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3.1. Hydrodynamic sub-model

Figure 2 3.1. Hydrodynamic sub-model

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Overview of gasification steps.[59,87] *Depends on moisture present in feed (wood). Table 1 whole gasification process is divided into three sub-models: kinet- ics, hydrodynamics and streams (material classification and prop- erties). These sub-models are further divided into classes (shown in Fig. 1) in order to have a realistic approach toward the overall gasification process. Out of various regimes of fluidization, this study focuses on bubbling regime. The Kinetic sub-model has sep- arate subroutines to take care of drying, devolatilization and gasi- fication. The streams that flow in and out of control volume are described in material sub-model (as solid liquid or gas). The vari- ous thermodynamic properties of these streams are calculated in this sub-model. It can be seen in the Fig. 1 that the information can flow both ways among the sub-models. The boundary of the investigated system is shown in Fig. 2. Bed geometry, temperature, pressure, composition and flow rate of the incoming streams are the inputs of the model. It is assumed that the cyclone instanta- neously separates the gas from the solids and no reaction takes place in the cyclone.
Overview of gasification steps.[59,87] *Depends on moisture present in feed (wood). Table 1 whole gasification process is divided into three sub-models: kinet- ics, hydrodynamics and streams (material classification and prop- erties). These sub-models are further divided into classes (shown in Fig. 1) in order to have a realistic approach toward the overall gasification process. Out of various regimes of fluidization, this study focuses on bubbling regime. The Kinetic sub-model has sep- arate subroutines to take care of drying, devolatilization and gasi- fication. The streams that flow in and out of control volume are described in material sub-model (as solid liquid or gas). The vari- ous thermodynamic properties of these streams are calculated in this sub-model. It can be seen in the Fig. 1 that the information can flow both ways among the sub-models. The boundary of the investigated system is shown in Fig. 2. Bed geometry, temperature, pressure, composition and flow rate of the incoming streams are the inputs of the model. It is assumed that the cyclone instanta- neously separates the gas from the solids and no reaction takes place in the cyclone.
Fig. 1. Simplified structure of model.
Fig. 1. Simplified structure of model.
Flow assumed in various version of two-phase theory [24]. VEB, visible flow in bubble phase; IFB, invisible flow in bubble phase (through flow); TFB, total flow in bubble phase; FDP, flow in dense phase; TF, total flow. Table 2
Flow assumed in various version of two-phase theory [24]. VEB, visible flow in bubble phase; IFB, invisible flow in bubble phase (through flow); TFB, total flow in bubble phase; FDP, flow in dense phase; TF, total flow. Table 2
Fig. 2. Modeling boundary for the gasification process.
Fig. 2. Modeling boundary for the gasification process.
List of primary equations used in hydrodynamic model. Table 3
List of primary equations used in hydrodynamic model. Table 3
Fig. 3. Mass exchange between the bubble and emulsion phases.
Fig. 3. Mass exchange between the bubble and emulsion phases.
Fig. 4. Simplified representation of gasification steps.
Fig. 4. Simplified representation of gasification steps.
Kinetics of primary devolatilization [88,89]. Table 4
Kinetics of primary devolatilization [88,89]. Table 4
List of gasification reaction present in model.
List of gasification reaction present in model.
Table 5 Fig. 5. Simplified model for volatilization. While modeling gasification, another popular simplification is to assume devolatilization as instantaneous [45-51]. Considering the fact that devolatilization is much faster than gasification it seems correct from first principle. The gas released during devola- tilization has to mix with the other up-flowing gas stream (e.g. gas- ifying agent and the gas released from char gasification). However, if all the devolatilized-gas is mixed at one point it will affect the equilibrium and bed hydrodynamics and hence will influence the final gas composition. Gas mixing, is a sensitive parameter and is
Table 5 Fig. 5. Simplified model for volatilization. While modeling gasification, another popular simplification is to assume devolatilization as instantaneous [45-51]. Considering the fact that devolatilization is much faster than gasification it seems correct from first principle. The gas released during devola- tilization has to mix with the other up-flowing gas stream (e.g. gas- ifying agent and the gas released from char gasification). However, if all the devolatilized-gas is mixed at one point it will affect the equilibrium and bed hydrodynamics and hence will influence the final gas composition. Gas mixing, is a sensitive parameter and is
Fig. 6. Gas mixing profile along the height. i: increasing; d: decreasing; e: equal; p: 1st increases then decreases. Char, is the product of the preliminary drying and devolatiliza- tion step. Char is often modeled as carbon. However, hydrogen and oxygen fractions in char are reported [53]. This is because char composition is strongly dependent on the devolatilization step. Char gasification too is a complex process due to the combination of different mechanisms like mass transfer, chemical reaction and heat transfer. The net carbon (char) after devolatilization is in- volved in a number of heterogeneous gasification reactions, as listed in Table 5. Data reveals that with 10% addition of raw wood ash in the gasifier, at temperature of 800 °C, the reactivity of wood has increased nearly 6 times [54,55]. Hence the catalytic activity of
Fig. 6. Gas mixing profile along the height. i: increasing; d: decreasing; e: equal; p: 1st increases then decreases. Char, is the product of the preliminary drying and devolatiliza- tion step. Char is often modeled as carbon. However, hydrogen and oxygen fractions in char are reported [53]. This is because char composition is strongly dependent on the devolatilization step. Char gasification too is a complex process due to the combination of different mechanisms like mass transfer, chemical reaction and heat transfer. The net carbon (char) after devolatilization is in- volved in a number of heterogeneous gasification reactions, as listed in Table 5. Data reveals that with 10% addition of raw wood ash in the gasifier, at temperature of 800 °C, the reactivity of wood has increased nearly 6 times [54,55]. Hence the catalytic activity of
