Analysis of the effect of steam-to-biomass ratio in fluidized bed gasification with multiphase particle-in-cell CFD Simulation

Biomass has been identified as a key renewable energy source to cope with upcoming environmental challenges. Gasification of biomass is becoming interested in large scale operation, especially in synthesis of liquid fuels. Bubbling and circulating fluidized bed gasification technology has overrun the interest over fixed bed systems. CFD studies of such reactor systems have become realistic and reliable with the modern computer power. Gasifying agent, temperature and steam or air to biomass ratio are the key parameters, which are responsible for the synthesis gas composition. Therefore, multiphase particle-in-cell CFD modeling was used in this study to analyze the steam to biomass, S/B, ratio in fluidized bed gasification. Due to the complexity of the full loop simulation of dual circulating fluidized bed reactor system, only the gasification reactor was considered in this study. Predicted boundary conditions were implemented for the particle flow from the combustion reactor. The fluidization model was validated against experimental data in beforehand where Wen-Yu-Ergun drag model was found to be the best. The effect of the S/B ratio was analyzed at a constant steam temperature of 1073K and a steam velocity of 0.47 m/s. Four different S/B of 0.45, 0.38, 0.28 and 0.20 were analyzed. The biomass was considered to be in complete dry condition where single step pyrolysis reaction kinetics was used. Each gasification simulation was carried out for 100 seconds. 8% reduction of hydrogen content from 57% to 49% and 17% increment of carbon monoxide from 13% to 30% were observed when the S/B was reduced from 0.45 to 0.20. Countable amounts of methane were observed at S/B of 0.28 and 0.20. The lower heating value of the product gas increased from 10.1 MJ/kg to 12.37 MJ/kg and the cold gas efficiency decreased from 73.2% to 64.6% when the S/B was changed from 0.45 to 0.20. The specific gas production rate varied between 1.64 and 1.04 Nm3/kg of biomass

Analyzing the effects of particle density, size, size distribution and shape for minimum fluidization velocity with Eulerian-Lagrangian CFD simulation

Fluidized bed reactor systems are widely used due to excellent heat and mass transfer characteristics followed by uniform temperature distribution throughout the reactor volume. The importance of fluidized beds is further demonstrated in high exothermic reactions such as combustion and gasification where fluidization avoids the hot spot and cold spot generation. A bed material, such as sand or catalyst, is normally involved in fluidized bed combustion and gasification of biomass. Therefore, it is vital to analyze the hydrodynamics of bed material, especially the minimum fluidization velocity, as it governs the fluid flowrate into the reactor system. There are limitations in experimental investigations of fluidized beds such as observing the bed interior hydrodynamics, where CFD simulations has become a meaningful way with the high computer power. However, due to the large differences in scales from the particle to the reactor geometry, complex interface momentum transfer and particle collisions, CFD modeling and simulation of particle systems are rather difficult. Multiphase particle-in-cell method is an efficient version of Eulerian-Lagrangian modeling and Barracuda VR commercial package was used in this work to analyze the minimum fluidization velocity of particles depending on size, density and size distribution. Wen-YU-Ergun drag model was used to model the interface momentum transfer where default equations and constants were used for other models. The effect of the particle size was analyzed using monodispersed Silica particles with diameters from 400 to 800 microns. Minimum fluidization velocity was increased with particle diameter, where it was 0.225 m/s for the 600 microns particles. The density effect was analyzed for 600 microns particles with seven different density values and the minimum fluidization velocity again showed proportionality to the density. The effect of the particle size distribution was analyzed using Silica. Particles with different diameters were mixed together according to pre-determined proportions as the final mixture gives a mean diameter of 600 microns. The 600 microns monodispersed particle bed showed the highest minimum fluidization velocity. However, some particle mixtures were composed with larger particles up to 1000 micron, but with a fraction of smaller particles down to 200 microns at the same time. This shows the effect of strong drag from early fluidizing smaller particles. The only variability for pressure drop during packed bed is the particle size and it was clearly observed in all three cases

Image Processing and Measurement of the Bubble Properties in a Bubbling Fluidized Bed Reactor

The efficiency of a fluidized bed reactor depends on the bed fluid dynamic behavior, which is significantly influenced by the bubble properties. This work investigates the bubble properties of a bubbling fluidized bed reactor using computational particle fluid dynamic (CPFD) simulations and electrical capacitance tomography (ECT) measurements. The two-dimensional images (along the reactor horizontal and vertical planes) of the fluidized bed are obtained from the CPFD simulations at different operating conditions. The CPFD model was developed in a commercial CPFD software Barracuda Virtual Reactor 20.0.1. The bubble behavior and bed fluidization behavior are characterized form the bubble properties: average bubble diameter, bubble rise velocity, and bubble frequency. The bubble properties were determined by processing the extracted images with script developed in MATLAB. The CPFD simulation results are compared with experimental data (obtained from the ECT sensors) and correlations in the literature. The results from the CPFD model and experimental measurement depicted that the average bubble diameter increased with an increase in superficial gas velocities up to 4.2 Umf and decreased with a further increase in gas velocities due to the onset of large bubbles (potential slugging regime). The bubble rise velocity increased as it moved from the lower region to the bed surface. The Fourier transform of the transient solid volume fraction illustrated that multiple bubbles pass the plane with varying amplitude and frequency in the range of 1–6 Hz. Further, the bubble frequency increased with an increase in superficial gas velocity up to 2.5Umf and decreased with a further increase in gas velocity. The CPFD model and method employed in this work can be useful for studying the influence of bubble properties on conversion efficiency of a gasification reactor operating at high temperatures.publishedVersio

