Canariis—and an advanced software ecosystem like AIMLUX.ai, Equitus.ai

 

Bridge the gap between heavy industrial equipment—like the packaged pumping, HVAC, and chiller/boiler systems manufactured by Canariis—and advanced software ecosystems like AIMLUX.ai, Equitus.ai, and ArcXA, you need an intelligent middle layer.

AIMLUX.ai proposal positions TruVolt.ai as that intelligent context layer. It augments traditional, rigid Proportional-Integral-Derivative (PID) and Internet Protocol (IP) control systems using a Triple Store Architecture / Resource Description Framework (RDF).

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The Challenge with Legacy Controllers

Traditional industrial systems run on PID loops (e.g., managing water pressure, flow, or temperature) and communicate via specific industrial IP protocols (like BACnet/IP or Modbus TCP).

• Limitation: PID controllers are "blind" to external context. A PID loop knows its current setpoint and error margin, but it doesn't know why it's pumping water, what the current electricity grid tariff is, or if a battery storage system (BESS) has cheap energy available. It operates in a silo.

Triple Store Architecture & RDF "Context Layer"

RDF Triple Store functions as a semantic graph database. Instead of storing data in rigid rows and columns, it stores information as a web of interconnected relationships using a [Subject---> Predicate ---> Object] format (a "triple").

Utilizing an ontology (such as Project Haystack or W3C Web of Things), TruVolt.ai creates a digital twin of the entire facility, mapping relationships like:

• [Chiller_Plant_1] -> [isManufacturedBy] -> [Canariis]

• [Canariis_Pump_A] -> [drawsPowerFrom] -> [Main_Electrical_Bus]

• [BESS_Unit_1] -> [suppliesPowerTo] -> [Main_Electrical_Bus]

• [Utility_Tariff] -> [hasCurrentPrice] -> ["$0.24/kWh"]

1. Abstracting the PID / IP Layer

Instead of rewriting the core PLC (Programmable Logic Controller) code or the low-level PID algorithms—which would disrupt Canariis’s pre-tested, factory-certified settings—TruVolt.ai sits above them.

It reads the real-time telemetry coming over IP protocols and maps those data points directly into the RDF graph. The PID controller handles the mechanical execution, while the RDF layer handles the strategic reasoning.

2. Orchestrating BESS and Utility Management

Because the Triple Store architecture models everything uniformly, it can easily cross-reference the state of the mechanical plant, the state of the Battery Energy Storage System (BESS), and real-time utility market data.

For example, if a Canariis chiller plant requires a high-flow startup sequence, TruVolt.ai uses its semantic context to instantly check:

1. Is the grid in a peak-pricing window?

2. Does the BESS have enough stored capacity to absorb the startup surge?

If yes, TruVolt.ai temporarily adjusts the PID setpoints or commands the BESS to discharge, shielding the facility from expensive demand charges.

3. Enabling Intelligent Inference and Automation

Triple stores allow for semantic reasoning and inference. If a rule states that "Any asset drawing power from Bus-A during peak hours must favor localized storage," the database automatically calculates and flags those assets dynamically. This allows AIMLUX.ai and its partner platforms (Equitus/ArcXA) to execute high-level Machine Learning and optimization algorithms without having to hardcode thousands of individual PLC rules.

Summary of the Architecture Flow