Process Risk Assessment Uses Big Data

1 minute read

Ankur Pariyani, Ulku Oktem, and Deborah Grubbe

Technology Update: Predictive, process risk assessment can use big data to assess risks dynamically and report automatically, empowering plant personnel to identify issues, taking necessary preventive measures to address them, avoiding a related shutdown incident or accident.

It’s a typical Monday morning scene at a refinery: the team (plant manager, supervisors, and head operators) gets together to review the past week’s performance and the coming week’s plans. They talk about the industrial fluid-catalytic-cracking-unit and the key question, “How was the catalyst stand pipe’s performance?” The team answers are: “Not great; there were more alarms than usual; and we’re not sure why.”

Plant management knows the regenerated catalyst stand pipe is prone to disturbances, which leads to frustrating operational “hiccups” (and trips) every now and then. It’s one of the most profitable units in the refinery, with a best-in-class historian and manufacturing intelligence software. The systems generate hundreds of thousands of data points. Yet, the magnitude of risks and reliability associated with the stand pipe (and how they change dynamically) remains unknown, creating challenges in managing its operation for optimum efficiency.

Risk Pyramid

This type of scene plays out often in refineries across the globe and indicates a growing problem as equipment ages and experienced operators retire. With recent advances in control and monitoring systems, facilities are getting overloaded with data—without clear insights into process performance, especially development of process risks. Hence, over the past few years, facilities have become data rich but information poor; this is typically referred to as the “big data challenge.”

Big data is indeed big. Typically, more than 5 billion data points are recorded every 6 months in a plant with about 320 tags, recording sensor measurements every second. It is often characterized by four Vs: volume, variety, velocity, and variability, which change with time. Lost in the big data flood are indicators that can help plants understand the dynamically changing risks and avoid some of the $10 billion losses the U.S. chemical and petrochemical industry experience annually (due to unexpected shutdowns).

Research shows that taking a different-in-kind approach to harnessing big data—based on processing the information directly with advanced data mining techniques—creates a wealth of insights that were previously unavailable. This has significant potential to transform the way facilities operate, and to reduce unexpected disruptions.

Keywords: risk assessment, big data, process industry, incident prevention