Determination of Boiling Range of Xylene Mixed in PX Device Using Artificial Neural Networks

TL;DR

This study develops artificial neural network models to accurately determine the boiling range of xylene mixed in PX devices, offering a safer and more precise alternative to traditional methods.

Contribution

The paper introduces two optimized ANN models for predicting xylene boiling points, improving accuracy and safety over conventional techniques.

Findings

MLFN-7 model achieves RMS error of 0.18 for initial boiling point

MLFN-4 model achieves RMS error of 0.75 for final boiling point

Models are robust and suitable for practical application

Abstract

Determination of boiling range of xylene mixed in PX device is currently a crucial topic in the practical applications because of the recent disputes of PX project in China. In our study, instead of determining the boiling range of xylene mixed by traditional approach in laboratory or industry, we successfully established two Artificial Neural Networks (ANNs) models to determine the initial boiling point and final boiling point respectively. Results show that the Multilayer Feedforward Neural Networks (MLFN) model with 7 nodes (MLFN-7) is the best model to determine the initial boiling point of xylene mixed, with the RMS error 0.18; while the MLFN model with 4 nodes (MLFN-4) is the best model to determine the final boiling point of xylene mixed, with the RMS error 0.75. The training and testing processes both indicate that the models we developed are robust and precise. Our research can effectively avoid the damage of the PX device to human body and environment.