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Table of Contents

    Topic review

    Fluidized Bed Reactors

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    Submitted by: Yong Sun

    Definition

    Fluidized gasification reactors can be used to produce hydrogen. They are operated in three modes including bubbling, circulating, and dual beds, as depicted inC. In a bubbling fluidized bed (BFB), the fuel is introduced from the bottom or side of the bed. The bed starts bubbling when the velocity of gasification agent is beyond the minimum fluidization velocity.

    1. Introduction

    The United Nations (UN) has promoted climate neutrality to produce no net greenhouse gas (GHG) emissions for years, as GHG emission has been considered one of the major causes of global warming [1]. GHG emissions in the atmosphere from fossil fuels, generated either by power plants or automobiles, have also risen and become a tremendous threat to environmental sustainability [2][3]. In recent years, a series of efforts shas been made, including using renewable resources or clean energy such as hydrogen fuels to mitigate the situation, reducing carbon dioxide emissions and in realizing sustainable development [4][5][6][7]. However, the conventional generation techniques of hydrogen are adopted from fossil fuels, including steam methane reforming (SMR) and derivations from natural gas, also known as “gray hydrogen” [8]. On a related note, hydrogen production using renewable resources is called “blue hydrogen” or biohydrogen (such as by the means of electrolysis, nuclear, solar photovoltaic-PV, wind, hydro or geothermal technologies), which is regarded as more environmentally friendly [3][5][6][9][10][11][12][13]. The current hydrogen generation technologies from different feedstocks are summarized in Figure 1. Apparently, the balance of feedstock between deploying fossil fuel and renewable resources for hydrogen generation has become lopsided, and this trend will become more prominent in the foreseeable future.
    While a large number of techniques are available for hydrogen generation, the employment of those techniques faces great challenges when it comes to considering the more complex factors (e.g., cost-effectiveness, reliability and efficiency). For example, electrolysis is considered to be not cost-effective, and bioprocessing through dark fermentation using biomass as the feedstock is not efficient due to its intrinsic, slow biological processing feature [14]. Recently, biomass gasification by fluidized bed reactors (FBRs) has been found to significantly enhance the efficiency of hydrogen production, but its obvious drawbacks, such as complex reaction mechanisms and catalyst usage, somehow limit its application [15][16]. For fluidized bed operation, many operational parameters (such as the carbon content, residence time, lower heating values and particle size) play vital roles in determining the expected outcomes (e.g., conversions and yield) [17], and there are very few examples in the literature that try to systematically correlate these critical operational parameters with the corresponding performances. Therefore, this initiates our interest in using our developed artificial neural networks, coupled with a response surface methodology (ANNs-RSM) algorithm, to assess the statistical significance of the investigated operational parameters upon the performances of FBRs during hydrogen generation.
    Figure 1. Hydrogen production from different resources via different technical routes. Left: blue hydrogen. Right: gray hydrogen.

