Abstract: Systems and methods for automated commissioning of virtualized distributed control systems are disclosed. An example method includes accessing a data structure including a list of configuration names for network cards associated with first and second host servers of a virtual process control environment. The first and second host servers implement virtual machines corresponding to workstations for a process control system. The example method also includes when configuring the first host server, assigning a first name to a first one of the network cards associated with the first host server. The example method further includes when configuring the second host server, assigning the first name to a second one of the network cards associated with the second host server based on a user selection of the first name from the list of configuration names. The second host server is configured after the first host server.

Abstract: A movable mirror device is provided as a unitary device with a position sensor and an analog-to-digital converter included in the unitary device.

Abstract: An on-line data analytics device can be installed in a process control system as a standalone device that operates in parallel with, but non-intrusively with respect to, the on-line control system to perform on-line analytics for a process without requiring the process control system to be reconfigured or recertified. The data analytics device includes a data analytics engine coupled to a logic engine that receives process data collected from the process control system in a non-intrusive manner. The logic engine operates to determine further process variable values not generated within the process control system and provides the collected process variable data and the further process variable values to the data analytics engine. The data analytics engine executes statistically based process models, such as batch models, stage models, and phase models, to produce a predicted process variable, such as an end of stage or end of batch quality variable for use in analyzing the operation of the on-line process.

Abstract: Methods and apparatus to provide redundancy in a process control system are disclosed. An example method includes receiving, at a first network host, first integrity messages transmitted from a second network host over a first network. The method includes detecting a network failure over a first communication path between a first network interface of the first network host and a second network interface of the second network host via first network when a first network interface of the first network host fails to receive one of the first integrity messages expected from a first network interface of the second network host. The method includes automatically establishing a second communication path between the first network host and the second network host in response to the detected network failure.

Abstract: A movable mirror device is provided as a unitary device with a position sensor and an analog-to-digital converter included in the unitary device.

Abstract: A process controller adaptation and tuning technique uses a closed loop adaptation cycle that performs an autocorrelation analysis on the prediction error or the control error of a process control system to determine if significant process model mismatch exists or to determine an increase or a decrease in process model mismatch over time. The adaptation and tuning technique may perform a controller tuning cycle when the determined model mismatch raises above a predetermined level.

Abstract: A movable mirror device is provided as a unitary device with a position sensor and an analog-to-digital converter included in the unitary device.

Abstract: A movable mirror device is provided as a unitary device with a position sensor and an analog-to-digital converter included in the unitary device.

Abstract: Process control system and methods are disclosed. An example method includes operating a first cluster including first virtual machines and first servers and operating a second cluster including second virtual machines and second servers. The example method also includes storing first data from the first virtual machines at a first data store of the first cluster and storing a replica of the first data at a second data store of the second cluster. The example method also includes storing second data from the second virtual machines at the second data store and storing a replica of the second data at the first data store and identifying a failure of the first cluster. The method also includes, in response to the failure, restarting the first virtual machines using the second servers and the replica of the first data at the second data store.

Abstract: A movement trainer for small animals has a water basin and a treadmill disposed in the water basin. The water basin has a peripheral side wall that is slanted along a peripheral surface into an interior of the water basin and forms a movement-limiting element for an animal provided in the movement trainer. An open space within the movement-limiting element is smaller than the running surface of the treadmill.

Abstract: Methods and apparatus to provide redundancy in a process control system are disclosed. An example method includes receiving, at a first network host, first integrity messages transmitted from a second network host over a first network. The method includes detecting a network failure over a first communication path between a first network interface of the first network host and a second network interface of the second network host via first network when a first network interface of the first network host fails to receive one of the first integrity messages expected from a first network interface of the second network host. The method includes automatically establishing a second communication path between the first network host and the second network host in response to the detected network failure.

