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Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants
Author(s) -
Esmaeili-Faraj Seyyed Hamid,
Vaferi Behzad,
Bolhasani Akbar,
Karamian Soroush,
Hosseini Shahin,
Rashedi Reza
Publication year - 2021
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.202000442
Subject(s) - olefin fiber , tonnage , artificial neural network , process engineering , range (aeronautics) , computer science , volumetric flow rate , approximation error , engineering , algorithm , chemistry , artificial intelligence , aerospace engineering , geology , oceanography , organic chemistry , polymer , physics , quantum mechanics
A neuro‐based computing technique is used for simulation of olefin plants at industrial scale. Artificial neural networks are applied to estimate the flow rate of the main products of the olefin unit from available information in terms of flow rate of feed streams and operating condition of furnaces. The structure of the smart model is determined through a trial‐and‐error procedure taking the real plant information over four successive years. The proposed paradigm estimates the tonnage of the product streams by an absolute average relative deviation in the range of 0.9 % for methane to 3.14 % for propylene. Results confirmed that this smart simulation not only presents accurate predictions, but is easy to use, straightforward, and can be simply employed for optimization and control of the unit.