    2. Statistical Analysis of Parameter upon Output

    In this review, among the different operational parameters, we choose seven parameters (temperature, residence time, equivalent ratio, steam-to-biomass ratio, carbon content, lower heating value and particle size) due to availability in reported literatures. Taking the feedstock sources for an example, different sources of feedstock may own various calorific values, carbon content, or moisture content that can significantly affect the conversion rate to hydrogen. The results are summarized in Table 1 and Table 2 (Table 1 for different types of FBGs and Table 2 for general FBGs that the types were not specified in the literatures). Using the collected references as training data set via ANNs-RSM algorithm, the predictions were made against the actual reported values from references. The results are shown in Figure 2. Apart from some values possessing relative higher uncertainties over ±20%, the majority of calculated data fall into the reasonable range, indicating that our constructed network can generate reliable predictions.
    Figure 2. Analysis result—actual versus prediction from ANNs modeling, where color bar represents the uncertainties.
    Table 1. Operational parameters versus corresponding hydrogen generation, where - represents the value that is not available from the literature (in this work, for easiness of data handling, the voids were replaced by the average reported value).
    Bed Type Feedstock Feedstock Particle Size (µm) Carbon Content (wt.%) LHV (MJ/Nm3) T/°C Process Time/min ER SBR Yield (Nm3/kg) Yield H2 Content/vol% (CCE) % Reference
    Bubbling Torrefied and raw pine 468 13.80 - 800 45 0.28 - 80.56 15.13 - [18]
    Wood sawdust 1500 - - 850 300 - - 1.15 42.00 85.00 [19]
    Rice husk 7500 11.69 3.84 600 - 0.20 - 0.50 2.70 95.00 [20]
    Wood-PET pellets 6000 12.16 19.19 800 90 0.28 - - 8.10 98.60 [21]
    Rice husk - 36.00 9.30 800 60 0.30 - - 12.50 - [22]
    MSW - 8.46 14.40 900 - 0.25 1.00 - - - [23]
    Cocoa shells 461 21.70 - 900 60 0.23 1.20 1.49 49.10 50.00 [24]
    Rice husk and coal 1575 22.37 - 850 210 0.26 1.21 - 8.64 89.00 [25]
    Pine sawdust - 12.60 - 600 120 - 0.20 1.03 38.60 71.20 [26]
    - - - 14.30 800 42 0.30 - - 4.00 76.00 [27]
    Pine sawdust and brown coal 4000 13.20 - 900 - 0.20 0.50 - 50.60 84.20 [28]
    Torrefied woodchips 240 22.82 19.26 850 30 0.22 1.20 1.12 28.66 89.20 [29]
    Carbonaceous feedstock 15,000 11.50 20.53 785 30 0.21 - 2.10 7.10 84.10 [30]
    Rice husk - 14.99 - 850 - 0.30 0.80 - 11.00 76.00 [31]
    Cypress wood chips - 20.64 15.80 700 - 0.30 1.20 - 0.59 - [32]
    Torrefied woodchips - 20.18 3.00 800 30 0.24 - 1.77 14.31 78.00 [33]
    Poultry litter 525 22.82 19.26 850 90 - 1.40 1.41 43.00 87.52 [34]
    - 310 8.81 5.36 700 30 0.30 0.24 1.36 17.58 88.00 [35]
    Spruce slice 615 - 20.05 809 60 0.20 - - 9.69 50.00 [36]
    Miscanthus 300 14.99 4.25 850 - 0.35 0.50 - 12.30 - [37]
    Torrefied and raw pine 630 - 5.55 915 60 0.32 - - 10.80 91.00 [38]
    Circulating Torrefied wood residues and mixed wood 5000 24.65 11.70 850 180 0.22 1.26 1.60 53.00 82.40 [39]
    Wood residue and Tabas coal 175 18.20 - 850 55 0.40 - - 52.70 - [40]
    Methane and biomass - - - 1000 - 0.21 1.00 - 28.00 - [41]
    Sub-bituminous coal and sawdust 3675 35.93 22.39 800 - 0.29 - 2.11 12.63 84.00 [42]
    - 1890 - 3.96 800 - 0.41 0.60 - 4.00 - [43]
    Dual PP plastic pellets, wood chips and plant capsules 660 8.01 26.00 900 10.67 0.30 - 2.53 29.70 82.00 [44]
    Rice straw 1250 18.74 - 800 120 0.24 - 1.20 5.38 84.77 [45]
    PE plastic bags, sawdust and PP plastic particles 780 5.00 - 900 - 0.30 0.50 - 53.10 - [46]
    PE plastic bags, sawdust and PP plastic particles 780 5.00 - 700 35 0.30 0.60 - 39.38 - [47]
    Volatile, fixed carbon and ash - 17.16 9.90 800 - 0.19 1.56 1.72 32.34 91.50 [48]
    Pine sawdust 200 12.73 11.40 850 120 - 0.30 10.51 47.30 64.00 [49]
      Biomass briquette - 18.71 11.00 670 300 0.19 - 1.20 24.00 98.82 [50]
      PE plastic bags, wood chips and PP particles 660 - - 900 35 0.30 0.60 - 50.96 92.59 [51]
    Table 2. Operational parameters of general fluidized bed (types not specified in literatures) versus corresponding hydrogen generation, where - represents the value that is not available from the literature (in this work, for easiness of data handling, the voids were replaced by the average reported value).
    Catalyst Feedstock Feedstock Particle Size (µm) Carbon Content (wt.%) LHV (MJ/Nm3) T/°C Process Time/min ER SBR Yield (Nm3/kg) Yield H2 Content/vol% CCE % References
    ZSM-5 zeolite Beech-wood and poly - - - 854 90 0.30 0.63 - - 98.20 [52]
    - Palm kernel shell and sub-bituminous coal 160 40.00 21.13 800 1440 0.60 0.20 - 12.00 82.80 [53]
    NiO/modified dolomite Coffee husk - - - 900 - 0.15 1.50 1.75 27.00 - [54]
    - Carbonaceous feedstock 275 0.80 - 820 - 0.19 1.00 2.00 40.00 - [55]
    - Citrus peel 500 40.31 4.65 750 20 0.30 1.25 0.69 26.00 87.00 [56]
    Ni/CeO2/Al2CO3 Wood residue - 49.18 - 823 44 0.17 0.71 1.66 42.52 93.56 [57]
    - Straw 7500 17.15 14.96 850 60 0.16 - 0.90 17.00 75.00 [58]
    Commercial Ni-catalyst *1 Almond shells - 11.00 - 815 60 - 0.49 1.70 55.30 - [59]
    Ternary molten carbonates Forestry biomass waste - 3.89 - 750 60 - 1.00 - 55.00 - [60]
    - Pine sawdust and MSW 2000 18.82 - 850 - 0.21 - 13.40 9.80 - [61]
    High-alumina bauxite Straw 7500 17.50 9.35 726 60 0.16 - - 14.90 70.99 [62]
    Calcium (Ca) Rice husk and bamboo dust 670 - 5.05 800 30 0.35 0.41 1.72 - 98.00 [63]
    Commercial Zeolite *2 Empty fruit bunch 3000 8.60 - 973 30 - 2.00 - 75.00 - [64]
    Industrial sludge derived catalysts - 320 10.35 4.84 800 50 0.30 1.00 - 12.46 100.00 [65]
    SCG ash - 1400 20.00 12.20 900 30 - 0.53 - 6.00 - [66]
    Coal bottom ash Palm kernel shell 750 14.25 12.50 692 60 - 1.50 - 79.77 59.90 [67]
    Calcined dolomite - 5000 35.20 - 1000 50 0.14 1.00 - 49.10 60.80 [68]
    Company information: *1 Johnson Matthey. *2 Zeolyst, Malaysia Sdn. Bhd., Malaysia.
    The types of fluidized bed reactors and their corresponding reported hydrogen contents from Table 1 and Table 2 were summarized and plotted in Figure 3. Obviously, different types of fluidized bed reactors from different reported sources tend to yield different reported values of hydrogen contents. In Figure 3, the top three reported hydrogen contents were annotated. For example, the hydrogen content could reach nearly 80% when almond shell was fed into fluidized bed gasifier using commercial nickel as catalyst. The bubbling fluidized bed reactor also generated hydrogen content reaching around 70% when empty fruit bunch was used as feedstock.
    Figure 3. Types of fluidized bed versus hydrogen yield (vol-%), the circles with number labelled represent hydrogen yield (%) and the top three hydrogen yield case are displayed in blue and purple.

