Systemic risks management in complex process plants: Challenges, opportunities, and emerging trends

The BP Deepwater Horizon oil spill disaster has reminded us, once again, the potential for systemic failures in complex engineered systems. But such systemic failures are not limited to the chemical and petrochemical industries alone. The 2003 Northeast electrical power blackout was a systemic failure. Financial disasters such as Enron, WorldCom, subprime derivatives market, and so on, also belong to the same class. Given the size, scope, and complexity of these modern engineered systems and their interactions, it is becoming increasingly difficult for people to anticipate, diagnose and control serious abnormal events in a timely manner. Practitioners in the process industries view this as the next major challenge in control systems research and application. There are two different, but related, components of the overall systemic risks management problem. One deals with the problem of process safety during real-time operations. The other deals with safety issues during the design and/or modifications of the plant or the processes. In this paper, I will present an overview of the challenges and the opportunities. Recent progress has promising implications on the use of intelligent systems for a variety of applications in the chemical, petrochemical, and pharmaceutical industries for inherently safer design, operator training, abnormal events management, and optimal process operations.

[1]  Venkat Venkatasubramanian,et al.  Spontaneous emergence of complex optimal networks through evolutionary adaptation , 2004, Comput. Chem. Eng..

[2]  Raghunathan Rengaswamy,et al.  A syntactic pattern-recognition approach for process monitoring and fault diagnosis , 1995 .

[3]  Venkat Venkatasubramanian,et al.  Neural network decomposition strategies for large-scale fault diagnosis , 1994 .

[4]  S. N. Kavuri,et al.  Using fuzzy clustering with ellipsoidal units in neural networks for robust fault classification , 1993 .

[5]  T. Andersen,et al.  Chemical Process Diagnosis using Safety Information , 1992 .

[6]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .

[7]  Venkat Venkatasubramanian,et al.  Integrated framework for abnormal event management and process hazards analysis , 2003 .

[8]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[9]  J. E. Jackson.  Multivariate Statistical Methods , 1986 .

[10]  Venkat Venkatasubramanian,et al.  An intelligent operator decision support system for abnormal situation management , 1999 .

[11]  George Stephanopoulos,et al.  Temporal Representation of Process Trends for Diagnosis and Control , 1992 .

[12]  Randall Davis,et al.  Diagnostic Reasoning Based on Structure and Behavior , 1984, Artif. Intell..

[13]  Jan Eric Larsson.  Diagnostic reasoning strategies for means-end models , 1994, Autom..

[14]  Venkat Venkatasubramanian,et al.  An object-oriented two-tier architecture for integrating compiled and deep-level knowledge for process diagnosis , 1988 .

[15]  John F. MacGregor,et al.  Process monitoring and diagnosis by multiblock PLS methods , 1994 .

[16]  Robert Milne,et al.  Strategies for Diagnosis , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Luis Puigjaner,et al.  Fault diagnosis support system for complex chemical plants , 2001 .

[18]  Venkat Venkatasubramanian,et al.  Automating HAZOP analysis of batch chemical plants : Part II. Algorithms and application , 1998 .

[19]  Venkat Venkatasubramanian,et al.  PCA-SDG based process monitoring and fault diagnosis , 1999 .

[20]  Mylaraswamy Dinkar.  DKit: A blackboard -based, distributed, multi -expert environment for abnormal situation management , 1996 .

[21]  R. Wennersten,et al.  Process modelling in fault diagnosis , 1996 .

[22]  Karl-Erik Årzén,et al.  Automating operating procedure synthesis for batch processes: Part I. Knowledge representation and planning framework , 1998 .

[23]  Masahiro Abe,et al.  Incipient fault diagnosis of chemical processes via artificial neural networks , 1989 .

[24]  Venkat Venkatasubramanian,et al.  Representing and diagnosing dynamic process data using neural networks , 1992 .

[25]  David M. Himmelblau,et al.  FAULT DIAGNOSIS IN COMPLEX CHEMICAL PLANTS USING ARTIFICIAL NEURAL NETWORKS , 1991 .

[26]  Qiang Luo,et al.  Robust isolable models for failure diagnosis , 1989 .

[27]  Venkat Venkatasubramanian,et al.  A hybrid framework for large scale process fault diagnosis , 1997 .

[28]  Venkat Venkatasubramanian,et al.  Model-based reasoning in diagnostic expert systems for chemical process plants , 1987 .

[29]  A. B. Bulsari,et al.  Neural Networks for Chemical Engineers , 1995 .

[30]  Lyle H. Ungar,et al.  Adaptive networks for fault diagnosis and process control , 1990 .

[31]  Thomas F. Edgar,et al.  Identification of faulty sensors using principal component analysis , 1996 .

