Richard D. Braatz

Fault Detection and Diagnosis in Industrial Systems

The appearance of this book is quite timely as it provides a much needed state-of-the-art exposition on fault detection and diagnosis, a topic of much interest to industrialists. The material included is well organized with logical and clearly identified parts; the list of references is quite comprehensive and will be of interest to readers who wish to explore a particular subject in depth. The presentation of the subject material is clear and concise, and the contents are appropriate to postgraduate engineering students, researchers and industrialists alike. The end-of-chapter homework problems are a welcome feature as they provide opportunities for learners to reinforce what they learn by applying theory to problems, many of which are taken from realistic situations. However, it is felt that the book would be more useful, especially to practitioners of fault detection and diagnosis, if a short chapter on background statistical techniques were provided. Joe Au

Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis

Abstract Principal component analysis (PCA) is the most commonly used dimensionality reduction technique for detecting and diagnosing faults in chemical processes. Although PCA contains certain optimality properties in terms of fault detection, and has been widely applied for fault diagnosis, it is not best suited for fault diagnosis. Discriminant partial least squares (DPLS) has been shown to improve fault diagnosis for small-scale classification problems as compared with PCA. Fishers discriminant analysis (FDA) has advantages from a theoretical point of view. In this paper, we develop an information criterion that automatically determines the order of the dimensionality reduction for FDA and DPLS, and show that FDA and DPLS are more proficient than PCA for diagnosing faults, both theoretically and by applying these techniques to simulated data collected from the Tennessee Eastman chemical plant simulator.

A tutorial on linear and bilinear matrix inequalities

Abstract This is a tutorial on the mathematical theory and process control applications of linear matrix inequalities (LMIs) and bilinear matrix inequalities (BMIs). Many convex inequalities common in process control applications are shown to be LMIs. Proofs are included to familiarize the reader with the mathematics of LMIs and BMIs. LMIs and BMIs are applied to several important process control applications including control structure selection, robust controller analysis and design, and optimal design of experiments. Software for solving LMI and BMI problems is reviewed.

Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis

Abstract Principal component analysis (PCA) is a well-known data dimensionality technique that has been used to detect faults during the operation of industrial processes. Dynamic principal component analysis (DPCA) and canonical variate analysis (CVA) are data dimensionality techniques which take into account serial correlations, but their effectiveness in detecting faults in industrial processes has not been extensively tested. In this paper, score/state and residual space PCA, DPCA, and CVA are applied to the Tennessee Eastman process simulator, which was designed to simulate a wide variety of faults occurring in a chemical plant based on a facility at Eastman Chemical. This appears to be the first application of residual space CVA statistics for detecting faults in a large-scale process. Statistics quantifying variations in the residual space were usually more sensitive but less robust to the faults than the statistics quantifying the variations in the score or state space. The statistics exhibiting a small missed detection rate tended to exhibit small detection delays and vice versa. A residual-based CVA statistic proposed in this paper gave the best overall sensitivity and promptness, but the initially proposed threshold for the statistic lacked robustness. This motivated increasing the threshold to achieve a specified missed detection rate.

End-to-End Continuous Manufacturing of Pharmaceuticals: Integrated Synthesis, Purification, and Final Dosage Formation†

A series of tubes: The continuous manufacture of a finished drug product starting from chemical intermediates is reported. The continuous pilot-scale plant used a novel route that incorporated many advantages of continuous-flow processes to produce active pharmaceutical ingredients and the drug product in one integrated system.

Advanced control of crystallization processes

Abstract A key bottleneck in the production of pharmaceuticals and many other products is the formation of crystals from solution. The control of the crystal size distribution can be critically important for efficient downstream operations such as filtration and drying, and product effectiveness (e.g., bioavailability, tablet stability). This paper provides an overview of recent developments in the control of crystallization processes, including activities in sensor technologies, model identification, experimental design, process simulation, robustness analysis, and optimal control.

Data-driven methods for fault detection and diagnosis in chemical processes

I. Introduction.- 1. Introduction.- II. Background.- 2. Multivariate Statistics.- 3. Pattern Classification.- III. Methods.- 4. Principal Component Analysis.- 5. Fisher Discriminant Analysis.- 6. Partial Least Squares.- 7. Canonical Variate Analysis.- IV. Application.- 8. Tennessee Eastman Process.- 9. Application Description.- 10. Results and Discussion.- V. Other Approaches.- 11. Overview of Analytical and Knowledge-based Approaches.- References.

Advances and New Directions in Crystallization Control

The academic literature on and industrial practice of control of solution crystallization processes have seen major advances in the past 15 years that have been enabled by progress in in-situ real-time sensor technologies and driven primarily by needs in the pharmaceutical industry for improved and more consistent quality of drug crystals. These advances include the accurate measurement of solution concentrations and crystal characteristics as well as the first-principles modeling and robust model-based and model-free feedback control of crystal size and polymorphic identity. Research opportunities are described in model-free controller design, new crystallizer designs with enhanced control of crystal size distribution, strategies for the robust control of crystal shape, and interconnected crystallization systems for multicomponent crystallization.

Mathematical modeling of drug delivery from autocatalytically degradable PLGA microspheres--a review.

PLGA microspheres are widely studied for controlled release drug delivery applications, and many models have been proposed to describe PLGA degradation and erosion and drug release from the bulk polymer. Autocatalysis is known to have a complex role in the dynamics of PLGA erosion and drug transport and can lead to size-dependent heterogeneities in otherwise uniformly bulk-eroding polymer microspheres. The aim of this review is to highlight mechanistic, mathematical models for drug release from PLGA microspheres that specifically address interactions between phenomena generally attributed to autocatalytic hydrolysis and mass transfer limitation effects. Predictions of drug release profiles by mechanistic models are useful for understanding mechanisms and designing drug release particles.

Optimal control and simulation of multidimensional crystallization processes

The optimal batch control of a multidimensional crystallization process is investigated. A high resolution algorithm is used to simulate the multidimensional crystal size distribution under the operations defined by two optimal control trajectories. It is shown that a subtle change in the optimal control objective can have a very large effect on the crystal size and shape distribution of the product crystals. The effect of spatial variation is investigated using a compartmental model. The effect of differing numbers of compartments on the size and shape distribution of the product crystals is investigated. It is shown that the crystal size distribution can be very different along the height of the crystallizer and that a solution concentration gradient exists due to imperfect mixing. The nucleation rate can be significantly larger at the bottom of the crystallizer and the growth rate can be much larger at the top. The high resolution method provides high simulation accuracy and fast speed, with the ability to solve large numbers of highly nonlinear coupled multidimensional partial differential equations over a wide range of length scales. A parallel programming implementation results in simulation times that are short enough for using the simulation program to compute optimal control trajectories.