Control inferential

In all the control systems considered so far, it has been assumed that measurements of the controlled variable were available. In some control applications, however, the process variable that is to be controlled cannot be conveniently measured on-line. For example, product composition measurements may require that a sample be sent to the plant laboratory from time to time. In this situation, measurement of the controlled variable may not be available frequently enough or quickly enough to be used for feedback control. 

---

The similarity between DMC and MAC has been formalized that formalism has been labeled the IMC structure. Garcia and Morari [42] produced the seminal work on IMC, summarizing DMC- and MAC-based results, developing the structure illustrated in Figure 9.1, and acknowledging that inferential control concepts [43] motivated the IMC structure. 

Brosilow, C.B. The Structure and Design of Smith Predictors from the Viewpoint of Inferential Control (1979) Joint Automatic Control Conference, Denver... 

The shortcoming of all methods for predetermining cure cycles that regulate secondary variables is that they deal only in expectations and probabilities. No matter how many eventualities are anticipated, there is always one more that is unexpected. Unexpected variations in material properties, process equipment malfunctions, and changes to geometries of tool and part all contribute to the uncertainty of the outcome. As a result, in-process, inferential control is needed for the process environment as well as the boundary conditions and material state. Inferential control is relatively new to the polymer processing industry, especially in complex processes where good models are not yet common. 

The main focus of this chapter, however, is on the tools that can use this sensor information for real-time control. These tools are still largely developmental because inferential control is relatively new to the composites industry and change is dependent on both technical merit and on other changes in the culture of the industry. 

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. 

One solution to this problem is to employ inferential control, where process measurements that can be obtained more rapidly are used with a mathematical model to infer the value of the controlled variable, as illustrated in Figure 12. For example, if the overhead product stream in a distillation column cannot be analysed on-line, measurement of a selected tray temperature may be used to infer the actual composition. If necessary, the parameters in the model may be updated, if composition measurement become available, as illustrated by the second measuring device in Figure 12 (dashed lines). 

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

The concentration of the product B, CB, is not measured on-line and a measurement is only available hourly from a lab. The control of the concentration is therefore based on inferential control in loop 4 using the reactor temperature T. The inferential controller will then, from a model of the process, infer what the concentration CB is and use this inferred measurement as the signal to the controller CC3 (where the first C refers to Concentration). 

The model in the inferential controller is updated hourly with the actual measurement of the concentration CB from the lab in loop 5 (CT5). 

Insights from nonlinear wave theory can also be used for designing new control strategies. A major problem in controlling product purities in separation as well as integrated reaction separation processes is often the lack of a cheap, reliable and fast online concentration measurement. This problem can be solved in two different ways (i) through simple inferential control, or (ii) model-based measurement. 

Joseph, B. and Brosilow, C.B., "Inferential Control of Processes Part 1. Steady-Stated Analysis and Design, Part III. Construction of Optimal and Suboptimal Dynamic Estimations," AlChE J. Vol. 24, No. 3, pp 485-492, pp 500-509, 1978a,b. 

FIGURE 15.58 Schematic for inferential control of the bottoms product composition of a distillation column. 

For an inferential control to be effective, the inferential measurement must correlate strongly with the controlled variable value, and this correlation should be relatively insensitive to unmeasured disturbances. The following are several examples that illustrate how inferential measurements can be effectively applied in the CPI. 

Tray temperatures correlate very well with product compositions for many distillation columns therefore, inferential control of distillation product composition is a widely used form of inferential control. Figure 15.58 shows the arrangement for inferential temperature control of the bottoms product composition for this column. Note that the tray temperature controller is cascaded to a flow controller. 

When is an inferential control configuration needed What do you think is its primary weakness Compare it to a simple feedback control configuration. Which one is preferable ... 

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. 

---