Supercritical Water Gasification Of Biomass Research Articles

Optimizing H2 production from biomass: A machine learning-enhanced model of supercritical water gasification dynamics

Supercritical water gasification (SCWG) is recognized as an efficient technology for biomass conversion, demonstrating substantial potential in the sustainable energy sector. This paper focuses on the H2 production by SCWG of biomass. The objective is to develop an accurate gasification kinetic model that can facilitate the optimization of industrial reactor designs. A series of SCWG experiments is conducted in a batch reactor system under temperature of 500–600 °C and residence time of 1–20 min. Based on the experimental results, a hybrid-driven model for SCWG reaction is established. Firstly, experimental data is utilized to delineate SCWG reaction mechanistic model and forecast gas yields with an acceptable error margin. Subsequently, a Wasserstein Generative Adversarial Network Gradient Boosting Regression Grid Search (WGAN-GBR-GRID) model is applied to acquire knowledge of the predictive errors from the mechanistic model and to develop a hybrid model. The hybrid-driven model significantly enhances the average relative prediction error from 15.56 % to 0.02 % when compared to the mechanistic model. This result underscores the potential of the hybrid model in optimizing SCWG technology and the Shapley Additive exPlanations (SHAP) values in feature analysis shows the possible shortcomings of mechanistic model, thereby providing a new perspective for SCWG reaction dynamics modeling.

Supercritical Water Gasification of Biomass on CSP Plants: Comparison of On-Sun and Off-Sun Designs

A comparative study of On-Sun and Off-Sun designs for supercritical water gasification (SCWG) of biomass on concentrated solar power (CSP) plants was here presented. In the On-Sun design, the tubular reactor was placed on a cavity receiver of a solar power tower (SPT) plant and the heat employed to produce the gasification came directly from the sun (viz. the solar radiation was concentrated, thanks to the heliostat field, onto the tube's surface). In the Off-Sun designs, the gasification of the biomass was carried out inside a heat exchanger and it was produced thanks to the heat absorbed from the hottest fluid, which can be obtained from CSP plants or electrical heating. This work numerically investigated the thermomechanical performance of both designs. The results stood out the relation between temperature-gas yield-thermal stress. In On-Sun configurations the gas yield was 5 times higher than those obtained in the Off-Sun configuration. However, the highest values of both temperature and stress may produce the failure of the reactor. This fact could be avoided in an Off-Sun reactor, since its uniform heating conditions mitigate the maximum temperature and stresses.

Data-driven interpretation, comparison and optimization of hydrogen production from supercritical water gasification of biomass and polymer waste: Applying ensemble and differential evolution in machine learning algorithms

Supercritical water gasification (SCWG) has been utilized for producing hydrogen from organic wastes, which is associated with sustainable development. Nevertheless, identifying the optimal SCWG conditions and appropriate catalysts for diverse waste materials to yield hydrogen-rich syngas is consistently a laborious and costly endeavor. The current research presents a comprehensive framework that combines machine learning models (Decision Tree, Ensembled Learning Tree, Support Vector Machine, and Gradient Boost Regression) with Differential Evolution Optimization (DEO) to predict and optimize hydrogen production from Supercritical Water Gasification (SCWG) using 24 different biomass characteristics and process parameters.The findings indicate that ELA-DEO emerges as the preferred method for forecasting hydrogen yield (R2test = 0.95, RMSETest = 0.091), demonstrating its effectiveness in handling intricate variable-target relationships. Conversely, Support Vector Machine (SVM) displayed weaker performance with an R2Test of 0.73 and RMSETest of 0.116. In the SHAP feature importance analysis, temperature, catalyst amount, catalyst type, hydrogen and oxygen were highlighted as important parameters affecting the process. In addition, by fine-tuning the machine learning hyperparameters, the DEO optimization method was used to maximize the production of H2. The optimized ELA-DEO model was used to identify the ideal features and conditions applicable to our experimental setup. Based on these findings, a recommended biomass table was finally formulated, which was validated by the ELA-DEO model. According to our laboratory results, Dunaliella salina produced 12 mmol of hydrogen with a 35% selectivity. Black liquor with 3% (wt) wood has the capacity to produce 17.21 mmol of hydrogen. Also, when crude glycerol was reacted with RuCl3, 18.7 mmol of hydrogen were generated. However, Dunaliella salina produced a 64% mole fraction of total gas output, which matched the maximum value predicted by our ELA-DEO models.

