Performance analysis of ultrasound-assisted synthesized nano-hierarchical SAPO-34 catalyst in the methanol-to-lights-olefins process via artificial intelligence methods

Mohammad Javad Azarhoosh; Rouein Halladj; Sima Askari; Abbas Aghaeinejad-Meybodi

doi:10.1016/j.ultsonch.2019.104646

Performance analysis of ultrasound-assisted synthesized nano-hierarchical SAPO-34 catalyst in the methanol-to-lights-olefins process via artificial intelligence methods

Ultrason Sonochem. 2019 Nov:58:104646. doi: 10.1016/j.ultsonch.2019.104646. Epub 2019 Jun 17.

Authors

Mohammad Javad Azarhoosh¹, Rouein Halladj², Sima Askari³, Abbas Aghaeinejad-Meybodi⁴

Affiliations

¹ Chemical Engineering Department, Faculty of Engineering, Urmia University, P.O. Box 57561-51818, Urmia, Iran.
² Faculty of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), P.O. Box 15875-4413, Tehran, Iran.
³ Department of Chemical Engineering, Science and Research Branch, Islamic Azad University, P.O. Box 14778-93855, Tehran, Iran.
⁴ Chemical Engineering Department, Faculty of Engineering, Urmia University, P.O. Box 57561-51818, Urmia, Iran. Electronic address: a.aghaeinejad@urmia.ac.ir.

PMID: 31450297
DOI: 10.1016/j.ultsonch.2019.104646

Abstract

The present study has focused on performance analysis of ultrasound-assisted synthesized nano-hierarchical silico-alumino-phosphate-34 (SAPO-34) catalyst during methanol-to-light-olefins (MTO) process. A classical method, i.e., multiple linear regression (MLR) and two intelligent methods, i.e., genetic programming (GP) and artificial neural networks (ANN) were used for modeling of the performance of nano-hierarchical SAPO-34 catalyst. We studied the influence of basic parameters for the sonochemical synthesis of nano-hierarchical SAPO-34 catalyst such as crystallization time, ultrasonic irradiation time, ultrasonic intensity, amount of organic template (diethylamine (DEA) and carbon nanotube (CNT)) on its performance (methanol conversion and light olefins selectivity) in MTO process. The results revealed that the models achieved using the GP method had the highest accuracy for training and test data. Therefore, GP models were used in the following to predict the effect of main parameters for the sonochemical synthesis of nano-hierarchical SAPO-34 catalyst. Finally, an optimal catalyst with the highest yield into light olefins was predicted using the genetic optimization algorithm. The genetic models were employed as an evaluation function in the genetic algorithm (GA). A good agreement between the outputs of GP models for the optimal catalyst and experimental results were obtained. The optimal ultrasound-assisted synthesized nano-hierarchical SAPO-34 was accompanied by light olefins selectivity of 77% and methanol conversion of 94% from the onset of the process after 9 h.

Keywords: Artificial neural network; Genetic algorithm; Genetic programming; MTO process; Multi-linear regression; Nano-hierarchical SAPO-34; Ultrasound-assisted synthesis.