Comparison of Conventional and Two-Zone Fluidized Bed Reactors for Methanol to Olefins. Effect of Reaction Conditions and the Presence of Water in the Feed

Zapater, Diego; Lasobras, Javier; Soler, J.; Herguido, J.; Menéndez, M.

doi:10.1021/acs.iecr.2c00323

Cited by 12 publications

(3 citation statements)

References 58 publications

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“…At the beginning of the reaction, this catalyst has a yield of byproducts close to zero, which could suggest that the secondary mechanism may relate to a slower adsorption reaction in acid sites. We found a similar conclusion in previous works, ,, where the deactivated catalyst showed a high byproduct yield when the olefin yield was close to zero. This mechanism needs the combined effect of temperature and mild acidity in the catalyst, which acid peaks in TPD were missing in the fresh catalyst (Figure ).…”

Section: Resultssupporting

confidence: 92%

Effect of Thermal, Acid, and Alkaline Treatments over SAPO-34 and Its Agglomerated Catalysts: Property Modification and Methanol-to-Olefin Reaction Performance

Zapater,

Lasobras,

Zambrano

et al. 2024

Ind. Eng. Chem. Res.

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zeolite is one of the most well-studied methanolto-olefin catalysts, with applications from laboratory to commercial scale. Here, we have studied the impact on the properties and performance of different modifications of a commercial zeolite, including thermal, acid, and alkaline treatments, along with its agglomeration with bentonite and alumina required in the technical catalyst. We prepared three zeolites and agglomerated them, making a total of seven materials, along with our benchmark catalyst. These were characterized and tested in a packed bed reactor. We analyzed the conversion, yield, and deactivation (coke) based on the effective acid site density ρ AS * (a parameter correlating acid strength, density, and micropore volume). Thermal treatment increased the effective acid site density of the commercial zeolite by 60%, while only a 10% increase was found in the parent agglomerated catalyst. Acid etching increased the effective acid site density by 80%, while the basic treatment completely amorphized the framework of the zeolite. After agglomeration, the performance of the catalysts (by means of olefin production and deactivation) correlated with effective acid site density. The catalysts based on thermally and acid-treated zeolites performed best, while they had the lowest effective acid site density.

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Section: Resultssupporting

confidence: 92%

Effect of Thermal, Acid, and Alkaline Treatments over SAPO-34 and Its Agglomerated Catalysts: Property Modification and Methanol-to-Olefin Reaction Performance

Zapater,

Lasobras,

Zambrano

et al. 2024

Ind. Eng. Chem. Res.

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“…Moreover, it must be mentioned that the role of H 2 O, − and H 2 , − as mitigating agents of coke deposition in the conversion of methanol/DME over acid catalysts is well established. Presumably, the role of H 2 O in attenuating the condensation of coke intermediates, and of H 2 in facilitating their hydrogenation, will be similar for the coke deposited on the In 2 O 3 –ZrO 2 catalyst, thereby mitigating its deactivation.…”

Section: Resultsmentioning

confidence: 99%

Kinetic Model for the Direct Conversion of CO₂/CO into Light Olefins over an In₂O₃–ZrO₂/SAPO-34 Tandem Catalyst

Portillo,

Parra,

Aguayo

et al. 2024

ACS Sustainable Chem. Eng.

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An original kinetic model is proposed for the direct production of light olefins by hydrogenation of CO 2 /CO (CO x ) mixtures over an In 2 O 3 −ZrO 2 /SAPO-34 tandem catalyst, quantifying deactivation by coke. The reaction network comprises 12 individual reactions, and deactivation is quantified with expressions dependent on the concentration of methanol (as coke precursor) and H 2 O and H 2 (as agents attenuating coke formation). The experimental results were obtained in a fixed-bed reactor under the following conditions: In 2 O 3 −ZrO 2 /SAPO-34 mass ratio, 0/1−1/0; 350−425 °C; 20−50 bar; H 2 /CO x ratio, 1−3; CO 2 /CO x ratio, 0−1; space time, 0−10 g In2O3−ZrO2 h mol C −1 , 0−20 g SAPO-34 h mol C −1; time, up to 500 h; H 2 O and CH 3 OH in the feed, up to 5% vol. The utility of the model for further scale-up studies is demonstrated by its application in optimizing the process variables (temperature, pressure, and CO 2 /CO x ratio). The model predicts an olefin yield higher than 7% (selectivity above 60%), a CO x conversion of 12% and a CO 2 conversion of 16% at 415 °C and 50 bar, for a CO 2 /CO x = 0.5 in the feed. Additionally, an analysis of the effect of In 2 O 3 −ZrO 2 and SAPO-34 loading in the configuration of the tandem catalyst is conducted, yielding 17% olefins and complete conversion of CO 2 under full water removal conditions.

