Process, self regulating

This is called an integrating (also capacitive or non-self-regulating) process. We can associate the name with charging a capacitor or filling up a tank. 

For self-regulating processes with little or no capacitance, a single-speed floating controller can be used, such as a single-speed reversible motor. The controller is usually provided with a neutral zone (dead zone), and when the measurement is within that zone, the output of the controller is zero. This dead zone is desirable, because otherwise the manipulated variable would be changing continually in one direction or the other. 

When a process is at steady state and it is upset by a step change, it usually starts to react after the dead time (Figure 2.37). After the dead time, most processes will reach a maximum speed (reaction rate), then the speed will drop (self-regulating process) or the speed will remain constant (integrating process). 

Reaction curve of a self-regulating process, caused by a step change of one unit in the controller output. Lr = /, is dead time, Rr is reaction rate, and K is process gain. 

If a process settles at a new steady state after an input change, the process is referred to as self-regulating. Levels in tanks, accumulators, and reboilers, and many pressure systems, behave as integrating processes. Consider the level in a tank for which both the flow in and the flow out are set independently. Initially, the flow out, is equal to the flow in, and the level is constant. Figure 15.8 shows the level as a function of time for a step change in the flow out at time equal to 10 s. Note that the level in the tank begins to decrease at a constant rate. This is an example of a non-self-regulating process, since the process does not move to a new steady state. 

To better understand the kinetic aspect of a model based on a self-regulating process of the diffusion, it was necessary to choose an electronic component better adapted than the Zener diode to the simulation of the phenomenon. 

Know the difference between a self-regulating process response and an integrating process response... 

Recognize the desired response pattern when a step change in setpoint is introduced to the controller for a self-regulating process variable with optimum... 

Measure the response time for a self-regulating process variable to reach the first peak after a step-up change in setpoint when tuning a self-regulating process response... 

The process dynamics of most process variables can be characterized as a self-regulating process response. One example is the response of a liquid flow rate when a valve position is opened. The liquid flow rate will increase from the initial flow rate to a new steady-state flow rate. Another example is the response of the temperature of a liquid flowing through a heat exchanger that is heated with steam. When the steam valve position is increased, the temperature of the liquid outlet will increase to a new steady-state temperature. 

The recommended method for tuning level control loops is given in Section 9.8. It is different from the method for tuning controllers with a self-regulating process variable response. 

The self-regulating process gain, Kp, can be measured from the setpoint change and controller output (valve position) change (Equation 9.2). 

This chapter described the concept of a self-regulating process response and an integrating process response. Several methods were described for tuning a control loop while the controller is in automatic output mode. These include the trial-and-error method, the Zlegler-Nlchols ultimate gain method, and the Robbins pattern recognition methods. 

As discussed in Chapter 9, the process dynamics of most process variables can be characterized as a self-regulating process response. When the controller output changes the automatic valve position of the manipulated variable, the process variable moves to a new steady-state value. 

The most common model of a self-regulating process response is a first-order plus dead time (FOPDT) response. 

The open-loop process gain, K, for a self-regulating process response is calculated from Equation 10.1. 

The fired heater that we have worked with is an example of a self-regulating process. Following the disturbance to the fuel valve the temperature will reach a new steady state without any manual intervention. Not all processes behave this way. For example, if we trying to obtain the dynamics for a future level controller we would make a step change to the manipulated flow. The level would not reach a new steady state unless some intervention is made. This non-self-regulating process can also be described as an integrating process. 

By including a bias (because it is not true that the PV is zero when the MV is zero) we can modify Equation (2.2) for a self-regulating process to... 

By replacing PV with its derivative we can therefore apply the same model identification techniques used for self-regulating processes. While for DCS-based controllers, PV and MV remain dimensionless, Kp must now have the units of reciprocal time. The units will depend on whether rate of change of PVis expressed in sec min or hr . Any may be used provided consistency is maintained. We will use min throughout this book. 

Figure 2.20 Mixed integrating and self-regulating process...

The term open-loop unstable is also used to describe process behaviour. Some would apply it to any integrating process. But others would reserve it to describe inherently unstable processes such as exothermic reactors. Figure 2.21 shows the impact that increasing the reactor inlet temperature has on reactor outlet temperature. The additional conversion caused by the temperature increase generates additional heat which increases conversion further. It differs from most non-self-regulating processes in that the rate of change of PV increases over time. It often described as a runaway response. Of course, the outlet temperature will eventually reach a new steady state when aU the reactants are consumed however this may be well above the maximum permitted. 

It is unlikely that the control engineer will find a published controller tuning method that will meet the needs of the process. This is despite a considerable amount of research work. In 2000 a survey (Reference 2) identified, for self-regulating processes, 81 published methods for tuning PI controllers and 117 for PID controllers. For integrating processes, it also found 22 methods for PI control and 15 for PID control. Every one of these methods has at least one flaw. 

The tuning constants are calculated for a self-regulating process from formulae developed by Chien (Reference 9) as... 

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