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: 52
    Citation - Scopus: 60
    Applied Machine Learning for Prediction of Waste Plastic Pyrolysis Towards Valuable Fuel and Chemicals Production
    (Elsevier, 2023) Cheng, Yi; Yang, Yang; Coward, Brad; Wang, Jiawei; Yıldız, Güray; Ekici, Ecrin; Yıldız, Güray
    Pyrolysis is a suitable conversion technology to address the severe ecological and environmental hurdles caused by waste plastics' ineffective pre- and/or post-user management and massive landfilling. By using machine learning (ML) algorithms, the present study developed models for predicting the products of continuous and non-catalytically processes for the pyrolysis of waste plastics. Along with different input datasets, four algorithms, including decision tree (DT), artificial neuron network (ANN), support vector machine (SVM), and Gaussian process (GP), were compared to select input variables for the most accurate models. Among these algorithms, the DT model exhibited generalisable and satisfactory accuracy (R2 > 0.99) with training data. The dataset with the elemental composition of waste plastics achieved better accuracy than that with the plastic-type for predicting liquid yields. These observations allow the predictions by the data from ultimate analysis when inaccessible to the plastic-type data in unknown plastic wastes. Besides, the combination of ultimate analysis input and the DT model also achieved excellent accuracy in liquid and gas composition predictions. © 2023 The Authors
  • Article
    Citation - WoS: 39
    Citation - Scopus: 42
    Prediction 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, Güray
    Char 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: 33
    Citation - Scopus: 39
    Bibliometric Analysis of Research Trends on the Thermochemical Conversion of Plastics During 1990-2020
    (Elsevier, 2021) Khatun, Roomana; Xiang, Huan; Yang, Yang; Wang, Jiawei; Yıldız, Güray
    The 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.