Pyrolysis Model Research Articles

Modelling and simulation of fast pyrolysis of pomace from three-phase olive mill targeting optimal yields of pyrolysis products

Biomass can be transformed into useful bioproducts (biofuels, biomaterials) through its thermochemical conversion. This study concerns modeling and simulation of fast pyrolysis of olive pomace from three-phase olive mill using Super Pro Designer (SPD) software, which was selected because of its vast databank of specific chemical compounds as well as specific unit operations specifically designed for modeling and simulation of biological, physical and chemical processes. The simulation was carried out at pyrolysis temperatures ranging from 400 to 675 °C and residence times varying between 0.1 and 15s. Simulation results indicate that fast pyrolysis yielded maximum bio-oil yield of 23.72% at 650 °C and a residence time of 0.1s, and maximum syngas yield of 41.17% at 675 °C and a residence time of 15s. Predicted product yields were in accordance with experimental data collected from the literature, with relative errors in the range of 9%, which may be due to variable feedstock properties. The developed model provides very useful information on olive pomace fast pyrolysis conditions.

Development of multi-scale pyrolysis and combustion model for fir wood

Development of multi-scale pyrolysis and combustion model for fir wood

Pyrolysis model parameterization and fire growth prediction: The state of the art

A failure to accurately predict fire growth on a solid object can be frequently traced to our inability to fully resolve coupling between the gas-phase processes of flaming combustion and condensed-phase processes of pyrolysis, responsible for generation of gaseous fuels. Two key quantities that define this coupling are the rate of heat flow (or heat flux) into the condensed phase and the rate of mass flow (or mass flux) of decomposition products into the gas phase. Recognizing that an improvement in our ability to compute the latter quantity may hold the key to accurate fire growth predictions, a systematic effort has been made by the fire science community to develop progressively more sophisticated models of pyrolysis. State-of-the-art pyrolysis solvers can simulate complex, multi-reaction thermal decomposition mechanisms coupled with physically diverse heat and mass transfer processes taking place in the condensed phase. However, parameterization of these models remains a non-trivial effort requiring a large number of careful measurements and inverse modeling, which is necessary because some of the processes cannot be fully decoupled through experimental design. Here we review formulation and parameterization of the state-of-the-art pyrolysis models, discuss their limitations and the impact of these limitations on fire growth predictions.

Semi-Analytical Modeling of Pyrolysis Front and Ignition Temperatures of Thermally Reactive Single Solid Particles

A semi-analytical approach is developed for predicting pyrolysis front temperature in a charring solid undergoing thermal decomposition. The pre-reaction heating stage is described using an analytical formulation and invoking the concept of thermal penetration depth. The solution for the solid conversion stage accounts for decomposition enthalpy, the convective flow of volatiles, and a reaction front characterized by a uniform temperature that progresses toward the inner layers. This method incorporates empirical relations into the analytical model. Two scenarios are considered. First, the solution of the pyrolysis model combined with the data of conversion time versus external heat flux leads to an algebraic expression that reveals the existence of a maximum pyrolysis-front temperature. Explicit relations are derived for both the extremum pyrolysis temperature and optimum applied heat flux. In the second case, an expression is derived for the ignition temperature of a solid fuel (e.g., wood) by incorporating ignition delay time measurements into the heating stage model. The newly derived expression allows the ignition temperature to be described as a function of the Biot number and external heat flux. The relation obtained for the ignition temperature explains the experimental trends reported in some previous studies where two local extremums were observed for the ignition temperature in the absence of volatile reactions.

Technoeconomic assessment of hydrogen production from natural gas pyrolysis in molten bubble column reactors

Natural gas pyrolysis to produce carbon and hydrogen, known as “turquoise hydrogen, using molten metals and salts is a promising route to clean hydrogen production. Methane pyrolysis produces significantly less or near-zero CO2. Additionally, the solid carbon can be separated and sold as a valuable co-product, making industrial-scale production of hydrogen via pyrolysis economically attractive. To better understand the potential of turquoise hydrogen, this study presents a comparative techno-economic assessment of molten media pyrolysis processes in bubble column reactors using Ni0.27Bi0.73, Ga, and KCl–MnCl2 (33:67). The study evaluates techno-economic assessment with a clear understanding of kinetics of natural gas pyrolysis and reactor process modeling. According to the modeling results, Ga had the lowest reactor cost and total bare erected cost. Nevertheless, the pyrolysis process that used inexpensive KCl–MnCl2 molten salt was more economically advantageous. Due to uncertainties in the density separation of solid carbon in the molten salt at temperatures above 1000 °C, Ni0.27Bi0.73 was selected as the promising molten medium for the long term. Sensitivity analyses were carried out to assess the impact of the costs of natural gas, hydrogen, and electricity on the industrial concept. In regions where gas is produced, such as Saudi Arabia, mature molten pyrolysis plants that use grid electricity at a rate of $48/MWh and solar photovoltaic systems with storage at a rate of $24/MWh can be economically feasible at natural gas prices of $132/ton and $198/ton, respectively, even if carbon is not sold. The cost of producing turquoise H2 is comparable to the global average for grey H2 and selling carbon can generate additional revenue or improve the profitability of the plant. The net energy demand of the molten pyrolysis plant is found to be approximately 21% of the energy requirement for current methods of green hydrogen production through PEM (proton exchange membrane) electrolysis.

