Performance analysis of ultrasound-assisted synthesized nano-hierarchical SAPO-34 catalyst in the methanol-to-lights-olefins process via artificial intelligence methods
Introduction
Ethylene, propylene, and butylene are used mainly as light olefins in the petrochemical industries [1], [2]. The methanol-to-light-olefins (MTO) process using a non-oil source as an alternative method has been considered due to the shortage of oil resources in the future and the industries growing demand to light olefins [3], [4]. Among the catalysts that used for the MTO process, SAPO-34 is the most proper catalyst due to small pores and moderate acidity [5], [6]. SAPO-34 is a silico-alumino-phosphate micro-pore molecular sieve with chabasite (CHA) structure that has a suitable catalytic performance in the MTO process. This catalyst has a very high selectivity for producing light olefins by almost complete methanol conversion, because of mild acidity, shape selectivity due to its small entry pores, and thermal stability [28]. One of the main problems of the MTO process is rapid deactivation of SAPO-34 catalyst, which could be because of the synthesis factors, hence, the catalyst performance could be optimized by adjusting the synthesis conditions [7], [8].
The ultrasonic-assisted synthesis of the SAPO-34 catalysts is one of the promising methods for improving its performance in the MTO process [9], [10], [11], which has also been adopted in the present study. In fact, the ultrasonic radiation to the initial solution during the preparation of the precursor gel (prior to the start of the hydrothermal process) leads to its uniformity and significantly improves the initial nucleation [12]. The results of previous studies were indicative high crystallinity, narrow distribution of particle size, and a significant increase in the external surface of the synthesized catalyst. The synthesized catalyst was along with a much higher selectivity and lifetime compared to the conventional catalysts [13], [14], [15].
Synthesize of SAPO-34 catalysts with a hierarchical structure is another approach for the increase of its performance [16], [17], [18], [19]. In this method, macro pores (pores with diameters (dp) > 50 nm) and Meso pores (2 nm < dp < 50 nm) are added to the structure of zeolite, which contains micro-pores (dp < 2 nm) [20], [21], [22]. Nowadays, this method has been examined in studies with a growing number in recent years [23], [24], [25]. In this method, in addition to the use of base micro-porous templates such as tetraethyl ammonium hydroxide (TEAOH), diethylamine (DEA), etc., other templates are also utilized to create meso-pores in the structure of the SAPO-34 microporous structure [26], [27], [28], [29].
Ren et al. [30] synthesized the hierarchical SAPO-34 base catalyst using a hydrothermal method and then placed it in nitric acid and oxalic acid solutions for 6 h. Finally, the catalysts were separated by filtration and calcinated after washing with water. The results indicated that the fabricated catalyst had a hierarchical structure and higher acidic sites in comparison with the base catalyst, hence increasing the lifetime of the catalyst in the MTO process (increasing from 210 to 390 min) and leading to a higher light olefins selectivity (from 92% to 94%) [30].
Mousavi et al. [31] used polyethylene glycol (PEG) as the agent to create the meso-pores in the hierarchical SAPO-34 catalyst. PEG leaving the structure in the calcination process in synthesized catalysts led to the creation of a hierarchical structure in the catalysts, which greatly enhanced the performance of the MTO process [31].
In the hydrothermal synthesis of the hierarchical SAPO-34 catalyst, Yang et al. [32] utilized phenylaminopropyltrimethoxysilane (PHAPTMS) as a silicon source and also as a template to create meso-pores as well as TEAOH as a microporous template. The use of PHAPTMS in the primary gel led to the creation of meso-pores inside the SAPO-34 microporous structure, and the hierarchical SAPO-34 catalysts were produced with an average particle size of 50 nm. In the MTO process, these catalysts displayed a high degree of stability and light olefins selectivity.
Rimaz et al. [33] applied carbon nanotube (CNT) as a hard template and DEA as a microporous template in the synthesis of hierarchical SAPO-34 using the dry gel technique. The results revealed an increase in the outer surface of the synthesized catalyst as well as the creation of a hierarchical structure, resulting in a significant improvement of the catalyst performance in the MTO process [33].
In this research work, an ultrasonic-assisted hydrothermal synthesis was applied to the synthesis of the nano-hierarchical SAPO-34 catalysts. These catalysts were synthesized with CNT as a template creating pores with meso and macro sizes and DEA as the microporous template [27]. Also, the effects of main parameters such as crystallization time, amount of templates, ultrasonic irradiation time and power intensity on the performance of synthesized nano-hierarchical SAPO-34 have been investigated and modeled in the MTO process.
For this purpose, first, a set of 40 catalysts was randomly synthesized and its performance was investigated in the MTO process. Common experimental design methods – i.e., full or partial factorial method, and response surface method (RSM) – need a large number of tests in case of the high number of input factors, which is not economically cost-effective. Therefore, in these methods the level numbers must be limited. In addition, due to the limited number of levels, the results of modeling are usually not along with high accuracy for experiments with parameter values other than level values. Therefore, in this study, a randomized test design method has been exploited in which each factor has an unspecified number of levels. The advantages of this method include reduction of the number of experiments required, lack of limitation in the experiments at specific levels of influential factors, and hence the higher accuracy of the presented models. Then, classical modeling methods such as MLR (multiple linear regression) and intelligent methods such as GP (genetic programming) and ANN (artificial neural networks) models were presented aiming to predict the effect of synthesis conditions on the performance properties of the synthesized catalysts in the MTO process such as light olefins selectivity and methanol conversion. In the following, among the above-mentioned models, a model with the highest accuracy and the highest prediction power was adopted to examine the influence of operating parameters and selection of optimum catalyst. Finally, the efficiency of the optimal predicted catalyst was compared with experimental results.
Section snippets
Synthesis and characterization of catalyst
For SAPO-34 synthesis, initial solution with the molar composition of 1Al2O3: 1P2O5: 0.6SiO2: xCNT: yDEA: 70H2O was used using aluminum isopropoxide (Merck, Germany, ≥98 wt%, 220418), tetraethylortosilicate (TEOS, Merck, Germany, ≥99 wt%, 8006580250) and phosphoric Acid (Merck, Germany, 85 wt%, 1005731000) as aluminum, silicon and phosphor source, respectively (x and y are varied in different samples). First, the required amount of distilled water, DEA (Merck, Germany, ≥99 wt%, 8030100500) and
Investigating the role the ultrasound-assisted hydrothermal synthesis
In this section, a comparison between structural characteristics and performance of two catalyst samples synthesized by ultrasound-assisted hydrothermal (SONO-SAPO-34) and hydrothermal (HT-SAPO-34) is investigated. In both catalysts, CNT/Al2O3, DEA/Al2O3 in initial gel and crystallization time are 1, 2 and 6 h, respectively. In SONO-SAPO-34 the, the obtained solution was placed under ultrasonic irradiation before crystallization step (US time = 20 min and US irradiation intensity = 243 W.m−2).
Conclusion
In the present study, the effect of main parameters on sonochemical synthesis of hierarchical SAPO-34 catalyst – i.e., crystallization time, ultrasonic irradiation time and power intensity and amount of organic templates (CNT and DEA) – on its performance in the MTO process was investigated and modeled using classical (MLR) and intelligent methods (ANN and GP). The results revealed that the models achieved using the GP method had the highest accuracy for training and test data. Therefore, these
Acknowledgment
The authors sincerely thank to the INIC (IRAN Nanotechnology Initiative Council, Iran) for all the support provided.
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