    3. Conclusions

    We compared the commonly used hydrogen production technologies including steam methane reforming, electrolysis, and biomass gasification. Among the technologies, biomass gasification using fluidized bed reactor was thoroughly reviewed, including the types and operating conditions. Biomass gasification can be considered as a promising alternative technology for hydrogen production owing to the renewable, abundant, carbon neutral, and cost-effective nature of the feedstock. Subsequently, biomass gasifiers including entrained flow gasifier, fixed bed and fluidized bed reactor (FBR) were compared. Due to the inherent advantage of enhanced mass and heat transfer, the FBR was identified as the most promising biomass gasification technique for hydrogen production. In addition, to quantitatively assess the pivotal operational parameters of FBR, seven key inputs and three outputs were extracted from the reported literatures as a training data set. These inputs are SBR, ER, temperature, PS of feedstock, residence time, LHV, and CC. The three outputs are HY, HC, and CCE. The results of the statistical analysis indicate that six binary parameters are statistically significant to the outputs. In terms of high HY, SBR, and ER, relatively low values were suggested for efficient reaction and economic considerations. A high HC was proposed based on a shorter reaction time within 180 min under 850 °C for biomass that contained high LHV and fine particle sizes. The optimal CCE values could be obtained within an ER range of 0.15 to 0.35, operating temperature of 700 to 850 °C, reaction time within 180 min, and with CC values beyond 8%, as inputs. This analysis may provide a revealing insight for users who wish to realize high working efficiency using biomass gasification technology for hydrogen production.

    The entry is from 10.3390/j4030022

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