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In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous (TP) and ammonia nitrogen (NH4-N).
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By using the soft structure mentioned in "Sensor and sensor glove" section, the sensing material's stretching is avoided and repeatability of bending is tested.
A description of the stepwise regression algorithm used by the soft sensor is provided in Section 4.
A multi-layer conformal skin structure of the sensor was created using the soft molding process along with a highly flexible rubber material.
The representative dataset used to construct the soft sensor consisted of daily measurements from 18 months worth of plant data, which leads to approximately 650 daily measurements.
Using the above mechanisms it provides means for automated data pre-processing, model validation, selection and adaptation which can be used to significantly simplify the soft sensor building and maintenance process.
The soft sensor used to achieve the above tasks is discussed in Section 4.
The soft sensor uses an automated stepwise regression algorithm to predict quality.
Section 3 describes the main dataset used to construct, and validate the soft sensor.
The soft sensor uses actual plant measurements from online sensors and laboratory data to calculate the final alumina strength.
The soft sensor uses a combination of historical process data recorded from online sensors and laboratory measurements to predict a key quality indicator, namely particle strength.
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