Optimization of the Shell and Tube Heat Exchanger

International Journal of Advanced Engineering Research and Science, 2017

The literature has different implementations and results for the mono-objective and multiobjective optimization of the shell and tube heat exchanger (STHE), most of them using evolutionary computation. However, there is a gap to find the optimal solution of this problem through direct search methods (numerical optimization). So, this paper uses the Pattern Search algorithm of MATLAB toolbox applied to this case study.

International Journal of Heat and Mass Transfer, 2013

In this paper, a multiobjective optimization of the heat transfer area and pumping power of a shell-andtube heat exchanger is presented to provide the designer with multiple Pareto-optimal solutions which capture the trade-off between the two objectives. Nine decision variables were considered: tube layout pattern, number of tube passes, baffle spacing, baffle cut, tube-to-baffle diametrical clearance, shell-tobaffle diametrical clearance, tube length, tube outer diameter, and tube wall thickness. The optimization was performed using the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) available in the multiobjective genetic algorithm module of MATLAB Ò. In order to verify the improvements in design that the method offers, two case studies from the open literature are presented. The results show that for both case studies, better values of the two objective functions can be obtained than the ones previously published. In addition, NSGA-II provides a Pareto front with a wider range of optimal decision variables. Ranking the Pareto-optimal solutions using a simple cost function shows that the costs for optimal design are lower than those reported in the literature for both case studies. The algorithm was also used to determine the impact of using continuous values of the tube length, diameter and thickness rather than using discrete standard industrial values to obtain the optimal heat transfer area and pumping power. Results show that using continuous values of these three decision variables only leads to marginally improved performance compared to discrete values.

2015

The extensive use of heat exchangers in the industry makes its optimization be crucial for raising efficiency and energy conservation. In the context of cleaner production and energy sustainability of the industrial sector, energy efficiency is a cornerstone to reduce fuel consumption. In this way the performance of the heat exchanger is a key factor. This work aims to contribute to energy efficiency, for this purpose a multiobjective optimization of the thermal and hydraulic design of heat exchangers of shell and tubes is implemented. A meta-heuristic technic of genetic algorithm, using two fitness functions, number of entransy dissipation and total cost was programed. Finally it is obtained the Pareto front with multiple solutions, these solutions where adjusted to the operating conditions.

Energy Conversion and Management, 2015

This paper comprehensively investigates performance of evolutionary algorithms for design optimization of shell and tube heat exchangers (STHX). Genetic algorithm (GA), firefly algorithm (FA), and cuckoo search (CS) method are implemented for finding the optimal values for seven key design variables of the STHX model. -NTU method and Bell-Delaware procedure are used for thermal modeling of STHX and calculation of shell side heat transfer coefficient and pressure drop. The purpose of STHX optimization is to maximize its thermal efficiency. Obtained results for several simulation optimizations indicate that GA is unable to find permissible and optimal solutions in the majority of cases. In contrast, design variables found by FA and CS always lead to maximum STHX efficiency. Also computational requirements of CS method are significantly less than FA method. As per optimization results, maximum efficiency (83.8%) can be achieved using several design configurations. However, these designs are bearing different dollar costs. Also it is found that the behavior of the majority of decision variables remains consistent in different runs of the FA and CS optimization processes.

Chemical Engineering and Processing: Process Intensification, 2006

Cost minimization of shell-and-tube heat exchangers is a key objective. Traditional design approaches besides being time consuming, do not guarantee the reach of an economically optimal solution. So, in this research, a new shell and tube heat exchanger optimization design approach is developed based on imperialist competitive algorithm (ICA). The ICA algorithm has some good features in reaching to the global minimum in comparison to other evolutionary algorithms. In present study, ICA technique has been applied to minimize the total cost of the equipment including capital investment and the sum of discounted annual energy expenditures related to pumping of shell and tube heat exchanger by varying various design variables such as tube length, tube outer diameter, pitch size and baffle spacing. Based on proposed method, a full computer code was developed for optimal design of shell and tube heat exchangers and different test cases are solved by it to demonstrate the effectiveness and accuracy of the proposed algorithm. Finally the results are compared to those obtained by literature approaches. The obtained results indicate that the ICA algorithm can be successfully applied for optimal design of shell and tube heat exchangers with higher accuracy in less computational time.

