Computer graphics processing and selective visual display system – Computer graphics processing – Graph generating
Reexamination Certificate
1999-07-01
2003-07-01
Kincaid, Kristine (Department: 2174)
Computer graphics processing and selective visual display system
Computer graphics processing
Graph generating
C345S440100, C345S440200, C345S960000, C345S950000, C345S950000
Reexamination Certificate
active
06587108
ABSTRACT:
FIELD OF THE INVENTION
The present invention is generally related to process control. More particularly, the present invention pertains to graphical user interfaces and displays for process control.
BACKGROUND OF THE INVENTION
Display technologies are emerging which have importance for a variety of applications. For example, various graphical user interfaces and displays have been developed for personal computing, financial services applications, etc. Recent advances in hardware and software technologies enable the development of powerful graphical user interfaces.
Various types of process control systems are presently in use, such as for control of processes operable under control of a single variable to processes controlled using controllers capable of controlling multiple variables. Control of a process is often implemented using microprocessor-based controllers, computers, or workstations which monitor the process by sending and receiving commands and data to hardware devices to control either a particular aspect of the process or the entire process as a whole. For example, many process control systems use instruments, control devices, and communication systems to monitor and manipulate control elements, such as valves and switches, to maintain one or more process variable values (e.g., temperature, pressure, flow, and the like) at selected target values. The process variables are selected and controlled to achieve a desired process objective, such as attaining a safe and efficient operation of machines and equipment utilized in the process. Process control systems have widespread application in the automation of industrial processes such as, for example, the processes used in chemical, petroleum, and manufacturing industries.
In recent years, advanced process control systems for controlling multivariable processes have been developed. For example, one type of process control is based on configuring or programming advanced controls based on engineer(s) knowledge (e.g., incorporating feed forward, signal selection, and calculation blocks) to continually push a process plant toward some known operating state. Another type of advanced process control is model-based predictive control. Model-based predictive control techniques have gained acceptance in the process industry due to their ability to achieve multivariable control objectives in the presence of dead time, process constraints, and modeling uncertainties.
In general, model-based predictive control techniques include algorithms which compute control moves as a solution to an optimization problem for minimizing errors subject to constraints, either user imposed or system imposed. A model-based predictive control algorithm can be generally described with reference to a multivariable process. Generally, the model-based predictive control includes two major portions: first, an optimization program is used to define the best place to run the process at steady state, and, second, a dynamic control algorithm defines how to move the process to the steady state optimum in a smooth way without violating any constraints. For example, at a specified frequency, e.g., every minute, the optimizer looks at the current state of the process and calculates a new optimum. From the optimizer, the controller knows where process variables should be in the final steady state. The control algorithm then calculates a dynamic set of changes for the process variables to move the process in a smooth way to the steady state with no dynamic violations of constraints. For example, 60-120 control moves may be calculated out into the future for a process variable. Generally, one of the calculated control moves is implemented and the rest thrown away. These steps are then reiterated. The control objective for the model-based predictive control is generally to provide for optimum controlled variables through calculation using a model based on economic values.
Model-based predictive control is performed using products available from several companies. For example, model based predictive control is performed by a Dynamic Matrix Control (DMC) product available from Aspen Tech (Cambridge, Mass.), and by a Robust Multivariable Predictive Control Technology (RMPCT) product available from Honeywell Inc. (Minneapolis, Minn.) which is a multi-input, multi-output control application product that controls and optimizes highly interactive industrial processes such as when used in suitable automated control systems.
Generally, a model-based predictive controller contains three types of variables; namely, controlled variables (CVs), manipulated variables (MVs), and disturbance variables (DVs) (sometimes also referred to as feed forward variables (FFs)). Controlled variables are those variables that the controller is trying to keep within constraints. Further, it may also be desirable to minimize or maximize some of the controlled variables (e.g., maximize the feed throughput process variable). Manipulated variables are those variables, such as valves, that the controller can open and close to try to achieve an objective of the controller (e.g., maximizing feed throughput) while maintaining all of the controlled variables within their constraints. Disturbance variables are those variables that can be measured, but not controlled. Disturbance variables assist the controller by providing needed information such as information regarding certain factors, e.g., outside air temperature. The controller can then recognize how such factors will affect other process variables in the controller, so as to better predict how the plant will react to measured disturbances.
A user of the model-based predictive controller (e.g., an engineer, an operator, etc.) has conventionally been provided with various types of information regarding the various process variables including information concerning the controlled variables, manipulated variables, and disturbance variables. For example, information such as predicted values, current values, and other relational information of variables relative to other variables has been provided to a user in the past by way of various interfaces and displays. The user can monitor such information and interact with the controller in various ways. For example, the user can turn the controller on and off, take individual process variables in and out of control, change various types of limits placed on process variables contained in the controller (e.g., change low or high limits for individual process variables), change the model of the controller, etc.
However, in order for the user to monitor the overall health of the controller effectively, and to interact with the controller in the required manner (e.g., changing limits of process variables), the user must be presented with suitable controller information. For example, an operator monitoring the controller should be presented with information regarding the relationship between manipulated variables and controlled variables, the limits to which process variables are constrained, the current values of the various process variables, etc. Such information should be presented in such a manner that a user can effectively understand the performance of the process and, for example, be able to detect and solve problems in the process. Although various types of screen displays have been used to present information regarding the controller to a user (e.g., those described in the Honeywell product publication entitled “Robust Multivariable Predictive Control Technology—RMPCT Users Guide for TPS (June, 1997) hereby incorporated herein by reference in its entirety and hereinafter referred to as “Honeywell Users Guide”) such that the user can monitor and manipulate parameters related to one or more process variables in the process being controlled thereby, the effectiveness of such an interface has been lacking and the users may have difficulties performing the required monitoring and control functions.
For example, one difficulty of monitoring multiple dynamic process variables in parallel is that generally a large
Bullemer Peter T.
Guerlain Stephanie A. E.
Jamieson Gregory A.
Gebhardt Mark J
Honeywell Inc.
Kincaid Kristine
Mackinnon Ian D
Miologos Anthony
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