Optimal design approach for the plate-fin heat exchangers using neural networks cooperated with genetic algorithms

Peng, Hao; Ling, Xiang

doi:10.1016/j.applthermaleng.2007.03.032

Cited by 161 publications

(62 citation statements)

References 8 publications

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“…The total entropy generation was considered as the objective function. Peng and Ling [20] demonstrated the successful application of the genetic algorithm combined with back propagation neural networks for the optimal design of plate-fin heat exchangers. Several investigators used other intelligent algorithms like the biogeography-based optimization algorithm [21], adaptive simulated annealing algorithm [22], bees algorithm [23], cuckoo search algorithm [24] and the Jaya algorithm [25], to optimize the heat exchangers.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of an Air-to-Air Heat Exchanger with Cross-Corrugated Triangular Ducts by Using a Particle Swarm Optimization Algorithm

et al. 2017

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Air-to-air heat exchangers with cross-corrugated triangular ducts are widely used in various industrial fields to recover waste heat. The geometric parameters of the heat exchangers greatly affect the performance and total annual cost of these systems. In this study, the effectiveness-number of transfer units (ε-NTU) method was utilized to develop the thermal mathematical model, which was verified by comparing it with previous research. The configuration parameters of the heat exchanger were optimized in this study. The particle swarm optimization (PSO) algorithm was applied using both single and multi-objective algorithm. The colburn factor (j factor), friction factor (f factor), and comprehensive thermal hydraulic performance index (JF factor) were considered as objective functions to be optimized using a single objective and multi-objective algorithm. Then, the entropy generation rate and total annual cost were optimized by using a multi-objective PSO algorithm. In addition, to identify the influential geometric parameters, a global sensitivity analysis was performed. The sensitivity analysis showed that the apex angle θ, channel height H, and heat exchanger height L h influenced the performance and annual total cost of these systems.

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Section: Introductionmentioning

confidence: 99%

Optimal Design of an Air-to-Air Heat Exchanger with Cross-Corrugated Triangular Ducts by Using a Particle Swarm Optimization Algorithm

et al. 2017

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“…So far, very few industries really put these methods into practical applications. To the best of our knowledge, very few references concern about the optimization of thermal performance of energy systems using such a powerful knowledge-based technique [22]. To address this challenge, we recently used a high-throughput screening (HTS) method combined with a well-trained ANN model to screen 3.5 × 10 8 possible designs of new WGET-SWH settings, in good agreement with the subsequent experimental validations [23].…”

Section: Introductionmentioning

confidence: 52%

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Liu

et al. 2017

International Journal of Photoenergy

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Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a highperformance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

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“…Peng and Ling [17] presented used of ANNs (artificial neural networks) for the prediction of pressure drop & heat transfer characteristics in the plate fin heat exchangers. The estimated result indicates that ANN models can be used for providing satisfactory estimations of both Colburn factor (j) & friction factor (f) in PFHEs.…”

Section: Development Of Experimental Analysis and Results In Platmentioning

confidence: 99%

Review of Experimental Analysis and Comparision of Results With Correlations Developed for Colburn Factor (J) and Friction Factor (F) in Plate Fin Heat Exchangers

2017

IJAERD

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Optimal design approach for the plate-fin heat exchangers using neural networks cooperated with genetic algorithms

Cited by 161 publications

References 8 publications

Optimal Design of an Air-to-Air Heat Exchanger with Cross-Corrugated Triangular Ducts by Using a Particle Swarm Optimization Algorithm

Optimal Design of an Air-to-Air Heat Exchanger with Cross-Corrugated Triangular Ducts by Using a Particle Swarm Optimization Algorithm

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Review of Experimental Analysis and Comparision of Results With Correlations Developed for Colburn Factor (J) and Friction Factor (F) in Plate Fin Heat Exchangers

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