Artificial Intelligence in Process Fault Diagnosis (eBook)

Methods for Plant Surveillance
eBook Download: EPUB
2024 | 1. Auflage
432 Seiten
Wiley (Verlag)
978-1-119-82591-3 (ISBN)

Lese- und Medienproben

Artificial Intelligence in Process Fault Diagnosis -  Richard J. Fickelscherer
Systemvoraussetzungen
157,99 inkl. MwSt
  • Download sofort lieferbar
  • Zahlungsarten anzeigen
Artificial Intelligence in Process Fault Diagnosis

A comprehensive guide to the future of process fault diagnosis

Automation has revolutionized every aspect of industrial production, from the accumulation of raw materials to quality control inspections. Even process analysis itself has become subject to automated efficiencies, in the form of process fault analyzers, i.e., computer programs capable of analyzing process plant operations to identify faults, improve safety, and enhance productivity. Prohibitive cost and challenges of application have prevented widespread industry adoption of this technology, but recent advances in artificial intelligence promise to place these programs at the center of manufacturing process analysis.

Artificial Intelligence in Process Fault Diagnosis brings together insights from data science and machine learning to deliver an effective introduction to these advances and their potential applications. Balancing theory and practice, it walks readers through the process of choosing an ideal diagnostic methodology and the creation of intelligent computer programs. The result promises to place readers at the forefront of this revolution in manufacturing.

Artificial Intelligence in Process Fault Diagnosis readers will also find:

  • Coverage of various AI-based diagnostic methodologies elaborated by leading experts
  • Guidance for creating programs that can prevent catastrophic operating disasters, reduce downtime after emergency process shutdowns, and more
  • Comprehensive overview of optimized best practices

Artificial Intelligence in Process Fault Diagnosis is ideal for process control engineers, operating engineers working with processing industrial plants, and plant managers and operators throughout the various process industries.

Richard J. Fickelscherer, PhD, PE has worked on advanced process control and process monitoring programs at DuPont, Exxon, Merck Pharmaceuticals, Koch Industries, and FMC, and has since developed and patented a Fuzzy logic-based compiler program to automate process fault analysis.


Artificial Intelligence in Process Fault Diagnosis A comprehensive guide to the future of process fault diagnosis Automation has revolutionized every aspect of industrial production, from the accumulation of raw materials to quality control inspections. Even process analysis itself has become subject to automated efficiencies, in the form of process fault analyzers, i.e., computer programs capable of analyzing process plant operations to identify faults, improve safety, and enhance productivity. Prohibitive cost and challenges of application have prevented widespread industry adoption of this technology, but recent advances in artificial intelligence promise to place these programs at the center of manufacturing process analysis. Artificial Intelligence in Process Fault Diagnosis brings together insights from data science and machine learning to deliver an effective introduction to these advances and their potential applications. Balancing theory and practice, it walks readers through the process of choosing an ideal diagnostic methodology and the creation of intelligent computer programs. The result promises to place readers at the forefront of this revolution in manufacturing. Artificial Intelligence in Process Fault Diagnosis readers will also find: Coverage of various AI-based diagnostic methodologies elaborated by leading expertsGuidance for creating programs that can prevent catastrophic operating disasters, reduce downtime after emergency process shutdowns, and moreComprehensive overview of optimized best practices Artificial Intelligence in Process Fault Diagnosis is ideal for process control engineers, operating engineers working with processing industrial plants, and plant managers and operators throughout the various process industries.

Richard J. Fickelscherer, PhD, PE has worked on advanced process control and process monitoring programs at DuPont, Exxon, Merck Pharmaceuticals, Koch Industries, and FMC, and has since developed and patented a Fuzzy logic-based compiler program to automate process fault analysis.

