Techniques are described for implementing automated control systems that manipulate operations of specified target systems, such as by modifying or otherwise manipulating inputs or other control elements of the target system that affect its operation (e.g., affect output of the target system). An automated control system may in some situations have a distributed architecture with multiple decision modules that each controls a portion of a target system and operate in a partially decoupled manner with respect to each other, such as by each decision module operating to synchronize its local solutions and proposed control actions with those of one or more other decision modules, in order to determine a consensus with those other decision modules. Such inter-module synchronizations may occur repeatedly to determine one or more control actions for each decision module at a particular time, as well as to be repeated over multiple times for ongoing control.

Real-time dynamic optimization of a process model in an online model-based process control computing environment. A mixed integer nonlinear programming (MINLP) solver utilizes a switch to activate and deactivate a first-principle model of a process unit. The switch enables MINLP behavior by attaching to the first-principle model.

A cascaded MPC system includes an upper tier controller and lower tier controller having stored constraints for controlling a process having manipulated variables (MVs), controlled variables (CVs), and conjoint manipulated variable (CMV). The upper tier passes a target value for the CMV to the lower tier which optimizes for determining a local optimal operating point for the MVs, CVs, and CMV, moves towards the target value starting at the CMVs local operating point, and optimizes for identifying of the constraints as selected constraint(s) when the moving is truncated, passes the selected constraint(s) to the upper tier which performs an overall optimization for the process using the selected constraint to generate an optimal value for the CMV that lower tier uses as a new CM V target value for redetermining updated local optimal operating points for the MVs, CVs, and CMV, and for controlling the process utilizing the updated operating points.

A priori geo-referenced data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the geo-referenced data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

Methods and systems are described for a computer-implemented complex adaptive systems metrology (CASM) technique for generating universally and mathematically standardized scores that quantify longitudinal evidence for either temporal-interaction scores or temporal-interaction benefit-and-harm scores to determine a quantitative significance estimate of scores for either standardized temporal-interaction scores or the standardized temporal-interaction benefit-and-harm scores.

A chemical dosing optimization apparatus includes a chemical dosing optimization part and a chemical dosing output control part, wherein the chemical dosing optimization part receives real-time data at least from a water treatment plant treating feed water by dosing a chemical and providing a treated water, analyzes the real-time data through a water treatment model in response to receiving the real-time data, derives a prediction value for predicting a state of the treated water of the water treatment plant, and derives a control value based on the prediction value through a controller, such that the control value is to set a minimum of a chemical dosage to be dosed in the feed water while the state of the treated water of the water treatment plant is maintained in a normal range, and wherein the chemical dosing output control part provides the control value to a water treatment control device.

According to an embodiment, an information processing device includes one or more processors configured to: extract a first subgraph indicating a connection relation of a first node that matches with a schema in which a connection relation between nodes is defined, among first nodes included in a first graph indicating a connection relation of the first nodes each associated with an attribute; determine a template that is output by a first model by inputting input information based on the first subgraph to the first model, the first model being learned to input the input information based on a subgraph indicating a connection relation between the nodes and to output the template of a formula; and set one of the attributes associated with the first nodes as the variable to be used in the determined template to generate the formula.

A method for recording data relating to the performance of an oil and gas flow network uses statistical data to represent raw data in a compact form. Categories are assigned to time intervals in the data. The method comprises: (1) gathering data covering a period of time, wherein the data relates to the status of one or more control point(s) within the flow network and to one or more flow parameter(s) of interest in one or more flow path(s) of the flow network; (2) identifying multiple time intervals in the data during which the control points and the flow parameter(s) can be designated as being in a category selected from multiple categories; (3) assigning a selected category of the multiple categories to each one of the multiple datasets that are framed by the multiple time intervals; and (4) extracting statistical data representative of some or all of the datasets identified in step (2) to thereby represent the original data from step (1) in a compact form including details of the category assigned to each time interval in step (3).

A multiple fluid model tool for training and/or refining of fluid models using disparate and/or aggregated machine data is presented. For example, a system includes a modeling component, a machine learning component, a three-dimensional design component and a data collection component. The modeling component generates a three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a machine learning process associated with the three-dimensional model. The three-dimensional design component provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the mechanical device on the three-dimensional model based on the one or more characteristics of the mechanical device. The data collection component collects machine data via a communication network to update the three-dimensional model associated with the three-dimensional design environment.

A method for managing crystallization process in a process control plant is provided. The method includes capturing process parameters of an operating reactor unit in a process control plant. The method includes predicting desired process parameters based on first set of parameters and the captured process parameters. The first set of parameters includes information related to process dynamics and process disturbances associated with the operating reactor unit. Furthermore, the method includes controlling process control loop associated with the operating reactor unit based on the desired process parameters and the first set of parameters.