Pyrolysis Behavior Of Biomass Research Articles

Coupling kinetic modeling with artificial neural networks to predict the kinetic parameters of pine needle pyrolysis

The pyrolysis behavior of biomass is critical for industrial process design, yet the complexity of pyrolysis models makes this task challenging. This paper introduces an innovative hybrid model to quantify the pyrolysis potential of pine needles, predicting the entire process of their pyrolysis behavior. Through experimental analyses and kinetic parameter calculations of pine needle pyrolysis, the study employs a kinetic model with a chemical reaction mechanism. Additionally, it introduces an improved dung beetle optimization algorithm to accurately capture the primary trends in pine needle pyrolysis. The developed artificial neural network model incorporates meta-heuristic algorithms to address process error factors. Validation is based on experimental data from TG at three different heating rates. The results demonstrate that the hybrid model exhibits strong predictive performance compared to the standalone model, with coefficients of determination (R²) of 0.9999 and 0.999 for predicting the conversion degree and conversion rate of untrained data, respectively. Additionally, the standard errors of prediction (SEP) are 0.249% and 0.449% for predicting the conversion degree and conversion rate of untrained data, respectively.

Effects of Na-salt solutions on the biomass pyrolysis and CO2-assisted gasification behavior

This study aims to explore the effects of different Na-salt solutions on the behavior of biomass pyrolysis and gasification. The same molar number of Na was introduced via the impregnation method. The distinction was that pine wood impregnated with neutral NaCl and Na2SO4 solutions was the Na in physical adsorption form, whereas the alkaline Na2CO3, CH3COONa, and NaOH solutions resulted in both carboxylate form and physical adsorption form. Thermogravimetric analysis results showed that impregnating alkaline Na solution was more potent than the neutral Na solution in reducing the thermal stability of pine wood structures. Results of pyrolysis and CO2-assisted gasification indicated that samples impregnated with the alkaline Na solutions obtained lower CO and higher CO2 evolution than that from the neutral Na solution during pyrolysis. The Na in carboxylate form was more difficult to volatilize, and formed more Na-containing active catalytic structures, resulting in biochar that provided higher reactivity in gasification. However, different Na solutions on the biochar's gasification reactivity showed the order of NaCl-loaded sample < CH3COONa-loaded sample ∼ NaOH-loaded sample < Na2SO4-loaded sample < Na2CO3-loaded sample. This can be attributed to the combined effects of Na-containing active catalytic structures, along with the active surface salt complex formed by molten Na species and biochar. As for the overall energy efficiency of pine wood impregnated with different Na solutions, gasification of Na2CO3-loaded sample provided the highest maximum overall energy efficiency (49.2 %). The results presented here provide useful insights for practical application of using syngas for electricity generation.

The exploration of pyrolysis characteristics, kinetics and product distribution of Bermuda grass via TG, Py-PIMS and Shuffled Complex Evolution

The pyrolysis behaviors of biomass play an important role in understanding energy utilization. The pyrolysis characteristics, kinetics and product distribution of Bermuda grass were investigated based on thermogravimetric (TG), pyrolysis with photoionization mass spectrometry (Py-PIMS) and Shuffled Complex Evolution algorithm (SCE). The temperature range for weight loss is 450–850 K and the pyrolysis process can be classified into two regions based on the variation of activation energy, with conversion rates of 0.10–0.40 (Region Ⅰ) and 0.40–0.75 (Region Ⅱ). According to the Flynn-Wall-Ozawa (FWO) and Kissinger-Akahria-Sunose (KAS) methods, the mean activation energy values for Region Ⅰ are 126.70 kJ/mol and 124.20 kJ/mol, respectively. while the values for Region Ⅱ are 161.70 kJ/mol and 159.90 kJ/mol, respectively. Then, a three-component parallel reaction model based on the SCE algorithm was proposed and the pyrolysis kinetic parameters were optimized. The results showed the predicted values using the optimized kinetic parameters are well agreeable with experimental data, which shows that the optimized kinetic parameters and the three-component parallel reaction model may be applicable to the Bermuda grass pyrolysis. Moreover, the Py-PIMS results confirmed that the fast pyrolysis products of Bermuda grass included furan derivatives, ketones, cyclopentanones, phenols, aromatic hydrocarbons, acids and esters.

