Correlations between tar content and permanent gases as well as reactor temperature in a lab-scale fluidized bed biomass gasifier applying different feedstock and operating conditions

“…As a result, the problems of low carbon conversion and high tar yield occur inevitably. 14,15 Increasing the IDT of biomass ash can carry out biomass FB gasification at high temperatures (900-1050 C), thereby achieving the purpose of high carbon conversion, low tar yield, and inhibiting slagging problems. The essence of increasing the IDT of biomass ash is that K-and Na-containing minerals with low MP react with external substances to convert into Kand Na-containing minerals with high MP.…”

Section: Introductionmentioning

confidence: 99%

“…To avoid slagging problems on the radiation heating surface during biomass FB gasification, low gasification temperature has to be chosen. As a result, the problems of low carbon conversion and high tar yield occur inevitably 14,15 . Increasing the IDT of biomass ash can carry out biomass FB gasification at high temperatures (900–1050°C), thereby achieving the purpose of high carbon conversion, low tar yield, and inhibiting slagging problems.…”

Section: Introductionmentioning

confidence: 99%

Effects of coal gangue on the ash initial deformation temperature and gasification characteristic of biomass

Fan,

Li,

Guo

et al. 2024

Asia-Pacific J Chem Eng

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The effects of coal gangue (CG) on the ash initial deformation temperature (IDT) and CO2 gasification characteristic of biomass (chestnut shell [CS] and soybean straw [SS]) were investigated by ash fusion tester and thermogravimetric analyzer, respectively. The IDTs of CS and SS ashes were low because of the presence of abundant low melting point (MP) airchildite (K2Ca(CO3)2) and arcanite (K2SO4), and they were efficiently improved by the CG additions of 2%–10% mass ratios. The increases in the IDTs of CS and SS ashes were resulted from the conversions of fairchildite and arcanite into high MP K‐Al silicates (kalsilite [KAlSiO4], leucite [KAlSi2O6], etc.). The gasification reactivities of CS/CG mixtures stayed below that of CS, and they decreased with increasing CG mass ratios (4%–10%) and increased with increasing temperatures (900–1000°C). At 900°C, CG addition caused a decrease in the gasification reactivity of SS. When the temperature reached 950°C, the CG addition of 4% mass ratio increased the gasification reactivity of SS instead. The synergistic effect between biomass and CG during cogasification was more significant at low CG mass ratio and high temperature, and the synergistic effect between SS and CG was more intense than that between CS and CG.

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“…This is because achieving precise control over the FBG process to attain an optimal synthetic gas composition, flow rate, and temperature represents a formidable challenge with significant repercussions for process efficiency and sustainability [9]. For instance, suboptimal control of synthetic gas productivity, its composition, as well as its temperature can directly affect energy conversion, thereby diminishing the overall profitability of the gasification process [10]. Additionally, elevated levels of contaminants, like tar in synthetic gas, may induce corrosion and equipment fouling, and hinder downstream equipment efficiency [11,12].…”

Section: Introductionmentioning

confidence: 99%

Advancing Process Control in Fluidized Bed Biomass Gasification Using Model-Based Deep Reinforcement Learning

Faridi,

Tsotsas,

Kharaghani

2024

Processes

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This study presents a model-based deep reinforcement learning (MB-DRL) controller for the fluidized bed biomass gasification (FBG) process. The MB-DRL controller integrates a deep neural network (DNN) model and a reinforcement learning-based optimizer. The DNN model is trained with operational data from a pilot-scale FBG plant to approximate FBG process dynamics. The reinforcement learning-based optimizer employs a specially designed reward function, determining optimal control policies for FBG. Moreover, the controller includes an online learning component, ensuring periodic updates to the DNN model training. The performance of the controller is evaluated by testing its control accuracy for regulating synthetic gas composition, flow rate, and CO concentration in the FBG. The evaluation also includes a comparison with a model predictive controller. The results demonstrate the superior control performance of MB-DRL, surpassing MPC by over 15% in regulating synthetic gas composition and flow rate, with similar effectiveness observed in synthetic gas temperature control. Additionally, this study also includes systematic investigations into factors like DNN layer count and learning update intervals to provide insights for the practical implementation of the controller. The results, presenting a 50% reduction in control error with the addition of a single layer to the DNN model, highlight the significance of optimizing MB-DRL for effective implementation.

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Correlations between tar content and permanent gases as well as reactor temperature in a lab-scale fluidized bed biomass gasifier applying different feedstock and operating conditions

Cited by 19 publications

References 41 publications

Multi-scale modelling of fluidized bed biomass gasification using a 1D particle model coupled to CFD

Multi-scale modelling of fluidized bed biomass gasification using a 1D particle model coupled to CFD

Effects of coal gangue on the ash initial deformation temperature and gasification characteristic of biomass

Advancing Process Control in Fluidized Bed Biomass Gasification Using Model-Based Deep Reinforcement Learning

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