Graph neural networks for soft sensors: Learning from process topology and operational data

Soft sensors estimate process variables that are difficult or impossible to measure directly by using mathematical models and available sensor data, e.g., product concentrations. Machine learning-based approaches have become popular for soft sensing tasks. These approaches offer automatic modeling using historical process data but lack basic process information, such as the process topology. This can lead to (1) modeling of correlations instead of causation between process measurements, (2) model deterioration in deployment due to unseen process scenarios, and (3) large data requirements. To overcome these shortcomings, we propose a novel ML modeling approach incorporating the process topology into soft sensor models for improved spatio-temporal modeling. For this, we propose process topology-aware graph neural networks. We combine process topology and sensor data by representing process data in a directed graph and leverage these process graphs to train graph neural networks. Our method demonstrates enhanced model robustness, reduced data requirements, and more intuitive data representations compared to standard black-box machine learning modeling approaches. Overall, this work introduces a new paradigm for soft sensing by directly embedding process information into the data, paving the way for more efficient and reliable digital twin applications. • Developed a graph-based representation method combining process topology and sensor data for soft sensor modeling. • Developed a graph neural network-based modeling approach for spatio-temporal soft sensors. • Introduced process case studies that are challenging to common, tabular data machine learning models. • Demonstrated advantages over pure ML models through case studies, especially in terms of data efficiency, causality enforcement, and transferability.