Artificial Neural Network and Support Vector Regression Modeling for Prediction of Mixing Time in Wet Granulation

Abstract

Purpose Every successful pharmaceutical product requires a rapid and reliable scale-up process from small laboratory quantities to a large commercial production to ensure the qualities of products. Despite best efforts, a pharmaceutical company must incur losses in material costs and working-hours before achieving optimal process parameters. One possible approach to shorten this tedious process is to leverage knowledge and experience using a computational algorithm that predicts the process parameters using related data. Method This study aimed to demonstrate the approach of embedded successful material attributes and process parameters of wet granulation as inputs into an artificial neural network (ANN) model and support vector regression (SVR) model to predict the total number of the impeller revolution for dry mixing and wet massing to produce the granules with the same qualities. ResultsThe SVR model performed better for prediction with RMSEs of 3.2552-4.0066. Using the SVR model using Gaussian kernel, which had the least RMSE, in the training stage and validation stage gave the values of MAPE of 17.12% and 12.32%, respectively. Conclusion The model can be implemented for the prediction of the number of impeller revolution by using the proposed parameters.

Multilayer perceptron and support vector regression models for feline parturition date prediction

“…In several fields of scientific research, SVR is considered an alternative model to MLP [ [41] , [42] , [43] , [44] , [45] ]. The concept behind SVR was to obtain the best-fit plane, known as a hyperplane, to cover the maximum number of data points in the data space.…”