Abstract: A method of routing a data packet between a first node and a second node on a communication network includes defining a first graph through the first node and the second node and zero or more intermediate nodes, associating several nodes which belong to the communication network with the first graph, associating a first unique graph identifier with the first graph and providing at least partial definitions of the first graph and the first unique identifier to at least some of the nodes associated with the first graph. The method then sends data packet with the graph identifier from the first node, and directs the data packet to the second node via the zero or more intermediate nodes using the graph identifier. This method may include forwarding the packet to a neighbor node of an intermediate node if the intermediate node and the neighbor node are nodes associated with the first graph and if the intermediate node and the neighbor node are connected by at least one direct communication connection.

Abstract: A batch modeling and analysis system uses a simple and computationally inexpensive technique to align data collected from an on-going, currently running or on-line batch process with a batch model formed for the batch process so as to enable the reliable determination of the current operational state of the on-line batch process with respect to the batch model. This data alignment technique enables further statistical processing techniques, such as projection to latent sources (PLS) and principle component analysis (PCA) techniques, to be applied to the on-line batch data to perform analyses on the quality of the currently running batch. These analyses, in turn, provide useful information to a user, such as a batch operator, that enables the user to determine the quality of the batch at the present time, based on the batch model, and the likelihood that the desired batch output quality metrics will be reached at the end of the batch run.

Abstract: A process control system includes economic models disposed in communication with process control modules, as well as with sources of economic data, such as cost, throughput and profit data, and uses the economic models to determine useful economic parameters or information associated with the actual operation of the process plant at the time the plant is operating. The economic models can be used to provide financial statistics such as profitability, cost of manufactured product, etc. in real time based on the actual current operating state of the process and the business data associated with the finished product, raw materials, etc. These financial statistics can be used to drive alarms and alerts within the process network and be used as inputs to process plant optimizers, etc. to provide for better or more optimal control of the process and to provide a better understanding of the conditions which lead to maximum profitability of the plant.

Abstract: Example methods and apparatus to predict process quality in a process control system are disclosed. A disclosed example method includes receiving process control information relating to a process at a first time including a first value associated with a first measured variable and a second value associated with a second measured variable, determining if a variation based on the received process control information associated with the process exceeds a threshold, if the variation exceeds the threshold, calculating a first contribution value based on a contribution of the first measured variable to the variation and a second contribution value based on a contribution of the second measured variable to the variation, determining at least one corrective action based on the first contribution value, the second contribution value, the first value, or the second value, and calculating a predicted process quality based on the at least one corrective action at a time after the first time.

Abstract: A process control system includes economic models disposed in communication with process control modules, as well as with sources of economic data such as cost, throughput and profit data, and uses the economic models to determine useful economic parameters or information associated with the actual operation of the process plant at the time the plant is operating. The economic models can be used to provide financial statistics such as profitability, cost of manufactured product, etc. in real time based on the actual current operating state of the process and the business data associated with the finished product, raw materials, etc. These financial statistics can be used to drive alarms and alerts within the process network and be used as inputs to process plant optimizers, etc. to provide for better or more optimal control of the process and to provide a better understanding of the conditions which lead to maximum profitability of the plant.

Abstract: An adaptive process controller performs continuously scheduled process model parameter interpolation to determine a particular set of process model parameters which are used to develop controller tuning parameters for controller tuning. More particularly, a state-based, adaptive PID controller described herein uses a new technique to determine an appropriate process model to be used to perform adaptive tuning over the various operating regions of the plant, and in particular, uses a process model parameter determination technique that enables continuously scheduled process model parameter update over the various plant operating regions or points. The use of this continuously scheduled process model parameter update method provides for smoother transitions between tuning parameters used in the PID controller during adaptive tuning procedures which are implemented based on changes in the operating region or the operating point of the process, thereby providing for better overall control.

