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

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@Article{AZARHOOSH:2019:US,

• author = "Mohammad Javad Azarhoosh and Rouein Halladj and Sima Askari and Abbas Aghaeinejad-Meybodi",
• title = "Performance analysis of ultrasound-assisted synthesized nano-hierarchical {SAPO-34} catalyst in the methanol-to-lights-olefins process via artificial intelligence methods",
• journal = "Ultrasonics Sonochemistry",
• volume = "58",
• pages = "104646",
• year = "2019",
• ISSN = "1350-4177",
• DOI = "doi:10.1016/j.ultsonch.2019.104646",
• URL = "http://www.sciencedirect.com/science/article/pii/S1350417719305103",
• keywords = "genetic algorithms, genetic programming, Ultrasound-assisted synthesis, Nano-hierarchical SAPO-34, MTO process, Multi-linear regression, Artificial neural network",
• 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 77percent and methanol conversion of 94percent from the onset of the process after",

}

Genetic Programming entries for Mohammad Javad Azarhoosh Rouein Halladj Sima Askari Abbas Aghaeinejad-Meybodi

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