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

2009 ◽  
Vol 48 (6) ◽  
pp. 2927-2935 ◽  
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

Hybrid Particle Swarm Optimization and Ant Colony Optimization Technique for the Optimal Design of Shell and Tube Heat Exchangers

2015 ◽  
Vol 10 (2) ◽  
pp. 81-96 ◽  
Author(s):  
Sandip K. Lahiri ◽  
Nadeem Muhammed Khalfe
Keyword(s):  
Particle Swarm Optimization ◽  
Heat Exchanger ◽  
Heat Exchangers ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Shell And Tube ◽  
Operational Constraints

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.

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

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.

scholarly journals Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2000
Author(s):  
Jin-Hwan Lee ◽  
Woo-Jung Kim ◽  
Sang-Yong Jung
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Design ◽  
Cost Function ◽  
Particle Swarm ◽  
Torque Ripple ◽  
Rate Of Change ◽  
Swarm Optimization ◽  
Traction Motor ◽  
Target Performance ◽  
The Cost

This paper proposes a robust optimization algorithm customized for the optimal design of electric machines. The proposed algorithm, termed “robust explorative particle swarm optimization” (RePSO), is a hybrid algorithm that affords high accuracy and a high search speed when determining robust optimal solutions. To ensure the robustness of the determined optimal solution, RePSO employs the rate of change of the cost function. When this rate is high, the cost function appears as a steep curve, indicating low robustness; in contrast, when the rate is low, the cost function takes the form of a gradual curve, indicating high robustness. For verification, the performance of the proposed algorithm was compared with those of the conventional methods of robust particle swarm optimization and explorative particle swarm optimization with a Gaussian basis test function. The target performance of the traction motor for the optimal design was derived using a simulation of vehicle driving performance. Based on the simulation results, the target performance of the traction motor requires a maximum torque and power of 294 Nm and 88 kW, respectively. The base model, an 8-pole 72-slot permanent magnet synchronous machine, was designed considering the target performance. Accordingly, an optimal design was realized using the proposed algorithm. The cost function for this optimal design was selected such that the torque ripple, total harmonic distortion of back-electromotive force, and cogging torque were minimized. Finally, experiments were performed on the manufactured optimal model. The robustness and effectiveness of the proposed algorithm were validated by comparing the analytical and experimental results.

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