Low-frequency process disturbances

Low-frequency process disturbances. The control system is expec ted to react to these disturbances. 

The objective of filtering and smoothing is to remove the last three components, leaving only the low frequency process disturbances. 

One of the most characteristic psychosocial characteristics of this type of work is the contrast between normal operation and process disturbances, when the operator is required to shift from passive supervision to rapid information processing and decision making. Another characteristic feature is the long interval between disturbances and, thereby, the low frequency of opportunities for operators to utilize, much less develop, their process skills. 

The accumulation of charges near the electrodes, however, has to be associated with ion diffusion phenomena inside the material. The lower the measurement frequency, the higher the depth reached inside the material of the disturbance caused by the alternating current, and the greater the amplification of the electrode polarization process. The mobilization of these charge carriers, which are no longer necessarily available for current conduction, then leads to an increase in the material s resistance at low frequencies. 

This pyroelectric effect can be utilized for sensors such as e.g. infrared cameras. However, in sensors that use the electromechanical effect p3To-electricity can be disturbing. The disturbing effects arise especially in low-frequency or quasi-static applications as the temperature drift is often a slow process. A one ohm resistance is applied in parallel to suppress this effect. That way, the pyroelectric induced charges are deflected and the cut-off frequency of the sensor is raised. 

Here, we focus on how to implement the optimal operation in the presence of low frequency disturbances. In typical oil/gas producing systems there are large uncertainties (e.g. reservoir properties, models) and few measurements, so methods that can help operate the process optimally when disturbances occur are of great value. 

The process sensitivity function for the modified reactor-separator problem is shown in Fig. 6. As seen from the figure, the recycle feedback increases the disturbance sensitivity by a factor 4,2 at low frequencies, while it in fact serves to slightly dampen the disturbance sensitivity, i.e., Sp < 1, at high frequencies. 

If theoretically this approach seems promising, experimental data analyses show that impedance measurements imder sliding are often disturbed at low frequencies due to the random fluctuations of potential or current. This "electrochemical noise" limits unfortunately to some extent the application of impedance measurements. Limitations frequently originate from the sliding action itself and more specifically from the localized damages induced in the contact area by the mechanical interaction. Notwithstanding that, the in-depth analysis of the electrochemical noise will surely in the future be fruitful since it will contain useful information on the progress of the process at both spatial and time scales. 

Since the second method does not test the process, the current value of loop gain is unknown until a disturbance Identifies It. Identification must then be carried out, and parameter adjustment made carefully to prevent overcorrection. Identification consists principally of factoring the response curve Into high- and low-frequency components whose ratio represents the dynamic gain of the closed loop. The load-response curve shown in Fig. 6.21 is so separated. 

The Bode plots are useful, since they can be used in controller tuning, which will be discussed in chapter 32. They also are useful if we know the fi equency of the disturbance that the process is subjected to. It can easily be seen that high fi equency disturbances have less impact on the process than low frequency disturbances. Controller tuning also depends on the disturbance frequency as will be shown later. 

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