A comprehensive study on the pyrolysis behavior of pine sawdust catalyzed by different metal ions under conventional and microwave heating conditions

The Russia-Ukraine conflict and the COVID-19 pandemic have made fossil energy more urgent, and the catalytic pyrolysis of biomass is conducive to energy transformation to achieve global sustainable development. In this paper, the influence mechanisms of different metal ions on biomass pyrolysis under conventional heating and microwave heating conditions were studied. Through thermogravimetric analysis, it was found that the existence of metal ions could change the pyrolysis behaviors of biomass, leading to different degrees of changes in the main pyrolysis temperature and range. Compared with conventional heating conditions, metal ion-loaded biomass samples exhibited higher heating rates under microwave heating conditions due to the possible hotspot phenomenon, resulting in increased gas yields and decreased bio-oil yields. Among them, the trivalent iron ion exhibited excellent catalytic properties for gas generation, with a high gas yield of 57.9% and a bio-oil yield of 12.1%. The components in bio-oil were greatly simplified by microwave irradiation, the number of the bio-oil compounds from the pyrolysis of Fe-loading pine sawdust was reduced to 77, and the GC-MS area of light compounds with carbon number less than 10 was increased to 84.4%. Phenol and furan in bio-oil are also catalytically converted into aromatic hydrocarbons, which are ideal chemical raw materials.

Fast pyrolysis behaviors of biomass with high contents of ash and nitrogen using TG-FTIR and Py-GC/MS

In order to examine the effective exploitation of high ash and high nitrogen content biomass, the fast pyrolysis behavior of mushroom bran (MB) and corn straw (CS) was studied utilizing high heating rate TG-FTIR and Py-GC/MS. TG was used to study the pyrolysis behavior of MB and CS under low and high heating rates followed by a seven-stage pyrolysis studies using Py-GC/MS. The results showed that at higher heating rates, there is an overlap of the decomposition of starch and hemicellulose, potentially leading to synergistic effects that would promote the formation of furan and furfural. At heating rates greater than 200 °C/min, nitrogen has a tendency to migrate from hydrogen cyanide to NH3 before 450 °C. Deoxidation and denitrification mainly occurred between the pyrolysis temperature range of 300 °C to 350 °C, which merits further research. Staged pyrolysis interrupts the oil's continuous deoxidation process in comparison to direct pyrolysis, and it reduces the selectivity of aldehydes and ketones in MB and CS pyrolysis by 12.57% and 12.25%, respectively. More importantly, the study of staged pyrolysis in this work provides detailed pyrolysis characteristics for high nitrogen and ash content biomass, allowing for a better understanding of the pyrolysis behavior of such biomass.

Comparison of Artificial Neural Networks and kinetic inverse modeling to predict biomass pyrolysis behavior

The prediction of biomass pyrolysis behavior has received immense research attention, which has challenged scientists as well as industrialists. Currently, the main predictive tool is inverse modeling which is based on a specific kinetic scheme. However, real biomass pyrolysis entails a complex reaction mechanism, the appropriate kinetic scheme is being explored. Because pyrolysis prediction is closely related to the accuracy of the proposed kinetic scheme, whether the prediction can be achieved without prior knowledge of the kinetic scheme is intriguing. Therefore, Artificial neural networks (e.g., back propagation neural network and Elman neural network) are applied for prediction based on unknown kinetic schemes. Specifically, Elman neural network, which is an uncommon biomass pyrolysis prediction method, has been applied. Moreover, the neural network structure that was subjected to different data sets was optimized to establish the optimal neuron number in the hidden layer, transfer function, and training function, which were based on thermogravimetric data over a wide heating rate range. Eventually, the predicted results agreed well with the experimental data. Moreover, compared with kinetic inverse modeling coupled with a common heuristic algorithm known as particle swarm optimization, our current ANNs results, which were based on the unknown kinetic scheme exhibited superior prediction ability, especially in the shoulder and peak regions of mass loss rate curves.

