Process control dynamic response

Probably the best way to illustrate what we mean by process dynamics and control is to take a few real examples. The first example describes a simple process where dynamic response, the time-dependent behavior, is important. The second example illustrates the use of a single feedback controller. The third example discusses a simple but reasonably typical chemical engineering plant and its conventional control system involving several controllers. 

Open-Loop versus Closed-Loop Dynamics It is common in industry to manipulate coolant in a jacketed reacdor in order to control conditions in the reacdor itself. A simplified schematic diagram of such a reactor control system is shown in Fig. 8-2. Assume that the reacdor temperature is adjusted by a controller that increases the coolant flow in proportion to the difference between the desired reactor temperature and the temperature that is measured. The proportionality constant is K. If a small change in the temperature of the inlet stream occurs, then depending on the value or K, one might observe the reactor temperature responses shown in Fig. 8-3. The top plot shows the case for no control (K = 0), which is called the open loop, or the normal dynamic response of the process by itself. As increases, several effects can be noted. First, the reactor temperature responds faster and faster. Second, for the initial increases in K, the maximum deviation in the reactor temperature becomes smaller. Both of these effects are desirable so that disturbances from normal operation have... 

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 ... 

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). 

From a dynamic-response standpoint, the adjustable speed pump has a dynamic characteristic that is more suitable in process-control apphcations than those characteristics of control valves. The small amphtude response of an adjustable speed pump does not contain the dead baud or the dead time commonly found in the small amphtude response of the control valve. Nonhnearities associated with frictions in the valve and discontinuities in the pneumatic portion of the control-valve instrumentation are not present with electronic... 

Basic process control system (BPCS) loops are needed to control operating parameters like reactor temperature and pressure. This involves monitoring and manipulation of process variables. The batch process, however, is discontinuous. This adds a new dimension to batch control because of frequent start-ups and shutdowns. During these transient states, control-tuning parameters such as controller gain may have to be adjusted for optimum dynamic response. 

The strategy depends on the situation and how we measure the concentration. If we can rely on pH or absorbance (UV, visible, or Infrared spectrometer), the sensor response time can be reasonably fast, and we can make our decision based on the actual process dynamics. Most likely we would be thinking along the lines of PI or PID controllers. If we can only use gas chromatography (GC) or other slow analytical methods to measure concentration, we must consider discrete data sampling control. Indeed, prevalent time delay makes chemical process control unique and, in a sense, more difficult than many mechanical or electrical systems. 

The dynamic response of most sensors is usually much faster than the dynamics of the process itself. Temperature sensors are a notable and sometimes troublesome exception. The time constant of a thermocouple and a heavy thermowell can be 30 seconds or more. If the thermowell is coated with polymer or other goo, the response time can be several minutes. This can significantly degrade control performance. 

The dynamic response of most transmitters is usually much faster than the process and the control valves. Consequently we can normally consider the transmitter as a simple gain (a step change in the input to the transmitter gives an instantaneous step change in the output). The gain of the pressure transmitter considered above would be... 

In the last chapter we used Laplace-domain techniques to study the dynamics and stability of simple closedloop control systems. In this chapter we want to apply these same methods to more complex systems cascade control, feedforward control, openloop unstable processes, and processes with inverse response. Finally we will discuss an alternative way to look at controller design that is called model-based control. 

Becanse there are many factors involved in the dynamic mechanical compression of polyolefin foams, the Taguchi method was employed in a Perkin Elmer DM A7 dynamic mechanical analyser to establish a method to improve the measurement process. The signal-to-noise ratio was measured to determine how the variability could be improved. Control and noise factors were evaluated and levels chosen, with details being tabulated. Appendix A describes some of the factors. Tests were conducted on two closed cell foams. NA2006 foam is 48 kg/cu m LDPE and NEE3306 foam is 32 kg/cu m EVA. Different factors were shown to influence results for E and tan delta but an optimum combination is proposed for the simultaneous measurement of both properties. The results were less variable as frequency was increased. Small differences in the dynamic response of different materials should be measurable because of the low variability in the experimental results. 18 refs. 

Dynamic response testing of process control instrumentation... 

