Location of Repository

Process and operator performance analysis in process operational safety

By Yussef Haji Ali Mirza Sezalli


Abnormal operation of chemical processec sa used by equipment and sensor faults, such as plugging of pipes, control system failure or improper operation by personnel can result in poor product quality, equipment damage, or a catastrophe process failure leading to loss of equipment and worker injury, as well as significant economic losses. It is estimated that the cost attributable to preventable\ud losses in the petrochemical industry only is around several billion pounds per year. Independent studies of case\ud histories by the Health and Safety Commission in the UK and by a Honeywell led industrial consortium in the US and world wide show that human errors represent the major cause\ud of failures. In contrast to this discovery, the majority of pervious studies on computer aided systems for fault detection and diagnosis has focussed on the process side only. It is now widely acknowledged that there is only limited information on how human factors can be assessed \ud and even less that is specific to chemical industry,\ud therefore research is much needed in this area.\ud \ud This study presents a methodology to involve human \ud factors into the development of systems for automatic identification and diagnosis of abnormal operations and\ud develops methods and techniques that can be used to simultaneously capture, characterise and assess the performance of operators as well as of the process. A joint process operator simulation platform was developed which was used as a test-bed for carrying out the studies. The process part is a simulator, which emulates in high fidelity the dynamic behaviour of the process, which is subject to influence of various disturbances and operators intervention. The operator module was developed as a real-time expert system, which emulates operator's behaviour in interpretation of received signals, planning and executions of the decisions. The interaction between the two modules\ud is managed through an interaction module, which handles the real-time exchange of data using DDE (Dynamic Data Exchange). The interaction module also contains the toolkits for analysing the dynamic behaviour of the joint process-operator system.\ud \ud The operator simulation module was developed based on a theoretical model of human behaviour, which breaks operator's activities into perception of signals an interpretation of the received information, planning for actions and execution of the decisions. The system was implemented as a real-time expert system using visual Prolog. Numerical models were also integrated into the expert system, e. g. stress models of operators. This flexible system allows studies on individual operators actions, stress, intervene time, the frequency of intervene and near-miss or near-hit in operation.\ud \ud As part of the effort to use the platform to develop methods and tools for characterising and assessing the dynamic behaviour of the joint process-operator system,\ud a digraph method for qualitative/quantitative modelling of the dynamic behaviour of the combined system was proposed.\ud The method involves categorical characterisation of\ud dynamic trends using principal component analysis and fuzzy c-means and sectioning of the clusters. An iterative method for determining the number of the clusters and sections\ud based on the global performance was derived. Compared with pervious studies on qualitative process modelling, the proposed approach is more accurate and has higher\ud resolution, and more importantly is able to deal with joint process-operator systems. \ud \ud The methods and systems developed were illustrated and fully tested using simulated and industrial case studies

Publisher: School of Chemical and Process Engineering (Leeds)
Year: 2001
OAI identifier: oai:etheses.whiterose.ac.uk:810

Suggested articles



  1. 1. "Safety improvement by multimedia operator education system, " doi
  2. A case study in hybrid process safety verification, " doi
  3. A comparison of principal component analysis, multi-way principal component analysis, tri-linear decomposition and parallel factor analysis for fault detection in a semiconductor etch process, " doi
  4. A data Base Oriented Dynamic Methodology for the failure analysis of closed loop control systems in process plants, " Reliability Engineering & System Safety, doi
  5. A fault detection and diagnosis for the continuous process with load-fluctuations using orthogonal wavelev' doi
