Adaptive process control

Tni Profile Gauge, Web measurement online, full web profiUng, statistical process control. Adaptive Technologies, Inc. 

A wide variety of particle size measurement methods have evolved to meet the almost endless variabiUty of iadustrial needs. For iastance, distinct technologies are requited if in situ analysis is requited, as opposed to sampling and performing the measurement at a later time and/or in a different location. In certain cases, it is necessary to perform the measurement in real time, such as in an on-line appHcation when size information is used for process control (qv), and in other cases, analysis following the completion of the finished product is satisfactory. Some methods rapidly count and measure particles individually other methods measure numerous particles simultaneously. Some methods have been developed or adapted to measure the size distribution of dry or airborne particles, or particles dispersed inhquids. 

The objective ia any analytical procedure is to determine the composition of the sample (speciation) and the amounts of different species present (quantification). Spectroscopic techniques can both identify and quantify ia a single measurement. A wide range of compounds can be detected with high specificity, even ia multicomponent mixtures. Many spectroscopic methods are noninvasive, involving no sample collection, pretreatment, or contamination (see Nondestructive evaluation). Because only optical access to the sample is needed, instmments can be remotely situated for environmental and process monitoring (see Analytical METHODS Process control). Spectroscopy provides rapid real-time results, and is easily adaptable to continuous long-term monitoring. Spectra also carry information on sample conditions such as temperature and pressure. 

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. 

Adaptive Control Process control problems inevitably require on-hne tuning of the controller constants to achieve a satisfactory degree of control. If the process operating conditions or the environment changes significantly, the controller may have to be retuned. If these changes occur quite frequently, then adaptive control techniques should be considered. An adaptive control system is one in which the controller parameters are adjusted automatically to compensate for changing process conditions. 

The purpose for which the analytical data are required may perhaps be related to process control and quality control. In such circumstances the objective is checking that raw materials and finished products conform to specification, and it may also be concerned with monitoring various stages in a manufacturing process. For this kind of determination methods must be employed which are quick and which can be readily adapted for routine work in this area instrumental methods have an important role to play, and in certain cases may lend themselves to automation. On the other hand, the problem may be one which requires detailed consideration and which may be regarded as being more in the nature of a research topic. 

Strang. G., Wavelets and dilation equations A brief introduction. SIAM Rev. 31, 614 (1989). Ungar, L. H., Powell, B. A., and Kamens, S. N., Adaptive Networks for fault diagnosis and process control. Comput. Chem. Eng. 14, 561 (1990). 

W. Luo, M.N. Karim, A.J. Morris and E.B. Martin, Control relevant identification of a pH waste water neutralisation process using adaptive radial basis function networks. Computers Chem. Eng., 20(1996)S1017... 

If the program continues and additional reductions are desired, more expensive and more complex projects begin to emerge (Phase II). These are often associated with equipment modifications, process modifications and process control and may include the addition or adaptation of auxiliary equipment for simple source treatment, possibly for recycle. This phase usually has little immediate ROI, and more inclusive approaches to assessing the economics of the operation (estimating costs for waste handling, long-term liability, risk) are needed to justify the continued pollution-prevention operation. 

It may be useful to point out a few topics that go beyond a first course in control. With certain processes, we cannot take data continuously, but rather in certain selected slow intervals (c.f. titration in freshmen chemistry). These are called sampled-data systems. With computers, the analysis evolves into a new area of its own—discrete-time or digital control systems. Here, differential equations and Laplace transform do not work anymore. The mathematical techniques to handle discrete-time systems are difference equations and z-transform. Furthermore, there are multivariable and state space control, which we will encounter a brief introduction. Beyond the introductory level are optimal control, nonlinear control, adaptive control, stochastic control, and fuzzy logic control. Do not lose the perspective that control is an immense field. Classical control appears insignificant, but we have to start some where and onward we crawl. 

Image analysis is an important aspect of many areas of science and engineering, and imaging will play an important role in characterizing self-assembled structures as well as in on-line process control. Development of effective noise identification and suppression, contrast enhancements, visualization, pattern recognition, and correlation algorithms should be co-opted where possible and adapted to the analysis of self-assembled structures. 

Seborg, Edgar, and Shah (A/ChE Journal 1986, Vol. 32, p, 881) give a survey of adaptive control strategies in process control. 

During the last two decades, chemists have become increasingly focused on how molecules interact, i.e. on supramolecular chemistry. Dynamic intermolecular processes provide opportunities for incorporation of control, adaptation and function in man-made materials, as observed in living systems. In biology, these processes are tightly controlled by the catalytic action of enzymes. In this chapter, we focus on enzymatically controlled supramolecular polymerisation, whereby self-recognising molecular building blocks assemble to form extended onedimensional (ID) structures, or supramolecular polymers, with unique adaptive features. 

Regardless of the quality of the model, process cycles designed with models have all of the same problems of process cycles designed by process science, DOE, or SPC. Preplanned regulation of secondary variables does not allow controlled adaptation to unanticipated disturbances in the cycle. 

Morant, F., Martinez, M. and Pic6, J. In Application of Artificial Intelligence in Process Control by Boullart, L., Krijgsman, A. and Vingerhoeds, R. A. eds. Section VI. Supervised adaptive control. 

M.A. Beyer, W. Grote, and G. Reinig. Adaptive exact linearization control of batch polymerization reactors using a Sigma-Point Kalman filter. Journal of Process Control, 18 663-675, 2008. 

X.Q. Xie, D.H. Zhou, and Y.H. Jin. Strong tracking filter based adaptive generic model control. Journal of Process Control, 9 337-350, 1999. 

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