Numerical analysis of wood biomass packing factor in a fixed-bed gasification process
Introduction
Biomass is a renewable energy source and its energy use in fixed bed gasifiers is an important process for power generation and cooking systems [1]. The comprehension of thermal, physical, and chemical phenomena involved in the biomass to gas (BTG) transformation enables improved reactor efficiency and reduced pollutant emissions [2]. The gasification process includes mass transfer mechanisms associated with drying, pyrolysis, oxidation, and reduction stages, and energy transfer mechanisms, such as convection and radiation. Therefore, acquisition of experimental data with complex parameters during the experimental stage is challenging [3]. Modeling helps to describe system behavior, enabling identification of complex phenomena, such as incident radiation, solid-gas heat transfer, diffusive parameters, and the kinetic interaction of particles in solid-gas reactions [4,5].
There is an ongoing line of work in the scientific community that study the conversion of biomass in fixed beds by means of CFD models using commercial software. The CFD models require a phenomenological description of the biomass conversion to gaseous fuels. This interaction between the solid and gas phases has been implemented through User Defined Functions (UDFs), where sub models created in C++ were adapted; these sub-models are embedded into the code and therefore are also solved by the CFD software [2,6,7].
CFD models of biomass combustion and/or gasification in fixed beds have been used to study the phenomenology involved during thermochemical conversion, various important operation parameters of the process, and chemical kinetic mechanisms, and physical properties of biomass have been analyzed. Several authors developed dimensional models for both combustion [2,3,8] and gasification [9,10] in fixed beds, with their work aiming to study the influence of the heterogeneous properties of the bed during combustion, and to compare temperature fields and gaseous concentrations in the two-dimensional domain during gasification. Other authors used numerical models for validating such parameters as combustion time, furnace temperature, combustion gases emissions (including NOx), carbon content in the ashes, and combustion total efficiency [11]. Regarding fixed bed combustion, several studies experimentally measured flame front velocity, process speed movement (which is dependent on the air supply velocity), biomass heating value, and particle size in order to obtain a transient behavior of the local temperature, oxygen consumption rate, and heat transfer phenomena using model predictions [12]. Other authors have also validated gas composition for different air flows in downdraft gasification, as well as the temporal and spatial evolution of temperature, comparing model results with experimental ones available in the literature [13]. For downdraft gasifiers, the Lagrangian model to predict the temperature field inside the reactor, and comparing the theoretical temperature distribution with experimental data has been proposed by Janajreh et al. [14]. Other studies have focused on the analysis of the effect of the geometry and configuration of the combustion chamber over combustion efficiency, emissions and process temperature [6]. Regarding biomass shape and size, there are thermochemical conversion models used to predict the intra-particle temperature gradient, mass loss rate, particle size and density [7]. Physicochemical and geometrical properties of biomass have also been studied during the operation of reactors and burners, with several authors presenting numerical models where two types of geometries can be evaluated, including cylindrical particles (horizontal and upright) and spherical ones [7]. Other authors have focused on the analysis of the biomass gasification process through the evaluation of different types of biomass with several particle geometries, considering physicochemical properties, such as density, void fraction, sphericity, surface/volume ratio [13] and particle diameter [6].
The literature also presents the characterization and analysis of the flame front and process velocity movement as functions of the input experimental settings, such as air mass flow, biomass heating value and particle size. Those studies analyze the transient evolution of the reaction front for all solid to gas conversion stages [13], studying mass and heat transfer phenomena (including radiation, convection, and conduction phenomena) [12].
In general, CFD numerical models enable the prediction of the reactor/plant behavior to the variation of several operating conditions [11], where the analyses of the channeling generated in the bed is highlighted. Channeling has been blamed of generating higher concentrations of nitrous oxides and unburned products under combustion regimes [8]. This work presents a fixed bed biomass combustion model that has been extended to tackle gasification conditions. The effect of the packing factor on the gasification process behavior was evaluated. The validating parameters are temperature fields, flame front velocity, biomass consumption rate, syngas volumetric flow, low heating gas, equivalence ratio, cold gas efficiency, and producer gas composition. Afterwards, a sensitivity analysis of convection and radiation heat transfer mechanisms was performed in function of biomass packing factor, enabling a better description of the phenomenology involved in the process with different physical properties of wood biomass. The model used in this work allows to study different types of biomass, such as energy crops, forest, agricultural waste, and others, as renewable feedstock for thermochemical processes. These solid biofuels are characterized by different densities (bulk and particle) [15]. The packing factor (PF) quantifies the relationship between these two parameters of significant importance for the biomass gasification process in fixed bed [16].
Section snippets
Materials and methods
According to the state of the art, the numerical analyses of the effect of biomass packing factor (PF = Average bulk density/Particle density) on fixed bed gasification/combustion by means of models or experimental tests are scarce. The PF parameter is a fundamental variable that affects gasification performance due to the random packing of biomass in a fixed bed [17]. Therefore, the packing factor is the process parameter (the operating condition as a function of biomass physical properties)
Model description
The fixed bed biomass gasification approach was developed by considering separately the solid and gaseous phases. The model presented in this work is an extended version of the model developed for biomass combustion in fixed beds. All the solid parameters are formulated as Eulerian scalars that are embedded into the code by User Defined Scalars (UDS). The interaction between phases is modelled as sources in the corresponding transport equations, which are introduced in the code by User Defined
Results and discussion
The aim of this work is twofold. First, to extend the CFD model initially developed for simulating combustion in a fixed bed to simulate the gasification process. Therefore, in this section, a detailed model validation is presented. Second, to take advantage of the model versatility and power calculation, this study evaluated the effect of the packing factor of wood biomass on the fixed bed gasification process.
Conclusions
The adaptation of the model to gasification conditions required modification of the chemical kinetics of pyrolysis, oxidation, and reduction processes using the adequate kinetic mechanism of the reactions for gasification. The numerical simulation of biomass gasification has a dimensional prediction because the average relative error of all the thermodynamic variables that were validated (flame front velocity, biomass consumption rate, volumetric gas flow, gas heating value, equivalence ratio,
Acknowledgments
The authors acknowledge the financial support of Universidad de Antioquia through the project “Sostenibilidad 2017–2018.” In addition, this work was financially supported by project ENE2015-67439-R of the Ministry of Economy and Competitiveness and the work of Sergio Chapela López has been supported by the grant BES-2016-076785 of the Ministry of Economy, Industry and Competiveness (Spain).
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