Browsing by Author "Ghernaout, Djamel"

Now showing 1 - 4 of 4

A sustainable bioadsorbent approach for tetracycline adsorption from aqueous solutions using calcined chicken bone waste: Optimization via response surface methodology
(Results in Engineering, 2026-01-17) Ziani, Salima; AitAli, Salima; Brahmi, Aghilas; Lakehal, Yanis Zakaria; Zaabar, Aida; Meski, Samira; Boudrahem, Farouk; Khireddine, Hafit; Ghernaout, Djamel; Mukalaz,Herbert; Belaadi, Ahmed
Pharmaceutical pollutants, particularly tetracycline (TC), are of growing concern due to their persistence in the environment and toxicity, which pose significant risks to both aquatic ecosystems and human health. This novel study investigates the potential of chicken bone waste (CBW) as a bioadsorbent for TC removal from aqueous solutions, presenting a sustainable and cost-effective alternative to traditional methods. CBW was dried at 100 °C and subsequently calcined at 500 °C and 900 °C to enhance its surface properties, porosity, and crystallinity. Characterization techniques, including thermogravimetry (TGA), X-ray diffraction (XRD), Fourier-ransform infrared (FTIR) spectroscopy, and BET/BJH analysis, revealed notable enhancements in the surface area, porosity, and crystallinity of CBW. Specifically, calcination increased the specific surface area from 16.47 ² to 32.90 ² Optimization of the adsorption process was achieved using a Central Composite Design (CCD) based on Response Surface Methodology (RSM). The optimal conditions for TC removal were pH 6.34, TC concentration of 73.76 mg/L, CBW-900 dosage of 2.65 g/L, and temperature of 45 °C, resulting in a 94 % removal efficiency. Adsorption isotherms and kinetics indicated a complex physisorption mechanism, best described by the modified Langmuir-Freundlich isotherm and pseudo-first-order kinetics. DFT, BET/BJH, and FTIR analyses further suggested that TC interacts with the CBW-900 surface via donor-acceptor mechanisms, hydrogen bonding, π-π stacking, and pore filling. CBW-900 exhibited excellent recyclability, maintaining over 70% removal efficiency after ten adsorption-desorption cycles. These findings highlight CBW as an
Forecasting the thermal degradation depending on the kinetics of dracaena Draco lignocellulosic fibers using an artificial neural network
(Journal of Natural Fibers;Taylor &Francis, 2025-07-17) Hadou, Abdelwaheb; Belaadi, Ahmed; Ghernaout, Djamel; Mukalazi, Herbert
In order to forecast the thermal degradation of Dracaena draco plant fibers (DDFs) using thermogravimetric analysis (TGA) at heating rates ranging from 5 to 30°C/min, this study employed artificial neural networks (ANNs). Hemicellulose, cellulose, and lignin break-down were represented by the three different degradation stages that were seen. The enhanced ANN27 model successfully captured pyrolysis behavior and degradation patterns, achieving a high prediction accuracy (R2 = 0.99966). The model performed well at lower heating rates (5 and 10°C/min), but because of bias and heteroscedasticity, adjustments are required at higher rates (15–30°C/min). In contrast to the experimental averages of 131.244 kJ/mol, 109.269 kJ/mol, and 131.694 kJ/mol, respectively, kinetic analysis showed that the ANN27-predicted activation energies (Ea) were 133.420 kJ/mol (KAS), 53.692 kJ/mol (FWO), and 133.784 kJ/mol (STR). Without requiring a lot of testing, our ANN method provides insights into DDF thermal behavior and optimizes processing settings by properly forecasting degradation curves
Unraveling the Pyrolysis mechanisms of Syagrus palm waste fibers through Gaussian deconvolution and kinetic modeling
(Journal of Natural Fibers, 2025-09-22) Ferfari, Oussama; Belaadi, Ahmed; Alshahranib, Hassan; Ghernaout, Djamel; Mukalazi, Herbert
The thermal decomposition kinetics and thermodynamics of Syagrus romanzoffiana waste rachis fibers (SrWRFs) were investigated through thermogravimetric analysis in a nitrogen atmosphere at heating rates (β) of 30, 40, and 50°C/min. The Coats–Redfern method was employed to determine kinetic parameters, including activation energy (Ea), pre-exponential factor (A), and reaction mechanisms. In contrast, thermodynamic properties such as enthalpy change (ΔH), Gibbs free energy (ΔG), and entropy change (ΔS) have been derived to evaluate the energy requirements and spontaneity of the pyrolysis process. A three-parallel Gaussian reaction model was employed to deconvolute the degradation profiles of hemicellulose, cellulose, and lignin, revealing distinct temperature intervals for each component: hemicellulose (200–345°C), cellulose (305–398°C), and lignin (220–650°C), with high fitting accuracy (R2 ≥ 0.99537). The kinetic analysis identified sigmoidal rate (SR) models (SR6, SR7, and SR8) as the most suitable, yielding Ea values ranging from 97.31 to 262.11 kJ/mol, which increased with higher heating rates. Thermodynamic results indicate that SrWRF pyrolysis is endothermic (ΔH > 0) and nonspontaneous (ΔG > 0), with negative entropy changes (ΔS) suggesting an increase in molecular order among the degradation products. The kinetic compensation effect was confirmed, demonstrating a linear relationship between lnA and Eₐ.
Water-absorption behavior of hybrid biocomposites based on Syagrus romanzoffiana Palm waste and short Sisal \fibers : application of ANN, RSM optimization, and Fick’s Diffusion Model
(Journal of Natural Fibers, 2026-09-29) Ghernaout, Djamel; Boumaaza, Messaouda; Belaadi, Ahmed; Elboughdiri, Noureddine; Benaissa, Mhamed; Bourchak, Mostefa; Mukalazi, Herbert; Salih, Alsamani Ahmed
This research advances sustainable production methods by focusing on the reuse of agricultural waste. The absorption behavior of hybrid biocomposites reinforced with biochar derived from Syagrus romanzoffiana palm waste (SrPW) and Agave sisalana fiber (AsF) in an epoxy matrix was examined. The primary objective was to assess how varying AsF content (10, 15, 20, and 30%) affects the dynamic behavior of hybrid biocomposites incorporating a fixed 2% of biochar. Response surface methodology (RSM) and artificial neural networks (ANN), optimized using a genetic algorithm, were employed to evaluate and simulate absorption characteristics. The ANN approach demonstrated superior accuracy and robustness compared to RSM, as indicated by high correlation coefficients (0.9999 in training, 0.9702 in testing, and 0.9970 in validation) between model predictions and experimental results. This predictive method minimizes the need for extensive experimental trials, thereby saving time and resources. The resulting green composite, based on epoxy, palm waste, and sisal, presents an environmentally friendly alternative for potential sustainable applications.