Title: Optimisation of propylene conversion response by neuro-fuzzy approach

Authors: Aiyoub Fazli Shahgoli; Yousef Zandi; Arian Heirati; Masoud Khorami; Peyman Mehrabi; Dalibor Petkovic

Addresses: Department of Civil Engineering, Islamic Azad University, Tabriz Branch, Tabriz, Iran ' Department of Civil Engineering, Islamic Azad University, Tabriz Branch, Tabriz, Iran ' Department of Civil Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran; Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia ' Department of Civil Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran; Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia ' Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran ' Pedagogical Faculty in Vranje, University of Niš, Partizanska 14, 17500 Vranje, Serbia

Abstract: Propylene polymerisation is a nonlinear process with complex phenomena during the reaction. In propylene polymerisation, first principle models are not appropriate to give an accurate behaviour of the reaction due to the difficulty of complex molecular structure. The difficulty is to determine the value distribution of several parameters. In addition, the morphological properties have also contributed to the difficulties of the model formulation. Therefore, empirical or semi-empirical model could be as an alternative to the mathematical model. To estimate the output propylene conversion response, the current study has built a procedure that simulates the output response regarding to different temperature, pressure and hydrogen concentration percentage with adaptive network-based fuzzy inference system (ANFIS) method. The outcomes have shown the effectiveness of ANFIS in the optimisation of propylene conversion response.

Keywords: polymerisation; propylene conversion; response; neuro-fuzzy; ANFIS.

DOI: 10.1504/IJHM.2020.109918

International Journal of Hydromechatronics, 2020 Vol.3 No.3, pp.228 - 237

Received: 10 Sep 2019
Accepted: 23 Dec 2019
Published online: 29 Sep 2020 *

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