1
MOTIVATIONS FOR AUTOMATING PROCESS FAULT ANALYSIS


Richard J. Fickelscherer, PE

Department of Chemical and Biological Engineering, State University of New York at Buffalo, Buffalo, New York, USA

OVERVIEW


This introductory chapter to our treatment establishes and briefly discusses the current motivations for directly automating process fault analysis. It begins by listing the various traditional methods currently employed for helping human operators perform more effective process fault management. Human limitations at actually performing such management are then enumerated. Also the advantages of human analysis versus those that computer automation currently possesses are compared. This comparison is made in order to identify the most feasible approaches/best possible pathways presently existing for actually automating process fault analysis. Please note that the literature references cited in this discussion (as well of those cited throughout the remainder of this treatment's various chapters and appendices written or co‐written by me) are chosen because they are the earliest mention of their respective particular observations that I have encountered in the open literature. These “old” citations are consequently meant to recognize and properly bestow the corresponding appropriate intellectual credit to those original pioneers in this continuing evolution of automated process fault analysis.

CHAPTER HIGHLIGHTS


  • Discussion of the changing role of human operators in modern processing plants.
  • Overviews of various traditional process fault management methods that are currently being employed by the processing industries in order to address these changes.
  • Descriptions of various human limitations encountered when actually performing process fault management.
  • Comparisons between human‐based and current computer‐based analysis advantages.
  • Current major motivations for further developing automated process fault analysis.

1.1 INTRODUCTION


Economic competition within the chemical process industries (CPI) has directly led to the construction and operation of larger, highly integrated and more fully automated production plants. As a result, the primary functions performed by the human process operators in these plants have changed. An unfortunate consequence of these changes is that those operators' ability to perform process fault management has been diminished.1 The underlying reasons behind this potentially highly dangerous situation and the various methods currently being used to counteract it are discussed here.

One continuing major trend driving the present modernization of the CPI has been the evermore increasing automation of all process control functions. The motivation for such automation is that it results in more accurately applying the best available process control strategies in a continuous, consistent, and dependable manner (Lefkowitz 1982; De Heer 1987). This automation has been made possible by advances in both computer technology and process control theory. Such advances have made automated control more economically feasible, reliable, and available (Lefkowitz 1982). Advancing process control computers have also provided a significant means for dealing with the diverse and complex information required to effectively operate a modern production plant (De Heer 1987). Together with continuing improvements in electronic instrumentation, these developments are directly allowing these plants to still operate effectively with considerably fewer human operators (Lefkowitz 1982).2

Another continuing trend for reducing CPI operating costs has been to maximize the availability of modern plants for production. This is typically accomplished by optimally scheduling the production runs and by minimizing the effects of unexpected production disruptions. A variety of methods are currently used to either eliminate or minimize the severity of unexpected production disruptions. None the less, as the complexity of the processing plants has increased, making these plants available for production has become much more difficult because the number of potential operating problems has also increased (Syrbe 1981). This tends to increase the frequency of unexpected production disruptions. Consequently, maximizing these plants availability for efficient process operation has become more dependent upon effectively managing their various potential operating problems (Linhou 1981).

1.2 THE CHANGING ROLE OF THE PROCESS OPERATORS IN PLANT OPERATIONS


The process operators' main task in plant operation is to continuously assess the process state (Lefkowitz 1982) and then, based upon that assessment, react appropriately. Process operators thus have three core primary responsibilities (Rijnsdorp 1986). The first is to monitor the performance of various control loops to make sure that the process is operating properly. Their second is to make adjustments to the process operating conditions whenever product quality or production efficiency fall outside predefined tolerance limits. The operators’ third, and by far most important, responsibility is to avoid emergency situations if at all possible, and if not, properly respond to them.3 This means effectively and reliably performing process fault management. Such management requires that the operators correctly detect, identify, and then implement the necessary counter actions required to eliminate the process fault or faults creating the emergency situation. If it is performed incorrectly, accidents can and have occurred on many occasions.4

The biggest change in the functions performed by the process operators has been directly caused by the increased automation of process control. Process operators now monitor and supervise, rather than manually control, process operations. Moreover, such functions are increasingly accomplished with interface technology designed to centralize control and information presentation (Visick 1986). As a result, their duties have become less interesting and their ability to manually control the process has diminished.5 Both situations have increased the job dissatisfaction experienced by the process operators (Visick 1986). This has also directly diminished the operators’ ability to perform process fault management.6

A second change in the functions performed by the operators has directly resulted from having fewer operators present in modern processing plants. Each operator has become responsible for a larger portion of the overall process system’s production. This increases the risk of accidents because relatively fewer operators are available at any given time to notice the development of emergency situations or help prevent such situations from causing major accidents. Besides their increased risk, the potential severity of these possible accidents has also increased because larger quantities of reactive materials and energy are now being processed. This makes the operators’ ability to perform effective process fault management much more critical for ensuring the safe operation of those plants.

