Comparative study of the effectiveness of time series and arima modeling x RNA approach in a Multi Layer Perceptron (MLP) model in predicting fouled in petroleum pre-processing heat exchangers

Abstract

This work presents a comparison between two approaches based on Artificial Intelligence techniques to predict critical deposition in the battery of shell and tube heat exchangers, used in petroleum pre-processing. Deposition or incrustation, which occurs on the thermal exchange surfaces of heat exchangers, during operation, can reduce the efficiency of this equipment and generate an increase in the cost of refining for the petrochemical plant due to the modification of ideal operating parameters and changes. process variables, such as flow, pressure and temperature, input and output of heat exchangers, as well as thermodynamic parameters, such as fouling resistance coefficient and global heat transfer coefficient. The proposed methods for comparing results used the analysis and prediction of time series based mainly on integrated autoregressive moving average (ARIMA) and Deep Learning models and, on the other hand, an artificial neural network (ANN) model based on the Multi Layer architecture. Perceptron (MLP) with the Backpropagation error backpropagation algorithm. The data was collected in exchangers of a petroleum preheating battery in the years 2014 to 2021. The general objective of this work is to present the results of both approaches and compare, since they aim to predict the deposition in the heat exchanger of the shell and tube type, thus helping to minimize maintenance costs and increase the energy efficiency of the petrochemical plant, making operations safer and more efficient, bringing significant benefits to the petroleum industry.