Abstract: Process control system and methods are disclosed. An example method includes operating a first cluster including first virtual machines and first servers and operating a second cluster including second virtual machines and second servers. The example method also includes storing first data from the first virtual machines at a first data store of the first cluster and storing a replica of the first data at a second data store of the second cluster. The example method also includes storing second data from the second virtual machines at the second data store and storing a replica of the second data at the first data store and identifying a failure of the first cluster. The method also includes, in response to the failure, restarting the first virtual machines using the second servers and the replica of the first data at the second data store.

Abstract: Systems and methods for automated commissioning of virtualized distributed control systems are disclosed. An example method includes accessing a data structure including a list of configuration names for network cards associated with first and second host servers of a virtual process control environment. The first and second host servers implement virtual machines corresponding to workstations for a process control system. The example method also includes when configuring the first host server, assigning a first name to a first one of the network cards associated with the first host server. The example method further includes when configuring the second host server, assigning the first name to a second one of the network cards associated with the second host server based on a user selection of the first name from the list of configuration names. The second host server is configured after the first host server.

Abstract: An on-line data analytics device can be installed in a process control system as a standalone device that operates in parallel with, but non-intrusively with respect to, the on-line control system to perform on-line analytics for a process without requiring the process control system to be reconfigured or recertified. The data analytics device includes a data analytics engine coupled to a logic engine that receives process data collected from the process control system in a non-intrusive manner. The logic engine operates to determine further process variable values not generated within the process control system and provides the collected process variable data and the further process variable values to the data analytics engine. The data analytics engine executes statistically based process models, such as batch models, stage models, and phase models, to produce a predicted process variable, such as an end of stage or end of batch quality variable for use in analyzing the operation of the on-line process.

Abstract: Example methods, apparatus and articles to test batch configurations are disclosed. A disclosed example method includes identifying, using a processor, an execution path through a batch configuration of a process control system, generating a test plan for the execution path, stimulating the process control system to execute the test plan, and recording a result of the test plan.

Abstract: Example methods, apparatus and articles of manufacture to test process control systems are disclosed. A disclosed example method includes obtaining user inputs to a first process control system, obtaining process inputs and first process outputs of the first process control system, providing the user inputs and the process inputs to a second process control system to operate the second process control system, obtaining second process outputs of the second process control system operated with the user inputs and the process inputs, and comparing the first and second process outputs to determine whether the second process control system is implemented as intended.

Abstract: A process controller adaptation and tuning technique uses a closed loop adaptation cycle that performs an autocorrelation analysis on the prediction error or the control error of a process control system to determine if significant process model mismatch exists or to determine an increase or a decrease in process model mismatch over time. The adaptation and tuning technique may perform a controller tuning cycle when the determined model mismatch raises above a predetermined level.

Abstract: Example methods, apparatus and articles to test batch configurations are disclosed. A disclosed example method includes identifying, using a processor, an execution path through a batch configuration of a process control system, generating a test plan for the execution path, stimulating the process control system to execute the test plan, and recording a result of the test plan.

Abstract: An MPC controller technique integrates feedback control performance better than methods commonly used today in MPC type controllers, resulting in an MPC controller that performs better than traditional MPC techniques in the presence of process model mismatch. In particular, MPC controller performance is enhanced by adding a tunable integration block to the MPC controller that develops an integral component indicative of the prediction or other control error, and adds this component to the output of an MPC controller algorithm to provide for faster or better control in the presence of model mismatch, which is the ultimate reason for the prediction error in the first place. This technique enables the MPC controller to react more quickly and to provide better set point change and load disturbance performance in the presence of model mismatch, without decreasing the robustness of the MPC controller.

Abstract: An MPC adaptation and tuning technique integrates feedback control performance better than methods commonly used today in MPC type controllers, resulting in an MPC adaptation/tuning technique that performs better than traditional MPC techniques in the presence of process model mismatch. The MPC controller performance is enhanced by adding a controller adaptation/tuning unit to an MPC controller, which adaptation/tuning unit implements an optimization routine to determine the best or most optimal set of controller design and/or tuning parameters to use within the MPC controller during on-line process control in the presence of a specific amount of model mismatch or a range of model mismatch.