Özet:

In this research, we present a multi-layered feed-forward neural network (ANN) model developed for prediction of dynamic viscosity of aqueous gelatin solutions using experimental data collected from a number of measurements. In ANN architecture, shear stress, shear strain, torque of spindle, the angular velocity of spindle together with mass concentrations of gelatin solutions were introduced as input neurons, whereas dynamic viscosity of aqueous gelatin solutions was assigned as a single output neuron to be predicted. Developed ANN model was trained using backpropagation algorithm optimized with Bayesian regulation. Optimal geometry of the hidden layer was first studied to search out the ANN architecture which yields the most accurate performance results. Mean squared error (MSE), mean absolute error (MAE), root-mean-squared error (RMSE), determination of coefficient (R^2), the variance accounted for (VAF) and regression analyses were used as performance assessment parameters for suggested network models. Sensitivity analysis was carried out to investigate the most effective input neuron strongly influencing the performance of the developed ANN model. As a result, the use of 8 neurons in the hidden layer has shown excellent performance results yielding the least MSE and the highest R^2 values compared to other suggested ANN models. Upon sensitivity analysis, the shear rate was found to be the most effective input neuron significantly affecting network performance. ANN-based predicted dynamic viscosity values were found to be in excellent agreement with measured viscosity values, demonstrating the robustness as well as the accuracy of the developed ANN model. Developed ANN model can, therefore, be effectively used to predict the dynamic viscosity of aqueous polymer solutions using the same input and output parameters in specific data range reported in this paper with statistical details.

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