The disclosure provides a method and system for optimizing production of a natural gas liquefaction process, the method comprising the steps of: selecting at least one manipulated variable (MV) for controlling the liquefaction process; selecting at least one control variable (CV), the at least one control variable at least comprising liquefied natural gas (LNG) throughput; providing at least one model, each model providing a dependency of the at least one control variable (CV) on the at least one manipulated variable (MV); using the at least one model to estimate LNG throughput for at least one of the manipulated variables (MV); obtaining process data from the liquefaction process, the process data at least including observed values of LNG throughput; creating a gain matrix based on said interdependencies; and using the gain matrix to optimize a process control system of the liquefaction process.

Disclosed is a process for creating an event prediction model that employs a data-driven approach for selecting the model's input data variables, which, in one embodiment, involves selecting initial data variables, obtaining a respective set of historical data values for each respective initial data variable, determining a respective difference metric that indicates the extent to which each initial data variable tends to be predictive of an event occurrence, filtering the initial data variables, applying one or more transformations to at least two initial data variables, obtaining a respective set of historical data values for each respective transformed data variable, determining a respective difference metric that indicates the extent to which each transformed data variable tends to be predictive of an event occurrence, filtering the transformed data variables, and using the filtered, transformed data variables as a basis for selecting the input variables of the event prediction model.

A system for flight control system using simulator data for an electric aircraft is presented. The system includes a computing device, the computing device configured to receive a plurality of measured flight data, simulate a plurality of aircraft performance model outputs as a function of a flight simulator and the plurality of measured flight data, determine a moment datum as a function of the plurality of measured flight data and the plurality of aircraft performance model outputs, generate an allocation command datum as a function of the moment datum and the plurality of aircraft performance model outputs, and perform a torque allocation on a flight component of a plurality of flight components as a function of the allocation command and the moment datum.

Provided is an apparatus including: a first acquisition unit acquiring an operation plan of a piece of equipment, and at least identification information of a parameter among target setting data used for learning of an operation model operating the piece of equipment, the target setting data including identification information of a parameter for which a target range is to be set among parameters relating to the piece of equipment and a target range set for the parameter; and a first learning processing unit performing, by using learning data including the identification information of the parameter and the operation plan acquired by the first acquisition unit, learning processing of a target setting model outputting at least one of the identification information or the target range of the parameter among the target setting data that should be used for learning of the operation model, in response to the operation plan being input.

A flow control device is configured to control fluid flow in an HVAC system. The flow control device includes a valve, an actuator configured to open and close the valve, and one or more sensors. The flow control device further includes a fault detection and correction agent configured to receive data from the one or more sensors, analyze the data according to a set of rules, and detect whether one or more faults have occurred. In response to detecting a fault, the fault detection and correction agent is configured to either operate the actuator to open or close the valve or initiate a corrective action to be taken by another device in the HVAC system.

An automatic optimization method and an automatic optimization system of a diagnosis model are provided. The automatic optimization method includes: obtaining equipment parameters; selecting a target model; selecting and converting a hyperparameter into a gene sequence, randomly generating a plurality of gene sequences to be optimized and adding them to a gene sequence set; performing a gene evolution process to generate a plurality of progeny gene sequences; performing a region search process on the plurality of progeny gene sequences to generate a plurality of new progeny gene sequences and add them to the gene sequence set; and in response to meeting the evolution completion condition, using the gene sequence set as an optimal gene sequence set for configuration of the target model and generation of a plurality of candidate diagnosis models.

There is provision of an operation support system for supporting an operation of a demand-supply system including a resource supplying facility and a resource demander facility. The operation support system receives an input of a demand-supply system model of the demand-supply system, facility-capacity information of the resource supplying facility, and analysis condition information defining a plurality of variables and an external variable of the demand-supply system, to generate a correlation logical formula representing a relation among the plurality of variables and the external variable. The operation support system further acquires performance data of the demand-supply system which includes actual values of the plurality of variables and the external variable during an operation, and renders an area represented by the correlation logical formula when the external variable is equal to an actual data included in the performance data and a point represented by the performance data of the plurality of variables.

A method includes obtaining data associated with operation of an industrial process controller and identifying impacts of operational problems of the industrial process controller. The method also includes generating a graphical display for a user, where the graphical display presents one or more recommended actions to reduce or eliminate at least one of the impacts of at least one of the operational problems. The method further includes triggering at least one of the one or more recommended actions based on input from the user. The method could also include executing one or more analytic algorithms to process the obtained data and identify the operational problems of the industrial process controller. Each of the one or more analytic algorithms could be instantiated as a container, and multiple containers could be instantiated and executed as needed. Results of executing the one or more analytic algorithms could be transformed into a standard format.

A building system including one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to determine an average of a minimum half of sorted energy consumption values for a first time period and determine an average of a maximum half of sorted energy consumption values for a second time period. The instructions also cause the processor to determine a ratio of the average of the minimum half of sorted energy consumption values for the first time period to the average of the maximum half of sorted energy consumption values for the second time period, compare the calculated ratio to an adaptively tunable threshold value and activate a system responsive to the calculated ratio exceeding the adaptively tunable threshold value.

A controller for a technical system is trained using a machine learning method. For this purpose, a chronological sequence of training data is detected for the machine learning method, the training data including both state data as well as control action data of the technical system. A change in the control action data over time is detected specifically and correlated with changes in the state data over time within different time windows, wherein a time window specific correlation value is ascertained in each case. A resulting time window is then ascertained on the basis of the ascertained correlation values, and the training data which is found within the resulting time window is extracted in a time window-specific manner. The controller is trained by means of the machine learning method using the extracted training data and thereby configured to control the technical system.