Neural Sewer Flow Control for Distant Facility Load Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional sewer management systems face challenges in effectively predicting and managing load values for fluid facilities, particularly distant facilities, due to the inability to account for changes in fluid condition attributes over time and varying processing capacities.
Innovation Solution
The implementation of a fluid stream management system that includes downstream and upstream processing sub-systems, neural networks, and fluid flow controllers to determine and adjust fluid flow based on flow condition attribute values, load values, and modified load values, using sensors, processors, and controllers to optimize fluid distribution and treatment across interconnected facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sewer management systems are used to manage fluid facilities, then the system structure is simple, but the ability to predict and manage load values for distant facilities is insufficient
Solution Approach 1:
The system divides the sewer network into multiple fluid facilities with distinct processing capacities. Each facility is monitored independently with sensors that measure flow condition attributes, allowing the system to manage complex networks through modular, segmented control units rather than treating the entire system as a single entity.
Solution Approach 2:
The system performs preliminary calculations of load values and modified load values using neural networks before fluid actually reaches distant facilities. By predicting future load conditions based on current flow attributes and processing capacities, the system can proactively manage fluid distribution to optimize facility utilization and prevent overload conditions.
2Reliability
If the system monitors and manages distant fluid facilities, then the management coverage is improved, but the computational complexity increases
Solution Approach 1:
The system introduces an intermediary computational layer that calculates modified load values by adjusting base load values based on fluid condition attributes such as flow rate, composition, and temperature. This intermediary calculation step translates complex multi-variable inputs into simplified control parameters that can be used for real-time decision-making about fluid routing and facility management.
Solution Approach 2:
The system continuously monitors flow condition attributes at multiple facilities and uses this feedback to dynamically adjust fluid distribution decisions. Sensors provide real-time data on flow rates, fluid composition, and facility status, which feeds back into the control system to optimize routing decisions and balance load across facilities based on current and predicted conditions.
3Productivity
If fluid flow is optimized across multiple facilities, then processing efficiency is improved, but the control system complexity increases
Solution Approach 1:
The control system dynamically adjusts fluid routing decisions based on real-time and predicted load conditions at various facilities. Rather than using fixed routing rules, the system continuously adapts flow distribution to match current facility capacities and forecasted demands, optimizing processing efficiency while managing control complexity through adaptive rather than static control logic.
Data Source
AI summary
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.