Prediction of Oil Sorption Capacity on Carbonized Mixtures of Shungite Using Artificial Neural Networks

نویسندگان

چکیده

Using the mixture of carbonized rice husk and shungite from Kazakhstan Koksu deposit experimentally determined oil sorption capacity contaminated soil with originating in Karazhanbas field, a set Artificial Neural Network (ANN) models were built for predictions. The ANN architecture design, training, validation testing methodology performed, prediction was evaluated. successfully trained capturing dependence on time ratio 10% 15% oil-contaminated soil. best ANNs revealed very good capability data subset, demonstrated by high coefficient determination values R2 = 0.998 0.981 mean absolute percentage errors ranging 1.60% to 3.16%. Furthermore, proved their interpolation ability utility predicting any moments investigated interval 60 days new ratios. developed open opportunities planning experiments, maximizing performance design dedicated equipment.

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ژورنال

عنوان ژورنال: Processes

سال: 2023

ISSN: ['2227-9717']

DOI: https://doi.org/10.3390/pr11020518