Online process supervision is enabled by smart sensor development, hence attracting increasing attention in pharmaceutical and chemical process engineering. Additional sensor data enable more precise process control as additional process parameters can be monitored. They are easy to integrate into modular plants, and their provided additional process parameters enable a more flexible operation of the apparatus due to the quick and more sensitive reaction to changing circumstances. An artificial intelligence-based optical sensor for the investigation of different operating states and droplet sizes within a liquid−liquid stirred DN32 extraction column in counter current flow is developed and presented in this work. Two operating states, the flooding state and the column's regular operating state, are differentiated as observable states. Additionally, the diameter of the rising liquid droplets of the disperse phase is categorized into different diameter classes. A control strategy for the extraction column is derived based on the results of the convolutional neural network-based image analysis. Thus, a robust soft sensor controlling the hydrodynamics of an extraction column was developed. The developed control strategy automatically leads the extraction column into a favorable hydrodynamically stable operation state.

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Flooding of separation columns is a severe limitation in the operation of distillation and liquid‐liquid extraction columns. To observe operation conditions, machine learning algorithms are implemented to recognize the flooding behavior of separation columns on laboratory scale. Besides this, the investigated columns already provided the modular automation interface Module Type Package (MTP), which is used for data access of necessary sensor data. Hence, artificial intelligence (AI) tools with deep learning offer high potential for the process industry and allow to capture operating states that are otherwise difficult to detect or model. However, the advanced methods are only hesitantly applied in practice due to complex combination of operational sensing, data analysis, and active control of the equipment. This article provides an overview on how AI‐based algorithms can be implemented in existing laboratory plants. Process sensor data as well as image data are used to model the flooding behavior of distillation and extraction columns for stable and robust operational conditions.

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Droplet generation in microfluidic devices has emerged as a promising approach for the design of highly controllable processes in the chemical and pharmaceutical industry. However, droplet generation is still not fully understood due to the complexity of the underlying physics. In this work, micro-computed tomography is applied to investigate droplet formation in a circular channel in a co-flow configuration at different flow conditions (Ca < 0.001). The application of an in-house developed scanning protocol assisted by comprehensive image processing allows for the time-resolved investigation of droplet formation. By tracking different droplet parameters (length, radii, volume, surface, Laplace pressure) the effect of flow conditions on droplet progression is determined. As characteristic for the squeezing regime, final droplet size was nearly independent of Ca for higher Ca tested. For lower Ca, the final droplet size increased with decreasing Ca, which points to the leaking regime that was recently introduced in the literature.

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