On control action

In Section 11.5, certain observations are made on the costs of control in relation to possible benefits and, in the light of this. Section 11.6 suggests possible future priorities on control action and research activities. Some of the more important points which emerge are ... 

Fig. 6. Variation of OCL nonlinearity as a function of the maximal magnitude of deviation of initial conditions. Results for three different penalty weights on control action a are shown.

Fig. 7. Variation of the OCL nonlinearity measure as a function of the penalty weight a on control action for the Hammerstein-type example system. Initial conditions lie within a radius...

Fig. 8. Optimal feedback control law for the Hammerstein example system for different values of the penalty weight on control action a.

Figure 13 shows the dynamic profiles of optimal state driving from steady state 1 to steady state 2 with different levels of constraints. Dynamic changes of product mass, undersized mass, and moisture content are shown in Figure 13 a, b, and c, respectively, under two constraint levels. Figure 13d depicts control profiles for these two cases. In addition to the constraints on control actions, the final time constraints to ensure the final steady-state status is imposed on the system. That is, the left-hand side of Eqs 5.14, 5.22, and 5.24 should be zero at the final time. However, it is not necessary to achieve zero exactly for the derivatives at the final time. We normally impose the final time constraints as dx(t/)/df < s in which x represents general state variables such as the number of particles, mass of powder, and moisture... 

Constraint control strategies can be classified as steady-state or dynamic. In the steady-state approach, the process dynamics are assumed to be much faster than the frequency with which the constraint control appHcation makes its control adjustments. The variables characterizing the proximity to the constraints, called the constraint variables, are usually monitored on a more frequent basis than actual control actions are made. A steady-state constraint appHcation increases (or decreases) a manipulated variable by a fixed amount, the value of which is determined to be safe based on an analysis of the proximity to relevant constraints. Once the appHcation has taken the control action toward or away from the constraint, it waits for the effect of the control action to work through the lower control levels and the process before taking another control step. Usually these steady-state constraint controls are implemented to move away from the active constraint at a faster rate than they do toward the constraint. The main advantage of the steady-state approach is that it is predictable and relatively straightforward to implement. Its major drawback is that, because it does not account for the dynamics of the constraint and manipulated variables, a conservative estimate must be taken in how close and how quickly the operation is moved toward the active constraints. 

Feedforward Control If the process exhibits slow dynamic response and disturbances are frequent, then the apphcation of feedforward control may be advantageous. Feedforward (FF) control differs from feedback (FB) control in that the primary disturbance or load (L) is measured via a sensor and the manipulated variable (m) is adjusted so that deviations in the controlled variable from the set point are minimized or eliminated (see Fig. 8-29). By taking control action based on measured disturbances rather than controlled variable error, the controller can reject disturbances before they affec t the controlled variable c. In order to determine the appropriate settings for the manipulated variable, one must develop mathematical models that relate ... 

Principal Option for Containment/ Recovery Excavation Vacuum extraction Temporary cap/cover Hydraulic modification No action Groundwater pumping Subsurface drains Hydraulic barriers Low permeability barriers No action Overflow/underflow containment (i.e. oil booms) Run off/run on control Diversion/collection No action Capping/ nsulation Operations modifications Gas collection/removal No action... 

In the CPI, the most extensively studied human-machine interface is in the central control room in automated plants where plant information is displayed on visual display units (VDUs) and appropriate control actions are made by the operating team. In the case of a highly automated plant, the primary role of the human is to respond to unexpected contingencies such as plant states that have not been anticipated by the designers of the automatic... 

As described earlier, in the first stage of perception, information is acquired via the senses from a number of sources. These may include gauges and chart recorders, VDU screens in a control room, verbal communication with individuals on the plant, or direct observation of process variables. In the short term, this information provides feedback with regard to specific control actions. 

The format of the procedure is also important in this respect. There may be situations where alternatives to prose are more efficient and acceptable. A flow diagram or a decision table may help the process worker to concentrate more easily on what indications are presented, and what decisions and control actions he or she has to make (see Wright. 1977). 

Corrosion monitors by themselves only warn of corrosion and must be coupled with the fifth phase of control, viz. remedial action, to be effective. In some cases of corrosion the remedial measure is known or easily deduced, but in others diagnostic work has to precede a decision on remedial action. 

During the forties and fifties, episodes of severe air pollution occurred In a number of urban and Industrial areas. They were responsible for 111 health and In some cases caused death among the populations concerned. As the scientific and public Information base on the adverse effects of urban air pollution Increased, so did public demand for control measures. As a result, many Industrial countries Introduced comprehensive air pollution control laws at various times from the mid-fifties onwards. Industrial response to these laws led to the application of control techniques which effectively reduced the emissions of some pollutants. However there are other sources and factors which can obscure the benefits of these control actions. For example, consider urban growth. In 1980 there were 35 cities with populations over 4 million. By the year 2000 this number will nearly double to 66, and by the 2025, this number will more than double to an estimated 135 (9). In developing countries, from 1980 to the year 2000, It Is estimated that twice as many people will live In cities of a total population of 1 million or more In Latin America (101 million to 232 million) and East Asia (132 million to 262 million). Three times as many people will live In cities of 1 million or more In South Asia (106 million to 328 million) and four times as many In Africa (36 million to 155 million) (10). Accompanying this rapid growth are Increases In Industrial activity... 

In designing a continuous plant, therefore, it is essential to establish the measurement and control strategy based on an understanding of which critical aspects are indicative of good or poor plant operation, and how deviation of these measurements can be exploited to perform a corrective process control action. Estabhshing suitable on-hne real-time measurement techniques may be a blocker to the implementation of continuous processing. In contrast, implementation of a successful measurement and control strategy may be the enabler for improved product yields and product quahty. 

The key recognitive skill required to carry out the above tasks is the formation of a mental model of the process operations that fits the current facts about the process and enables the operators to correctly assess process behavior and predict the effects of possible control actions. Correct mental models of process operations have allowed operators to overcome the weakness of lost sensors and conflicting trends, even under the pressure of an emergency (Dvorak, 1987), whereas most of the operational mishandlings are due to an erroneous perception as to what is going on in the process (O Shima, 1983). 

Figure 4.5 Influence of oxidant stress on action potentials recorded In an isolated rabbit ventricular myocyte, (a) Control action potential, (b) Action potential recorded 3 min after exposure to oxidant stress induced by the photoactivation of rose bengal (50 nu). (c) Spontaneous and repetitive action potential discharges induced 6.5 min after exposure to rose bengal. Action potentials were recorded via a 2.5 MQ suction electrode and a current-clamp amplifier. The cell was stimulated at 0.1 Hz with a 2 ms suprathreshold current pulse and, when the cell showed automaticity (after 6 min), stimulation was stopped. Redrawn from Matsuura and Shattock (1991b).

The effective controller action is represented by its effect on U and hence on Fw, where... 

We certainly want to respond very differently if the temperature of a chemical reactor is changing at a rate of 100°C/s as opposed to l°C/s. In a way, we want to "project" the error and make corrections accordingly. In contrast, proportional and integral controls are based on the present and the past. Derivative controller action is based on how fast the error is changing with time (rate action control). We can write... 

To reduce derivative kick (the sudden jolt in response to set point changes), the derivative action can be based on the rate of change of the measured (controlled) variable instead of the rate of change of the error. One possible implementation of this idea is in Fig. 5.3. This way, the derivative control action ignores changes in the reference and just tries to keep the measured variable constant.2... 

---