A first valve of a manifold for a fluid distribution system may regulate a fluid flow to a first fluid handling device (“FHD”). A second valve of the manifold may communicate with a second FHD, a reservoir, or a recirculation line. A target flow condition for the manifold may be determined by a manifold control system (“MCS”) based on a device setting received for the first FHD. The MCS may determine a fluid distribution system operation for obtaining the target flow condition based on the target flow condition, a flowrate of the fluid flow, and an operational state of a supply device. The operation may include the MCS controlling at least one of the supply device, the first valve, and the second valve to change the flowrate. The MCS may continuously operate at least one manifold valve to maintain the target flow condition once exhibited by the manifold.

Disclosed herein is a method of repairing a component. The method includes scanning a damaged area of the component, and preparing a repair plan in response to the scanning. The method may also include providing the repair plan to a guided tool having a position correcting controller, and removing damaged material from the component in preparation for a repair operation. An apparatus is also disclosed that includes a computing device configured for performing actions. The computing device includes a processor and a local memory. The actions include detecting damage to the component, recording position information of the detected damage, and incorporating the position information in the repair plan.

A system and method for controlling operations of a fluid distribution may include a first manifold receiving a next mode of operation for the fluid distribution system. The first manifold may calculate first and second flow requirements for the first and second manifolds that may respectively include a first and second total flowrates from the first and second manifolds. The first manifold may determine required operation states for valves of the first manifold and the second manifold for the next mode based on the first and second flow requirements. The first manifold may be controllably operated to cause the second manifold and a supply device of the fluid distribution system to operate in the required operation states and provide first and second flow requirements. The first manifold may direct the second manifold to independently balance individual outlet flowrates of the second manifold while continuing to provide the second flow requirements.

Various embodiments described herein relate to management of industrial process optimization related to batch operations. In this regard, an optimization request to optimize an industrial process that produces an industrial process product is received. In response to the optimization request, product spent characteristics for one or more blending components of a batch operation subprocess are determined. Also in response to the optimization request, demand data for one or more feed products associated with the one or more blending components is updated based on the product spent characteristics and inventory data indicative of an inventory level for the one or more feed products. Furthermore, a control signal configured based on the demand data is transmitted to a controller configured for optimization associated with the industrial process that produces the industrial process product.

A system for controlling an operation of a machine for performing a task is disclosed. The system submits a sequence of control inputs to the machine and receives a feedback signal. The system further determines, at each control step, a current control input for controlling the machine based on the feedback signal including a current measurement of a current state of the system by applying a control policy transforming the current measurement into the current control input based on current values of control parameters in a set of control parameters of a feedback controller. Furthermore, the system may iteratively update a state of the feedback controller defined by the control parameters using a prediction model predicting values of the control parameters and a measurement model updating the predicted values to produce the current values of the control parameters that explain the sequence of measurements according to a performance objective.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.

A control method includes: monitoring a statistic obtained by performing a multivariate analysis on a plurality of parameters; extracting, from the plurality of parameters, a predetermined number of higher-order parameters in terms of an influence degree on fluctuation of the statistic; generating a plurality of experimental patterns according to an experimental design method; acquiring a measurement result of a specific parameter indicating quality of a product when at least one device is controlled according to each of the plurality of experimental patterns; setting a new target value of the predetermined number of higher-order parameters in order to stabilize a value of the specific parameter within a management range based on the measurement result; and controlling the at least one device such that the predetermined number of higher-order parameters approaches the new target value.

Disclosed are embodiments for automatically resolving faults in a complex network system. Some embodiments monitor one or more of system operational parameter values and message exchanges between network components. A machine learning model detects a fault in the complex network system, and an action is selected based on a cause of the fault. After the action is applied to the complex network system, additional monitoring is performed to either determine the fault has been resolved or additional actions are to be applied to further resolve the fault.

A method for automatically adapting a predictive model used to control a heating, ventilation, or air conditioning (HVAC) system in a building to compensate for a detected fault in the HVAC system is shown. The method includes obtaining an indication of the detected fault in the HVAC system or a zone in the building. The method further includes determining a predicted impact of the detected fault on an operational performance of the HVAC system. The method further includes adjusting one or more parameters of the predictive model based on the predicted impact of the detected fault to generate a fault-adapted predictive model. The method further includes operating the HVAC system to control an environmental condition of the building using the fault-adapted predictive model.