Processes control multivariable systems

Finally, process control systems allow the unit to operate smoothly and safely. At the next level, an APC package (whether within the DCS framework or as a host-based multivariable control system) provides more precise control of operating variables against the unit s constraints. It will gain incremental throughput or cracking severity. 

A more complicated situation in process regulation occurs when among various chemical properties there is not one that can explicitly be indicated as a key test, so that more than one sensor (1,2,..., n) has to be used, in which case one refers to "multivariable systems in process control 6. 

For now let us say merely that the control system shown in Fig. 1.5 is a typical conventional system It is about the minimum that would be needed to run this plant automatically without constant operator attention. Notice that even in this simple plant with a minimum of instrumentation the total number of control loops is lO. We win find that most chemical engrneering processes are multivariable. 

In Chap. 15 we reviewed a tittle matrix mathematics and notation. Now that the tools are available, we will apply them in this chapter to the analysis of multivariable processes. Our primary concern is with closedloop systems. Given a process with its matrix of openloop transfer functions, we want to be able to see the effects of using various feedback controllers. Therefore we must be able to find out if the entire closedloop multivariable system is stable. And if it is stable, we want to know how stable it is. The last question considers the robustness of the controller, i.e., the tolerance of the controller to changes in parameters. If the system becomes unstable for small changes in process gains, time constants, or deadtimes, the controller is not robust. 

Unfortunately much of Ais interaction analysis work has clouded the issue of how to design an effective control system for a multivariable process. In most process control applications the problem is not setpoint responses but load responses. We want a sy stem that holds the process at the desired values in the face of load disturbances. Interaction is therefore not necessarily bad, and in fact in some systems it helps in rejecting the effects of load disturbances. Niederlinski [AIChE J 1971, Vol 17, p. 1261) showed in an early paper that the use of decouplers made the load rejection worse. 

In theory, the internal model control methods discussed for SISO systems in Chap. 11 can be extended to multivariable systems (see the paper by Garcia and Morari in lEC Process Design and Development, Vol. 24, 1985, p. 472). 

As a result, there has been a lot of research activity in multivariable control, both in academia and in industry. Some practical, useful tools have been developed to design control systems for these multivariable processes. The second edition includes a fairly comprehensive discussion of what 1 feel are the useful techniques for controlling multivariable processes. 

The behaviour of many control loop components can be described by first-order differential equations provided that certain simplifying assumptions are made. Great care should be taken that the assumptions made are reasonable under the conditions to which the component is subjected. Two examples of a first-order system are described—a measuring element and a process. An illustration of a multivariable system which approximates to first order with respect to each input variable can be found in Example 7.11. 

A bioprocess system has been monitored using a multi-analyzer system with the multivariate data used to model the process.27 The fed-batch E. coli bioprocess was monitored using an electronic nose, NIR, HPLC and quadrupole mass spectrometer in addition to the standard univariate probes such as a pH, temperature and dissolved oxygen electrode. The output of the various analyzers was used to develop a multivariate statistical process control (SPC) model for use on-line. The robustness and suitability of multivariate SPC were demonstrated with a tryptophan fermentation. 

Control system Effective multivariable process control system in place for online applications... 

Previous process controllability work on analyzing the effect of delays on achievable control performance has centered on defining and computing the effective delay(s) in multivariable systems. This work has generated some useful controllability measures, which are reviewed below, but does not in itself provide an answer to the key question of whether a particular disturbance can... 

The detection and diagnosis tasks can be carried out on the process measurements to obtain critical insights into the performance of not only the process itself but also the automatic control system that is deployed to assure normal operation. Today, the integration of such tasks into the process control software associated with Distributed Control Systems (D-CS) is in progress. The technologies continue to advance, especially in the incorporation of multivariate statistics as well as recent developments in signal processing methods such as wavelets and hidden Markov models. 

TJ Harris, CT Seppala, and LD Desborough. A review of performance monitoring and assessment techniques for univariate and multivariate control systems. J. Process Control, 9 1-17, 1999. 

A Norvilas, A Negiz, J DeCicco, and A Cinar. Intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring. J. Process Control, 10(4) 341-350, 2000. 

J Schaefer and A Cinar. Multivariable MPC system performance assessment, monitoring, and diagnosis. J. Process Control, 14(2) 113-129, 2004. 

The difficulties are aggravated by the fact that chemical processes are largely nonlinear, imprecisely known, multivariable systems with many interactions. The measurements and manipulations are limited to a relatively small number of variables, while the control objectives may not be clearly stated or even known at the beginning of the control system design. 

Design of Control VI Systems for Multivariable Processes Introduction to Plant Control... 

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