Mathematical Programming Model for Heat-Exchanger Network Synthesis Including Detailed Heat-Exchanger Designs. 2. Network Synthesis

Mizutani, Fabio T.; Pessoa, Fernando; Queiroz, Eduardo M.; Hauan, Steinar; Grossmann, Ignacio E.

doi:10.1021/ie020965m

Cited by 63 publications

(71 citation statements)

References 17 publications

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“…Depending on the flow regime and the operating conditions, the heat transfer coefficient on tube side can be calculated from the following correlations [22,23].…”

Section: Shell and Tube Side Heat Transfer Coefficientmentioning

confidence: 99%

Gravitational search algorithm for economic optimization design of a shell and tube heat exchanger

Mohanty

2016

Applied Thermal Engineering

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“…Depending on the flow regime and the operating conditions, the heat transfer coefficient on tube side can be calculated from the following correlations [22,23].…”

Section: Shell and Tube Side Heat Transfer Coefficientmentioning

confidence: 99%

Gravitational search algorithm for economic optimization design of a shell and tube heat exchanger

Mohanty

2016

Applied Thermal Engineering

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“…Case 1: 1.44(MW) duty, kerosene-crude oil exchanger [19] Case 2: 0.46(MW) duty, distilled water-raw water exchanger [16] Case 3: 4.34(MW) duty, Process fluid-water exchanger [20].…”

Section: Case Studiesmentioning

confidence: 99%

“…The third case studies were analyzed by Ponce-Ortega et al [17] using GA approach and taken from literature [20].…”

Section: Case Studiesmentioning

confidence: 99%

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

Lahiri

Khalfe

2015

Chemical Product and Process Modeling

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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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“…These curves demonstrate in separate diagrams the impact on heat transfer area of variations in decision variables such as tube length, tube diameter and tube arrangement. Using Delaware's approach, Ponce et al [15] described shell side flow and used a genetic algorithm with a few decision variables to minimize their objective function (total cost), and observed a decrease in pressure drop and total cost compared with a similar analysis in another reference [16]. Munawar and Babu [17] used differential completion for optimizing a shell and tube heat exchanger.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Finned Shell and Tube Heat Exchanger Using a Multi-Objective Optimization Genetic Algorithm

2015

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Abstract:Heat transfer rate and cost significantly affect designs of shell and tube heat exchangers. From the viewpoint of engineering, an optimum design is obtained via maximum heat transfer rate and minimum cost. Here, an analysis of a radial, finned, shell and tube heat exchanger is carried out, considering nine design parameters: tube arrangement, tube diameter, tube pitch, tube length, number of tubes, fin height, fin thickness, baffle spacing ratio and number of fins per unit length of tube. The "Delaware modified" technique is used to determine heat transfer coefficients and the shell-side pressure drop. In this technique, the baffle cut is 20 percent and the baffle ratio limits range from 0.2 to 0.4. The optimization of the objective functions (maximum heat transfer rate and minimum total cost) is performed using a non-dominated sorting genetic algorithm (NSGA-II), and compared against a one-objective algorithm, to find the best solutions. The results are depicted as a set of solutions on a Pareto front, and show that the heat transfer rate ranges from 3517 to 7075 kW. Also, the minimum and maximum objective functions are specified, allowing the designer to select the best points among these solutions based on requirements. Additionally, variations of shell-side pressure drop with total cost are depicted, and indicate that the pressure drop ranges from 3.8 to 46.7 kPa. OPEN ACCESSSustainability 2015, 7 11680

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Mathematical Programming Model for Heat-Exchanger Network Synthesis Including Detailed Heat-Exchanger Designs. 2. Network Synthesis

Cited by 63 publications

References 17 publications

Gravitational search algorithm for economic optimization design of a shell and tube heat exchanger

Gravitational search algorithm for economic optimization design of a shell and tube heat exchanger

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

Optimization of a Finned Shell and Tube Heat Exchanger Using a Multi-Objective Optimization Genetic Algorithm

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