Modeling Multicomponent Fuel Droplet Vaporization with Finite Liquid Diffusivity Using Coupled Algebraic-Dqmom with Delumping

Multicomponent fuel droplet vaporization models for use in combustion CFD codes often prioritize computational efficiency over model complexity. This leads to oversimplifying assumptions such as single component droplets or infinite liquid diffusivity. The previously developed Direct Quadrature Method of Moments (DQMoM) with delumping model demonstrated a computationally efficient and accurate approach to solve for every discrete species in a well-mixed vaporizing multicomponent droplet. To expand the method to less restrictive cases, a new solution technique is presented called the Coupled Algebraic-Direct Quadrature Method of Moments (CA-DQMoM). In contrast to previous moment methods for droplet vaporization, CA-DQMoM solves for the evolution of two liquid distributions by coupling a monovariate, homogeneous DQMoM approach with additional algebraic moment equations, allowing for a more complex droplet vaporization model with finite rates of liquid diffusion to be solved with computational efficiency. To further decrease computational expense, an approximation that employs the same nodes for both distributions can be used in certain cases. Finally, a delumping technique is adapted to the finite diffusivity model to reconstruct discrete species information at minimal computational cost. The model is proven to be accurate relative to a full discrete component model for both a kerosene droplet comprised of 36 species and a multicomponent droplet of 200 species while maintaining the computational efficiency of continuous thermodynamics models. The combined accuracy and computational efficiency demonstrated by the CA-DQMoM with delumping model for a multicomponent fuel droplet with finite liquid diffusivity makes it ideal for incorporation into CFD models for complex combustion process

Direct Quadrature Method of Moments with Delumping for Modeling Multicomponent Droplet Vaporization

A multicomponent droplet vaporization model which combines the computational efficiency of continuous thermodynamic approaches with the detailed species information provided by discrete component models has been developed. The Direct Quadrature Method of Moments (DQMoM) is used to efficiently solve for the evolution of the nodes and weights of the equivalent liquid-phase mole fraction distribution without assuming any functional form. The novelty of the approach is an inexpensive delumping procedure that is used to reconstruct the time-dependent mole fractions and fluxes for all discrete species. When applied to a vaporizing kerosene droplet, agreement between the full discrete component model, which solves ODEs for every individual species, and DQMoM with delumping, which solves only a few ODEs, is excellent. This computationally inexpensive model is well-suited for implementation in CFD codes with detailed kinetic mechanisms, as it enables accurate calculation of species source terms from the droplets without incurring an unrealistic computational cost

An Efficient Coal Pyrolysis Model for Detailed Tar Species Vaporization

An accurate and computationally efficient model for the vaporization of many tar species during coal particle pyrolysis has been developed. Like previous models, the molecular fragments generated by thermal decomposition are partitioned into liquid metaplast, which remains in the particle, and vapor, which escapes as tar, using a vapor-liquid equilibrium(VLE) sub-model. Multicomponent VLE is formulated as a rate-based process, which results in an ordinary differential equation (ODE) for every species. To reduce the computational expense of solving many ODEs, the model treats tar and metaplast species as a continuous distribution of molecular weight. To improve upon the accuracy of previous continuous thermodynamic approaches for pyrolysis, the direct quadrature method of moments (DQMoM) is proposed to solve for the evolving distributions without assuming any functional form. An inexpensive delumping procedure is also utilized to recover the time-dependent mole fractions and fluxes for every discrete species. The model is well-suited for coal-to-chemicals processes, and any application which requires information on a range of tar species. Using a modified CPD model as the basis for implementation of the VLE submodel, agreement between the full discrete model and DQMoM with delumping is excellent, with substantial computational savings

A Hybrid Droplet Vaporization-Chemical Surrogate Approach for Emulating Vaporization, Physical Properties, and Chemical Combustion Behavior of Multicomponent Fuels

The complex nature of multicomponent aviation fuels presents a daunting task for accurately simulating combustion behavior without incurring impractical computational costs. To reduce computation time, chemical fuel surrogates comprised of only a few species are used to emulate the combustion of complex pre-vaporized fuels. These surrogates are often unable to match the vaporization behavior and physical properties of the real fuel and fail to capture the effect of preferential vaporization on combustion behavior. Therefore, a computationally efficient, hybrid droplet vaporization-chemical surrogate approach has been developed which emulates both the physical and chemical properties of a multicomponent kerosene fuel. The droplet vaporization/physical portion of the hybrid uses the Coupled Algebraic–Direct Quadrature Method of Moments with delumping to accurately solve for the evolution of every discrete species in a vaporizing fuel droplet with the computational efficiency of a continuous thermodynamic model. The chemical surrogate portion of the hybrid is linked to the vaporization model using a functional group matching method, which creates an instantaneous surrogate composition to match the distribution of chemical functional groups (CH2, (CH2)n, CH3 and Benzyl-type) in the vaporization flux of the full fuel. The result is a hybrid method which can accurately and efficiently predict time-dependent, distillation-resolved combustion property targets of the vaporizing fuel and can be used to investigate the effects of preferential vaporization on combustion behavior

