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دسته بندی:
یادگیری ماشین - machine learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms
ترجمه فارسی عنوان مقاله:
تولید هیدروژن از طریق گاز زدایی زیست توده و الگوریتم های یادگیری ماشین نظارت شده
منبع:
Sciencedirect - Elsevier - International Journal of Hydrogen Energy, 44 (2019) 17260-17268: doi:10:1016/j:ijhydene:2019:02:108
نویسنده:
Emine Elmaslar Ozbas a,*, Dogukan Aksu b, Atakan Ongen a, Muhammed Ali Aydin b, H. Kurtulus Ozcan
چکیده انگلیسی:
Prediction of clean hydrogen production via biomass gasification by supervised machine
learning algorithms was studied. Lab-scale gasification studies were performed in a steel
fixed bed updraft gasifier having a cyclone separator. Pure oxygen, and dried air with
varying flow rates (0.05e0.3 L/min) were applied to produce syngas (H2, CH4, CO). Gas
compositions were monitored via on-line gas analyzer. Various regression models were
created by using different Machine Learning (ML) algorithms which are Linear Regression
(LR), K Nearest Neighbors (KNN) Regression, Support Vector Machine Regression (SVMR)
and Decision Tree Regression (DTR) algorithms to predict the value of H2 concentration
based on the other parameters that are time, temperature, CO, CO2, CH4, O2 and heating
value. The highest hydrogen value in syngas was found around 35% vol. after gasification
experiments with higher heating value (HHV) of approximately 3400 kcal/m30.05 L/min and
0.015 L/min were the optimum flow rates for dried air and pure oxygen, respectively. In
modeling section, it was observed that H2 concentrations were being reflected effectively
by the concentrations estimated through the proposed model structures, and by having r2
values of 0.99 which were ascertained between actual and model results.
Keywords: Machine learning algorithms | Gasification | Biomass | Hydrogen
قیمت: رایگان
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