Molecular Dynamics Simulations Guide the Gasification Process of Carbon-Supported Nickel Catalysts in Biomass Supercritical Water.

In this study, the Density Functional Theory (DFT) Calculations for Molecules and Clusters-ADF module is employed to model carbon-supported nickel catalysts and lignin monomers, integrating the ReaxFF module to simulate molecular dynamics under supercritical water conditions, with a focus on lignin decomposition reactions. Molecular dynamics models for supercritical water gasification are established under various conditions such as catalyst presence, water molecule quantities, and reaction temperature. By comparing simulation systems under different conditions, the yields of and variations in combustible gases (hydrogen and carbon monoxide) are summarized. Introducing heteroatoms into the lattice of the carbon support can alter the electronic structure within graphene, thereby influencing its electrical and electrochemical properties, increasing the number of active sites, and significantly enhancing electrocatalytic activity. Simulation results indicate that carbon-supported nickel metal catalysts can promote the cleavage of C-C bonds in lignin monomers, thereby increasing the rate of water-gas shift reactions and boosting hydrogen production in the system by 105%. Increasing water molecule quantities favored water-gas shift reactions and hydrogen generation while lowering carbon monoxide formation. Moreover, elevating reaction temperatures led to increased hydrogen and carbon monoxide production rates, which were particularly pronounced between 2500 K and 3500 K. These findings offer crucial theoretical insights for advancing efficient hydrogen production through biomass supercritical water gasification.

Comparison of CO2 with H2O as the transport medium in a biomass supercritical water gasification system

Comparison of CO2 with H2O as the transport medium in a biomass supercritical water gasification system

Investigating Salt Precipitation in Continuous Supercritical Water Gasification of Biomass

The formation of solid deposits in the process of supercritical water gasification (SCWG) is one of the main problems hindering the commercial application of the process. Seven experiments were conducted with the grass Reed Canary Grass with different preheating temperatures, but all ended early due to the formation of solid deposits (maximum operation of 3.8 h). The position of solid deposits in the lab plant changed with the variation in the temperature profile. Since the formation of solid deposits consisting of salts, coke, and corrosion products is a severe issue that needs to be resolved in order to enable long-time operation, inner temperature measurements were conducted to determine the temperature range that corresponds with the zone of solid formation. The temperature range was found to be 400 to 440 °C. Wherever this temperature was first reached solid deposits occurred in the system that led to blockage of the flow. Additional to the influence of the temperature, the influence of the flow direction (up-flow or down-flow) on the operation of the continuous SCWG plant was examined. If salts are not separated from the system sufficiently, up-flow reactors should be avoided because they amplify the accumulation of solid deposits leading to a shortened operation time. The heating concept coupled with the salt separation needs to be redesigned in order to separate the salts before entering the gasification reactors. Outside of the determined temperature zone no deposition was visible. Thus, even though the gasification efficiency was low it could be shown that the operation was limited to the deposits forming in the heating section and not by incomplete gasification in the reactor where T > 600 °C.

