Understanding optimizers


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Tobias Scheele, vice president, design, simulation and optimization for Invensys explains how optimizers extend the ability to improve plant performance.

Data validation and reconciliation

The old computer programming axiom of “garbage in, garbage out” certainly applies to optimization, especially in the case of online optimization. As mentioned earlier, in those cases where online closed-loop optimization is applied, these solutions don’t generally address process dynamics in any comprehensive manner. Consequently they are executed only when the process has reached steady state, requiring that the tools have sufficient facilities to detect that the process has reached steady state from the last set of moves requested by the optimizer. These steady state detection tools are used in conjunction with data reconciliation tools to spot, correct, and remove or even replace process values prior to the optimizer initiating its calculation sequence to arrive at the next solution. 


Constraints or limits are present in every context of optimization, whether it is MPC optimization or online/offline optimization. The process presents many constraints which must be respected and are therefore integrated programmatically into the software. Constraints might be with respect to product qualities or safety or equipment limits. In any case they must be respected and not violated. Sometimes, the optimizer cannot find a feasible solution with the existing constraints and a constraint must be relaxed in order to find a feasible solution. In these cases the constraints selected to be relaxed or given up are determined ahead of time and ranked as to which constraint is least important.

Benefits, costs, and maintenance

Six to nine months is typical ROI for optimization applications, but there is wide variation across industries. Some of the greatest benefits have been achieved when they are implemented as part of an MPC program and when regulatory and advanced regulatory control layers are well maintained and operating at peak performance prior to commissioning.

Although optimization projects may compete for resources, it is worth noting that the optimization itself does not change the process or the existing automation hardware, except for the addition of another server class machine connected to the process or business network. 

Maintenance must also be factored into any discussion of ROI on optimizers. Some assume that once commissioned, optimizers require no ongoing maintenance. However, operational constraints, models, and objectives change over time and the optimizer must be changed accordingly. Ongoing software and application support can average 10% of the original project cost.

Future requirements

Despite their benefits, developing rigorous first principle models can be time-consuming. Combining empirical and first principle models is an emerging trend, which promises to reduce development time and ease future model maintenance. Integration of historians, feed property databases, and various planning tools is another trend, which can reduce staff time needed to update information manually while at the same time reducing errors. Lastly, the new generation of users coming into the workplace demands a level of ease of use not found in the previous generation of tools. Drag and drop, copy and paste, and touchscreen enabled are all trends that will simplify the use and adoption of software tools. The inclusion of features that are consistent with the mainstream desktop- and iPad-enabled generation will be ongoing. 

Also, the technical capabilities to conceive, develop, commission, and maintain an optimization system are always in short supply. Getting staff with the right skills is a prerequisite to a successful project in addition to identifying and enlisting a site champion. The right skills might be within your company or within the supplier organization, but not in your geographic location, so we are seeing an increasing trend where multiple locations and time zones are accommodated by the optimization tool set, allowing multiple persons in differing locations to work collaboratively in real time. Two or more users should be able to view the same workspace at the same time with both making changes with the underlying database managed in near real time. The successful tools of the future will support the transition of design models developed for steady state design to be seamlessly translated into the online optimization for both steady state and dynamic applications, giving much greater leverage and reuse than has been possible with previous generations of tools.

Tom Kinney is vice president, optimization, for Invensys.

Key concepts:

  • In most process manufacturing applications, the number of process variables requires sophisticated analysis to determine optimum operating conditions.
  • Optimizers operate hand-in-hand with advanced process techniques to drive profitability.
  • Accurate process models are necessary to ensure that all relevant operating constraints and safety measures are included in the calculations.



See related stories about advanced process control below.

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Anonymous , 12/28/13 04:25 AM:

I will talk about refinery optimisation.
Planning & scheduling are like headquarters that define strategy and tactics to optimize the refinery. Then process optimisers are soldiers that shall apply strategy on the battlefield, i.e. every minute how you can best operate your various units to achieve the overall goals. Hence the complexity threshold for process optimisation is reduced, and the need for extremely detailed model is also reduced.
Decisions like producing more naphtha versus kerosene/gasoil are totally out of the hand of process optimisation. This is decided by planning and scheduling. Process optimisation shall enforce it by pushing production against constraints. When designing process optimiser, I will define how planning and scheduling will set their “orders”.
There is also “local” optimization, i.e. the global strategy does not depend upon the choice of the local optimum. Typically, energy optimisation is often local. For instance, considering 2-cut distillation with 3 manipulated variables (condenser, reboiler and reflux). Once your cut is defined to achieved global strategy (2 variables fixed), one variable remains for energy optimisation.
Because process optimisation is “manipulating” variables to enforce targets, it shall follow basic control principle like Shannon theorem. Hence, process optimiser shall have “fast” frequency, which means you shall reconcile your process model versus the plant taking into account process dynamics. Tools like SMOC, that runs every minute, are well tailored to enforce targets, push against constraints and solve local optimum.
Tools with only steady state models does not fit because their models are updated only at steady state and does not achieve the minimum frequency. A good example is outside temperature in summer time. Running optimisation every 6 hours does not work. Often your process constraint is cooling capacity. Early in the morning, optimiser foresees extra cooling capacity because of the night, but you can’t achieve it because sun is back. In the evening it is the other way around, you don’t use the extra capacity because optimiser has run versus the afternoon heat. Steady state optimisers are always late.
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