Data-derived analysis and inference for an industrial deethanizer

This paper presents an application of data-derived approaches for analyzing and monitoring industrial processes. The discussed methods are used in visualizing process measurements, extracting operational information, and designing estimation models for primary process variables otherwise difficult to measure in real-time. Emphasis is given to the modeling of the data with two classical machine learning paradigms; the self-organizing map (SOM) and the multi-layer perceptron (MLP). The effectiveness of the proposed approach is validated on an industrial deethanizer, where the goal is to identify operational modes and most sensitive variables for this full-scale unit, as well as design an inferential model for a critical process variable, the bottom ethane concentration. The study led to the definition of a fully automated monitoring tool to be implemented online in the plant's distributed control system. The results confirmed the potential of the data-derived approach, and based on the analysis, the existing control configuration of the unit could be redefined toward more consistent operations. Because it is general and modular by design, the tool can be easily used for other processes.