  6. (1980). A graphical approach to cause and effect analysis of chemical processing system, " doi
  7. A hybrid ANN-ES system for dynamic fault diagnosis of hydro-cracking processo"
  8. (1996). A hybrid hierarchical neural network-fUzzy expert system approach to chemical process fault diagnosis, " Fuzzy Set System, doi
  9. A massive parallel architecture for a self-organising neural pattern recognition machine Computer Vision Graphics and Image doi
  10. A methodology for the quantitative evaluation of NPP fault diagnostic systems' dynamic aspects, "Annals offuclear Energy, doi
  11. (1990). A model based system for fault diagnosis of chemical process plan,
  12. A modular methodology for fast fault detection and classification in power systems, " doi
  13. A non-probabilistic prospective and retrospective human reliability analysis method: application to railway system, " doi
  14. A plant-wide industrial process control problems,
  15. A similarity-based approach to interpretation of sensor data using adaptive resonance theory, " doi
  16. Adaptive batch monitoring using hierarchical PCA, " doi
  17. Adaptive network for fault diagnosis and process control, " doi
  18. (1976). Adaptive pattern classification and universal recording: 11 feedback, expectation, olfaction, illusions: 'Bio Cybernet,
  19. Adequately address abnormal operations, "
  20. Aiding the analysis of human action in large-scale system: An intelligent interface approach, "
  21. Aiding the analysis of human actions in large-scale system: an intelligent interface approach, " doi
  22. ALPHATECH, In: Burlington, Massachusetts & Serfaty D-4ptima, "Adaptive team coordination, " Human Factor,
  23. An algorithm for diagnosis of system failures in the chemical process, " doi
  24. An experimental study on estimating human error probability (HEP) parameter for PSA/HRA by using human model simulation, " doi
  25. An overview of multivariate statistical process control in continuous and batch process performance monitoring, " Transaction of the Institute of Measurement and Control, doi
  26. Application of fuzzy causal networks to waste water treatment plants, " doi
  27. Application of wavelets and neural networks to diagnostic system development, 1, feature extraction, " doi
  28. Application of wavelets and neural networks to diagnostic system development, 2, an integrated framework and its application: ' doi
  29. Applications of artificial neural networks in chemical engineering: ' doi
  30. ART2: self-organization of stable category recognition codes foranalogue input patterns, " doi
  31. (1988). Artificial neural network models for knowledge representation in chemical engineering, " doi
  32. Auto-associate neural networks, "
  33. Automatic classification for mining process operational data: 'Industrial Engineering Chemistry Research, doi
  34. Batch process monitoring for consistent production, " doi
  35. Batch tracking via non-linear principal component analysis: ' AICMJournal, doi
  36. Bayesian networks for knowledge discovery, " In: Fayyad UM,
  37. Case study investigating the application of neural networks for process modelling and condition monitoring, " doi
  38. (1997). Casual graphs and rule generation: application of fault diagnosis of dynamic process, "
  39. Challenges in the industrial applications of fault diagnostic systems: ' doi
  40. (1981). Classification system for reporting events involving human malfunctions, "
  41. Clustering of infrared spectra of lubricating base oils using adaptive resonance theory, " doi
  42. (1993). Cockpit resource management, "
  43. Cognitive environment simulation: a tools for modelling operator cognitive performance during emergencies, "
  44. Cognitive ergonomics and the reliability of cognition, "
  45. Cognitive task analysis of complex work domain: acase study, " Reliability Engineering and System Safety, doi
  46. (1991). Cognitive task analysis? " In: Weir
  47. Combining conceptual clustering and principal component analysis for state space based process monitoring: ' doi
  48. Computer-aided synthesis of fault-trees, " doi
  49. Computer-assisted Markov failure modelling of process control system. ' doi
  50. Conceptual design of multi-human Machine interface, " Control Engineering Practice, doi
  51. Cooperative human-machine interface for plant-wide control and communication, " doi
  52. (1993). Coordination in hierarchical infonnation process structures, "