Pore-resolving Simulation of Char Particle Gasification Using Micro-CT

Understanding the interaction between transport, reaction and morphology at the scale of individual char particles is important for optimizing solid fuel gasification and combustion processes. However, most particle-scale models treat porous char particles as an effective porous continuum, even though the presence of large, irregular macropores, voids and fractures render such upscaled treatments mathematically invalid, and the models non-predictive. A new modeling framework is therefore proposed to elucidate the impact of morphology on char particle gasification and combustion. A pore-resolving, transient, three-dimensional simulation for gasification of a realistic coal char particle is developed based on X-ray micro-computed tomography (micro-CT). The large macropores and voids resolved by micro-CT are explicitly represented in the particle’s geometry and conservation equations based on first principles are solved in those regions. Upscaled, effective-continuum equations are applied only within the micro- and meso-porous grains surrounding the voids, where such equations are mathematically appropriate. To assess the impact of the realistic particle morphology, a second model which employs effective-continuum equations everywhere and assumes spherical symmetry is also developed for a particle having the same initial mass, volume, porosity, surface area and equivalent diameter as the pore-resolving model. The results indicate that large, irregular voids enhance mass transport throughout the particle and affect its overall conversion behavior when reactions occur under intra-particle diffusion control

Characteristics and Applications of Biochars Derived from Wastewater Solids

Pyrolysis is a thermochemical decomposition process that can be used to generate pyrolysis gas (py-gas), bio-oil, and biochar as well as energy from biomass. Biomass from agricultural waste and other plant-based materials has been the predominant pyrolysis research focus. Water resource recovery facilities also produce biomass, referred to as wastewater solids, that could be a viable pyrolysis feedstock. Water resource recovery facilities are central collection and production sites for wastewater solids. While the utilization of biochar from a variety of biomass types has been extensively studied, the utilization of wastewater biochars has not been reviewed in detail. This review compares the characteristics of wastewater biochars to more conventional biochars and reviews specific applications of wastewater biochar. Wastewater biochar is a potential candidate to sorb nutrients or organic contaminants from contaminated wastewater streams. While biochar has been used as a beneficial soil amendment for agricultural applications, specific research on wastewater biochar is lacking and represents a critical knowledge gap. Based on the studies reviewed, if biochar is applied to land it will contain less organic micropollutant mass than conventional wastewater solids, and polycyclic aromatic hydrocarbons are not likely to be a concern if pyrolysis is conducted above 700 °C. Wastewater biochar is likely to serve as a better catalyst to convert bio-oil to py-gas than other conventional biochars because of the inherently higher metal (e.g., Ca and Fe) content. The use of wastewater biochar alone as a fuel is also discussed. Finally, an integrated wastewater treatment process that produces and uses wastewater biochar for a variety of food, energy, and water (FEW) applications is proposed

IMECE2009-12985 REDUCED ORDER MODELING OF ENTRAINED FLOW SOLID FUEL GASIFICATION

ABSTRACT Reduced order models that accurately predict the operation of entrained flow gasifiers as components within integrated gasification combined cycle (IGCC) or polygeneration plants are essential for greater commercialization of gasification-based energy systems. A reduced order model, implemented in Aspen Custom Modeler, for entrained flow gasifiers that incorporates mixing and recirculation, rigorously calculated char properties, drying and devolatilization, chemical kinetics, simplified fluid dynamics, heat transfer, slag behavior and syngas cooling is presented. The model structure and submodels are described. Results are presented for the steady-state simulation of a two-metric-tonne-per-day (2 tpd) laboratory-scale Mitsubishi Heavy Industries (MHI) gasifier, fed by two different types of coal. Improvements over the state-of-the-art for reduced order modeling include the ability to incorporate realistic flow conditions and hence predict the gasifier internal and external temperature profiles, the ability to easily interface the model with plant-wide flowsheet models, and the flexibility to apply the same model to a variety of entrained flow gasifier designs. Model validation shows satisfactory agreement with measured values and computational fluid dynamics (CFD) results for syngas temperature profiles, syngas composition, carbon conversion, char flow rate, syngas heating value and cold gas efficiency. Analysis of the results shows the accuracy of the reduced order model to be similar to that of more detailed models that incorporate CFD. Next steps include the activation of pollutant chemistry and slag submodels, application of the reduced order model to other gasifier designs, parameter studies and uncertainty analysis of unknown and/or assumed physical and modeling parameters, and activation of dynamic simulation capability

