Control system inferential

The major problem for control based on material states, however, is the quality control culture that requires that parts be accepted based on adherence to a preset cycle within specified limits. Because state-based inferential control systems could theoretically come up with a new cure cycle every time, this sort of specification cannot be used with such systems. Specifications instead have to be in terms of the process plan used for the cure. The satisfactory completion of a certain cure history without alarm states would be assumed to produce an acceptable part. Once the culture was able to accept that difference for autoclave curing, production costs at the U.S. air force s McClellan AFB Logistic Center were substantially reduced [32], This type of specification could also give material review boards a head start on investigations because they would know that a part did not meet specification as well as what sorts of flaws might result from the deviation. The experience at McClellan is that there are fewer parts to review. It is even conceivable that, with improvements to sensors, much of the current postcure nondestructive evaluation used to verily the quality of parts could be incorporated into the process, building quality in rather than inspecting it in after the fact. 

Propose a control system based on feed-forward - feedback control, cascade control and inferential control to achieve these control objectives. 

The material of the subsequent four chapters (Chapter 19, 20, 21, and 22) should be viewed as an introduction to the analysis and design of the control systems above. The subject is quite involved, and the interested reader should consult the references at the the end of Part V. In particular, the discussion on the adaptive and inferential control is limited to a simple qualitative presentation of these control systems, since a more rigorous presentation goes beyond the scope of this text. Nevertheless, in Chapter 31, the interested reader will find a mathematical treatment of the adaptive control system design. 

What happens, though, if the disturbances cannot be measured None of the control configurations studied so far can be used to control an unmeasured process output in the presence of unmeasured disturbances. This is the type of control problems where inferential control is the only solution. Let us now examine the structure of an inferential control system. 

Equation (22.5) provides the needed estimator which relates the unmeasured controlled output to measured quantities like m and z. Figure 22.6b shows the structure of the resulting inferential control system. Notice that the estimated value of the unmeasured output plays the same role as a regular measured output that is, it is compared to the desired set point and the difference is the actuating signal for the controller. 

Having developed the four process transfer functions it is easy to design the inferential control system (Figure 22.7b). 

Chapter 22. The book by Shinskey is once more a valuable guide for the design of useful adaptive and inferential control systems. The mathematical treatment of the subject is simple and to the point. The general reader will find very instructive the following papers on adaptive control ... 

V.28 (a) Show how would you construct an inferential control system for a nonisothermal CSTR using temperature measurements to regulate the exit concentration at a desired value. The reaction is exothermic and the reacting mixture is cooled with water which flows through a jacket around the reactor. 

Construct an inferential control system that uses temperature measurements along the length of a tubular catalytic reactor in order to control the exit concentration. The reaction is exothermic and is cooled by a coolant (see Figure PII.9). 

Since the catalyst activity decays, add an adaptation mechanism to the inferential control system. The adaptive system will adjust the parameters of the inferential controller using as information exit composition measurements which are taken periodically with a gas chromatograph. 

Part V (Chapters 19 through 22) deals with the description, analysis, and design of more complex control systems, with one controlled output. In particular, Chapter 19 introduces the concept of feedback compensation with Smith s predictor, to cope with systems possessing large dead times or inverse response. Chapter 20 describes and analyzes a variety of multiloop control systems (with one controlled output) often encountered in chemical processes, such as cascade, selective, and split-range. Chapter 21 is devoted exclusively to the analysis and design of feedforward and ratio control systems, while Chapter 22 makes a rather descriptive presentation of adaptive and inferential control schemes why they are needed and how they can be used. 

We notice, therefore, that the availability of a good mathematical model for the process is indispensable for the design of good inferential control systems. 

Figure 22.6 (a) Process with need for inferential control (b) corresponding inferential control system. 

In References for Part VII the reader can find additional entries on the applications of self-tuning regulators. A very good source for the problems and design of inferential control systems is the work of C. Brosilow and his coworkers ... 

V.32 (a) Construct an inferential control system to regulate the distillate composition of a distillation column when the following transfer functions are provided ... 

On-line adaptation is not limited to feedback systems. On-line process identification can be coupled easily with feedforward, inferential, and other control systems, thus expanding the range of their applicability. Adaptation is particularly valuable for feedforward and inferential systems because they rely heavily on good process models for their successful implementation. 

Describe an on-line adaptive procedure for a typical feedforward control system (see Chapter 21). Do the same for the inferential control of a distillation column (see Example 22.S). 

FIGURE 57.5 General structure of a dryer inferential control system. 

Typical automatic batch-dryer control systems use the exhaust-air temperature as the controlled variable to determine when to end the drying process. Shinskey [37] and Fadum and Shinskey [38] have described an alternative control scheme employing inferential control. The system has proven to be an effective substitute for online moisture analysis in terminating drying. This method is based on the following equation ... 

Although many process variables are easily measured, lack of on-line sensors for key polymer properties renders quality control of polymer plants difficult. Process control schemes based on process variables p, T, flow-rate and feedstock compositions) alone are no longer sufficient, because these cannot reveal all material property variations. Significant efforts are being spent on improvements to process control systems, as exemplified by the numerous attempts to monitor polymer properties during processing, such as composition, density, viscosity and dispersion of a minor phase, etc., all of which are somehow difficult to measure. The development of an on-line inferential system for polymer property is a very active research area of polymerisation reactor control [1]. A schematic of inferential systems is illustrated in Fig. 7.1. For highest quality... 

Control StniCturB CSS. As mentioned in the previous section, it is desirable to use inferential temperature measurements instead of direct composition measurements whenever possible. However, the results show that control structure CS7 is not an effective control structure, at least with the optimum steady-state design studied here. It appears that some direct composition information about the reactant inventory inside the system is required for a more effective control system because the column is designed for neat... 

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