Search Results (205)

Mango peel (MP), an abundant agro-industrial residue, was evaluated as a solid biofuel using combined physicochemical characterisation and non-isothermal thermogravimetric kinetics (TGA). Fourier transform infrared (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) revealed hydroxyl-rich surfaces and porous microstructures. Thermogravimetric combustion, conducted at heating rates of 20–80 K min⁻¹, displayed three distinct stages. These stages correspond to dehydration (330–460 K), hemicellulose/cellulose oxidation (420–590 K), and cellulose/lignin oxidation (540–710 K). Kinetic analysis using six model-free methods (Friedman (FR), Flynn–Wall–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS), Starink (STK), Kissinger (K), and Vyazovkin (VY)) yielded activation energies (E_a) of 52–197 kJ mol⁻¹, increasing with conversion (mean E_a ≈ 111 kJ mol⁻¹). Coats–Redfern (CR) fitting confirmed a three-dimensional diffusion mechanism (D3, R² > 0.99). Thermodynamic analysis revealed that the formation of the activated complex is endothermic, with activation enthalpy (ΔH) values of 45–285 kJ mol⁻¹. The process was found to be non-spontaneous under the studied conditions, with Gibbs free energy (ΔG) values ranging from 83 to 182 kJ mol⁻¹. With a high heating value (HHV) of 21.9 MJ kg⁻¹ and favourable combustion kinetics, MP is a promising supplementary fuel for industrial biomass boilers. However, its high potassium oxide (K₂O) content requires dedicated ash management strategies to mitigate slagging risks, a key consideration for its practical, large-scale application. Full article

TGA kinetic analysis can assess the thermal stability and degradation properties of PCMs by calculating activation energies and onset degradation temperatures, which are critical elements when developing optimal PCM composition and assessing long-term performance in thermal energy storage applications. In this study, we utilize a thermogravimetric analyzer to examine the thermal stability of both solar salt phase change material (i.e., commonly used in medium-temperature applications) (NaNO₃ + KNO₃) and a composite eutectic PCM mixture (i.e., PCM with 20% biochar). The activation energies of both the pure solar salt and composite solar salt PCM sample were evaluated using a variety of different kinetic models such as Kissinger–Akahira–Sunose (KAS), Flynn–Wall–Ozawa (FWO), and Starink. For pure PCM, the mean activation energies calculated using the KAS, FWO, and Starink methods are 581.73 kJ/mol, 570.47 kJ/mol, and 581.31 kJ/mol, respectively. Conversely, for the composite solar salt PCM sample, the calculated experimental average activation energies are 51.67 kJ/mol, 62.124 kJ/mol, and 51.383 kJ/mol. Additionally, various machine learning models, such as linear regression, decision tree regression, gradient boosting regression, random forest regression, polynomial regression, Gaussian process regression, and KNN regression models, are developed to predict the degradation behaviour of pure and composite solar salts under different loading rates. In the machine learning models, the mass loss of the samples is the output variable and the input features are PCM type, heating rate, and temperature. The machine learning models had a great prediction performance based on experimental TGA data, with KNN regression outperforming the other models by achieving the lowest RMSE of 0.0318 and the highest R² score of 0.977. Full article

Straw biomass is a renewable but problematic fuel due to its high alkali and chlorine content, which can cause slagging and corrosion during combustion. To mitigate these issues, this study investigates the influence of aluminosilicate additives on the thermal behavior and combustion characteristics of straw biomass. Laboratory-scale testing is carried out using thermogravimetric analysis under atmospheric air, showing the TG, DTG, and DSC profiles of samples (kaolinite, halloysite, straw biomass, and straw biomass with 4 wt.% of halloysite). Additionally, the main combustion parameters, like the ignition temperature, the maximum peak temperature, the burnout temperature, and some combustion indexes, are presented. The results show the effect of a heating rate in the range of 5–20 °C/min. Moreover, in this study, two non-isothermal model methods (Kissinger and Ozawa) are used to estimate energy activation. While halloysite slightly affects the combustion indexes and marginally reduces energy activation, its overall influence does not significantly alter combustion efficiency. These findings support the potential and safe use of halloysite for the biomass combustion process. Full article

