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

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

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

Key Performance Monitoring and Diagnosis in Industrial Automation Processes

With ever increasing global competition, monitoring and diagnosis methods based on key performance indicator (KPI) are increasingly receiving attention in the process industry. Primarily due to the scale and complexity of modern automation processes, application of signal processing and model-based monitoring methods is too costly and time-consuming. On the other hand, due to the availability of cheap measurement and storage systems, a large amount of process and KPI data is obtained. As a result, developing data-driven KPI monitoring methods has become an area of great interest in both academics and industry. Therefore, this thesis is focused on the data-driven design of systematic KPI monitoring and diagnosis systems for industrial automation processes. Depending on the relationship between the low-level process variables and the high-level KPIs, industrial processes can be classified into three groups: 1. Static processes (SPs) are those described by algebraic equations; 2. Lumped-parameter processes (LPPs) are those described by ordinary differential equations; and 3. Distributed-parameter processes (DPPs) are those described by partial differential equations. For each of these groups of processes, analytical redundancy plays a very important role when developing efficient process monitoring tools. For SPs, multivariate-statistics-based methods have been used. However, their applicability is restricted by high mathematical complexity, high design costs and low diagnostic performance. For this reason, an alternative improved method has been proposed in this thesis. For LPPs, complex model-based methods have been implemented. Therefore, to reduce the design costs required for monitoring LPPs, efficient Subspace identification based approaches are presented. Finally, since there are very few available model-based methods for DPPs, this thesis presents novel approaches for KPI monitoring in DPPs. For all these methods, the design procedures are based on the process I/O data and do not require advanced mathematical knowledge. After performance degradation has been detected, it is important to identify the root causes to prevent further losses. In industrial processes, performance degradation is more often caused by multiplicative faults. In this work, a new data-driven multiplicative fault diagnosis approach is proposed. This approach aims at assisting the maintenance personnel by narrowing down the investigation scope. As a result, overall equipment effectiveness (OEE) can be significantly improved. To show the effectiveness of the proposed approaches, case studies on the Tennessee Eastman benchmark process, the continuous stirred tank heater benchmark and the simulated drying section of a paper machine have been performed. The proposed methods worked successfully with these processes.Key Performance Überwachung und Diagnose in industriellen Automatisierungsprozessen Im Rahmen einer stetigen Zunahme des globalen Wettbewerbs erhalten Key Performance Indikator (KPI) basierte Überwachungs- und Diagnosetechniken zunehmend Aufmerksamkeit in der Prozessindustrie. Vor allem vor dem Hintergrund von Umfang und Komplexität moderner Automatisierungsprozesse ist die Anwendung von Signalverarbeitung und modellbasierten Überwachungstechniken zu teuer und zu zeitaufwendig. Andererseits ist häufig auf Grund der Verfügbarkeit von günstigen Mess- und Speichersystemen, eine große Menge von Prozess- und KPI-Daten vorhanden. Daher ist die Entwicklung von datenbasierten Verfahren ein Forschungsfeld, welches sowohl im akademischen als auch im industriellen Bereich mit großem Interesse verfolgt wird. Dementsprechend liegt der Fokus der vorliegenden Arbeit auf einem systematischen und datenbasierten Entwurf von KPI-Überwachungs- und -Diagnosesystemen für industrielle Automatisierungsprozesse. Anhand der Beziehung zwischen den low-level Prozessgrößen und den high-level KPIs können industrielle Prozesse in drei Gruppen eingeteilt werden: 1. Statische Prozesse (SP) sind Prozesse, die sich durch algebraische Gleichungen beschrieben lassen; 2. Konzentrierte-Parameter Prozesse (KPP) sind Prozesse, welche durch gewöhnliche Differentialgleichungen beschrieben werden; und 3. Verteilte-Parameter Prozesse (VPP) sind Prozesse, welche durch partielle Differentialgleichungen beschrieben werden. Für jede dieser Gruppen spielt das Konzept der analytischen Redundanz eine sehr wichtige Rolle bei der Entwicklung von effizienten Prozessüberwachungs-Tools. Für SP, sind multivariate statistische Verfahren verwendet worden. Allerdings ist deren Anwendbarkeit durch hohe mathematische Komplexität, einen hohen Entwurfsaufwand und eine geringen Diagnoseleistung beschränkt. Aus diesem Grund wird ein alternatives, verbessertes Verfahren in dieser Arbeit vorgeschlagen. Für KPP, sind komplexe modellbasierte Methoden implementiert worden. Um die Entwicklungskosten für die Überwachung der KPP zu reduzieren, wird eine effiziente Methode, basierend auf Subspace-Identifikation, vorgestellt. Da es nur sehr wenige modellbasierte Methoden für VPP gibt, präsentiert diese Arbeit schließlich neue Verfahren für die KPI- Überwachung in VPP. Alle vorgestellten Verfahren basieren auf den Prozess E/A Daten und erfordern daher keine tiefergehenden mathematischen Kenntnisse über den Prozess. Nach erfolgreicher Erkennung des Leistungsabfalls eines KPI, ist es in einem nächsten Schritt erforderlich die Ursache zu identifizieren, um weitere ökonomische Verluste zu verhindern. In industriellen Prozessen wird ein Leistungsabfall häufig durch multiplikative Fehler verursacht. In dieser Arbeit wird ein neues datenbasiertes, multiplikatives Fehlerdiagnoseverfahren vorgeschlagen. Dieses Verfahren soll der Unterstützung des Wartungspersonals dienen, indem eine Eingrenzung der Problemursache vorgenommen wird. Als Ergebnis kann somit die OEE (Overall Equipment Effectiveness) deutlich verbessert werden. Um die Wirksamkeit der vorgeschlagenen Verfahren zu demonstrieren, wurden verschiedene Fallstudien an Hand des „Tennessee Eastman“ Benchmark, des „continuous stirred tank heater“ Benchmark und einer simulierten Trockenpartie einer Papiermaschine durchgeführt. Die Effektivität der vorgeschlagenen Methoden konnte an Hand der aufgeführten Benchmark Prozesse erfolgreich gezeigt werden

