Process variables, disturbance

An open-loop system positions the manipulated variable either manually or on a programmed basis, without using any process measurements. This operation is acceptable for well-defined processes without disturbances. An automanual transfer switch is provided to allow manual adjustment of the manipulated variable in case the process or the control system is not performing satisfac torily. 

A regulator is a compact device that maintains the process variable at a specific value in spite of disturbances in load flow. It combines the functions of the measurement sensor, controher, and final control element into one self-contained device. Regulators are available to control pressure, differential pressure, temperature, flow, hquid level, and other basic process variables. They are used to control the differential across a filter press, heat exchanger, or orifice plate. Regulators are used for monitoring pressure variables for redundancy, flow check, and liquid surge relief. 

Investigate the system under dynamic conditions with disturbances acting over many circuits and monitor how the process variables change with time, resulting in a judicious recommendation regarding the control of the system. 

All control modes previously described can return a process variable to a steady value following a disturbance. This characteristic is called "stability."... 

Copper production is quite a complex process to plan and to schedule due to the many process interdependencies (shared continuous casters and cranes, emission level restrictions, limited material availability, to name a few). This makes it very difficult to foresee the overall consequences of a local decision. The variability of the raw material has alone a significant impact on the process, various disturbances and equipment breakdowns are common, daily maintenance operations are needed and material bottlenecks occur from time to time. The solution that is presented here considers simultaneously, and in a rigorous and optimal way, the above mentioned aspects that affect the copper production process. As a consequence, this scheduling solution supports reducing the impact of various disturbance factors. It enables a more efficient production, better overall coordination and visualization of the process, faster recovery from disturbances and supports optimal... 

A database (historian) for process variables, costs and revenues, operating conditions, disturbances, and so on. 

As Figure 1 shows, the main stages of the proposed methodology are the preliminary data analysis, the identification of all process variables (outputs, inputs and disturbance variables), the transfer function model (i.e. the mathematical model that describes the... 

The monitoring uses formulas that take into account feed flow rates, targets calculated by the optimization layer of multivariable control, controlled variables upper and lower limits and other parameters. The economic benefits are based on the degrees of freedom and the active constraints at the steady state predicted by the linear model embedded in the controller. In order to improve the current monitoring, parameters dealing with process variability will be incorporated in the formulas. By doing this, it will be also possible to quantify external disturbances that affect the performance of the advanced control systems and identify regulatory control problems. 

As in most processes, a distillation column and other separation processes must be maintained at operating conditions that result in products meeting certain specifications. To achieve this objective on a continuous basis the process is equipped with an automatic control system. Various disturbances can occur during the operation of the process, such as variations in ambient conditions or in the feed flow rate or composition. This can move the process away from design steady-state conditions, causing the products to be off-specification. The automatic controller counters the disturbances by adjusting the operating conditions such as to maintain the process variables at acceptable values. 

Process dynamics is another important factor that must be considered. In a distillation column, for instance, the time elapsed between changing the reflux rate and observing a change in a product composition could be measured in hours. With this response time, and in the absence of dynamic prediction capability, the controller will start taking action hours after a disturbance occurs, and it would take even longer for the correction to take effect. Linear predictions are commonly used to forecast trends of process variables but many processes, particularly multistage separations, are often highly nonlinear. Substantial improvement can be achieved with a nonlinear model. 

The method has been demonstrated on a continuous stirred tank reactor (CSTR) simulation to identify an abnormal inlet concentration disturbance [340]. The jacketed CSTR, in which an exothermic reaction takes place, is under level and temperature control. An important process variable is the coolant flow rate through the jacket, that is related to the amount of heat produced in the CSTR, and it indirectly characterizes the state of the process. This variable will be monitored in this classification scheme. 

When or SPE charts exceed their control limits to signal abnormal process operation, variable contributions can be analyzed to determine which variable (s) caused the inflation of the monitoring statistic and initiated the alarm. The variables identified provide valuable information to plant personnel who are responsible for associating these process variables with process equipment or external disturbances that will influence these variables, and diagnosing the source causes for the abnormal plant behavior. The procedure and equations for developing the contribution plots was p-resented in Section 3.4. 

Contribution plots presented in Section 7.4 provide an indirect approach to fault diagnosis by first determining process variables that have inflated the detection statistics. These variables are then related to equipment and disturbances. A direct approach would associate the trends in process data to faults explicitly. HMMs discussed in the first three sections of this chapter is one way of implementing this approach. Use of statistical discriminant analysis and classification techniques discussed in this section and in Section 7.6 provides alternative methods for implementing direct fault diagnosis. 

Fortunately, process control problems are most usually concerned with maintaining operating variables constant at particular values. Most disturbances to the process involve only small excursions of the process variables about their normal operating points with the result that the system behaves linearly regardless of how nonlinear the descriptive equations may be. Thus Eq. (1) is a nonlinear differential equation since both Cp and U are functions of 80 but for small changes in 8 average values of CP and U may be regarded as constants, and the equation becomes the simplest kind of first order linear differential equation. 

When the Laplace transform of a differential equation is taken, a term must be included for the initial conditions, i.e., for the conditions at t = 0. This term is zero for the special case when the dependent variable and all its derivatives with respect to time are zero at t = 0. Now the typical process control situation is that the process variables are normally constant with time except for occasional disturbances which temporarily derange the system. Thus the period of time which is of especial interest in control analysis is the time from the start of a disturbance until the system returns to its normal controlled condition. At t = 0 for this period all the time derivatives of the dependent variable are zero since all conditions are steady, and for convenience the steady initial value of the variable can be taken as zero. Hence for control purposes the initial conditions terms in the Laplace transformation may be eliminated. 

Consider the behavior of the variable x shown in Figure 1.5. Notice that at time t = t0 the constant value of x is disturbed by some external factors, but that as time progresses the value of x returns to its initial value and stays there. If x is a process variable such as temperature, pressure, concentration, or flow rate, we say that the process is stable or self-regulating and needs no external intervention for its stabilization. It is clear that no control mechanism is needed to force x to return to its initial value. 

In Part III we studied the dynamic behavior of various typical processing systems under the influence of changes in the input variables (disturbances or manipulated variables). In doing so, we were not concerned about having the system respond in a specific manner. In other words, we were not interested in controlling the behavior of the process. 

The secondary processes usually disturb the gel chromatographic separation or complicate the processing of the chromatographic analytical data. That is why it is necessary to remove, or at least to suppress, the secondary processes in common experimental practice by the appropriate choice of the operational variables. For example, the effects of adsorption, thermodynamic partition and incompatibility can be diminished by the choice of gel and eluent, while the ionic effects are suppressed by adding a suitable salt into eluent, and the concentration effects are not important when working with very low sample concentration or when applying the thermodynamically poor solvent as mobile phase. 

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