Hence the present study explores the use of a non-traditional optimization technique; called particle swarm optimization (PSO), for design optimization of shell-and-tube heat exchangers from economic view point. Minimization of total annual cost is considered as an objective function. Three design variables such as shell internal diameter, outer tube diameter and baffle spacing are considered for optimization. Two tube layouts viz. triangle and square are also considered for optimization. Four different case studies are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using PSO technique are compared with those obtained by using genetic algorithm (GA).     

考虑压降的换热网络优化设计

提出考虑压降的有分流换热网络合成的优化策略,在分级超结构的基础上,建立换热网络同步综合的MINLP数学模型,以年度总费用最小为优化目标,采用改进粒子群算法对模型求解,并通过两个算例验证了模型和方法的有效性。     

A practical algorithm for optimal operation management of distribution network including fuel cell power plants

Fuel cell power plants (FCPPs) have been taken into a great deal of consideration in recent years. The continuing growth of the power demand together with environmental constraints is increasing interest to use FCPPs in power system. Since FCPPs are usually connected to distribution network, the effect of FCPPs on distribution network is more than other sections of power system. One of the most important issues in distribution networks is optimal operation management (OOM) which can be affected by FCPPs. This paper proposes a new approach for optimal operation management of distribution networks including FCCPs. In the article, we consider the total electrical energy losses, the total electrical energy cost and the total emission as the objective functions which should be minimized. Whereas the optimal operation in distribution networks has a nonlinear mixed integer optimization problem, the optimal solution could be obtained through an evolutionary method. We use a new evolutionary algorithm based on Fuzzy Adaptive Particle Swarm Optimization (FAPSO) to solve the optimal operation problem and compare this method with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO) and Tabu Search (TS) over two distribution test feeders.     

Optimal design of plate-fin heat exchangers by a hybrid evolutionary algorithm

This study explores the first application of a Genetic Algorithm hybrid with Particle Swarm Optimization (GAHPSO) for design optimization of a plate-fin heat exchanger. A total number of seven design parameters are considered as the optimization variables and the constraints are handled by penalty function method. The effectiveness and accuracy of the proposed algorithm is demonstrated through an illustrative example. Comparing the results with the corresponding results using GA and PSO reveals that the GAHPSO can converge to optimum solution with higher accuracy.     

An imperialist competitive algorithm for optimal design of plate-fin heat exchangers

This paper presents the application of imperialist competitive algorithm (ICA) for optimization of a cross-flow plate fin heat exchanger. Minimization of total weight and total annual cost are considered as objectives. Seven design parameters, namely, heat exchanger length at hot and cold sides, fin height, fin frequency, fin thickness, fin-strip length and number of hot side layers are selected as optimization variables. A case study from literature is presented to show the effectiveness of the proposed algorithm. The numerical results reveal that ICA can find optimum configuration with higher accuracy in less computational time when compared to conventional genetic algorithm (GA).     

Thermodynamic and Economic Optimization of Plate Fin Heat Exchangers Using the Bees Algorithm

This study presents the successful application of the bees algorithm (BA) for optimal design of a cross‐flow plate fin heat exchanger by offset strip fins. The ε – NTU method is used to approximate the heat exchanger effectiveness and pressure drop. Two different objective functions including the minimization of total annual cost (sum of investment and operational costs) and total number of entropy generation units for certain heat duty required under given space constraints are considered as targets of optimization separately. Based on the applications, seven design parameters (heat exchanger length at hot and cold sides, fin height, fin frequency, fin thickness, fin‐strip length, and number of hot side layers) are selected as optimization variables. Two examples from the literature are presented to illustrate the efficiency and accuracy of the proposed algorithm. Results showed that the BA can detect an optimum configuration with higher speed (short computational time) and accuracy compared to the imperialist competitive algorithm (ICA) and the genetic algorithm (GA). © 2013 Wiley Periodicals, Inc. Heat Trans Asian Res, 43(5): 427–446, 2014; Published online 3 October 2013 in Wiley Online Library ( wileyonlinelibrary.com/journal/htj ). DOI 10.1002/htj.21087     

