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Hydrogen Production by Fluidized Bed Reactors: A Quantitative Perspective Using the Supervised Machine Learning Approach

Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
School of Computer Science, University of Nottingham Ningbo, Ningbo 315100, China
Department of Chemistry, University College London (UCL), 20 Gordon Street, London WC1H 0AJ, UK
National Engineering Laboratory of Cleaner Hydrometallurgical Production Technology, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
School of Engineering, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA 6027, Australia
Authors to whom correspondence should be addressed.
J 2021, 4(3), 266-287;
Submission received: 16 June 2021 / Revised: 29 June 2021 / Accepted: 2 July 2021 / Published: 7 July 2021
(This article belongs to the Special Issue Hydrogen Production by Fluidized Bed Reactor)


The current hydrogen generation technologies, especially biomass gasification using fluidized bed reactors (FBRs), were rigorously reviewed. There are involute operational parameters in a fluidized bed gasifier that determine the anticipated outcomes for hydrogen production purposes. However, limited reviews are present that link these parametric conditions with the corresponding performances based on experimental data collection. Using the constructed artificial neural networks (ANNs) as the supervised machine learning algorithm for data training, the operational parameters from 52 literature reports were utilized to perform both the qualitative and quantitative assessments of the performance, such as the hydrogen yield (HY), hydrogen content (HC) and carbon conversion efficiency (CCE). Seven types of operational parameters, including the steam-to-biomass ratio (SBR), equivalent ratio (ER), temperature, particle size of the feedstock, residence time, lower heating value (LHV) and carbon content (CC), were closely investigated. Six binary parameters have been identified to be statistically significant to the performance parameters (hydrogen yield (HY)), hydrogen content (HC) and carbon conversion efficiency (CCE) by analysis of variance (ANOVA). The optimal operational conditions derived from the machine leaning were recommended according to the needs of the outcomes. This review may provide helpful insights for researchers to comprehensively consider the operational conditions in order to achieve high hydrogen production using fluidized bed reactors during biomass gasification.

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. The quantitative assessment of reported references for hydrogen generation from different FBRs, to the best of our knowledge, has not been reported before.

2. Analysis Approach

2.1. Literature Collections and Scoping

In this review, the literature utilized was primarily collected through keyword searches in scientific data bases including Science Direct, Web of Science and Google Scholar. The keyword combinations used in the academic search tool were primarily set as “fluidized bed”, “hydrogen production” and “biomass gasification”. To keep up with the most recent research in this field, selected literature were restricted within the last five years, from 2016 to 2021, in five main subject areas (energy, chemical engineering, chemistry, material science and environmental sciences). As shown in Figure 2, besides 2018, the total number of publications in hydrogen generation from FBRs experienced a steady increase from 2016 to 2020. Among the subject areas, energy (En) related research accounts for up to 40% of the total publications each year, indicating great research potential and applications in the field. Chemical engineering (CE) contributes around 20% of the total publications, while other areas individually account for about 10%. The total number of publications in the last three years (2019–2021) also experience a steady increase, indicating an increased research interest in this field.

2.2. Methodology for Data Training and Predictions

Artificial neural network (ANN) is a well-developed soft computing technique. Inspired by the human neurological system, ANN is constructed in layers, and information is fed forward to these layers. The configuration of the networks adopts 10 × 10 nodes (two hidden layers) feed forward progression using Gaussian as the transformation function, which is often widely adopted as a supervised machine learning configuration setup [18]. In this work, 52 collected references with seven inputs (namely temperature, residence time, equivalent ratio, steam-to-biomass ratio, carbon content, lower heating value, and particle size) and three outputs (hydrogen yield, hydrogen content, and carbon conversion efficiency) were used as a training data set. The cross-out validation technique was deployed, and the mean square error (MSE) and mean absolute relative residuals (MARR) were computed as:
MSE % = 1 N s a m j = 1 N s a m ( r | i s a m r i c a l ) 2 × 100 %
MARR % = 1 N s a m j = 1 N e x p ( | r i s a m r i c a l | r i s a m ) × 100 %
where Nsam refers to the number of data set, and i s a m and r i c a l are actual and prediction number, respectively. The acceptable uncertainties are ±10%. The response surface methodology (RSM) was used for data matrix generation, and the corresponding outputs were produced from ANNs via data training. The detailed data handling and algorithm specifications can be found from our previously reported works [18,19,20]. After the completion of the supervised data learning, analysis of variation (ANOVA) using commercial Design Expert® Version 11 software package (Stat-Ease, Inc., Minneapolis, MN, USA) was deployed for statistical analysis.

