Enhancing Mass Transfer Coefficient Prediction from Field Emission Scanning Electron Microscope Images Through Convolutional Neural Networks and Data Augmentation Techniques

Abstract

Listen

Translate article

With the growing demand for drug products requiring lyophilization, it is essential to either expand aseptic drying capacity or improve the efficiency of existing capacity through process intensification, ensuring that resources are utilized to their full potential. In this regard, mathematical models are highly recommended to assist professionals in process optimization. To effectively utilise these models, it is also essential to develop robust techniques for determining key parameters, including the product resistance to vapour flow. Traditional experimental methods for evaluating this coefficient are time-intensive and/or require the insertion of probes into the product, which is not feasible at a manufacturing scale. This study addresses these challenges by introducing a novel deep learning framework designed to predict the mass transfer coefficient directly from Field Emission Scanning Electron Microscope images. This approach significantly streamlines the evaluation process, leveraging the high-resolution capabilities of Field Emission Scanning Electron Microscope for detailed analysis. In this work, we focus on advanced Field Emission Scanning Electron Microscope image processing, choice of strategic convolutional neural network configuration, and thorough model performance evaluation to predict the mass transfer coefficient. Given the frequent scarcity of datasets in this field, we have employed data augmentation techniques to enhance the robustness of our model. The results demonstrate good predictive accuracy (error on the interpolation test data lower than 5%), highlighting the potential of this framework to facilitate the assessment of mass transfer coefficients in freeze-dried products.