Continuous Digester Optimization via Soft Sensor and MPC

The patent replaces expensive, complex physics-based models with simpler, computationally lightweight empirical models that are easier to implement and maintain. These simplified models achieve adequate control accuracy without requiring high-level expertise for calibration and tuning, making them practically feasible for average process engineers while reducing implementation and maintenance costs.

The patent transforms the complex physics-based model into a simplified empirical model by changing the mathematical parameters and relationships. The empirical model uses straightforward mathematical relationships between measurable process variables and Kappa number, avoiding the complex nonlinear differential equations of physics-based models while maintaining sufficient control accuracy for practical applications.

The patent implements preliminary action by using the simplified empirical model to predict Kappa number trends and initiate control adjustments before the full effect of disturbances is observed. The model-based predictive control calculates future Kappa values based on current process conditions and anticipated changes, allowing the system to proactively adjust cooking parameters to prevent quality deviations rather than reactively correcting them after delays.

The patent enhances the feedback mechanism by continuously comparing predicted Kappa values from the empirical model with actual measurements and using this information to dynamically adjust control parameters. The feedback loop operates more effectively because the simplified model provides timely predictions without the computational burden of complex physics-based models, enabling faster corrective actions while maintaining quality consistency.