This article aimed to incorporate the coordinated construction of classifiers to develop a model for predicting the pyrolysis of loose biomass. For the purposes of application, the ground form of pine cone was used to perform the thermogravimetric analysis at heating rates of 5, 10, and 15 °C∙min⁻¹. The supervised machine learning technique was considered to estimate the kinetic parameters associated with the thermal decomposition of the material. Here, the integral as well as differential form of the isoconversional method was used along with the Kissinger method for the maximum reaction rate determination. Python (version 3.13.2), along with PyCharm (2024.3.3) as an integrated development environment (IDE), was used to develop code for the given problem. The TG model obtained through the boosting technique provided the best fitting for the experimental dataset of raw pine cone, with the root squared error varying from ±1.82 × 10⁻³ to ±1.84 × 10⁻³, whereas it was in the range of ±1.78 × 10⁻³ to ±1.83 × 10⁻³ for processed pine cone. Similarly, the activation energies derived through the trained models of Friedman, OFW, and KAS were 176 kJ-mol⁻¹, 151.60 kJ-mol⁻¹, and 142.04 kJ-mol⁻¹, respectively, for raw pine cone. It was seen that the boosting technique did not provide a reasonable fit if the number of features was increased in the kinetic models. This happened owing to an inability to maintain a tradeoff between variance and bias. Moreover, the multiclassification in pyrolysis kinetics through the proposed scheme was not able to capture the distribution pattern of target values of the differential method. With the increase in the heating rates, the noise level in the predicted model was also relatively increased. Full article

The pyrolysis of non-recyclable construction, renovation, and demolition (CRD) wood waste is a complex thermochemical process involving devolatilization, diffusion, phase transitions, and char formation. CRD wood, a low-ash biomass containing 24–32% lignin, includes both hardwood and softwood components, making it a viable heterogeneous feedstock for bioenergy production. Thermogravimetric analysis (TGA) of CRD wood residues was conducted at heating rates of 10, 20, 30, and 40 °C/min up to 900 °C, employing model-fitting (Coats–Redfern (CR)) and model-free (Ozawa–Flynn–Wall (OFW), Kissinger–Akahira–Sunose (KAS), and Friedman (FM)) approaches to determine kinetic and thermodynamic parameters. The degradation process exhibited three stages, with peak weight loss occurring at 350–400 °C. The Coats–Redfern method identified diffusion and phase interfacial models as highly correlated (R² > 0.99), with peak activation energy (E_a) at 30 °C/min reaching 114.96 kJ/mol. Model-free methods yielded E_a values between 172 and 196 kJ/mol across conversion rates (α) of 0.2–0.8. Thermodynamic parameters showed enthalpy (ΔH) of 179–192 kJ/mol, Gibbs free energy (ΔG) of 215–275 kJ/mol, and entropy (ΔS) between −60 and −130 J/mol·K, indicating an endothermic, non-spontaneous process. These results support CRD wood’s potential for biochar production through controlled pyrolysis. Full article

Identifying sustainable and efficient methods for the degradation of plastic waste in landfills is critical for the implementation of the Saudi Green Initiative, the European Union’s Strategic Plan, and the 2030 United Nations Action Plan, all of which are aimed at achieving a sustainable environment. This study assesses the influence of palm fronds (PFR) on the thermal degradation of polypropylene plastic (PP) using TGA/FTIR experimental measurements, thermo-kinetics, and machine learning convolutional deep learning neural networks (CDNN). Thermal degradation operations were conducted on pure materials (PFR and PP) as well as mixed (blended) materials containing 25% and 50% PFR, across degradation temperatures ranging from 25 to 600 °C and heating rates of 10, 20, and 40 °C·min⁻¹. The TGA data indicated a synergistic interaction between the agricultural waste (PFR) and PP plastic, with decreased thermal stability at temperatures below 500 °C, attributed to the hemicellulose and cellulose present in the PFR biomass. In contrast, at temperatures exceeding 500 °C, the presence of lignin retards the degradation of the PFR biomass and blends. Activation energy values between 81.92 and 299.34 kJ·mol⁻¹ were obtained through the application of the Flynn–Wall–Ozawa (FWO) and Kissinger–Akahira–Sunose (KAS) model-free methods. The application of CDNN facilitated the extraction of significant features and labels, which were crucial for enhancing modeling accuracy and convergence. This modeling and simulation approach reduced the overall cost function from 41.68 to 0.27, utilizing seven hidden neurons, and 673,910 epochs in 13.28 h. This method effectively bridged the gap between modeling and experimental data, achieving an R² value of approximately 0.992, and identified sample composition as the most critical parameter influencing the thermolysis process. It is hoped that such findings may facilitate an energy-efficient pathway necessary for the thermal decomposition of plastic materials in landfills. Full article

