Data Science and Applications

Yazarlar: Esra Yel, Gülay Tezel, Sait Ali Uymaz

Konular:

Anahtar Kelimeler:-

Özet: Waste material was fragmented into gas, liquid and solid fractions by pyrolysis. Recently the solid fraction (char) has been used as filler in epoxy composites. Type and properties of filler affect water absorption of epoxy composites. A recent water absorption database (of 1512 data) has been obtained experimentally. Accordingly, type of pyrolysed plastic, waste pre–washing, pyrolysis temperature, additive dosage and water exposure time were input parameters in the estimation model developed with multilayer perceptron artificial neural network (MLP ANN) to predict the absorbed water quantity as output. Four datasets were derived with data preprocessing. Among all the configurations worked up, 0.991 training and 0.986 testing R² were attained as the highest R² values under conditions including 2e4 iterations, lr 0.04, mc 0.9, first hidden layer of 22 nodes, and second hidden layer of 15 nodes. The R² value attained in the optimum configuration and the average R² attained via 5-fold cross-validation are close to each other for both training and test. The established model will help users to predict the quantity of water that absorbed upon exposure. This will give idea about the availability of that composite for using it for particular purposes.

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