CFD-DEM simulation of air-blown gasification of biomass in a bubbling fluidized bed gasifier: Effects of equivalence ratio and fluidization number

Hwang, In Sik; Sohn, Jung-Ho; Lee, Uen Do; Hwang, Jungho

doi:10.1016/j.energy.2020.119533

Cited by 40 publications

(8 citation statements)

References 25 publications

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“…Therefore, the chemical reactions are simplified into several global reactions. Moreover, reaction kinetics dominate the thermochemical behaviors in the reactor, which have various values according to different literature 39–41 . The discrepancies between numerical results and experimental data are inevitable but acceptable.…”

Section: Resultsmentioning

confidence: 99%

“…The investigated object is a lab-scale BFB reactor, consistent with the experimental test rig used by Udomsirichakorn et al 20 (Figure 4A). As shown in Figure 4B, the height and inner diameter of [39][40][41] The discrepancies between numerical results and experimental data are inevitable but acceptable. Hence, the present model can reasonably predict the thermochemical properties of both the conventional and AER gasification processes in the BFB reactor.…”

Section: Numerical Settingsmentioning

confidence: 96%

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Numerical study of biomass gasification combined with CO₂ absorption in a bubbling fluidized bed

et al. 2023

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Biomass gasification combined with CO 2 absorption-enhanced reforming (AER) in a bubbling fluidized bed (BFB) reactor is numerically studied via the multiphase particle-in-cell (MP-PIC) method featuring thermochemical and polydispersity submodels. A novel bubble detection algorithm is proposed for efficiently characterizing bubble morphology. The effects of several crucial operating parameters on the microscale particle behaviors, mesoscale bubble dynamics, and macroscale reactor performance of the AER gasification process are analyzed. Compared with conventional gasification, AER gasification reduces the CO 2 concentration by 33.58% but elevates the H 2 concentration by 32.13%. Higher operating temperature and steam-tobiomass (S/B) ratio promote H 2 generation but deteriorate gasification performance.A lower operating pressure improves gas-solid contact efficiency and gasification performance as the increased operating pressure inhibits bubble dynamics and particle kinematics. Compared with pure sand as bed material, the mixed bed material (CaO:sand = 1:1) significantly improves gasification performance by enhancing H 2 generation and CO 2 removal.

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Section: Resultsmentioning

confidence: 99%

Section: Numerical Settingsmentioning

confidence: 96%

Numerical study of biomass gasification combined with CO₂ absorption in a bubbling fluidized bed

et al. 2023

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“…Yang et al 63 revealed that the particle size distribution has a large impact on the bubble dynamic and morphology. Furthermore, the large bubbles have a high fraction of H 2 , CH 4 , and CO. Hwang et al 332 found that the fluidization number (superficial gas velocity/minimum fluidization velocity) of 3.56 and the equivalence ratio (actual air-fuel ratio/stoichiometric air-fuel ratio) of 0.3 are optimal for better yield of products. Gonzaĺez et al 333 concluded that a higher packing factor of 0.59 could increase the solid temperature and decrease the drying and pyrolysis rates in the fixed bed biomass gasifier.…”

Section: Drying In Thermochemical Converting Reactormentioning

confidence: 99%

Recent Advances in CFD Simulations of Multiphase Flow Processes with Phase Change

Zhu

Zhang

et al. 2023

Ind. Eng. Chem. Res.

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Phase change is a phenomenon that has been extensively utilized in chemical engineering, energy, electronics, vehicle, and space exploration. From both the science and engineering perspectives, gaining a profound understanding of the intricacies of multiphase flow processes accompanied by heat and mass transfer due to phase change is of fundamental importance. Indeed, the computational fluid dynamics (CFD) has been increasingly applied as an effective tool to give an in-depth and efficient analysis of the phase change processes, thereby enhancing our understanding and capacity to control and optimize the phase change effects in these processes. This paper seeks to provide a comprehensive review of the utilization of CFD methodologies in the simulations of phase change flows across various applications, including temperature management, drying, separation and purification, and others. First, the CFD models at various scales that are developed to elucidate the phase change processes are summarized. Second, the noteworthy advances in the recent CFD simulation work on various phase change processes are presented, respectively. Finally, the challenges and potential prospects to facilitate the application of the phase change flow are discussed. The current review aims to offer insightful guidance for the CFD modeling of phase change processes in numerous engineering application scenarios.

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“…In this zone the CO 2 and water vapor entrained by the gas flow have been reduced to CO and H 2 [27][28][29]. Equivalence Ratio ER The ratio of actual air fuel to the stoichiometric air fuel [30].…”

Section: Rztmentioning

confidence: 99%

Artificial Neural Networks and Gradient Boosted Machines Used for Regression to Evaluate Gasification Processes: A Review

Sedej¹,

Mbonimpa²,

Sleight³

et al. 2022

JEPT

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Waste-to-Energy technologies have the potential to dramatically improve both the natural and human environment. One type of waste-to-energy technology that has been successful is gasification. There are numerous types of gasification processes and in order to drive understanding and the optimization of these systems, traditional approaches like computational fluid dynamics software have been utilized to model these systems. The modern advent of machine learning models has allowed for accurate and computationally efficient predictions for gasification systems that are informed by numerous experimental and numerical solutions. Two types of machine learning models that have been widely used to solve for quantitative variables that are of predictive interest in gasification systems are gradient boosted machines and artificial neural networks. In this article, the reviewed literature used either gradient boosted machines or artificial neural networks to successfully model gasification systems. The review of such literature allows for a comparison in machine learning model architecture and resultant accuracy as well as an insight into what parameters are being used to inform the models and to make predictions.

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CFD-DEM simulation of air-blown gasification of biomass in a bubbling fluidized bed gasifier: Effects of equivalence ratio and fluidization number

Cited by 40 publications

References 25 publications

Numerical study of biomass gasification combined with CO₂ absorption in a bubbling fluidized bed

Numerical study of biomass gasification combined with CO₂ absorption in a bubbling fluidized bed

Recent Advances in CFD Simulations of Multiphase Flow Processes with Phase Change

Artificial Neural Networks and Gradient Boosted Machines Used for Regression to Evaluate Gasification Processes: A Review

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CFD-DEM simulation of air-blown gasification of biomass in a bubbling fluidized bed gasifier: Effects of equivalence ratio and fluidization number

Cited by 40 publications

References 25 publications

Numerical study of biomass gasification combined with CO2 absorption in a bubbling fluidized bed

Numerical study of biomass gasification combined with CO2 absorption in a bubbling fluidized bed

Recent Advances in CFD Simulations of Multiphase Flow Processes with Phase Change

Artificial Neural Networks and Gradient Boosted Machines Used for Regression to Evaluate Gasification Processes: A Review

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Product

Resources

About

Numerical study of biomass gasification combined with CO₂ absorption in a bubbling fluidized bed

Numerical study of biomass gasification combined with CO₂ absorption in a bubbling fluidized bed