2019
DOI: 10.1016/j.ultsonch.2019.104646
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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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Cited by 36 publications
(22 citation statements)
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“…While this review has only focused on just four techniques, many other methods are emerging, including the use of ultrasound, [121][122][123] and developing composite materials. [124][125] While newer techniques can provide excellent control over particle sizes and crystallization rates, less focus is being paid to tailoring silicon incorporation, which remains a key catalytic factor.…”
Section: Summary Of Synthetic Methodsmentioning
confidence: 99%
“…While this review has only focused on just four techniques, many other methods are emerging, including the use of ultrasound, [121][122][123] and developing composite materials. [124][125] While newer techniques can provide excellent control over particle sizes and crystallization rates, less focus is being paid to tailoring silicon incorporation, which remains a key catalytic factor.…”
Section: Summary Of Synthetic Methodsmentioning
confidence: 99%
“…The genetic programming models and the experimental results showed the agreement outputs. The optimal hierarchical SAPO-34 nanoparticles exhibited 94% methanol conversion and 77% light olefins selectivity after 9 h [55]. Interestingly, the SAPO-34 catalysts which were synthesized using morpholine with an inexpensive cationic surfactant Reprinted with permission from Reference [51].…”
Section: Methanol and Ethanol To Light Olefinsmentioning
confidence: 99%
“…The genetic programming models and the experimental results showed the agreement outputs. The optimal hierarchical SAPO-34 nanoparticles exhibited 94% methanol conversion and 77% light olefins selectivity after 9 h [55]. Interestingly, the SAPO-34 catalysts which were synthesized using morpholine with an inexpensive cationic surfactant (polydiallyldimethylammonium chloride, PDADMAC) showed a 12% enhanced selectivity for ethylene and propylene and a catalytic lifetime more than five times longer than those of conventional catalysts in MTO reactions.…”
Section: Methanol and Ethanol To Light Olefinsmentioning
confidence: 99%
“…In another research, a smart model based on an adaptive neural fuzzy inference system and artificial bee colony for prediction of a coking time factor was developed by Peng et al [20]. Multiple linear regression, ANNs, and genetic programming were utilized by Azarhoosh et al to evaluate conversion and selectivity of a nano-hierarchical, silico-alumino-phosphate-34 catalyst [21] under the effects of the main influential parameters such as ultrasonic irradiation, crystallization time, ultrasonic intensity, and the amount of organic template.…”
Section: Introductionmentioning
confidence: 99%