Gasification Process Simulator is developed for modeling the gasification process and predict the corresponding products.
1-D kinetics simulator
The 1-D kinetics simulator can provide fast and accurate predictions of syngas generation.
A comprehensive gasification reaction scheme is separated into four inter-linking stages. Each stage is simulated in an individual module. The whole process of gasification is governed by the mass conversion equation and energy conversion equation.
The graphic user interface of the 1-D kinetics simulator:
Ong, Z., Cheng, Y., Maneerung, T., Yao, Z., Tong, Y. W., Wang, C. H., & Dai, Y. (2015). Co‐gasification of woody biomass and sewage sludge in a fixed‐bed downdraft gasifier. AIChE Journal, 61(8), 2508-2521.
2-D kinetics simulator
The 2-D kinetics simulator can provide accurate predictions of both syngas and biochar products
To facilitate the optimization of the energy efficiency and economic viability of gasification systems, a comprehensive fixed-bed gasification model has been developed to predict the product rate and quality of both biochar and syngas. A coupled transient representative particle and fix-bed model was developed to describe the entire fixed-bed in the flow direction of primary air. A three-region approach has been incorporated into the model, which divided the reactor into three regions in
terms of different fluid velocity profiles, i.e. natural convection region, mixed convection region, and forced convection region, respectively.
In the 2-D kinetics simulator, the reactor was discretized in the axial direction and the particle domain was discretized in the radial direction so the model can also be described as 1D+1D. A 1-D model was developed to describe the entire fixed-bed in the moving direction z of feedstock. It was assumed that all the species were well-mixed and all the variables were uniform in the radial direction. In this model, the entire packed bed fluid model was coupled with the representative particle model. The reactor was discretized in the z-direction. In each cell, one representative particle was chosen and modeled as a shrinking sphere. The reactor was divided into three regions in terms of different velocity profiles: natural convection region, mixed convection region, and forced convection region. A parameter Lm was used to determine the boundary of the mixed convection region. During the reaction, the biomass particle size decreased with its density being constant. The biomass particle was impervious with intraparticle diffusion and all the reaction details were lumped at the gas-solid
interface. The presented model considered drying, pyrolysis, homogeneous gas reactions, and heterogeneous combustion/gasification reactions, respectively. In the gas phase, eight species (O2, N2, CO, CO2, H2, H2O, CH4, and tar) were considered. The solid phase was woodchips. In the solid phase, all the components obtained from approximate
analysis (moisture, volatiles, fixed carbon, and ash) and ultimate analysis (C, H, O, N) were treated as the dependent variables of time and space.
The woody biomass gasification simulation results of the equivalence ratio (ER, the ratio of the actual fuel/air ratio to the stoichiometric fuel/air ratio) effects on temperature and particle size distributions, syngas production rate and compositions, biochar production rate and compositions are shown in Figures 1-3, respectively.
Fig. 1. (a) Temperature distribution along the axis direction under different ER. (b) Particle size distribution along the axis direction under different ER.
Fig. 2. (a) Effects of ER on producer gas flow rate, producer gas HHV, and CGE; (b) Effects of ER on producer gas composition.
Fig. 3. (a) Effects of ER on carbon content, biochar production rate, and the total amount of carbon; (b) Effects of ER on biochar composition.
Yao, Z., You, S., Ge, T., & Wang, C. H. (2018). Biomass gasification for syngas and biochar co-production: Energy application and economic evaluation. Applied Energy, 209, 43-55.
3-D CFD simulator
In the 3-D computational fluid dynamic (CFD) simulator, the Eulerian-Eulerian three-phase flow model was employed to describe the flow behavior of each phase, with the reacting gas flow as the primary phase and both the biomass and char as the secondary phase. The species transport model was used to describe the species conservation in the gas phase and homogeneous reactions.
Yan, W. C., Shen, Y., You, S., Sim, S. H., Luo, Z. H., Tong, Y. W., & Wang, C. H. (2018). Model-based downdraft biomass gasifier operation and design for synthetic gas production. Journal of Cleaner Production, 178, 476-493.
A hybrid peripheral fragmentation and shrinking-core model for biomass particle in gasification
The hybrid peripheral fragmentation and shrinking-core model can predict the syngas generation, biochar production, and particulate matter (PM) emission.
In this model, the biomass particle was assumed to be a porous media in which homogeneous reactions happened in the interstitial gas phase and heterogeneous reactions took place at the gas-solid interface. As the thermal chemical reactions going on, the density of the solid phase was assumed to be consistent, while the porosity inside the particle built up
with time. When the porosity reached a certain critical value, peripheral fragmentation took place, which led to the shrinkage of the particle and PM emissions. The shrinking of particle size was assumed proportional to the total measured volume of PM.
The biomass particle was discretised in the radial direction and in each cell, four solid species (water, volatiles, fixed carbon, ash) and eleven gaseous species (CO, CO2, H2O, H2, O2, CH4, primary tar, secondary tar, Ar, NO and SO2) were considered in the model. The biomass particle was discretized in the radial direction of the particle and the governing equations were derived by finite volume approach. The total mass, species mass, energy, and momentum balances were applied to the gas mixture of a finite volume.
The operating conditions inside the fixed bed reactor were used as initial and boundary conditions for the single-particle model. The particle properties were also used as the input of the single-particle model. After solving the mass and energy balance equations, the critical value for peripheral fragmentation was chosen from calculated porosity profiles. The measured size-dependent PM number and mass concentration were used for the empirical correlation of mass loss due to peripheral fragmentation, which would further lead to the shrinking particle size. Thus, a progressive shrinking particle model was integrated with the peripheral fragmentation model. The model is capable of predicting syngas generation,
biochar production, and PM emission from gasification process and it could serve as the basis for further implementation in the largescale fixed-bed simulation.
A data-driven model was developed based on the experimental results of PM emission under different reaction conditions.
The numerical model of PM emission was designed to be a function of both reaction conditions and time. The experimental results were considered as signals in the time domain, and the time signals were transformed by applying signal analysis for the numerical PM emission model. Discrete wavelet transform (DWT) technique was employed to analyze the time signals and a radial basis function (RBF)-based response surface model was developed to represent the effect of different reaction conditions.
The simulation results of PM emissions, biochar, and syngas are shown in Figures 4-6, respectively.
Fig. 4. PM emission of 10mm particle under Air gasifying agent. (A) generation rate of PM between 0.25 μm and 0.5 μm. (B) generation rate of PM between 0.5 μm and 1 μm. (C) generation rate of PM between 1 μm and 2.5 μm. (D) generation rate of PM between 2.5 μm and 32 μm.
Fig. 5. Mass transient validations for biochar
Fig. 6. Composition validations for syngas generated
Yao, Z., He, X., Hu, Q., Cheng, W., Yang, H., & Wang, C. H. (2020). A hybrid peripheral fragmentation and shrinking-core model for fixed-bed biomass gasification. Chemical Engineering Journal, 400, 124940.
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