Kinetic parameters and reaction mechanism study of biomass pyrolysis by combined kinetics coupled with a heuristic optimization algorithm

Kinetic reaction mechanism plays a key role in accurately predicting biomass pyrolysis behavior. However, the order-based reaction mechanism is usually defined as the default in the existing kinetic studies. For the purpose of exploring the exact kinetic reaction mechanism of biomass pyrolysis, the combined kinetic method is tried here by coupling two common kinetic models (one-step global reaction and three-parallel reaction). The decomposition rate expressed by the method is related to three key parameters, whose values are corresponding to the establishment of reaction mechanism. The combined kinetic method is further coupled with a heuristic optimization algorithm to obtain more exact kinetic parameters in order to explore the reaction mechanism. The results show that it is hard to infer the reaction mechanism based on one-step global reaction, while based on three-parallel reaction, the respective reaction mechanism of each component can be established as Power law for cellulose, Order-based for hemicellulose and lignin by comparing with the thermogravimetric data. More importantly, the established mechanism and optimized parameters can be used to accurately predict the micro-scale pyrolysis behavior of biomass (beech) at a wider range of heating rates.

Insights into the influence of methanolysis on the physicochemical structure variation and pyrolysis reactivity of wheat straw

Alkanolysis had become the promising approach for extracting organic compounds of lignocellulosic biomass, but its effect on the pyrolysis behaviors of biomass still received little attention. In this research, the influence of methanolysis pretreatment on the physicochemical structure variation, pyrolysis kinetics, and thermodynamic of wheat straw (WS) was systematically investigated using four multiple direct tools. C element content of pretreated WS greatly increased and O-containing functional groups was extracted, such as C-O bonds. Thermal decomposition behaviors of WS and pretreated WS were conducted by Thermogravimetric analyzer (TGA) at the heating rate of 10, 20, and 30 °C/min, respectively. Three model-free methods such as Flynn-Wall-Ozawa (FWO), Kissinger-Akahira-Sunose (KAS), and Starink were applied to evaluate the pyrolysis kinetics and thermodynamics parameters of three samples. The average Ea value obtained using three model-free methods for R250 °C (340.84, 345.88, and 345.02 kJ/mol) were higher in comparison to WS (192.48, 191.15, and 192.39 kJ/mol) and R220 °C (175.05, 180.10, and 177.63 kJ/mol). The pyrolysis kinetic model of WS and R220 °C followed D3 or R3 (0.1 ≤α ≤ 0.7) and F2 diffusion model (α ≥ 0.7). D3 or R3 (0.1 ≤α ≤ 0.7) and F3 diffusion model (α ≥ 0.7) can accurately describe the kinetic model of R250 °C. Combined with thermodynamic data analysis of three samples, R250 °C required more external energy for pyrolysis process due to more existence of lignin. This research could provide the new understandings for the pyrolysis process of liquefaction residue of biomass.

Co-pyrolysis of corn stalk and coal fly ash: A case study on catalytic pyrolysis behavior, bio-oil yield and its characteristics

In this paper, industrial solid waste coal fly ash and agricultural solid waste corn stalk were used as the research object. The aim is to realize the comprehensive utilization of solid waste and obtain high-quality bio-oil. Thus, the effects of pyrolysis conditions on the pyrolysis process were discussed, and the catalytic effects of coal fly ash were explored using the TGA, SEM-EDS, XRD, FTIR, and GC-MS. The results of TGA coupled with the Coats-Redfern model indicate that the total apparent activation energy varied from 408.40 kJ/mol to 387.39 kJ/mol and co-pyrolysis is a endothermic process while the initial pyrolysis temperature(T i ) is advanced after adding coal fly ash. The highest yield of bio-oil is 30.06 ± 0.03 wt%. It is obtained at 500 °C with a 50% mass fraction of coal fly ash. Additionally, ketonization reactions are promoted, leading to the content of ketones in bio-oil increased by about 10.28%. The selectivity of hydrocarbons is also improved, especially, aliphatic hydrocarbons increase by about 7.89%. The reasons are deeply analyzed and discussed. Overall, the results demonstrate that the participation of coal fly ash not only improves the pyrolysis behavior of biomass but also promotes the enrichment of ketone chemicals in the bio-oil.