3. Source of Hydrogen

3.1. Steam Methane Reforming (SMR)

Currently, approximately 95% of the world’s hydrogen is derived from fossil fuels through such processes as natural gas steam reforming, coal gasification, and petroleum refining. Chemical processing allows the transformation of hydrocarbon fuels to hydrogen under various conditions. The most common pathway to produce hydrogen in the industry is natural gas reforming, also known as steam methane reforming (SMR) [21,22,23]. This is an endothermic process that applies high-temperature steam to convert methane into carbon dioxide and hydrogen. Methane enters the reactor as feedstock, mixing and reacting with water to form carbon monoxide and hydrogen in the temperature and pressure range of 700 to 1000 °C and 3–25 bar, respectively, in the presence of a catalyst [24,25]. Meanwhile, the formed carbon monoxide is shifted with steam to produce carbon dioxide and extra hydrogen through a process known as the water–gas shift (WGS) reaction.
SMR: CH4 + H2O ↔ CO + 3H2; ΔH298 = 206 kJ mol−1
WGS: CO + H2O ↔ CO2 + H2; ΔH298 = −41 kJ mol−1
The overall reaction is expressed as follows:
CH4 + 2H2O ↔ CO2 + 4H2; ΔH298 = 165 kJ mol−1
Despite achieving the desired conversion, the final products may contain an excessive amount of carbon dioxide, which necessitates further separation, using techniques such as pressure swing adsorption, to purify the hydrogen-rich syngas [24]. On an industrial scale, SMR can achieve the highest hydrogen production efficiency compared to other techniques. Notwithstanding the well-established SMR technology, thermochemical conversion techniques such as auto-thermal reforming, partial oxidation, and plasma catalytic reforming were developed and applied by researchers for efficient hydrogen generation [23,26,27,28]. However, most of the abovementioned thermochemical processes mainly use fossil fuels, generate greenhouse gas emissions, and consume a considerable amount of energy, especially when combined with carbon capture process. The other two important technologies, electrolysis and biomass gasification for hydrogen production, were reviewed in the following section, and a comparison of these three technologies were summarized in Table 1.

3.2. Electrolysis

Electrolysis is a hydrogen production approach that dissociates hydrogen from water by applying an induced electric current from an electrolyzer. It is an endothermic reaction, meaning that energy input (electricity) is required during the process. Electrolysis technique is clean, as it only generates hydrogen and oxygen from water molecules without any other harmful emissions [29,30]. The overall splitting reaction that occurs in the electrolysis cell, within the electrolyzer, is demonstrated in Equation (6):
2H2O → 2H2 + O2
Ionic reactions take place at the two electrodes (anode and cathode) within the electrolysis cell, which are connected through electrolytes. At present, the commonly used electrolytes are alkaline, acidic or salt solutions. In an acidic cell, water molecules are primarily converted to oxygen and positively charged protons at the anode. The protons subsequently move forward to the cathode through the electrolyte, whilst electrons are transported to the cathode through an external circuit (such as a direct current). Finally, protons and electrons react at the cathode to form hydrogen gas in the presence of a catalyst [31]. This process can be described by the following equations:
Anode reaction: 2H2O → 4H+ + O2 + 4e
Cathode reaction: 2H+ + 2e → H2
In the case of an alkaline electrolyte, the electric potential drives the negative hydroxide ions toward the anode and the positive protons to the cathode to form hydrogen gas, as shown below [32]:
Anode reaction: 4OH → 2H2O + O2 + 4e
Cathode reaction: 2H2O + 2e → H2 + 2OH
Among the three electrolytic media, the alkaline electrolyte is more desirable considering potential corrosion or spoilage of some metal electrodes in acidic electrolyte, as well as the possible undesired by-product generation in a salt solution. Recent studies explored many other harsh challenges such as high working temperature and molten hydroxides, the optimized working conditions and the reaction mechanism, to fill the gaps in reference electrodes for hydrogen production under high temperature electrolyte conditions [33,34]. Apart from the conventional options, novel alternatives such as proton exchange membrane (PEM) electrolyzer technology, alkaline anion exchange membrane (AAEM), and solid oxide water electrolysis (SOWE), etc., are well-developed [35,36,37,38]. Unfortunately, their excessive cost could hinder their large-scale application in hydrogen production, especially, the use of precious metal catalysts including iridium, ruthenium, palladium and platinum during PEM electrolysis [39]. Apart from that, costly electrode materials and high energy consumption also impede the widely application of water electrolysis in large commercial plants. On the other hand, although electrolysis is regarded as a clean process, it could be indirectly associated with carbon emissions if the electricity input is sourced from fossil fuels combustion, unless the “green” electricity comes from renewable sources such as wind or solar energy [40]. Overall, electrolysis can be a promising method for clean hydrogen production, but it has not yet been recognized as cost-effective and widely deployed compared to fossil fuels. Its future development should be focused on the exploitation of inexpensive catalysts and materials, higher productivity, and usage of larger amounts of affordable and renewable electricity [41,42].

