Chemical Property-Guided Neural Networks for Naphtha Composition
  Prediction
Chonghyo Joo Jeongdong Kim Hyungtae Cho Jaewon Lee Sungho Suh Junghwan Kim
The naphtha cracking process heavily relies on the composition of naphtha,
which is a complex blend of different hydrocarbons. Predicting the naphtha
composition accurately is crucial for efficiently controlling the cracking
process and achieving maximum performance. Traditional methods, such as gas
chromatography and true boiling curve, are not feasible due to the need for
pilot-plant-scale experiments or cost constraints. In this paper, we propose a
neural network framework that utilizes chemical property information to improve
the performance of naphtha composition prediction. Our proposed framework
comprises two parts: a Watson K factor estimation network and a naphtha
composition prediction network. Both networks share a feature extraction
network based on Convolutional Neural Network (CNN) architecture, while the
output layers use Multi-Layer Perceptron (MLP) based networks to generate two
different outputs - Watson K factor and naphtha composition. The naphtha
composition is expressed in percentages, and its sum should be 100%. To enhance
the naphtha composition prediction, we utilize a distillation simulator to
obtain the distillation curve from the naphtha composition, which is dependent
on its chemical properties. By designing a loss function between the estimated
and simulated Watson K factors, we improve the performance of both Watson K
estimation and naphtha composition prediction. The experimental results show
that our proposed framework can predict the naphtha composition accurately
while reflecting real naphtha chemical properties.