Empirical Models with Self-Assessment Capabilities for On-Line Industrial Applications

Self-assessment capabilities are critical for the longevity of online empirical models in industrial settings. A generic structure of an on-line model supervisor, consisting of within-the-range indicator, confidence of prediction, performance indicator, novelty/outlier detector, and model fault detector, is proposed in the paper. Several methods for confidence limits calculations, such as ensembles of analytic neural networks and symbolic regression models generated by genetic programming, linearized models based on transforms, derived by genetic programming, and a strangeness measure, based on support vector machines for regression, have been explored and their performance was compared in a case study for emission estimation on-line model. Some of the self-assessment capabilities for detection of unacceptable on-line performance and model and process faults are illustrated with industrial applications in the chemical industry.