Paper Info
Title
Modeling of Chemical Plant's Rectifying Towers Using Artificial Neural Networks
Abstract
An artificial neural network based modeling method of a chemical plant's rectifying towers is presented in this paper. There are many approaches on chemical plant modeling. Some of them use neural networks to model some part of chemical plants or processes. This paper also tries to model a component of chemical plants. Standard multilayer perceptron (MLP) and back-propagation (BP) learning algorithm are used in this study. Using actual data obtained from real operation of rectifying towers MLP is trained at first and then tested for real data not used for training. Experimental results for two O2 production increase cases, 3000nm3/h and 5000nm3/h, NN based modeling shows that the model mimics well actual rectifying towers. In the experiments, 22 inputs are selected as inputs and 5 outputs are selected as outs to model rectifying towers.
Year
DOI
Venue
2011
10.1109/CICSyN.2011.42
CICSyN
Keywords
Field
DocType
poles and towers,neural network,chemical plant,o2 production increase case,chemical industry,actual data,learning (artificial intelligence),multilayer perceptron,artificial neural network based modeling method,artificial neural networks,chemical plant modeling,backpropagation,multilayer perceptrons,rectifying towers,oxygen production,chemical plant rectifying tower,real operation,rectifying tower,artificial neural network,chemical processes,modeling,backpropagation learning algorithm,chemical engineering,towers mlp,chemicals,back propagation,production,learning artificial intelligence,atmospheric modeling
Computer science,Multilayer perceptron,Chemical plant,Artificial intelligence,Artificial neural network,Backpropagation,Machine learning
Conference
ISBN
Citations 
PageRank 
978-0-7695-4482-3
0
0.34
References 
Authors
2
5
Name
Order
Citations
PageRank
Jonghwa Kim162346.51
Suyeon Jeong210.73
Bok-Jin Oh300.34
Doo-Hyun Choi46512.25
Jinhee Lee58021.11