3.3. Gasification of Biomass

Biomass is an abundant renewable organic resource that incorporates forest residues (such as wood logs, straw), agricultural residues (such as cornstalk, rice husk), municipal wastes (such as sewage slurry), biological residues (such as shells), etc. [48,49,50]. Recently, there is an ever-increasing attention on bioenergy due to its low cost, versatile applications and carbon neutrality [50]. Biomass can be directly used as combustion fuels or upgraded as a feedstock through a variety of methods including thermal conversions (such as gasification, pyrolysis and torrefaction) [49,51,52], chemical conversions (such as Fischer–Tropsch synthesis which converts biomass-based products into a synthetic lubrication oil and synthetic fuel) [53,54,55,56], biological conversions (such as fermentation, composting, anaerobic digestion) [57,58,59], electrochemical conversions (such as electro-catalytic oxidation), etc. [60]. Among such numerus pathways, biomass gasification, as a matured technology either commercially or pilot scale demonstration, has been intensively explored for converting organic materials to hydrogen and other products.
Gasification is a highly endothermic process that includes a series of reactions such as pyrolysis, char gasification, carbon residues combustion, and tar cracking or reforming. Biomass gasification implements biomass as feedstock, operating at high temperatures (700 to 1000 °C) with an oxidizing agent (air/oxygen/steam) supply to produce hydrogen, carbon monoxide, carbon dioxide, and other products such as char, tar, nitrogen, methane, etc. [47]. The produced syngas can be transported to power plants for power and electricity generation or used as a chemical feedstock for processes such as methanol production. A typical air gasification reaction of biomass can be expressed as:
Biomass + Air → H2 + CO + CO2 + N2 + CH4 + Tar + Char + Hydrocarbons
Due to the non-combustible nitrogen content of air and incomplete reaction, the hydrogen production efficiency of the air gasifier is generally lower than oxygen and steam gasifiers. Although the hydrogen content of oxygen gasification is enriched, the use of pure oxygen is commercially uneconomical. The steam gasification is a viable pathway of treating biomass and has the highest conversion efficiency of the three oxidizing agents, which is around 53 to 55 vol% of H2 production, whereas that of air gasification is around 8 to 10 vol% [61]. Additionally, the carbon-to-hydrogen mass ratio can be reduced through water–gas shift reaction, which further increases the hydrogen content and calorific value of the yield gases:
Biomass + steam → H2 + CO + CO2 + tar + char
Water–gas shift reaction converts carbon monoxide to more hydrogen:
CO + H2O → H2 + CO2
Biomass gasification is versatile for adapting various types of biomass in terms of producing hydrogen and syngas. However, the process is limited by thermodynamic equilibrium and undesired products such as tar, coupled with other challenges such as types of gasification reactors, use of catalyst, operating parameters, etc. [44,62]. Typical gasification reactors are classified as entrained flow gasifiers (EFG), fixed bed gasifiers (FXBG) and fluidized bed gasifiers (FBG), as illustrated in Figure 3. In an EFG as shown in Figure 3A, biomass can be fed at the top with the gasifying agent (downflow system) or the bottom through side burners (upflow system). Normally, biomass is heated in a temperature range of 1200 to 2000 °C at 20 to 70 bar, during which particles travel with oxygen and steam along the reactor in a very short residence time [63]. Since the gas flow velocity in the system is high enough, both the biomass fuels and the formed synthesis gas are entrained in the same flow direction. Meanwhile, the high temperature and pressure can result in almost complete reaction that high quality of synthesis gas can be obtained. Most importantly, the high temperature ensures a full destruction of the undesired volatile components such as tar. However, it is challenging that a pre-treatment of the biomass fuel is required to ensure unhindered flow and stable heat and mass transfer during the reaction [63]. The fuels must be dried and milled into finely ground particles with size diameter of hundred micrometers before it can be introduced to the systems. Nevertheless, due to different source and the intrinsic physical properties of biomass, the particle-stated biomass fuels still suffer from poor mobility with flows. This may lead to insufficient mixing, and therefore restrict the overall efficiency. Besides, the operating temperature is beyond the ash fusion temperature and slagging could be a challenging issue in the system. In addition, keeping a high flow demands large oxidant input into the system [63,64].
