Systems for self-organizing data collection and storage in a refining environment are disclosed. An example system may include a swarm of mobile data collectors structured to interpret a plurality of sensor inputs from sensors in the refining environment, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of a plurality of refining system components disposed in the refining environment, and wherein the plurality of refining system components is structured to contribute, in part, to refining of a product. The self-organizing system organizes a swarm of mobile data collectors to collect data from the system components, and at least one of a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs.

Methods and systems for sensor fusion in a production line environment are disclosed. An example system for data collection in an industrial production environment may include an industrial production system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the components; a sensor communication circuit to interpret a plurality of sensor data values in response to a sensed parameter group; and a data analysis circuit to detect an operating condition of the industrial production system based at least in part on a portion of the sensor data values; and a response circuit to modify a production related operating parameter of the industrial production system in response to the detected operating condition.

A manifold can determine a turnover scheme including a target turnover schedule of target turnover levels, based on an operation schedule and efficiency setting for a fluid distribution. Each target turnover level can correspond to a volume of fluid to be cycled through the fluid distribution system over a period of time. The manifold can operate respective valves and a supply device based on a target turnover level and determine a current turnover level from a flowrate detected by a flow sensor for at least one of the valves. The manifold can receive a current usage of the fluid distribution system and determine a required turnover level. An override status for the turnover scheme can be based on the efficiency setting and a comparison of the current, target, and the required turnover levels, and the manifold can operate respective valves and the supply device based on the override status.

A model predictive maintenance (MPM) system for building equipment includes an equipment controller configured to operate the building equipment to affect a variable state or condition in a building and an operational cost predictor configured to predict a cost of operating the building equipment over a duration of an optimization period. The MPM system includes a maintenance cost predictor configured to predict a cost of performing maintenance on the building equipment over the duration of the optimization period and an objective function optimizer configured to optimize an objective function to predict a total cost associated with the building equipment over the duration of the optimization period. The objective function includes the predicted cost of operating the building equipment and the predicted cost of performing maintenance on the building equipment.

A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.

Systems and methods for production line optimization include: receiving, from each of a plurality of equipment along the production line, equipment data at each interval of a plurality of intervals, the equipment data including equipment speed data, equipment state data, and equipment fault data; analyzing the equipment data with one or more optimization rules to identify an optimization setting; and, outputting the optimization setting to a production line device for modification of the production line.

Methods and systems for data collection in an environment including pumps and fans are disclosed. A monitoring system may include a data collector communicatively coupled to a plurality of input channels, wherein the input channels are communicatively coupled to sensors measuring operational parameters of a pump or fan. A data storage may store one or more frequencies related to an operation of the pump or fan, and a data acquisition circuit may interpret a plurality of detection values from the collected data. A frequency evaluation circuit may detect a signal on one of the input channels at a frequency higher than the one or more frequencies at which the pump or fan operates.

A method includes receiving, from sensors, current trace data including current sensor values associated with producing products. The method further includes processing the current trace data to identify features of the current trace data and providing the features of the current trace data as input to a trained machine learning model that uses a hyperplane limit for product classification. The method further includes obtaining, from the trained machine learning model, outputs indicative of predictive data associated with the hyperplane limit and processing the predictive data and the hyperplane limit to determine: first products associated with a first product classification and second products associated with a second product classification based exclusively on the subset of the plurality of features; and third products associated with the first product classification or the second product classification based on an additional feature not within the subset.

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.

Systems, methods and apparatus include a computer-implemented method that performs the following. First inputs are received from a first monitoring of the main power distribution system (MPDS) at a safety instrumented system (SIS) logic solver. The first inputs include power status information for equipment monitored by a main distribution switchgears (MDS). A second input is received at the SIS logic solver, from a second monitoring of the MPDS. The second input includes power status information for equipment monitored by a motor control center (MCC). SIS logic solver logic in the SIS logic solver is executed by the SIS logic solver using at least one of the first and second MPDS inputs. Upon confirmation of a power failure in the MPDS detected instantly by MDS or MCC functional safety capable controllers, the SIS logic solver logic generates an output signal to cut an incoming feed to processing plants, leading to discard power failure as a worst credible design case scenario for flare and disposal relief systems, the worst credible design case scenario caused by at least one of the first inputs and the second inputs.