Fig. 7. Model algorithm for the dense zone (bottom bed). The energy conservation equation is solved across the entire zone, and it is assumed that, within a zone, the temperature is uniform. This assumption is based on efficient solid mixing and the domi- nance of the solid’s heat capacity within a fluidized bed. The overall
Fig. 7. Model algorithm for the dense zone (bottom bed). The energy conservation equation is solved across the entire zone, and it is assumed that, within a zone, the temperature is uniform. This assumption is based on efficient solid mixing and the domi- nance of the solid’s heat capacity within a fluidized bed. The overall
Model input, data extracted from tables in reference [73]. Table 7 pellets, consisted of four variables that could be independently var- ied: the flow rates of biomass, air, steam and oxygen. The flow rates of air and oxygen determined the oxygen content in the en- riched air. The experimental program comprised tests with air- oxygen-steam mixtures in different proportions i.e. to tests vary- ing stoichiometric ratio (SR), steam-to-biomass ratio (SBR), and the oxygen percentage of the enriched air (OP). For their study, they varied SR, SBR and OP (between the indicated ranges: SR from 0.24 to 0.38, SBR from 0 to 0.63 and OP from 21% to 40%) to explore the effects of these variables on the quality and composition of the produced gas. Table 7 lists the key operating parameters for Fig. 8. The details of the model inputs and operating parameters can be found elsewhere [75-76]. It can be seen in Fig. 8 that the model predictions are in good agreement with the measured values: all the predictions are within +5% of the measured value. During each of the test runs, many of the operating parameters (stoichiometric ratio, steam-to-biomass ratio, temperature, etc.) were changed. Therefore, it is not easy to find the exact cause for the observed change in gas composition among the test runs. However, the authors [73] have tried to give a detailed explanation.
Model input, data extracted from tables in reference [73]. Table 7 pellets, consisted of four variables that could be independently var- ied: the flow rates of biomass, air, steam and oxygen. The flow rates of air and oxygen determined the oxygen content in the en- riched air. The experimental program comprised tests with air- oxygen-steam mixtures in different proportions i.e. to tests vary- ing stoichiometric ratio (SR), steam-to-biomass ratio (SBR), and the oxygen percentage of the enriched air (OP). For their study, they varied SR, SBR and OP (between the indicated ranges: SR from 0.24 to 0.38, SBR from 0 to 0.63 and OP from 21% to 40%) to explore the effects of these variables on the quality and composition of the produced gas. Table 7 lists the key operating parameters for Fig. 8. The details of the model inputs and operating parameters can be found elsewhere [75-76]. It can be seen in Fig. 8 that the model predictions are in good agreement with the measured values: all the predictions are within +5% of the measured value. During each of the test runs, many of the operating parameters (stoichiometric ratio, steam-to-biomass ratio, temperature, etc.) were changed. Therefore, it is not easy to find the exact cause for the observed change in gas composition among the test runs. However, the authors [73] have tried to give a detailed explanation.
Fig. 9. Effect of different gas-mixing patterns on the gas composition for test run T1 of Table 7. There are some extensive models that exist today [9,69-72], but it is difficult to check their robustness. Most of them are very spe- cific and are formulated to study a particular case. It is hard to gen- eralize these models, required are not pub because most of the key information or data ished. An effort has been made here to vali- date this model with as diverse data as possible. Fig. 8 is based on the study of Cam poy et al. [73]. They used wood pallets with empirical formula CH;.409¢4 (dry, ash free basis). The moisture and ash content were 6.3% and 0.5% (wt basis). The test rig is de- tailed elsewhere [74 . The operating mode for all the tests in the pilot-scale FBG was auto-thermal and adiabatic. Also, the temper- ature of the gasification agent was fixed at 400 °C for all tests. The mode of operation t hey used for conducting the tests, for wood
Fig. 9. Effect of different gas-mixing patterns on the gas composition for test run T1 of Table 7. There are some extensive models that exist today [9,69-72], but it is difficult to check their robustness. Most of them are very spe- cific and are formulated to study a particular case. It is hard to gen- eralize these models, required are not pub because most of the key information or data ished. An effort has been made here to vali- date this model with as diverse data as possible. Fig. 8 is based on the study of Cam poy et al. [73]. They used wood pallets with empirical formula CH;.409¢4 (dry, ash free basis). The moisture and ash content were 6.3% and 0.5% (wt basis). The test rig is de- tailed elsewhere [74 . The operating mode for all the tests in the pilot-scale FBG was auto-thermal and adiabatic. Also, the temper- ature of the gasification agent was fixed at 400 °C for all tests. The mode of operation t hey used for conducting the tests, for wood
Fig. 10. Comparison of model prediction for steam as the gasifying medium.. In Fig. 8, both model predictions and measured data show that the percentage of hydrogen varied widely (19-35%), while the methane fraction in the produced gas was almost constant
Fig. 10. Comparison of model prediction for steam as the gasifying medium.. In Fig. 8, both model predictions and measured data show that the percentage of hydrogen varied widely (19-35%), while the methane fraction in the produced gas was almost constant
Model input parameters (*: 15% of total carbon present in biomass).
Model input parameters (*: 15% of total carbon present in biomass).
Model input parameters [79].
Model input parameters [79].
Related topics:
Mechanical EngineeringChemical EngineeringFluidized BedBiomass gasificationFluidized Bed ReactorCritical Parameter
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