• Document: LOOP CONTROL AND MORE CONTROL FUNCTIONS
  • Size: 3.06 MB
  • Uploaded: 2018-12-05 20:08:56
  • Status: Successfully converted


Some snippets from your converted document:

Industry Automation and Drive Technologies - SCE LOOP CONTROL AND MORE CONTROL FUNCTIONS OBJECTIVE In this chapter, the students get to know the essential components and the demands on a block for continuously controlling process variables; they will also learn to set up and configure a temperature control by using the blocks CTRL_PID and PULSEGEN. THEORY IN SHORT In the process industry, certain process variables have to be kept to a certain value (disturbance variable), or process variables have to be set stability-oriented to specified setpoints (response to setpoint changes). To this end, control loops are used as shown in Figure 1. Figure 1: Control Loop For our plant, the reactor temperature has to be set to a certain value for the reaction control to be in accordance with the specification. The disturbance variables are the ambient temperature and the materials that are used having different temperatures. For the temperature to be regulated, we first have to ascertain it by measurement. This measured value that corresponds to the actual value of the process variable is then compared with the desired value (setpoint). The difference between theactual value and the setpoint is called (system) deviation. If the system deviation is known, counter-measures can be derived. Regarding temperature regulation, the heater is switched on if the measured actual value is lower than the specified setpoint. For the process to handle this autonomously, a controller is needed. A controller that calculates the manipulated variable based only on the current deviation is called a proportional controller (P-controller for short). In practice, controllers prevailed that can, with the aid of a few parameters, be used for a wide range of processes, the so-called PID controllers. The PCS7 Standard Library V71 contains proven blocks that implement this function. Below, the block CTRL_PID is used. TIA Training Manual Page 1 of 36 Module P01_06 Status: 12/2010 PCS 7 for Universities Industry Automation and Drive Technologies - SCE THEORY INTRODUCTION The P-controller mentioned above is the simplest one. It works according to the principle: the larger the current deviation, that larger the manipulated value. That is, its behavior is derived directly from the current system deviation –which makes it fast and dynamically relatively favorable. However, certain disturbances are not completely compensated; i.e., a system deviation always remains. Not every system tolerates a sustained system deviation. For that reason, additional steps have to be taken. One option is adding an integral component, which changes the P- controller into a PI controller. The effect of the integral component is this: a sustained system deviation is totaled. Thus, the manipulated value increases although the system deviation remains the same. If abrupt disturbances occur in a system, they can be quickly counteracted with an additional differentiating component. The D-component calculates the manipulated variable using the time deviation of the system deviation. However, this behavior causes stochastic disturbances (noise). Here, an effective compromise has to be found. . A combination of P, I and D components is referred to as a PID controller. In the process industry, 95% of applications are implemented with these controllers since the PID controller is set with only three parameters (gain, TN (reset time, integral action time) and TV (rate time, derivative time). These few parameters already allow for a good adaptation to numerous different dynamic processes. However, setting the parameter presupposes knowledge of the system to be controlled. The knowledge about the system can be gained from experience, it can be ascertained experimentally, or it can be calculated by modeling the process. For a wide range of processes that are not dominated by delays and respond in a similar manner to positive as well as negative changes of manipulated variable interventions, it was possible to come up with different rules for controller adjustment suitable in practice. Examples are the rules for controller adjustment according to Chien, Hrones and Reswick [1], the me

Recently converted files (publicly available):