Image‐based characterization of flocculation processes through PLS inspired representation learning in convolutional neural networks

Monitoring of flocculation processes such as those used in downstream processing of a fermentation broth is essential for process control. One approach is to apply microscopic imaging combined with image analysis for characterizing the state of the process. In this work, we investigate and compare the use of supervised feedforward convolutional neural network (CNN) architectures to predict the process states from the image information and compare the results with the traditional alternative of characterizing flocs based on manually engineered image features guided by human expertise. From a well‐defined image data set representing six process states, the objective is to establish end‐to‐end classification models which are accurate but at the same time learn meaningful latent variable space representations. Specifically, we evaluate three different CNN architectures with varying degrees of regularization and compare results with logistic regression models based on inputs from two different traditional feature engineering methods. By applying global average pooling as a structural regularizer to the CNN architecture, we significantly improve the generalization performance in comparison with the classification accuracies of the traditional feature engineered models. Furthermore, we show that by imposing a projection to latent structures (PLS) like regularization framework onto the CNN, it can also learn a latent variable representation that mimics the features selected by human expertise. This work explores the use of convolutional neural network (CNN) to monitor industrial flocculation processes, comparing them with traditional methods guided by human expertise. By evaluating different CNN architectures and traditional feature engineering methods, we aim to establish accurate classification models, while learning meaningful and interpretable latent variable space representations.