Emnet, LLC

Xylem Vue Inc.

Timothy P Ruggaber

Luis Montestruque

Ruben Kertesz

Bryant McDonnell

Timothy P Braun

Alireza Partovi

Fluid stream management systems and methods relating thereto are described. The fluid management system includes a neural network, which comprises: (i) an input layer that is communicatively coupled to one or more fluid facility sensors and/or one or more pre-processing flow sensors such that one or more of the flow condition attribute values are received the neural network; (ii) one or more intermediate layers, which are configured to constrain one or more of the flow condition attribute values to arrive at modified flow condition attribute values; (iii) an output layer, which transmits, one or more modified flow condition attribute values to a downstream control device. This control device and other computation devices perform certain calculations that ultimately inform a flow controller, which in turn, instructs a flow-directing device regarding management of fluid streams.

Fluid stream management systems and methods thereof

The present invention is a method and system for control of sewer systems. The invention may implement ABRTC (RTC) to dynamically control of sewer systems to accomplish a desired outcome, such as CSO or SSO reduction or reduce water pollution or reduce surface flooding. In one embodiment, an Agent-Based RTC System assigns an agent to each sewer asset. Each agent measures the current state of its corresponding asset and assigns a virtual cost to any incoming flow to the asset based on the asset hydraulic or water quality state. The virtual cost is communicated to other assets upstream or downstream that may change the hydraulic or water quality state of the asset communicating the virtual cost. The network of agents may thus control the sewer to achieve the desired objective, such as asset hydraulic or water quality state.

System and method for agent-based control of sewer infrastructure

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South Bend, IN, United States of America

Timothy P Ruggaber