Analysis of biomass and sewage sludge devolatilization using the distributed activation energy model

Carbon fiber is a kind of new polymer material with excellent mechanical properties and being applied widely. The process of carbon fiber prepared by bamboo tar, including extraction, condensation, spinning, oxidation and carbonation, is influenced by the pyrolysis kinetics significantly. In this paper, the thermogravimetric analysis (TGA) of bamboo tar produced in the process of pyrolysis and gasification of the bamboo which is known as Phylostachys sulphurea, was analyzed by the distributed activation energy model (DAEM) to understand the kinetic properties and parameters of bamboo tar. The thermogravimetric analysis of bamboo tar which is used as the raw material of carbon fiber was conducted under 5 different heating rates (i.e. 5, 10, 15, 30 and 50 °C/min, etc.) in nitrogen atmosphere. The results show that the activation energy of bamboo tar and the exponential factor increased significantly with the increase of the heating rate, and the low heating rate is advantageous to the extraction of bamboo tar solvent and the thermal polycondensation, which can provide scientific reference for the optimization of carbon fiber technology. The thermal weight results show that the temperature range of bamboo tar being decomposed rapidly is 213°C-410°C. The ranges of the activation energy were calculated by DAEM, which have small difference in comparisons with five heating rates when the conversion rate is at 0.1-0.6 and the average value of the activation energy is 119 kJ/mol. The stability range of the activation energy is enlarged when the conversion rate is greater than 0.6 and heating rate increases. Keywords: bamboo tar, carbon fiber, thermogravimetric analysis (TGA), distributed activation energy model (DAEM) DOI: 10.25165/j.ijabe.20181106.3839 Citation: Zhang H, Yan B B, Lei T Z, Liu T, Hu J J, Li Y M, et al. Thermogravimetric analysis of bamboo-tar under different heating rates based on distributed activation energy model. Int J Agric & Biol Eng, 2018; 11(6): 180–186.

Maximum volatile release (MVR) methods, including single point (MVR-S) and multiple (MVR-M) points, are proposed to extract the kinetics used to predict the fuel bean straw pyrolysis process. The simulation results were compared to those of the distributed activation energy model (DAEM). For the TGA (thermogravimetric analysis) experiments, fuels were heated from ambient temperature to 1,173 K at the heating rates of 10, 20, 40, 60, 80 K min−1, and the corresponding maximum volatile release points were obtained. For the MVR-S method, both the activation energy (E) and pre-exponential factor (B) increased with increasing heating rates. For the pyrolysis weight loss process, the DAEM showed best agreement with the experimental data, followed by the MVR-M, and then the MVR-S method. However, for weight loss rate prediction, MVR-S method had the best match with the experimental data, whereas the DAEM and MVR-M method generated more errors.

Analysis of coals and biomass pyrolysis using the distributed activation energy model

Coupling DAEM and CFD for simulating biomass fast pyrolysis in fluidized beds

An innovative evaluation method for kinetic parameters in distributed activation energy model and its application in thermochemical process of solid fuels

Pyrolysis characteristics and kinetics of low rank coals by distributed activation energy model

The precursor of LiNiPO4 was synthesized by solid-state reaction at low-heating temperature using LiOH·H2O and NH4NiPO4·H2O as raw materials. LiNiPO4 was obtained by calcining the precursor. Based on the advanced isoconversional procedure and the distributed activation energy model (DAEM), the activation energies calculated indicated that the thermal process involved two stages which stage II was a kinetically complex process, but stage I was single-step process. The most probable mechanism for the stage I is random nucleation and subsequent growth. DAEM and nonlinear model-fitting method were applied to study the stage II of decomposition process of the precursor. The distributions of activation energy, f(E a) and values of preexponential factor A of the stage II of the thermal decomposition of precursor were obtained on the basis of DAEM. The results of nonlinear model-fitting method showed the most probable mechanisms of the parallel reactions for stage II are chemical reaction and nucleation.

Assessment of thermokinetic behaviour of tannery sludge in slow pyrolysis process through artificial neural network

This article focuses on the influence of relevant parameters of biomass pyrolysis on the numerical solution of the isothermal nth-order distributed activation energy model (DAEM) using the Rayleigh distribution as the initial distribution function F(E) of the activation energies. In this study, the integral upper limit, the frequency factor, the reaction order and the scale parameters are investigated. This paper also derived the asymptotic approximation for the DAEM. The influence of these parameters is used to calculate the kinetic parameters of the isothermal nth-order DAEM with the help of thermo-analytical results of TGA/DTG analysis.

