MPC Maintenance for DMC/RMPCT- COLUMBO (Closed-Loop Universal Multivariable Optimizer)
PiControl is pleased to release brand new software and technology for 2018. A unique new product called COLUMBO – ClOsed Loop Universal Multivariable Optimizer for maintaining and improving DMC, RMPCT and all other MPCs.
MPC Model Maintenance and Improvements: DMC (Dynamic Matrix Control), RMPCT (Robust Model Predictive Control Technology), PredictPro, Connoisseur and other MPCs (Model Predictive Controllers) are widely used in chemical, petrochemical, paper, power plant and oil refining industries. With time, due to aging equipment, hardware changes, changes in process, operating and economic conditions and process nonlinearities dynamic models can change significantly causing the MPC control quality to deteriorate. MPCs are often turned off with subsequent loss of benefits and profits. This can be a control engineer’s nightf MPC control problems and deterioration in control quality are most definitely changes in the dynamic models inside the MPC. The shapes of the MPC models is the heart and soul of the MPC system. Unfortunately, when MPC models change, or were wrong to begin with, it is a very difficult job to fix the models and identify the correct models. This is because several MVs (manipulated variables) are associated various CVs (controllemare and headache for the plant management that spent a lot of money on the MPC project. PiControl’s COLUMBO provides an amazing new and novel method to identify the correct models. COLUMBO is a process control technology marvel with true breakthrough technology and algorithm.
Step Tests and Conventional Method of Dynamic Model Identification: Currently practiced conventional known methods of model Identification typically involve making small step tests on the setpoints of slave PIDS in auto mode (often called closed-loop tests). Or they involve making step tests on control valves (final control element) in manual mode (often called open-loop tests). Note that closed-loop tests still involve steps in most cases and most methods. In these conventional methods, slave PIDs may be in auto mode but stepping on their setpoints is still required. The above is current and conventional thinking and methodology.
DMC3 and Calibrate Function from Aspen: Even in the new and latest DMC3 Calibrate method – step tests are superimposed on top of the MV move trajectories from a DMC controller that is active (on). So here, the DMC controller is on, but step tests are still needed for the DMC3 Calibrate to work. Step tests are “open-loop” by definition.
Types of Dynamic Models: Transfer Function Models or Step Response Coefficients: Most MPCs characterize process dynamics using either transfer function parameters or step response coefficients. COLUMBO can identify and improve models in both formats. COLUMBO can be used to improve the model accuracy and control performance of any MPC (COLUMBO is not MPC vendor specific) but works with any MPC system from an y MPC vendor.
Matlab ARMAX Box Jenkins Models: ARMAX (auto regressive moving average with exogenous inputs) and Box Jenkins models have been used for several decades and are the main modeling methodology provided by Matlab from Mathworks and Labview from National Instruments. These methods do not work well with complete closed-loop data without any step tests at all and/or slave PIDs or MVs in cascade modes. They also do not work well with unmeasured disturbances and noise. These methods offer what they call “noise models” for separating noise from the true process dynamics, but when unmeasured disturbances comprise of a mixture of fast random noise, medium frequency drifts and slow unmeasured disturbances, the noise model concept is not useful.
MPC Model Identification: Most models are based on making small steps of about 1-3% of the current MV values. So, if a setpoint of an MV (a slave PID) is (say) 100 kg/h, then changes from 100 kg/h to 102 kg/h or 98 kg/h or so) are made. If the step changes are too large (say going from 100 kg/h to 120 kg/h and then to 90 kg/h), this may upset the process and the step cannot be help for too long and the process gain of the model cannot be unambiguously determined and there will be a lot of uncertainty in the data. COLUMBO overcomes the problem by analyzing closed-loop data with MPC running, a function not possible with other current MPC algorithms and software products.
Identify and Improve MPC dynamic models with COLUMBO breakthrough MPC model technology: COLUMBO is the latest breakthrough technology for identifying open-loop dynamic models using completely closed-loop data. No step tests are required. Slave PID controllers can be in auto mode and even in cascade mode. How does this work? Please read below…
COLUMBO closed-loop system identification method: Currently, MPC models are built using small steps (1-3% of current setpoints). When the MPC is running, it uses these models that were identified using the small steps to calculate the trajectory for various MVs in closed-loop mode. When a feedforward changes (measured disturbance changes), the MPC calculates compensating moves based on the models that were identified using the small steps. The difference now is that a measured disturbance signal (feedforward in MPC) may have changed by 20% (not 1-2% like during step tests for model identification) and the larger change (20%) can lead to nonlinearities that cannot be seen during the 1-2% smaller moves. Also, during MPC closed-loop mode, several MVs are changed simultaneously (in a correlated manner) and this is also different compared to the model identification phase where typically only one MV is moved at a time to avoid complications due to correlations. So, there are two main differences between the MPC model identification stage (to identify MPC models) and the closed-loop stage with MPC running (to calculate MV trajectories in order to keep the CVs at their targets) and these are: Moves are 1-2% of the3 current slave setpoints during model identification stage but could be 1-30% of the current slave setpoints with MPC running trying to provide control. 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 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 MPC is running, the MPC moves various MVs (slave PID setpoints) simultaneously in order to keep the CVs at their targets. The differences between the open-loop model identification stage and the closed-loop stage with MPC on and running can be significant especially since there is nonlinearity of varying magnitude in all real-life chemical processes. The closed-loop behavior with multiple MVs changing by larger amounts is often different compared to the open-loop behavior with smaller changes and only one MV changing at a time. These differences lead to model prediction errors and is often the root cause of poor control in many MPC systems. When these problems occur, the fixing and the solution are not easy. COLUMBO was designed to address these problems and help the control engineer come up with improve the MPC models and thereby the MPC control quality and performance. If after a few months or years, if an MPC is not performing well and if you turn off the MPC and resume step tests again in an effort to re-identify the MPC open-loop models because things have changed the problems caused by the differences explained in the A and B bullets above will still cause a potential error in the real MPC models when the MPC is running and moving several MVs simultaneously with larger step sizes.
For more info, see: https://www.picontrolsolutions.com/columbo/
