Single loop variables

Single-loop variables These include pressure and levels. They are controlled in order to achieve the first objective, i.e., setting stable conditions for column operation. The set points at which these are controlled are established by stability considerations alone, regardless of product specifications. 

Shinskey (1984) has shown that there are 120 ways of connecting the five main parts of measured and controlled variables, in single loops. A variety of control schemes has been devised for distillation column control. Some typical schemes are shown in Figures 5.22a, b, c, d, e (see pp. 234, 235) ancillary control loops and instruments are not shown. 

After proper pairing of manipulated and controlled variables, we still have to design and tune the controllers. The simplest approach is to tune each loop individually and conservatively while the other loop is in manual mode. At a more sophisticated level, we may try to decouple the loops mathematically into two non-interacting SISO systems with which we can apply single loop tuning procedures. Several examples applicable to a 2 x 2 system are offered here. 

The control valve is a variable restriction in a pipeline which receives its position command from a controller—either in the form of a single loop regulator or as part of a more complex control system. As such, the control valve constitutes by far the most common final control element although increasing use is being made of variable speed pumps and fluidics(64) to control the flowrates of process fluids. 

Controlled variables include product compositions (x,y), column temperatures, column pressure, and the levels in the tower and accumulator. Manipulated variables include reflux flow (L), coolant flow (QT), heating medium flow (Qb or V), and product flows (D,B) and the ratios L/D or V/B. Load and disturbance variables include feed flow rate (F), feed composition (2), steam header pressure, feed enthalpy, environmental conditions (e.g., rain, barometric pressure, and ambient temperature), and coolant temperature. These five single loops can theoretically be configured in 120 different combinations, and selecting the right one is a prerequisite to stability and efficiency. 

Plant-wide control is concerned with designing control systems for a large number of individual process units that may be highly interacting. A typical plant-wide control system will consist of many single-loop controllers as well as multi-variable controllers such as Model Predictive Control (MPC),1 10 and may involve thousands of measurements, hundreds to thousands of manipulated variables and hundreds of disturbance variables. Fortunately, a plant with a large number of processing units can be analysed as smaller clusters of units. 

Application to Simultaneous Phase and Chemical Equilibrium. The single-stage process with simultaneous phase and chemical equilibrium is another application of the inside-out concept where the Newton-Raphson method has been employed in a judicious way in the inside loop. There would appear to be no reaction parameter having characteristics that make it suitable as an outside loop iteration variable in the spirit of the inside-out concept. On the other hand, the chemical equilibrium relationships are simple in form, and do not introduce new thermophysical properties that depend in a complicated way on other variables. Thus it makes sense to include them in the inside loop, and to introduce the reaction extents as a new set of inside loop variables. 

In the era of single-loop control systems in chemical processing plants, there was little infrastructure for monitoring multivariable processes by using multivariate statistical techniques. A limited number of process and quality variables were measured in most plants, and use of univariate SPM tools for monitoring critical process and quality variables seemed appropriate. The installation of computerized data acquisition and storage systems, the availability of inexpensive sensors for typical process variables such as temperature, flow rate, and pressure, and the development of advanced chemical analysis systems that can provide reliable information on quality variables at high frequencies increased the number of variables measured at... 

The most popular tool for monitoring single-loop feedback and feedforward/feedback controllers is based on relative performance with respect to minimum variance control (MVC) [53, 102[. The idea is not to implement MVC but to use the variance of the controlled output variable that would be obtained if MVC were used as the reference point. The variation of the inflation of the controlled output variance indicates if the process is operating as expected or not. Furthermore, if the variance with a MVC is larger than what could be tolerated, this indicates the need for modification of operating conditions or process. 

Judicious application of this control strategy on essentially linear single variable control systems which don t exhibit a prolonged delay (dead time) between action by the manipulated variable and measured response by the controlled variable has proven quite effective. Fortunately most single loop control systems exhibit this behavior. 

The feedback control configuration involves one measurement (output) and one manipulated variable in a single loop. There are, however, other simple control configurations which may use ... 

Before proceeding we should emphasize that these control systems involve loops that are not separate but share either the single manipulated variable or the only measurement. In this respect the multiple-loop control systems of this chapter are generically different from those we will study in Chapters 23 and 24. 

