EO Reactor Catalyst Selectivity Estimation With AI Digital Twin

The patent creates a digital twin - a virtual copy of the physical EO reactor system - that replicates reactor behavior and catalyst performance. This digital model allows optimization calculations and predictions to be performed in the virtual environment, with results transferred back to control the physical system, thereby improving catalyst selectivity and activity without directly modifying the physical reactor structure.

The patent replaces traditional mechanical and manual optimization methods with artificial intelligence algorithms and genetic programming. Instead of relying on physical experimentation and manual adjustment of process parameters, the system uses computational models to predict optimal operating conditions, substituting complex mechanical trial-and-error processes with intelligent software-based optimization.

The patent implements a closed-loop feedback system where process data from the physical EO reactor is continuously collected, analyzed by AI algorithms, and used to generate optimization recommendations that are fed back to control the reactor. This feedback mechanism enables real-time adjustment of operating conditions to maintain optimal catalyst selectivity and activity, transforming static operation into dynamic optimization.

The digital twin framework serves multiple functions simultaneously: it acts as a predictive model for catalyst performance, an optimization engine for determining optimal operating conditions, a simulation environment for testing scenarios, and a control system for implementing adjustments. This multi-functionality consolidates what could be separate complex systems into a unified platform.

The patent introduces AI algorithms and genetic programming models as intermediaries between raw process data and catalyst performance metrics. These computational models act as mediators that translate complex, indirect measurements of reactor conditions into precise estimates of catalyst selectivity and activity, bridging the gap between observable process parameters and difficult-to-measure catalytic performance.

The patent transforms the measurement approach by changing from direct physical measurement of catalyst properties to computational estimation based on process parameter analysis. The AI models analyze changes in operating parameters (temperature, pressure, flow rates, composition) and use these parameter changes to infer catalyst performance, converting a difficult direct measurement problem into a more manageable data analysis problem.