Pyrolysis Kinetics Of Biomass Research Articles

Investigation on thermochemical characteristics and pyrolysis kinetics of lignocellulosic biomass for biofuel production feasibility

Investigation on thermochemical characteristics and pyrolysis kinetics of lignocellulosic biomass for biofuel production feasibility

Coupling coarse‐grained DEM‐CFD and intraparticle model for biomass fast pyrolysis simulation and experiment validation

AbstractThe understanding of complex fluidization hydrodynamics and chemical reactions in biomass fast pyrolysis fluidized bed reactors is lacking and requires further investigation. It is urgent to develop accurate mathematical models capable of describing the complex multiphase reaction system of biomass pyrolysis. A comprehensive multiscale model based on coarse‐grained discrete element method (DEM)‐computational fluid dynamics (CFD) was developed in open‐source MFiX code. It incorporates detailed biomass pyrolysis kinetics and an intraparticle model. To validate the model, measurements were conducted in a fluidized bed pyrolyzer, including quantifying the segmental pressure drop along the height of the bed and determining the yields and compositions of gas, liquid, and solid products. The particle mixing and segregation, axial distribution and residence time, Lacey index, and pyrolysis products were investigated. The study provides experimental and theoretical foundations for designing multiphase fluidized bed reactors and advancing biomass thermochemical conversion.

Effect of different pre-treatment techniques on thermogravimetric characteristics and kinetics of lignocellulosic biomass pyrolysis

In this study, effect of pre-treatments on biomass pyrolysis kinetics was evaluated. Fir wood sawdust (F), pine wood sawdust (P) and hazelnut shell (H) were selected as raw biomass sample. Acidic pre-treatment was applied by using 1M-H2SO4 at room conditions and hydrothermal pre-treatment applied by using high pressure and temperature reactor. Thermogravimetric analysis was executed to specify thermal degradation behavior of the samples. Thermogravimetric analysis was also used to determine kinetic parameters of thermal degradation for biomass and pre-treated biomass samples. Having knowledge thermal degradation behavior and kinetic parameters of pyrolysis process such as activation energy and reaction rate are very important for reactor design. Kissinger-Akahira-Sunose (KAS), Ozawa-Flynn-Wall (OFW) and Coats-Redfern (CR) methods were used to investigate the effect of different pre-treatment techniques on the kinetic parameters. Activation energies of pyrolysis reactions for F, P and H were found as 158.47 ± 1.08 kJ/mol, 126.0 ± 1.78 kJ/mol and 158.66 ± 1.24 kJ/mol, respectively. The calculated activation energy values increased while acidic and hydrothermal pre-treatment were applied to each biomass samples. In conclusion, acidic and hydrothermal pre-treatments influenced the serial and parallel reaction rates during the thermal degradation process and the calculated activation energy values changed accordingly. Inorganics that catalyzed the pyrolysis reactions were removed by acid pre-treatment. The condensed species during hydrothermal pre-treatment prevented the easy volatilization. As a result of experimental studies and kinetic calculations, it has been determined that pre-treatments applied to biomass increased the activation energy value in thermal degradation reactions.

Application of machine learning methods for lignocellulose biomass pyrolysis: Activation energy prediction from preliminary analysis and conversion degree

The investigations of biomass pyrolysis kinetics are traditionally accomplished through experiments at the expense of extensive resources worldwide. The estimation of activation energy (Eα) of biomass pyrolysis is a crucial and essential aspect of process optimization and control. In this study, machine learning methods were applied for the first time to predict the Eα values of the pyrolysis process based on the preliminary analysis of the biomass feedstocks and conversion degree (α). A total of 1523 sets of Eα values calculated via three frequently-used model-free kinetic methods were collected from literature and modeled by using artificial neural network (ANN), random forest (RF), and support vector machine (SVM) algorithms. The training and optimization of the models showed that the RF algorithm exhibited satisfactory accuracy, making it a promising tool for a quick prediction of Eα values. Moreover, the feature importance analysis indicated that Eα mainly depended on the α, C content, and ash content of the biomass. This work suggested the effective application of machine learning in determining Eα accurately, which could help in understanding the pyrolysis mechanisms, reducing the experimental workload, and improving the optimization of the pyrolysis process.