Abstract: Disclosed are systems and methods for on-line monitoring of operation of a process in connection with process measurements indicative of the operation of the process. In some cases, the operation of the process is simulated to generate model data indicative of a simulated representation of the operation of the process and based on the process measurements. A multivariate statistical analysis of the operation of the process is implemented based on the model data and the process measurements. The output data from the multivariate statistical analysis may then be evaluated during the operation of the process to enable the on-line monitoring of the process involving, for instance, fault detection via classification analysis of the output data.

Abstract: A method of routing a data packet between a first node and a second node on a communication network includes defining a first graph through the first node and the second node and zero or more intermediate nodes, associating several nodes which belong to the communication network with the first graph, associating a first unique graph identifier with the first graph and providing at least partial definitions of the first graph and the first unique identifier to at least some of the nodes associated with the first graph. The method then sends data packet with the graph identifier from the first node, and directs the data packet to the second node via the zero or more intermediate nodes using the graph identifier. This method may include forwarding the packet to a neighbor node of an intermediate node if the intermediate node and the neighbor node are nodes associated with the first graph and if the intermediate node and the neighbor node are connected by at least one direct communication connection.

Abstract: An appendable system includes a plurality of appendable devices that are adapted to interoperate with each other and/or a workstation via a communication network to monitor and/or control a process. Each of the appendable devices can communicate with one or more sensors and/or control outputs and includes a housing that facilitates mounting of the appendable device to a surface.

Abstract: Disclosed is a controller having a processor and a control module adapted for periodic execution by the processor and configured to be responsive to a process variable to generate a control signal for a process. An iteration of the periodic execution of the control module involves implementation of a routine configured to generate a representation of a process response to the control signal. The routine is further configured to maintain the representation over multiple iterations of the periodic execution of the control module and until an update of the process variable is available. In some cases, the update of the process variable is made available via wireless transmission of the process signal. In those and other cases, the controller may be included within a process control system having a field device to transmit the process signal indicative of the process variable non-periodically based on whether the process variable has changed by more than a predetermined threshold.

Abstract: Example methods and apparatus to predict process quality in a process control system are disclosed. A disclosed example method includes receiving process control information relating to a process at a first time including a first value associated with a first measured variable and a second value associated with a second measured variable, determining if a variation based on the received process control information associated with the process exceeds a threshold, if the variation exceeds the threshold, calculating a first contribution value based on a contribution of the first measured variable to the variation and a second contribution value based on a contribution of the second measured variable to the variation, determining at least one corrective action based on the first contribution value, the second contribution value, the first value, or the second value, and calculating a predicted process quality based on the at least one corrective action at a time after the first time.

Abstract: An adaptive process controller performs continuously scheduled process model parameter interpolation to determine a particular set of process model parameters which are used to develop controller tuning parameters for controller tuning. More particularly, a state-based, adaptive PID controller described herein uses a new technique to determine an appropriate process model to be used to perform adaptive tuning over the various operating regions of the plant, and in particular, uses a process model parameter determination technique that enables continuously scheduled process model parameter update over the various plant operating regions or points. The use of this continuously scheduled process model parameter update method provides for smoother transitions between tuning parameters used in the PID controller during adaptive tuning procedures which are implemented based on changes in the operating region or the operating point of the process, thereby providing for better overall control.

Abstract: Example methods and apparatus to standardize data properties in a process control environment are disclosed. A disclosed example method includes associating a function block with a primary key. The example method also includes associating at least one data property associated with the process control environment with the primary key and storing an instance of the at least one data property in a history database associated with the process control environment.

Abstract: A diagnostic system for use in a process control system collects and stores in a database information pertaining to the operation of the process control system, and that uses an expert engine to apply rules for analysis to the information in the database to determine solutions to problems. The database stores various types of information such as event and alarm data, notices of scheduled maintenance and changes to operating parameters, and historical data related to previous changes to the process control system that are relevant to determining both the source of the problems detected in the process control system and the steps necessary to either further analyze or correct the detected problems. The diagnostic system identifies the source of the problem and identifies and runs the appropriate analytical tools or takes remedial measures based on the rules for analysis for the expert engine.