Optimized Water Distribution
Use Case Problem:
SmartCloud's Industrial AI technologies enable water distribution solutions that optimize operational goals in real time, such as for costs, quality, and compliance.
Meeting water demand is increasingly difficult, particularly in stressed water grids.
Decreasing supplies require water utilities to meet growing demand by better utilizing resources. Optimizing water resources is complicated by many situational factors, including grid network system dynamics and the sheer volume of grid measurements to monitor, not to mention missing, delayed, and corrupted measurements. There are also regulatory permits to contend with, plus water quality regulations, demand variance, and energy cost containment for pumps and purification facilities.
Use Industrial AI to model, simulate, forecast and optimize water grid in real time.
AI modeling: Represent grid system dynamics by combining semantic web modeling of water grid data, which captures connected relationships of equipment, with first-principle engineering models.
AI reasoning: Employ neural networks and other machine learning techniques to forecast water consumption, in addition to using genetic algorithms, linear programming, and other techniques to optimize overall operation.
Simulations: Use AI-driven water grid models to run simulations that assess alternate operating scenarios.
AI Agents: Process data flows with AI agents to ensure missing, delayed, or corrupted measurements do not adversely impact decisions.
Logic flow orchestration: Calculate water grid control settings in real time by orchestrating data collection, running of models, reasoning logic, simulations, and forecasts. Communicate decisions as guidance to operators (open-loop control) or commands to the control system for automatic actions (closed-loop control).
Optimize water grid in real time taking into account many situational factors and constraints to better achieve operational performance goals and utilization of assets such as wells.