Developing a hybridized thermodynamic and data-driven model for catalytic supercritical water gasification of biomass for hydrogen production

Supercritical water gasification (SCWG) of biomass offers a promising pathway for sustainable energy production. This study presents a novel approach that combines an artificial intelligence (AI) technique with the Gibbs minimization (GM) method to enhance the GM approach by involving the catalyst effect on the gasified yield. The proposed strategy incorporates a modified Gibbs energy calculation, incorporating a delta term that quantifies the influence of the catalyst’s properties on the Gibbs free energy. The delta term is determined using experimental data, including characteristics of biomass and the employed catalysts. An artificial neural network (ANN) model is developed to predict the delta value and is integrated with the GM method to improve the estimation of gasified product yields in the presence of a catalyst. The ANN model establishes correlations between the catalyst specifications such as pore volume, specific surface area, and pore diameter, and delta value. The trained model can predict the delta term, allowing the catalyst’s role to be considered within the GM technique. This integration addresses a limitation of the conventional thermodynamic model, which typically ignores the catalyst’s influence. The developed ANN model exhibits high accuracy, with an R2 value of 0.92 and a mean squared error (MSE) of 0.042. The hybridized GM approach substantially improves the hydrogen yield prediction compared to the basic model, enhancing (reducing) the MSE and mean absolute error by 98% and 93%, respectively. Furthermore, sensitivity analysis reveals that increasing temperature and reducing pressure contribute to improved hydrogen production. Additionally, lower biomass concentrations are shown to be beneficial for enhancing the hydrogen yield.

System development and thermodynamic performance analysis of a system integrating supercritical water gasification of black liquor with direct-reduced iron process

Hydrogen metallurgy is an effective way to decarbonize the steel industry. In this study, a new system integrating biomass supercritical water gasification (SCWG) with hydrogen generation-shaft furnace-electric arc furnace (HSE) was proposed. The thermodynamic performance of SCWG-HSE system were analyzed and optimized. The results show that the gasification and energy efficiency of SCWG system increase with an increase in the gasification temperature. The carbon emission of the SCWG-HSE system are effectively reduced, and the product yield and energy efficiency of the system are improved by increasing gasification temperature and recovering furnace top gas. The energy and exergy efficiency of the SCWG-HSE system are 46.66% and 40.17%, respectively. The exergy destruction of SCWG gasifier and electric arc furnace are the major exergy damage sources of the SCWG-HSE system. The exergy efficiency of the ironmaking system reaches 51.60%, and the energy consumption and carbon emission per unit product are 18.49 GJ/t and 902.40 kg/t, respectively. Compared with the traditional Coking-Blast furnace ironmaking process, the energy consumption and carbon emission of this system are reduced by 7.32% and 40.84% respectively. This work is expected to open a new way for the application of biomass in the low-carbon steel industry.

High value-added syngas production by supercritical water gasification of biomass: Optimal reactor design

Supercritical water gasification (SCWG) emerges as a highly encouraging technique for the efficient generation of syngas from biomass feedstock. The performance of the SCWG reactor plays a pivotal role in determining the overall success of the process. This study employs a mathematical modeling and optimization method to design an optimal tubular-type reactor for the SCWG of biomass. First, the reactor is divided into cells, and mathematical models are established to describe the reactions within each cell, as well as the mass balances between cells. Afterwards, the optimal reactor design problem is formulated as a non-linear optimization problem, which involves integration of all the individual models along the reactor length. The objective is to maximize the high heating value (HHV) of gaseous products and simultaneously satisfying multiple constraints. Once the problem is solved, the optimal decision variables (i.e., reactor axial fluid temperature profiles, diameter and length) are obtained. Here, two reactor design scenarios are investigated to showcase the adaptability of this optimization method. The obtained outcomes indicate that non-isothermal reactors with a zigzag-shaped temperature profiles are more advantageous than traditional isothermal ones for high value-added syngas production. Specifically, optimal non-isothermal reactors can achieve a remarkable increase of 50.08% (i.e., 26.94 vs 17.95 MJ/kg glycerol) in the HHV of gaseous products compared to isothermal reactors. These findings provide valuable insights on designing efficient SCWG reactors for industrial high value-added syngas production.

