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Tailoring Big Data Analytics for Optimizing Ethylene Plant Asset Performance

Source: AIChE
  • Type:
    Conference Presentation
  • Conference Type:
    AIChE Spring Meeting and Global Congress on Process Safety
  • Presentation Date:
    March 27, 2017
  • Duration:
    30 minutes
  • PDHs:

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Getting the best out of the existing ethylene assets is a key result area for

manufacturers. Enhancing the capability to run the plant reliably and safely while

producing the required quality product and understanding the gaps between the best

production that can be achieved and current operation and finding ways to close this

gap are always operational goals.

The information to make this happen resides in the data that is typically collected,

partially used, and mostly archived in manufacturing and business databases. The

volume of available data, the velocity at which this data is collected and the sheer

variety of data available from the plant and the supply chain (albeit often of

questionable veracity in raw form) makes it impossible for unaided operating personnel

to assimilate and get his/her mind around this data overload. However this “Big data“

analysis problem has been getting significant press recently due to successful

application in diverse applications.

Applying the “Big Data Analytics” concept to ethylene manufacturing requires tailoring

to take advantage of past knowledge and best practice. Additionally, combining it with

techniques including Machine learning and the Industrial Internet of Things is necessary

to achieve the goal. When combined with a methodology to ensure implementation

and results, delivery of 5% - 20% increased throughput, improved reliability of operation

and a substantial improvement in furnace run lengths and hence uptime, while

proactively preventing safety incidents is achieved at ethylene sites where it has been


This methodology allows operating personnel to drive operations based on data enhanced

insights made readily available to them. It enables operations personnel and

managers with the ability to uncover truths and insights that aren’t readily obvious or

don't align with conventional wisdom or intuition. The analysis from this methodology

allows predictions and provides better prescriptive insights to better determine the best

course of action, while continuously growing the experience “database” of managers

and operating personnel at a faster pace over time. The methodology allows the

improvements to be tracked, quantified and controlled, so that it can be sustained and

improved continuously.

This paper describes how this Big Data Analytics methodology has been successfully

applied to several ethylene plants and case example are discussed.

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