Effect of pretreatment with Phanerochaete chrysosporium on physicochemical properties and pyrolysis behaviors of corn stover

Fungal pretreatment can selectively degrade partial biomass components, which undoubtedly exerts a significant influence on biomass pyrolysis behavior. The corn stover was pretreated with Phanerochaete chrysosporium, and its influence on the physicochemical properties and pyrolysis behaviors of biomass together with the product characteristics were investigated. The Phanerochaete chrysosporium was more active to degrade hemicellulose and lignin. The hemicellulose and lignin contents in corn stover were decreased by 35.14 % and 31.80 %, respectively, after five weeks pretreatment, compared to the untreated sample. The reaction activation energy decreased from 52.89 kJ·mol−1 for the untreated sample to 40.88 kJ·mol−1 for the sample pretreated for five weeks. The Phanerochaete chrysosporium pretreatment was beneficial to the biochar production but exerted an unfavorable effect on the texture structure. The Phanerochaete chrysosporium also had an obvious influence on the bio-oil compositions. This study can provide a scientific reference for the application of biological pretreatment for biomass pyrolysis technology.

Effect of torrefaction pretreatment on the fast pyrolysis behavior of biomass: Product distribution and kinetic analysis on spruce-pin-fir sawdust

Torrefaction pretreatment is of great significance to the enhancements of biomass characteristics. Product distribution and kinetics analysis of spruce-pin-fir (SPF) sawdust and its torrefaction samples were studied during the fast pyrolysis process. Under anaerobic conditions, torrefied biomass was prepared at different temperatures in a slot-rectangular spouted bed reactor. Fast pyrolysis experiments were conducted in a tube furnace with online mass spectrometry. The effects of torrefaction degree and pyrolysis temperature on product distribution were discussed. The main gaseous products, ie. CO, CO2, H2, and CH4, were investigated, and kinetic data of gaseous product formation was solved based on the isoconversional method. Other volatiles were the main product during the fast pyrolysis process, and the char yield was the lowest. With the increase of torrefaction degrees, the yield of char increased from 19.80 % to 22.09 % at 800 °C. The pyrolysis gas yield decreased from 25.78 % to 17.18 % as the torrefaction temperature increased from 240 °C to 300 °C. The gaseous products were mainly CO and CO2. The content of CO tended to increase, and the content of CO2 decreased when the pyrolysis temperature increased. The order of activation energy was E (CO2)>E (H2) >E (CO) >E (CH4) during the fast pyrolysis of SPF. The activation energy of the CO2, CH4, and H2 increased with the increase of the torrefaction temperature. According to different evaluation indexes, the optimal setting of reaction conditions is determined.

Effect of alkali and alkaline metals on gas formation behavior and kinetics during pyrolysis of pine wood

