Kineticore
Kineticore delivers a catalogue of plug-and-play, physics-informed digital twin modules that integrate into SCADA to maximize operational margins and predict faults within weeks
About Us
The technology is powered by Physics-Informed Neural Networks (PINNs) that fuse machine learning with the laws of thermodynamics and chemical kinetics. While traditional “black box” AI requires years of historical data and fails during unpredictable operational anomalies, Kineticore embeds physical reality into the neural network’s core architecture.
This physics-first approach eliminates “pilot purgatory,” shortening deployment from years to under four weeks. Out of the box, the modules deliver 85–95% fault prediction accuracy and real-time process guidance. By monitoring critical equipment (like ESPs) and optimizing energy-intensive thermal assets (like EAFs or chemical reactors), Kineticore provides operators with immediate, actionable recommendations.
Ultimately, the technology shifts the industrial paradigm from defensive cost-cutting to aggressive margin expansion—turning complex physical processes into predictable, highly resilient, and insurable operations on Earth and in space
Relevant Challenges
Energy Efficiency
Kineticore optimizes submersible pump and pump station performance using 85% accurate predictive analytics to solve water resource energy efficiency
Resource Efficiency
Kineticore maximizes resource efficiency by optimizing performance parameters to guide operators in real time across metals and minerals (EAF, LF), chemicals (reactors, concentration units, distillation columns), pulp and paper (defiblers, paper machines, heaters), and oil and gas (reactors, carbon furnaces, air compressors, air separation units)
Extraction
Kineticore guides operators in real time by solving physical ODEs/PDEs dynamically to adapt flotation processes to changing raw material qualities
Energy Consumption
Kineticore maximizes data center energy efficiency by optimizing heat pumps and chillers using models trained on thermodynamic cycles for diverse refrigerants
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