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“…Light olefins, e.g., ethylene, propylene, and butane, have received great attention as they are essential materials for the polymer industry. − With the gradual depletion of petroleum, there is an urgent need to use nonpetroleum as materials to replace traditional olefin production routes . Methanol to olefins (MTO) is an important process used to produce light olefins from coal. − The reaction kinetics model is crucial to describe the distribution of MTO reaction products. , It can meet the major demand for a cyber-physical systems (CPS) model base in the construction of an intelligent factory for the coal chemical industry in reactor simulation, design, scale-up, and optimization. Therefore, the formulation of an accurate and efficient MTO reaction kinetics model is a challenging issue.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Modeling of Methanol to Olefin Reaction Kinetics Based on the Artificial Neural Network

Wang,

Sun

et al. 2024

Ind. Eng. Chem. Res.

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Methanol to olefin (MTO) is an important coalbased process to ensure olefin yield. The current reaction kinetics model as a crucial tool of process regulation lacks real-time, accuracy, and simplicity in describing the catalyst deactivation. To fill in the gap, a reaction kinetics hybrid model of MTO was developed by a machine learning approach. Specifically, a catalyst deactivation model was first built by an artificial neural network (ANN) and then integrated into a lump-based reaction kinetics model over a fresh SAPO-34 catalyst. Reaction kinetics parameters were estimated by the genetic algorithm. A priori knowledge was extracted by analyzing the deactivation experimental data and used in the training of ANN to enhance its extrapolability. The developed model of MTO agreed well with the kinetics experimental data considering the catalyst deactivation. The model provided a satisfactory predictive performance. Results show that a higher reaction temperature, longer reaction space time, and shorter catalyst residence time are beneficial to the yield of ethylene and propylene. The hybrid model developed in this work is expected to apply to the process control of MTO. The proposed hybrid modeling approach could be used as theoretical guidance for other catalytic reaction kinetics modeling.

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Comparison of Conventional and Two-Zone Fluidized Bed Reactors for Methanol to Olefins. Effect of Reaction Conditions and the Presence of Water in the Feed

Cited by 12 publications

References 58 publications

Effect of Thermal, Acid, and Alkaline Treatments over SAPO-34 and Its Agglomerated Catalysts: Property Modification and Methanol-to-Olefin Reaction Performance

Effect of Thermal, Acid, and Alkaline Treatments over SAPO-34 and Its Agglomerated Catalysts: Property Modification and Methanol-to-Olefin Reaction Performance

Kinetic Model for the Direct Conversion of CO₂/CO into Light Olefins over an In₂O₃–ZrO₂/SAPO-34 Tandem Catalyst

Hybrid Modeling of Methanol to Olefin Reaction Kinetics Based on the Artificial Neural Network

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Comparison of Conventional and Two-Zone Fluidized Bed Reactors for Methanol to Olefins. Effect of Reaction Conditions and the Presence of Water in the Feed

Cited by 12 publications

References 58 publications

Effect of Thermal, Acid, and Alkaline Treatments over SAPO-34 and Its Agglomerated Catalysts: Property Modification and Methanol-to-Olefin Reaction Performance

Effect of Thermal, Acid, and Alkaline Treatments over SAPO-34 and Its Agglomerated Catalysts: Property Modification and Methanol-to-Olefin Reaction Performance

Kinetic Model for the Direct Conversion of CO2/CO into Light Olefins over an In2O3–ZrO2/SAPO-34 Tandem Catalyst

Hybrid Modeling of Methanol to Olefin Reaction Kinetics Based on the Artificial Neural Network

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Kinetic Model for the Direct Conversion of CO₂/CO into Light Olefins over an In₂O₃–ZrO₂/SAPO-34 Tandem Catalyst