This study examines the acid and alkaline treatment of pine wood to help understand the effect of alkali and alkaline earth metals (AAEMs) content on the pyrolysis behavior at different temperatures. Acid and alkali pretreatment of pine wood were conducted to modify AAEMs content by ion-exchanging method. Thermal kinetic behavior of the pretreated samples was first conducted by a thermogravimetric analyzer (TGA) at different heating rates that provided activation energies of pyrolysis. Gas formation behavior of the samples for different extents of conversion was carried out in a fixed-bed reactor at two different temperatures of 823 K and 1073 K. Evolutionary behavior of the gas components during pyrolysis, including flow rate, and yield and their energy content were measured and compared. Results showed that the values of activation energies increased with the extent of conversion for all the pretreated samples examined. The effect of AAEMs on pyrolysis behavior of biomass varied with the extent of conversion. The presence of AAEMs in biomass decreased the decomposition energy at low conversion while it greatly improved under high conversion. Besides, alkali treated sample with higher AAEM content enhanced the gas and char yield while it reduced the bio-oil production at low temperature with low conversion. However, at high temperature the opposite trend was observed. Presence of AAEMs was favorable for the generation of H2, CO, CO2 and CnHm at low temperature, while it showed an inhibition effect on CO and CnHm yield and syngas energy at high temperature. The catalytic mechanism of AAEMs on the pyrolysis behavior at different temperatures was discussed based on activation energy and gaseous formation. Results revealed decomposition of carboxylate at low temperature and formation of stable biomass-Na (BM-Na) structure at high temperature that led to the variation of activation energy and changed gaseous products yield and syngas energy. Acid washing pretreatment was found to be effective to enhance bio-oil yield at low temperature and increase syngas energy at high temperature. Syngas yield of acid pretreated sample was 19.6% higher than that raw sample, while alkali pretreatment was favorable for enhanced char yield. These results help to achieve the desired yield of gas or solid using AAEM modification.

Effects of torrefaction on slow pyrolysis of the sorghum straw pellets: a kinetic modeling study

The slow pyrolysis of pretreated sorghum straw pellets (torrefied at 503, 533, 553 and 573 K) was investigated. The product yields of the torrefaction and slow pyrolysis and the total product yields of the two steps were measured separately. The results suggested that the increase in torrefaction temperature increased the carbon content and calorific value and decreased the hydrogen and oxygen content of the solid products. Compared to the raw sorghum straw pellets, the total yield of the gases obtained from slow pyrolysis of the torrefied sorghum straw decreased, while the char yield did not change significantly. It is demonstrated that torrefaction had little effect on the char yield of the biomass slow pyrolysis. The results of ultimate analysis showed that the carbon content (C) in bio-oil produced from torrefied sorghum straw pellets increased with the increased torrefaction temperature, while the oxygen (O) and hydrogen content (H) in bio-oil decreased. Based on the results of ultimate analysis and mass conservation calculation, a simplified one-step chemical equation was developed for the slow pyrolysis of raw and torrefied sorghum straw, and the stoichiometric coefficients of the equation were calculated by elemental balance and mass conservation. A two-step kinetic model was proposed for the pyrolysis of biomass, the apparent activation energy, the pre-exponential factor and the exponents of the two reaction models increased with the increased torrefaction temperature. The apparent activation energy and pre-exponential factor of the reaction increased proportionally with the increased C/O ratio of the reactants. The combination of the one-step chemical equation and the two-step kinetic model can effectively describe the volatiles release behavior of biomass pyrolysis and can be used to predict the relative yield of char, bio-oil and some major gas species.

The effects of char and potassium on the fast pyrolysis behaviors of biomass in an infrared-heating condition

During the solar pyrolysis of biomass, the heat transfer of biomass is restrained due to uncontrolled heating area and accumulation effect. So the co-pyrolysis reaction behaviors of biomass and char, and the secondary pyrolysis reactions of biomass under co-pyrolysis situation are worth exploring. The effects of char and potassium on fast pyrolysis products distribution by fully mixing and extruding sample materials under various heating rates (5 °C/s, 20 °C/s, 100 °C/s) and pyrolysis temperature (350 °C, 425 °C, 500 °C) were discussed. The results showed that high temperature was favorable to the release of volatiles, and higher heating rate promoted decomposition of bio-oils into gaseous products. Higher heating rate contributed to the release of O element and the accumulation of C element in bio-char to some extent. The char enhanced the secondary pyrolysis reaction resulting further decomposition of small molecular acids and the conversion of guaiacyl-contained structure into simple phenols and CH4 by demethylation and demethoxylation reaction. The influences of potassium were concentrated in increasing the reactivity of biomass pyrolysis, which promoted the conversion of bio-oils into gas (especially CO2) and the demethylation reaction of guaiacyl at low temperature.