Validation of the fire dynamics simulator (FDS) model for fire scenarios with two liquid pool fires in multiple compartments

A validation study on the fire dynamics simulator (FDS) model for fire scenarios with multiple liquid pool fires occurring in multiple compartments was performed. For fire growth in the open simulation, the mass loss rate per unit area (MLRPUA) time curve obtained experimentally was used as the input. In the blind simulation, the pyrolysis model was used. For the ventilation flow rate curve, the supply/exhaust flow rate curve measured in the experiment was used as the input for the open simulation, whereas the quadratic fan curve model was used for the blind simulation. The time-averaged datasets of the output parameters from the FDS model and experiment were plotted as data points on scatter plots. In terms of bias factor and model standard deviation of output parameters, the scatter plots under the open simulation conditions were compared with those of NUREG-1824. Under blind simulation conditions, the bias factor and model standard deviation of output parameters increased significantly. A sensitivity analysis was performed on input parameters, such as the MLRPUA time curve, supply/exhaust flow time curve, compartment leak area, and maximum differential pressure in the compartment. Within the given ranges of the sensitivity input parameter values, only changing the MLRPAU time curve significantly affected the bias factor and model standard deviation of the output parameters.

Scale modeling of thermo-structural fire tests of wood members

Standard fire resistance testing is essential to qualifying wood-based materials to be approved for use in building assemblies. In a previous study, we developed a theoretically based, experimentally validated scaling methodology for reducing the size of the test article while maintaining the same thermal and structural response exhibited in the large-scale test. This paper is focused on demonstrating these scaling laws for wood with combined thermo-structural loading. Due to load capacity limits, dimensional lumber boards at ½-scale and ¼-scale were subjected to combined bending and thermal loading. Samples were placed in static three-point bending with the loading scaled to have structural similitude, while simultaneously, the bottom surface was exposed to a scaled fire exposure. Analytical modeling of wood pyrolysis demonstrated that, due to char kinetics as the heating rate is increased in the tests, equivalently less char is formed in the reduced-scale tests. Therefore, we developed a char timescale correction factor, calculated from both model predictions and measured charring rates, which modified the previous Fourier number time scaling laws. Scaling exposure time by our updated thermo-structural test scaling laws, the smaller ¼-scale test deflection results predict the behavior of the equivalent larger ½-scale tests by between 11.0% to 16.1%.

Kinetic study of hydrogen abstraction reactions from n-propyl/n-butylcyclohexane by hydrogen atom

Hydrogen abstraction reactions of alkylated cycloalkane by radicals play a fundamental role in combustion and atmospheric chemistry. In this work, we select two representative molecules in alkylated cycloalkane fuels, n-propylcyclohexane (nPCH) and n-butylcyclohexane (nBCH), to investigate the kinetics of their hydrogen abstraction reactions with hydrogen atom. The rate constants over a broad temperature range of 298–2000 K were calculated by using canonical transition state theory coupled with the multistructural torsional anharmonicity effect (TST/MS-T). We stress the particular importance of considering the MS-T anharmonicity for the investigated species involved in the H-abstraction pathways. Due to the approximate cancelation of the anharmonicity factor between the transition-state species and the reactant, the anharmonicity factors for reactions are spread over a narrower range of 0.7–3.3. The anharmonicity will slightly accelerate the H-abstraction for the nBCH system over the whole temperature range. The current results show that the multistructural torsional anharmonicity will influence the accurate estimation of competing relationships. We found that the total H-abstraction rate constants of the nBCH system by the TST/MS-T method are larger by factors of 1.2–1.3 than those for the nPCH system. The present data show that nPCH + H channels are less important by factors of 1.41–5.48 than currently estimated by the commonly used nPCH pyrolysis model. The H-abstraction ratios from the cyclic ring and side chain groups reveal that the rate rule for secondary carbon in chain alkanes does not represent the reactivity of all cycloalkanes well, e.g. the ratio between the cyclic ring and side chain group is high up to 2.8.