  53. Counter-propagation networks, "Applied Optics.
  54. (1999). Data mining and knowledge discovayfor process monitoring and control,
  55. (1998). Data mining for monitoring and diagnosis of the MT13E reactor and reactive distillation process, "
  56. (2000). Data-driven methods for fault detection and diagnosis in chemicalprocesses, " doi
  57. Design of human-errortolerant interface using fuzzy logic, "Application ofArtiflicial Intelligence, 13,179,2000. Bezdek J. "Pattern recognition with fuzzy objective function algorithms, "
  58. (1996). Detection and diagnosis of abnormal batch operations based on multi-way principal component analysis: ' world batch forum, doi
  59. Development of an support system for man-machine system design information, " doi
  60. (1987). Development of the dynamic fault tree using Markovian process and super-components, " Reliability Engineering and System Safety,
  61. Diagnosis using back-propagation neural networks-analysis and criticism, "
  62. Digraph based models for automated HAZOP analysis, " Reliability Engineering& System Safety, doi
  63. (2000). Discovery of operational spaces from process data for production of multiple grades of products, " doi
  64. (1986). Doing time: putting qualitative reasoning on firmer ground, " National conference on artificial Intelligence,
  65. Dynamic event tree analysis- an application to SGTRý'
  66. Dynamic probabilistic model-based expe rt system for fault diagnosis, " doi
  67. (1992). Dynamic response of human operators, " Wright air development centre.
  68. (1960). Element of the Theory of Markov Processes and Their, 4pplication"
  69. (1986). Expert systems in on-line process control, " Proceeding third international conference chemical process control,
  70. Fault classification in power systems using artificial neural networks, " Engineering Intelligent System Elec.,
  71. Fault detection and diagnosis using multivariate statistical techniques, " Chemical Engineering Research and Design,
  72. Fault detection in an industrial process using real-time expert systems in conjunction with a dynamic simulation, " IChe; nE Research EventlSecond European Conferencefor Young Researchers,
  73. Fault diagnosis model based on Petri net with ftizzy colours, " doi
  74. Fault-diagnostic system using analytical fiizzy redundancy, " Engineering Applications ofArtificial Intelligence,
  75. (1998). Feature extraction and knowledge discovery in process operation analysis, "
  76. Fime dependent unavailability analysis of nuclear safety systems, " doi
  77. (1985). Forty-five years in man-machine systems: history and trends, "
  78. Frarnework for enhancing fault diagnosis capabilities of artificial neural networks: ' doi
  79. (1980). Fuzzy sets and systems, Theory and applications, " doi
  80. (1999). Fuzzy sets, " Information& Control, 8,338,1965. 194 Zhang H, Tangirala AK & Shah SL. "Dynamic process monitoring using multiscale PCA, "
  81. (1995). Graphical beliefmodelling, " London,
  82. Graphical models for discovering knowledge, " In
  83. Guidelines for developing signed directed graph models, " MIT,
  84. Handbook of human reliability analysis with emphasis on nuclear power plant applications, "
  85. (1999). Hazop and hazan: identifying and assessing process industry hazards, " Institution of Chemical Engineers,
  86. (1990). Human error, " Cambridge, UK: doi
  87. (1989). Human factors in haza dous situation, " Proceeding of a Royal Society Discussion Meeting
  88. (1997). Human factors in safely-critical systems, " ButterworthHeinemann,
  89. (1993). Human reliability analysis, " Context and Control,
  90. Human reliability analysis. Where should thou turn? " Reliability Engineering& System Safety, 29,283,1990. Dougherty EM. "Issues of human reliability in risk analysis, " doi
  91. (1996). Improved process understanding using multiway principal component analysis, " doi
  92. Improvements of the accuracy of fault diagnosis systems, using signed digraph graphs, " doi
  93. Improving the speed of multi-way algorithms: doi
  94. In: Woburn & Massachusetts, "Adaptive team coordination, " Human Factor,
  95. Inferential estimation of polymer quality using stacked neural network, " doi
  96. (1986). Information processing and human-machine interaction: An approach to cognitive engineering: 'North-Holland,
