Aspen Nonlinear Controller

(formerly Aspen Apollo)

Non-linear model predictive control

Additional Resources

Product Brochure

Aspen Nonlinear Controller is a multivariable model predictive controller capable of full automation and dynamic optimization of product grade transitions. It uses a combination of simple first-principle models (general equations) in conjunction with nonlinear approximation technology that can be safely used directly within a controller. Aspen Nonlinear Controller is a core element of AspenTech’s aspenONE® Advanced Process Control applications.

Features

Full non-linear model predictive control: Fully nonlinear (dynamic and steady state, interacting nonlinearities).
Reduced modeling complexity: Ability to capture all relationships in a single model per controlled variable.

All parts of the solution reference the same model. Models are gain, time constant, and delay type models.
State-space model formulation with Extended Kalman Filter for unmeasured disturbance handling.
Little or no step testing required.

Less reliance on lab feedback during transitions: Highly accurate models, reliable process gain profiles.
Constraint ranking: Targets and limits for different variables can be ranked so that under conditions of infeasibility, the optimizer will give up on lower ranked constraints first.
Dead time and dynamics: Independent dead time alignment for each pair of relationships. General state space models and parametric dynamics supported.
Guaranteed gain and extrapolation: Bounded Derivative networks guarantee gains will be within specified bounds. Models extrapolate sensibly outside data in existing operating regions.
Variable dynamics support: Variable dynamics and deadtimes are supported. Model dynamics can be adjusted online in real-time.
Gain constrained dynamics: Accurate modeling of non-linear gains across the entire operating space.
Ease of use: Operator interfaces designed by operators for operators.
Interactive graphical simulation environment: Simulate open loop and closed loop performance. Validate controller tuning including move plan, optimization, and unmeasured disturbance rejection.

Benefits

Full automation and optimization of even complex-grade transition strategies
Easily model multiple catalysts and donors in a single model
Faster grade transitions
Reduced maintenance compared to multiple model per CV designs
Robust solution (a single model per CV eliminates model conflict problems associated with controllers where the inferential model is different to the control model)