Refined bleached deodorized palm oil quality prediction using multivariate statistical process control tools

Multivariate statistical process control (MSPC) has been widely used for quality prediction and monitoring in palm oil refinery processes. Currently, the refined, bleached deodorized palm oil (RBDPO) quality is determined based on the relationship between crude palm oil quality and process parameters, with the assumption that the process is static and not affected by the time-varying characteristic of the palm oil refinery process. However, the prediction is less accurate since the generated regression coefficients from static prediction models do not reflect the current process status and remain constant over time. Therefore, this study was conducted to introduce a new framework for regression coefficients improvement via dynamic prediction models. The dynamic prediction models were developed by integrating the MSPC prediction tool with time-series expansion methods where the prediction models were adapted to new process dynamics. Data collected from an industrial palm oil refining plant were used as the case study in this research. Four MSPC models, namely linear principal component regression (PCR), linear partial least squares (PLS), nonlinear principal component regression based on nonlinear iterative partial least squares algorithm (NIPALS-PCR) and nonlinear partial least squares based on nonlinear iterative partial least square algorithm (NIPALS-PLS) were used to determine the relationship between the quality and process variables. Time-series expansion methods were used to trace the dynamic behaviour based on five approaches, namely static, moving window (MW), recursive window (RW), exponentially weighted moving window (EWMW) and exponentially weighted recursive window (EWRW). The findings show that the combination of the linear prediction model with the time-series expansion method showed a more reliable prediction performance than the nonlinear prediction model. The performance of the PCR EWMW model in predicting the RBDPO quality is improved by 12.02 % (11.96 % for free fatty acid, 6.92 % for moisture content, 16.13 % for iodine value and 13.01 % for colour) compared to other prediction models. The sensitivity of the regression coefficients was also improved where the regression coefficients fluctuated very smoothly and showed high convergence to zero value when using the PCR EWMW model. This shows that the implementation of the linear dynamic prediction model was better than the static prediction model. Therefore, the linear dynamic prediction model for quality prediction was the best for it has the greatest prediction improvement and showed a better trend of the regression coefficient

