Performance Enhanced SAPO-34 Catalyst for Methanol to Olefins: Template Synthesis Using a CO2-Based Polyurea

The effects of templates and seeds on the properties of nanosheet SAPO-34 molecular sieves and their catalytic performance in the MTO reaction

Nanoscale SAPO-34 molecular sieves were synthesized by adding different types of seed into hydrothermal synthesis systems with tetraethylammonium hydroxide (TEAOH) and triethylamine (TEA) & tetraethylammonium bromide (TEABr) as templates. The effects of different types of template and seed on the crystal structure, morphology, grain size and acidity of the molecular sieves were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), N2 isothermal adsorption–desorption and ammonia temperature-programmed desorption (NH3-TPD). The methanol-to-olefins (MTO) reaction performance of the synthesized samples was investigated in a fixed-bed reactor. The results showed that crystalline supernatant and seed soaking solution could be used as liquid seeds to assist in the synthesis of SAPO-34 molecular sieves with a lamellar structure. The yield of SAPO-34 synthesized by seed increased from 38.64 to 59.68%, and the methanol conversion rate was significantly improved as compared with that of SAPO-34 synthesized without seed. The nano-thickness of SAPO-34 synthesized with TEA&TEABr instead of TEAOH as template decreased from 100–150 to 40–50 nm, and the lifetime increased from 360 to 400 min with the original yield kept constant.

Propene Adsorption-Chemisorption Behaviors on H-SAPO-34 Zeolite Catalysts at Different Temperatures

Propene is an important synthetic industrial product predominantly formed by a methanol-to-olefins (MTO) catalytic process. Propene is known to form oligomers on zeolite catalysts, and paramters to separate it from mixtures and its diffusion properties are difficult to measure. Herein, we explored the adsorption–chemisorption behavior of propene by choosing SAPO-34 zeolites with three different degrees of acidity at various adsorption temperatures in an ultra-high-vacuum adsorption system. H-SAPO-34 zeolites were prepared by a hydrothermal method, and their structural, morphological, and acidic properties were investigated by XRD, SEM, EDX, and temperature-programmed desorption of ammonia (NH3-TPD) analysis techniques. The XRD analysis revealed the highly crystalline structure which posses cubic morphology as confirmed by SEM images. The analysis of adsorption of propene on SAPO-34 revealed that a chemical reaction (chemisorption) was observed between zeolite and propene at room temperature (RT) when the concentration of acidic sites was high (0.158 mmol/g). The reaction was negligible when the concentration of the acidic sites was low (0.1 mmol/g) at RT. However, the propene showed no reactivity with the highly acidic SAPO-34 at low temperatures, i.e., −56 °C (using octane + dry ice), −20 °C (using NaCl + ice), and 0 °C (using ice + water). In general, low-temperature conditions were found to be helpful in inhibiting the chemisorption of propene on the highly acidic H-SAPO-34 catalysts, which can facilitate propene separation and allow for reliable monitoring of kinetic parameters.

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

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.