This study presents an Artificial Neural Network (ANN) model for predicting the dynamic viscosity of H2O/KCOOH (potassium formate) solution. The model accounts for the effect of temperature and concentration in salt and it covers the concentrations typical for brine (0–50%) and desiccant (60–80%) applications, including also pure water. The model shows a fair agreement in predicting experimental data: the mean absolute percentage error (MAPE) is 0.92%. The characteristic parameters of the ANN model are fully reported in the paper.
Application of Artificial Neural Network (ANN) for modelling H2O/KCOOH (potassium formate) dynamic viscosity
Longo, Giovanni A.
;Ortombina, Ludovico;Zigliotto, Mauro
2018
Abstract
This study presents an Artificial Neural Network (ANN) model for predicting the dynamic viscosity of H2O/KCOOH (potassium formate) solution. The model accounts for the effect of temperature and concentration in salt and it covers the concentrations typical for brine (0–50%) and desiccant (60–80%) applications, including also pure water. The model shows a fair agreement in predicting experimental data: the mean absolute percentage error (MAPE) is 0.92%. The characteristic parameters of the ANN model are fully reported in the paper.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