Comprehensive insights into the influences of acid-base properties of chemical pretreatment reagents on biomass pyrolysis behavior and wood vinegar properties

Pyrolysis of biomass is an effective approach to produce high-value added solid, liquid and gaseous products. Wood vinegar (WV) is obtained as one of the main liquid products of biomass pyrolysis and has been used as the sustainable chemicals in agriculture. In this study, the effects of acid-base properties of chemical reagents on the biomass pyrolysis behavior and WV properties were investigated, and the mechanism was further discussed. The results indicated that acid-base properties of chemical reagents exhibited the significant effects on pyrolysis behavior and WV properties. Alkaline compounds showed the more obviously effects on changes in biomass chemical structure rather than acids, while acids removed the metallic species more efficiently than alkaline compounds. All chemical pretreatments increased the cellulose crystallinity, and the alkaline compounds were more favorable than the acidic compounds for increasing the crystallinity. Meanwhile, chemical pretreatments changed the chemical structure of biomass and promoted the pyrolysis process. All chemical pretreatments increased the organic content of WV. The relative content of phenols increased after all pretreatments, which increased more obviously by the acids pretreatments. Whereas, the relative content of acids decreased after pretreated by inorganic acids, and increased after pretreated by organic acids and alkaline compounds.

Catalytic Conversion of Biomass for Aromatics Over HZSM-5 Modified by Dawson-Type Phosphotungstic Acid

The multi-functional zeolites modified with different phosphotungstic acid (PW) ratios were prepared and characterized. Thermogravimetric tests coupled with kinetics analysis were performed to evaluate catalytic pyrolysis behavior of biomass; The presence of HZSM-5 inhibited the product diffusion, causing a slight delay of pyrolysis; The PW modification made the reaction more complex; reaction order increased from 1.96 to 2.12. The activation energy decreased from 53.32 to 31.15 kJ/mol with the increase of PW. Then, fixed-bed catalytic experiments were further conducted to investigate aromatics production; 10%PW modification gave the appropriate acidic distribution and pore structure, resulting in oxygen being more likely to be removed in the form of COx. Although the organic yield was only 10.73%, HHV reached 38.01 MJ/kg. The organic phase catalyzed by 10%PW/HZSM-5 exhibited higher aromatization degree, and the structures of benzene ring were mainly single-rings. The oxygenates (especially for phenols from 16.88% to undetected) reduced obviously with increasing PW loading, and the 10%PW modification gave the highest content (peak area, %) of desirable mono-aromatic hydrocarbons (52.29%). The GC/MS analysis results were basically consistent with the 1H/13C NMRs. Besides, the 10%PW/HZSM-5 had the highest catalytic stability and the spent catalyst could recover high activity after regeneration. Therefore, catalytic pyrolysis of biomass using Dawson-structured PW-modified HZSM-5 is a promising approach for production of light aromatic hydrocarbons.

Simulation of Batch Slow Pyrolysis of Biomass Materials Using the Process-Flow-Diagram COCO Simulator

The commercial COCO simulation program was used to mimic the experimental slow pyrolysis process of five different biomasses based on thermodynamic consideration. The program generated the optimum set of reaction kinetic parameters and reaction stoichiometric numbers that best described the experimental yields of solid, liquid and gas products. It was found that the simulation scheme could predict the product yields over the temperature range from 300 to 800 °C with reasonable accuracy of less than 10% average error. An attempt was made to generalize the biomass pyrolysis behavior by dividing the five biomasses into two groups based on the single-peak and two-peak characteristics of the DTG (derivative thermogravimetry) curves. It was found that this approximate approach was able to predict the product yields reasonably well. The proposed simulation method was extended to the analysis of slow pyrolysis results derived from previous investigations. The results obtained showed that the prediction errors of product yields were relatively large, being 12.3%, 10.6%, and 27.5% for the solid, liquid, and gas products, respectively, possibly caused by differing pyrolysis conditions from those used in the simulation. The prediction of gas product compositions by the simulation program was reasonably satisfactory, but was less accurate for predicting the compositions of liquid products analyzed in forms of hydrocarbons, aromatics and oxygenated fractions. In addition, information on the kinetics of thermal decomposition of biomass in terms of the variation of fractional conversion with time was also derived as a function of temperature and biomass type.

