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: 3Citation - Scopus: 3Continuous Flow Pyrolysis of Virgin and Waste Polyolefins: a Comparative Study, Process Optimization and Product Characterization(Springer, 2024) Ekici, Ecrin; Yildiz, Guray; Yildiz, Magdalena Joka; Kalinowska, Monika; Seker, Erol; Wang, JiaweiUnder optimal process conditions, pyrolysis of polyolefins can yield ca. 90 wt % of liquid product, i.e., combination of light oil fraction and heavier wax. In this work, the experimental findings reported in a selected group of publications concerning the non-catalytic pyrolysis of polyolefins were collected, reviewed, and compared with the ones obtained in a continuously operated bench-scale pyrolysis reactor. Optimized process parameters were used for the pyrolysis of waste and virgin counterparts of high-density polyethylene, low-density polyethylene, polypropylene and a defined mixture of those (i.e., 25:25:50 wt %, respectively). To mitigate temperature drops and enhance heat transfer, an increased feed intake is employed to create a hot melt plastic pool. With 1.5 g<middle dot>min-1 feed intake, 1.1 L<middle dot>min-1 nitrogen flow rate, and a moderate pyrolysis temperature of 450 degrees C, the formation of light hydrocarbons was favored, while wax formation was limited for polypropylene-rich mixtures. Pyrolysis of virgin plastics yielded more liquid (maximum 73.3 wt %) than that of waste plastics (maximum 66 wt %). Blending polyethylenes with polypropylene favored the production of liquids and increased the formation of gasoline-range hydrocarbons. Gas products were mainly composed of C3 hydrocarbons, and no hydrogen production was detected due to moderate pyrolysis temperature.Article Citation - WoS: 52Citation - Scopus: 60Applied 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, GürayPyrolysis 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