Fixed bed gasifier has a bed of solid fuel particles in a cylindrical space, as demonstrated in Figure 3B. FXBGs normally operate in a temperature range of 300 to 1000 °C without extra pressure exerted to the system. FXBGs are categorized into updraft and downdraft types. In these configurations, the gasification agent enters the reactor in different directions [45,65]. In updraft FXBG (see Figure 3B), the biomass fuel is introduced from the top entrance and the gasification agent (steam and oxygen) from the bottom of the reactor. The biomass descends through the bed while being heated and converted to synthesis gas. The gasification process includes drying, pyrolysis, reduction, and oxidation, after which the synthesis gas leaves the gasifier through the top exit. Hydrogen content produced by the updraft FXBG is higher than EFG, but contains higher fractions of tar (10 to 20 g/m3) and a huge amount of other pyrolysis products, which need to be removed before further utilization [65]. For downdraft FXBGs, the reactor through an inlet and the targeted syngas is separated from the bottom, as shown in Figure 3B. The downdraft FXBGs can enable hydrocarbon cracking and depressing tar content [43,45]. However, downdraft FXBGs usually have a lower gas yield than upward FXBGs and are therefore limited to upscaling for commercial production. Overall, although fixed bed gasifiers produce cleaner product gas, relative high tar content and low conversion rate severely limit FXBGS to small scale applications [43,65].
Owing to excellent solid–gas contact and promising heat and mass transfer, fluidized bed gasifiers exhibit better performance for biomass gasification than EFGs and FXBGs. FBGs are viable to deal with a variety of types and sizes of biomass and have great potential to scale up for commercial applications [66]. In a fluidized bed, the biomass is introduced above a dense fluid bed (typically quartz sand or catalytic particles) and upward flow of gasification reactants, supplied to the bottom of the gasifier, serve as fluidizing medium. The bed operating temperature is normally within a range of 700 to 1000 °C, which is below the ash fusion or softening point to avoid ash agglomeration and blockage or defluidization of the bed [66,67]. Biomass settles to the hot bed surface and is heated rapidly, enabling drying and pyrolysis to take place. The uniform high temperature profile maintained in FBGs is beneficial for achieving a high carbon conversion efficiency and reduction of tar and light hydrocarbons. Meanwhile, constant fluidization facilitates continuous and proper mixing between the oxidant and biomass particles, promoting a high reaction efficiency. However, char combustion under such temperature partly restricts gasification process and negatively impacts the entire process. In addition, tar formation is the main barrier towards higher yield of synthesis gas [68,69].
Fluidized gasification reactors are operated in three modes including bubbling, circulating, and dual beds, as depicted in Figure 3C. 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. The product syngas is extracted from the top of the reactor and cleaned in a cyclone, as shown in Figure 3C. BFB demonstrates capability to treat high-moisture biomass, but significant instability in the presence of bubbling and rising gas channeling [70,71]. Circulating fluidized bed (CFB) features two operating units, which are a fast velocity riser reactor and a circulating loop cyclone (Figure 3C middle). In this concept, biomass is treated under a higher gas velocity (superficial flow velocity) than BFB in a more drastic fluidization state. A mixture of product syngas and bed particles rise to the top and separated in cyclones, where the issued-out solids recirculate into a riser from the bottom. Therefore, greater overall efficiency could be realized through the looping design [46]. Dual fluidized bed (DFB) systems or two-stage fluidized bed systems comprise of two fluidized bed reactors interlinked with a solid looping configuration, as illustrated in Figure 3C (right) [72]. They can be a combination of two BFBs, two CFBs or a BFB and a CFB. The two connected fluidized beds are independently accountable for pyrolysis and combustion reactions [73]. In the combustion chamber, air and fuels are fed in and solid reactants are heated and combusted. Consequently, combustion turns flue gas, at the top of reactor, into a connected cyclone, after which the bed materials are transported to the second FBG. Biomass gasification takes place with the help of a hot bed material and steam to produce syngas gas and char. Later, the bed material and char are transferred back to the first FBG for char combustion, repeating the previous steps. By this means, the two processes do not interfere with each other, and high conversion efficiency is ensured [72,74]. Table 2 summarizes different types of gasifiers for biomass gasification, as well as their operational conditions, advantages and limitations.

