Online local learning based adaptive soft sensor and its application to an industrial fed-batch chlortetracycline fermentation process

This work presents a new method for adaptive soft sensor development by further exploiting just-in-time modeling framework. In the presented method, referred to as online local learning based adaptive soft sensor (OLLASS), the samples used for local modeling are selected based on the mutual information (MI) weighted or neighbor sample based similarity measure. Then, two adaptive methods, namely self-validation and neighbor-validation, are developed to adaptively select the optimal local modeling size for scenarios without and with the neighbor output information, respectively. Further, a real-time performance improvement strategy is used to enhance the online modeling efficiency. Moreover, an online dual updating strategy is proposed to activate infrequent local model updating and model output offset updating in turn, which allows significantly reducing the online computational load by avoiding unnecessary local model reconstruction while at the same time maintaining high estimation accuracy by performing offset compensation. A maximal similarity replacement rule using MI weighted similarity measure is used for database updating. The superiority of the proposed OLLASS method over traditional soft sensors in terms of the estimation accuracy, adaptive capability and real-time performance is demonstrated through an industrial fed-batch chlortetracycline fermentation process.