[32]  Venkat Venkatasubramanian,et al.  PHASuite: An Automated HAZOP Analysis Tool for Chemical Processes , 2005 .

[33]  Alan S. Willsky,et al.  A survey of design methods for failure detection in dynamic systems , 1976, Autom..

[34]  Benjamin Kuipers,et al.  The Limits of Qualitative Simulation , 1985, IJCAI.

[35]  Michael L. Mavrovouniotis,et al.  Reasoning with Orders of Magnitude and Approximate Relations , 1987, AAAI.

[36]  John F. MacGregor.  STATISTICAL PROCESS CONTROL OF MULTIVARIATE PROCESSES , 1994 .

[37]  G. Stephanopoulos,et al.  Representation of process trends—Part I. A formal representation framework , 1990 .

[38]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[39]  M. Iri,et al.  An algorithm for diagnosis of system failures in the chemical process , 1979 .

[40]  Michèle Basseville,et al.  Detection of Abrupt Changes in Signals and Dynamical Systems , 1985 .

[41]  Harry Markopolos.  No One Would Listen , 2010 .

[42]  Venkat Venkatasubramanian,et al.  A neural network methodology for process fault diagnosis , 1989 .

[43]  Venkat Venkatasubramanian,et al.  PHASUITE: AN AUTOMATED HAZOP ANALYSIS TOOL FOR CHEMICAL PROCESSES Part I: Knowledge Engineering Framework , 2005 .

[44]  Venkat Venkatasubramanian,et al.  A theory of design of complex teleological systems: Unifying the Darwinian and Boltzmannian perspectives , 2007, Complex..

[45]  A qualitative shape analysis formalism for monitoring control loop performance , 2001 .

[46]  Venkat Venkatasubramanian,et al.  A wavelet theory-based adaptive trend analysis system for process monitoring and diagnosis , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[47]  Karl-Erik Årzén,et al.  Automating operating procedure synthesis for batch processes: Part II. Implementation and application , 1998 .

[48]  V. Venkatasubramanian,et al.  Automating HAZOP analysis of batch chemical plants: Part I. The knowledge representation framework , 1998 .

[49]  Venkat Venkatasubramanian,et al.  DROWNING IN DATA: Informatics and modeling challenges in a data‐rich networked world , 2009 .

[50]  Michelle Girvan,et al.  Optimal design, robustness, and risk aversion. , 2002, Physical review letters.

[51]  Alan S. Willsky.  Detection of abrupt changes in dynamic systems , 1985 .

[52]  Olivier Raiman,et al.  Order of Magnitude Reasoning , 1986, Artif. Intell..

[53]  J. Hess.  A Year Later , 2002 .

[54]  T. McAvoy,et al.  Batch tracking via nonlinear principal component analysis , 1996 .

[55]  Herbert A. Simon,et al.  Causality in Device Behavior , 1989, Artif. Intell..

[56]  J. Macgregor,et al.  Monitoring batch processes using multiway principal component analysis , 1994 .

[57]  Venkat Venkatasubramanian,et al.  Intelligent systems for HAZOP analysis of complex process plants , 2000 .

[58]  B. Babu,et al.  ADAPTIVE NETWORKS FOR FAULT DIAGNOSIS AND PROCESS CONTROL , 2002 .

[59]  M. Kramer.  Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[60]  V. Venkatasubramanian.  Inexact Reasoning in Expert Systems: A Stochastic Parallel Network Approach , 1985, CAIA.

[61]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[62]  Liya Hou,et al.  Diagnosis of multiple simultaneous fault via hierarchical artificial neural networks , 1994 .

[63]  Venkat Venkatasubramanian,et al.  Automatic generation of qualitative descriptions of process trends for fault detection and diagnosis , 1991 .

[64]  S. Low,et al.  The "robust yet fragile" nature of the Internet. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[65]  Thomas F. Edgar,et al.  Smart Process Manufacturing—A Vision of the Future , 2009 .

[66]  Kenneth D. Forbus.  Qualitative Process Theory , 1984, Artificial Intelligence.

[67]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[68]  M. Laser.  Recent Safety and Environmental Legislation , 2007 .

[69]  Edwin H. Neave,et al.  The subprime mortgage market: familiar lessons in a new context , 2007 .

[70]  Venkat Venkatasubramanian,et al.  Representing bounded fault classes using neural networks with ellipsoidal activation functions , 1993 .

[71]  Venkat Venkatasubramanian.  Systemic failures: challenges and opportunities in risk management in complex systems , 2011 .

[72]  Ramaswamy Vaidyanathan,et al.  Process fault detection and diagnosis using neural networks , 1990 .

[73]  Dominique Lapierre,et al.  Five Past Midnight in Bhopal , 2001 .