A wide range of monomers, including styrene, can be polymerised in this way. Cu(I)/ligand is a commonly used metal complex which can act as a catalyst. These metal complexes must have the ability to be oxidised to a higher oxidation state. In the case of copper, the oxidised form of the metal is Cu(II), the deactivator of the process. The dynamic equilibrium of this method is responsible for the well-defined behaviour of these kinds of polymerisations. This equilibrium can, in its turn, be controlled by the ratio of concentrations of both the metal-complex forms. In this chapter, preliminary research results are described concerning the voltammetric determi-... 

Early applications of MPC took place in the 1970s, mainly in industrial contexts, but only later MPC became a research topic. One of the first solid theoretic formulations of MPC is due to Richalet et al. [53], who proposed the so-called Model Predictive Heuristic Control (MPHC). MPHC uses a linear model, based on the impulse response and, in the presence of constraints, computes the process input via a heuristic iterative algorithm. In [23], the Dynamic Matrix Control (DMC) was introduced, which had a wide success in chemical process control both impulse and step models are used in DMC, while the process is described via a matrix of constant coefficients. In later formulations of DMC, constraints have been included in the optimization problem. Starting from the late 1980s, MPC algorithms using state-space models have been developed [38, 43], In parallel, Clarke et al. used transfer functions to formulate the so-called Generalized Predictive Control (GPC) [19-21] that turned out to be very popular in chemical process control. In the last two decades, a number of nonlinear MPC techniques has been developed [34,46, 57],... 

The process is subjected to a number of disturbances, and the control structure handles all of them quite effectively. Dynamic responses to changes in the setpoint of the temperature controller in the first reactor are shown in Figure 6.109. At 0.1 h, the setpoint is increased from 245 to 255°C. At 3 h, it is decreased to 235°C. Decreasing the temperature in the first reactor results in an increase in throughput. The synthesis gas feedrate, the product rate, and the vent rate all increase. The opposite occurs when the temperature is increased. This indicates that the reaction is equilibrium-limited, not kinetically limited. Decreasing temperature increases the equilibrium constant of exothermic reactions. 

The plant control system functions to limit temperature rates of change during plant load change and upset events. This is achieved at two different levels. In the time asymptote the control system through control variable set points (with values assigned as a function of steady-state power) takes the plant to a new steady-state condition. The set point values are chosen so that hot side temperatures remain little changed. In the shorter term the control system manages the dynamic response of the plant so that the transition between steady states is stable and with minimal overshoot of process variables. 

Dynamic matrix control (DMC) is also an MVC technique, but it uses a set of linear differential equations to describe the process. The DMC method obtains its data from process step responses and calculates the required manipulations utilizing an inverse model. Coefficients for the process dynamics are determined by process testing. During these tests, manipulated and load variables are perturbed, and the dynamic responses of all... 

After satisfying all of the basic regulatory requirements, we usually have additional degrees of freedom involving control valves that have not been used and setpoints in some controllers that can be adjusted. These can be utilized either to optimize steady-state economic process performance (e.g., minimize energy, maximize selectivity) or to improve dynamic response. 

It now looks as if we have achieved the best of all worlds a thermally efficient process with an easy-to-control reactor Can this be true Not quite. What we forget are the undesirable effects on the reactor that thermal feedback introduces. In Chap. 4 we explained in detail how7 process feedback is responsible for the same issues we tried to avoid in the first place by selecting an adiabatic plug-flow reactor. It is necessary that we take a close look at the steady-state and dynamic characteristics of FEHE systems. 

Figure 5.26 Dynamic response of HDA reactor inlet temperature to -8°C setpoint change for three different process and control configurations.

Step 8. The previous steps have left us at this point with two unassigned control valves, which are the reflux flows to each column. As discussed in Chap. 6, these are independent variables and can be fixed by flow controllers. We do not need dual composition control for the irreversible case because only one end of both columns is a product stream leaving the process. These two reflux flowrates are available in Step 9 to use as optimizing variables or to improve dynamic response. However, we may need dual composition control in the DIB column for the reversible case as mentioned in Steps 4 and 5. 

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