Model predictive controller (MPC) maintenance and improvement is a remote consulting service that fixes poor MPC performance on any vendor software, DMC, RMPCT, Predict Pro, Connoisseur, or any other, using COLUMBO to refit the controller's dynamic models directly from MV, CV, and FF data you already export from your MPC, so prediction errors fall without step tests or downtime.
You export MV, CV, and FF data from your running MPC into Excel files, whatever vendor software it runs on, so the engagement starts from data your plant already collects rather than a new instrumentation project.
COLUMBO's optimizer reads the Excel files and refits your existing MPC models, improving their accuracy and reducing prediction errors with a methodology not available in any other product.
PiControl engineers check your MPC against six common root causes of poor performance, so the fix targets what is actually degrading the controller instead of guessing.
The engagement is scoped around the controller you already run rather than a generic template, so the deliverable is a set of refitted models with measurably lower prediction error you can validate against your own historian.
Model predictive controller performance degrades for identifiable reasons, and a PiControl MPC maintenance engagement checks your controller against each one. Our engineers review the MV, CV, and FF data you export and diagnose which of the following are limiting your MPC's performance, then fix them using COLUMBO and direct process engineering review.
Because the review works from exported data rather than a live connection into your control system, it applies the same way regardless of which MPC package generated the data.
Every MPC's control quality depends on the accuracy of its dynamic models, and those models drift as feed composition, catalyst activity, and operating conditions change. COLUMBO's optimizer refits the existing models and reduces prediction errors using a methodology not available in any other product, so the controller keeps making the multivariable moves it was installed to make instead of getting switched to manual.
Because COLUMBO reads MV, CV, and FF data directly from Excel files already exported from the MPC, an engagement does not require new instrumentation, a new host server, or new connections into the control system. The same historian data used to run the MPC is the same data used to refit it.
That vendor independence is what lets one methodology cover the whole installed base. COLUMBO has analyzed closed-loop data from active DMC, RMPCT, Predict Pro, and Connoisseur installations and refitted the dynamic models inside each without taking the controller offline for step tests.
Our engineers run every MPC maintenance engagement on COLUMBO, PiControl's closed loop universal multivariable optimizer, so the consulting result is reproducible rather than personal to one engineer.
COLUMBO reads Excel data files containing MV, CV, and FF data exported from your model predictive controller. Its optimizer then refits the existing MPC models and improves their accuracy, reducing prediction errors with a fast, novel methodology not available in any other product on the market.
MPC maintenance and improvement consulting is delivered remotely. You export MV, CV, and FF data from your MPC into Excel files and send them to PiControl, so an engagement can start without a site visit, a new OPC connection, or any interruption to the running controller.
Scope is set by which of the six common root causes apply to your MPC. Some engagements center on refitting the dynamic models with COLUMBO; others also review MV and CV selection, the tuning of the slave PID loops feeding the MPC, or whether certain CVs and MVs would perform better moved to DCS-based advanced process control (APC). PiControl scopes the engagement to your controller rather than applying a fixed package to every project.
Vendor compatibility, what COLUMBO fixes, what data to send, and how to get started.
Ready to fix your model predictive controller, whatever vendor software it runs on? Schedule a consultation and a PiControl engineer will review your MV, CV, and FF data, scope the engagement against the six common root causes, and outline the improvement you can expect before any work begins.