In recent years there has been extensive interest in feedback control systems that automatically adjust their controller settings to compensate for changes in the process or the enviornment. Such systems are referred to as adaptive controllers. This survey paper reviews the current state of the art in adaptive control from a process control perspective and describes leading design techniques. Potential operating problems associated with adaptive control schemes are considered. A survey of experimental applications of adaptive control systems to process control problems is also included. SCOPEProcess control systems inevitably include adjustable controller settings that facilitate process operation over a wide range of conditions. Typically, controller settings are tuned after the control system has been installed using time-consuming, trial-and-error procedures. If process conditions change significantly, then the controller must be retuned in order to obtain satisfactory control.In recent years, there has been extensive interest in adaptive control systems that automatically adjust the controller settings to compensate for unanticipated changes in the process or the environment. Adaptive control schemes provide systematic, flexible approaches for dealing with uncertainties, nonlinearities, and time-varying process parameters. Consequently, adaptive control systems offer significant potential benefits for difficult process control problems where the process is poorly understood andlor changes in unpredictable ways. The practical benefits of adaptive control have been documented in a wide variety of successful industrial applications.Although adaptive control has been a reputable research area for about thirty years, it is only in the last decade that it has achieved prominence as one of the

This article describes principal component analysis (PCA) of the power spectra of data from chemical processes. Spectral PCA can be applied to the measurements from a whole unit or plant because spectra are invariant to the phase lags caused by time delays and process dynamics. The same comment applies to PCA using autocovariance functions, which was also studied. Two case studies are presented. One was derived from simulation of a pulp process. The second was from a refinery involving 37 tags. In both cases, PCA clusters were observed which were characterised by distinct spectral features. Spectral PCA was compared with PCA using autocovariance functions. The performance was similar, and both offered an improvement over PCA using the time domain signals even when time shifting was used to align the phases. r

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Stored process data in the form of high fidelity time trends are a resource for data-driven process analyses such as statistical monitoring, minimum variance control loop benchmarking, fault detection, data reconciliation and development of inferential sensors. However, many commercial data historians compress the data before archiving it and a question therefore arises of how useful the compressed data are for the intended purposes.This article examines the impact of compression on data-driven methods and presents an automated algorithm by which the presence of piecewise linear compression may be inferred during the pre-processing phase of a data-driven analysis.The results show that compression interferes with many types of data-driven analyses and the paper strongly recommends caution in the use of compressed process data archives.

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