Optimization in heat exchangers using artificial intelligence: A hybrid neural network approach for predicting deposit accumulation and equipment efficiency
Abstract
This article presents an approach based on artificial intelligence techniques to predict critical deposition in heat exchangers used in petroleum preprocessing. Deposition on the heat exchange surface during operation can reduce equipment efficiency and cause maintenance problems. The proposed method uses a recurrent neural network in a Multi Layer Perceptron (MLP) model. Data was collected from exchangers of a petroleum preheating battery with data from 2014 to 2021. The neural network was trained with the data to predict the occurrence of deposition. The results showed that the neural network is capable of accurately predicting the occurrence of deposition in heat exchangers. Predicting deposition in advance can help minimize maintenance costs and increase energy efficiency, making operations safer and more efficient. Thus, the proposed approach can bring significant benefits to the petroleum industry by allowing early prediction of critical deposition in heat exchangers.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2023 Adroaldo Santos Soares, Ana Laise do Nascimento dos Santos, Oberdan Rocha Pinheiro, Marcelo Albano Moret Simões Gonçalves, Fernando Luiz Pellegrini PessoaAuthor(s)
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Adroaldo Santos Soares
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Ana Laise do Nascimento dos Santos
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Oberdan Rocha Pinheiro
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Marcelo Albano Moret Simões Gonçalves
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Fernando Luiz Pellegrini Pessoa