Abnormal Situation Prevention in Refinery Units

From my days as a young systems engineer working on offshore oil & gas platforms in the Gulf of Mexico, I know that abnormal situations in our processes are something we all wish to avoid. A 1999 study by the ASM consortium estimated $10 billion USD in losses for U.S. process manufacturers due to abnormal situations. The question is how best to prevent these abnormal situations from occurring in the first place.

Emerson’s Ravi Kant and Roger Pihlaja recently presented a paper, “Abnormal Situation Prevention (ASP) in Complex Systems” at the recent NPRA Q&A and Technology Forum.

In their presentation they stress that the potential severity and cost of an incident increases if timely corrective action is not taken. An example cited from a refinery abnormal situation is the failure of a butterfly valve. After going several hours without detection by the automation system or operations personnel, it caused the Cat Cracker (FCCU) to shut down. In a matter of minutes this caused the refinery to shutdown, resulting in more that $1 million USD per day in lost revenue.

Ravi and Roger explained how abnormal situation prevention (ASP) technology embedded in the sensors, actuations, and machinery health are closest to the process and have access to better information. This ASP technology can predict root causes of abnormal situations through high-frequency spectral and statistical data analysis within these smart devices. The main reason for doing this analysis closest to the process is that the sampling frequency is greater–22 samples per second, instead of 1 sample per second to 1 sample per minute typical at the automation system level.

Data analysis at this higher frequency can uncover process anomalies including drift, bias, excessive noise, process spikes, and plugged conditions. Some of the detection and prediction algorithms and techniques which are employed include: polynomial extensible regression, principal component analysis, statistical process control, decision trees, fuzzy logic, and neural networks.

They cited some specific ASP applications in refineries including early detection of catalyst losses, catalyst circulation issues, afterburn conditions, column and heater coking, temperature runaway, and acid levels outside optimal or safe levels. The key to detecting these process conditions is sharing this data analysis at from the field device level, up through the equipment level, up through the process unit level to the operators and plant maintenance staff. Digital communications technologies like Foundation fieldbus and HART provide the information path.

Roger also shared with me other high-frequency data dependant ASP applications in the process including:

  • Plugged impulse line detection for DP flow transmitters
  • Flame instability
  • Stick/slip in FCC solids transfer lines
  • Stirred tank vessel agitator diagnostics
  • Continuous rotary drum vacuum filter diagnostics
  • Fouling & DP level transmitter plugging in evaporators
  • Detection of developing ASP issues like arching, bridging, & rat-holing in bulk solids storage vessels
  • In-situ proof testing of emergency relief systems

Work continues to refine and extend these predictive ASP technologies to more smart field devices to increase the “eyes and ears” on the process in order to avoid the costs and losses associated with abnormal situations.

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