This Lab was part of PI World 2018 in San Francisco. The Lab manual used during the instructor led interactive workshop is attached. Lab VM is available via OSIsoft Learning
In a crude oil refinery, gasoline is produced in the stabilizer (distillation) column. Gasoline RVP is one of the key measurements used to run and adjust the column operations. Refineries that do not have an on-line RVP analyzer have to use lab measurements - available only a few times - say, a couple of samples, in a 24-hour operation.
As such, column process values (pressure, temperature, flow etc.) and historical RVP lab measurements can be used via machine learning models to predict RVP more often (say, every 15 minutes or even more frequently) to guide the operator.
In the hands-on portion, you
- Review the AF model
- Use PI Integrator to prepare and publish historical data (to a SQL table) - this data is used for model development
- Review the step-by-step machine learning model development process in Python/Jupyter
- Deploy the model for real-time operations
- Use PI Integrator to stream real-time stabilizer process data to Kafka. And, using Python and kafka consumer, calculate the model-predicted RVP and write it back to PI via PI WebAPI