Hybrid Adaptive Calibration Methods and Ensemble Strategy for Prediction of Cloud Point in Melamine Resin Production
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In melamine resin production process, it is essential to supervise the condensation process. Monitoring the value of the cloud point indicates the best point of time to stop the condensation. Currently, the supervision is conducted manually by operators, which from time to time need to draw and analyze samples from the production process. In order to increase efficiency and to improve quantification quality, in this paper we investigate the usage of non-linear chemometric models, which are calibrated based on near infrared (FTNIR) process spectrum measurements. 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 appear on-line
over time due to long-term fluctuations (e.g., caused by dirt) and changes in the composition of the educt, often leading to severe error drifts of static models. Extracting the most informative wavebands prior to model training is essential to avoid a curse of dimensionality; this is achieved by a new extended variant of forward
selection, termed as forward selection with bands (FSB). Furthermore, variants of how to integrate auxiliary sensor information (temperature, pH value, pressure) togetherwith the FTNIR spectra are presented (hybridity). A specific ensemble strategy is developed which is able to properly compensate noise in repeated spectrum measurements.
Results on high-dimensional data from four independent types of melamine resin show that 1) our non-linearmodelingmethodology can outperform state-of-the-art linear and non-linear chemometric modeling
methods in terms of validation error, 2) the ensemble strategy is able to improve the performance of models without ensembling significantly and 3) incrementalmodel updates are necessary in order to keep the predictive quality of the models high by preventing drifting residuals.