Modelling of tar reduction in Biomass Fuelled Gasification using Biomass Char as a Catalyst
Bibliographic record
Abstract
A project is carried out to develop a new process for tar reduction downstream of a biomass gasifier making use of cheap and active materials as catalysts. In the first stage of the project screening of different types of catalysts was carried out in a fixed bed reactor. This stage was concluded with the selection of char as a good candidate for catalytic tar reduction. In the second stage of the project, fixed and fluidized bed experiments have been carried out to characterize wood char as a catalyst using naphthalene as a model tar component. It was proven that char is a catalyst of high potential. In the third stage of the project, partly presented in this paper, a fluidized bed reactor model and design for catalytic tar reduction using char as a catalyst is aimed. A single char particle model for tar reduction is implemented to get better understanding of the process. The effect of different parameters such as particle size, temperature, environment composition will be simulated and compared with experiments carried out in a fixed bed reactor. The model foumulation and the main experimental results are presented in this paper. INTRODUCTION Biomass can be converted to energy carriers by biological or thermochemical processes. Among the various thermochemical processes gasification has attracted significant interest. This is because of the higher efficiencies produced by this technology either for small or large-scale systems. One of the most important technical barriers in biomass gasification development is the removal of tars. These tars are always present in producer gas from the gasifier as a side product. If tar content in the exit gas is high, it can condense in the gas transfer lines and cause severe plugging problems resulting in serious operational interruptions. Catalytic tar conversion has the potential to increase conversion efficiencies of gasification while simultaneously eliminating the need for collection and disposal of tar. The attractiveness of char as a catalyst for tar reduction originates from its low price and its natural production inside the gasifier. The char catalytic activity for tar reduction can be related to the pore size, the surface area, and the ash or mineral content. Molina et al. [ 1] reviewed the different parameters affecting char reactivity and the influence of some factors affecting the gasification rates. They also summarized the most important kinetic models proposed for char gasification, as well as the Published in Science in Thermal and Chemical Biomass Conversion, Victoria, Vancouver Island, BC, Canada, 30 Aug to 2 Sep 2004. effectiveness of these models for prediction of char reactivity. Golfier et al. [ 2] modelled the steam gasification of single char particle. They confirmed the numerical model with experiments. Srinivas et al. [ 3] developed a model for the gasification of a single-char particle. They considered diffusion effects, change in surface area during gasification but neglect temperature gradient in the particle. Dutta et al. [ 4] studied the reactivity of coal and char in carbon dioxide atmosphere. They found that each char sample has its own characteristic rate curve. They introduced a parameter that represents the change in available pore surface areas of the particles during reaction into the rate equation to account for the rate conversion curves. In a previous work, char showed high potential for tar reduction [ 5, 6, 7]. However, this catalyst is not inert and is consumed by gasification reactions with steam and CO2. Char gasification reactions affect the pore size, structure and number in the char particle in addition to the particle consumption. This causes a strong effect on char activity and performance for tar elimination. In order to develop a reactor model, a single char particle model for tar reduction is implemented to get better understanding of the process in terms of : • The behavior of a single char particle with respect to the gasification reactions • The change of the surface area and other key parameters of the char particle during the gasification • Tar concentration profiles in and around the char particle A model for the tar elimination using a porous char particle in an environment of N2, H2O, H2, CO2, CO, CH4 and naphthalene (model tar compound) is formulated. The effect of different parameters such as particle size, temperature, environment composition will be simulated. This paper presents the formulated model and the main experimental results that the model will be compared with. MODEL FORMULATION The single char particle model describes the gasification of char-carbon and naphthalene (model tar compound) conversion. It includes the diffusive transport and the kinetics of reactants and products in a porous spherical char particle. ASSUMPTIONS The model is based on the following main assumptions • Spherically symmetric particle, modelled as 1-D system • Fixed environment consisting of N2, H2O, CO2, H2, CH4 and naphthalene. The concentrations of all species and the temperature in the environment are known (quasi steady state). • The heterogeneous reactions 1-4 take place evenly in the whole char particle, i.e., porous particle model is assumed [ 1 0 (1 ) n X ρ ρ − = − , n=0], and the homogeneous gas phase reactions 5 and 6 take place every where, (see Table 1). • The gas film around the particle is characterized by the mass transfer coefficient and it is assumed the same for all species. • Tar is represented by the model tar compound “Naphthalene”. • The effective diffusivity inside the particle is taken as 2 eff D D e = ⋅ Published in Science in Thermal and Chemical Biomass Conversion, Victoria, Vancouver Island, BC, Canada, 30 Aug to 2 Sep 2004. MODEL KINETICS The main reactions considered in the model are given in Table 1. The used reaction rates and the kinetic data are given in Table 2. Table 1 Main reactions Table 2 Reaction rates and kinetic data Reaction Rate of reaction Ko E (kJ/kmol) Ref. C−steam 0.51 1 1 2 1 0.51 .[ ] ( ) s k H O r P R T η ⋅ = ⋅ 7 1.71 10 ⋅ (s.bar) -211000 [8] (7) C−CO2 0.3 2 2 2 2 0.3 .[ ] ( ) s k CO r P R T η ⋅ = ⋅ 6 3.1 10 ⋅ (s.bar) -215000 [9] (8) C−O2 3 3 3 2 .[ ] s r k O η = ⋅ 7 6.360 10 ⋅ 113000 [10] (9) C−H2 2 4 4 4 . s H r k P η = ⋅ 10 9.14 10 ⋅ -149050 [3] (10) Shift 1855.6 ( 1.89645 ) 10 T K − + = [3] (11) Naphthalene 6 6 app naphthalene r k C η = ⋅ 1520 (s) -33780 [5] (12) The relative surface area given by Dutta et al. [4] is used. It is defined as the ratio of the available specific surface area at any stage of conversion to the specific pore surface area at zero conversion. (13) The heterogeneous reaction rates are based on unit internal surface area. Converting them to unit volume of the particle, for the j reaction make them function of both carbon conversion and the available pore surface area (1 ) j sj o b r r S a X w ρ = ⋅ ⋅ ⋅ ⋅ − ⋅ (j=1-4) (14) Heterogeneous reactions Steam gasification 2 2 C H O CO H + → + (1) CO2 gasification 2 2 C CO CO + → (2) Hydrogasification 2 4 2 C H CH + → (3) Oxidation 2 2 (2 2 ) 2 C O CO CO γ γ γ + → − + (4) Homogeneous reactions Water gas shift reaction 2 2 2 CO H O CO H + + (5) Naphthalene steam reforming 10 8 2 2 10 10 14 C H H O CO H + → + (6) 1 100 X a X e νγ γ − = ± Published in Science in Thermal and Chemical Biomass Conversion, Victoria, Vancouver Island, BC, Canada, 30 Aug to 2 Sep 2004. Carbon conversion is defined as amount of carbon gasified divided by the original amount of carbon. (15) The Thiele modulus is defined as
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".