Fast pyrolysis multiphase CFD-kinetics model in a drop tube reactor

Experimental investigation of biomass pyrolysis kinetics is challenging due to simultaneous complex chemical reactions, interphase heat and mass transfer. A CFD model and simulation overcome the challenges faced in experimental work and provides a detailed characterization of pyrolysis kinetics. In this work, a granular multiphase flow model with 17 heterogeneous reactions, 53 interphase mass transfer, and 35 pyrolysis species and Nitrogen (36 species) transport models was implemented to investigate the pyrolysis kinetics using a continuous flow drop tube reactor (DTR). The model predicted that 45 wt% biofuel, 25 wt. % permanent gases, 10 wt% water and 20 wt% biochar is produced from a biomass feedstock at a reactor wall temperature of 773 K. The majority of biofuel (35 wt%) is produced from cellulose. While hemicellulose and lignin mainly produce permanent gases (16 wt%) and biochar (8 wt%), respectively. Product characterization and visualization of the pyrolysis process in a virtual environment mimicking the actual reactor in real-time sheds light on the pyrolysis reaction processes lacking in the literature.

A comparative study of GA, PSO and SCE algorithms for estimating kinetics of biomass pyrolysis

A comparative study of GA, PSO and SCE algorithms for estimating kinetics of biomass pyrolysis

Pyrolysis activation energy of cellulosic fibres investigated by a method derived from the first order global model

The pyrolysis kinetics of cellulosic fibres, a natural cotton yarn (NCY) and a mercerized cotton yarn (MCY), has been explored with a modified first order global analysis method (FOG), via a series of non-isothermal experiments, using thermogravimetric analysis (TGA). The modified FOG analysis routine was developed to overcome discrepancy in heating rate and the difference between exact results and approximations in integrals. The intrinsic pyrolysis activation energy, with temperature range tending to zero, was found to be independent of heating rate and approximation used, giving average values of 153 ± 2 kJ/mol for NCY and 192 ± 7 kJ/mol for MCY. This proves the applicability of the reported analysis routine under the conducted TGA measurements. The reasons for different values were hypothesized to be the difference in chemical composition and crystalline structure. The findings provide a new approach in the investigation on pyrolysis kinetics of biomass and factors impacting their pyrolytic behaviour.

A method for addressing compensation effect in determining kinetics of biomass pyrolysis

Compensation effect is an unsolved issue when determining pyrolysis kinetics of biomass using inverse modelling and optimization algorithms, implying no unique solution can be obtained. To address this problem, a new method coupling Gauss multi-peak fitting method, Kissinger method, a numerical model, and Shuffled Complex Evolution (SCE) optimization algorithm is proposed to extract kinetics from microscale thermogravimetric analysis (TGA) experiments and avoid attainment of unreasonable good-fit solutions. TGA tests of beech wood at nitrogen atmosphere were conducted at three heating rates. Gauss multi-peak fitting method was employed to separate the overlapped peaks in TGA curves and identify the contribution of each elemental component reaction. The kinetics of individual reactions were estimated by Kissinger method to provide an initial solution for SCE optimization. Then, narrow initial search ranges were determined to further refine the kinetics by SCE. By compiling previous data in literature and our optimization results, it was found compensation effect exists for individual basic components, hemicellulose, cellulose and lignin, following a linear correlation between lnA andEa, and among multiple components. Pyrolysis of hemicellulose can be modelled by either first-order or high-order reactions, while cellulose pyrolysis is more likely a first-order reaction. Nevertheless, the long tail in DTG curves associated with decomposition of lignin can only be captured by a high-order reaction. Although the Kissinger solution of lignin cannot be used in selecting an appropriate search range for SCE optimization, the narrow search ranges of the remaining kinetic parameters can ensure the accuracy and convergency efficiency of optimization.

