Smart plant operations: Vision, progress and challenges

T he process industry is involved with the conversion of raw materials, through a series of chemical processing steps, to valued products and is a key economic sector in the U.S. and globally. The global market share and business performance of the process industry is heavily based on the value that can be generated from its assets which are comprised of process sites, people and materials, as well as intellectual property in the form of product knowledge, process expertise and physical properties of materials. While the range of valuable assets is large, nearly all the economic value in terms of operating profit in the process industry is a direct result of plant operations. This realization has motivated extensive research, over the last 40 years, on the development of advanced operation and control strategies to achieve economically optimal plant operation by regulating process variables at appropriate values. Figure 1 depicts the existing paradigm that couples plant management with process feedback control. This paradigm has been widely adopted by the process industries and extensively studied by the process systems engineering community. This paradigm features two distinct levels of plant operations: a plant management level and a process control level. At the plant management level, an optimization problem is solved on the basis of a (typically) steady state model of the plant to compute the economically optimal values for the process variables, while in the process control level, feedback control systems are used to regulate the process variables at the specified values. Additionally, the important task of plant monitoring — that is the determination of abnormal, potentially faulty plant behavior by proper analysis of plant sensor data — and the incorporation of plant operator input take place at the plant management level. While the paradigm of Figure 1 has undoubtedly been a successful one, over the last few years there have been numerous calls (e.g.,) for expanding this paradigm in a number of directions. Specifically, while economic prosperity has always Perspective

[1]  P. Christofides,et al.  Nonlinear and Robust Control of PDE Systems: Methods and Applications to Transport-Reaction Processes , 2002 .

[2]  Michael Baldea,et al.  Dynamics and control of integrated networks with purge streams , 2006 .

[3]  Panagiotis D. Christofides,et al.  Fault‐tolerant control of process systems using communication networks , 2005 .

[4]  Theodora Kourti,et al.  Multivariate SPC Methods for Process and Product Monitoring , 1996 .

[5]  Panagiotis D. Christofides,et al.  Fault‐tolerant control of nonlinear process systems subject to sensor faults , 2007 .

[6]  B. Bequette Nonlinear control of chemical processes: a review , 1991 .

[7]  Ignacio E. Grossmann,et al.  Enterprise‐wide optimization: A new frontier in process systems engineering , 2005 .

[8]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

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

[10]  P. R. Kumar,et al.  New technological vistas for systems and control: the example of wireless networks , 2001 .

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

[12]  Ignacio E. Grossmann,et al.  Retrospective on optimization , 2004, Comput. Chem. Eng..

[13]  Barry M. Wise,et al.  The process chemometrics approach to process monitoring and fault detection , 1995 .

[14]  Panagiotis D. Christofides,et al.  Isolation and handling of actuator faults in nonlinear systems , 2008, at - Automatisierungstechnik.

[15]  Michael Baldea,et al.  Control of integrated process networks—A multi-time scale perspective , 2005 .

[16]  Miguel J. Bagajewicz,et al.  Design and retrofit of sensor networks in process plants , 1997 .

[17]  Karlene A. Hoo,et al.  Process Data Analysis and Interpretation , 1999 .

[18]  Panagiotis D. Christofides,et al.  Lyapunov-based Model Predictive Control of Nonlinear Systems Subject to Data Losses , 2007, ACC.

[19]  James F. Davis,et al.  Structuring diagnostic knowledge for large-scale process systems , 1998 .

[20]  Panagiotis D. Christofides,et al.  Integrated fault-detection and fault-tolerant control of process systems , 2006 .

[21]  P. Daoutidis,et al.  Feedback control of nonlinear differential-algebraic-equation systems , 1995 .

[22]  James F. Davis,et al.  Clustering in wavelet domain: A multiresolution ART network for anomaly detection , 2004 .

[23]  B. Erik Ydstie New vistas for process control: Integrating physics and communication networks , 2002 .

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

[25]  Nael H. El-Farra,et al.  Actuator fault isolation and reconfiguration in transport‐reaction processes , 2007 .

[26]  Ali Cinar,et al.  Control of complex distributed systems with distributed intelligent agents , 2007 .

[27]  Panagiotis D. Christofides,et al.  Control of Nonlinear and Hybrid Process Systems: Designs for Uncertainty, Constraints and Time-Delays , 2005 .

[28]  Dale E. Seborg,et al.  Nonlinear Process Control , 1996 .

[29]  Dal Vernon C. Reising,et al.  An integrated decision support framework for managing and interpreting information in process diagnosis , 2003 .

[30]  Michael Baldea,et al.  Control of integrated process networks - A multi-time scale perspective , 2007, Comput. Chem. Eng..

[31]  Ahmet Palazoglu,et al.  Introduction to Process Control , 2005 .

[32]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[33]  P. Daoutidis,et al.  Control of nonlinear differential algebraic equation systems , 1999 .

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

[35]  A. Negiz,et al.  Statistical monitoring of multivariable dynamic processes with state-space models , 1997 .

[36]  Kendell R. Jillson,et al.  Process networks with decentralized inventory and flow control , 2007 .

[37]  Panagiotis D. Christofides,et al.  Lyapunov-Based Model Predictive Control of Nonlinear Systems Subject to Data Losses , 2007, IEEE Transactions on Automatic Control.

[38]  Panagiotis D. Christofides,et al.  Model-Based Control of Particulate Processes , 2002 .

[39]  Miguel J. Bagajewicz,et al.  Rigorous Methodology for the Design and Upgrade of Sensor Networks Using Cutsets , 2006 .

[40]  Jianliang Wang,et al.  Reliable Hinfinity controller design for linear systems , 2001, Autom..

[41]  I. Nimmo,et al.  Adequately address abnormal operations , 1995 .