Molecular Dynamics Investigation of the Gasification and Hydrogen Production Mechanism of Phenol in Supercritical Water

Supercritical water gasification is an efficient and clean method for converting biomass into hydrogen-rich gas. Phenol plays a crucial role as an intermediate product in biomass supercritical water gasification, and studying its reaction pathway in supercritical water is essential for understanding the chemical reaction mechanism and optimizing biomass energy conversion processes. In this paper, we investigated the conversion mechanism of phenol gasification and hydrogen production in supercritical water using a combined approach of reactive force field (ReaxFF) and density functional theory (DFT). We determined the decomposition pathways and product distribution of phenol in supercritical water. The calculation results demonstrate that in the supercritical water system, the efficiency of phenol conversion for hydrogen production is approximately 27 times higher than that of hydrogen production through gasification in the pyrolysis state. Moreover, both the carbon conversion rate and hydrogenation rate in the supercritical water system are significantly higher compared to those in the pyrolysis system. Furthermore, we found that the energy in the supercritical system is approximately half that of the pyrolysis system, favoring the ring-opening reactions of phenol and promoting hydrogen production. In contrast, the pyrolysis system produces a greater quantity of aromatic compounds, leading to tar formation and having significant implications for both the reaction process and reactor design. Additionally, we conducted comparative experiments between the supercritical water gasification process and the pyrolysis process to explore the advantages of supercritical water gasification.

AI Prediction of H2 Production in a Biomass Supercritical Water Gasification Fluidized Bed under Pulsating Inlets

AI Prediction of H₂ Production in a Biomass Supercritical Water Gasification Fluidized Bed under Pulsating Inlets

High Dimensional Model Representation Approach for Prediction and Optimization of the Supercritical Water Gasification System Coupled with Photothermal Energy Storage

Supercritical water gasification (SCWG) coupled with solar energy systems is a new biomass gasification technology developed in recent decades. However, conventional solar-powered biomass gasification technology has intermittent operation issues and involves multi-variable characteristics, strong coupling, and nonlinearity. To solve the above problems, firstly, a solar-driven biomass supercritical water gasification technology combined with a molten salt energy storage system is proposed in this paper. This system effectively overcomes the intermittent problem of solar energy and provides a new method for the carbon-neutral process of hydrogen production. Secondly, the high dimensional model representation (HDMR) approach, as a surrogate model, was used to predict the production and lower heating value of syngas developed in Aspen Plus, which were validated using experimental data obtained from the literature. The ultimate analysis of biomass, temperature, pressure, and biomass-to-water ratio (BWR) were selected as input variables for the model. The non-dominated sorted genetic algorithm II (NSGA II) was considered to maximize the gasification yield of H2 and the LHV of syngas in the SCWG process for five different types of biomass. Firstly, the results showed that HDMR models demonstrated high performance in predicting the mole fraction of H2, CH4, CO, CO2, gasification yield of H2, and lower heating value (LHV) with R2 of 0.995, 0.996, 0.997, 0.996, 0.999, and 0.995, respectively. Secondly, temperature and BWR were found to have significant effects on SCWG compared to pressure. Finally, the multi-objective optimization results for five different types of biomass are discussed in this paper. Therefore, these operating parameters can provide an optimal solution for increasing the economics and characteristics of syngas, thus keeping the process energy efficient.

Catalytic Biomass Gasification in Supercritical Water and Product Gas Upgrading

AbstractThe gasification of biomass with supercritical water, also known as SCWG, is a sustainable method of hydrogen production. The process produces a mixture of hydrogen, carbon oxides, and hydrocarbons. Upgrading this mixture through steam or dry reforming of hydrocarbons to create synthesis gas and then extra hydrogen is a viable way to increase hydrogen production from biomass. This literature review discusses combining these two processes and recent experimental work on catalytic SCWG of biomass and its model compounds and steam/dry reforming of produced hydrocarbons. It focuses on catalysts used in these processes and their key criteria, such as activity, selectivity towards hydrogen and methane, and ability to inhibit carbon formation and deposition. A new criterion is proposed to evaluate catalyst performance in biomass SCWG and the need for further upgrading via reforming, based on the ratio of hydrogen bound in hydrocarbons to total hydrogen produced during SCWG. The review concludes that most catalysts used in biomass SCWG trap a large proportion of hydrogen in hydrocarbons, necessitating further processing of the product stream.

