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Simulation of the L-valve in the circulating fluidized bed with a coarse-grained discrete particle method

Particuology 2024年第7期90卷 266-280页

作者： Ruixiang Tang Zheng Zou Ji Xu Junwu Wang Qingshan Zhu Hongzhong Li State Key Laboratory of Mesoscience and Engineering Institute of Process EngineeringChinese Academy of SciencesP.O.Box 353Beijing100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing100049China Innovation Academy for Green Manufacture Chinese Academy of SciencesBeijing100190China

Stable and controllable solid flow is essential in circulating fluidized bed (CFB) systems. The L-valve is a typical non-mechanical valve that can provide flexible solid feeding. The investigation of the solid circulation rate and the hydrodynamic characteristics of the L-valve is crucial to its design and operation. The gas-solid flow in the L-valve of a full-loop CFB is studied with the coarse-grained discrete particle method (EMMS-DPM). Good agreements on the solid circulation rate and the pressure drop through the L-valve are achieved between the simulated and experimental data. The solid circulation rate increases linearly with the aeration velocity until the stable particle circulation of the CFB is destroyed. The flow patterns in the horizontal section of L-valve are gas-solid slug flow above the stationary solid layer and the moving solid layer, respectively. The effects of L-valve geometric parameters on the solid flow characteristics are also investigated. The results indicate that reducing the diameter and length of the horizontal section of L-valve can improve the solid transport efficiency, especially at low aeration velocity. Besides, the solid conveying capacity and flow stability are improved when the sharp bend of L-valve is modified to be a gradual bend.

关键词： L-valve Circulating fluidized bed Full loop discrete particle method Gas-solid flow

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Quantifying growth and breakage of agglomerates in fluid-particle flow using discrete particle method

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Chinese Journal of Chemical Engineering 2018年第5期26卷 914-921页

作者： Lingfeng Zhou Junwu Wang Wei Ge Shiwen Liu Jianhua Chen Ji xu Limin Wang Feiguo Chen Ning Yang Rongtao Zhou Lin Zhang Qi Chang Philippe Ricoux Alvaro Fernandez State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Tianjin University Tianjin 300072 China TOTALSA - R&D Groupe Paris 92069 France Total Research & Technology Feluy Belgium Germany

The cohesive solids in liquid flows are featured by the dynamic growth and breakage of agglomerates, and the difficulties in the development, design and optimization of these systems are related to this significant *** this paper, discrete particle method is used to simulate a solid–liquid flow system including millions of cohesive particles, the growth rate and breakage rate of agglomerates are then systematically investigated. It was found that the most probable size of the agglomerates is determined by the balance of growth and breakage of the agglomerates the cross point of the lines of growth rate and breakage rate as a function of the particle numbers in an agglomerate, marks the most stable agglomerate size. The finding here provides a feasible way to quantify the dynamic behaviors of growth and breakage of agglomerates, and therefore offers the possibility of quantifying the effects of agglomerates on the hydrodynamics of fluid flows with cohesive particles.

关键词： Agglomerate Growth and breakage Quantification discrete particle method

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Gas–solid reactor optimization based on EMMS-DPM simulation and machine learning

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Particuology 2024年第6期89卷 131-143页

作者： Haolei Zhang Aiqi Zhu Ji Xu Wei Ge State Key Laboratory of Mesoscience and Engineering Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing 100049China Innovation Academy for Green Manufacture Chinese Academy of SciencesBeijing 100190China

Design,scaling-up,and optimization of industrial reactors mainly depend on step-by-step experiments and engineering experience,which is usually time-consuming,high cost,and high *** numerical simulation can reproduce high resolution details of hydrodynamics,thermal transfer,and reaction process in reactors,it is still challenging for industrial reactors due to huge computational *** this study,by combining the numerical simulation and artificial intelligence(AI)technology of machine learning(ML),a method is proposed to efficiently predict and optimize the performance of industrial reactors.A gas–solid fluidization reactor for the methanol to olefins process is taken as an example.1500 cases under different conditions are simulated by the coarse-grain discrete particle method based on the Energy-Minimization Multi-Scale model,and thus,the reactor performance data set is *** develop an efficient reactor performance prediction model influenced by multiple factors,the ML method is established including the ensemble learning strategy and automatic hyperparameter optimization technique,which has better performance than the methods based on the artificial neural ***,the operating conditions for highest yield of ethylene and propylene or lowest pressure drop are searched with the particle swarm optimization algorithm due to its strength to solve non-linear optimization *** show that decreasing the methanol inflow rate and increasing the catalyst inventory can maximize the yield,while decreasing methanol the inflow rate and reducing the catalyst inventory can minimize the pressure *** two objectives are thus conflicting,and the practical operations need to be compromised under different circumstance.

关键词： discrete particle method Artificial intelligence Machine learning particle swarm optimization Industrial reactor optimization

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3D coarse-grained DPM simulation of the MIP reaction-regeneration loop

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Particuology 2024年第6期89卷 57-66页

作者： Yanhao Ye Shuai Zhang Ji Xu Wei Ge State Key Laboratory of Mesoscience and Engineering(formerly State Key Laboratory of Multiphase Complex Systems) Institute of Process EngineeringChinese Academy of SciencesBeijing100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing100049China

The reactor-regenerator loop is the core facility of the maximizing iso-paraffin(MIP)*** the discrete particle method(DPM)simulation can provide detailed information at the particle scale,it has been unable to simulate such a complex loop system due to limitations of coarse-grained(CG)models,computing software,and *** this study,a newly proposed soft-shell CG-DPM model with a CG ratio of up to 800 is used to simulate a 3.5 Mt/a industrial-scale MIP reactor-regenerator *** solid fraction distribution obtained is found to agree well with in-situ *** properties including the distribution of solid fraction,gas and solid velocity,standard derivation of solid fraction with time,temporal distribution of the flow field,and particle residence time distribution are measured in the simulation,which are meaningful to better design and operate such systems in the future.

关键词： discrete particle method Coarse-grain Maximizing iso-paraffins Full-loop Reaction-regeneration system

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