Setting point

Setting Point.—When a large quantity of the substance is available a very speedy determination of its setting point (freezing point) may be made as follows. The method is used largely on the technical scale, and is specially suitable for controlling chemical operations. 

The same apparatus (Fig. 8) is used for the distillation of a liquid and for determining its boiling point. A pure liquid should boil at a constant temperature, and the whole should pass over within a very small range. 

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Displacer units are tolerant of specific gravity shifts within their range, require no power to operate, are intrinsically safe, and can be easily field cahbrated. A single displacer unit can control up to three alarm set points or up to three pumps with different spans. They can be caHbrated for set points or pump spans from 0.15—30 m from the top of the vessel mounting. 

The second area, the implementation of a modem process monitoring and control system, is the most dramatic current appHcation of CAD/CAM technology to the chemical process industry. The state of the art is the use of computer graphics to display the process flow diagram for sections of the process, current operating conditions, and controUer-set points. The process operator can interact directly with the control algorithms through the... 

Feedback Control In a feedback control loop, the controlled variable is compared to the set point R, with the difference, deviation, or error e acted upon by the controller to move m in such a way as to minimize the error. This ac tion is specifically negative feedback, in that an increase in deviation moves m so as to decrease the deviation. (Positive feedback would cause the deviation to expand rather than diminish and therefore does not regulate.) The action of the controller is selectable to allow use on process gains of both signs. 

On/Off Control An on/off controller is used for manipulated variables having only two states. They commonly control temperatures in homes, electric water-heaters and refrigerators, and pressure and liquid level in pumped storage systems. On7off control is satisfac-toiy where slow cychng is acceptable because it always leads to cycling when the load hes between the two states of the manipulated variable. The cycle will be positioned symmetrically about the set point only if the normal value of the load is equidistant between the two states of the manipulated variable. The period of the symmetrical cycle will be approximately 40, where 0 is the deadtime in the loop. If the load is not centered between the states of the manipulated variable, the period will tend to increase, and the cycle follows a sawtooth pattern. 

Proportional Control A proportional controller moves its output proportional to the deviation in the controlled variable from set point ... 

ProportionaJ-plus-Integral (PI) Control Integral action eliminates the offset described above by moving the controller output at a rate proportional to the deviation from set point. Although available alone in an integral controller, it is most often combined with proportional action in a PI controller ... 

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 ... 

The effect of the disturbance on the controlled variable These models can be based on steady-state or dynamic analysis. The performance of the feedforward controller depends on the accuracy of both models. If the models are exac t, then feedforward control offers the potential of perfect control (i.e., holding the controlled variable precisely at the set point at all times because of the abihty to predict the appropriate control ac tion). However, since most mathematical models are only approximate and since not all disturbances are measurable, it is standara prac tice to utilize feedforward control in conjunction with feedback control. Table 8-5 lists the relative advantages and disadvantages of feedforward and feedback control. By combining the two control methods, the strengths of both schemes can be utilized. 

Note that the characteristic equation wiU be unchanged for the FF + FB system, hence system stability wiU be unaffected by the presence of the FF controller. In general, the tuning of the FB controller can be less conservative than wr the case of FB alone, since smaller excursions from the set point will residt. This in turn woidd make the dynamic model Gp(.s) more accurate. 

Foxboro developed a self-tuning PID controller that is based on a so-called expert system approach for adjustment of the controller parameters. The on-line tuning of K, Xi, and Xo is based on the closed-loop transient response to a step change in set point. By evaluating the salient characteristics of the response (e.g., the decay ratio, overshoot, and closed-loop period), the controller parameters can be updated without actually finding a new process model. The details of the algorithm, however, are proprietary... 

In continuous processes where automatic feedback control has been implemented, the feedback mechanism theoretically ensures that product quality is at or near the set point regardless of process disturbances. This, of course, requires that an appropriate manipulated variable has been identified for adjusting tne product quality. However, even under feedback control, there may be daily variations of product quahty because of disturbances or equipment or instrument malfunctions. These occurrences can be analyzed using the concepts of statistical quahty control. 

A key feature of MFC is that future process behavior is predicted using a dynamic model and available measurements. The controller outputs are calculated so as to minimize the difference between the predicted process response and the desired response. At each sampling instant, the control calculations are repeated and the predictions updated based on current measurements. In typical industrial applications, the set point and target values for the MFC calculations are updated using on-hne optimization based on a steady-state model of the process. Constraints on the controlled and manipulated variables can be routinely included in both the MFC and optimization calculations. The extensive MFC literature includes survey articles (Garcia, Frett, and Morari, Automatica, 25, 335, 1989 Richalet, Automatica, 29, 1251, 1993) and books (Frett and Garcia, Fundamental Process Control, Butterworths, Stoneham, Massachusetts, 1988 Soeterboek, Predictive Control—A Unified Approach, Frentice Hall, Englewood Cliffs, New Jersey, 1991). 

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