Sensitivity Analysis and Effect of Simulation parameters of CPFD Simulation in Fluidized Beds

Fluidized bed technology is broadly applied in industry due to its distinct advantages. CFD simulation of fluidized beds is still challenging compared to single-phase systems and needs extensive validation. Multiphase particle-in-cell is a recently developed lagrangian modeling technique and this work is devoted to analyze the sensitivity of grid size, time step, and model parameters, which are the essences of accurate results. Barracuda VR 17.1.0 commercial CFD package was used in this study. 500µm sand particles and air was used as the bed material and fluidization gas respectively. Five different grids, having 27378, 22176, 16819, 9000 and 6656 computational cells were analysed, where five different time steps of 0.05, 0.01, 0.005, 0.001 and 0.0005 were used for each grid. One velocity step was maintained for 8 seconds. The bed pressure drop at packed bed operation was high for simulations with reduced time steps while equal pressure drops were observed during fluidization for all time steps. Time steps of 0.0005s and 0.001s and 0.005s produced equal result of 0.15 m/s for minimum fluidization velocity, irrespective of the grid size. The results from time steps of 0.05 and 0.01 are converged to the results from time steps of 0.005 and 0.001 by increasing simulation time per one velocity step.Sensitivity Analysis and Effect of Simulation parameters of CPFD Simulation in Fluidized BedsacceptedVersionpublishedVersionNivå

Sintering Behaviors of Synthetic Biomass Ash

Entrained flow gasification of biomass provides the opportunity to convert low-grade biogenic feedstocks to high-grade synthetic fuels. For a top-fired entrained flow slagging biomass gasifier, the thermophysical properties of the ash and slag limit process operation and affect process energy efficiency. The biomass ash has to be molten and slag viscosity has to be in a certain range for it to flow out of the gasifier. However, direct sampling, analysis, and evaluation of slag formation and behaviors are often challenging as entrained flow biomass gasification operates at high temperatures (i.e., 1200-1500°C) continuously. One alternative is to study synthetic ash's melting and sintering behaviors at elevated temperatures, which represent the major inorganic constituents in biomass ash. For thermochemical conversion of biomass, K, Ca and Si are typically the most common ash-forming elements. In this work, the synthetic ashes were prepared by mixing model compounds K2O, CaO and SiO2 in different mole ratios, which were pressed to form pellets. The selection of mole ratios was based on thermodynamic calculations that indicate that the tested model compound mixtures melt and flow with desired viscosity at certain temperature ranges. The pressed synthetic ashes were preheated at 900 °C for 8 hours to thermally homogenize them. Then the premelted synthetic ashes were heated at 1000 and 1400 °C in a muffle furnace with a residence time of 1 and 8 hours in air to study fusion behaviors and slag formation tendency, and were cooled down to room temperature gradually after the sintering test. The sintered residues were collected and analyzed by SEM/EDX to study the interactions of the model compounds and identify chemical compositions. The results showed that the mole ratios of model compounds have recognizable impacts on the composition, formation and transformation of mineral phases in residues from sintering tests. A strong correlation was also found between the sintering intensity of the synthetic ash and the mole ratios of model compounds.publishedVersio

Life Cycle Assessment under Uncertainty: A Scoping Review

Today, life cycle assessment (LCA) is the most widely used approach to model and calculate the environmental impacts of products and processes. The results of LCAs are often said to be deterministic, even though the real-life applications are uncertain and vague. The uncertainty, which may be simply ignored, is one of the key factors influencing the reliability of LCA outcomes. Numerous sources of uncertainty in LCA are classified in various ways, such as parameter and model uncertainty, choices, spatial variability, temporal variability, variability between sources and objects, etc. Through a scoping review, the present study aims to identify and assess the frequency with which LCA studies reflect the uncertainty and what are the tools to cope with the uncertainty to map the knowledge gaps in the field to reveal the challenges and opportunities to have a robust LCA model. It is also investigated which database, methodology, software, etc., have been used in the life cycle assessment process. The results indicate that the most significant sources of uncertainty were in the model and process parameters, data variability, and the use of different methodologies and databases. The probabilistic approach or stochastic modeling, using numerical methods such as Monte Carlo simulation, was the dominating tool to cope with the uncertainty. There were four dominant LCA methodologies: CML, ReCiPe, IMPACT 2002+, and TRACI. The most commonly used LCA software and databases were SimaPro® and Ecoinvent®, respectively

Techno-Economic and Life Cycle Cost Analysis through the Lens of Uncertainty: A Scoping Review

