Ghysels, Stef

Maziarka, Przemyslaw

Ronsse, Frederik

English

Ghent University Academic Bibliography

Kineic schemesCOMPARISON BETWEEN SIMPLE AND DETAILED KINETIC SCHEMES OF BIOMASS THERMAL DEGRADATION –TWO DIMENSIONAL CFD MODELLING OF PYROLYSIS OF ANISOTROPIC LARGE WOOD PARTICLESRESEARCH GROUP THERMOCHEMICAL CONVERSION OF BIOMASSPrzemysław Maziarka, Stef Ghysels, Frederik RonsseInhomogeneity of biomass composition and a lack of knowledge about thedetailed influence of the process parameters causes a problem in theprediction of the pyrolysis outcome with appropriate accuracy. A betterunderstanding of the biomass single particle behaviour during the pyrolysisprocess, with numerical models, would improve the prediction accuracy andin the future, increase the possibility of applying this biomass processingtechnology on a larger scale.This work aimed to identify a kinetic model which will show the lowestdiscrepancy with the experimental data of pyrolysis of a porous single largewood particle. Kinetics were compared between simple competitive schemeand detailed schemes, represented by the improved Ranzi scheme and theRanzi-Anca-Couce (RAC) scheme.• All models were validated with experimental data of Bennadji et al. (2014)•Particle’s thermo-physical parameters were applied accordingly to Park etal. (2010). The exception was radial thermal conductivity of biomass,which was chosen to be 20% higher.•Particle had cylindrical shape (Ø1.905cm x 4cm). Initial conditions:absolute solid density of 1500 kg/m3, the porosity of 0.68. Initial bio-composition (wt. %): 44.1 CELL, 21.8 HCE, 11.3 LIGC, 4.4 LIG-H, 18.4 LIG-O.• A two dimensional, CFD model was built using the commercial softwareCOMSOLTM v. 5.4. The domain was modelled as 2D-axisymmetric and wasmeshed into 1600 elements (20x80). Differential equations werediscretized in the first order scheme.Fig.3. Temperature center simulations results against experimental data from Bennadji et al. (2014)Fig. 5. Boundary gases outflow in function of time simulations results against experimental data from Bennadji et al. (2014)AcknowledgmentsThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 721991ConclusionsTab. 1. Models components gouverning equations and boundary conditionsSimple - Shafizadeh’s and Chin’s (1978) competitive scheme; 3 reactions and 4 compounds. Biomass is not divided into bio-components and productsare modelled as lumped groups: Gas (1:1 mixture of CO2 and CO), Tar (benzene) and Char (carbon)Detailed models distinguish the biomass bio-components composition (lignin in 3 artificial forms), char is modelled as carbon and metaphase traps:•Ranzi - The improved Ranzi model of fast pyrolysis (Deblagi et al. 2018); 25 reactions and 48 compounds•RAC - The Ranzi-Anca-Course intermediate pyrolysis scheme (Anca-Couse et al. 2017); 24 reactions and 33 compounds (+5 parameters of charring)Fig. 2. The Ranzi-Anca-Couse (RAC) intermediate pyrolysis schemeFig. 1. The Ranzi fast pyrolysis schemeIntroduction and scopeFig. 4 . Products yields simulations results against experimental data from Bennadji et al. (2014) Fig. 6. Results of particle's reactions thermal effect at different time (shown as 3D axisymmetric)100s 200s 300sRanziModelRACModel• All models agree well with experimental data, but the RAC model wasthe best; Simple (R2=0.965), Ranzi (R2=0.965) and RAC (R2=0.995)• Only detailed models were able to predict the temperature peakbetween 350-500s. The RAC model predicts the peak most accurately.