4. 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 3 and Table 4 (Table 3 for different types of FBGs and Table 4 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 4. 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.
The types of fluidized bed reactors and their corresponding reported hydrogen contents from Table 3 and Table 4 were summarized and plotted in Figure 5. Obviously, different types of fluidized bed reactors from different reported sources tend to yield different reported values of hydrogen contents. In Figure 5, 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.
The statistical analysis of the impact of seven process parameters on the corresponding three outputs using ANOVA are shown in Figure 6. In this work, instead of investigating the statistical significance of singular process parameters upon the responses, we mainly focused on statistical significance of the binary combined parameters upon those three responses, which is often more meaningful from a practical operation point of view. Among all the investigated binary combined parameters (total 7 × 6 = 42 different combinations, see Figure 6A), six binary combined parameters (Figure 6B) were identified and found to be statistically significant to the response.
The statistical analysis of the impact of the binary combined factors on responses are shown in Figure 7. Figure 7A shows the steam-to-biomass ratio (SBR) and equivalence ratio (ER) versus hydrogen yield (HY). SBR refers to the ratio between steam content and biomass fed to the gasifier, and ER represents the actual air-to-biomass ratio with respect to stoichiometry for complete combustion [88,113]. Both SBR and ER are significant parameters that need to be maintained at optimal values in order to achieve relatively high HY. High HY values were found at both high SBR and ER regions in Figure 7A. It can be explained that a higher value of SBR (i.e., 1.5–2.0) tends to increase HY on account of the water–gas shift reactions. Meanwhile, higher ER (above 0.5) indicates the availability of more oxidizing agent in the system, which enhances the oxidation reaction. It also maintains continuous tar cracking and eventually boosts the total amount of producer gas, although HY does not experience significant growth [113]. It should be noted that both SBR and ER should not go beyond a limit (mentioned above) because more oxidizing agent in the gasifier would lead to incomplete gasification and an increase in carbon-contained gases, leading to adverse effect on HY. In addition, both high values could result in more energy and material consumption. Interestingly, high HY is also found in the region of lower SBR and ER values. For example, reduction of ER to a range within 0.2 to 0.3 facilitates hydrogen production and other gaseous content. However, low ER below 0.15 (Figure 7A) can leave a proportion of unconverted char and tar in the system. If SBR is too low, a deficiency of the water–gas shift process would likewise restrict the final HY. Figure 7B demonstrates the particle size (PS) and ER against HY. It is suggested that under a low ER, small feedstock particle size (e.g., 3000 µm) enriches HY, as large surface area favors efficient heat transfer during gasification. Conversely, larger sized biomass feedstock suffers from poor thermal decomposition, leading to high volatile content, which in return produces high tar content and low HY [122]. Figure 7C depicts the influence of residence time (Time) and temperature (Temp) on hydrogen content (HC) in the producer gas. Although the results suggested that longer residence time (such as 1500 min) also leads to higher HC, it is regarded as inefficient considering factors such as a noticeable amount of energy, material input, and other economic considerations. On the other hand, adequate residence time should be ensured, as both temperature and reactions need certain periods to attain steady state. This corresponds to the predictions in Figure 7C that the optimal residence time is controlled within 180 min. Conversely, higher temperatures intensify HC values due to the intrinsic endothermic process of hydrogen production and a decrease in carbon monoxide. However, it is also dependent on the moisture content of the feedstock, as part of the energy would be consumed during drying. Therefore, lower temperature may impede effective processing of biomass with high moisture content [123]. Nevertheless, the char combustion demands extra energy input at high temperature conditions, i.e., over 850 °C, where HC is limited under this circumstance. As for lower heating value (LHV), there is an increasing trend of HC directly associated with larger LHV of biomass fuels, as shown in Figure 7D. This suggests that favor high LHV of the feedstock favors HC at any instance. Carbon conversion efficiency (CCE) is determined by the mass of carbon in producer gas over the mass of carbon in biomass feedstock. A High CCE value implies high hydrogen to carbon monoxide ratio as well as high tar conversion [124]. The analysis results, as illustrated in Figure 7F, shows the estimated optimal CCE values fall within the ER and temperature range of 0.15 < ER < 0.35 and 700 to 850 °C, respectively. Restricting the residence time within 180 min, a high CCE can be achieved by selecting biomass with a wide range of carbon content (CC) over 8%, as seen in Figure 7F. Overall, the parametric factors surveyed above have a joint influence on hydrogen production and a rough prediction can be made based on those given values. Therefore, this may serve as a guide for users when considering hydrogen production from biomass using fluidized bed gasifiers.