  97. Integrating human factors and systems engineering to reduce the risk of operator "ERROR", " Safety Science,
  98. (1991). INTEGRATION: the key to second-generation applications, "
  99. (1999). Introduction pattern recognition: statistical, structural, neural and ftizzy logic approaches, " Bunke H& Wang PS, Eds. Imperial College-Press,
  100. Knowledge based design of human-machine interface"
  101. (2000). Knowledge discovery from data for process performance monitoring and improvement: '
  102. Knowledge discovery from process operational data for assessment and monitoring of operator's performance, doi
  103. (2001). Knowledge discovery from process operational data using PCA and fiazy clustering, " Engineering Application of Artificial Intelligence, In press, doi
  104. Knowledge extraction in chemical process control, " Chemical Engineering Communications, 130,251,1994. 182 Kazuo F, Taro S& Shunsuke K. "Behavioural simulation of a nuclear power plant operator crew for human-machine system design, "
  105. Learning dynamic fault models based on a fuzzy set covering method, " doi
  106. (1986). Learning internal representations hy error propagation, in parallel distrihution processing, " doi
  107. (1996). Loss prevention in the process industries: hazard identification, doi
  108. (1959). Method ofLogict"
  109. (1995). Modelling and safety verification of discrete / continuous processing systems using discrete time domain models: ' doi
  110. Modelling and simulation of human behaviour for safety analysis and control of complex system, " Safety Science, doi
  111. (1999). Modelling and simulation of human behaviour in system control, " doi
  112. Monitoring, diagnosis and control of industrial process,
  113. Multi-way principal component- and PLSanalysis, " doi
  114. Multilevel PCA and inductive learning for knowledge extraction from operational data of batch processes, " doi
  115. Multiple-fault diagnosis under uncertain conditions by the quantification of qualitative relations, " doi
  116. Multiscale PCA with application to multivariate statistical process monitoring, " doi
  117. Multivariate image analysis for real-time process monitoring and control, " doi
  118. Multivariate quality control, illustrated by the air testing of sample bombsights, "
  119. Multivariate SPC charts for batch processes, " doi
  120. Multivariate statistical analysis of an emulsion batch process, " doi
  121. Multivariate statistical monitoring of process operating performance: ' doi
  122. Neural nets, fuzzy sets and digraphs in safety and operability studies of refinery reaction processes, " doi
  123. Non-linear dynamic principal component analysis for on-line process monitoring and diagnosis, "
  124. Non-linear principal component analysis using auto-associative neural networks, "AIChE
  125. Non-linear principal component analysis-based on principal curves and neural networks, doi
  126. Observations and problems applying ART2 for dynamic sensor pattern interpretation, " doi
  127. (1994). On the design of flight-deck procedures, " NASA contractor report 177642.
  128. PCA of wavelet transformed process data for monitoring: 'Intelligent Data Analysis, doi
  129. (1981). Petri Net Yheo? y and the Modelling 0 Prentice-Hall,
  130. Petri net-digraph models for automating HAZOP analysis of batch process plants, " doi
  131. Petri-net base machine tool failure diagnosis,
  132. Predictive on-line monitoring of continuous processes, " doi
  133. Principal curves, " doi
  134. Process analysis, monitoring and diagnosis, using multivariate projection method, " doi
  135. (2000). Process controL designing processes and control systems for dynamic performance, " 2nd ed,
  136. Process fault detection and diagnosis using neutral networks-1. Steady-state processes, " doi
  137. Process monitoring and diagnosis by multi block methods, "
  138. Product design through multivariate statistical analysis of process data: 'AIChE doi
  139. (1988). Prototype for integrated hazard analysis, "
  140. (1995). Qualitative process modelling-a fuzzy signed directed graph method, " doi
  141. Quantifying signed directed-graphs with the fuzzy set for fault-diagnosis resolution improvemeW' doi
  142. Reasoning about physical systems: Shallow versus deep models, '
  143. (1996). Reasoning in time: modelling, analysis and pattern recognition of temporal process trends, " doi