Non-isothermal pyrolysis of xylan, cellulose and lignin: A hybrid simulated annealing algorithm and pattern search method to regulate distributed activation energies

Biomass transformation into fuels, platform chemicals and materials contributes to developing sustainability and carbon neutralization. Pyrolysis is one of the most important approaches to valorize biomass. The distributed activation energy model (DAEM) is a comprehensive kinetic model for describing biomass pyrolysis kinetics. The estimation of the DAEM parameters is difficult because of the complex structure of the DAEM equation. This work formulated the numerical calculation approach of the DAEM and proposed a hybrid simulated annealing (SA) algorithm and pattern search (PS) method for optimizing the DAEM parameters. The DAEM kinetic parameters were evaluated by simultaneously fitting the experimental kinetic data of cellulose, xylan and lignin pyrolysis at various heating rates with the DAEM using the proposed hybrid optimization method. The results showed that the proposed hybrid optimization method could effectively and accurately determine the DAEM parameters and the DAEM with the optimal parameters could reproduce the experimental kinetic data of xylan, cellulose and lignin pyrolysis very well. The distributed activation energies of xylan, cellulose and lignin predicted by hybrid optimization were centered at 162.8, 222.8 and 236.7 kJ mol−1, respectively, with standard deviations of 4.6, 0.8 and 25.9 kJ mol−1, respectively. The reaction orders of xylan, cellulose and lignin pyrolysis are 1.7, 1.1, and 2.5, respectively.

Insight into derivative Weibull mixture model in describing simulated distributed activation energy model and distillers dried grains with solubles pyrolysis processes

The kinetics of biomass pyrolysis is fundamental for exploring its mechanisms and optimizing its processes, which is helpful for designing its systems. The derivative Weibull mixture model was proposed for kinetic description of the simulated distribution energy model (DAEM) processes and distillers dried grains with solubles (DDGS) pyrolysis processes. The conversion rate data of these processes at different heating rates could be accurately described by the derivative Weibull mixture model. Moreover, the proposed model could effectively smooth the noises contained in the experimental conversion rate data of DDGS pyrolysis. The derivative Weibull mixture model separated DDGS pyrolysis reactions into several individual processes, and provided some data required for further isoconversional kinetic analysis. The predicted curves from the derivative Weibull mixture model allowed us to obtain the effective activation energies of DDGS pyrolysis, which varied significantly from 170 to 330 kJ mol−1 in the conversion range between 0.1 and 0.9.

Pyrolysis of burnt maritime pine biomass from forest fires

Forest fires in Portugal give rise to a large amount of burnt wood, a waste, and as such with few applications. Its energy recovery can be seen to manage these residues with advantages for the environment and as a source of income for the populations that depend on the primary sector of the economy. Burnt pine wood was evaluated as raw material for pyrolysis. The collected biomass samples, from the maritime pine trunk, were cut to separate the burnt part (external layer) from the part not affected by the fire, giving rise to the two distinct biomass samples, raw and burnt. The lignocellulosic composition was assessed by thermogravimetry and the differences in composition arise from the fact that the raw sample is sapwood, whereas the burnt sample is mainly cambium. Biomass pyrolysis kinetics evaluated by the KAS method led to an activation energy of 192 kJ/mol for raw and 169 kJ/mol for burnt biomasses. Biomass was pyrolyzed in a fixed bed pyrex reactor for temperatures in the 673–773 K range corresponding to the maximum thermal degradation rate assessed by thermogravimetry. The burnt biomass showed lower bio-oil yields (maximum 31%), which increase with the pyrolysis temperature. Besides, burnt biomass bio-char yield was always greater than the analog produced from raw biomass over the entire temperature range. The catalyzed pyrolysis tests using carbonate catalysts (limestone, CaCO3, Na2CO3, and Li2CO3) showed improved pyrogas which are compatible with the gasification role of alkali carbonate catalysts reported in the literature.

Comparative study on pyrolysis kinetics of agroforestry biomass based on distributed activation energy model method

Pyrolysis behavior and kinetics of three typical agroforestry biomasses including apricot shell, wheat straw and poplar sawdust were investigated by thermogravimetric mass spectrometry (TG-MS). The results show that the differences of the main components make the three biomasses exhibit different characteristics in the main reaction range (200–450 °C). It is found that the average activation energy of apricot shell, straw and sawdust is 188.22, 220.77, and 175.87 kJ/mol, respectively based on the typical isoconversional methods. The average activation energy of each component in biomass was calculated by the distributed activation energy model (DAEM) method, indicating that there is a fourth component with high average activation energy in biomass (297.44 kJ/mol for apricot shell, 284.35 kJ/mol for straw and 309.96 kJ/mol for sawdust). The activation energy of hemicellulose and cellulose shows an increasing order of straw < apricot shell < sawdust. The two kinds of kinetics methods are complementary to each other. The overall calculation results by the isoconversional method are close to those by the single-component distributed activation energy model method, but the isoconversional method is simpler; while the distributed activation energy model method can be used to obtain the kinetic parameters of different components of raw materials, which makes up for the deficiency of the isoconversional method. A combined use of the two methods can form a more comprehensive understanding of the pyrolysis reaction.

