Adaptive control inferential

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. 

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 ... 

Identify the transfer functions that must be evaluated to design the foregoing adaptive and inferential controllers. 

What information on the dynamics of the catalytic reactor do you need in order to design the foregoing inferential and adaptive control schemes ... 

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. 

Shinskey (1994) and Bequette (1998) have provided informative overviews of these methods and related techniques. Other enhanced single-loop control strategies considered earher in this chapter, namely, inferential control, selectors, and adaptive control, can also be classified as nonhnear control strategies. 

Figure 22.8 Adaptive inferential control for a distillation column.

Show that the inferential control employed for process A or B in item 13 (above) can be improved through an adaptive mechanism that uses the direct composition measurement every 2 to 3 hours. (Consult Example 22.5.)... 

Suppose that the overall heat transfer coefficient between the cooling water and the reacting mixture drops with time due to fouling. Construct an adaptive scheme which uses intermittently exit composition measurements to adjust the parameters of the inferential controller. 

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. 

Inferential control, 16-18, 21, 438-43 adaptive, 442 the need of a model, 47 references, 447... 

How can we use the tremendous computational power of a computer to implement some advanced notions of process control, such as feedforward, adaptive, inferential, optimizing, and so on ... 

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). 

Kooi, S., 1994, Adaptive Inferential Control of Wood Chip Refiner, Tappi Journal 77(11) 185-194. 

In the field of polymer reactor engineering, the calorimetric estimation and control problems have been extensively studied with simulations and experiments [1, 33, 37,39]. EJCF [33,37] and L [39] observers have been employed to estimate the heat generation rate, on the basis of an off-line fitted heat transfer model [38, 39]. Various control techniques have been employed among them are adaptive, inferential, model predictive, and geometric control [1, 38, 39]. The robustness of the controller is shown by its successful implementations, regardless of the particular estimation and control techniques employed. Recently [15], it has been formally established, and experimentally demonstrated, the feasibility of jointly estimating the heat generation rate and the heat transfer coefficient in an exothermic reactor. 

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