On the Parametric Study of Lubricating Oil Production using an Artificial Neural Network (ANN) Approach
This is an incremental application of an existing ANN method to optimize lubricant extraction in the petroleum industry.
The study tackled predicting lubricating oil production flow rates from industrial plant data using an Artificial Neural Network (ANN), achieving successful modeling of input-output relationships.
In this study, an Artificial Neural Network (ANN) approach is utilized to perform a parametric study on the process of extraction of lubricants from heavy petroleum cuts. To train the model, we used field data collected from an industrial plant. Operational conditions of feed and solvent flow rate, Temperature of streams and mixing rate were considered as the input to the model, whereas the flow rate of the main product was considered as the output of the ANN model. A feed-forward Multi-Layer Perceptron Neural Network was successfully applied to capture the relationship between inputs and output parameters.