Fisher-rosemount Systems, Inc

Terry Blevins

Mark J Nixon

Wilhelm K Wojsznis

Ken J Beoughter

Dirk Thiele

John Michael Lucas

Ron Ottenbacher

Ken Beoughter

John M Lucas

Methods, apparatus, and articles of manufacture to collect process control data are disclosed. An example method to collect process control data includes registering an electronic device description describing a parameter to be measured and a measurement sampling rate, measuring the parameter based on the measurement sampling rate, storing data representative of the measured parameter in a data structure, and transferring data in the data structure to a first process control device via a process control network in response to at least one of a request for the data, a condition associated with the data, or an event associated with the data.

Methods and apparatus to collect process control data

Example apparatus and methods to access information associated with a process control system are disclosed. A disclosed example method involves receiving a first user-defined parameter name to reference a first datum value in a first data source. The first one of a plurality of data source interfaces is enabled to access the first datum value in the first data source. The example method also involves enabling referencing the first datum value in the first data source based on the first user-defined parameter name. In addition, data source interface software is then generated to access the first datum value in the first data source in response to receiving a first data access request including the first user-defined parameter name.

Apparatus and methods to access information associated with a process control system

An integrated optimization and control technique integrates an optimization procedure, such as a linear or quadratic programming optimization procedure, with advanced control, such as model predictive control, within a process plant in which the number of control and auxiliary variables can be greater than the number of manipulated variables within the process plant. The technique first determines a step response matrix defining the correlation between changes in the manipulated variables and each of the process variables that are used during optimization. A subset of the control variables and auxiliary variables is then selected to be used as inputs to a model predictive control routine used to perform control during operation of the process and a square M by M control matrix to be used by the model predictive control routine is generated. Thereafter, during each scan of the process controller, the optimizer routine calculates the optimal operating target of each of the complete set of control and auxiliary variables and provides the determined target operating points for each of the selected subset of control and auxiliary variables to the model predictive control routine as inputs. The model predictive control routine determines changes in the manipulated variables for use in controlling the process from the target and measured values for each of the subset of the control and auxiliary variables and the M by M control matrix.

Integrated model predictive control and optimization within a process control system

An interface or display routine is provided for use in viewing and configuring a function block that performs integrated optimization and control within a process control system. The interface routine may enable a user to view or configure variables, values or other parameters associated with the integrated optimization and control block within the process control system. For example, the interface routine may display the current operating state of the integrated function block, may enable a user to select inputs and output of the function block for use in providing integrated optimization and control, may enable a user to select a particular or desired optimization function for use in the function block, etc. The interface routine may also display the multiple input output curves associated with the optimizer and the controller sections of the integrated function block in a manner that provides ease of view and selection of these curves as part of the algorithm used by the integrated function block.

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Round Rock, TX, United States of America

Terry Blevins