Model Predictive Control -- Where Have We Been and Where Are We Going -- Part 3

McMillan, Weiner and Darby Discuss Practical Considerations in MPC Setup, Maintenance and Improvement to Meet Economic Objectives

By Greg McMillan, Stan Weiner

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Greg: Comment on operator involvement.

Mark: Operator involvement is critical to the success of an MPC project, beginning the day the project team shows up at the plant. Operators are the best source for knowing how the plant operates and its associated challenges. The operators need to be convinced that the controller will help them, not cause problems. We want them to become vested in the long-term success of the MPC.

Stan: How do you make operator involvement more effective?

Mark: Part is training and part is operator goodwill and communication. The operator needs to understand the "what and why" of the actions taken by the MPC. Operator graphics that include past and future trends of predictions can help understanding by showing where the controller has been and where it is going. Active constraint indications, which are common in MPC displays, also help. Today there are better tools for detailing the "why" behind MPC decisions. Operator feedback is important to ensuring the long-term success of the MPC. There may be new situations that demand a better operational understanding or a MPC modification.

Greg: What about the setting of limits?

Mark: Constraint limit setting is an important issue that needs to be consistent across all operating shifts. Setting constraint limits too narrowly will reduce MPC benefits or hide controller problems. As a result, there has been a tendency towards reducing the limits that the operator can change. In this way, if an MPC is not performing well, it will be switched off and force corrective action. Periodic review of limits by operations, planning/scheduling, and control personnel is an important activity.

Read Also: Model Predictive Control - Past, Present and Future - Part 2

Stan: What types of MPC metrics do you find useful?

Mark: Many MPCs are on-stream more than 90% of the time. You need something more than just service factor. The real question is MV utilization and how much time is spent at MV limits versus CV limits. An MV pegged at its limit is not producing an economic return. It is also important to monitor the active constraint sets. Operation against new set of constraints should be investigated, as it could be indicative of incorrect limit setting or an actual process change, or a problem in the process or instrumentation. A trend of the bias correction or the uncorrected prediction can provide an indication of model accuracy. Ultimately, you would like the MPC tools to provide diagnostics for MPC performance problems. This is another active development area for MPC technology suppliers.

Greg: Online process metrics have been misleading in some cases, exhibiting inverse response and deceptive short-term inefficiencies. It appears synchronization of process inputs with process outputs may be needed for metrics such as yield and energy efficiency during transitions and upsets because of process dynamics. The metrics may also need some filtering to deal with synchronization errors and noise. What do you see in terms of effectiveness of process metrics?

Mark: As new steady states are achieved, synchronization issues disappear. Online process metrics are particularly effective when they show improvement in plant performance compared to operation before the new or modified MPC went online. This is relatively straightforward when the controller is operating with the same economic objectives. It is a challenge when the objectives change, as the question becomes what to compare current operation against. One approach is to develop an economic model with assumptions for how the process would be controlled without MPC (e.g., the proximity to constraint limits). This is an area that could benefit from standardized approaches.

Greg: We conclude this series with a wrap-up of advanced control myths.

Myth 5 – You can skip the step (bump) tests. Bumps tests are essential for identifying instrumentation and regulatory loop tuning problems. The tests also provide a clearer view for the process engineer and MPC engineer to see what was real, unexpected and strange. As a minimum, a step in each direction should be made at each operating point. Steady-state process simulations can help identify gains for changes in operating conditions and help confirm test results. The old rule is true: If you can see the model from a trend, it is there. Sometimes, the brain can estimate the process gain and dead time better than software.

Myth 6 – You need to completely know your process before you start a MPC application. This would be nice, but often the benefits provided by a model stem from the knowledge discovered during the systematic building and identification procedures. Frequently, the understanding gained by developing models leads to immediate benefits in terms of better setpoints, instrumentation, and valves. The commissioning of the MPC locks in the benefits for varying plant conditions.

Myth 7 – Optimization by pushing constraints will decrease on-stream time. The converse is true. MPC will recognize future violations for unforeseen problems and will back off from the edge to increase on-stream time.

Greg: And a bonus item: This excellent paper on a small MPC application by one of the ISA Mentor program protégés.

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