Explainable artificial neural network as a soft sensor to predict the moisture content in a continuous granulation line

The application of artificial neural networks (ANNs) has the potential to fundamentally change the pharmaceutical industry, making manufacturing more agile, robust, efficient and reliable. Although ANNs' application as data-driven soft sensors has a particular potential, the black-box nature of most models creates mistrust and prevents their widespread application. Therefore, this study focuses on the development of an explainable ANN used as a soft sensor to monitor an integrated, continuous manufacturing process based on twin-screw granulation. Our goal was to estimate the moisture content, a critical quality attribute of granules only based on the applied process parameters without any direct measurements. Two separate ANNs - a multilayer perceptron (MLP) and a Nonlinear Autoregressive with Exogenous Inputs (NARX) - were built and compared with a near-infrared (NIR) spectra-based method. The validation of the methods - carried out by performing off-line loss-on-drying measurements - revealed that the accuracy of the ANNs and the NIR models was comparable, and the moisture content could be determined with a root mean square error of prediction below 1 % in all cases. Additionally, the explainability of an MLP was also investigated by SHAP analysis, revealing which parameters impacted the prediction and strength of their impact, making the technology transparent and providing valuable insight into the model. This study highlights the potential of ANNs applied as data-driven soft sensors, offering a viable, orthogonal alternative to traditional analytical methods that is cost-efficient and enhances process understanding.