Million Dollar Savings by optimizing parameters of Compressor loop using Cognitive Analytics.

About Customer

  • Customer is one of the large petroleum refinery and petrochemical manufacturer in the world.
  • One of the challenges this producer facing was low efficiency of their Crack Gas Compressor (CGC) of the Ethylene plant.
  • For a typical 500 KTA capacity ethylene plant, CGC Power accounts for 30 MWh of electricity equivalent to $ 20 million per year.
  • The Customer wants to optimize CGC operating parameters to improve its efficiency and lower energy consumption.

Objective / Challenges

  • Customer technical services team was constantly observing various operating parameters to analyze CGC efficiencies based on plant data from DCS, PLC & LIMS and descriptive data analytics.
  • They were not able to correlate operating parameters to the CGC efficiency and unable to visualize key indicators.
  • This is due to the following reasons
    • A lot of explanatory parameters (Over 100 operating parameters).
    • The volume of data was very high to analyze.
    • Building correlations between parameters was not possible as they were highly interdependent.
  • Due to the above challenges, a customer had engaged us to use advanced analytics to solve the problem.
Challenges

Solution to problem

  • We have used cognitive analytics and data sciences to correlate a large amount of plant data across several operating parameters consisted of 40 measured variables for a duration of 4 years.
  • Multivariate analysis was used to contextualize a large volume of data sets.
  • Machine learning capabilities, take deep dive into historical process data, identifying patterns and relationships among discrete process steps and inputs.
  • This information was transcended to provide real-time predictions of efficiency and thus, optimizing the parameters that have the greatest effect on CGC efficiency.

Benefits realized

  • Cognitive analytics and data sciences helped to correlate a large amount of plant data across several operating parameters.
  • Predict optimal operating values for key process parameters upon which CGC efficiency was highly dependent.
    • Overall, the facility realized a 5% reduction in power consumption equivalent to 1 MW and
    • It was feasible to extend the turnaround maintenance schedule by 6 ~8 months (i.e. 15%). The total saving was about a million dollars.