Applying AI/ML in Ethylene for Real Time Intelligence

Title: Applying AI/ML in ethylene for real time intelligence

Author: Sameer Thorat

Digital Transformation is driving major improvements in the process manufacturing industry. This is particularly seen in ethylene plants. The inherent complexities in the ethylene process makes application of data analytics a significantly challenging process but the insights that create the returns are many. With right domain expertise, in-depth process knowledge and appropriate data science techniques combined with first principal models, a customized data analytics solution can be applied in the ethylene process to meet individual plant needs and effectively drive improvements. Digitalization has made it possible to implement innovative solutions that efficiently provide real time intelligence for operations.

Digital transformation principally aims to enhance the traditional technical and manufacturing skills sets with new advanced techniques that together drive manufacturing process to new levels of excellence by identifying opportunities and eliminating inefficiencies in the process. Advance analytics provides the right information at right time to drive the right decisions without overwhelming operations with “data overload”. Driven by technology, Industry 4.0 is transforming the ethylene process and reshaping operation paradigms to create greater efficiency and synergies in operating strategy and solve challenging problems.

This paper discusses the various opportunities in the ethylene process and the enormous improvements possible from application of AI/ML solutions. Challenges in achieving the desired objectives of various stakeholders and approaches to ensure successful application of AI/ML models will be addressed. Demonstration of apt usage of technology to successfully drive benefits from the analytics based solutions will be provided.

The Industry 4.0 Digitalization initiative is driving industry specific advanced analytics to leverage both historic and real-time data to achieve improvements that are difficult to accomplish using traditional approaches. Substantial economic gains can be achieved when data analytics is coupled with the overall plant operating strategy. This paper will cover some of case studies to demonstrate on how manufacturers were able to improve their ethylene unit performance effectively by the “Digital Transformation” process.