Prognostication of lignocellulosic biomass pyrolysis behavior using ANFIS model tuned by PSO algorithm

In-depth knowledge on pyrolysis behavior of lignocellulosic biomass is pivotal for efficient design, optimization, and control of thermochemical biofuel production processes. Experimental thermogravimetric analysis (TGA) is usually employed to peruse the pyrolysis kinetics of biomass samples. In addition to that, the main constituents of biomass (i.e., cellulose, hemicellulose, lignin) as well as the process heating rate can excellently reflect its pyrolysis characteristics through modeling techniques. However, the application of statistical and phenomenological models for extremely complex and highly nonlinear phenomena like lignocellulose pyrolysis is challenging. To address this challenge, adaptive network-based fuzzy inference system (ANFIS) was consolidated with particle swarm optimization (PSO) algorithm to prognosticate the kinetic constants of lignocellulose pyrolysis. More specifically, the PSO algorithm was applied to tune membership function parameters of the ANFIS model. Three ANFIS−PSO topologies were designed and trained to estimate the kinetic constants of lignocellulose pyrolysis, i.e., energy of activation, pre-exponential coefficient, and order of reaction. The input variables of the developed models were biomass main constituents and the process heating rate. The developed models could predict the kinetic constants of lignocellulosic biomass pyrolysis with an R2 > 0.970, an MAPE < 3.270%, and an RMSE < 5.006. The pyrolysis behaviors of three different biomass feedstocks (unseen data to the developed models) were adequately prognosticated with an R2 > 0.91 using the developed models, further confirming their fidelity. Overall, the lignocellulose pyrolysis behavior could be reliably and accurately estimated using the trained ANFIS–PSO approaches as an alternative to the TGA measurements. In order to make practical use of the trained models, a handy freely-accessible software platform was designed using the selected ANFIS−PSO models for approximating biomass pyrolysis kinetics.

Contrasting the Pyrolysis Behavior of Selected Biomass and the Effect of Lignin

This study was aimed at comparing the pyrolysis behavior of several selected biomass samples, namely, pine wood, poplar wood, wheat straw, and sugarcane bagasse, with a particular attention to the effect of lignin. Raw samples were first treated using Soxhlet solvent extraction with a 2:1 (v/v) mixture of toluene/ethanol to remove wax. Lignin was then removed by soaking the dewaxed samples in a 1.0 M sodium chlorite solution at 343 K till the solids became white. Fourier transform infrared (FTIR) spectroscopy analysis was applied to characterize the surface functional groups of the samples. The morphology of the samples before and after delignification treatment was analyzed using scanning electron microscope (SEM). The pyrolysis behavior of the raw and treated biomass samples was studied using a thermogravimetric analyzer (TGA) operating in nitrogen at a constant heating rate of 10 K min−1 from room temperature to the final temperature 823 K. The FTIR and SEM results indicated that lignin can be successfully removed from the raw biomass via the chemical treatment used. As expected, the pyrolysis behavior differed significantly among the various raw biomass samples. However, the pyrolysis behavior of the delignified samples showed almost identical thermal behavior although the temperature associated with the maximum rate of pyrolysis was shifted to a lower temperature regime by ca. 50 K. This suggests that the presence of lignin significantly affected the biomass pyrolysis behavior. Thus, the pyrolysis behavior of the biomass cannot be predicted simply from the individual components without considering their interactions.

Mathematical insight to non-isothermal pyrolysis of pine needles for different probability distribution functions

ABSTRACTThe purpose of this paper is to analyze the different probability distribution functions for predicting the behavior of biomass pyrolysis mathematically. Comparative analyses of predicted results obtained for nth order distributed activation energy model (DAEM) are performed with the help of graphical representation. Asymptotic approximation is obtained for the wide distribution of f (E). The temperature distribution for pyrolysis of pine needles is assumed to be non-isothermal.