5. Conclusions

In this paper, the commonly used hydrogen production technologies including steam methane reforming, electrolysis, and biomass gasification were reviewed and compared. Among the mentioned 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. However, besides the parametric conditions mentioned in the content, other essential factors such as the types and amount of catalyst were not assessed in this paper. This is because currently, the data could not be quantified as an effective data input to the analysis system. Therefore, both the qualitative and quantitative evaluation of this factor will be conducted in our future study.

Author Contributions

Z.L.: drafting, writing and analysis; Y.W. (Yixiao Wang): data curation; X.Z.: data curation; A.Y.: writing; L.F.: writing and drafting; D.M.: writing and drafting; H.J.: programming and supervision; Y.L.: writing and supervision; J.H.: supervision; Y.W. (Yunshan Wang): project management; G.Y.: project management; Y.S.: writing, supervision and project management. All authors have read and agreed to the published version of the manuscript.


Funding was acquired through the University of Nottingham Ningbo China (FoSE New Researchers Grant I01210100011), the Faculty Inspiration Grant of University of Nottingham (FIG2019), the Qianjiang Talent Scheme (QJD1803014), the National Key R&D Program of China (Grant: 2018YFC1903500), the Ningbo Science and Technology Innovation 2025 Key Project (Grant 2020Z100) and the Ningbo Municipal Commonweal Key Program (Grant 2019C10033 and 2019C10104).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.


The authors also sincerely appreciate the critical and insightful comments raised by those anonymous reviewers for significantly improving the quality of this work.

Conflicts of Interest

The authors declare no conflict of interest.


AAEMAlkaline anion exchange membrane
ANNsArtificial neural networks
ANNs-RSMArtificial neural networks coupled with response surface methodology
ANOVAAnalysis of variation
BFBBubbling fluidized bed
CCCarbon content
CCECarbon conversion efficiency
CEChemical engineering
CFBCirculating fluidized bed
DFBDual fluidized bed
ESEnvironmental sciences
FBGFluidized bed gasifiers
FBRFluidized bed reactors
EFGEntrained flow gasifiers
EREquivalence ratio
FXBGFixed bed gasifiers
GHGGreenhouse gas
HCHydrogen content
HYHydrogen yield
i s a m Actual
LHVLower heating value
MARRMean absolute relative residuals
MSMaterial science
MSEMean square error
NsamNumber of data set
PEMProton exchange membrane
PSParticle size
r i c a l Prediction
RSMResponse surface methodology
SBRSteam-to-biomass ratio
SMRSteam methane reforming
SOWESolid oxide water electrolysis
UNUnited Nations
WGSWater-gas shift