Optimizing heat exchanger networks with genetic algorithms for designing each heat exchanger including condensers

This paper presents a procedure for the design of the components of a heat exchanger network (HEN). The procedure first uses pinch analysis to maximize heat recovery for a given minimum temperature difference. Using a genetic algorithm (GA), each exchanger of the network is designed in order to minimize its total annual cost. Eleven design variables related to the exchanger geometry are considered. For exchanger involving hot or cold utilities, mass flow rate of the utility fluid is also considered as a design variable. Partial or complete condensation of hot utility fluid (i.e., water vapor) is allowed. Purchase cost and operational cost are considered in the optimization of each exchanger. Combining every exchanger minimized cost with the cost of hot utility and cold utility gives the total cost of the HEN for a particular ΔT_min. The minimum temperature difference yielding the more economical heat exchanger network is chosen as the optimal solution. Two test cases are studied, for which we show the minimized total cost as a function of the minimum temperature difference. A comparison is also made between the optimal solution with the cost of utilities and without it.     

基于温差均匀性因子协进化的双层算法同步优化换热网络

针对同时存在整型变量和连续型变量的换热网络综合问题,提出一种双层优化方法。外层以换热网络的温差均匀性因子作为网络结构性能的评价指标,通过蒙特卡洛随机抽样技术产生试探结构,采用整型优化算法逐步进化外层结构;内层以最小年综合费用作为优化指标,采用动态更新子群的改进粒子群算法优化连续变量。优化结果表明,温差均匀性因子可以有效评价换热网络的结构性能,从而指导结构的进化;改进的粒子群算法具有更强的全局搜索能力,相关算例均找到了更优的网络设计,应用于工业生产实际,可以有效节约成本。     

Thermoeconomic optimization of the geometry of an air conditioning precooling air reheater dehumidifier

Exergy method of optimization for the geometrical parameters of an air conditioning precooling air reheater with turbulent flow is developed in this paper. The method is based on exergy, economic analysis and optimization theory. As there are humid air streams involved in the heat transfer process, then there are irreversibilities or exergy destruction, which is due to pressure losses, temperature difference and specific humidity gradient. These principle components of total irreversibility are not independent and there is a trade‐off between them. Therefore, the purpose of this research paper is to study the effect of the geometry and the specific humidity of the two streams on the irreversibilities of a crossflow precooling air reheater dehumidifier. Also, the optimum balance between the three components of irreversibility is determined thereby giving the optimum solution for heat exchanger area. The total cost function is expressed on an annualized basis of the sum of the precooler capital cost and the running cost attributable to the precooler irreversibility. This total cost function is optimized in this paper according to the optimum heat transfer area and the total irreversibilities. Two optimum heat transfer areas were found for minimum total irreversibility and minimum total annual cost for a specific example. Finally, the relations between the typical operational variables such as heat transfer area, Reynolds numbers and the total annual cost for the precooler is developed and presented in graphs, which allow the calculation of the optimal heat transfer area, which gives the optimum irreversibility and minimum total annual cost. Copyright © 2005 John Wiley & Sons, Ltd.     

蚁群算法在换热网络优化中的应用

提出将蚁群算法应用于换热网络优化中,按照相等的能量份额将各股热流体分解成能量集合,热流体能量通过换热器在与冷流体换热的过程中得到分配,换热器单元面积得到相应地调整．能量分配过程中换热网络得到优化,从而使年综合费用减少的换热器面积不断积累,最终形成了一个最优的换热网络结构．通过具体算例验证了该方法的可行性和有效性,最终优化的结果证明该方法具有较强的全局搜索能力,能够应用于复杂换热网络的优化问题中．     

Investigating the Optimum Operational Strategy of Energy Storage Tank by Using Particle Swarm Algorithm

Optimal design of an energy storage tank system is presented in this study. Total annual cost is considered as the objective. To minimize the total annual cost, 24 design parameters including the operational strategy of the chiller in each hour during a sample day are selected. A Particle Swarm Optimization (PSO) algorithm is used for three different strategies including partial storage (PS), full storage (FS), and variable storage (VS), separately. In addition, this procedure is performed for both electrical and absorption chillers. There was a 25.21% and 13.80% improvement in total annual cost observed in the VS strategy compared with the PS and FS strategies, respectively, in the case of an electrical chiller. Furthermore, 23.47% and 8.01% improvement in total annual cost is observed in the VS strategy compared with the PS and FS strategies, respectively, in the case of an absorption chiller. Moreover, the electrical chiller was found to be more suitable in this study but no sensible difference is observed in the FS strategy. Finally the optimum results of the PSO algorithm is compared with the Genetic Algorithm (GA) and differences are reported.     

