Process-control comparator

There are adaptive PCs. They are control system that changes the settings in response to changes in machine performance to bring the product back into its preset requirements or specification. The shift is maintained so that the control has adapted to changing conditions. It is a technique typically used to modify a closed loop control system. The process control comparator is the portion of the control elements that determines the feedback error on which a controller acts. 

The sweeping test, following X, Y and Z axes, on the aluminum plate containing four standard defects and the processing software comparing between the impedance variation of the sane reference and the sample to be controlled allow the reconstitution of an image of the piece. 

Statistical Control. Statistical quahty control (SQC) is the apphcation of statistical techniques to analytical data. Statistical process control (SPC) is the real-time apphcation of statistics to process or equipment performance. Apphed to QC lab instmmentation or methods, SPC can demonstrate the stabihty and precision of the measurement technique. The SQC of lot data can be used to show the stabihty of the production process. Without such evidence of statistical control, the quahty of the lab data is unknown and can result in production challenging adverse test results. Also, without control, measurement bias cannot be determined and the results derived from different labs cannot be compared (27). 

Historical DataBase Subsystem We have discussed the use of on-hne databases. An historical database is built similar to an on-line database. Unlike their on-line counterparts, the information stored in a historical database is not normally accessed directly by other subsystems for process control and monitoring. Periodic reports and longterm trends are generated based on the archived data. The reports are often used for long-term planning and system performance evaluations such as statistical process (quality) control. The trends may be used to detect process drifts or to compare process variations at different times. 

Single-loop controllers provide both the process control functions and the operator interface function. This makes them ideally suited to very small applications, where only two or three loops are required. However, it is possible to couple single-loop controllers to a personal computer (PC) to provide the operator interface function. Su(m installations are extremely cost effec tive, and with the keen competition in PC-based produc ts, the capabilities are comparable and sometimes even better than that provided by a DCS. However, this approach makes sense only up to about 25 loops. 

Adequate process control and its associated instrumentation are essential to have product quality control. In some cases the goal is precise adherence to a control point. In others it is simply to maintain the temperature within a comparatively narrow range. 

For uniform and stable extrusion it is important to check periodically the drive system, the take-up device, and other equipment, and compare it to its original performance. If variations are excessive, all kinds of problems will develop in the extruded product. An elaborate process-control system can help, but it is best to improve stability in all facets of the extrusion line. Some examples of instabilities and problem areas include... 

Metrics for this might include number of excursions from statistical process control, but one very useful metric for controllability is process capability, or more accurately, process capability indices. Process capability compares the output of an in-control process to the specification limits by using capability indices. The comparison is made by forming the ratio of the spread between the process specifications (the specification width ) to the spread of the process values. In a six-sigma environment, this is measured by six standard deviation units for the process (the process width ). A process under control is one where almost all the measurements fall inside the specification limits. The general formula for process capability index is ... 

Chapter 2 is employed to provide a general introduction to signal and process dynamics, including the concept of process time constants, process control, process optimisation and parameter identification. Other important aspects of dynamic simulation involve the numerical methods of solution and the resulting stability of solution both of which are dealt with from the viewpoint of the simulator, as compared to that of the mathematician. 

If very close control is desired, then any disturbance due to steam pressure changes should be minimized. Figure 7-9 shows how this can be done using a cascade control system. In this case, the temperature of the process stream is measured and compared to its desired value, as before. The output of the controller, however, instead of affecting the control valve, regulates the set point of a second controller, the steam-pressure controller. This controller compares the set point determined by the first controller with the pressure downstream of the steam valve. 

LOPA is a semi-quantitative tool for analyzing and assessing risk. This method includes simplified methods to characterize the consequences and estimate the frequencies. Various layers of protection are added to a process, for example, to lower the frequency of the undesired consequences. The protection layers may include inherently safer concepts the basic process control system safety instrumented functions passive devices, such as dikes or blast walls active devices, such as relief valves and human intervention. This concept of layers of protection is illustrated in Figure 11-16. The combined effects of the protection layers and the consequences are then compared against some risk tolerance criteria. 

Figure 4 shows the impact of process intensification for this hypothetical case. With a temperature increase of only 41°C, the number of reactors for such comparatively big units is reduced from 20, hardly feasible in view of costs and process control, to four, feasible for the same reasons. Thus, the costs decrease by almost a factor of five (not exactly, since fixed costs have a small share). Another 21°C temperature increase halves the number of reactors again, and at 249°C, which is 149°C higher than the base temperature, an equivalent of 0.2 micro-structured reactors is needed. This means that practically one micro-structured reactor is taken and either reduced in plate number or in the overall dimensions. The costs of all microstructured reactors scales largely with their number only at very low numbers do fixed costs for microfabrication lead to a leveling off of the cost reduction. 

Scale-up can also have a significant effect on the basic process control system and safety systems in a reactive process. In particular, a larger process will likely require more temperature sensors at different locations in the process to be able to rapidly detect the onset of out-of-control situations. Consideration should be given to the impact of higher-temperature gradients in plant-scale equipment compared to a laboratory or pilot plant reactor (Hendershot 2002). 

Feedback control. The traditional way to control a process is to measure the variable that is to be controlled, compare its value with the desired value (the setpoint to the controller) and feed the difference (the error) into a feedback controller that will change a manipulated variable to drive the controlled variable back to the desired value. Information is thus fed back from the controlled variable to a manipulated variable, as sketched in Fig. 1.7. 

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