Ethane Steam Cracking Inferential Model Development using Artificial Neural Network

被引：0

作者：

Rosli, Mohd Nazarudin ^{[1
]}

Aziz, Norashid ^{[1
]}

机构：

[1] Univ Sains Malaysia, Sch Chem Engn, Engn Campus, Perai 14300, Pulau Pinang, Malaysia

来源：

MATERIALS TODAY-PROCEEDINGS | 2019年 / 19卷

关键词：

Ethane steam cracking; ASPEN Plus Dynamics; artificial neural network;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

During normal operation, the product yield distribution of ethane steam cracking is obtained using analyzers and lab sampling to measure the cracking severity. Since analyzers take time to produce result, primarily depending on analyzers and lab testing to determine main product yield will hinder immediate control action to the process. In this study, data generated from ASPEN Plus Dynamics is used as feed data to develop the inferential model for product yield prediction based on real time operating parameters. The finalized model is a 4x2 ANN model, with 10 hidden nodes. Mean Squared Error (MSE) analysis shows that the model can accurately predict ethane and ethylene yields of the process, with MSE of 2.78x10(-5) and 3.41x10(-6), respectively. On top of that, R-squared analysis shows goodness-of-fit of 99.4% and 98.4%, respectively. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页码：1451 / 1458

页数：8

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