Deploy Fast. Learn Continuously. Optimize Relentlessly.
RLTune: The Real-Time Autonomous Optimization Engine
Overview
Using safe, constrained reinforcement learning, RLTune learns directly from live plant behavior and continuously improves control decisions in real time. It enhances existing automation systems without replacing them.
Whether reducing turbidity in water treatment, improving product consistency in food and beverage, or optimizing battery charge/discharge cycles in microgrids, RLTune delivers measurable improvements in efficiency, stability, and operating cost in complex, dynamic environments.
Why RLTune?
Traditional control strategies assume stability. Industrial processes evolve.
PID and MPC systems are effective, but they require manual retuning, accurate process models, and relatively stable operating assumptions. When feedstock changes, seasons shift, equipment ages, or upstream disturbances occur; performance drifts.
RLTune continuously adapts.
RLTune learns directly from real operating data and updates its control strategy in real time — operating strictly within operator-defined guardrails.
What Makes RLTune Different
RLTune does not require first-principles models or high-fidelity digital twins. It learns from the real plant using a constrained, safe-by-design form of reinforcement learning - eliminating the need for time-consuming simulation models or extensive historical data.
Automatically adjusts to seasonal variability, influent changes, equipment wear, and process disturbances — without manual retuning cycles.
Balances competing goals such as throughput, energy usage, chemical consumption, stability, and product quality while maintaining compliance in regulated environments. Objectives are aligned to plant-level KPIs using weighted or hierarchical strategies.
Applicable to any continuous or batch process with measurable feedback and controllable actuators. Proven applications include:
- Chemical dosing and aeration optimization
- Battery charge/discharge optimization
- Reactor and distillation control
- Yield and consistency optimization in food & beverage
Operates alongside existing SCADA, DCS, and PLC systems while strictly enforcing operator-defined constraints. No replacement of safety logic. No disruption to core control architecture.
How RLTune Works
RLTune sits alongside your existing control system and operates within defined actuator and safety boundaries.
Unlike static control logic, the policy evolves as plant conditions change.
Observe
RLTune ingests live process signals - including actuator positions, sensor feedback, and relevant operating context - directly from your automation layer.
Learn
Using constrained reinforcement learning, the system models the relationship between control actions and plant-level outcomes under real operating conditions. Learning occurs under strict guardrails and reflects true plant dynamics.
Optimize
RLTune continuously evaluates and updates its control policy to improve defined KPIs - such as stability, energy efficiency, chemical usage, throughput, or quality - while respecting all operational constraints.
Deployment & Integration
RLTune is designed for structured, low-disruption deployment in live operating environments.
Implementation begins with secure OPC-UA connectivity to your automation layer and mapping of actuators, sensors, and plant-level KPIs. In collaboration with your team, we define and rigorously validate operational guardrails — including actuator limits, rate of change constraints, and safety thresholds.
Once validated, RLTune operates under controlled authority within those defined boundaries. Learning and optimization occur under real plant conditions, enabling adaptation to true process dynamics rather than simulated assumptions.
Deployment is focused and time-bound, requiring no major infrastructure changes or process redesign. Most plants achieve stable, measurable performance improvements within the first month of controlled operation.
Technical Requirements
RLTune is lightweight and purpose-built for OT environments.
Any continuous closed loop process with feedback loop and:
At least one controllable actuator (VFD, valve, pump, blower, dosing skid, etc.)
At least one measurable feedback signal (flow, pH, turbidity, pressure, conductivity, temperature, vibration, etc.)
- Vendor-agnostic OPC-UA connectivity
- Compatible with SCADA, DCS, PLCs, historians, and IoT gateways
- Fully on-premise operation — no required cloud connectivity
- Learns from live plant data
- No digital twin required
- No extensive historical dataset required
- Low compute footprint (local server or VM)
Engineered for Safety, Trust, and Control
RLTune is purpose-built for safety-critical industrial environments.
Operates Within Guardrails
It operates strictly within operator-defined guardrails, including actuator limits and rate-of-change constraints – and cannot override existing PLC, DCS, or safety system logic. Operators retain full, instant override authority at all times.
Constantly Monitors Performance and Adapts
The system continuously monitors performance and data integrity. If anomalies, drift, or connectivity issues are detected, RLTune automatically reverts to a safe fallback condition, leaving baseline control logic unaffected.
Completely Auditable
All control actions and state transitions are logged for full traceability and compliance.
Cybersecure
Customer data remains fully isolated within your environment. RLCore does not use customer data to train external models, and no data leaves your premises without explicit authorization.
From Periodic Retuning to Continuous Optimization
Add adaptive intelligence to the automation systems you already trust - and unlock sustained, measurable performance gains.