This paper describes an intelligent controller that combines an auto-calibration state-trajectory-based control method with a simple algorithm that enables fault detection.
In the Calibrate state, the machine is sent to the Record state to obtain a certain number of calibration data readings, the default being eight data points.
This factor was determined using the calibration procedure stated in the Unisense manual: a 2-ml sample of LBK was sparged with 20% H2/N2 and assayed at 37°C with a stir bar (200 rpm).
For calibration, maximal reduction state of NAD(P H was achieved after the addition of 2 mmol/l sodium cyanide, and minimal reduction state was achieved after the addition of 200 μmol/l DNP.
This contribution describes realization of test bench for calibration of steady state magnetic sensors based on Hall effect.
The problems related to the tuning of the genetic search and to the virgin state calibration of the model are also discussed.
It is also shown that in the presence of large transients, the adaptive control response can be improved by using manifold pressure time constant values that are based upon steady state calibration values, during transients.
Calibration results with state of the art optimization methods are reported, which can serve as a reference for comparison with new methodologies.
In this paper, an algorithm to minimize the data collection period for calibration of steady-state virtual sensors is developed based on sensor accuracy, reliability and applicability.
When calculating heat transfer coefficients by steady-state calibration, an Extended Kalman Filter method was employed to predict the thermal equilibrium of radiometer to shorten the time.