Machine learning approach for the prediction of biomass pyrolysis kinetics from preliminary analysis

The pyrolytic behavior of lignocellulosic biomass is highly complex, and its kinetic behavior varies with operating conditions and the type of biomass. To reduce timescales, cost and rigorous calculations associated with new set of experimentation used for the estimation of kinetic parameters, model-based predictions are recommended. In the present work, Artificial Neural Network (ANN) based machine learning models are developed to predict the biomass pyrolysis kinetics. Data sets of thermogravimetric analysis and feedstock characterization from a diverse range of biomass were used to develop and test the networks. Four models were developed in this study based on proximate analysis (ANN-1), ultimate analysis (ANN-2), combined proximate and ultimate analysis (ANN-3) and the combined proximate, ultimate, and biochemical analysis (ANN-4). A total of 704 kinetic datasets were extracted and recalculated with the Coats-Redfern Method from which 662, 585, 465 and 133 datasets were used to develop models sequentially. The developed models, in particular ANN-3 and ANN-4 have shown a competitive prediction capability (R2 ~ 0.99, RRMSE <10.0%, and MAE < 0.071). Relative importance of each input (biomass properties & heating rate) on outputs (kinetic parameters) was also studied. Biochemical analysis was found to have higher contribution (~38%) in comparison to ultimate (~29%) followed by proximate analysis (~22%) and the pyrolysis kinetics were found to be affected by the heating rate to the extent of ~10%. The developed models were found to be accurate enough in predicting the pyrolysis kinetics for any new biomass feedstocks based on preliminary analysis.

RETRACTED: Chemistry-Informed Neural Networks modelling of lignocellulosic biomass pyrolysis

Biomass pyrolysis is a complicated reaction process that involves complex components and reaction pathways. Due to measurement limitations, the intermediate components are difficult to be detected, therefore their detailed kinetics are still not well established. To address this issue, novel Chemistry-Informed Neural Networks (CINNs) were developed to derive the lignocellulosic biomass pyrolysis kinetics from the thermogravimetric analysis (TGA) measurements in published literature. The derived pyrolysis kinetics, involving eight species and eleven reactions, could accurately reproduce the pyrolysis process for both the seen and unseen samples with R2>0.95. The comparisons with the CRECK multi-step and Bio-CPD models also demonstrated the advantages of the derived kinetics in predicting both the final volatiles yield and the pyrolysis process for various biomass types. This study explored a new tool for establishing solid fuel conversion kinetics from TGA measurements using chemistry-informed machine learning approaches.

Application of a hybrid PSO-GA optimization algorithm in determining pyrolysis kinetics of biomass

A hybrid optimization algorithm, combining both Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), is proposed to gain the favorable features of each individual algorithm when determining the pyrolysis kinetics of biomass. High convergence efficiency and the capability of avoiding being trapped in local optimal solution are primarily associated with PSO and GA, respectively. Gene operations in GA, including selection, crossover and mutation, are partially incorporated into PSO to increase the population diversity. Pyrolysis of beech wood was experimentally studied at three heating rates, and a numerical solver was established to simulate the pyrolysis details. In order to demonstrate the improved performance of PSO-GA, two pyrolysis models with given reaction schemes and kinetic parameters were adopted to create the acritical thermogravimetric analysis (TGA) curves. Then the kinetics was estimated using PSO-GA and individual GA and PSO. Subsequently, the experimental data were analyzed with the same manner. The results show that PSO-GA has the highest possibility of obtaining desired outcomes followed by PSO and then GA. With fixed population size, PSO-GA converges to a lower fitness function value, corresponding to higher accuracy. The attained kinetics of wood falls into the reported ranges in the literature. In some scenarios, the optimized results of hemicellulose and lignin contradict with the existing conclusions even though the global curves match the experimental measurements well. This implies the general concept of the pyrolysis process should also be given adequate consideration to avoid potential compensation effect when encountering complex issues.