Investigation of the thermal behavior of Ca(NO3)2·4H2O and its application for the regeneration of HNO3 and CaO

Nitric acid pressure leaching has great potential in treating limonitic laterite. A new method is designed to improve the downstream process. The resulting calcium nitrate solution is concentrated/crystallized/pyrolyzed to achieve the regeneration of nitric acid and calcium oxide. Calcium nitrate pyrolysis is the core process to realize the new method, but the dynamic analysis of this process is relatively lacking and the pyrolysis mechanism is controversial. In this paper, TG-DSC, TG-MS and XRD were applied to systematically study the thermodynamics and phase evolution of calcium nitrate tetrahydrate. It is found that pyrolysis of calcium nitrate tetrahydrate can be divided into four stages: removing free water and part of crystal water, removing residual crystal water, stable existence of anhydrous calcium nitrate, and its decomposition after melting. Calcium nitrite and nitrogen are absent in the process of calcium nitrate pyrolysis. Three iso-conversion methods, including Flynn-Wall-Ozawa method, Kissinger-Akahira-Sunose method and Starink method, were applied to determine activation energies of calcium nitrate pyrolysis as 224.4 kJ/mol, 221.8 kJ/mol and 222.2 kJ/mol. The kinetic mechanism model of calcium nitrate pyrolysis determined by Melak method is closer to An model, which is the random nucleation and growth reaction. Calcium nitrate pyrolysis process further expands the industrial application of nitric acid pressure leaching.

Numerical simulation of the coupled multiphysics fields and reactions during the microwave pyrolysis of wood particles

Microwave heating has gained significant attention as an alternative to conventional methods for biomass pyrolysis. To investigate the electromagnetic-thermal-hydraulic-chemical processes during biomass pyrolysis under microwave irradiation, a three-dimensional comprehensive pyrolysis model was developed. The model numerically described the physicochemical processes of the electromagnetic wave propagation in the reactor, heat transfer and fluid flow inside porous wood particles, pyrolysis reactions, and phase changes. The model was validated by comparing the temperature profiles of biomass samples under varying microwave powers against literature data. The modeling results demonstrated that there was a strong interaction between the distributions of the microwave electromagnetic fields in the cavity and that inside the wood particles; these distributions changed with the size, shape, and location of the biomass sample in the cavity. Using the model, the thermal response of wood particles to microwave radiation was determined. The predicted results showed that a millimeter-sized wood particle was large enough to straddle multiple high-energy microwave regions in the cavity, resulting in more than one intraparticle hotspots. The transient dielectric loss inside wood particles is nonuniform due to the enhancement of the local relative permittivity by the local hotspots. The maximum permeability within the wood particles increases by an order of magnitude during microwave pyrolysis at 1 kW. The hotspots have a considerable influence on the structural homogeneity and stability of biochar due to the formation of large thermal and expansion stresses around the hotspot locations. Moreover, the energy efficiency (ηe) of converting microwave electromagnetic energy into the thermal energy of wood particles is sensitive to the input microwave power and particle shape. For instance, ηe ranges from 12.44% to 36.15% under the microwave powers of 0.8–1 kW for the pyrolysis of a cylindrical wood particle with aspect ratios of 0.25–2. This work provides the fundamental technical support for the industrial application of microwave pyrolysis.

A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches

Biofuels from lignocellulosic biomass converted via thermochemical technologies can be renewable and sustainable, which makes them promising as alternatives to conventional fossil fuels. Prior to building industrial-scale thermochemical conversion plants, computational models are used to simulate process flows and conditions, conduct feasibility studies, and analyse process and business risk. This paper aims to provide an overview of the current state of the art in modelling thermochemical conversion of lignocellulosic biomass. Emphasis is given to the recent advances in artificial intelligence (AI)-based modelling that plays an increasingly important role in enhancing the performance of the models. This review shows that AI-based models offer prominent accuracy compared to thermodynamic equilibrium modelling implemented in some models. It is also evident that gasification and pyrolysis models are more matured than thermal liquefaction for lignocelluloses. Additionally, the knowledge gained and future directions in the applications of simulation and AI in process modelling are explored.

