Innovation, best practices and optimization

Some of Tai-Ji’s main features include:

• Automatic test signal design and generation
• Automatic step testing—implementation of the steps to the process—in a closed-loop or open-loop mode
• Real-time monitoring of steps, variable limits and current process operation
• Real-time adjustment of variables stepped, their step sizes and Tai-Ji limits
• Model identification with model quality analysis providing immediate feedback as to test step performance and data quality

Meanwhile, Process Doctor prepares the application for its step test by surveying regulatory controller performance, and identifying bad actors, such as controllers that aren’t performing well, and helping to determine if they need tuning, maintenance, or repair. Once the regulatory controllers are working well, the step test is ready to begin.

Tkatch says users should ensure that their data is being archived in at least two different places, set up the Tai-Ji modeling analysis using common sense combinations of variables, and keep the model analysis setup simple, but inclusive. Then, after approximately 24 hours of stepping, models can be run. Model results are graded using estimated error bounds, including:

• A = very good, high quality model
• B = good (collecting more data may improve this model)
• C = marginal (Larger steps will generally improve this model)
• D = poor model, or no model (additional data collection required)

Tkatch adds that model identification is successful if most expected models have A and B grades, but this doesn’t not mean that all models must be A or B. Next, he advises users to:

• Look at step response plots, and use a scaled-response option to identify dominant models versus less dominant models when evaluating model qualities.
• Use frequency response to judge the spread of model errors. Low frequencies need to be accurate.
• Use model simulation plots to assess predictions. Error of less than 30% is adequate in practice.
• Use slicing if CVs are saturating or there are calibration periods in the data set, or huge unmeasured disturbances.
• For existing transforms, ensure that these transforms are applied before sending data to the model block.
• Then, if an MV has consistently poor models, focus on increasing the step sizes or frequency content.
• Once satisfied with models for some of the variables, remove them from the step test simply by un-checking the select box in the web interface. These variables will not be stepped any longer, but the rest of the test continues.

So far, Hovensa has now used Tai-Ji for stepping and model identification on five process units. These include revamping its Crude Unit 6 in 2005 with two sub-controllers on its crude tower and stabilizer; revamping its fluid catalytic cracking (FCC) unit in 2006 with three controllers; and revamping its BTX unit and distillate desulphurization unit in 2007. Tkatch adds that Hovensa plans to conducted closed loop tests on its Crude Unit 5 and Vacuum Unit 3 this year.