Prediction of Pyrolysis Kinetics of Biomass: New Insights from Artificial Intelligence-Based Modeling

The present work introduces a quantitative structure-property relationship (QSPR)-based stochastic gradient boosting (SGB) decision tree framework for simulating and capturing of the thermal decomposition kinetics of biomass considering effective parameters of the ultimate analysis (such as carbon, hydrogen, oxygen, nitrogen, and sulfur content) and process heating rate. Through a total of 149 pyrolysis kinetics, this study developed an artificial model and subjected it to training and testing phases. The proposed model was validated using error analysis, sensitivity, regression, and outlier detection. The coefficient of determination (R2) and mean relative error (%MRE) were calculated to be 0.993 and 4.354%, respectively, suggesting good performance in the estimation of the pyrolysis kinetic parameters. Also, the sensitivity results indicated the process heating rate to have the strongest effect on the model output with a relevancy factor of 0.43. Eventually, the proposed model showed superior performance compared to earlier frameworks.

Pyrolysis of agricultural waste in a thermogravimetric analyzer: Studies of physicochemical properties, kinetics behaviour, and gas compositions

Biomass is employed in pyrolysis to generate a substance that can be employed as a feedstock for speciality chemicals as well as a source of energy. Since the oil crisis in the middle of the 1970s, significant progress has been made to transform biomass into liquid fuels and chemicals. To make the process more efficient and optimized, kinetic understanding is required before moving on to applied pyrolysis. Thus, the present study explored the pyrolysis kinetics of low-value agricultural waste biomass at varied heating rates in a thermogravimetric analyzer (TGA). Six model-free methodologies were utilized to analyse the kinetics parameters (Kissinger-Akahira-Sunose (KAS), Ozawa-Flynn-Wall (FWO), Distributed Activation Energy Model (DAEM), Coats-Redfern (CR), Vyazovkin model (VZ), and master plot). In addition, a TGA-FTIR analyzer was employed to investigate the gas composition online. The physicochemical study’s findings suggested that it could be used as a feedstock for pyrolysis in the bioenergy sector. Furthermore, TGA-FTIR analyzer established the highest discharge of CO2 (28.73 and 33.14 %) and carbonyl products (20.60 and 29.35 %) for WS and CS, respectively. The average apparent activation energies (AAAE) for KAS, OFW, DAEM, and VZ are also determined to be 237.12, 186.40, 170.50, and 163.63 kJ mol−1 for WS and 176.37, 170.51, 196.12, and 194.91 kJ mol−1, for CS respectively. Further, the master plot and thermodynamic study of biomass exposed that pyrolysis passed through many multifaceted reaction mechanisms (Diffusion, nucleation, phase boundary-controlled etc.) during pyrolysis. Overall, pyrolysis techniques can be used to increase the energy density of lignocellulosic residues in an environmentally friendly manner.

Application of a Hybrid PSO-GA Optimization Algorithm in Determining Pyrolysis Kinetics of Biomass

Application of a Hybrid PSO-GA Optimization Algorithm in Determining Pyrolysis Kinetics of Biomass

A Method for Addressing Compensation Effect in Determining Kinetics of Biomass Pyrolysis

A Method for Addressing Compensation Effect in Determining Kinetics of Biomass Pyrolysis

Simple microwave pyrolysis kinetics of lignocellulosic biomass (oil palm shell) with activated carbon and palm oil fuel ash catalysts

Abstract The important parameters characterizing microwave pyrolysis kinetics, namely the activation energy (E a) and the rate constant pre-exponential factor (A), were investigated for oil palm shell mixed with activated carbon and palm oil fuel ash as microwave absorbers, using simple lab-scale equipment. These parameters were estimated for the Kissinger model. The estimates for E a ranged within 31.55–58.04 kJ mol−1 and for A within 6.40E0–6.84E+1 s−1, in good agreement with prior studies that employed standard techniques: Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC). The E a and A were used with the Arrhenius reaction rate equation, solved by the 4th order Runge-Kutta method. The statistical parameters coefficient of determination (R 2) and root mean square error (RMSE) were used to verify the good fit of simulation to the experimental results. The best fit had R 2 = 0.900 and RMSE = 4.438, respectively, for MW pyrolysis at power 440 W for OPS with AC as MW absorber.