WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - WoS: 6Citation - Scopus: 8Thermal Liquefaction of Olive Tree Pruning Waste Into Bio-Oil in Water and Ethanol With Naoh Catalyst(Elsevier B.V., 2024) Öcal,B.; Recepoğlu,Y.K.; Yüksel,A.In this study the effect of catalysts and solvents at varying temperatures on the production of bio-oil from olive tree pruning waste (OPW). The thermal liquefaction process was conducted at 200 °C, 225 °C, and 250 °C for 90 min, employing either water or ethanol as solvents, with alkaline catalysts (0.125 M, 0.25 M, and 0.5 M NaOH) introduced for the first time. Raw material, solid byproducts, and bio-oil samples underwent FTIR analysis for structural changes, TGA for proximate analysis, and GC-MS for bio-oil analysis. Results revealed that NaOH enhanced biomass conversion in water, yet didn't increase bio-oil yield, whereas in ethanol, biomass conversion was relatively lower, but bio-oil yield improved despite the adverse effects of catalyst. The highest biomass conversion (94 %) was achieved at 250 °C with 0.5 M NaOH, but the maximum bio-oil yield (25 %) occurred without a catalyst in water. Conversely, the highest bio-oil yield (55 %) was attained using ethanol without a catalyst at 250 °C. © 2024 Energy InstituteArticle Citation - WoS: 39Citation - Scopus: 42Prediction of Char Production From Slow Pyrolysis of Lignocellulosic Biomass Using Multiple Nonlinear Regression and Artificial Neural Network(Elsevier, 2021) Li, Ting Yan; Xiang, Huan; Yang, Yang; Wang, Jiawei; Yıldız, GürayChar produced from lignocellulosic biomass via slow pyrolysis have become one of the most feasible alternatives that can partially replace the utilisation of fossil fuels for energy production. In this study, the relationship between compositions of lignocellulosic biomass, operating conditions of slow pyrolysis, and characteristics of produced char have been analysed by using multiple nonlinear regression (MnLR) and artificial neural networks (ANN). Six input variables (temperature, solid residence time, production capacity, particle size, and fixed carbon and ash content) and five responses (char yield, and fixed carbon, volatile matter, ash content, HHV of produced char) were selected. A total of 57 literature references with 393-422 datasets were used to determine the correlation and coefficient of determination (R-2) between the input variables and responses. High correlation results (>0.5) existed between pyrolysis temperature and char yield (-0.502) and volatile matter of produced char (-0.619), ash content of feedstock and fixed carbon (-0.685), ash content (0.871) and HHV (-0.571) of produced char. Whilst the quadratic model was selected for the regression model, then the model was further optimised by eliminating any terms with p-values greater than 0.05. The optimised MnLR model results showed a reasonable prediction ability of char yield (R-2 = 0.5579), fixed carbon (R-2 = 0.7763), volatile matter (R-2 = 0.5709), ash (R-2 = 0.8613), and HHV (R-2 = 0.5728). ANN model optimisation was carried out as the results showed trainbr training algorithm, 10 neurons in the hidden layer, and tansig and purelin transfer function in hidden and output layers, respectively. The optimised ANN models had higher accuracy than MnLR models with the R-2 greater than 0.75, including 0.785 for char yield, 0.855 for fixed carbon, 0.752 for volatile matter, 0.951 for ash and 0.784 for HHV, respectively. The trained models can be used to predict and optimise the char production from slow pyrolysis of biomass without expensive experiments.