Rockwell Automation Technologies Incorporated

Scotty Bromfield

David Christopher Mazur

Rob Alan Entzminger

Andrew J Schaeffler

Timothy L Stanford

Peter A Morell

Barry N Elliott

Nicole R Bulanda

Fabio M Mielli

Corey A Peterson

Steven Marshall

Mithun Mohan Nagabhairava

Pieter Wolmarans

Jonathan Armstrong

Chidiebere U Egbuna

Andries Ernst Kruger

Steven Clohessy

Techniques to facilitate adaptive optimization and control of flotation cell processing are disclosed herein. In at least one implementation, a computing system receives a plurality of flotation cell process variables associated with a flotation cell process. The flotation cell process variables are fed into a machine learning model associated with the flotation cell process to determine improved settings for the flotation cell process. The improved settings for the flotation cell process are provided to an industrial controller that controls at least one aspect of the flotation cell process to improve the flotation cell process.

Adaptive control of industrial automation for mining flotation cells

Techniques to facilitate synchronization of industrial assets in an industrial automation environment are disclosed herein. In at least one implementation, a computing system receives time-series industrial process data associated with a plurality of process subsystems of an industrial automation process. The time-series industrial process data is fed into a machine learning model associated with the industrial automation process to dynamically generate a process duration prediction for a first one of the process subsystems and responsively determine an updated set point for a second one of the process subsystems based on the process duration prediction for the first one of the process subsystems. The updated set point for the second one of the process subsystems is provided to an industrial controller associated with the second one of the process subsystems.

Synchronization of industrial automation process subsystems

A method for operating a plurality of geographically distributed compressors, wherein the outputs of the geographically distributed compressors are coupled to a compressed air distribution system within an industrial automation environment, is provided. The method includes receiving performance data from the plurality of compressors, and receiving current environment data from a plurality of sensors within the industrial automation environment, including at least some sensors within the compressed air distribution system. The method also includes assigning a guide vane weight to each compressor based at least in part on a capacity of each compressor, identifying a target system air pressure, and processing the performance data, current environment data, guide vane weights, and target system air pressure to determine control settings for each of the plurality of compressors.

Industrial control system for distributed compressors

A compressor controller for operating a compressor within an industrial automation environment is provided. The compressor controller includes a control module, configured to control the compressor via control settings, and a machine learning module, coupled with the control module. The machine learning module is configured to receive a set of supervised data related to the compressor, and to train with the supervised data to produce a Newtonian physics model representing the inputs and outputs of the compressor within the industrial automation environment. The machine learning module is also configured to receive performance data related to the compressor, receive environment data related to the compressor, and to process the performance data and environment data to produce predicted future performance data for the compressor, and to produce control settings for the compressor.

Industrial control system with machine learning for compressors

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Johannesburg, South Africa

Scotty Bromfield