One method used to help reduce the risk of a major accident has been the addition of emergency interlock systems to the overall process control strategy. Such systems are designed to automatically shut down the process during dangerous emergency situations, thereby reducing the likelihood of accidents occurring that could directly threaten human and environmental safety or damage the process equipment. Emergency interlock systems therefore help ensure that the process operation is safe during such emergencies by decreasing the effects of human error in such situations (Kohan 1984).7 Eliminating any accidents also protects the operational integrity of the process system, which in turn allows it to be restarted more quickly after these automatic shutdowns.

However, the wide‐spread use of emergency interlock systems has caused the operators’ primary focus in plant operations to change from that of process safety to that of economic optimization (Lees 1981). In emergency situations, the operators are now more concerned with taking the corrective actions required for continuing to keep the process system operating rather than those which will safely shut it down. They rely upon the interlock system to handle any emergency shutdowns, trusting that it will take over once operating conditions become too dangerous to let production continue.

A potential problem with this strategy is that, in order to keep the process system operating, the operators may take actions that counteract the symptoms of a fault situation without correcting that situation itself (Goff 1985). Such behavior by the operators may cause them to inadvertently circumvent the protection of the emergency interlock system, thereby creating a situation which they falsely believe to be within that protection. Another potential problem of this strategy is that the emergency interlock system may fail, which again will create a situation in which the operators falsely believe that the process system is still protected by it. These potential problems can be reduced by: (i) prudently designing those interlock systems, (ii) being certain to add sufficient redundancy to detect critically dangerous situations (Kohan 1984), (iii) establishing formal policy by which particular interlocks can be bypassed during process...

Erscheint lt. Verlag 23.1.2024
Sprache englisch
Themenwelt Naturwissenschaften Chemie
Schlagworte AI • Artificial Intelligence • chemical engineering • Chemie • Chemische Verfahrenstechnik • Chemistry • Computer-aided Engineering • Computergestützte Verfahrenstechnik • Computer Science • Fehlerdiagnose • Industrial Chemistry • Informatik • KI • Künstliche Intelligenz • Technische u. Industrielle Chemie
ISBN-10 1-119-82591-1 / 1119825911
ISBN-13 978-1-119-82591-3 / 9781119825913
Haben Sie eine Frage zum Produkt?
EPUBEPUB (Adobe DRM)
Größe: 8,2 MB

Kopierschutz: Adobe-DRM
Adobe-DRM ist ein Kopierschutz, der das eBook vor Mißbrauch schützen soll. Dabei wird das eBook bereits beim Download auf Ihre persönliche Adobe-ID autorisiert. Lesen können Sie das eBook dann nur auf den Geräten, welche ebenfalls auf Ihre Adobe-ID registriert sind.
Details zum Adobe-DRM

Dateiformat: EPUB (Electronic Publication)
EPUB ist ein offener Standard für eBooks und eignet sich besonders zur Darstellung von Belle­tristik und Sach­büchern. Der Fließ­text wird dynamisch an die Display- und Schrift­größe ange­passt. Auch für mobile Lese­geräte ist EPUB daher gut geeignet.

Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen eine Adobe-ID und die Software Adobe Digital Editions (kostenlos). Von der Benutzung der OverDrive Media Console raten wir Ihnen ab. Erfahrungsgemäß treten hier gehäuft Probleme mit dem Adobe DRM auf.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen eine Adobe-ID sowie eine kostenlose App.
Geräteliste und zusätzliche Hinweise

Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.

Mehr entdecken
aus dem Bereich