Preventing Abnormal Situations in Refineries and Petrochemical Plants

by | May 2, 2006 | Downstream Hydrocarbons, Industry

Recently an email came in that said Refineries and Petrochemicals specialist, Ravi Kant, and ASP Validation and Verification Engineer, Ahmad Hamad, in our Performance Technologies division, won the Fuels & Petrochemical‘s Award for best paper (out of more than 80 papers) at the AIChE 2006 Spring National Meeting.
This was something I had to get my hands on and find out why, and extend hearty congratulations to Ahmad and Ravi. The predictive PlantWeb technologies developed by this team find their way into AMS Suite software products, Rosemount transmitters, and other Emerson smart field devices.
With many industries like refining and petrochemicals running near full capacity, abnormal situation prevention provides a method for early detection with problems in the process and provides an opportunity for timely corrective action–before down time, quality issues, or even safety issues occur.
The paper, Advances in Abnormal Situation Prevention in Refineries and Petrochemical Plants, looks at traditional ways of preventive maintenance and the drawbacks in performing unnecessary maintenance, sometimes requiring down time, and being unable to detect abnormal situations.
It also explores other techniques for abnormal situation management. These solutions use knowledge-based diagnostics with data drawn from the continuous historian to develop a multivariate model. The source data from the historian is typically very low frequency from once per second to once per minute. This approach fails to detect abnormal situation which can develop rapidly. It also often fails to find problems with machinery, devices, and transmitters in the process. An example might be a stuck valve.
Ahmad and Ravi describe how advances in microprocessor performance and digital communications like Foundation Fieldbus and HART make it possible to do high frequency diagnostics within smart field devices. Emerson Process Management has developed Abnormal Situation Prevention (ASP) blocks in smart field devices like Rosemount 3051s transmitter, which capture high frequency process data at 22 samples per second. The blocks perform statistical, frequency-based, auto-regression, wavelets and other diagnostic measures to try to discover problems in the process in their earliest stage. And automation systems like the DeltaV and Ovation systems can turn the most critical of these alerts from these ASP blocks into operator and maintenance alarms for corrective action to begin.
The paper describes for cases where this early detection can prevent abnormal situations from occurring. These include: coke detection in refineries, catalyst circulation in fluid catalytic cracking (FCC) units, maltrays detection in crude columns, and gas turbine abnormalities. These are but a few of the critical applications where abnormal situation prevention technology can be applied.
Like anything else, the closer you can get to the source of the abnormal situation, and the earlier you can identify it, the sooner you can mitigate or prevent the situation from occurring.

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