A nitroguanidine propellant containing 4,4′,6,6′-tetra(azido)hydrazo-1,3,5-triazine (TAHT) was designed by replacing part of the nitroguanidine (NGu) in the nitroguanidine (NGu) propellant with TAHT. Three samples of the NGu propellant were prepared with different amounts of TAHT. The amounts of TAHT were 0%, 15%, and 20%, respectively. The effect of TAHT on the thermal behavior of the NGu propellant was studied by differential scanning calorimetry (DSC) and thermogravimetric analysis (TG). The apparent activation energy ($E_a$) of the decomposition reaction of the propellant samples was calculated using the Kissinger equation and the Ozawa equation. The thermal decomposition process of TAHT was studied using thermogravimetric–mass spectrometric–infrared (TG-MS-FTIR) triple technology. The results show that a small amount of TAHT slightly improves the thermal stability of the NGu propellant. TAHT can significantly reduce the mass loss rate of the propellant. Adding 20% of TAHT can reduce the maximum mass loss rate of the NGu propellant by 27%. It inhibits the thermal decomposition of the propellant. The $E_a$ values of the propellant calculated using the Kissinger equation were 192.8, 174.7, and 169.4 kJ·mol⁻¹, respectively, while the activation energies calculated using the Ozawa method were 190.7, 173.5, and 168.5 kJ·mol⁻¹, respectively. The consistency between these results indicates that adding TAHT can significantly reduce the thermal decomposition rate of the NGu propellant. During thermal decomposition, TAHT will generate a polyazide compound, and a dicyanopolymer is formed. This polyazide compound rapidly forms on the surface of the propellant, which explains the inhibiting effect of TAHT on the NGu propellant. Full article

The research investigates the thermal behavior of mixed systems based on natural and artificial cellulose fibers used as precursors for carbon nonwoven materials. Flax and hemp fibers were employed as natural components; they were first chemically treated to remove impurities and enriched with alpha-cellulose. The structure, chemical composition, and mechanical properties of both natural and viscose fibers were studied. It was shown that fiber properties depend on the fiber production process history; natural fibers are characterized by a high content of impurities and exhibit high strength characteristics, whereas viscose fibers have greater deformation properties. The thermal behavior of blended compositions was investigated using TGA and DSC methods across a wide range of component ratios. Carbon yield values at 1000 °C were found to be lower for blended systems containing 10–40% by weight of bast fibers, with carbon yield increasing as the quantity of natural fibers increased. Thus, the composition of the cellulose composite affects carbon yield and thermal processes in the system. Using the Kissinger method, data were obtained on the value of the activation energy of thermal decomposition for various cellulose and composite systems. It was found that natural fiber systems have three-times higher activation energy than viscose fiber systems, indicating their greater thermal stability. Blends of natural and artificial fibers combine the benefits of both precursors, enabling the deliberate regulation of thermal behavior and carbon material yield. This approach opens up prospects for the creation of functional carbon materials used in various high-tech areas, including thermal insulation. Full article

This study presents a comprehensive kinetic and thermodynamic investigation of dried orange peel (OP) combustion, employing thermogravimetric analysis (TGA) and differential thermogravimetry (DTG) at high heating rates (20–80 K min⁻¹). This gap in high heating rate analysis motivates the novelty of present study, by investigating OP combustion at 20, 40, 60, and 80 K min⁻¹ using TGA, to closely simulate rapid thermal conditions typical of industrial combustion processes. Thermal decomposition occurred in three distinct stages corresponding sequentially to the dehydration, degradation of hemicellulose, cellulose, and lignin. Activation energy (E_a) was calculated using six model-free methods—Friedman (FR), Flynn–Wall–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS), Starink (STK), Kissinger (K), and Vyazovkin (VY)—yielding values between 64 and 309 kJ mol⁻¹. The E_a increased progressively from the initial to final degradation stages, reflecting the thermal stability differences among biomass constituents. Further kinetic analysis using the Coats–Redfern (CR) model-fitting method identified that first-order (F1), second-order (F2), and diffusion-based mechanisms (D1, D2, D3) effectively describe OP combustion. Calculated thermodynamic parameters—including enthalpy (ΔH), Gibbs free energy (ΔG), and entropy (ΔS)—indicated the endothermic and increasingly non-spontaneous nature of the reactions at higher conversions. These findings demonstrate the potential of OP, an abundant agricultural waste product, as a viable bioenergy resource, contributing valuable insights into sustainable combustion processes. Full article

