The authors present a digital signal processor (DSP) architecture whose powerful CPU is optimized to solve a state variable filter algorithm, but is not limited to that application. State variable filters perform low ...
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作者：
Y. IdaSection 2
Development & Engineering Department2 Process Control Systems Business Division Yokogama Electric Corporation Musashino Tokyo Japan
In plant control, sophisticated controlsystems are implemented by closely combining sequence-control and feedback-control functions. In order to construct such a control system with CAD (computer-aided design) induce...
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In plant control, sophisticated controlsystems are implemented by closely combining sequence-control and feedback-control functions. In order to construct such a control system with CAD (computer-aided design) induced techniques, the author uses loop diagrams for feedback control and logic diagrams for sequence control in an engineering workstation. The operating condition of the control system is also monitored using input diagrams.< >
A feasible directions algorithm is proposed for efficient solution of a min-max formulation for model predictive control, which arises when there is uncertainty in the model of the system. The output of the model is a...
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A feasible directions algorithm is proposed for efficient solution of a min-max formulation for model predictive control, which arises when there is uncertainty in the model of the system. The output of the model is assumed to be a linear function of some unknown parameters. The algorithm always tries to choose the steepest possible descent direction.< >
Recognition and interpretation of real-time process trends is the center-piece of the socalled intelligent controller, which recognizes faults, performance degradation, and scopes the problems associated with efficien...
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Recognition and interpretation of real-time process trends is the center-piece of the socalled intelligent controller, which recognizes faults, performance degradation, and scopes the problems associated with efficient disturbance rejection and process model/controller adaptation. In this paper we will show how a unifying symbolic representation of process trends leads to a systematic extraction of their temporal features and precise classification of trends. Such classification may proceed in a syntactic, semi-quantitative, or fully quantitative manner, depending on the characteristics of the problem being solved. Furthermore, we will argue that a multi-scale representation of process trends is essential in pattern recognition, and we will show how the unifying representation of trends allows for the decomposition of signals at various levels of abstraction.
A new self-tuning PID controller has been developed for distributed processcontrolsystems. The self-tuning PID controller is able to identify the process dynamics using a short period of process behaivior such as a ...
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A new self-tuning PID controller has been developed for distributed processcontrolsystems. The self-tuning PID controller is able to identify the process dynamics using a short period of process behaivior such as a setpoint change under the closed loop control condition, and to tune PID parameters based on the identified iodei parameters for both setpoint tracking and disturbance regulation characteristics, which makes it easy to set up the inital PID parameters and adapt the PID values to the process dynamics changes. This paper describes the basic principles of the self-tuning controllers, and explains the functions to be implemented on. Finally, some application results illstrate the effectiveness of the self-tuning controllers.
The authors describe two architectures: the Automated process Monitoring (APROM) System, which examines a large combination of trend charts and shows the symptoms of problems to the engineers by sending a high level m...
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The authors describe two architectures: the Automated process Monitoring (APROM) System, which examines a large combination of trend charts and shows the symptoms of problems to the engineers by sending a high level message and a chart when a process variable is out of control; and the Interactive Data Extraction and Analysis (IDEA) System, which provides a simple menu interface to get a block of data from a local, or remote host database. Once the user receives the right data block, the user can interactively exercise many options of plots and statistical data analysis functions. An application is described by a profile which can be modified easily by users.< >
作者：
R.A. HilhorstJ. van AmerongenP. LöhnbergH.J.A.F. TullekenControl
Systems and Computer Engineering Group Dept of Electrical Engineering University of Twente and Mechatronics Research Centre Twente P. O. Box 217 7500 AE Enschede The Netherlands Process Systems Group
Dept of Mathematics & Systems Engineering Koninklijke/Shell-Laboratorium (Shell Research B.V.) P.O. Box 3003 1003 AA Amsterdam The Netherlands
The intelligence of controllers has increased over the decades. However, the number of applications of adaptive controllers is still restricted, due to practical limits of the implemented continuous adaptation. For pr...
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The intelligence of controllers has increased over the decades. However, the number of applications of adaptive controllers is still restricted, due to practical limits of the implemented continuous adaptation. For processes which operate only in a limited number of modes (so called mode-switch processes), constant adaptation is not needed or desired. In this paper an intelligent extension of adaptive control will be presented, in which process behaviour can be stored in a memory, retrieved from it and evaluated for each mode of operation. This intelligent memory concept leads to an adaptive control structure which, after a learning phase, quickly adjusts the controller parameters based on retrieval of old information, without the need to relearn every time. This approach has been tested on a simulation model of an assembly robot, but it is directly applicable to many processes in the (petro)chemical industry.
The authors study sensor failure in noise-perturbed discrete-time linear systems represented by the usual state space model Kalman filtering. The Bayesian approach to failure detection is used. The best estimates are ...
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The authors study sensor failure in noise-perturbed discrete-time linear systems represented by the usual state space model Kalman filtering. The Bayesian approach to failure detection is used. The best estimates are obtained from the outputs of a linearly growing bank of Kalman filters (KFs), giving conditional distributions which are Gaussian mixtures. A method originally introduced by D.J. Salmond (1989, 1990) for dealing with clutter in target tracking problems is used here for combining components of this mixture in a way which causes minimum distortion. By using this, an approximate algorithm can be derived, which uses no more than a fixed number of KFs. The algorithm is straightforward to implement and demonstrated excellent performance.< >
The authors present a digital signal processor (DSP) architecture whose powerful CPU is optimized to solve a state variable filter algorithm, but is not limited to that application. State variable filters perform low ...
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The authors present a digital signal processor (DSP) architecture whose powerful CPU is optimized to solve a state variable filter algorithm, but is not limited to that application. State variable filters perform low pass filtering and generate the derivatives of the filtered signal. These signals are used in systems for real-time process identification. In order to minimize the number of components, program and data memory, timers, and peripheral control logic are integrated on the DSP chip. Software development for this DSP is supported by an assembler, simulator and high level language compiler. In addition to the presentation of the architecture, a design methodology is introduced which uses rapid prototyping techniques to verify system design and algorithms in their real-time environment.< >
作者：
NYER, EBOETTCHER, GMORELLO, BEvan K. Nyer is an expert in the research and application of technology to ground water cleanups. As vice president with Geraghty & Miller Inc.
he is responsible for engineering services including hazardous and solid waste management environmental and natural resource management remediation activities and designing treatment systems for contaminated sites throughout the United States and in foreign countries. He has designed more than 100 ground water treatment systems. Nyer travels throughout the country teaching treatment techniques at seminars and universities. He has written numerous papers on ground water decontamination and other water and waste water cleanup techniques. He is responsible for bringing to the field many innovative techniques for biological treatment of water soils and in situ treatment and the application of existing technologies to ground water contamination. He is a member of the Water Pollution Control Federation The National Water Well Association The American Institute of Chemical Engineers and The American Society of Civil Engineers. Bridget Morello received a B.S. Che from the University of South Florida in 1987 and is currently working for Geraghty & Miller's Process Group in Tampa
Florida. She is mainly involved in treatability evaluation and design of ground water treatment systems. Gary Boettcher is a project scientists with Geraghty & Miller Inc. in Tampa
Florida. He received his B.S. degree in microbiology from the University of South Florida and is currently pursuing a Master of Public Health (MPH) degree. He is involved in investigation treatability and design of biological remediation systems.
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