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This work presents a new method for identifying the bubbling regime of a fluidized bed gasification reactor. The method has been developed using experimental measurements and a computational model. Pressure drops are measured in experiments, and pressure drop as well as solid volume fraction fluctuations are calculated by implementing the model. Experiments are carried out with sand and limestone particles of mean diameter 346 μm and 672 μm, respectively. A computational particle fluid dynamics (CPFD) model has been developed for the reactor and implemented using a commercial CPFD software Barracuda VR. The model is validated against experimental measurements. The validated model is used to analyse the fluctuation of pressure drop and solid volume fraction as a function of superficial air velocity. The change in standard deviation of pressure drop and solid volume fraction fluctuation is used to predict the transition from one regime to another. The method can be used in the design and operation of a bubbling fluidized bed gasification reactor. The results show that the minimum fluidization velocity for sand and limestone are 0.135 m/s and 0.36 m/s, respectively and are independent of the particle aspect ratio. Both types of particle beds make the transition into bubbling regime as soon as they get fluidized. The bed aspect ratios have almost no effect on the onset of bubbling f uidization regime. The slugging velocity decreases with increasing aspect ratio for both types of particles. The operating range of the bubbling fluidized bed for sand particle is 0.2–0.4 m/s and 0.5–0.8 m/s for the limestone particles.

The efficiency of a gasification process is directly related to the rate of biomass conversion into product gas. The rate of fuel conversion depends on the interaction of fuel with the bed material, the gasifying agent, and the residence time of fuel particles. The interactions and the residence time depend on the fuel feeding positions along the height of the reactor. Thus, the fuel feeding position in a gasification reactor is an important parameter that influences the efficiency of the gasification process; longer residence time of the fuel particles in the bed enables efficient carbon conversion and less tar formation. In this work, in-bed and on-bed feed positions of the fuel particles have been investigated using a computational particle fluid dynamics (CPFD) model. The model is developed and validated against experimental data obtained from a bubbling fluidized bed gasification reactor. Experiments were carried out in a 20 kW pilot scale bubbling fluidized bed gasification reactor. Wood pellets of 3–30 mm length and 5 mm diameter are fed into the reactor at a mass flow rate of 2.4 kg/h. The molar flow rate of the producer gas, which typically consists of CH4, CO, CO2, and H2 for both the in-bed and on-bed cases, is calculated by the CPFD model. The results show that CO and CH4 concentrations increase in the product gas when the biomass is fed at the location near to the bottom of the bed, while CO2 and H2 increase in the case of on-bed feed. The fuel particles segregate, followed by partial combustion of the smaller fuel particles on the bed surface in the case of on-bed feed. The total mass of the bed including unreacted char is higher for on-bed feed, indicating that the char is consumed slowly. The CPFD model can predict the product gas compositions, the fuel conversion, changes in the bed hydrodynamics, and the product gas yield at different feeding positions of the fuel particles. Thus, the model can be useful for design purposes.

Experiments were performed in a pilot-scale bubbling fluidized bed gasification reactor with air as a fluidizing agent. Birch wood chips and sand particles were used as biomass and bed materials. Average molar product gas composition was 0.214 of CO, 0.212 of CO2, 0.103 of H2, 0.074 of CH4 and 0.397 of N2. A kinetics-based model was developed for the gasification process and simulated using commercial software Barracuda®. The model is validated against the measured gas compositions. The validated model was used to study the product gas compositions for olive waste and straw pellets. The effects of equivalence ratio (ER) on the product gas composition for birch wood was also studied in one of the simulations. Birch wood gave the highest (20.5 mole %) CO production rate and lowest (9.0 mole %) H2 production rate. The product gas flow rate was 1.96 Nm3 per kg of biomass and the lower heating value of the product gas was 6.65 MJ/Nm3. The CO concentration decreased from 25 to 13.2 mole %, whereas CO2 concentration increased from 17 to 19.5 mole % when increasing ER from 0.2 to 0.3. The CO and H2 concentrations for the olive waste were 8.1 and 56.1 mole %. The CO and H2 concentrations for the straw pellets were 6 and 73.4 mole %.

The fluidization behaviour depends on particle properties such as particle size, sphericity, density and the properties of the fluidizing agent. In this study, the effects of different particle sizes on fluidization behaviour were investigated. Experiments were done by mixing sand particles of mean diameter 293 μm (small particle) and 750 μm (large particle). The experiment with 20% small particles and 80% large particles gave a reduction in minimum fluidization velocity of 60.8% compared to the minimum fluidization velocity with only large particles. CPFD simulations were performed using the commercial software Barracuda®. There is a good agreement between the results from the experiments and the simulations. The minimum fluidization velocity is also calculated using different theoretical equations based on the average particle size for the mixture. The obtained experimental results were compared with the minimum fluidization velocity calculated using different equations available in the literature. There are significant differences in minimum fluidization velocities obtained from the different empirical equations. The pressure drop profiles for large and small particles follow the trends presented in the literature. The experimental minimum fluidization velocities were found to be 0.46 and 0.092 m/s for the large and small particles respectively.

Renewable energy sources have significant potential for limiting climate change and reducing greenhouse gas emissions due to the increased global energy demand. Fluidized bed gasification of biomass is a substantial contribution to meeting the global energy demand in a sustainable way. However, ashrelated problems are the biggest challenge in fluidized bed gasification of biomass. Bed agglomeration is a result of interaction between the bed material and alkali metals present in the biomass ash. The agglomerates interfere with the fluidization process and might result in total de-fluidization of the bed. The study focuses on ash challenges related to the fluidization behavior in gasification of biomass. A model is developed and verified against results from previous performed experiments in a cold flow model of a bubbling fluidized bed. The commercial computational particle fluid dynamics (CPFD) software Barracuda Virtual Reactor is used for the computational study. The simulations show that the CPFD model can predict the fluidization process of an agglomerated fluidized bed gasifier.

The energy from biomass can be utilized through the thermochemical conversion processes of pyrolysis and gasification. Biomass such as wood chips is heated in a gasification reactor to produce a synthesis gas containing CO, H2 and CH4. The gas can be further processed to bioproducts or fuels. The thermochemical process involves devolatilization of wood followed by steam gasification, CO2 gasification, methanation, water gas shift reactions and methane reforming. To optimize the performance of the reactor, it is important to study each of the reactions separately. The reactions are simulated individually using the chemical process optimization software Aspen Plus. The results are compared with simulations performed with the Computational Particle Fluid Dynamic (CPFD) software Barracuda VR 15. The CPFD methodology solves the fluid and particle equations in three dimensions with the transient flow and is time-consuming. Aspen Plus is one dimensional and solves the included reactions fast. The results of the Aspen Plus and CPFD simulations, given as product gas compositions (CO, CO2, CH4 and H2), show that each reaction contributes to the product gas composition differently. Comparison between Aspen Plus and CPFD simulations of individual gasification reactions show good agreement. However, when all reactions are included in the simulations, there is a deviation in the volume fraction of product gas composition.