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Basic control modes

In general, a proportional controller provides a fast response when compared with other controllers, but a sustained error occurs where the PV does not return to the set point even when steady state is reached. This sustained error is called offset and is undesirable in most cases. Therefore, it is necessary to eliminate offset by combining proportional control with one of the other basic control modes. [Pg.101]

Now that the various basic control modes have been described, it is desirable to be able to choose a particular control mode for a specific process. Figure 4.18 graphically outlines a procedure for control nnode selection. [Pg.111]

Choose control mode IC. Design basic functions. [Pg.27]

The coefficients of this mode-coupling functional are the basic control parameters of this idealized version of MCT. One sees that Eqs. [46] and [47] amount to a set of nonlinear equations for the correlators S(q,t) that must be solved self-consistently. [Pg.27]

The dynamic simulation file prepared in Aspen Plus is exported in Aspen Dynamics [10]. We select the flow-driven simulation mode. Aspen Dynamics files have already implemented the basic control loops for levels and pressures. Units with fast dynamics, such as the evaporator or some heat exchangers, may be handled as steady state. The implementation of control loops for the key operational units, chemical reactor and distillation columns, take into account some specific issues from the plantwide perspective, which are developed in detail in Luyben et al. [8]. [Pg.162]

For this, refer to Fig. V1/5.0.3-1A. This is shown in cormection with ISA 84, but it is applicable for lEC 61511 (Fig. 4). At the begirming, it is tested whether it is for instmmentation. If not, then for safety, other means of risk reduction is used. If it is for instmmentation but not safety-related, then basic controls for asset protection is chosen. If it is for safety related instrumentation, then, for continuous mode safety instmmented control function is the choice. On the contrary, for demand mode, safety instmmented preventive or mitigating functions are chosen. Other means of risk reduction are shown by the dotted box. These explanations (Clauses 5.0.2 and 3) are valid for ISA 84 00.01 also. [Pg.448]

Figure 1.2 The basic operating modes of industrial-scale cultivation of suspended cells. The simplest mode is batch culture without any feed except possibly add or base for pH control. The productivity of a simple batch can be considerably increased and extended by feeding fresh, usually concentrated sterile medium until the culture vessel is completely full. The continuous... Figure 1.2 The basic operating modes of industrial-scale cultivation of suspended cells. The simplest mode is batch culture without any feed except possibly add or base for pH control. The productivity of a simple batch can be considerably increased and extended by feeding fresh, usually concentrated sterile medium until the culture vessel is completely full. The continuous...
As mentioned the most significant risk relevant to DP operations is loss of the position control ability (Bakken and Smedvig, 2001, Fougere, 2005). Normally, there are two basic failure modes for loss of positions they are drive-off and drift-off. For drive off, the OSV/MODUs go off position by their own thrusters force, because inaccurate position data are received, which lead to that the DP system drives the installation to a wrong position. For drift-off, the force generated by thrusters could not resist the external environmental force the installation will drift off its original position by the force of the wind, waves as well as the currents. Figure 1 shows the causes of the loss of position incidents from year 1994-2003 (Tjallema et al., 2007). [Pg.746]

Non-linear physical models tend to be relatively complex and nsnally leqniie considerable development effort to arrive at an adeqnate form. In addition, they reqnire intimate process knowledge and understanding. Their primary nse has been in process design and simnlation. It is usually the steady-state version of these models that is used to size new equipment or to simulate the process at different conditions such as feed rate, temperature and pressure, etc. These steady-state models are also useful for supervisory control where they can be used in an on-line or offline mode to optimize the process conditions at various time intervals and readjust the setpoints of the controllers in a basic control structure. [Pg.327]


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