AUTHOR=Ren Yu , Liao Zuwei , Yang Yao , Sun Jingyuan , Jiang Binbo , Wang Jingdai , Yang Yongrong 

TITLE=Direct prediction of steam cracking products from naphtha bulk properties: Application of the two sub-networks ANN

JOURNAL=Frontiers in Chemical Engineering

VOLUME=Volume 4 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/chemical-engineering/articles/10.3389/fceng.2022.983035

DOI=10.3389/fceng.2022.983035

ISSN=2673-2718

ABSTRACT=Steam cracking of naphtha is an important process for the production of olefins. Applying artificial intelligence helps achieve high-frequency real-time optimization strategy and process control. This work employs an artificial neural network (ANN) model with two sub-networks to simulate the naphtha steam cracking process. In the first feedstock composition ANN, the detailed feedstock compositions are determined from the limited naphtha bulk properties. In the second reactor ANN, the cracking product yields are predicted from the feedstock compositions and operating conditions. The combination of these two sub-networks has the ability to accurately and rapidly predict the steam cracking products directly from limited naphtha bulk properties without being limited by naphtha compositions. With the special design of the first feedstock composition ANN, the prediction accuracy of cracking products is significantly improved. The mean absolute error of 11 cracking products is 0.53wt% for 472 test sets. The proposed ANN strategy provides the possibility to predict, optimize, and control naphtha steam cracking processes with variable feedstock compositions.