In a multi-input multi-output (MIMO) control system (Fig. 12.14), there are several controlled variables (vector y) that should be kept on set-points (vector r) faced to disturbances (vector d) by means of appropriate manipulated variables (vector u). The feedback controller K provides the algorithm that will ensure the link between the manipulated (inputs) and controlled (outputs) variables. In this chapter we will consider a decentralised control system that makes use of multi-SISO control loops, which means that a single controlled variables is controlled by a single manipulated variable. This arrangement is typical for plantwide control purposes. However, there will be interactions between different loops. These Interactions can be detrimental, or can bring advantages. Therefore, the assessment of interactions is a central issue in the analysis of MIMO systems. 

Single-loop controllers are usually the only backup required. Sometimes a cascade backup in which the computer adjusts the secondary loop through the direct digital controller mode of the primary is desirable. The backup modes include manual, local secondary-set, and local cascade-set. To assure complete backup synchronization, the primary controller tracks the secondary variable through a feedback connection whenever the cascade mode is deactivated. The result is procedureless, nonshift switching of on and off computer control. Cascade backup is required only on loops that can break up an entire system. 

The strategy most widely used in the wet-end today is single-loop control. One input variable, e.g. total consistency controls one output variable, e.g. retention aid dosage. Due to the fact that most parameters are impacted through various variables. 

A processor does not have control of all the steps from basic material to finished product. Material processing capability is limited by what is received and by when inspection is made or required so selection tests are important and must be subject to change (see Chapter 10). In turn, the process line has many variables that must be coordinated. A practical procedure today, with all closed-loop process control systems, is to subdivide the controls into distinct subsystems (Fig. 3-33). They can then be controlled within single control loops or by simple intermeshed circuits. A single-loop feedback circuit has one input and one output signal disturbances that affect the process are registered by the controller directly, by an ad-... 

Figure 6 (A) A series-tuned, balanced-matched, inductively coupled sample (primary) coil. This example consists of a single loop of conductor with two symmetrically positioned fixed tuning capacitors, 2Q, and a variable capacitor, Q, for fine adjustment of the resonance frequency. The series tuning capacitors lower the coil voltage and should reduce dielectric losses. A secondary (impedance-matching) coil is required for coupling the primary to the spectrometer. (B) A circuit for a balanced-matched, inductively coupled surface coil. Lg is the inductance of the sample (primary) coil, which is tuned by Q and Q. Lm is the inductance of the matching (secondary) coil. The impedance matching can be fine-adjusted using Cm. (Reproduced with permission from Cady EB (1990). Magnetic Resonance Spectroscopy. New York Plenum Plenum.)...

PI control Simple proportional-integral SISO loops provide effective control of the vast majority of all chemical plants. These systems require process understanding to set up, rational tuning methods, the use of overrides to handle constraints and split-ranged valves to handle the case where several manipulated variables can be used to control a single controlled variable. 

I Single-loop PID control, with appropriate loop pairing for multivariable processes (since such processes are mostly linear, with no difficult dynamics and little or no interactions among the process variables). 

The idea of the simpler single-temperamre loop-control strategy is to use an aqueous reflux flowrate to hold some tray temperature inside the column and to ratio the other two manipulated variables (reboiler duty and entrainer makeup) to the feed flowrate. From the earlier sensitivity analysis in Figure 9.9a, the most sensitive control point inside the column is Stage 6, which is close to the top of the column. Since the main acetic acid product is drawn out from the bottom of the column, an alternative control point is to select the second most sensitive control point, which is closer to the bottom of the column. This alternative control point will be Stage 16. Thus two single-loop-control stmctuies will be evaluated using closed-loop dynamic tests. They are ... 

Third, we only aim at the synthesis of single-loop logic algorithms. In other words, we assume that the only loop is the one that is achieved in the schema by the recursion on the induction parameter, and that none of the instances of the predicate-variables is defined recursively (possibly as a divide-and-conquer logic algorithm). 

The process module has as input the initiating event that is under examination. Four initiating events were modelled (LOCA, LOOP, LOFW, Transient) using a single enumerative variable scenario that determines the used initiating event. The variable scenario forces certain physical parameters to have a particular value. For example, in all initiating events the reactor water level becomes low. Consequently, the corresponding variables in the model shall also indicate that the reactor water level is low. The process module consists of case clauses that implement these kinds of rules for all scenarios. The variable scenario also has an additional possible value FREE. In this case the physical parameters of the plant experience no restrictions what so ever the values of the parameters are selected non-deterministically. 

Jntil now, discussion has been confined to control systems with a single manipulated variable. Furthermore, only one controlled variable has been allowed to be independently specified. But any process capable of manufacturing or refining a product cannot do so within a single control loop. In fact each unit operation requires control over at least two variables product rate and quality. 

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