A method for obtaining new plant growth data according to an experimentation objective, the method including defining an experimentation objective; defining a set of experimentation alternatives; obtaining a data set of experimentation alternative conditions; obtaining a trained plant growth model including plant growth model parameters; defining an experimental design utility function based on the experimentation objective; selecting an experimentation plan from the set of experimentation alternatives by using the trained plant growth model, the experimental design utility function, and the data set of experimentation alternative conditions; and performing the selected experimentation plan to obtain new plant growth data.

A method including receiving a sequence of thermal data of a plant, wherein said sequence is sampled at least one location of said tissue while said tissue is being thermally disturbed, processing said thermal data to derive thermal values associated with each of said tissue locations, deriving at least one thermal variable at least one location on said plant, based, at least in part, on said processing, calculating a variance value of all said thermal variables associated with each of said locations and determining a state of said plant based on at least one location at which said variance value exceeds a predetermined threshold. The disclosure also includes a system and computer product for real time sorting of plants.

A control parameter optimizing system and an operation optimizing apparatus equipped therewith are provided, the system being applicable to an existing plant without modifying the control panel or equipment of the plant, the system further being capable of optimizing the operation control of the plant in accordance with diverse operational requirements. The system includes an objective function setting section, a plant model, and a control parameter optimizing section. The control parameter optimizing section includes an optimization control parameter selecting section and an optimization control parameter adjusting section. The optimization control parameter selecting section selects as an optimization control parameter the control parameter for optimizing an objective function based on control logic information extracted from a power plant. The optimization control parameter adjusting section adjusts the value of the optimization control parameter using the plant model in such a manner as to optimize the objective function.

Techniques for reconciling discrepancies between the runtime operation of a process plant and the configuration for the process plant allow for the process plant to be operated in a predictable and consistent manner. Additionally, techniques for reconciling discrepancies in the process plant enable inappropriate parameter values to be detected and reconciled efficiently and before such inappropriate values are included into configuration. Such techniques reduce the risk of downtime for online operation of the process plant to troubleshoot object configuration. A configuration engineer may provide one or more reconciliation instructions to reconcile the discrepancy. A configuration application then updates the process control environment of the process plant in accordance with the one or more reconciliation instructions. In some cases, the discrepancy is resolved by updating a configuration file for the object. In other cases, the discrepancy is resolved by updating the runtime instantiation of the object.

A plant operating condition setting support system for supporting the setting of a plant operating condition includes: a learning device that learns a regression model for calculating, from values of a plurality of state parameters indicating an operating condition of a plant and values of a plurality of manipulation parameters set to control an operation of the plant, a predicted value of an output indicating a result of operating the plant when the values of the plurality of manipulation parameters are set in the operating condition indicated by the values of the plurality of state parameters; and an operating condition setting support device that calculates the values of the plurality of manipulation parameters that should be set to control the operation of the plant, by using the regression model learned by the learning device.

A computing system for receiving operational data including process parameters generated by sensors in a plant. An analysis engine uses the operational data to automatically provide a first listing of worst performing process parameters, that when a selected poor performing process parameter is chosen generates a ranked filtered view of equipment parameters for associated processing equipment that may be affected by the selected poor performing parameter and a filtered view of recommendations for recognizing action(s) to fix the associated processing equipment and/or the selected poor performing process parameter, and/or a second listing of worst performing processing equipment that when a selected poor performing processing equipment is chosen generates a ranked filtered view of suspected process parameters that may be affected by the selected poor performing processing equipment along with a filtered view of recommendations for recognizing action(s) to fix the selected poor performing processing equipment and the suspected process parameters.

A control parameter optimizing system and an operation optimizing apparatus equipped therewith are provided, the system being applicable to an existing plant without modifying the control panel or equipment of the plant, the system further being capable of optimizing the operation control of the plant in accordance with diverse operational requirements. The system includes an objective function setting section, a plant model, and a control parameter optimizing section. The control parameter optimizing section includes an optimization control parameter selecting section and an optimization control parameter adjusting section. The optimization control parameter selecting section selects as an optimization control parameter the control parameter for optimizing an objective function based on control logic information extracted from a power plant. The optimization control parameter adjusting section adjusts the value of the optimization control parameter using the plant model in such a manner as to optimize the objective function.

The present teachings relate to a method comprising a plurality of sensors, and one or more functionally connected processing units, the method comprising: providing, at any of the one or more processing units, time-series residual data of a sensor object; the sensor object being a group of at least some of the sensors from the plurality of sensors, and wherein the residual data comprises, for each of the sensors of the sensor object, a residue signal which is a difference between the sensor's measured output and the sensor's expected output, monitoring, via any of the one or more processing units, a level signal; wherein the level signal is indicative of a collective time-based variation of the time-series residual data, monitoring, via any of the one or more processing units, an association signal; wherein the association signal is indicative of the variation and/or association structure of the time-series residual data, generating, via any of the one or more processing units, an anomaly event signal when at a given time a value of the level signal and/or a value of the association signal changes from an expected value of the respective signal at or around that time. The present teachings also relate to a monitoring and/or control system for a plant comprising a plurality of sensors, wherein the system comprises one or more processing units configured to perform the method steps of any of the steps herein disclosed, and a computer software product.

The invention refers to an edge computing device 120 for processing data 112 acquired with respect to a production process of an industrial plant comprising a plant control system 110, wherein the control system comprises a server 111. The device comprises a first unit 125 configured to be communicatively coupled to the server for receiving the data from the system. A second unit 121 is configured to provide a container runtime environment 122 configured to run a container on the second unit. A process container 123 is configured to run on the environment, wherein the process container comprises a program configured to process data acquired with respect to a production process of the industrial plant when running inside the process container. The first unit is communicatively coupled to the second unit for providing the received data to the environment, wherein the program is configured to process the provided data.

A method of monitoring a plant by using a Mahalanobis distance calculated from data of a plurality of variables each of which indicates a state of the plant includes: a dividing step of dividing a range of a single variable which indicates a state of the plant into a plurality of first range bands on the basis of a frequency distribution of the single variable; and a unit space creating step of creating a plurality of unit spaces which serve as a basis of calculation of the Mahalanobis distance on the basis of the respective data of the plurality of variables respectively corresponding to a plurality of second range bands of the single variable determined on the basis of the plurality of first range bands.