Energy Systems Engineering / Enerji Sistemleri Mühendisliği
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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.