A Dynamic Kinetic Model for Methanol to Light Olefins Reactions over a Nanohierarchical SAPO-34 Catalyst: Catalyst Synthesis, Model Presentation, and Validation at the Bench Scale

Production of eco friendly DME fuel over sonochemically synthesized UiO66 catalyst

The ultrasound-assisted preparation of UiO-66 was carried out at T = 80-220 °C, and the catalytic performances were evaluated in methanol conversion. Also, physicochemical properties were assessed by XRD, SEM, PSD, FTIR, N2 adsorption-desorption, TG-DTG, and NH3-TPD analysis. The characterization proved that increasing the synthesis temperature positively affected the crystallinity, specific surface area, thermal stability, and acidity of the catalysts. Besides, the catalysts' performance was investigated in the methanol conversion reaction (T = 350-450 °C, P = 1 atm, and WHSV = 5 h-1), leading to the DME (Dimethyl Ether) production. Rising reaction temperature increased the methanol conversion and DME yield. The synthesized sample at 220 °C had the best properties and performance with conversion and yield of about 38% and 51%, respectively. The stability test for the UiO-66-220 (University of Oslo 66) catalyst was performed at 450 °C for 12 h, and the activity remained stable for about 5 h. Furthermore, the used catalyst was characterized via XRD and TG analysis.

Presenting a Four-Lump Dynamic Kinetic Model for Methanol to Light Olefins Process Over the Hierarchical SAPO-34 Catalyst Using Power Law Models

OBJECTIVES

A four-lump dynamic kinetic model on the hierarchical SAPO-34 catalyst in the methanol to light olefins (MTO) process has been presented using the power law models. Since decreased catalyst activity in the MTO process is common, for the applicability of the proposed model, the function of catalyst activity was computed as a function of the coke percentage deposited on the catalyst.

MATERIALS AND METHODS

The reactant and products were divided into four lumps, including methanol and dimethyl ether (DME), light olefins (ethylene and propylene), light paraffin (methane, ethane, and propane) and heavier hydrocarbons from C4. The one-dimensional ideal plug reactor was used for the simulation of the MTO reactor. The kinetic parameters and the catalyst activity function were predicted using the particle swarm optimization (PSO) algorithm.

RESULTS

The comparison of product distribution in the experimental model and the results of the kinetic model indicated the high accuracy of the presented model. The effect of operational parameters such as temperature and weight hourly space velocity (WHSV) on the mole percent of light olefins was investigated using the proposed kinetic model. The optimized value of temperature and WHSV to reach the maximum yield of light olefins was respectively 460 ˚C and 4.2 h^-1.

CONCLUSION

The passive kinetic coefficients were estimated in the reaction rate constant and catalyst activity function with the help of the PSO optimization algorithm. The mole fraction of different products and the reactant arising from modeling at the reactor outlet was compared with experimental results, which indicated the high accuracy of the presented kinetic model. The results also revealed that the selection of high and low temperatures and WHSV decreases the yield of light olefins and the lifetime of the catalyst.

CFD Modeling of Methanol to Light Olefins in a Sodalite Membrane Reactor using SAPO-34 Catalyst with In Situ Steam Removal

AIMS AND OBJECTIVE

In this work, the performance of a sodalite membrane reactor (MR) in the conversion of methanol to olefins (MTO process) was evaluated for ethylene and propylene production with in situ steam removal using 3-dimensional CFD (computational fluid dynamic) technique.

METHODS

Numerical simulation was performed using the commercial CFD package COMSOL Multiphysics 5.3. The finite element method was used to solve the governing equations in the 3- dimensional CFD model for the present work. In the sodalite MR model, a commercial SAPO-34 catalyst in the reaction zone was considered. The influence of key operation parameters, including pressure and temperature on methanol conversion, water recovery, and yields of ethylene, propylene, and water was studied to evaluate the performance of sodalite MR.

RESULTS

The local information of component concentration for methanol, ethylene, propylene, and water was obtained by the proposed CFD model. Literature data were applied to validate model results, and a good agreement was attained between the experimental data and predicted results using CFD model. Permeation flux through the sodalite membrane was increased by an increase of reaction temperature, which led to the enhancement of water stream recovered in the permeate side.

CONCLUSION

The CFD modeling results showed that the sodalite MR in the MTO process had higher performance in methanol conversion compared to the fixed-bed reactor (methanol conversion of 97% and 89% at 733 K for sodalite MR and fixed-bed reactor, respectively).

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.