AI Plant Loading Model for Adaptive Process Flow Optimization

The patent creates a digital twin (virtual copy) of the processing plant that mirrors the physical system's behavior. This digital twin is trained on historical operational data to learn and predict plant loading characteristics, replacing the need for repeated resource-intensive simulations while maintaining high adaptability to process flow changes.

The patent transforms the simulation approach by changing from solving constrained optimization models with fixed parameters to using a machine learning model with learned parameters from historical data. This allows the system to adapt to new process flows by retraining on new data rather than requiring complete re-simulation, reducing resource requirements while improving adaptability.

The patent performs preliminary training of the machine learning model using historical operational data before actual prediction is needed. This pre-learning phase captures the complex relationships in the system, enabling rapid predictions later without requiring time-consuming repeated simulations when process flows change.

The patent replaces the mechanical simulation process (solving constrained optimization models) with an intelligent system (machine learning model). This substitution eliminates the need for repeated computational simulations, dramatically reducing prediction time while maintaining accuracy through patterns learned from historical data.