Columbo Closed Loop MPC – Model Predictive Control Software for DMC – Dynamic Matrix Control Model Improvement. Use data in Excel files from DMC (Dynamic Matrix Control) from Aspen Tech, or from RMPCT (Robust Model Predictive Control Technology) from Honeywell, or Predict Pro from Emerson and use it to generate and improve correct models for the various MV-CV pairs. Amazing new optimization technology does not need step tests as required by Aspen tech, Honeywell and others, Works entirely in the time domain, easy to use, compact and practical.
Do you design and support Model Predictive Control (MPC) like DMC, RMPCT, PredictPro, Connoisseur or traditional Advanced Process Control (APC) inside a DCS or PLC?
When Model Predictive Controls (MPC) or Advanced Process Controls (APC) deteriorate due to bad models or changes in plant or process conditions, you may see ugly oscillations like below:
Model Predictive Controls (MPC) can have 10s or 100s of dynamic models. One or more could be wrong. Bad (wrong) Model Predictive Control (MPC) dynamic models produce a bias (model prediction error) between the predicted signal and measured signal coming from the sensor.
How to identify the bad (wrong) Model Predictive Control (MPC) models and how to generate correct models?
This has been a long challenging problem and a nightmare for the process control staff responsible for Model Predictive Control (MPC) quality and performance. Now COLUMBO offers a breakthrough, novel and state-of-the art solution like never before.
COLUMBO will help you to improve Model Predictive Control (MPC) models with either open-loop or completely closed-loop data. Upon CV target changes, you will see improved control, tighter control and stability as shown below:
COLUMBO will also help to improve Model Predictive Control (MPC) performance and quality upon a change in a feedforward signal (a measured disturbance) as shown below:
A) Moves are 1-2% of the3 current slave setpoints during model identification stage but could be 1-30% of the current slave setpoints with Model Predictive Control (MPC) running trying to provide control.
B) During model identification stage, only one MV is moved at a time followed by waiting time to try to see the new steady state. This is recommended by all Model Predictive Control (MPC) vendors as a good guideline to avoid correlation problems as if more than one MV (slave PID setpoint) is changed at the same time, then there is ambiguity and uncertainty regarding the impact of the multiple variables and subsequently dynamic models could be wrong due to correlations. However, when the Model Predictive Control (MPC) is running, the Model Predictive Control (MPC) moves various MVs (slave PID setpoints) simultaneously in order to keep the CVs at their targets.
How does COLUMBO work? What are details about the COLUMBO algorithm and internals? The COLUMBO algorithm is a PiControl invention and PiControl corporate intellectual property and cannot be disclosed in open literature. All that can be revealed is that COLUMBO tries to identify new models based on the measured data (CVs) and the MV trajectory calculated by the Model Predictive Control (MPC) system or the Advanced Process Control (APC) system. Internally, COLUMBO is equipped with a powerful and new optimizer designed for handling multiple inputs, both analog and digital subject to constraint limits and nonlinear math processing capabilities. Written in C++, COLUMBO code is super-fast and super-compact allowing the optimization calculations to complete and converge in an amazingly short time. See below an overview of how COLUMBO works: