Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation

By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection TOLPP2 and SPEOLPP are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies

Latent variable modeling approaches to assist the implementation of quality-by-design paradigms in pharmaceutical development and manufacturing

With the introduction of the Quality-by-Design (QbD) initiative, the American Food and Drug Administration and the other pharmaceutical regulatory Agencies aimed to change the traditional approaches to pharmaceutical development and manufacturing. Pharmaceutical companies have been encouraged to use systematic and science-based tools for the design and control of their processes, in order to demonstrate a full understanding of the driving forces acting on them. From an engineering perspective, this initiative can be seen as the need to apply modeling tools in pharmaceutical development and manufacturing activities. The aim of this Dissertation is to show how statistical modeling, and in particular latent variable models (LVMs), can be used to assist the practical implementation of QbD paradigms to streamline and accelerate product and process design activities in pharmaceutical industries, and to provide a better understanding and control of pharmaceutical manufacturing processes. Three main research areas are explored, wherein LVMs can be applied to support the practical implementation of the QbD paradigms: process understanding, product and process design, and process monitoring and control. General methodologies are proposed to guide the use of LVMs in different applications, and their effectiveness is demonstrated by applying them to industrial, laboratory and simulated case studies. With respect to process understanding, a general methodology for the use of LVMs is proposed to aid the development of continuous manufacturing systems. The methodology is tested on an industrial process for the continuous manufacturing of tablets. It is shown how LVMs can model jointly data referred to different raw materials and different units in the production line, allowing to understand which are the most important driving forces in each unit and which are the most critical units in the line. Results demonstrate how raw materials and process parameters impact on the intermediate and final product quality, enabling to identify paths along which the process moves depending on its settings. This provides a tool to assist quality risk assessment activities and to develop the control strategy for the process. In the area of product and process design, a general framework is proposed for the use of LVM inversion to support the development of new products and processes. The objective of model inversion is to estimate the best set of inputs (e.g., raw material properties, process parameters) that ensure a desired set of outputs (e.g., product quality attributes). Since the inversion of an LVM may have infinite solutions, generating the so-called null space, an optimization framework allowing to assign the most suitable objectives and constraints is used to select the optimal solution. The effectiveness of the framework is demonstrated in an industrial particle engineering problem to design the raw material properties that are needed to produce granules with desired characteristics from a high-shear wet granulation process. Results show how the framework can be used to design experiments for new products design. The analogy between the null space and the Agencies’ definition of design space is also demonstrated and a strategy to estimate the uncertainties in the design and in the null space determination is provided. The proposed framework for LVM inversion is also applied to assist the design of the formulation for a new product, namely the selection of the best excipient type and amount to mix with a given active pharmaceutical ingredient (API) to obtain a blend of desired properties. The optimization framework is extended to include constraints on the material selection, the API dose or the final tablet weight. A user-friendly interface is developed to aid formulators in providing the constraints and objectives of the problem. Experiments performed industrially on the formulation designed in-silico confirm that model predictions are in good agreement with the experimental values. LVM inversion is shown to be useful also to address product transfer problems, namely the problem of transferring the manufacturing of a product from a source plant, wherein most of the experimentation has been carried out, to a target plant which may differ for size, lay-out or involved units. An experimental process for pharmaceutical nanoparticles production is used as a test bed. An LVM built on different plant data is inverted to estimate the most suitable process conditions in a target plant to produce nanoparticles of desired mean size. Experiments designed on the basis of the proposed LVM inversion procedure demonstrate that the desired nanoparticles sizes are obtained, within experimental uncertainty. Furthermore, the null space concept is validated experimentally. Finally, with respect to the process monitoring and control area, the problem of transferring monitoring models between different plants is studied. The objective is to monitor a process in a target plant where the production is being started (e.g., a production plant) by exploiting the data available from a source plant (e.g., a pilot plant). A general framework is proposed to use LVMs to solve this problem. Several scenarios are identified on the basis of the available information, of the source of data and on the type of variables to include in the model. Data from the different plants are related through subsets of variables (common variables) measured in both plants, or through plant-independent variables obtained from conservation balances (e.g., dimensionless numbers). The framework is applied to define the process monitoring model for an industrial large-scale spray-drying process, using data available from a pilot-scale process. The effectiveness of the transfer is evaluated in terms of monitoring performances in the detection of a real fault occurring in the target process. The proposed methodologies are then extended to batch systems, considering a simulated penicillin fermentation process. In both cases, results demonstrate that the transfer of knowledge from the source plant enables better monitoring performances than considering only the data available from the target plant

Principal component analysis vs. independent component analysis for damage detection

In previous works, the authors showed advantages and drawbacks of the use of PCA and ICA by separately. In this paper, a comparison of results in the application of these methodologies is presented. Both of them exploit the advantage of using a piezoelectric active system in different phases. An initial baseline model for the undamaged structure is built applying each technique to the data collected by several experiments. The current structure (damaged or not) is subjected to the same experiments and the collected data are projected into the models. In order to determine whether damage exists or not in the structure, the projections into the first and second components using PCA and ICA are depicted graphically. A comparison between these plots is performed analyzing differences and similarities, advantages and drawbacks. To validate the approach, the methodology is applied in two sections of an aircraft wing skeleton powered with several PZTs transducers.Postprint (published version

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries

Transfer learning for batch process optimal control using LV-PTM and adaptive control strategy

In this study, we investigate a data-driven optimal control for a new batch process. Existing data-driven optimal control methods often ignore an important problem, namely, because of the short operation time of the new batch process, the modeling data in the initial stage can be insufficient. To address this issue, we introduce the idea of transfer learning, i.e., a latent variable process transfer model (LV-PTM) is adopted to transfer sufficient data and process information from similar processes to a new one to assist its modeling and quality optimization control. However, due to fluctuations in raw materials, equipment, etc., differences between similar batch processes are always inevitable, which lead to the serious and complicated mismatch of the necessary condition of optimality (NCO) between the new batch process and the LV-PTM-based optimization problem. In this work, we propose an LV-PTM-based batch-to-batch adaptive optimal control strategy, which consists of three stages, to ensure the best optimization performance during the whole operation lifetime of the new batch process. This adaptive control strategy includes model updating, data removal, and modifier-adaptation methodology using final quality measurements in response. Finally, the feasibility of the proposed method is demonstrated by simulations

Development and Application of Chemometric Methods for Modelling Metabolic Spectral Profiles

The interpretation of metabolic information is crucial to understanding the functioning of a biological system. Latent information about the metabolic state of a sample can be acquired using analytical chemistry methods, which generate spectroscopic profiles. Thus, nuclear magnetic resonance spectroscopy and mass spectrometry techniques can be employed to generate vast amounts of highly complex data on the metabolic content of biofluids and tissue, and this thesis discusses ways to process, analyse and interpret these data successfully. The evaluation of J -resolved spectroscopy in magnetic resonance profiling and the statistical techniques required to extract maximum information from the projections of these spectra are studied. In particular, data processing is evaluated, and correlation and regression methods are investigated with respect to enhanced model interpretation and biomarker identification. Additionally, it is shown that non-linearities in metabonomic data can be effectively modelled with kernel-based orthogonal partial least squares, for which an automated optimisation of the kernel parameter with nested cross-validation is implemented. The interpretation of orthogonal variation and predictive ability enabled by this approach are demonstrated in regression and classification models for applications in toxicology and parasitology. Finally, the vast amount of data generated with mass spectrometry imaging is investigated in terms of data processing, and the benefits of applying multivariate techniques to these data are illustrated, especially in terms of interpretation and visualisation using colour-coding of images. The advantages of methods such as principal component analysis, self-organising maps and manifold learning over univariate analysis are highlighted. This body of work therefore demonstrates new means of increasing the amount of biochemical information that can be obtained from a given set of samples in biological applications using spectral profiling. Various analytical and statistical methods are investigated and illustrated with applications drawn from diverse biomedical areas