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Figure 1. Hydrogen production from different resources via different technical routes. Left: blue hydrogen. Right: gray hydrogen.
Figure 1. Hydrogen production from different resources via different technical routes. Left: blue hydrogen. Right: gray hydrogen.
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Figure 2. Summary of publication from Scopus for hydrogen generation by subject areas, where En refers to energy, CE refers to chemical engineering, CH refers to chemistry, MS refers to material science, and ES refers to environmental sciences.
Figure 2. Summary of publication from Scopus for hydrogen generation by subject areas, where En refers to energy, CE refers to chemical engineering, CH refers to chemistry, MS refers to material science, and ES refers to environmental sciences.
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Figure 3. Schematic illustration of different types of gasifiers: (A) entrained flow, (B) fixed bed (left: updraft and right: downdraft) and (C) Fluidized bed (left: bubbling fluidized bed, middle: circulating fluidized bed and right: dual fluidized bed).
Figure 3. Schematic illustration of different types of gasifiers: (A) entrained flow, (B) fixed bed (left: updraft and right: downdraft) and (C) Fluidized bed (left: bubbling fluidized bed, middle: circulating fluidized bed and right: dual fluidized bed).
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Figure 4. Analysis result—actual versus prediction from ANNs modeling, where color bar represents the uncertainties.
Figure 4. Analysis result—actual versus prediction from ANNs modeling, where color bar represents the uncertainties.
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Figure 5. 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.
Figure 5. 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.
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Figure 6. Statistical analysis for the impact of process parameters upon responses, (A) graph column of parameters and responses, (B) ANOVA of all parameters towards the responses, where HY represents hydrogen yield, HC refers to hydrogen content, and CCE refers to carbon conversion efficiency (%), SBR refers to steam-to-biomass ratio (-), ER refers to equivalence ratio, LHV refers to lower heating value (MJ/Nm3), CC refers to carbon content (%), PS refers to particle size (μm) and Temp refers to temperature (°C).
Figure 6. Statistical analysis for the impact of process parameters upon responses, (A) graph column of parameters and responses, (B) ANOVA of all parameters towards the responses, where HY represents hydrogen yield, HC refers to hydrogen content, and CCE refers to carbon conversion efficiency (%), SBR refers to steam-to-biomass ratio (-), ER refers to equivalence ratio, LHV refers to lower heating value (MJ/Nm3), CC refers to carbon content (%), PS refers to particle size (μm) and Temp refers to temperature (°C).
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Figure 7. Statistical analysis for the impact of process parameters upon responses, (A) SBR-ER-HY, (B) PS-ER-HY, (C) Time-Temp-HC, (D) PS-LHV-HC, (E) ER-Temp-CCE and (F) CC-Time-CCE.
Figure 7. Statistical analysis for the impact of process parameters upon responses, (A) SBR-ER-HY, (B) PS-ER-HY, (C) Time-Temp-HC, (D) PS-LHV-HC, (E) ER-Temp-CCE and (F) CC-Time-CCE.
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Table 1. Comparisons of hydrogen production technologies by steam methane reforming, electrolysis and biomass gasification.
Table 1. Comparisons of hydrogen production technologies by steam methane reforming, electrolysis and biomass gasification.
Production TechnologyFeedstockProcesses InvolvedOverall ReactionsEfficiencyAdvantagesLimitationsReferences
Steam Methane ReformingMethane and steam
  • Reforming of steam and methane
  • Water–gas shift
CH4 + 2H2O ↔ CO2 + 4H270–85%
  • Mature commercially available technology
  • Highest hydrogen production efficiency
  • Use of fossil fuels or refinery byproducts cause GHG emission
  • Additional heat transfer units
ElectrolysisWater and electricity
  • Ionic reactions
2H2O → 2H2 + O2; 2H2O → 4H+ + O2 + 4e; 2H+ + 2e → H2 *50–70%
  • Abundant water and electricity sources
  • No GHG emission and other by-products unless fossil fuels used as electricity source
  • Expensive catalyst and materials
  • Corrosion of electrodes
Biomass gasificationBiomass
  • Pyrolysis
  • Char gasification, carbon residues combustion
  • Tar cracking or reforming
Biomass + Air/oxygen/steam → H2 + CO + CO2 + (N2) + CH4 + Tar + Char + Hydrocarbons35–55%
  • Abundant and renewable resources
  • Carbon neutral
  • Cost-effective
  • Thermodynamic equilibrium and other byproducts
  • Relatively low efficiency
* Here, only acidic electrolyte case is shown as a demonstration.
Table 2. Comparison of different types of gasifiers for biomass gasification including their operational conditions, advantages and limitations.
Table 2. Comparison of different types of gasifiers for biomass gasification including their operational conditions, advantages and limitations.
Gasifier TypesDesign ConfigurationsDescriptionsT (°C)P (bar)AdvantagesLimitationsReferences
Entrained flowUpflow system
  • Biomass fed at the bottom with the gasifying agent
  • Syngas exits from the top
  • High quality of synthesis gas
  • Elimination of tar
  • Short residence time
  • High gas flow velocity, high temperature and pressure, large oxidant demand
  • High energy consumption
  • Complex pretreatment
Downflow system
  • Biomass fed at the top with the gasifying agent
  • Syngas exits from the bottom
Fixed bedUpdraft
