Ensembled Self-Adaptive Fuzzy Calibration Models for On-line Cloud Point Prediction

In this paper we investigate the usage of non-linear chemometric models, which are calibrated based on near infrared (FTNIR) spectra, in order to increase efficiency and to improve quantification quality in melamine resin production. They rely on fuzzy systems model architecture and are able to incrementally adapt themselves during the on-line process, resolving dynamic process changes, which may cause severe error drifts of static models. The most informative wavebands in NIR spectra are extracted by a new variant of forward selection, termed as forward selection with bands (FSB) and used as inputs for the fuzzy models. A specific ensemble strategy is developed which is able to properly compensate noise in repeated spectra measurements. Results on high-dimensional data from four independent types of melamine resin show that 1.) our fuzzy modeling methodology can outperform state-of-the-art chemometric modeling methods in terms of validation error, 2.) the ensemble strategy is able to improve the performance of models without ensembling and 3.) incremental model updates are necessary in order to prevent drifting residuals.