This study examines the thermal characteristics and kinetics of oil shale combustion using thermogravimetric analysis (TGA) at various heating rates. The combustion process includes three stages: dehydration, main combustion (70–80% mass loss), and mineral decomposition. Kinetic analysis using model-free (Ozawa–Flynn–Wall, Kissinger) and model-based (multi-step reaction kinetics) methods revealed that the second-order reaction model (F2) had the highest accuracy. Oil shale combustion involves multi-step reactions, with activation energy and pre-exponential factors varying nonlinearly with conversion rates. Combining model-free and model-based methods provides insights for optimizing combustion processes and equipment design for the efficient utilization of unconventional energy resources. Full article

With the development of high-speed and high-temperature equipment, thermal barrier materials are facing increasingly harsh service environments. The addition of YAG to Y₂Hf₂O₇ has been proposed in order to improve its long-term high-temperature performance. In this work, Y₂Hf₂O₇/Y₃Al₅O₁₂ composite powders were synthesized by combustion synthesis with urea, glycine, EDTA, citric acid, and glucose as fuels, while hafnium tetrachloride, yttrium nitrate hexahydrate, and aluminum nitrate nonahydrate were used as raw materials. The effects of fuels on the morphology and phase composition of synthetic powders were studied. Chemical reaction kinetic parameters were established by the Kissinger, Augis and Bennett, and Mahadevan methods. Y₂Hf₂O₇ and Y₃Al₅O₁₂ are the main components in the powders synthesized with urea as fuel, while YAlO₃ and Y₂Hf₂O₇ are the main phases with the other fuels. SEM and TEM analysis reveal that the powders prepared by the solution combustion method exhibit a typical porous morphology. When urea is used as fuel, the powders show a uniform elemental distribution, distinct ceramic grain crystallization, clear grain boundaries, and a uniform distribution of alternating grains. Compared to several other fuels, urea is more suitable for the preparation of Y₂Hf₂O₇/Y₃Al₅O₁₂ composite powders. In the process of preparing powders with urea, the activation energies for the combustion reaction calculated using the three methods are 100.579, 104.864, and 109.148 kJ·mol⁻¹, while the activation energies related to crystal formation are 120.397, 125.001, and 129.600 kJ·mol⁻¹, respectively. Full article

The fallen leaf has the potential to be energy-valorized in cities with sustainability goals. Thermochemical characterization of garden waste through pyrolysis and combustion kinetics will establish the reactivity of this lignocellulosic biomass as biofuel for thermochemical conversion processes for energy recovery. Herein, the thermal degradation of two types of pellets produced from fallen leaf (pellets without glycerol PG0, and pellets with 5 wt% glycerol PG5) are characterized under inert and oxidative atmospheres using three different approaches: thermogravimetry (TG) and differential thermogravimetry (DTG) analyses, TG-based reactivity, and reaction kinetics from three model-free isoconversional methods. The model-free isoconversional methods are Flynn–Wall–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS), and Friedman, which were applied for estimating the kinetic parameters, activation energy (Eα) and pre-exponential factor, using different heating rates (20, 30, and 40 °C/min) to ensure reliable data interpretation. The pyrolysis results showed that PG5 was more reactive compared to PG0 because the addition of glycerol during the pelletizing process increased the volatile matter and oxygen content in PG5. Likewise, the higher reactivity of PG5 under pyrolysis was determined by average activation energy (Eα) with an average value of 96.82 kJ/mol compared to 106.46 kJ/mol for PG0. During the combustion process, Eα was 90.70 kJ/mol and 90.29 kJ/mol for PG0 and PG5, respectively. Finally, both materials exhibited higher reactivity under an oxidative atmosphere. Therefore, according to our results, the pellets produced from leaf litter can be used as biofuels for thermochemical processes, highlighting that using glycerol as a binder favors the reactivity of the densified garden waste. Full article