• Models predict the products yields with good accuracy (5-10%differenced deviation).• The Ranzi model gives a better prediction of yields of all products incomparison to the RAC model.Center Temperature and products yield Boundary gases outflowThermal effects of the reactions• On Fig. 5 shows simulation results of boundarygases outflow from the particle for the Ranzi andthe RAC kinetic scheme• The Simple model was not taken into account(lumped groups of products)• Both models show moderate agreement withexperimental data for the outflow of the CO2 andCO, over-predicting its formation n first 200s ofprocess• Both models strongly over-predicts the formationof the methane in the first 300s; the RAC modelperform better at the end of conversion• The Ranzi model show better agreement for theprediction of the methanol formation• For the formaldehyde and acetic acid, the Ranzimodel strongly under-predicts its formation, inopposition to the RAC model which shows goodagreement with experimental data• The thermal effects of the specific reactioncontained in the Ranzi and the RAC scheme• The Ranzi model shows more extreme changes inthe thermal effects compared to the RAC scheme• The existence of extreme changes in thermaleffect cause problems with the model’ssimulation stability and hinder the model fromachieving the convergence01002003004000 100 200 300 400 500 600 700 800Mole fraction [PPM]Time [s]CO2Exp.Ranzi Mod.RAC Mod.0501001502002500 100 200 300 400 500 600 700 800Mole fraction [PPM]Time [s]COExp.Ranzi Mod.RAC Mod.0204060800 100 200 300 400 500 600 700 800Mole fraction [PPM]Time [s]CH4 Exp.Ranzi Mod.RAC Mod.0204060801000 100 200 300 400 500 600 700 800Mole fraction [PPM]Time [s]CH3OH Exp.Ranzi Mod.RAC Mod.0501001502002500 100 200 300 400 500 600 700 800Mole fraction [PPM]Time [s]HCHO Exp.Ranzi Mod.RAC Mod.02550751001251500 100 200 300 400 500 600 700 800Mole fraction [PPM]Time [s]CH3COOH Exp.Ranzi Mod.RAC Mod.010203040506070Char Tar GasYield  [wt. %]Exp.Simple Mod.Ranzi Mod.RAC Mod.501001502002503003504004505000 100 200 300 400 500 600 700 800Center temperature [oC]Time [s]Exp.Simple Mod.Ranzi Mod.RAC Mod.Component Gouverning equations Boundary conditionsMassSolids 𝜕𝜕𝑡< 𝜌𝑆 >= ሶ𝜔𝑆 𝐷𝑒𝑓𝑓𝛻(< 𝜌s > ) |𝑥=𝑥𝑝 = 0Gas compounds 𝜕𝜕𝑡< 𝜌𝑖 > +𝛻 < 𝑢𝐺𝜌𝑖 >= ሶ𝜔𝑖 𝐷𝑒𝑓𝑓𝛻(< 𝜌𝑖 >𝐺) |𝑥=𝑥𝑝 = ℎ𝑚 𝜌𝑖,∞−< 𝜌𝑖 >𝐺 |𝑥=𝑥𝑝Momentum Gases < 𝑢𝐺 >=𝐾𝐺,𝑒𝑓𝑓𝜇𝐺𝛻(< 𝑃𝐺> ) < 𝑃𝐺 > |𝑥=𝑥𝑝 = 𝑃∞Energy Total 𝜕𝑇𝜕𝑡< 𝜌𝑆 > 𝐶𝑃,𝑆 + 𝜀𝐺 < 𝜌𝐺 >𝐺 𝐶𝑃,𝐺 + 𝛻𝑇 𝜀𝐺෍𝑖=1𝑁< 𝑢𝑖𝜌𝑖 > 𝐶𝑃,𝑖 = 𝛻 𝜆𝑒𝑓𝑓𝛻𝑇 + 𝑄 𝛻 𝜆𝑒𝑓𝑓𝛻𝑇 |𝑥=𝑥𝑝 = ℎ𝑇 𝑇𝑓𝑙𝑜𝑤,∞ − 𝑇 |𝑥=𝑥𝑝 + 𝜎𝜔 𝑇𝑤𝑎𝑙𝑙4 − 𝑇4|𝑥=𝑥𝑝Detailed models show better agreement with experimental data than a simple kinetic scheme and are able to predict the composition of the vapours produced during the pyrolysis of the large wood particle. Between two detailed models, the RAC kinetic scheme performs better than Ranzi model in terms of the center temperature, gas composition, and its implementation do not introduce significant model instability during the simulation.ContactPrzemyslaw.Maziarka@UGent.behttp://biosys.ugent.be

Comparison between simple and detailed kinetic schemes of biomass thermal degradation : two dimensional CFD modelling of pyrolysis of anisotropic large wood practice

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Comparison between simple and detailed kinetic schemes of biomass thermal degradation : two dimensional CFD modelling of pyrolysis of anisotropic large wood practice

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Ghent University Academic Bibliography