ρ-Matriqs: India's First Analytical Digital Twin
We fuse advanced multi-sensor data, powered by AI/ML, to deliver predictive control over material flows for unprecedented purity, stability, and ESG performance.
We fuse advanced multi-sensor data, powered by AI/ML, to deliver predictive control over material flows for unprecedented purity, stability, and ESG performance.
Our platform integrates advanced multi-sensor data fusion, advanced AI intelligence, and a proactive control engine into a unified Analytical Digital Twin solution.
advanced Multi-Sensor IIoT Architecture combines Density/Mass-Volumetric measurement, Chemical analysis, and integrated Weighting & Thermal Monitoring.
The Density(ρ) & Chemistry Fusion Engine powers Predictive Quality Control (PQC), Deep Learning for material classification, and Multi-Objective Optimization for purity, energy, and cost.
Explainable AI (XAI) for auditability, Generative AI for optimal blend simulation, closed-loop control for process adjustment, and Predictive Failure Analysis (PFA) for zero downtime.
Our Platform-as-a-Service or Software-as-a-Service (PaaS/SaaS) model features role-based KPI dashboards, seamless integration with MES/ERP, and a Time-series Database (TSDB) for long-term optimization.
Current industrial practices suffer from measurement latency and resource inconsistency. The ρ-Matriqs platform provides the real-time, predictive control that eliminates these costly pain points.
Traditional quality control (XRF/OES) is slow (3-5 hours), making process adjustments REACTIVE. This leads to off-spec products, material giveaway, and high energy costs.
Lack of real-time material identification causes tramp element contamination (Cu, Sn) in metals and low purity yield in recycling streams (rPET). Waste value is lost instantly.
Variations in bulk material density (ρ), moisture, and particle size cause furnace instability (BF, Kiln), leading directly to unexpected breakdowns and high energy consumption.
Black-box AI solutions offer optimizations without justification. Operators cannot trust or audit the system, creating high operational risk and hindering regulatory compliance.
Relying on single sensors (Chemical, volume, scale or weight) fails due to dust, heat, and material segregation, resulting in unreliable raw density (ρ) data for control loops.
Inability to track material mass balance, carbon impact, and resource efficiency in real-time makes ESG reporting complex and hinders efforts toward decarbonization goals.
Our ρ-Matriqs Analytical Digital Twin delivers predictive control across nine core industrial platforms.
Choose India’s first Analytical Digital Twin for bulk materials, ensuring predictive control, superior ESG performance, and exponential economic return.
Our fusion of advanced multi-sensor data provides instantaneous material identity, eliminating laboratory latency for proactive decision-making.
Directly supports Net-Zero and Circular Economy goals by maximising critical mineral recovery and reducing energy consumption in production.
Leverage Explainable AI (XAI) for transparent auditing and Generative AI for simulating optimal material blends and process innovation.
A scalable, recurring revenue model built on a secure Multi-Sensor IIoT architecture, guaranteeing long-term support and rapid feature deployment.
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