Deep learning-based modelling of pyrolysis

Deep learning-based modelling of pyrolysis

High temperature oxidation of residual oil pyrolysis intermediates for modeling gasification and combustion processes

Developing chemical kinetic models for residual oil pyrolysis, gasification, and combustion is very challenging due to the large number of components and molecular complexity. This study provides experimental data for model development and validation for two residual oils, heavy fuel oil (HFO) and vacuum residual oil (VRO). This study aims to identify and quantify the HFO and VRO pyrolysis intermediates and validate an oxidation chemical kinetic model of the pyrolysis intermediates to provide data for future residual oil model development using hybrid chemistry (HyChem) and functional groups for mechanism development (FGMech) approaches. The pyrolysis experiments were conducted in a bench-scale tubular reactor at atmospheric pressure and a temperature of 1273 K. Detailed compositions of the yields from the pyrolysis of HFO and VRO were analyzed using gas chromatography-flame ionization detection (GC-FID) for the gas yields and gas chromatography-mass spectrometry/flame ionization detection (GC–MS/FID) for the liquids. Elemental analysis was conducted for the solid residuals. The experiment results show similar pyrolysis products from HFO and VRO. A surrogate of major components of the gas products and representatives of benzene and naphthalene derivatives (major compounds identified in liquid products) of the liquid products was formulated and its chemistry was studied by measuring ignition delay times (IDTs) in a shock tube. IDT measurements were conducted for stoichiometric and rich (ϕ = 1 and 3) mixtures, pressures of 20 and 40 bar, and diluents of nitrogen and carbon dioxide. Brute force IDT sensitivity analyses were used to study the effect of pressure, equivalence ratio and diluents on the reactivity of the mixture.

Experimental and theorical study on the pyrolysis behaviors of 2,3,3,3-tetrafluroropropene

The pyrolysis behaviors of 2,3,3,3-tetrafluroropropene (R1234yf) had been first studied in a flow reactor pyrolysis apparatus coupled with the synchrotron vacuum ultraviolet photoionization mass spectrometry and gas chromatography. The pyrolysis temperature, reaction products and intermediates were measured at atmospheric pressure. The experimental results revealed that the initial pyrolysis temperature of R1234yf was 1040K and the thermal decomposition was accelerated with the increase of temperature. It was found that the pyrolysis reaction of R1234yf will be replaced by a self-polymerization reaction at 1160 K. In order to explore the chemical process of R1234yf pyrolysis, a detailed kinetic simulation was performed and compared with experimental results. The calculated results indicated that the unimolecular decomposition paths took a dominant role in the whole pyrolysis processes. The intermediates of C2H2, C2HF, CF2, CHF3, CF4 and CF3CF3 were detected in the pyrolysis experiments. Moreover, the distribution characteristics and sensitivity analysis of the vital fluorinated species of CF2, C2H2, CF4, CF3CF3 and HF were discussed. All of these founding could provide reliable data for the development of pyrolysis models and guidance for the use of R1234yf in the refrigeration system.

Modeling the thermophoretic impact on nanoparticle production in an enclosed FSP reactor

For the accurate modeling and simulation of the flame spray pyrolysis (FSP) process, all the essential phenomena need to be taken into account. A validated model could then be used for process optimization and design with reasonably reduced costs. In that sense, this work uses a combination of computational fluid dynamics (CFD) and a population balance model (PBM) to investigate the influence of thermophoresis and other parameters (related to sintering and turbulent particle diffusion) in the final produced particle size. This work simulates the flame spray using CFD under an Eulerian–Lagrangian framework. Furthermore, a monodisperse solver for a bivariate PBM is implemented into ANSYS Fluent to simulate the formation and evolution of nanoparticles inside the reactor. We investigate the production of zirconium dioxide (ZrO2) nanoparticles and use experimental data from the literature for model validation. The model produces accurate results for some of the investigated cases.

Machine learning based prediction of biomass pyrolysis with detailed reaction kinetics for thermally-thick particles: from 1D to 0D

In reactor-scale CFD modeling of biomass pyrolysis with thermally-thick particles, zero-dimensional (0D) models coupled with lumped kinetics are commonly used, as they are simple and computationally efficient. However, intra-particle heat transfer, which cannot be directly implemented in 0D models, has significant effects on pyrolysis behaviors of thermally-thick biomass particles. Additionality, lumped kinetics usually fails to predict detailed composition of pyrolysis products. To overcome these issues, a widely-used one-dimensional (1D) model that can directly incorporate intra-particle heat transfer was employed with a detailed pyrolysis kinetics in this work to develop a corrected 0D (Cor-0D) model for accurate CFD modeling of biomass pyrolysis inside thermally-thick particles. Correction coefficients of external heat transfer, particle diameter, and pyrolysis reactions were introduced by comparing predictions of the 1D model with those of the 0D model quantitatively to reflect the effects of respective factors. The comparison demonstrates that if correction coefficients are properly determined, predictions of the developed Cor-0D model are in good agreement with experimental data as well as those of the employed 1D model under various conditions, while the 0D model overestimates mass loss rate and particle heating rate for thermally-thick biomass particles. Considering that correction coefficients are case dependent and determination of their values are tedious, artificial neural network (ANN) was used to correlate correction coefficients as functions of convective heat transfer coefficient, particle size, gas temperature, moisture content, and particle’s dimensionless temperature to derive an ANN-Cor-0D model. Results show that the ANN-Cor-0D model has the same performance as the Cor-0D model.