基于遗传算法的污水换热器的优化研究

以污水换热器的年总费用为目标函数,应用遗传算法对污水换热器进行了设计参数的优化计算.介绍了遗传算法工具箱的使用方法.该方法克服了传统优化算法的缺陷;优化结果更加符合工程实际.并指出了减小污水换热器年总费用的主要方向.     

Thermoeconomic analysis and multiobjective optimization of tubular heat exchanger network using different shapes of nanoparticles

In this study, the effects of different nanoparticle shapes including blades, cylinders, and bricks on the thermoeconomic optimization of tubular heat exchanger (HE) networks have been studied. Boehmite alumina was used as nanoparticle and water as the base fluid. Optimal results were proposed to improve both thermal effectiveness and total annual cost simultaneously by eight design parameters. In addition, optimization was performed for four nanofluid mass flow rates, 0.5, 1, 1.5, and 2 kg/s. The results show that the application of nanoparticles was accompanied by an improvement in the thermal economic parameters of the HE network, and this improvement rate was noticeable in higher values of effectiveness. However, the results indicate a nanofluid optimal mass flow rate of 1.5 kg/s, whereas there was no significant improvement by increasing mass flow rate to 2 kg/s. The optimum results in this paper showed that the best‐studied nanoparticle shape was blade, followed by brick and cylinder shapes. For example, 9.60%, 10.68%, and 11.20% improvements in the effectiveness were obtained in cylindrical, brick, and blade forms at a fixed cost of 3000 $/year, compared with BF. These improvements are 10.40%, 11.25%, and 11.52%, respectively, for the constant value of total annual cost = 4400 $/year.     

A new methodology for simultaneous optimization of capital and operating cost targets in heat exchanger network design

A new approach has been developed for establishing cost-optimal targets for a heat exchanger network (HEN). We formulate a mixed-integer-linear-programming (MILP) transportation problem that simultaneously optimizes for heat exchanger units, heat exchange area and loads on each utility. Heat exchange matches are placed between each hot stream in each temperature interval and all cold streams in all the subsequent lower temperature intervals. The objective function is linear and minimizes the total annual cost of the HEN subject to heat balance constraints. The temperature intervals are generated with a temperature shift just greater than zero. Each temperature interval is small enough that we can linearize the log-mean temperature difference on each match while maintaining the accuracy. Flowrate continuity constraints are written on utility streams so that we could account for non-point utilities. Furthermore, constraints on heat exchange area that occur in retrofit scenarios are incorporated into the model. The solution of the optimization model provides the heat loads on each utility, in case of multiple hot and cold utilities, the heat load on every match pair, the heat exchange area target and the number of units target such that the total annual cost is minimum. Using these targets, it is straightforward to construct heat exchanger networks.     

Application of a Genetic Algorithm for Thermal Design of Fin-and-Tube Heat Exchangers

Instead of the traditional trial-and-error process, a genetic algorithm (GA) is successfully applied to thermal design of fin-and-tube heat exchangers (FTHEs). The design method uses a GA to search and optimize structure sizes of FTHEs. The minimum total weight or total annual cost of FTHEs is taken as the objective function in the GA, respectively. Seven design parameters are varied for the optimization objectives. The implementation of the design method consists of a GA routine and a thermal design routine. In the GA routine, binary coding for tournament selection, uniform crossover, and one-point mutation is adopted. In the thermal design routine, thermal design of the FTHE is carried out according to the conditions of the structure sizes that the genetic algorithm generated, and the log-mean temperature difference method is used to determine the heat transfer area under the combined structure sizes for a given heat duty. Optimization shows that it is possible to achieve a great reduction in cost or weight, whenever such objectives have been chosen for minimization. The method is universal and may be used for thermal design and optimization of FTHEs under different specified duties.     

