Numerical simulation of scale-up effects of methanol-to-olefins fluidized bed reactors

Lu, Bona; Zhāng, Jìngyuàn; Luo, Hao; Wang, Wei; Li, Hua; Ye, Mao; Liu, Zhongmin; Li, Jinghai

doi:10.1016/j.ces.2017.05.007

Cited by 64 publications

(23 citation statements)

References 36 publications

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“…The association of this activity distribution function with the yields of products is not trivial in reactions with complex networks, such as the MTO and DTO processes. A simplified solution was the assumption of concentration-independent expressions for deactivation kinetics. , Among the works that relate deactivation to the content of coke, several authors simulated the expected coke distribution function resulting from the RTD of the catalyst during the MTO process. , Nonetheless, most of the complex models for fluidized bed reactors neglect this issue by operating at full conversion and excess of catalyst. , …”

Section: Introductionmentioning

confidence: 99%

Reactor–Regenerator System for the Dimethyl Ether-to-Olefins Process over HZSM-5 Catalysts: Conceptual Development and Analysis of the Process Variables

Cordero-Lanzac

Aguayo

Bilbao

2020

Ind. Eng. Chem. Res.

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The dimethyl ether (DME)-to-olefins (DTO) process is an interesting alternative to the methanol-to-olefins process because of the easier production of DME, its higher reactivity, and the moderate deactivation exhibited by HZSM-5 catalysts. A design of the reactor−regenerator system has been developed for the prospective implementation of the DTO process using the kinetic models of two catalysts based on zeolites with different acidities (Si/Al ratios of 15 and 140). These kinetic models have been obtained from DTO experimental runs carried out in a packed bed reactor at 325−400 °C, up to 6.5 g h mol C−1 and 15 h on stream. The reactor−regenerator model considers the activity distribution function of the catalyst in both fluidized bed units by applying the population balance theory. The influence of process variables (space time, temperature, reactant dilution in water or N 2 , catalyst acidity, and mean residence time of the catalyst) on the activity distribution function, conversion, and selectivity to products, mainly olefins, has been studied.

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Section: Introductionmentioning

confidence: 99%

Reactor–Regenerator System for the Dimethyl Ether-to-Olefins Process over HZSM-5 Catalysts: Conceptual Development and Analysis of the Process Variables

Cordero-Lanzac

Aguayo

Bilbao

2020

Ind. Eng. Chem. Res.

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“…For the virtual experiments, even though it is attractive because both true key hydrodynamic parameters and the corresponding interelectrode capacitance can be known, the disadvantage is that the state-of-the-art CFD models cannot always predict all flow patterns encountered in a real fluidized bed. 58,59 In addition, the calculated interelectrode capacitance, even though incorporated with the effect of noise, still deviates from the measured data in experiments. In the future, if these problems can be well addressed, the challenging inverse problem of ECT can be truly avoided and key hydrodynamic parameters with higher accuracy can be directly derived from the normalized capacitance measurements by the trained machine learning model.…”

mentioning

confidence: 80%

“…Therefore, it may be difficult to construct a one‐to‐one relationship between the normalized capacitance measurements of ECT and the corresponding key hydrodynamic parameters obtained by other measurement techniques. For the virtual experiments, even though it is attractive because both true key hydrodynamic parameters and the corresponding interelectrode capacitance can be known, the disadvantage is that the state‐of‐the‐art CFD models cannot always predict all flow patterns encountered in a real fluidized bed . In addition, the calculated interelectrode capacitance, even though incorporated with the effect of noise, still deviates from the measured data in experiments.…”

Section: Power and Limitations Of The Machine Learning Approachmentioning

confidence: 99%

A machine learning approach for electrical capacitance tomography measurement of gas–solid fluidized beds

Guo

Yang

et al. 2019

AIChE Journal

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Electrical capacitance tomography has been widely used to obtain key hydrodynamic parameters of gas-solid fluidized beds, which is normally realized by first reconstructing images and then by analyzing these images. This indirect approach is time-consuming and hence difficult for on-line monitoring. Meanwhile, considering recurrence of similar flow patterns in fluidized beds, most of these calculations are repetitive and should be avoided. Here, we develop a machine learning approach to address these problems. First, superficial gas velocity linear-increasing strategy is used to perform highthroughput experiments to collect a large amount of training samples. These samples are used to train the map from normalized capacitance measurements to key parameters that obtained by an iterative image reconstruction algorithm off-line. The trained model can then be used for on-line monitoring. Preliminary tests revealed that the trained models show good prediction and generality for the estimation of the overall solid concentration and the equivalent bubble diameter.

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“…From the computational cost point of view, the number of catalyst particles rapidly increase in scaling‐up the gas‐solid reactors, including the CFBs. For example, in the development of the MTO reactors, the catalyst inventory in the pilot‐, demo‐, and commercial‐scale reactors are typically in the kilogram, hundred kilogram, and hundred ton ranges, respectively, corresponding to 1e10 , 1e12, and 1e14 real particles roughly . Although current simulation corresponds to the demo‐scale reactor according to the number of particles, the methods developed in this work could be readily extended to larger gas‐solid reactors if more computing resource is available because they are very suitable to large‐scale parallel computing.…”

Section: Conclusion and Prospectsmentioning

confidence: 99%

Virtual process engineering on a three‐dimensional circulating fluidized bed with multiscale parallel computation

Liu

et al. 2019

J Adv Manuf & Process

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The combination of (quasi-)real-time simulation on industrial processes and virtual reality technologies may lead to a new paradigm of research and development in chemical engineering, that is, virtual process engineering (VPE). However, as the main engines of VPE, accurate and efficient simulation methods are still in urgent demand. In this paper, with substantial improvements to a discrete particle method (DPM), namely, energy-minimization multiscale (EMMS)-DPM, such an engine was established for a typical multiphase system, the circulating fluid beds (CFBs).To improve the accuracy, the coupling of particle and fluid flow solvers was updated to be more sensitive to local flow structures, and different drag laws were used for different flow regimes. To speed up the computation, heterogeneous central processing unit (CPU)-graphics processing unit (GPU) computing is developed for solving the fluids and particles. A two-layer domain decomposition method is developed to improve the load balance for real applications with complex geometries. Thus, a computer virtual experiment on a three-dimensional (3D) full-loop CFB has been achieved by simulating 1.27 × 10 11 real particles with 1.27 × 10 8 coarse-grained particles at the speed of 1.5 × 10 7 particle updates per GPU per second on 135 NVIDA K80 GPUs. To our knowledge, this is the largest-scale and highest-performance DPM simulation of a 3D full-loop CFB in terms of the computational particles used. It is a strong indication that VPE can be realized for industrial systems in the near future.K E Y W O R D S discrete particle method, energy-minimization multiscale model, three-dimensional circulating fluidized bed, virtual process engineering

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Numerical simulation of scale-up effects of methanol-to-olefins fluidized bed reactors

Cited by 64 publications

References 36 publications

Reactor–Regenerator System for the Dimethyl Ether-to-Olefins Process over HZSM-5 Catalysts: Conceptual Development and Analysis of the Process Variables

Reactor–Regenerator System for the Dimethyl Ether-to-Olefins Process over HZSM-5 Catalysts: Conceptual Development and Analysis of the Process Variables

A machine learning approach for electrical capacitance tomography measurement of gas–solid fluidized beds

Virtual process engineering on a three‐dimensional circulating fluidized bed with multiscale parallel computation

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