Abstract
In this paper, we present an application of data derived approaches for analyzing and monitoring an industrial deethanizer column. The discussed methods are used in visualizing process measurements, extracting operational information and designing an estimation model. Emphasis is given to the modeling of the data obtained with standard paradigms like the Self-Organizing Map (SOM) and the Multi-Layer Perceptron (MLP). The SOM and the MLP are classic methods for nonlinear dimensionality reduction and nonlinear function estimation widely adopted in process systems engineering; here, the effectiveness of these data derived techniques is validated on a full-scale application where the goal is to identify significant operational modes and most sensitive process variables before developing an alternative control scheme.
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