A Review of the Design and Performance of Catalysts for Hydrothermal Gasification of Biomass to Produce Hydrogen-Rich Gas Fuel.

Supercritical water gasification has emerged as a promising technology to sustainably convert waste residues into clean gaseous fuels rich in combustible gases such as hydrogen and methane. The composition and yield of gases from hydrothermal gasification depend on process conditions such as temperature, pressure, reaction time, feedstock concentration, and reactor geometry. However, catalysts also play a vital role in enhancing the gasification reactions and selectively altering the composition of gas products. Catalysts can also enhance hydrothermal reforming and cracking of biomass to achieve desired gas yields at moderate temperatures, thereby reducing the energy input of the hydrothermal gasification process. However, due to the complex hydrodynamics of supercritical water, the literature is limited regarding the synthesis, application, and performance of catalysts used in hydrothermal gasification. Hence, this review provides a detailed discussion of different heterogeneous catalysts (e.g., metal oxides and transition metals), homogeneous catalysts (e.g., hydroxides and carbonates), and novel carbonaceous catalysts deployed in hydrothermal gasification. The article also summarizes the advantages, disadvantages, and performance of these catalysts in accelerating specific reactions during hydrothermal gasification of biomass, such as water-gas shift, methanation, hydrogenation, reforming, hydrolysis, cracking, bond cleavage, and depolymerization. Different reaction mechanisms involving a variety of catalysts during the hydrothermal gasification of biomass are outlined. The article also highlights recent advancements with recommendations for catalytic supercritical water gasification of biomass and its model compounds, and it evaluates process viability and feasibility for commercialization.

Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation

Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation

Catalytic gasification of indole in supercritical water with non-noble bimetallic catalysts

Indole is commonly reported as a N-containing heterocyclic compounds in biomass supercritical water gasification (SCWG), and is difficult to be degraded to small molecule substances. In this work, Ni/AC, Ni–Cu/AC, Ni–Co/AC, Ni–Ce/AC were prepared and tested for catalytic SCWG of indole. The results showed that Ni–Cu/AC had the highest activity for converting indole and producing gases, leading to the highest hydrogen gasification efficiency (135.1%) and carbon gasification efficiency (33.1%). During the catalytic SCWG of indole, the addition of auxiliary metal Cu in multi-component catalyst showed synergistic effects by changing reaction pathways and reaction rates. It significantly promoted water-gas shift and steam reforming reactions, so forming the highest H2 yield of 2.62 mol mol−1. Furthermore, large specific surface, Ni reduction and high catalyst stability point to potential advantages of the Ni–Cu/AC catalyst for catalytic SCWG of recalcitrant N-containing compounds.

Partial oxidation gasification kinetics of indole in supercritical water for hydrogen production

Indole is one of refractory intermediate products in hydrothermal conversion of biomass, and its decomposition significantly restricts the improvement of gasification efficiency and hydrogen production in biomass supercritical water gasification (SCWG). In order to improve gasification efficiency of indole and moderate reaction conditions for hydrogen production in biomass SCWG, indole (0.25 mol/L) was gasified in supercritical water at 540–600 °C, 25 MPa, 0–0.8 of oxidation coefficients (OC) in min-batch reactors. Corresponding pathways and kinetics of partial oxidation of indole in supercritical water for hydrogen production were explored. The results showed that the addition of oxidant (hydrogen peroxide) promoted steam reform and decomposition, especially ring-opening reactions, so increasing the gasification efficiency of indole and the yields of H2 and CO2 in gaseous products. The maximum hydrogen gasification efficiency could reach up to 60.3% at OC = 0.6, which was about twice that of non-oxidant. Moreover, 29 groups of reaction rate constants, activation energies and Arrhenius constants were obtained in the established kinetic model.

