Selection of Manipulated Variables

Once the controlled variables have been specified, the control structure depends only on the choice of manipulated variables. For a given process, selecting different manipulated variables will produce different control structure alternatives. These control structures are independent of the controller structure, i.e., pairing of variables in a diagonal multiloop SISO structure or one full-blown multivariable 

For example, in a distillation column the manipulated variables could be the flow rates of reflux and vapor boilup R — V) to control distillate and bottoms compositions. This choice gives one possible control stmcture. Alternatively we could have chosen to manipulate the flow rates of distillate and vapor boilup D V). This yields another control structure for the same basic distillation process. 

The Morari resiliency index (MRI) discussed in Chap. 16 can be used to 

This selection of control structure is independent of variable pairing and controller tuning. The MRI is a measure of the inherent ability of the process (with the specified choice of manipulated variables) to handle disturbances, changes in operating conditions, etc. 

The problem of the effect of scaling on singular values is handled by expressing the gains of all the plant transfer functions in dimensionless form. The gains with engineering units are divided by transmitter spans and multiplied by 

Guideline 7. Select manipulated variables that rapidly affect the controlled variables. This precludes the selection of inputs that affect the outputs with large delays or time constants. 

Guidettne 8. Select manipulated variables that affect the controlled variables directly rather than indirectly. For example, when appropriate for the design of an exothermic reactor, it is preferable to inject a coolant directly rather than use a cooling jacket. 

Guidettne 9. Avoid recycling disturbances. It is better to eliminate the effect of disturbances by allowing them to leave the process in an exiting stream rather than having them propagate through the process hy the manipulation of a feed or recycle stream. 

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Manipulated variables (inputs). These are available degrees of freedom left after considering the basic control. The selection of manipulated variables could also be the object of a local controllability analysis. A typical example is the use of SVD for the selection of the sensitive stage for inferential quality control in a distillation column. 

Problem Definition Selection of Controlled Variables 13.2.1 Engineering Judgment / 13.2.2 Singular Value Decomposition Selection of Manipulated Variables Elimination of Poor Pairings BET Tuning... 

Selection of Manipulated Variables. The two obvious candidates are F i and F 2> since both affect the two outputs directly and rapidly (Guidelines 7 and 8). However, linear and nonlinear combinations of these flow rates are also possible. As shown in Example 21.4, a quantitative analysis is needed to make the best selection. ... 

Selection of Manipulated Variables. F, is adjusted to achieve its setpoint (Guideline 8). Fy has a rapid, direct effect on the vessel pressure, Pf, and almost no effect on any other output (Guidelines 7 and 8). For similar reasons, is selected to control the liquid level, h. Fyy is selected because it directly controls the flash temperature, 2 (Guideline 8). ... 

An additional difficulty in the control of polymer properties is that in some cases the control problem is multivariable, in the sense that there are interactions between the molecular weight and composition loops and therefore when a manipulated variable is chosen to control molecular weight it may also affect composition. It is important to use process knowledge to validate the selection of manipulated variables. For example, for the polymerization reactor shown in Figure 12.33, process simulations showed that one way to decouple polymer quality control is to take advantage of the fact that polymer composition is naturally very sensitive to changes in reactor feed composition but inherent viscosity is relatively insensitive... 

Dynamic Heat Transfer Option Figure 3.99 shows the parameters that must be specified when this heat transfer option is selected. The manipulated variable with this option is the flowrate of the cooling water. The temperature of the inlet cooling water is... 

Optimization implies maximum profit rate. An objective function is selected, and manipulated variables are chosen that will maximize or minimize that function. Unit optimization addresses several columns in series or parallel. It is concerned with the effective allocation of feedstocks and energy among the members of that system. Plantwide optimization involves coordinating the control of distillation units, furnaces, compressors, etc., to maximize profit from the entire operation. All lower-level control functions respond to set points received from higher-level optimizers. 

The above time-scale decomposition provides a transparent framework for the selection of manipulated inputs that can be used for control in the two time scales. Specifically, it establishes that output variables y1 need to be controlled in the fast time scale, using the large flow rates u1, while the control of the variables ys is to be considered in the slow time scale, using the variables us. Moreover, the reduced-order approximate models for the fast (Equation (3.11)) and slow (the state-space realization of Equation (3.16)) dynamics can serve as a basis for the synthesis of well-conditioned nonlinear controllers in each time scale. 

The chemical plant will be first decomposed to its subsystems of unit operations with functional uniformity and common objectives in terms of economics, operation and control. Within each subsystem resulting from the decomposition of the overall plant, we employ the same general approach described in the previous section to (a) classify the plant constraints at the current optimum, into active and inactive ones (b) select the controlled variables for the decentralized subsystem controllers (c) select the manipulated variables for each subsystem (d) establish the initial search direction (ISD) to achieve feasibility and (e) to define the resulting new search directions (NSD) that will ultimately lead to the new optimum operation. 

The optimal robust controller designed with one of the new synthesis techniques is generally not of a form that can be readily implemented. The main benefit of the new synthesis procedure is that it allows the designer to establish performance bounds that can be reached under ideal conditions. In practice, a decentralized (multiloop) control structure is preferred for ease of start-up, bumpless automatic to manual transfer, and fault tolerance in the event of actuator or sensor failures. Indeed, a practical design does not start with controller synthesis but with the selection of the variables that are to be manipulated and measured. It is well known that this choice can have more profound effects on the achievable control performance than the design of the controller itself. This was demonstrated in a distillation example [17, 18] in which a switch from reflux to distillate flow as the manipulated variable removes all robustness problems and makes the controller design trivial. 

Select manipulated variables. Find the set of manipulated variables that gives the largest minimum singular value of the steady-state gain matrix. 

Before selecting the controlled and manipulated variables for a control system, one must determine the number of manipulated variables permissible. As discussed under Degrees of Freedom in Section 4.2, the number of manipulated variables cannot exceed the number of degrees of freedom, which are determined using a process model according to... 

The temperature of the catalyst is not controlled in the investigated FCC unit yet, so a control loop should be designed to improve the operation of the actual control structure. In the first step, the analysis of degrees of freedom is carried out and a possible manipulated variable is found the coke formation in the reactor can be controll by the feed flow of the bottom product of the main distillation column (BMC) which contains heavy hydrocarbons. (This main distillation column separates the products of the FCC unit.) This flow is free for this control and it is selected as manipulated variable. 

Frequently a situation is encountered where two or more variables must not be allowed to pass specified limits for reasons of economy, efficiency, or safety. If the number of controlled variables exceeds the number of manipulated variables, whichever ones are in most need must logically be selected for control. (This is the case of the squeaky wheel getting the grease.) Automatic selector units are available for this type of service. They are employed in four basic areas of application ... 

B. 2. Employ multivariable control for highly interactive processes. So far we have assumed that a multiloop control approach will be sufficient and that multi-variable control will not be necessary. One way to help ensure that this assumption will eventually be validated is to design the individual control loops so they interact as little as possible by careful selection of controlled variables and their pairing with manipulated variables. For example, adjusting the value of wi and the ratio of wilwi (in order to control W4 and x j), respectively), instead of directly controlling the two flow rates individually, is one way of physically decoupling the two control loops (see Chapter 18). 

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