Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 6Citation - Scopus: 7Unveiling the Conditioning Correlation in Ex-Situ Catalytic Pyrolysis of Waste Polyolefins Towards Designated Conversion Into Valuable Products(Elsevier, 2024) Xiang, Huan; Wang, Jiawei; Ma, Peng; Cheng, Yi; Yildiz, GurayThe ex-situ catalytic pyrolysis of waste polyolefin plastics holds promise for producing aromatics and light olefins, with potential integrations in the low-carbon olefin processing industry for producing ethylene, propylene, butadiene, or aromatic hydrocarbons. Employing ZSM-5(50) zeolite, selected for its substantial specific surface area and total pore volume, facilitated the catalytic pyrolysis of household plastic waste through an exsitu pyrolysis-catalysis approach. This study explored the impact of operating parameters, T 1-T 2- C/P mass ratio, namely pyrolysis temperature, catalytic vapor upgrading temperature, and the catalyst/plastic mass ratio, on pyrolysis product yields and distributions. Higher T 2 benefited gas production, accompanied by a notable decrease in C 4 content in gaseous products. A larger C/P mass ratio provided more active sites for pyrolysis reactions, but higher T 2 induced coke formation on the catalyst, leading to ZSM-5(50) deactivation and inhibiting further gas production. Positive effects of T 2 and the C/P mass ratio were observed for the concentration of BTX in the produced oil. The quadratic fitting was engaged in characterising the reaction conditions. Specifically, the 500 -550 -0.25 run achieved the maximum C 2 yield of 30.3 wt%, the 500 -350 -0.4 run obtained the highest yield of C 3 and C 4 of 75.4 wt%, and the run of 575 -450 -0.25 yielded the highest amount of BTX of 17.2 wt%. These findings provide valuable insights into the designated conditioning of catalytic pyrolysis for plastic waste valorisation.Article 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.Article Citation - WoS: 33Citation - Scopus: 39Bibliometric Analysis of Research Trends on the Thermochemical Conversion of Plastics During 1990-2020(Elsevier, 2021) Khatun, Roomana; Xiang, Huan; Yang, Yang; Wang, Jiawei; Yıldız, GürayThe aim of this bibliometric analysis was to evaluate the trends in literature and the impact of publications that have been published during the period 1990-2020, in the field of thermochemical conversion of plastics, namely gasification, liquefaction and pyrolysis. SCOPUS was used and data was vetted via MS Excel, with analysis being completed via MS Excel and VOSViewer. A total of 1705 publications were used in the study, and China was identified as the most productive country. Pyrolysis was the most researched technology with over 88% of publications, while liquefaction accounted for less than 3% of the total publications. Across all three technologies, polyethylene (PE) was the most commonly occurring type of plastic. Journal of Analytical and Applied Pyrolysis had the highest number of publications and total citations. However, Energy Conversion and Management had a higher impact factor and higher average citations per publication. University of Alicante was identified as the most productive university with a total of 45 publications, while University of Leeds was the most commonly cited with an average of 65 citations per publication. The keyword analysis showed that copyrolysis with biomass and catalytic pyrolysis are gaining increased interests.