Efficient Pyrolysis Model for Large Biomass Particles with Arbitrary Shapes Based on a Composed-Sphere Concept and Voronoi Tessellation

Efficient Pyrolysis Model for Large Biomass Particles with Arbitrary Shapes Based on a Composed-Sphere Concept and Voronoi Tessellation

Sensitivity Analysis of Oil and Gas Production in the In Situ Pyrolysis of Oil Shale

The Maoming basin in south China is rich in oil shale resources, whereas the Youganwo formation is a potential area for large-scale in situ exploration because of its relatively large thickness, high oil content, and good continuity. Based on previous geologic studies on the Maoming basin and the laboratory experimental data, in situ pyrolysis models for shale oil are established using the CMG-STARS software to simulate the in situ heating and reactions. To analyze the major influencing factors, the CMOST module is employed to conduct the sensitivity analysis with accumulated oil and gas production as the target functions. The initial kerogen concentration, formation permeability, activation energy of pyrolysis reaction, frequency factor, heating rate, heating power, heat loss, heat conductivity, heat capacity, and bottom-hole pressure are chosen to be the controlling parameters. A total of 128 cases are calculated for each target function with the fractional factorial sampling method. The sensitivity analysis results demonstrate that the heating power, initial kerogen concentration, and reaction activation energy are the major influencing factors for oil and gas production. The simulation results also reveal that high non-uniformity of the temperature field will cause the light oil to convert to gas and char at the high-temperature zone near the heater well. It is suggested that proper control of formation temperature, such as increasing permeability to promote oil and gas flow and form a more uniform temperature field, will help to prevent oil production loss caused by secondary reactions.

Prediction of tar yield produced from devolatilisation of empty fruit bunch in a fluidised-bed reactor using pseudo-equilibrium model

In this work, a pseudo-equilibrium model (PEM) is developed for prediction of yield and product from devolatilisation of empty fruit bunch (EFB). The yield of individual species (H2, CO, CO2, CH4, bio-oil, tar, and char) from the primary decomposition of EFB is expressed empirically. Subsequently, they are the inputs to the secondary reaction region. In the secondary reactions zone, the water-gas shift reaction (WGSR) is treated under equilibrium conditions, but kinetically modified by a factor to predict the concentration of the four gases (H2, CO, CO2, and H2O) which participated in this reaction. Meanwhile, the methane, bio-oil and tar are modelled separately in a purely kinetic environment by applying a plug flow reactor (PFR) model to estimate the conversion of both species. The model is further to investigate the effects of changes in the operating conditions of devolatilisation such as temperature and carrier gas flow rate. The results showed a good prediction for tar yield with low root mean square (RMS = 0.003) compared to experiments, and conversion (59.7%) compared to experiments (51.5%) at 850 °C. A sensitivity analysis for the pyrolysis model was conducted to investigate the effects of process temperature and nitrogen gas flowrate. Tar yield was significantly reduced at high temperatures. Meanwhile, the change of nitrogen flowrate resulted in slight increase of tar yield. Reasonable predictions were obtained for other products.

Modelling of the swelling behaviour of a fire retarded material under a cone calorimeter

Modelling the swelling behaviour of intumescent materials is an important and challenging issue because this phenomenon influences their pyrolysis process. It must be therefore considered to improve the predictability of pyrolysis models. The objective of this work was to implement a swelling model in the pyrolysis code Gpyro in order to predict the behaviour of a sample of ethylene vinyl acetate containing aluminium trihydroxide (EVA/ATH) polymer studied by cone calorimetry. This polymer is used for making the external sheath of electrical cables. Unlike existing works in the literature, the model implemented here, and adapted to EVA/ATH, does not require information on the densities of the initial and final materials, which makes it more predictive. To characterize the swelling, experiments were carried out to measure the evolution of the sample thickness as a function of time. The mass loss and the back surface temperature of the sample were also measured. This was done under different operating conditions. These measurements are compared with the swelling predicted by the model and the results were found to be in good agreement. The model can be extended easily to other materials.