Applied Thermal Engineering, 2007

In the computer-based optimization, many thousands of alternative shell and tube heat exchangers may be examined by varying the high number of exchanger parameters such as tube length, tube outer diameter, pitch size, layout angle, baffle space ratio, number of tube side passes. In the present study, a genetic based algorithm was developed, programmed, and applied to estimate the optimum values of discrete and continuous variables of the MINLP (mixed integer nonlinear programming) test problems. The results of the test problems show that the genetic based algorithm programmed can estimate the acceptable values of continuous variables and optimum values of integer variables. Finally the genetic based algorithm was extended to make parametric studies and to find optimum configuration of heat exchangers by minimizing the sum of the annual capital cost and exergetic cost of the shell and tube heat exchangers. The results of the example problems show that the proposed algorithm is applicable to find optimum and near optimum alternatives of the shell and tube heat exchanger configurations.

Applied Thermal Engineering, 2009

This paper presents an approach based on genetic algorithms for the optimal design of shell-and-tube heat exchangers. The approach uses the Bell-Delaware method for the description of the shell-side flow with no simplifications. The optimization procedure involves the selection of the major geometric parameters such as the number of tube-passes, standard internal and external tube diameters, tube layout and pitch, type of head, fluids allocation, number of sealing strips, inlet and outlet baffle spacing, and shellside and tube-side pressure drops. The methodology takes into account the geometric and operational constraints typically recommended by design codes. The examples analyzed show that genetic algorithms provide a valuable tool for the optimal design of heat exchangers.

International journal of electrical and computer engineering systems

A new approach to optimize the design of a shell and tube heat exchanger (STHX) is developed via a genetic algorithm (GA) to get the optimal configuration from a performance point of view. The objective is to develop and test a model for optimizing the early design stage of the STHX and solve the design problem quickly. GA is implemented to maximize heat transfer rate while minimizing pressure drop. GA is applied to oil cooler type OKG 33/244, and the results are compared with the original data of the STHX. The simulation outcomes reveal that the STHX's operating performance has been improved, indicating that GA can be successfully employed for the design optimization of STHX from a performance standpoint. A maximum increase in the effectiveness achieves 57% using GA, while the achieved minimum increase is 47%. Furthermore, the average effectiveness of the heat exchanger is 55%, and the number of transfer units (NTU) has improved from 0.475319 to 1.825664 by using GA.

International Journal of Engineering Research and Technology (IJERT), 2020

https://www.ijert.org/sequential-quadratic-programming-algorithm-based-optimization-of-shell-and-tube-type-heat-exchangers https://www.ijert.org/research/sequential-quadratic-programming-algorithm-based-optimization-of-shell-and-tube-type-heat-exchangers-IJERTCONV8IS10057.pdf Shell and Tube type heat exchangers are having special importance in boilers, oil coolers, condensers and pre-heaters. These are also widely used in process applications as well as the refrigeration and air conditioning industry. The robustness and medium weighted shape of Shell and Tube type heat exchangers make them well suited for high pressure operations. The basic configuration, the thermal analysis and design of such exchangers form an included part of the mechanical, thermal and chemical engineering scholars for their curriculum and research activity. Traditional design approaches using graph sheets are time consuming, these may not considered all the variables and constraints simultaneously. On the other hand some new evolutionary algorithms viz. Genetic Algorithm (GA), Particle swarm optimization(PSO),Imperialist competitive algorithm (ICA) are not simple to understand by every designer and are not easy to be implemented. Therefore, in present work, a new shell and tube heat exchanger optimization design approach is discussed based on sequential quadratic programming (SQP). The SQP algorithm has some good features in reaching to the global minimum in comparison to other evolutionary algorithms. In present study, SQP technique has been applied to minimize the total cost which includes capital investment and total discounted operating cost. The design variables considered in the present work are tube outer diameter, shell diameter and baffle spacing. A matlab code is developed based on SQP for optimal design of shell and tube heat exchangers. The different test cases are solved using code to demonstrate the effectiveness and accuracy of the proposed algorithm. The results using developed code are compared to those obtained from previous literatures. It is found that the SQP algorithm is simple and it can be successfully applied for optimal design of shell and tube heat exchangers with higher accuracy.

This paper presents a study about the design optimization of shell-and-tube heat exchangers. The formulated problem consists of the minimization of the thermal surface area for a certain service, involving discrete decision variables. Additional constraints represent geometrical features and velocity conditions which must be complied in order to reach a more realistic solution for the process task. The optimization algorithm is based on a search along the tube count table where the established constraints and the investigated design candidates are employed to eliminate nonoptimal alternatives, thus reducing the number of rating runs executed. The performance of the algorithm and its individual components are explored through two design examples. The obtained results illustrate the capacity of the proposed approach to direct the optimization towards more effective designs, considering important limitations usually ignored in the literature.