Development of a Partitioning Kinetic Model of Biomass Gasification in Supercritical Water with a Fluidized Bed Reactor

Biomass supercritical water gasification technology is considered an effective way to realize the utilization of biomass, and further development of this technology depends on an accurate kinetic model. In this work, the reactor was distributed into a nozzle affected zone, a dense phase zone, and a freeboard zone according to the characteristic of the physical and chemical field, and a reaction kinetic model suitable for each zone was developed, which could be used for dynamic and scale-up studying of the reactor. First, three groups of gasification experiments (feedstock and biochar with low and high concentrations) were conducted to obtain the gas yield for calculation of the kinetic parameter, and obtain the evolution of the specific area of biochar for the calculation of the porous structure constant ψ. Then, detailed reaction pathways for biomass and biochar were proposed and a random pore kinetic model taking specific surface area into account was developed based on the first order of each reactant.

Supercritical water gasification of lignocellulosic biomass: Development of a general kinetic model for prediction of gas yield

• A general kinetic model of biomass supercritical water gasification was developed. • The kinetic model was validated successfully based on results from real biomass. • Temperature played the most important role in producing gaseous species. • The effect of residence time on gas formation was found to be minor. • SCWG of lignin produced the highest yield of hydrogen. Supercritical water gasification (SCWG) utilizes water as the reaction medium to convert biomass into a mixture of gases (i.e., H 2 , CO 2 , CO, CH 4 , and other short-chain hydrocarbons) at typical operating temperature ≥ 400 °C and pressure ≥ 23 MPa. Understanding kinetics of SCWG processes is critically important for future development and scale-up application of the SCWG technology. Thus, this study aimed to develop a general kinetic model to predict the yields of gases from SCWG of various lignocellulosic feedstocks with varying contents of cellulose, hemicellulose and lignin. The model was established based on the experimental results from K 2 CO 3 -catalyzed SCWG of various biomass model compounds including cellulose, xylan and lignin at 450–550 °C for 10–50 min, followed by validation of the model using the experimental data obtained from SCWG of real biomass (i.e., corn stalk and pinewood) under same reaction conditions. The validation results showed that the general kinetic model developed could accurately predict the yields of gases from real biomass SCWG and the quantitative influences of temperature and residence time on the gas yield in biomass SCWG. Additionally, the kinetic model has been validated other SCWG data in the literature obtained with various facilities and different lignocellulosic biomass feedstocks. The development of the general kinetic model provides an applicable way to assess the potential of SCWG of various lignocellulosic biomass feedstocks for producing combustible gases at various conditions.

Interpretable machine learning for predicting and evaluating hydrogen production via supercritical water gasification of biomass

Supercritical water gasification (SCWG) of biomass for hydrogen production is a clean and promising technology. However, due to many factors involved in SCWG process, including biomass properties and process parameters, it is time consuming and capital intensive to evaluate the multi-dimensional relationship between them, as well as the hydrogen production capability using the experimental method. Therefore, it is necessary to develop an accurate model to predict and evaluate SCWG process in an economic way. This study established four machine learning models to predict hydrogen production via SCWG of biomass, interpreted the inner workings of the optimal model and evaluated the performance of SCWG. The results suggested that random forest (RF) model outperformed gaussian process regression, artificial neural network and support vector machine models for predicting H2 yield (R2 = 0.9782). Feature importance and partial dependence analysis combined with the RF model were used to visually present the relative importance and average partial relationship selected biomass properties and process parameters for H2 yield. The contour plots based on the RF model indicated that maximum hydrogen reaction efficiency (45.6%) and exergy efficiency (43.3%) were achieved when biomass with a high O content and low H/C ratio were used as feedstock. This study demonstrated that the machine learning model is a practical tool for predicting hydrogen production. Meanwhile, the interpretation of the model can clarify the influence of the variables involved in SCWG on hydrogen production, which is helpful for selecting the appropriate feedstock and optimize the process parameters in the experiment or practical engineering.