Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
2 results
Search Results
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: 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