Process controls setting

Computers continued to get smaller, faster, and cheaper. The accuracy and speed of chemical analysis continued to improve. With real-time chemical analysis of process streams and a mathematical model for each unit in the process, the computer was programmed to set controllers on process streams using the new process control algorithms. With some additional programming and with the process control set points adjusted by the computer control system, the quality of the product improved. The physical property data for the chemicals in the process streams required for these model calculations were built into the process control program. 

The ATV (autotune variation) method determines the ultimate gain and period in a manner similar to the ultimate method, but ATV tests can be implemented without unduly upsetting the process. Controller settings can be calculated, and the controller can then be tuned on-line to meet the selected dynamic performance. 

One example of controls used with injection molding is seen in Figures 7-8-10. Based on the process-control settings, different behaviors of the plastics will occur. Some examples of these behaviors are shown in Figures 7-11 for injection molding and 7-12 for extrusion. 

There are many examples of action to eliminate or reduce problems. As an example there is the Quality System Regulation (QSR). FDA requires details on how products such as medical devices are manufactured. The details of the process are documented so that once a product produced in USA is approved, following what was in the QSR preparation can only produce the product. No change can be made. The exact plastic composition has to be used, process control settings remain the same, etc. Literally if a waste paper basket had been identified and located in a specific location in the plant, you can not relocate, change its size, etc. It has been reported that to make a change could cost literally a million dollars. Result of the QSR regulation is too ensure the safety of a person when the medical device is used. 

In order to achieve the target (epitaxial layer of 6 pm thickness), for each possible Resource (like the epitaxy equipment EPI 1 in the example) at least one Instruction Set instance is needed, which comprises of the applied Recipe, some Process Settings, and the Process Control Settings. 

An instance of Process Settings comprises of aU Parameters which can be used in order to adjust the result. In the discussed example, only Process Duration was chosen. By increasing the Process Duration, the epitaxial layer thickness is getting higher, or reverse. The Process Control Settings shall be discussed in httle more depth in the sequel. In this example, the used instance of Process Control Settings is MySPCForEpiThicknessOfS pm which comprises of the information which is needed to parameterize statistical process control (SPC) in the context of gauging layer thicknesses. 

Fig. 9.11 Associations between the product and the process control settings...

The concept Process Control Settings represents the parameterization of Process Control Model. By separating it into two concepts the more stable knowledge is represented by Process Control Model instances while the product-specific parameterization is performed through Process Control Settings instances. 

Fig. 9.12 Process control settings for statistical process control (SPC)...

In petrochemical plants, fans are most commonly used ia air-cooled heat exchangers that can be described as overgrown automobile radiators (see HeaT-EXCHANGEtechnology). Process fluid ia the finned tubes is cooled usually by two fans, either forced draft (fans below the bundle) or iaduced draft (fans above the bundles). Normally, one fan is a fixed pitch and one is variable pitch to control the process outlet temperature within a closely controlled set poiat. A temperature iadicating controller (TIC) measures the outlet fluid temperature and controls the variable pitch fan to maintain the set poiat temperature to within a few degrees. 

In order to operate a process facility in a safe and efficient manner, it is essential to be able to control the process at a desired state or sequence of states. This goal is usually achieved by implementing control strategies on a broad array of hardware and software. The state of a process is characterized by specific values for a relevant set of variables, eg, temperatures, flows, pressures, compositions, etc. Both external and internal conditions, classified as uncontrollable or controllable, affect the state. Controllable conditions may be further classified as controlled, manipulated, or not controlled. Excellent overviews of the basic concepts of process control are available (1 6). 

There are special numerical analysis techniques for solving such differential equations. New issues related to the stabiUty and convergence of a set of differential equations must be addressed. The differential equation models of unsteady-state process dynamics and a number of computer programs model such unsteady-state operations. They are of paramount importance in the design and analysis of process control systems (see Process control). 

Simulation of Dynamic Models Linear dynamic models are particularly useful for analyzing control-system behavior. The insight gained through linear analysis is invaluable. However, accurate dynamic process models can involve large sets of nonlinear equations. Analytical solution of these models is not possible. Thus, in these cases, one must turn to simulation approaches to study process dynamics and the effect of process control. Equation (8-3) will be used to illustrate the simulation of nonhnear processes. If dcjdi on the left-hand side of Eq. (8-3) is replaced with its finite difference approximation, one gets ... 

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). 

Sample Transport Transport time, the time elapsed between sample withdrawal from the process and its introduction into the analyzer, shoiild be minimized, particiilarly if the analyzer is an automatic analyzer-controller. Any sample-transport time in the analyzer-controller loop must be treated as equivalent to process dead time in determining conventional feedback controller settings or in evaluating controller performance. Reduction in transport time usually means transporting the sample in the vapor state. 

The process controller is the master of the process-control system. It accepts a set point and other inputs and generates an output or outputs that it computes from a rule or set of rules that are part of its internal configuration. The controller output seiwes as an input to another controller or, more often, as an input to a final control element. The final control element is the device that affects the flow in the piping system of the process. The final control element seiwes as an interface between the process controller and the process. Control valves and adjustable speed pumps are the principal types discussed. 

Currently, the trend in process control is away from centrahzed process control and toward an increased number of small distributed-control or PLC systems. This trend will put emphasis on the evolution of the fieldbus controller and continued growth of the PC-based controller. Also, as hardware and software improves, the functionality of the controller will increase, and the supporting hardware will be physically smaller. Hence, the traditional lines between the DCS and the PLC will become less distinct as systems will be capable of supporting either function set. 

With respect to their response, the discussion should emphasize why these are important anci why they adjust certain control settings. Among the deviations on which analysts should focus the discussion are the high and low alarm settings. Some alarms will require rapid response. Alarms may give insight into equipment-operation boundaries as well as process constraints. 

Process equipment function changes with different steps in process sequence (e.g., same vessel used as feed tank, reactor, crystallizer pump used to pump in/out). Instrumentation and controls not kept in phase with the current process step (e.g., control set points, interlocks etc.). 

Repetitive, irregular or non-symmetrical features require greater process control and complex set-up or tooling requirements. This can be an added source of variability. 

Avoid the use of instrumentation that may have low first cost, but is very expensive to operate or maintain. Blind controllers, for example, are completely unsatisfactory for most applications. The author has seen examples of temperature controllers set at the factory, but with no method of readout or calibration. These almost always require retrofitting of additional instrumentation later. Internal level floats on process vessels that require plant... 

G. D. Anderson s article recommends initial controller settings for those control loops set on automatic rather than manual for a plant startup. For liquid level, the settings depend upon whether the sensor is a displacer type or differential pressure type, or a surge tank (or other surge) is installed in the process ... 

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