| CPC G01N 33/34 (2013.01) [D21F 7/00 (2013.01); D21J 1/00 (2013.01); G06N 20/00 (2019.01)] | 15 Claims |
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1. A computer-implemented method for estimating at least one quality parameter of paperboard produced in a paperboard production subprocess of a paperboard processing pipeline during the production by a data-driven module comprising a preprocessing module and a machine-learning module, the method comprising:
acquiring sensor data from at least one processing step of the processing pipeline and transferring the sensor data to a data repository,
determining at least one historical feature by the preprocessing module by at least partially evaluating historical sensor data that was acquired during at least one previously produced batch of paperboard and retrieved from the data repository,
training the machine-learning module to reproduce from the at least one historical feature a target value of the at least one quality parameter, wherein the target value is determined for a previously produced batch of paperboard corresponding to the historical sensor data from which the historical feature was determined,
determining at least one real-time feature by the preprocessing module from a stream of current sensor data being acquired from a currently produced batch of paperboard and retrieved from the data repository,
determining an estimate for the at least one quality parameter from the at least one real-time feature by the trained machine-learning module and providing the estimate as an output value,
adjusting the paperboard production subprocess in response to the output value not being within a predetermined range,
wherein at least one range for the at least one quality parameter is provided as additional input to the data-driven module, wherein for each range a probability value for the respective quality parameter to fall within the respective range is determined as an output value,
wherein for one or more of the probability values determined by the data-driven module for the range of the at least one quality parameter, each probability value is compared with a probability limit assigned to the respective range and the respective quality parameter, resulting in one Boolean comparison value per range and quality parameter, and
wherein a predetermined Boolean expression depending on the one Boolean comparison value is evaluated, wherein an alarm is triggered, when the predetermined Boolean expression returns logical true.
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