Design optimization of shell-and-tube heat exchangers using single objective and multiobjective particle swarm optimization

Abstract Owing to the wide utilization of shell and tube heat exchangers (STHEs) in industrial processes, their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which gradually change the design and geometric parameters until satisfying a given heat duty and set of geometric and operational constraints. Although well proven, this kind of approach is time-consuming and may not lead to cost-effective design. The present study explores the use of non-traditional optimization technique called hybrid particle swarm optimization (PSO) and ant colony optimization (ACO), for design optimization of STHEs from economic point of view. The PSO applies for global optimization and ant colony approach is employed to update positions of particles to attain rapidly the feasible solution space. ACO works as a local search, wherein ants apply pheromone-guided mechanism to update the positions found by the particles in the earlier stage. The optimization procedure involves the selection of the major geometric parameters such as tube diameters, tube length, baffle spacing, number of tube passes, tube layout, type of head, baffle cut, etc. and minimization of total annual cost is considered as design target. The methodology takes into account the geometric and operational constraints typically recommended by design codes. Three different case studies are presented to demonstrate the effectiveness and accuracy of proposed algorithm. The examples analyzed show that the hybrid PSO and ACO algorithm provides a valuable tool for optimal design of heat exchanger. The hybrid PSO and ACO approach is able to reduce the total cost of heat exchanger as compare to cost obtained by previously reported genetic algorithm (GA) approach. The result comparisons with particle swarm optimizer and other optimization algorithms (GA) demonstrate the effectiveness of the presented method.

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Optimal Design of Shell-and-Tube Heat Exchangers Using Particle Swarm Optimization

Industrial & Engineering Chemistry Research ◽

10.1021/ie800728n ◽

2009 ◽

Vol 48 (6) ◽

pp. 2927-2935 ◽

Cited By ~ 40

Author(s):

Mauro A. S. S. Ravagnani ◽

Aline P. Silva ◽

Evaristo C. Biscaia ◽

Jose A. Caballero

Keyword(s):

Particle Swarm Optimization ◽

Optimal Design ◽

Heat Exchangers ◽

Particle Swarm ◽

Swarm Optimization ◽

Shell And Tube

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DIFFERENTIAL EVOLUTION ALGORITHM APPLIED IN A NUMERICAL COST OPTIMIZATION DESIGN OF A SHELL AND-TUBE HEAT EXCHANGER

Revista de Engenharia Térmica ◽

10.5380/reterm.v16i1.62185 ◽

2017 ◽

Vol 16 (1) ◽

pp. 11

Author(s):

L. C. Martinez ◽

V. C. Mariani ◽

L. S. Coelho ◽

E. H. V. Segundo

Keyword(s):

Particle Swarm Optimization ◽

Heat Exchange ◽

Heat Exchangers ◽

Quantum Particle ◽

Particle Swarm ◽

Swarm Optimization ◽

Total Cost ◽

Tube Heat Exchanger ◽

Quantum Particle Swarm Optimization ◽

Shell And Tube

Shell-and-tube heat exchangers are the most common heat exchangers that can be found in several industrial applications. The reduction of the investment cost and the operation of this equipment it’s one of main industrial designers and entrepreneurs aim. With the intention of reducing total costs of a shell-and-tube heat exchangers, as proposed by Caputo et al. (2008), employed in this present study the optimization technique called Differential Evolution (DE), which basically consists in a calculation mechanism, supported on operators of “crossing” and “mutation” differential, through mathematical and heuristics arguments that indicate your adequacy for function optimization. This study is defined as a mono-objective optimization problem and the total cost of a shell-and-tube heat exchanger is the objective function. To this, it was taken as a design variable intern diameter tube, the outer diameter of the shell and the spacing between baffles or deflectors. The results reached in this work were compared with the same problem when used GA (Genetics Algorithms), PSO (Particle Swarm Optimization), QPSO (Quantum Particle Swarm Optimization) and QPSOZ (Quantum Particle Swarm Optimization by Zaslavskii). Regarding the literature, the capital investment in the heat exchange reduces corresponding in 15.2% and consequently the depreciation charge of the equipment decrease approximately 12.5%. In general, the total cost of the shell-and-tube heat exchange in analysis, presented a reduction of 15%, showing the potential of applied method in this study, the technique DE.

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Multiswarm Multiobjective Particle Swarm Optimization with Simulated Annealing for Extracting Multiple Tests

Scientific Programming ◽

10.1155/2020/7081653 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Toan Bui ◽

Tram Nguyen ◽

Huy M. Huynh ◽

Bay Vo ◽

Jerry Chun-Wei Lin ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Simulated Annealing ◽

Particle Swarm ◽

Test Duration ◽

Swarm Optimization ◽

Multiple Tests ◽

Multiple Choice Tests ◽

Multiobjective Particle Swarm Optimization ◽

Choice Tests ◽

Single Objective

Education is mandatory, and much research has been invested in this sector. An important aspect of education is how to evaluate the learners’ progress. Multiple-choice tests are widely used for this purpose. The tests for learners in the same exam should come in equal difficulties for fair judgment. Thus, this requirement leads to the problem of generating tests with equal difficulties, which is also known as the specific case of generating tests with a single objective. However, in practice, multiple requirements (objectives) are enforced while making tests. For example, teachers may require the generated tests to have the same difficulty and the same test duration. In this paper, we propose the use of Multiswarm Multiobjective Particle Swarm Optimization (MMPSO) for generating k tests with multiple objectives in a single run. Additionally, we also incorporate Simulated Annealing (SA) to improve the diversity of tests and the accuracy of solutions. The experimental results with various criteria show that our approaches are effective and efficient for the problem of generating multiple tests.

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

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2010.03.001 ◽

2010 ◽

Vol 30 (11-12) ◽

pp. 1417-1425 ◽

Cited By ~ 145

Author(s):

V.K. Patel ◽

R.V. Rao

Keyword(s):

Particle Swarm Optimization ◽

Heat Exchanger ◽

Design Optimization ◽

Optimization Technique ◽

Particle Swarm ◽

Swarm Optimization ◽

Tube Heat Exchanger ◽

Particle Swarm Optimization Technique ◽

Shell And Tube

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Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

Complex & Intelligent Systems ◽

10.1007/s40747-020-00263-z ◽

2021 ◽

Author(s):

Wei Li ◽

Xiang Meng ◽

Ying Huang ◽

Soroosh Mahmoodi

Keyword(s):

Particle Swarm Optimization ◽

Learning Strategies ◽

Learning Strategy ◽

Particle Swarm ◽

Population Diversity ◽

Selection Strategy ◽

Rapid Loss ◽

Solution Quality ◽

Swarm Optimization ◽

Multiobjective Particle Swarm Optimization

AbstractMultiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

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Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

Mathematical Problems in Engineering ◽

10.1155/2014/823659 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Ya-zhong Luo ◽

Li-ni Zhou

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Optimization Techniques ◽

Mission Design ◽

Trajectory Design ◽

Time Transfer ◽

Pareto Solution ◽

Swarm Optimization ◽

Multiobjective Particle Swarm Optimization ◽

Asteroid Mission

A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO) is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

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