Converting food waste into biofuel resources is considered a promising approach to address the rapid increase in energy demand, reduce dependence on fossil fuels, and decrease environmental hazards. In Egypt, large quantities of fried tilapia fish waste are produced in restaurants and households, posing challenges for proper waste management due to its decaying nature. The current study investigates the kinetic triplet and thermodynamic parameters of fried tilapia fish waste (FTFW) pyrolysis. Kinetic analysis was carried out using four iso-conversional models, Friedman, Kissinger–Akahira–Sunose (KAS), Flynn–Wall–Ozawa (FWO), and Starink, at heating rates of 10, 15, and 20 °C/min. The study findings indicate that FTFW decomposes within the temperature range of 382–407 °C. The estimated activation energy using the Friedman, FWO, KAS, and Starink methods ranged from 43.2 to 208.2, 31.3 to 148.3, 22.3 to 179.3, and 24.1 to 181.3 kJ/mol, respectively, with average values of 118.4, 96.7, 109.7, and 100.5 kJ/mol, respectively. The average enthalpy change determined using the Friedman, FWO, KAS, and Starink methods was 113.45, 91.78, 95.58, and 104.73 kJ/mol, respectively. The average values of Gibbs free energy change for the Friedman, KAS, FWO, and Starink, methods were 192.71, 171.04, 174.83, and 183.99 kJ/mol, respectively. Full article

The research presented in the article was undertaken in order to better investigate the generation potential of the Oligocene Menilite Formation due to its importance as source rocks within the Outer Carpathian Basin. The non-isothermal decomposition of the selected Carpathian source rock was studied to determine the kinetic parameters of the pyrolysis process. The kinetic parameters of bulk rock and separated kerogen were determined using the model-free Kissinger, Kissinger–Akahira–Sunose (KAS), and Friedman methods. The pyrolysis process exhibits a complex reaction mechanism. The obtained apparent activation energy (Ea) and pre-exponential factor (A) values depend on the extent of conversion, suggesting that the process involves multiple reaction steps. This dependence is very similar when calculated using both isoconversional methods, Friedman and KAS; however, the calculated values of the kinetic parameters differ depending on the method used. It was found that the activation energy of kerogen is lower than that of bulk rock, and the reaction maximum was shifted to higher temperatures. This shift is attributed to the presence of clay minerals in the rock. The values of average activation energy and the pre-exponential factor found in this study are relatively high, possibly due to the nature of the short-chain organic matter contained in the source rock. Full article

Finding reliable, sustainable, and economical methods for addressing the relentless increase in plastic production and the corresponding rise in plastic waste within terrestrial and marine environments has garnered significant attention from environmental organizations and policymakers worldwide. This study presents a comprehensive analysis of the low-heating-rate thermal degradation of high-density polyethylene (HDPE) plastic in conjunction with date seed powder (DSP), utilizing thermogravimetric analysis coupled with Fourier transform infrared spectroscopy (TGA/FTIR), machine learning convolutional deep neural networks (CDNNs), multiple linear regression model (MLRM) and thermokinetics. The TGA/FTIR experimental measurements indicated a synergistic interaction between the selected materials, facilitated by the presence of hemicellulose and cellulose in the DSP biomass. In contrast, the presence of lignin was found to hinder degradation at elevated temperatures. The application of machine learning CDNNs facilitated the formulation and training of learning algorithms, resulting in an optimized architectural composition comprising three hidden neurons and employing 27,456 epochs. This modeling approach generated predicted responses that are closely aligned with experimental results ($R^2~0.939$) when comparing the responses from a formulated MLRM model ($R^2~0.818$). The CDNN models were utilized to estimate interpolated thermograms, representing the limits of experimental variability and conditions, thereby highlighting temperature as the most sensitive parameter governing the degradation process. The Borchardt and Daniels (BD) model-fitting and Kissinger–Akahira–Sunose (KAS) model-free kinetic methods were employed to estimate the kinetic and thermodynamic parameters of the degradation process. This yielded activation energy estimates ranging from 40.419 to 91.010 kJ·mol⁻¹ and from 96.316 to 226.286 kJ·mol⁻¹ for the selected kinetic models, respectively, while the D2 and D3 diffusion models were identified as the preferred solid-state reaction models for the process. It is anticipated that this study will aid plastic manufacturers, environmental organizations, and policymakers in identifying energy-reducing pathways for the end-of-life thermal degradation of plastics. Full article