Researchers have long been interested in developing new economic assessment methods to provide credible information and facilitate the sustainable development of new technologies and products. The techno-economic analysis (TEA) and the life cycle cost analysis (LCCA) are the most widely used approaches for modeling and calculating processes’ economic impacts. A simulation-based TEA is a cost-benefit analysis that simultaneously considers technical and economic factors. In addition, the method facilitates the development of the entire project and provides a systematic approach for examining the interrelationships between economic and technological aspects. When it comes to economic studies, it is intimately bonded with uncertainty. There are numerous uncertainty sources, classified in various ways. The uncertainty reflects “an inability to determine the precise value of one or more parameters affecting a system.” The variability refers to the different values a given parameter may take. This implies that a probability density function (PDF), for instance, can be employed to estimate and quantify the variability of a given parameter. The bias refers to “assumptions that skew an analysis in a certain direction while ignoring other legitimate alternatives, factors, or data.” The present study identifies the frequency with which TEA/LCCA studies address uncertainty and gaps within the selected papers through a scoping review. The results indicate that the uncertainty associated with economic factors and model uncertainties were the main sources of uncertainty in TEA and LCCA. Moreover, possibilistic approaches such as the Monte Carlo methodology were the most frequently used tool to cope with the uncertainties associated with LCCA and TEA

A Scoping Review on Environmental, Economic, and Social Impacts of the Gasification Processes

In recent years, computer-based simulations have been used to enhance production processes, and sustainable industrial strategies are increasingly being considered in the manufacturing industry. In order to evaluate the performance of a gasification process, the Life Cycle Thinking (LCT) technique gathers relevant impact assessment tools to offer quantitative indications across different domains. Following the PRISMA guidelines, the present paper undertakes a scoping review of gasification processes’ environmental, economic, and social impacts to reveal how LCT approaches coping with sustainability. This report categorizes the examined studies on the gasification process (from 2017 to 2022) through the lens of LCT, discussing the challenges and opportunities. These studies have investigated a variety of biomass feedstock, assessment strategies and tools, geographical span, bioproducts, and databases. The results show that among LCT approaches, by far, the highest interest belonged to life cycle assessment (LCA), followed by life cycle cost (LCC). Only a few studies have addressed exergetic life cycle assessment (ELCA), life cycle energy assessment (LCEA), social impact assessment (SIA), consequential life cycle assessment (CLCA), and water footprint (WLCA). SimaPro® (PRé Consultants, Netherlands), GaBi® (sphere, USA), and OpenLCA (GreenDelta, Germany) demonstrated the greatest contribution. Uncertainty analysis (Monte Carlo approach and sensitivity analysis) was conducted in almost half of the investigations. Most importantly, the results confirm that it is challenging or impossible to compare the environmental impacts of the gasification process with other alternatives since the results may differ based on the methodology, criteria, or presumptions. While gasification performed well in mitigating negative environmental consequences, it is not always the greatest solution compared to other technologies

Aspen Plus simulation of biomass gasification for different types of biomass

A steady-state Aspen Plus model was developed for biomass gasification in a fluidized bed reactor. A combination of different Aspen Plus unit operations was used to model the gasification process. The model was used to predict the gasifier performance for different operating conditions like temperature, Steam to Biomass Ratio (STBR) and biomass loadings. Further, the gas compositions were compared for different types of biomass feed. The gasification reactor is based on Gibbs minimization with restricted equilibrium approach. Hydrogen production was around 50% for all the biomasses while CO production varies from 8% (Pig manure) to 24.5% (Olive residue) at 700°C. H2/CO ratio increases with an increase in STBR for all the biomass and the ratio was the highest for the pig manure and lowest for the olive residue. Olive residue, wood residue and miscanthus gave the H2/CO ratio of 1.5-2.1, which are more suitable as a feedstock in Fischer-Tropsch synthesis depending upon the operating temperature, a catalyst used and other operating conditions. For the wood residue, an increase in temperature increases the H2 and CO production whereas CO2 and CH4 concentration decreases and becomes stable after 700°C. H2 concentration increased from 46 % to 54 % and CO concentration decreases from 30% to 20% with an increase in STBR from 0.6 to 1 for the wood residue

Analysing the effect of temperature for steam fluidized-bed gasification of biomass with MP-PIC simulation

Gasification in fluidized beds is an outstanding technology in biomass to energy conversion. The multiphase particle-in-cell modelling has reduced the computational time related to CFD simulations of dense gas-solid systems like fluidized bed gasification. Barracuda VR commercial CFD package was used to analyse the effect of reactor temperature in steam gasification of biomass. The product gas composition, lower heating value and the cold gas efficiency were compared for steam at 873K, 973K and 1073K. The steam-to-biomass ratio was maintained at a constant value of 0.45. The hydrogen content of the product gas changed from 36% to 57% as the temperature was increased from 873K to 1073K whereas the carbon monoxide content changed from 33% to 13%. The lower heating value and the cold gas efficiency changed from 10.4 MJ/kg to 10.1 MJ/kg and 76.6% to 73.2% respectively within the same temperature range. The formation of tar was not modelled and the gas composition showed high sensitivity towards the reactor temperature