Project Details
Projekt
Hybrid Physics-Neural Network Soft Sensors for Dynamic Operation of Liquid-Liquid Separation Processes
Subject Area
Chemical and Thermal Process Engineering
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 466656378
The goal of this project is to explore the potential of physics-informed neural networks for inferring poorly measurable conditions from liquid-liquid separation processes for which mechanistic descriptions are only partially available. As specific example, we investigate a horizontal, continuously operated gravity settler for the separation of liquid-liquid dispersions, a common low-energy separation unit in chemical, biotechnological, and recycling processes, whose dynamic operation cannot be described solely on the basis of mechanistic models. We aim to develop a reliable soft sensor for the dispersion layer height, i.e., the principal separation performance indicator in the settler, by combining machine learning with mechanistic modeling through physics-based regularization. To generate training data, we will operate a gravity settler on a technical scale in a closed-loop continuous mode that allows to vary and measure operating conditions, material system, dispersion, and phase separation parameters. We will compare the hybrid physics-neural network to a fully data-driven benchmark, e.g., a recurrent neural network, to validate our expectation that the hybrid model requires less training data, generalizes better, and makes more physically-consistent predictions. A demonstration of the hybrid soft sensor in control and an assessment of model validity range will conclude the work. Our project addresses three central challenges defined in the SPP 2331, namely, the introduction of physical laws in ML models, optimal decision making, and increasing trust in ML applications.
DFG Programme
Priority Programmes
