Application of neural networks to predict the transient performance of a Run-Around Membrane Energy Exchanger for yearly non-stop operation

Akbari, Soheil; Simonson, Carey J.; Besant, Robert W.

doi:10.1016/j.ijheatmasstransfer.2012.04.033

Cited by 15 publications

(2 citation statements)

References 33 publications

(51 reference statements)

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“…Outros trabalhos utilizaram redes neurais artificiais para modelar trocadores de calor para tarefas não relacionadas à incrustação, como os desenvolvidos em [5], [6], [7] e [8].…”

Section: B Definição Do Modelounclassified

Predição da Incrustação em um Trocador de Calor Baseada em Redes Neurais Artificiais

Silva¹

2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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The tendency to fouling is a common problem that affects an oil refinery's processing units. Residues naturally present on the oil or chemical products added during its transport deposit and foul tubes and internal surfaces of equipments. A fouled heat exchanger loses its capacity to adequately heat the oil, needing to be shut down periodically for cleaning. Previous knowledge of the best period to shut down the exchanger may improve the energetic and production efficiency of the plant. In this work we develop a system to predict the fouling on a heat exchanger from a real refinery, based on data collected in a partnership with Petrobras. Recurrent Neural Networks are used to predict the heat exchanger's flow in future time. This variable is the main indicator of fouling, because its value decreases gradually as the deposits in the tubes reduce their diameter. The prediction could be used to help plan the maintenances, providing previous information about the fouling evolution.

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“…Outros trabalhos utilizaram redes neurais artificiais para modelar trocadores de calor para tarefas não relacionadas à incrustação, como os desenvolvidos em [5], [6], [7] e [8].…”

Section: B Definição Do Modelounclassified

Predição da Incrustação em um Trocador de Calor Baseada em Redes Neurais Artificiais

Silva¹

2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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“…Diaz et al [11][12][13] and Parcheco-Vega et al [14][15][16] did a lot of researches in simulating and controlling heat exchanger performance using ANNs. Akbari [17,18] used a Neural Network for heat transfer data analysis. Islamoglu et al [19,20] predicted heat transfer rate of a wire-on-tube heat exchanger and made heat transfer analysis for air flowing in corrugated channels.…”

Section: Introductionmentioning

confidence: 99%

Predicting thermal–hydraulic performances in compact heat exchangers by support vector regression

Peng

Ling

2015

International Journal of Heat and Mass Transfer

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Classification and Types of Energy Recovery Systems

Ahmad

Riffat

2020

Energy Recovery Technology for Building Applications

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Application of neural networks to predict the transient performance of a Run-Around Membrane Energy Exchanger for yearly non-stop operation

Cited by 15 publications

References 33 publications

Predição da Incrustação em um Trocador de Calor Baseada em Redes Neurais Artificiais

Predição da Incrustação em um Trocador de Calor Baseada em Redes Neurais Artificiais

Predicting thermal–hydraulic performances in compact heat exchangers by support vector regression

Classification and Types of Energy Recovery Systems

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