Systems for self-organizing data collection and storage in a manufacturing environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the manufacturing system, 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 at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.

The present disclosure describes systems for data collection in an industrial environment. A system can include an industrial system including a plurality of components, at least one component operatively coupled to a sensor, and a sensor communication circuit to interpret a plurality of sensor data values in response to a sensed parameter group. A pattern recognition circuit may determine a recognized pattern value in response to at least a portion of the data values, wherein the recognized pattern value includes a secondary value. A sensor learning circuit may update the sensed parameter group in response to the recognized pattern value and adjust the interpreting the plurality of sensor data values in response to the updated sensed parameter group. The pattern recognition circuit and the sensor learning circuit iteratively determine the recognized pattern value and update the sensed parameter group to improve a sensing performance value.

Computer-based process modeling and simulation methods and systems combine first principles models and machine learning models to benefit where either model is lacking. In one example, input values (measurements) are adjusted by first principles techniques. A machine learning model of the chemical process of interest is trained on the adjusted values. In another example, a machine learning model represents the residual (delta) between a first principles model prediction and empirical data. Residual machine learning models correct physical phenomena predictions in a first principles model of the chemical process. In another example, a first principles simulation model uses the process input data and predictions of the machine learning model to generate simulated results of the chemical process. The hybrid models enable a process engineer to troubleshoot the chemical process, enable debottlenecking the chemical process, enable optimizing performance of the chemical process at the subject industrial plant, and enable automated process control.

Systems and methods are provided for improving skincare product formulations to address predicted skin trends. A biomarker analysis system is used to determine concentrations of various protein biomarkers of a user. A skin diagnosis computing device uses the protein biomarker concentrations to determine one or more skin trends, and determines one or more skincare product ingredients to address the skin trends. A skincare product is compounded that includes the one or more skincare product ingredients.

Examples of techniques for monitoring and controlling the pH of a drilling fluid are disclosed. In one example implementation, a method may include monitoring, by a first sensor, a first pH-value of the drilling fluid prior to the drilling fluid being heated. The method may further include monitoring, by a second sensor, a second pH-value of the drilling fluid subsequent to the drilling fluid being heated. The method may further include determining, by a processing system, an amount of additive being added to the drilling fluid to alter the pH of the drilling fluid.

The present disclosure describes systems for self-organized, network-sensitive data collection in an industrial environment. A system can include an industrial system including a plurality of components, at least one operatively coupled to a sensor, a sensor communication circuit to interpret sensor data values, and a system collaboration circuit to communicate a portion of the data values to a storage target according to a sensor data transmission protocol. A transmission environment circuit may determine transmission conditions corresponding to the communication of the portion of data values to the storage target and a network management circuit update the data transmission protocol in response to the transmission conditions.

An apparatus, methods and systems for collecting data related to an industrial environment are disclosed. A monitoring system can include a data collector communicatively coupled to a plurality of input channels relating to an aspect of an industrial production process, a data storage structured to store a plurality of detection values, a data analysis circuit structured to interpret a subset of the detection values to determine a state value comprising at least one of a process state or a component state, an optimization circuit structured to analyze a subset of the detection values and the state value, using at least one of a neural net or an expert system, to provide an adjustment recommendation, and an analysis response circuit structured to adjust the industrial production process in response to the adjustment recommendation.

An automated seed treatment system is adapted for on-site operation at a retail seed distributor. A sealed seed-treater vessel is configured to apply a plurality of chemical treatments to a batch of seed based on a seed treatment recipe. A programmable system controller is electrically coupled to a pump controller of each of a plurality of pump-stations. The programmable system controller is configured to receive a material transfer indication from each of the plurality of pump-stations and issue commands to the pump controller of each pump-station in response to the seed treatment recipe. The programmable system controller is configured to collect operational data representing at least consumption of chemical from the chemical container at each of the pump-stations based on the corresponding material transfer indication during seed treatment and to provide the operational data to a remotely hosted information system located remotely from the site of the retail seed distributor and accessible to at least one third party that is distinct from the retail seed distributor.

A non-transitory computer readable medium includes instructions configured to adapt a controller to maintain a first target environment in a bioreactor vessel containing a population of cells for a first incubation period to produce a population of genetically modified cells from the population of cells, initiate a flow of media to the bioreactor vessel, maintain a second target environment in the bioreactor vessel for a second incubation period to produce an expanded population of genetically modified cells.

Systems, methods and apparatus for data collection in an industrial environment are described. The system may include a multi-sensor acquisition component, the multi-sensor acquisition component including a plurality of inputs and outputs, a plurality of sensors operatively coupled to at least one of a plurality of components of an industrial process, and each communicatively coupled to at least one of the plurality of inputs of the multi-sensor acquisition component, a sensor data storage profile circuit structured to determine a data storage profile, a sensor communication circuit communicatively coupled to the plurality of outputs of the multi-sensor acquisition component, a sensor data storage implementation circuit structured to sensor data values in response to the data storage profile; and a data marketplace circuit structured to store at least a second portion of the plurality of sensor data values on a data marketplace.