Process control, multivariable

QA, quality attributes SPC/MSPC, statistical process control/multivariate statistical process control CPPs, critical process parameters. 

While the single-loop PID controller is satisfactoiy in many process apphcations, it does not perform well for processes with slow dynamics, time delays, frequent disturbances, or multivariable interactions. We discuss several advanced control methods hereafter that can be implemented via computer control, namely feedforward control, cascade control, time-delay compensation, selective and override control, adaptive control, fuzzy logic control, and statistical process control. 

Bristol, E.H., 1966. On a new measure of interactions for multivariable process control, IEEE Transactions of Automatic Control, AC-11, 133. 

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. 

MacGregor, J. F., Marlin, T. E., Kresta, J. V., and Skagerberg, B., Multivariate statistics methods in process analysis and control. In Chemical Process Control, CPCIV, (Y. Arkun and W.H. Ray, eds.). CACHE, AIChE Publishers, New York, 1991. 

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. 

MacGregor, J. F.. and Kourti, T., Statistical process control of multivariate processes, Coni. Eng. Prac. 3, 404-414 (1995). 

Martin, E. B., Morris, A. J., and Zhang, J. (1996). Process performance monitoring using multivariate statistical process control. IEE Proc. Control Theory 143, 132. 

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. 

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. 

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. 

All experiments were set np so as to be realistic scenarios with respect to multivariate statistical process control (MSPC). The pilot plant grannlator is operated exactly as the industrial scale counterpart, but most of the experiments inclnded mnch more severe variation than will usually be found in an otherwise stable industrial production sitnation of similar duration (e.g. it is normally not necessary to change nozzle types, or to change prodnct types within snch short intervals). 

R. Heikka, P. Minkkinen, and V-M Taavitsainen, Comparison of variable selection and regression methods in multivariate calibration of a process analyzer. Process Control and Quality, 6, 47-54 (1994). 

DRIFT-IR) spectroscopy was also used for polymorphic characterization. The authors detail the application of multivariate techniques, multivariate statistical process control (MSPC), PC A and PLS, to the spectroscopic data for a simple yet powerful, rapid evaluation of the given crystalhzation process. ... 

At-line PAT data nsed directly without conversion back to the original variables space - e.g., MVI (Mnltivariate Identification), MSPC (multivariate statistical process control). 

It is vitally important that the multivariate nature of data related to a process be assessed to develop an understanding of a process and to assess quality. Process data together with appropriate chemometric models can provide information about (1) product quality inferentially from process conditions (2) process consistency (process signature, statistical process control) (3) analyzer reliability and (4) operational knowledge that can aid in scale-up and process transfers. ... 

The control can be enabled by multivariate statistical process control (MSPC) using process models, control charts and the like. 

In recent years the term qualimetrics has been coined to refer to the use of chemometrics for the purposes of quality control (Massart et al. 1997). ft relates particularly to the use of multivariate analysis of process control measurements. Other texts on quality assurance in chemical laboratories include the latest edition of Garfield s book published by AOAC International (Garfield et al. 2000), material published through the Valid Analytical Measurement program by the LGC (Prichard 1995), and books from the Royal Society of Chemistry (Parkany 1993,1995 Sargent and MacKay 1995). Wenclawiak et al. (2004) have edited a series of Microsoft PowerPoint presentations on aspects of quality assurance. 

A technique known as multivariate statistical process control (MVSPC). 

Bristol, E. H. IEEE Trans. Autom. Control AC-11 (1966) 133. On a new measure of interaction for multivariable process control. 

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. 

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