Multiobjective thermo‐economic and thermodynamics optimization of a plate–fin heat exchanger

In the present work, a multiobjective heat transfer search (MOHTS) algorithm is proposed and investigated for thermo‐economic and thermodynamic optimization of a plate–fin heat exchanger (PFHX). Heat exchanger effectiveness and total annual cost (TAC) are considered as thermo‐economic objective functions. Similarly, entropy generation rate and heat exchanger effectiveness are considered as thermodynamic objective functions. Six design variables including flow length of cold and hot streams, no flow length, fin height, fin pitch, and fin offset length are considered as decision variables. Effectiveness and accuracy of the proposed algorithm are evaluated by analyzing application examples of a PFHX. The results obtained using the proposed algorithm for thermo‐economic considerations are compared with the available results of NSGA‐II and TLBO in the literature. Results show that 3.56% to 10.29% reductions in TAC with 0.48% to 0.81% higher effectiveness are observed using the proposed approach compared to TLBO and NSGA‐II approaches. Additionally, the distribution of each design variable in its allowable range is also shown for thermo‐economic consideration to identify the level of conflict on objective functions. The sensitivity analyses of design variables on the objective functions value are also performed in detail.     

Economic optimization of shell and tube heat exchanger based on constructal theory

In this paper, the new approach of constructal theory has been employed to design shell and tube heat exchangers. Constructal theory is a new method for optimal design in engineering applications. The purpose of this paper is optimization of shell and tube heat exchangers by reduction of total cost of the exchanger using the constructal theory. The total cost of the heat exchanger is the sum of operational costs and capital costs. The overall heat transfer coefficient of the shell and tube heat exchanger is increased by the use of constructal theory. Therefore, the capital cost required for making the heat transfer surface is reduced. Moreover, the operational energy costs involving pumping in order to overcome frictional pressure loss are minimized in this method. Genetic algorithm is used to optimize the objective function which is a mathematical model for the cost of the shell and tube heat exchanger and is based on constructal theory. The results of this research represent more than 50% reduction in costs of the heat exchanger.     

Alternative Approach in Optimization of Plate Type Heat Exchangers

Plate type heat exchangers are widely used in process industries for gas/gas applications. Typically, these exchangers prove to be very efficient, especially as air preheaters in process furnaces or in equipment used in environmental protection (e.g., in units for thermal disposal of wastes).

For economic reasons, there is a need for a new optimization approach for plate type heat exchanger design and operation. The objective function is to achieve a minimal total annual cost of heat exchangers. Pressure drop and heat transfer are interdependent, and both of them strongly influence capital and operating costs of any heat transfer system. In designing a heat exchanger, it is necessary to determine the optimal dimensions of the apparatus with the given conditions of the equipment operation.

The goal is to obtain the most economically optimal design. An economic assessment allows a comparable estimation of various alternatives. The total annual cost consisting of fixed and variable costs of the heat exchanger was selected as a criterion that summarizes different factors of influence into one objective function. Major cost components of a heat exchange system are as follows: capital, operating and maintenance costs of air and flue gas fans, and capital and maintenance costs of the plate type heat exchanger.

The application of the developed optimization approach is demonstrated through practical industrial examples.     

Studies on the retrofit of heat exchanger network based on the hybrid genetic algorithm

The retrofit of heat exchanger networks (HENs) is an important branch of investigation for systematic heat integration. The studies on the economical and efficient retrofit techniques are very important for the high energy-consumption enterprises to save energy, protect environment and improve their market competitiveness. Because the retrofit of HEN is an optimization problem normally solved by a mixed integer nonlinear program (MINLP) which requires enormous solution space, it is very difficult to solve it with the traditional optimization methods. In this paper, by the analysis of an existing heat exchanger network, the hybrid genetic algorithm is applied to obtain the optimal retrofitted HEN with full utilization of the existing heat exchangers and structures. Two examples are taken to show the better effect of the retrofit method with the optimal new heat exchangers and re-piping cost and energy saving.