The utilisation of rice husk as a feedstock for alternative fuel production is an area of growing interest due to its abundance and potential as a renewable energy source. This study reports investigation of kinetics and thermodynamics of rice husk pyrolysis in an inert atmosphere. Experimental analysis, including proximate, ultimate, structural composition and thermogravimetric analyses (TGA), were conducted to determine the proxanal, ultanal, chemical composition and pyrolysis characteristics of rice husk under inert conditions. Kinetics of the pyrolysis process was evaluated using distributed activation energy model (DAEM) while thermodynamic parameters such as activation energy (E A), changes in enthalpy (ΔH), entropy (ΔS),and Gibbs free energy (ΔG) were computed to provide insights into the reaction mechanisms. The feedstock exhibited ash content, fixed carbon, volatile matter, and higher heating value on dry basis of 13.56wt%, 16.37wt%, 70.07wt% and 15.22MJ/kg respectively while the result of ultimate analysis showed carbon, hydrogen, nitrogen, sulphur and oxygen content of 40.95, 5.67, 0.22, 0.09 and 52.17wt%. The chemical composition result (water extractives-free) indicated cellulose, hemicellulos and lignin content of 39.12, 17.56 and 22.43 wt%. The kinetics analysis revealed that the pyrolysis process followed a single-order reaction mechanism and irreversible, suggesting that the pyrolysis reactions proceed in a direction that favors the formation of products. The average E A, ΔH, ΔS and ΔG required for the pyrolysis reactions was established to be 140.56 kJ/mol, 137.00 kJ/mol, -135.25 J/mol and 222.38 kJ/mol respectively. The comprehensive assessment of the feedstock affirms its suitability for alternative fuel production towards a greener energy landscape.

In order to deal with the shortage of oil and natural gas, the supplement energies, such as oil sludge, waste plastics, waste rubber needed to be studied. The pyrolysis and combustion methods have advantages when they are chosen to treat oil sludge. The investigation of kinetics is significantly important to know the kinetic parameters and the reaction mechanisms and to provide important theoretical basis for the process design of pyrolysis and combustion to treat oil sludge. Most previous studies on pyrolysis and combustion kinetics of oil sludge regarded pyrolysis process or combustion process, and rarely split pyrolysis or combustion process to calculate kinetic parameters separately. In this work, the experiment of pyrolysis and combustion were carried out by thermal gravimetric analyzer. After splitting the pyrolysis and combustion processes into two stages, respectively, the methods of Coats-Redfern and distributed activation energy model (DAEM) were used to calculate the kinetic parameters. Based on the results of thermogravimetric analysis, the pyrolysis and combustion process can be divided into two stages. For the pyrolysis process, the first stage is mainly with the evolution of adsorbed volatile hydrocarbon and the rupture of weaker chemical bond and the second stage is the cracking of organic macromolecules. Meanwhile, in the first stage of combustion process, small molecular organic matter in dry basis oil sludge will volatilize and then burn on the surface of the crucible and the gas phase outside the crucible, and in the second stage, macromolecular organic matter will volatilize, pyrolyze and burn, and the diffusion of oxygen into the crucible will cause the fixed carbon to burn. According to the kinetic results of the pyrolysis process, the activation energy of the first stage calculated by the Coats-Redfern model are all less than 40 kJ/mol, and that of the second stage increases from 148.14 kJ/mol to 184.97 kJ/mol with the increase of the heating rate. The results of DAEM model show that with the increase of the conversion rate, the activation energy of the first stage increases from 60.82 kJ/mol to 81.05 kJ/mol, and that of the second stage increases from 120.60 kJ/mol to 237.07 kJ/mol. For the combustion process, the activation energy calculated using Coats-Redfern model decreases from 120.18 kJ/mol to 73.48 kJ/mol in the first stage, and that of the second stage is relatively low. The results of DAEM model show that with the increase of the conversion rate, the activation energy decreases from 129.53 kJ/mol to 62.36 kJ/mol in the first stage and that in the second stage is stable at about 70 kJ/mol.

Pyrolysis kinetics and thermal characteristics of microalgae Nannochloropsis oculata and Tetraselmis sp.

This article applies and critically compares essentially all methods (based on Arrhenius temperature dependence and power law reduced mass) to deduce the degradation parameters of polyether ether ketone (PEEK) and its carbon fibre composites, PEEK+CF, from thermogravimetric analysis experiments in nitrogen. The methods used include classical (and variations) analytical approximate methods, a method applying the genetic algorithm for optimization and the Distributed Activation Energy Model, usually not discussed with the other methods. The analytical methods are fast and easy to implement but they are limited to degradation kinetics having a single peak or individual separated peaks in the mass loss rate. By contrast, the genetic algorithm gives good results if a postulated mechanism of several Arrhenius reactions of arbitrary order is considered. Moreover, the Distributed Activation Energy Model provides the possibility to assess the assumption of overlapping first-order reactions having various activation energies. The degradation of PEEK and its composite is basically characterized by two regimes, one fast degradation regime (570 °C–630 °C approximately) and one much slower degradation regime (630 °C–800 °C approximately) at a mass degradation rate with time being simply proportional to the heating rate, a very remarkable new result by itself. However, the focus of the modelling in this work lies in the first fast degradation regime. It is shown that the Distributed Activation Energy Model and the genetic algorithm can simulate well the experimental results noting, however, that the Distributed Activation Energy Model is based on more physically determined activation energies. By contrast, the classical methods (Kissinger and modified Ozawa methods) are not able to reproduce the mass loss rates in the initial fast degradation regime (570 °C–630 °C) of pure PEEK and PEEK+CF composite. Although a physical explanation of the degradation mechanisms is desirable, the end game is to find out whether a method even not including the whole truth about a process is yet capable of reproducing the measurements accurately.

Pyrolysis kinetic and product analysis of different microalgal biomass by distributed activation energy model and pyrolysis-gas chromatography-mass spectrometry.

Insight into the pyrolysis kinetics of cellulose, xylan and lignin with the addition of potassium and calcium based on distributed activation energy model