Metamodeling of the deposition process in oil pre-processing to optimise the cleaning of the heat exchanger network: A systematic review

Abstract

Identifying and analysing possible metamodelling techniques to optimise the performance of heat exchangers in oil pre-processing from the point of view of the deposition process is of great importance for evaluating the performance of heat exchangers in different operating and maintenance configurations in order to increase their energy efficiency, since during the operation of heat exchanger networks, deposition on the heat exchange surfaces is common, reducing their effectiveness. In this article, a systematic review was carried out to study the metamodelling techniques and optimisation tools used. The results of the study showed that there are some techniques used such as: Recurrent Neural Networks (RNN); Multi-Layer Perceptron (MPL); Long Short-Term Memory (LSTM); Gated Recurrent Unit (GRU); Recurrent Convolutional Neural Network (RCNN), and tools that will be covered in this study.

DOI:https://doi.org/10.56238/sevened2024.003-009