  144. Reliability analysis and operator modelling, " Reliability Engineering and System Safety, doi
  145. (1994). Reliability and Safety Assessment of Dynamic Process Systems, doi
  146. Representation of process Trend-1. A formal representation framework, " doi
  147. Representation of process Trend-2. The problem of scaleand qualitative scaling, "
  148. Representation of process trends -IV. Induction of real-time patterns from operating data for diagnosis and supervisory control: ' doi
  149. Risk assessment for dynamic systems: An overview, " doi
  150. Robust fault diagnosis based on clustered symptom trees: ' Control Engineering Practice, doi
  151. Seagrave FL "Process modelling using stacked neural network, "
  152. Self-organised formation of topologically correct feature maps, " doi
  153. Sensor fault detection via multi-scale analysis and dynamic PCA, " Industrial Engineering Chemishy Research, 38,1489,1999. MacCallum KJ. "Understanding relationships in marine system design, "
  154. Signed digraph based multiple fault diagnosis" doi
  155. Software sensor design using Bayesian automatic classification and back propagation neural networks, " doi
  156. Some mathematical notes on three-mode factor analysis, " doi
  157. Statistical data analysis of a chemical plant, " doi
  158. Statistical monitoring of multivariable dynamic processes with state-space models, " doi
  159. (1999). Statistical process and controller performance monitoring: A tutorial on current methods and future directions, " doi
  160. Statistical process control of multivariate processes, " doi
  161. Subspacc approach to multidimensional fault idcntirication and reconstruction, "
  162. (1997). Symbolic model verifier for safe chemical process sequential control system, " doi
  163. (1994). System reliability theory: Models and statistical methods, " doi
  164. Tbe theory of signal detectability, "
  165. (1997). The application of fuzzy qualitative simulation in safety and operability assessment of process plants, " doi
  166. The application of fuzzy qualitative simulation in safety and operability assessment of process plants" doi
  167. The ART of adaptive pattern recognition by a selforganising neural network, " Computer, 21,77,1988. Catino CA, Grantham SD and Ungar LH. "Automatic generation of qualitative models of chemical process units, "
  168. The DYLAM Approach for the Reliability Analysis of Dynamic Systems, " In: Aldemir doi
  169. (1986). The Handbook of 4rtificial Intelligence, " Volume III
  170. The impact of expert-system-based training on calibration of decision confidences in emergency management, " Computer in Human Behaviour,
  171. The internal model principle of linear control theory, " doi
  172. (1949). The mathematical theory of communications, " University of Illinois press. Urbana IL.
  173. (1986). the PDP Research group. "Parallel distribution processing: qploration in the microstructure of cognition, "
  174. The process chemometrics approach to process monitoring and fault detection, " doi
  175. The propagation of faults in process plants, " Reliability Engineering 16,39,1986. doi
  176. The role of knowledge base systems in fault diagnosis in applications using human operators: ' doi
  177. (1985). The rule based system technology in design of human machine system, " Proceedings of 2"d IFAC11FORSIM4 conference on analysis, design and evaluation ofman-machine systems. Survey lecture.
  178. (1997). TMDOCTOR: A fuzzy rule and case-base expert system for turbo-machinery diagnosis, "
  179. (1999). To err is human..., " The Chemical Engineer,
  180. University- industry cooperative study on plant operations, " doi
  181. (1995). University-industry cooperative study on plant operations, " doi
  182. Use of fuzzy cause-effect digraph for resolution fault4iagnosis for process plants . 1. Fuzzy cause-effect digraph, " doi
  183. (2000). Use of Principal Component Analysis and Fuzzy Clustering for Knowledge Discovery from Process Operational Data; '
  184. Verification of a logically controlled, solids transport system using symbolic model checking, " doi
  185. (1998). version 52, Fuzzy logic toolbox, doi
  186. (1984). Vickers logical troubleshooting in hydraulic systems, " Troubleshooting Guide, Vickers systems, UK,
  187. Why human error modelling has failed to help systems development, " Interacting with Computers, doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.