Aspen Apollo™

Non-linear model-predictive controller

Aspen Apollo is a non-linear, multi-variable 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 non-linear approximation technology that can be safely used directly within a controller. Aspen Apollo is a core element of AspenTech’s aspenONE™ Advanced Process Control applications.

Features

  • Full non-linear model-predictive control: Supports dynamic and steady state, as well as interacting nonlinearities.
  • Reduced modeling complexity: Captures all relationships in a single model per controlled variable.
    • All components of the solution reference the same model. Models are gain, time-constant, and delay-type models.
    • State-space model formulation features Extended Kalman Filter for unmeasured disturbance handling.
    • Requires little or no step testing.
  • Highly accurate models, reliable process gain profiles: Eliminates reliance on lab feedback during transitions.
  • Constraint ranking: Allows different variables to be ranked for targets and limits so that under certain conditions, the optimizer will focus on higher ranked constraints first.
  • Dead time and dynamics: Supports independent dead-time alignment for each pair of relationships, as well as general state space models and parametric dynamics.
  • Guaranteed gain and extrapolation: Ensures that bounded derivative network gains will be within specified bounds; models extrapolate sensibly outside data in existing operating regions.
  • Variable dynamics support: Supports variable dynamics and dead times; model dynamics can be adjusted online in real-time.
  • Gain constrained dynamics: Provides accurate modeling of non-linear gains across the entire operating space.
  • Ease of use: Implements interfaces designed by operators for operators.
  • Interactive graphical simulation environment: Simulates open loop and closed loop performance, helping you validate controller tuning including move plan, optimization, and unmeasured disturbance rejection.

Benefits

  • Full automation and optimization of 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)

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