Micro-CT-Based Approaches for Quantifying the Morphology of Pulverized Char Particles

Morphological analysis of pulverized coal char particles using two-dimensional (2-D) cross-sectional imaging has been widely employed, but its accuracy has not been adequately assessed. In this study, pulverized coal char particles are imaged in three dimensions (3-D) using high-resolution X-ray microcomputed tomography (micro-CT). Particle volume, macropore volume, and macroporosity are measured in three dimensions and analyzed as a function of distance from the particle center using averaging at each radial location. A technique for extracting each particle’s average wall thickness, another morphological parameter used for classification, is also presented based on micro-CT imaging. When applied to pulverized bituminous coal char particles, the micro-CT-based analysis revealed a similar spatial distribution of macroporosity among a population that would typically be classified as containing both group II (mixed porous-solid) and group III (dense) particles. Wall thicknesses determined by micro-CT were generally well predicted by a model representing the particles as thick- and thin-walled cenospheres. Comparisons between 2-D and 3-D techniques reveal significant differences because of the use of just a single cross-sectional image in 2-D approaches. A new method for estimating macroporosity from 2-D imaging, called the cylindrical stacking method, is proposed for cases in which the micro-CT analysis is not feasible

Pore-Resolving Simulations to Study the Impacts of Char Morphology on Zone II Combustion and Effectiveness Factor Models

Combustion and gasification of pulverized char often occur under zone II conditions, in which the rate of conversion depends on both heterogeneous reaction and gas transport within the particle\u27s porous structure. The morphology of porous char has a strong influence on intra-particle diffusion, and thus, on the overall conversion rate. Because pulverized coal and biomass char particles are often irregularly shaped and contain pores and voids which can approach the size of the particles themselves, conventional models based on spherical symmetry and coarse-grained, upscaled, effective continuum conservation equations are not applicable or appropriate. A recent 3-D, pore-resolving CFD simulation approach based on real char particle geometries obtained from X-ray micro-computed tomography (micro-CT) obviates the need to upscale over large heterogeneities and to make oversimplifying geometric assumptions. The micro-CT-based pore-resolving approach is employed here to study zone II combustion for fifty pulverized, porous coal char particles produced at a high heating rate. The large pores often present in char particles enhance reactant transport throughout the particles, even within the micro- and meso-porous carbon surrounding the large pores. This is particularly the case for network-type particle structures, due to the prominence of channels that extend from the particle surface. Because reactor-scale codes often employ one-dimensional models to calculate the reaction rates of tracked particles, pore-resolving simulations are used to assess the accuracy of existing effectiveness factor models for real char. Cenospherical particles can be reasonably modeled using an effectiveness factor solution for hollow spheres, but the behavior of more complex network morphologies is not well-predicted by any of the effectiveness factor models examined

A Hybrid Droplet Vaporization-Adaptive Surrogate Model Using an Optimized Continuous Thermodynamics Distribution

Liquid transportation fuels are composed of hundreds of species, necessitating the use of surrogates in CFD simulations. Surrogates composed of a few species are often formulated to emulate the combustion properties targets (CPTs) of pre-vaporized fuels but fail to reproduce their vaporization behavior, implying that such surrogates cannot replicate the CPTs in the presence of preferential vaporization. The prevailing approach to this problem proposes a physical–chemical surrogate formulated to match the fuel’s distillation curve in addition to its CPTs. However, the physical–chemical surrogate approach requires more species, may not reproduce the instantaneous (distillation-resolved) CPTs, and is not well-suited to conditions in which non-surrogate species surround the droplets. A recent hybrid approach addresses these shortcomings by combining a continuous thermodynamic model (CTM) for droplet vaporization with an adaptive chemical surrogate formulated using functional group matching (FGM). Whereas the hybrid model previously required a delumping calculation to recover discrete fluxes prior to FGM, the approach is modified here to directly predict the fluxes of functional groups using the CTM, increasing its flexibility for high-pressure applications. To this end, a novel, purely mathematical distribution variable is proposed to correlate key functional groups, in addition to thermophysical and transport properties. The accuracy and flexibility of both hybrid approaches compare favorably with the physical–chemical surrogate method. While droplet vaporization rates are well-represented by both methods, functional group fluxes and instantaneous CPTs are predicted more accurately by the hybrid methods, illustrating their potential for improving the accuracy of Eulerian-phase solvers in the presence of preferential vaporization