gopal

Apply Predictive Machine Learning Models to Operations - Predict Gasoline RVP

Blog Post created by gopal on Jan 23, 2020

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 frequently (every 15 minutes or even faster) to guide the operator.

 

Stablizer (distillation) column producing gasoline in an oil refinery

Figure: Stablizer column 

 

AF data model

Figure: Stablizer column - AF  data model

 

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

 

Stabilizer historical process data and lab RVP used for model development

Figure: Stablizer column - historical process data and lab RVP measurements 

 

RVP Jupyter Python kafka consumer

Figure: Python Jupyter notebook - shows Kafka consumer and WriteValuesToPI  snippet

 

 

Gasoline RVP predicted values

Figure: Stablizer column - historical lab RVP measurements overlaid with predicted RVP 

Outcomes