Development and optimization of a molecular-level model for methanol-to-olefins (MTO) reaction-regeneration process

doi:10.16085/j.issn.1000-6613.2025-0555

Abstract

Abstract:

Methanol-to-olefins (MTO) is a successful non-petroleum route for producing light olefins such as ethylene and propylene, and process conditions significantly influence product distribution. To accurately simulate and optimize the product distribution, a molecular-level model for the MTO reaction-regeneration process based on industrial-scale data was developed. The model included sub-models for MTO kinetics, reactor, coke-burning kinetics, and regenerator. The reaction pathway was determined by the hydrocarbon pool mechanism, and the reaction network was developed. Then, the kinetics model was coupled with a fluidized bed reactor model to describe the conversion behavior of various components in the reactor, and the regenerator model was constructed using the same approach. The developed reactor model and regenerator model were hence coupled to simulate the reaction-regeneration process. The model was validated using industrial production data through single-point calibration and long-term prediction. The results demonstrated that the model could accurately predict the distribution and yield of key products such as ethylene, propylene, and C₄. On this basis, a quantitative relationship between process conditions and key product distribution was obtained by sensitivity analysis and optimization algorithm. The optimization results demonstrated that the developed MTO model had good prediction and optimization capabilities. This work provided an accurate computational tool for process simulation and optimization of industrial MTO plants.

Key words: kinetic modeling, process simulation, fluidized-bed, algorithm, optimization

摘要：

甲醇制烯烃（MTO）是非石油路线生产乙烯、丙烯等低碳烯烃的重要途径，工艺条件对产物分布具有重要的影响。为实现MTO过程产物分布的精准计算与优化，本文对MTO反应-再生过程构建了分子级模型，依托实际工业装置实现产物分布的模拟与优化。该过程模型包括MTO反应动力学模型、反应器模型、再生器烧焦动力学模型以及再生器模型。基于烃池机理确定反应路径，编写反应网络与动力学模型，将动力学模型与流化床反应器模型耦合以描述各物质在反应器中的转化行为，再生器模型也采用相同思路进行构建。之后将反应器模型与再生器模型耦合，形成MTO反应-再生过程模型。采用工业生产的数据分别进行单点校正和长周期预测，结果表明：建立的模型可准确预测乙烯、丙烯、C₄等关键产品的分布及收率。在此基础上，通过灵敏度分析和优化算法获得了工艺条件与关键产物分布的定量关系，搭建的MTO模型具备良好的预测和优化能力，可为工业MTO装置的过程模拟与工艺优化提供精准计算工具。

关键词: 动力学模型, 过程模拟, 流化床, 算法, 优化

CLC Number:

TQ11

ZHAO Xiangyu, XU Dongyu, CHEN Zhengyu, XU Chunming, ZHANG Linzhou. Development and optimization of a molecular-level model for methanol-to-olefins (MTO) reaction-regeneration process[J]. Chemical Industry and Engineering Progress, 2025, 44(8): 4785-4794.

赵翔宇, 徐东宇, 陈政宇, 徐春明, 张霖宙. 甲醇制烯烃反应-再生过程分子级模型构建及优化[J]. 化工进展, 2025, 44(8): 4785-4794.

