Abstract
Soft sensor techniques have been widely adopted in chemical industry to estimate important indices that cannot be online measured by hardware sensors. Unfortunately, due to the instinct time-variation, the small-sample condition and the uncertainty caused by the drifting of raw materials, it is exceedingly difficult to model the fed-batch processes, for instance, rubber internal mixing processing. Meanwhile, traditional global learning algorithms suffer from the outdated samples while online learning algorithms lack practicality since too many labelled samples of current batch are required to build the soft sensor. In this paper, semi-supervised hybrid local kernel regression (SHLKR) is presented to leverage both historical and online samples to semi-supervised model the soft sensor using proposed time-windows series. Moreover, the recursive formulas are deduced to improve its adaptability and feasibility. Additionally, the rubber Mooney soft sensor of internal mixing processing is implemented using real onsite data to validate proposed method. Compared with classical algorithms, the performance of SHLKR is evaluated and the contribution of unlabelled samples is discussed.
Highlights
Fed-batch processes play an important role in chemical and biochemical industry. ey are widely adopted in the production of a vast range of fermentation-derived products such as fine-chemical industry, pharmaceuticals and food products
In order to leverage those unused widely existed unlabelled data, we proposed recursive weighted kernel regression (RWKR) [23] before, which has already been validated in penicillin production process so sensor modelling
It behaves not promising for some other fed-batch processes, such as rubber internal mixing, since it behaves much more dri ing and the time-based weighting mechanism does not work since the Mooney viscosity of rubber is not monotonic increased as the penicillin concentration in penicillin fermentation process. erefore, in this paper, semi-supervised hybrid local kernel regression (SHLKR) is proposed to fully leverage both labelled and unlabelled data selected from historical and online data
Summary
Fed-batch processes play an important role in chemical and biochemical industry. ey are widely adopted in the production of a vast range of fermentation-derived products such as fine-chemical industry, pharmaceuticals and food products. We explore the potential of the hybrid local semi-supervised mechanism to leverage both unlabelled and labelled data via the proposed time window mixed with both historical and online samples. In order to leverage those unused widely existed unlabelled data, we proposed recursive weighted kernel regression (RWKR) [23] before, which has already been validated in penicillin production process so sensor modelling It behaves not promising for some other fed-batch processes, such as rubber internal mixing, since it behaves much more dri ing and the time-based weighting mechanism does not work since the Mooney viscosity of rubber is not monotonic increased as the penicillin concentration in penicillin fermentation process. Erefore, in this paper, semi-supervised hybrid local kernel regression (SHLKR) is proposed to fully leverage both labelled and unlabelled data selected from historical and online data.
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