Sirish L. Shah - Böcker

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies. . . , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for widerand rapid dissemination. Benchmarking is a technique first applied by Rank Xerox in the late 1970s for business processes. As a subject in the commercial arena, benchmarking thrives with, for example, a European Benchmarking Forum. It has taken rather longer for benchmarking to make the transfer to the technical domain and even now the subject is making a slow headway. Akey research step in this direction was taken by Harris (1989) who used minimum variance control as a benchmark for controller loop assessment. This contribution opened up the area and a significant specialist literature has now developed. Significant support for the methodologywas given by Honeywell who have controller assessment routines in their process control applications software; therefore, it is timely to welcome a (first) monograph on controller performance assessment by Biao Huang and Sirish Shah to the Advances in Industrial Control series.

The design of advanced control algorithms has largely preoccupied the control practitioners efforts. The rationale has been that systems which are difficult to control need advanced optimal, non-linear, adaptive or like control algorithms for better regulation. Although there are a variety of advanced control design techniques, few techniques exist for computing objective measures of control loop performance from routine operating industrial process data. The purpose of this book is to expose the reader to the most recent techniques for univariate and multivariate controller performance assessment. Specifically, this text ponders questions such as the following: Is your controller healthy? Is it doing its job well? How can one obtain a non-invasive or a model-free assessment of controller performance from routing closed-loop data? The theoretical results are supported by simulation, experimental (in laboratory) and industrial evaluations.

In the era of big data, process industries face the challenge of analyzing massive and complex data to extract information for effective process monitoring. This book introduces a novel paradigm called visual analytics. Visual analytics transforms chronological process data into visual formats to uncover patterns. This paradigm allows process experts to relate visual patterns to operational conditions and, consequently, support informed decision-making. The book explores three pathways within the visual analytics paradigm: (i) feature engineering, in which predefined mappings are used to convert time series data into visual representations; (ii) architecture engineering, which develops neural network architectures to directly learn visual representations; and (iii) data engineering, which employs contrastive learning to highlight differences and similarities in the data without relying on annotations.

This brief reviews concepts of inter-relationship in modern industrial processes, biological and social systems. Specifically ideas of connectivity and causality within and between elements of a complex system are treated; these ideas are of great importance in analysing and influencing mechanisms, structural properties and their dynamic behaviour, especially for fault diagnosis and hazard analysis. Fault detection and isolation for industrial processes being concerned with root causes and fault propagation, the brief shows that, process connectivity and causality information can be captured in two ways:·      from process knowledge: structural modeling based on first-principles structural models can be merged with adjacency/reachability matrices or topology models obtained from process flow-sheets described in standard formats; and·      from process data: cross-correlation analysis, Granger causality and its extensions, frequency domain methods, information-theoretical methods, and Bayesian networks can be used to identify pair-wise relationships and network topology.These methods rely on the notion of information fusion whereby process operating data is combined with qualitative process knowledge, to give a holistic picture of the system.

The objective of this workshop was to bring together engineers from industry and scientists from universities to focus attention on new developments and practical enhancements for using adaptive control in industry. The workshop provided a forum for a tutorial introduction to the state-of-the-art in adaptive control and helped focus attention on an in-depth view of the problems and needs of adaptive control engineers in industry. The volume includes papers concerned with recent theoretical advances in adaptive control, experimental application of adaptive control in industry and the role of filters in adaptive control.

were published in the series as the contributed volume, Process Control Performance Assessment: From Theory to Implementation with Andrzej Ordys, Damian Uduehi, and Michael Johnson as Editors (ISBN 978-1-84628-623-0, 2007). Along with this good progress in process controller assessment methods, researchers have also been investigating techniques to diagnose what is causing the process or control loop degradation. This requires the use of on-line data to identify faults via new diagnostic indicators of typical process problems. A significant focus of some of this research has been the issue of valve problems; a research direction that has been motivated by some industrial statistics that show up to 40% of control loops having performance degradation attributable to valve problems. Shoukat Choudhury, Sirish Shah, and Nina Thornhill have been very active in this research field for a number of years and have written a coherent and consistent presentation of their many research results as this monograph, Diagnosis of Process Nonlinearities and Valve Stiction. The Advances in Industrial Control series is pleased to welcome this new and substantial contribution to the process diagnostic literature. The reader will find the exploitation of the extensive process data archives created by today’s process computer systems one theme in the monograph. From another viewpoint, the use of higher-order statistics could be considered to provide a continuing link to the earlier methods of the statistical process control paradigm.