Figures/Tables 11

反应	指前因子	活化能/J·mol^-1
MTO反应
2CH₃OH $→$ C₂H₆O+H₂O	2.022×10⁵	1.066×10⁵
C₂H₆O $→$ CH₄+CO+H₂	6.389×10²	5.827×10⁴
C₂H₆O $→$ C₂H₄+H₂O	6.983×10¹	2.533×10⁴
C₂H₆O+CH₃OH $→$ C₃H₆+2H₂O	8.518	9.586×10³
C₂H₆O+2CH₃OH $→$ C₄H₈（1-丁烯）+3H₂O	8.162×10^-1	1.245×10⁴
C₂H₆O+2CH₃OH $→$ C₄H₈（异-丁烯）+3H₂O	6.650×10^-2	9.813×10³
C₂H₆O+2CH₃OH $→$ C₄H₈（2-丁烯）+3H₂O	2.481	1.275×10⁴
C₂H₆O+3CH₃OH $→$ C₅H₁₀+4H₂O	6.166	1.969×10⁴
C₂H₆O+4CH₃OH $→$ C₆+5H₂O	2.333×10^-5	1.959×10³
C₂H₄+H₂ $→$ C₂H₆	0.299	2.161×10⁴
C₃H₆+H₂ $→$ C₃H₈	1.940×10^-2	1.853×10⁴
C₄H₈+H₂ $→$ C₄H₁₀	0.255	2.026×10⁴
C₅H₁₀+H₂ $→$ C₅H₁₂	1.020×10^-3	2.401×10⁴
C₅H₁₀ $→$ C₂H₄+C₃H₆	7.603×10^-4	8.733×10³
2C₄H₈ $→$ C₂H₄+2C₃H₆	0.900	8.733×10³
2C₃H₆ $→$ 3C₂H₄	5.000×10^-2	8.733×10³
烧焦反应
CH _q +(0.5+0.25q)O₂ $→$ CO+0.5qH₂O	3.127×10⁶	1.332×10⁵
CH _q +(1+0.25q)O₂ $→$ CO₂+0.5qH₂O	3.600×10⁸	1.450×10⁵
2CO+O₂ $→$ 2CO₂	3.000	7.048×10⁴
2CO+O₂ $→$ 2CO₂	0.100	8.559×10⁴

反应	指前因子	活化能/J·mol^-1
MTO反应
2CH₃OH $→$ C₂H₆O+H₂O	2.022×10⁵	1.066×10⁵
C₂H₆O $→$ CH₄+CO+H₂	6.389×10²	5.827×10⁴
C₂H₆O $→$ C₂H₄+H₂O	6.983×10¹	2.533×10⁴
C₂H₆O+CH₃OH $→$ C₃H₆+2H₂O	8.518	9.586×10³
C₂H₆O+2CH₃OH $→$ C₄H₈（1-丁烯）+3H₂O	8.162×10^-1	1.245×10⁴
C₂H₆O+2CH₃OH $→$ C₄H₈（异-丁烯）+3H₂O	6.650×10^-2	9.813×10³
C₂H₆O+2CH₃OH $→$ C₄H₈（2-丁烯）+3H₂O	2.481	1.275×10⁴
C₂H₆O+3CH₃OH $→$ C₅H₁₀+4H₂O	6.166	1.969×10⁴
C₂H₆O+4CH₃OH $→$ C₆+5H₂O	2.333×10^-5	1.959×10³
C₂H₄+H₂ $→$ C₂H₆	0.299	2.161×10⁴
C₃H₆+H₂ $→$ C₃H₈	1.940×10^-2	1.853×10⁴
C₄H₈+H₂ $→$ C₄H₁₀	0.255	2.026×10⁴
C₅H₁₀+H₂ $→$ C₅H₁₂	1.020×10^-3	2.401×10⁴
C₅H₁₀ $→$ C₂H₄+C₃H₆	7.603×10^-4	8.733×10³
2C₄H₈ $→$ C₂H₄+2C₃H₆	0.900	8.733×10³
2C₃H₆ $→$ 3C₂H₄	5.000×10^-2	8.733×10³
烧焦反应
CH _q +(0.5+0.25q)O₂ $→$ CO+0.5qH₂O	3.127×10⁶	1.332×10⁵
CH _q +(1+0.25q)O₂ $→$ CO₂+0.5qH₂O	3.600×10⁸	1.450×10⁵
2CO+O₂ $→$ 2CO₂	3.000	7.048×10⁴
2CO+O₂ $→$ 2CO₂	0.100	8.559×10⁴

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