Application of raman spectroscopy in pharmaceuticals

Experimental research on the use of Raman spectroscopy as an in- and on-line sensing tool and a complementary characterization technique for pharmaceutical applications is presented in this thesis. In the first chapter following a broad overview, the use of Raman spectroscopy, together with x-ray powder diffraction, scanning electron microscopy (SEM) and differential scanning calorimetry, for multilevel characterization of cryomilled powders, and the melt-grown amorphous phase of griseofulvin, a model active pharmaceutical ingredient (API), is presented and discussed in detail. A key feature was the observation of a broad inelastic background superimposed on the Raman spectra of cryomilled powders, which is attributed to lattice disorder and Mie scattering generated by mechanical processing and sub-micron particle interfaces. In the following chapter, polymorphs of another model API, acetaminophen (APAP), were studied by Raman spectroscopy with supporting information obtained from x-ray diffraction, SEM images and intrinsic dissolution profiles. An important result was the stabilization and characterization of the metastable type II orthorhombic phase of APAP which is highly desired for its unique tabletting properties which are important for pharmaceutical manufacturing. Stabilization of metastable type II APAP was achieved by micronizing or nanocoating stable monoclinic crystallites of type I APAP. In addition, as an Appendix to the thesis, micro-Raman spectroscopy of single crystal APAP as a function of crystal orientation and of temperature was measured to provide an understanding of the lattice properties of APAP for input into models to predict its behavior under mechanical milling conditions widely used in pharmaceutical processing. Molecular behavior obtained from the above studies guided simulated in-line and off-line characterization of griseofulvin as thin gel films made from micronized powders and nanosuspensions. By employing complementary near infrared and Raman imaging for newly developed films, it was possible to extract valuable information on the spatial distribution and crystallinity of the embedded particles in a polymeric matrix at different scales of scrutiny. Chemometrics processing of spectroscopic data for films and nanosuspensions allowed for qualitative and quantitative particle size determinations of the API’s in the films and nanosuspensions. In the final chapter a photonic crystal substrate for surface enhanced Raman spectroscopic (SERS) sensing was employed to detect and study griseofulvin and APAP down to 10-8 M levels with enhancement factors approaching 1099. Detection sensitivities of the aromatic griseofulvin and APAP molecules were also compared with those of less aromatic and non-aromatic energetic molecules in order to understand the Raman enhancement process

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Sustainable Agriculture and Soil Conservation

Soil degradation is one of the most topical environmental threats. A number of processes causing soil degradation, specifically erosion, compaction, salinization, pollution, and loss of both organic matter and soil biodiversity, are also strictly connected to agricultural activity and its intensification. The development and adoption of sustainable agronomic practices able to preserve and enhance the physical, chemical, and biological properties of soils and improve agroecosystem functions is a challenge for both scientists and farmers. The Special Issue entitled “Sustainable Agriculture and Soil Conservation” collects 12 original contributions addressing the state of the art of sustainable agriculture and soil conservation. The papers cover a wide range of topics, including organic agriculture, soil amendment and soil organic carbon (SOC) management, the impact of SOC on soil water repellency, the effects of soil tillage on the quantity of SOC associated with several fractions of soil particles and depth, and SOC prediction, using visible and near-infrared spectra and multivariate modeling. Moreover, the effects of some soil contaminants (e.g., crude oil, tungsten, copper, and polycyclic aromatic hydrocarbons) are discussed or reviewed in light of the recent literature. The collection of the manuscripts presented in this Special Issue provides a relevant knowledge contribution for improving our understanding on sustainable agriculture and soil conservation, thus stimulating new views on this main topic

Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201