Thermal Design Optimization of Shell-and-Tube Heat Exchanger Liquid to Liquid to Minimize Cost using Combination Bell-Delaware Method and Genetic Algorithm

Setiawan, Reza; Hrdlička, František; Darmanto, Prihadi Setyo; Fahriani, Vera Pangni; Pertiwi, Suciani Rahma

doi:10.17977/um016v4i12020p014

Cited by 3 publications

(4 citation statements)

References 13 publications

(16 reference statements)

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“…Fouling resistances are calculated based on literature data for various fluid types and operating temperatures. The overall heat transfer coefficient is calculated using the formula [11]: (12) The minimum and maximum thermal capacity, C min and C max , respectively, are defined as below, (13) (14)…”

Section: Shell and Tube Heat Transfer Coefficientmentioning

confidence: 99%

“…Many standards for STHX aimed to help designers, engineers, and users work more efficiently. Many producers and consumers widely use tubular exchanger manufacture association (TEMA) standards, covering manufacturing tolerances, thermal relationships, performance data, installation, maintenance and operation, vibration standards, mechanical standards, and recommended good practices [12].…”

Section: Introductionmentioning

confidence: 99%

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Genetic algorithm for the design and optimization of a shell and tube heat exchanger from a performance point of view

Bakr

Ahmed

Haikal

et al. 2022

Int. j. electr. comput. eng. syst. (Online)

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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.

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Section: Shell and Tube Heat Transfer Coefficientmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic algorithm for the design and optimization of a shell and tube heat exchanger from a performance point of view

Bakr

Ahmed

Haikal

et al. 2022

Int. j. electr. comput. eng. syst. (Online)

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“…Studi optimasi dilakukan pada shell-and-tube heat exchanger fluida cair ke cair untuk mendapatkan harga termurah menggunakan kombinasi metode Bell-Delaware dan algoritma genetika. Hasil optimasi menghasilkan penurunan total harga sebesar 28,83 % pada studi kasus pertama dan 52,56 % pada studi kasus kedua jika dibandingkan dengan total harga dari desain asli [7].…”

Section: Pendahuluanunclassified

Optimasi Umur Pakai dan Efektivitas Boiler Akibat Korosi Menggunakan Algoritma Particle Swarm

Setiawan¹,

Fahriani²,

Riyadi³

2021

JSE

Self Cite

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One of the causes of boiler pipe breakage is corrosion. The prediction of the lifetime of the tube and superheater effectiveness on the boiler shall be calculated to determine and predict the failure time. The prediction of the lifetime of the tube is a prediction of the time of the failure of the superheater tube in the boiler so that the return can be performed. The effectiveness of the superheater in the boiler is a comparison of the heat transfer rate of the overheated tubes inside the boiler. The research method was research and development, a computational program using an optimization process. The variables used in the study were steam input temperature, metal tube temperature, steam outlet temperature, natural gas input temperature, natural gas mass flow rate, vapour mass flow rate, and natural gas outlet temperature. This study takes a case study of the superheater boiler from the company. The results of calculation of the life expectancy of the operating conditions shall be 11 years when the boiler data sheet is used and 14 years for using the actual boiler data. Superheater effectiveness in boilers with 56% is a data sheet on boilers and 56% of the actual data. Optimization of the particle swarm algorithm predicted a 29 year lifespan. The effectiveness of the superheater in the boiler is 86%.

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“…Optimization is a method to get the best results related to given parameters [1]. Optimization is one of the manufacturing processes used to ensure optimum results [2].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Melt and Coolant Temperature on Defects of Injection Molded Toothbrush Handle

Susetyo,

Andoko,

Pradana

2023

jtmt

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The toothbrush handle is an injection molded product that rejects up to 10%. One of the factors that cause defects is the injection molding process settings, namely melting and cooling temperature. The purpose of this optimization is to obtain the optimum value of melt and cooling temperature parameters on product quality (minimum defects) of toothbrush handles using RSM. The methods used include simulation using ANSYS to obtain mold temperature, Autodesk Moldflow to obtain product defects and quality prediction based on input parameters of melt temperature (190o , 200o , and 210o C) and coolant temperature (22o , 24o , and 26o C), and Minitab 19 for RSM optimization. The simulation results that cooling temperature and melt temperature that are too low and high result in high defect values (weld line and shrinkage) in the product, resulting in low quality prediction values. Based on the results of the optimized simulation, the best injection molding setting is at a melt temperature of 200oC and a cooling temperature of 24oC which obtains a toothbrush handle product quality response variable of 78.04% with a minimum weld line value of 0.0277o and a minimum shrinkage depth of 0.009 mm.

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Thermal Design Optimization of Shell-and-Tube Heat Exchanger Liquid to Liquid to Minimize Cost using Combination Bell-Delaware Method and Genetic Algorithm

Cited by 3 publications

References 13 publications

Genetic algorithm for the design and optimization of a shell and tube heat exchanger from a performance point of view

Genetic algorithm for the design and optimization of a shell and tube heat exchanger from a performance point of view

Optimasi Umur Pakai dan Efektivitas Boiler Akibat Korosi Menggunakan Algoritma Particle Swarm

Optimization of Melt and Coolant Temperature on Defects of Injection Molded Toothbrush Handle

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