A processor may receive an input dataset. The input dataset may include a plurality of waterpipe components and one or more performance factors of the waterpipe system. A processor may generate a digital twin of the waterpipe system using the input dataset. A processor may simulate, using the digital twin, one or more features of the waterpipe system. The simulating may include a forecast having one or more predicted conditions associated with the waterpipe system.

A building system includes one or more storage devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to receive an unstructured user question from a user device of a user and query a graph database based on the unstructured user question to extract context associated with the unstructured user question from contextual information of a building stored by the graph database, wherein the graph database stores the contextual information of the building through nodes and edges between the nodes, wherein the nodes represent equipment, spaces, people, and events associated building and the edges represent relationships between the equipment, spaces, people, and events. The instructions further cause the one or more processors to retrieve data from one or more data sources based on the context and compose a presentation based on the retrieved data.

An information map is obtained by an agricultural system. The information map maps values of a topographic characteristic to different geographic locations in a field. An in-situ sensor detects values of an environmental characteristic as an agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts the environmental characteristic at different locations in the field based on a relationship between the values of the topographic characteristic and the values of the environmental characteristic detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

A method which includes steps of providing a state space model of behaviour of a physical system, the model including covariances for state transition and measurement errors, providing a data based regression model for prediction of state variables of the physical system, observing a state vector comprising state variables of the physical system, determining a prediction vector of state variables based on the state vector, using the regression model, and combining information from the state space model with predictions from the regression model through a Bayesian filter, is provided.

A variable refrigerant flow (VRF) system for a building includes an outdoor VRF unit, a plurality of indoor VRF units, a battery, and a predictive VRF controller. The outdoor VRF unit includes powered VRF components configured to apply heating or cooling to a refrigerant. The indoor VRF units are configured to use the heated or cooled refrigerant to provide heating or cooling to a plurality of building zones. The battery is configured to store electric energy and discharge the stored electric energy for use in powering the powered VRF components. The predictive VRF controller is configured to optimize a predictive cost function to determine an optimal amount of electric energy to purchase from an energy grid and an optimal amount of electric energy to store in the battery or discharge from the battery for use in powering the powered VRF components at each time step of an optimization period.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing material processing. In one aspect, a method includes collecting, from a set of sensors, a set of current manufacturing conditions. Based on the set of current manufacturing conditions collected from the sensors, a set of current qualities of a material currently being processed by manufacturing equipment is determined. A baseline production measure for processing the material according to the set of current qualities is obtained. A candidate set of manufacturing conditions that provide an improved production measure relative to the baseline production measure is determined. A set of candidate qualities for the material produced under the candidate set of manufacturing conditions is determined. A visualization that presents both of the set of candidate qualities of the material and the set of current qualities of the material currently being processed is generated.

The present disclosure provides a method for managing a pipe network of natural gas. The method may comprise: obtaining pipe network information of natural gas in at least one area, the pipe network information including a running time of a system of the pipe network of the natural gas and gas leakage information of thepipe network; extracting feature information based on the running time and the gas leakage information;predicting a maintenance time of the pipe network by inputting the feature information into a maintenance time prediction model.

Responding to grid events is provided. The system determines, based on an event, to modify an electrical load of a site. The system selects a parameter for the site to adjust to modify the electrical load. The system identifies a script constructed from previously processed interactions between a human-machine interface of the building management system to adjust the parameter for the site. The system establishes a communication session with a remote access agent executed by a computing device of the site to invoke the building management system of the site. The system generates a sequence of commands defined by the script to adjust the one or more parameters for the site. The system transmits the sequence of commands to cause the remote access agent to execute the sequence of commands on the human-machine interface of the building management system to modify the electrical load of the site.

In order to control a technical system, a system state of the technical system is continually detected. By a trained first control model, a subsequent state of the technical system is predicted on the basis of a sensed system state. Then, a distance value is determined for a distance between the predicted subsequent state and an actually occurring system state. Furthermore, a second control model is trained by the trained first control model to predict the distance value on the basis of a sensed system state and on the basis of a control action for controlling the technical system. A subsequent state predicted by the first control model is then modified on the basis of a distance value predicted by the trained second control model. The modified subsequent state is output in order to control the technical system.

A field device, method, and non-transitory computer readable medium provide for cost of lost opportunity to operators in real-time using an advance process control. The field device includes a memory and a processor operably connected to the memory. The processor receives current values and average values for controlled variables and manipulated variables; determines costs of lost opportunity for each of controlled variable variance issues, limit issues, model quality issues, inferential quality issues, and variable model issues based on the current values and the average values of the controlled variables; and stores the costs of lost opportunity for the field device.