Real Time Digital Twin Plant Strategy
Current process control approaches in industrial processing plants are limited by their reactive nature, lack of predictive capabilities, and reliance on human operators or inflexible PID-based logic. While simulation tools exist, they are often offline and disconnected from the actual plant data.
Our approach revolutionizes the process by combining physics-based process models with machine learning (hybrid AI), enabling a live, self-updating digital twin of the process plant.
This twin acts as an intelligent advisor, capable of forecasting the system's behavior and autonomously suggesting control actions to improve sustainability, performance, and reliability.