This subject of this thesis is the detection and diagnosis of distributed disturbances in chemical processes. A distributed disturbance affects many variables such as feed, product and recycle flows, column temperature and product composition. It may upset just a single unit for example a distillation column, it may be plant-wide if it affects a complete production process or even site-wide if utilities such as the steam system are involved. Disturbances have an impact on profitability because production and throughput may have to back away from their maximum settings to accommodate process variability. The research has used signal processing, spectral analysis and non-linear time series analysis of measurements from routine process operations and has led to new applications of these methods in chemical process diagnosis. In particular, the use of principal component analysis on the power spectra of process measurements has given a breakthrough in the analysis of non-steady processes because the spectra are invariant to the lags and time delays that can make PCA unreliable in the time domain. The thesis offers novel methods and theoretical insights to support the industrial activity of detection and diagnosis of distributed disturbances. A key insight has been that non- linearity in the time trends of plant measurements is greatest in those measurements closest to the root cause because mechanical filtering by the plant makes the signals more linear as the disturbance propagates away from the source. A non-linearity index derived from process measurements can therefore locate the root cause of a disturbance. A feature of the work has been its focus on industrial implementation. The methods are demonstrated with data from real processes and care was taken to devise robust default settings of parameters in the algorithms to facilitate their application in unseen plants. As demonstrated in a case study, the outcomes of the work will significantly reduce the time spent on analysis and focus attention towards root causes of faults so that maintenance effort is directed effectively.

Production plants used in modern process industry must produce products that meet stringent environmental, quality and profitability constraints. In such integrated plants, non-linearity and strong process dynamic interactions among process units complicate root-cause diagnosis of plant-wide disturbances because disturbances may propagate to units at some distance away from the primary source of the upset. Similarly, implemented advanced process control strategies, backup and recovery systems, use of recycle streams and heat integration may hamper detection and diagnostic efforts. It is important to track down the root-cause of a plant-wide disturbance because once corrective action is taken at the source, secondary propagated effects can be quickly eliminated with minimum effort and reduced down time with the resultant positive impact on process efficiency, productivity and profitability. In order to diagnose the root-cause of disturbances that manifest plant-wide, it is crucial to incorporate and utilize knowledge about the overall process topology or interrelated physical structure of the plant, such as is contained in Piping and Instrumentation Diagrams (P&IDs). Traditionally, process control engineers have intuitively referred to the physical structure of the plant by visual inspection and manual tracing of fault propagation paths within the process structures, such as the process drawings on printed P&IDs, in order to make logical conclusions based on the results from data-driven analysis. This manual approach, however, is prone to various sources of errors and can quickly become complicated in real processes. The aim of this thesis, therefore, is to establish innovative techniques for the electronic capture and manipulation of process schematic information from large plants such as refineries in order to provide an automated means of diagnosing plant-wide performance problems. This report also describes the design and implementation of a computer application program that integrates: (i) process connectivity and directionality information from intelligent P&IDs (ii) results from data-driven cause-and-effect analysis of process measurements and (iii) process know-how to aid process control engineers and plant operators gain process insight. This work explored process intelligent P&IDs, created with AVEVA® P&ID, a Computer Aided Design (CAD) tool, and exported as an ISO 15926 compliant platform and vendor independent text-based XML description of the plant. The XML output was processed by a software tool developed in Microsoft® .NET environment in this research project to computationally generate connectivity matrix that shows plant items and their connections. The connectivity matrix produced can be exported to Excel® spreadsheet application as a basis for other application and has served as precursor to other research work. The final version of the developed software tool links statistical results of cause-and-effect analysis of process data with the connectivity matrix to simplify and gain insights into the cause and effect analysis using the connectivity information. Process knowhow and understanding is incorporated to generate logical conclusions. The thesis presents a case study in an atmospheric crude heating unit as an illustrative example to drive home key concepts and also describes an industrial case study involving refinery operations. In the industrial case study, in addition to confirming the root-cause candidate, the developed software tool was set the task to determine the physical sequence of fault propagation path within the plant. This was then compared with the hypothesis about disturbance propagation sequence generated by pure data-driven method. The results show a high degree of overlap which helps to validate statistical data-driven technique and easily identify any spurious results from the data-driven multivariable analysis. This significantly increase control engineers confidence in data-driven method being used for root-cause diagnosis. The thesis concludes with a discussion of the approach and presents ideas for further development of the methods.

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La demande sociétale en protéines végétales a augmenté d’environ 50% durant la dernière décennie. Les nouvelles tendances alimentaires et l’intérêt pour leurs propriétés font de ces protéines une alternative intéressante dans de nombreux secteurs industriels à moyenne et haute valeur ajoutée. L'objet de cette thèse est de progresser dans l'étude d’un procédé simple et robuste permettant la mise au point d'une éco-filière de production de protéine. L’objectif est d’étudier l’extraction et le procédé de purification de la protéine, à partir des plantes sélectionnées, afin de garantir la conservation de ses propriétés technologiques moussantes, émulsifiantes et gélifiantes. / Social requirements for vegetal proteins increased almost by 50% in last decade. New food trends and their interesting functional properties make these proteins a preferential choice for industrial sectors of middle and high added-value. The aim of this PhD Thesis is to contribute to the analysis of a simple and robust process able to favour an eco-industry for protein production. The objective is to study the extraction and the purification of the protein, from selected green-plants, in order to guarantee techno-functional properties such as foaming, emulsifying and gelling powers