  • Biomass fed at the top and gasifying agent from the bottom of reactors
  • Syngas exits from the top
300–1000Atmospheric pressure
  • Higher gas yield
  • Higher fractions of tar and other byproducts
  • Biomass fed at the top and gasifying agent from the middle of reactors
  • Syngas exits from the bottom
300–1000Atmospheric pressure
  • Hydrocarbon cracking and limited tar content
  • Lower gas yield
  • tar formation and other byproducts
Fluidized bedBubbling
  • Biomass fed at the bottom or side and gasifying agent from the bottom of reactors
  • Syngas exits from the bottom
  • Capable of treating high moisture biomass
  • Uniform high temperature profile
  • Betting mixing
  • High carbon conversion efficiency
  • No ash agglomeration
  • Tar formation and other byproducts
  • Biomass fed to the bed and gasifying agent from the bottom of reactors
  • Syngas exits from the top and partially recycled from cyclone and sent back to gasifier
  • Capable of treating high moisture biomass
  • Uniform high temperature profile
  • Better mixing
  • High carbon conversion efficiency
  • No ash agglomeration
  • Extra operating units
  • Two operation units combined pyrolysis and combustion
  • Higher working efficiency
  • Flexible and independent working units
  • Char removal
  • Higher energy demand
  • Extra operating units
Table 3. 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).
Table 3. 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 TypeFeedstockFeedstock Particle Size (µm)Carbon Content (wt.%)LHV (MJ/Nm3)T/°CProcess Time/minERSBRYield (Nm3/kg)Yield H2 Content/vol%(CCE) %Reference
BubblingTorrefied and raw pine46813.80-800450.28-80.5615.13-[75]
Wood sawdust1500--850300--1.1542.0085.00[76]
Rice husk750011.693.84600-0.20-0.502.7095.00[77]
Wood-PET pellets600012.1619.19800900.28--8.1098.60[90]
Rice husk-36.009.30800600.30--12.50-[91]
Cocoa shells46121.70-900600.231.201.4949.1050.00[71]
Rice husk and coal157522.37-8502100.261.21-8.6489.00[78]
Pine sawdust-12.60-600120-0.201.0338.6071.20[93]
Pine sawdust and brown coal400013.20-900-0.200.50-50.6084.20[79]
Torrefied woodchips24022.8219.26850300.221.201.1228.6689.20[95]
Carbonaceous feedstock15,00011.5020.53785300.21-2.107.1084.10[96]
Rice husk-14.99-850-0.300.80-11.0076.00[97]
Cypress wood chips-20.6415.80700-0.301.20-0.59-[98]
Torrefied woodchips-20.183.00800300.24-1.7714.3178.00[99]
Poultry litter52522.8219.2685090-1.401.4143.0087.52[100]
Spruce slice615-20.05809600.20--9.6950.00[102]
Torrefied and raw pine630-5.55915600.32--10.8091.00[104]
CirculatingTorrefied wood residues and mixed wood500024.6511.708501800.221.261.6053.0082.40[83]
Wood residue and Tabas coal17518.20-850550.40--52.70-[46]
Methane and biomass---1000-0.211.00-28.00-[81]
Sub-bituminous coal and sawdust367535.9322.39800-0.29-2.1112.6384.00[80]
DualPP plastic pellets, wood chips and plant capsules6608.0126.0090010.670.30-2.5329.7082.00[85]
Rice straw125018.74-8001200.24-1.205.3884.77[86]
PE plastic bags, sawdust and PP plastic particles7805.00-900-0.300.50-53.10-[87]
PE plastic bags, sawdust and PP plastic particles7805.00-700350.300.60-39.38-[88]
Volatile, fixed carbon and ash-17.169.90800-0.191.561.7232.3491.50[84]
Pine sawdust20012.7311.40850120-0.3010.5147.3064.00[89]
Biomass briquette-18.7111.006703000.19-1.2024.0098.82[105]
PE plastic bags, wood chips and PP particles660--900350.300.60-50.9692.59[106]
Table 4. 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).
Table 4. 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).
CatalystFeedstockFeedstock Particle Size (µm)Carbon Content (wt.%)LHV (MJ/Nm3)T/°CProcess Time/minERSBRYield (Nm3/kg)Yield H2 Content/vol%CCE %References
ZSM-5 zeoliteBeech-wood and poly---854900.300.63--98.20[107]
-Palm kernel shell and sub-bituminous coal16040.0021.1380014400.600.20-12.0082.80[108]
NiO/modified dolomiteCoffee husk---900-0.151.501.7527.00-[109]
-Carbonaceous feedstock2750.80-820-[110]
-Citrus peel50040.314.65750200.301.250.6926.0087.00[111]
Ni/CeO2/Al2CO3Wood residue-49.18-823440.170.711.6642.5293.56[112]
Commercial Ni-catalyst *1Almond shells-11.00-81560-0.491.7055.30-[114]
Ternary molten carbonatesForestry biomass waste-3.89-75060-1.00-55.00-[115]
-Pine sawdust and MSW200018.82-850-0.21-13.409.80-[69]
High-alumina bauxiteStraw750017.509.35726600.16--14.9070.99[116]
Calcium (Ca)Rice husk and bamboo dust670-5.05800300.350.411.72-98.00[117]
Commercial Zeolite *2Empty fruit bunch30008.60-97330-2.00-75.00-[118]
Industrial sludge derived catalysts-32010.354.84800500.301.00-12.46100.00[67]
SCG ash-140020.0012.2090030-0.53-6.00-[119]
Coal bottom ashPalm kernel shell75014.2512.5069260-1.50-79.7759.90[120]
Calcined dolomite-500035.20-1000500.141.00-49.1060.80[121]
Company information: *1 Johnson Matthey. *2 Zeolyst, Malaysia Sdn. Bhd., Malaysia.
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Lian, Z.; Wang, Y.; Zhang, X.; Yusuf, A.; Famiyeh, L.; Murindababisha, D.; Jin, H.; Liu, Y.; He, J.; Wang, Y.; et al. Hydrogen Production by Fluidized Bed Reactors: A Quantitative Perspective Using the Supervised Machine Learning Approach. J 2021, 4, 266-287.

AMA Style

Lian Z, Wang Y, Zhang X, Yusuf A, Famiyeh L, Murindababisha D, Jin H, Liu Y, He J, Wang Y, et al. Hydrogen Production by Fluidized Bed Reactors: A Quantitative Perspective Using the Supervised Machine Learning Approach. J. 2021; 4(3):266-287.

Chicago/Turabian Style

Lian, Zheng, Yixiao Wang, Xiyue Zhang, Abubakar Yusuf, Lord Famiyeh, David Murindababisha, Huan Jin, Yiyang Liu, Jun He, Yunshan Wang, and et al. 2021. "Hydrogen Production by Fluidized Bed Reactors: A Quantitative Perspective Using the Supervised Machine Learning Approach" J 4, no. 3: 266-287.

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