aspenONE^® V7 Manufacturing
and Supply Chain

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Control

Modeling — For Control

The Aspen Control Platform (ACP) provides three different controller formulations. The ACP provides the industry-leading Aspen DMCplus® formulation based on the FIR model form, as well as linear MIMO State Space and non-linear MISO State Space models.

Switching between formulations is accomplished with a simple mouse click. Older versions of Aspen Nonlinear Controller and Aspen DMCplus controllers are easily imported with automatic model, script, and tuning conversion.

The modeling tools are implemented in a modern (.NET) drag-and-drop environment for model building, simulation, configuration and deployment. The entire control application can be built, simulated, and deployed within aspenONE Advanced Process Control, providing a true competitive advantage with a seamless interchange between the three industry-leading control formulations.

Formulation	Prediction	Filter	Optimizer	Controller
FIR (Aspen DMCplus)	FIR models. Current CV predictions are kept in memory and current moves are added to it.	Bias is added to each output. First order time constant can be used to filter the update. Rotation factor used for ramps.	Upper and lower ranked limits for CVs (linear or quadratic type). Ranked ETs can be specified on inputs and outputs. Costs on inputs and outputs. Algorithm is a revised simplex method for LPs. Interior point for QPs. Interior point can also be used for LPs.	Number of moves from 8 to 64 is specified. Matrix inversion is used to create an analytical solution to the unconstrained problem. "Clipping" of the inputs is used to enforce constraints. Option to use QP solution. Also, option added to treat CV con-straints more rigorously. Problem solution time is cubic in number of MV and number of moves.
State Space MISO	MISO models. State of the linear part of each model is maintained and updated each cycle.	Bias is added to each output. Noise ratio can be used to filter. Internal states are not updated (except through prediction). Rotation factor used for ramps.	Upper and lower ranked limits for CVs (linear or quadratic type). Ranked targets can be specified on inputs and outputs (target can be an ET or upper or lower limit or minimum move). Costs on inputs and outputs. Algorithm is an SQP. Each QP iteration uses the interior point method. Trust regions are used to expand the search region. This usually takes 11 QP iterations.	Number of moves for each MV is specified. Algorithm is an SQP. Each QP sub-problem is solved with an active set method. A line search is used to expand the search region. This usually takes 2 to 6 QP iterations. Problem solution time is cubic in number of MVs and number of moves. Dependence on number of states is not known but probably small (and models usually low order).
State Space MIMO	MIMO models. State of the model is maintained and updated each cycle.	Internal states are updated using a Kalman filter. Typical noise and disturbance used for tuning. Input and "internal" disturbances can be specified.	Upper and lower ranked limits for CVs (only quadratic). Ranked ETs can be specified on inputs and outputs. Costs on inputs and outputs. Algorithm is an interior point method.	Number of moves is specified (no limit). Algorithm is an interior point method – extensively customized to take advantage of the problem structure. Problem solution time is cubic in number of MVs, quadratic in number of states, and linear in the number of moves.