Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Industrial management: Avoiding alarms

28.08.2014

An intelligent system that predicts when alarms might be triggered could greatly improve the management of industrial plants.


Operators managing a complex industrial plant can greatly benefit from models that predict when problems may arise.

© Georgijus Pavlovas/iStock/Thinkstock

A*STAR researchers have developed an anticipatory alarm system based on dynamic models of industrial processes using concepts similar to extreme weather forecasting.

A large industrial plant such as an oil refinery contains many interdependent units. In such a complex system, many things could potentially go wrong, which explains why engineers need sophisticated alarms to help them deal with abnormal situations. Having too many alarms, however, is almost as problematic as having none — especially if all of the alarms go off at the same time.

Arief Adhitya and co-workers at the A*STAR Institute of Chemical and Engineering Sciences in Singapore and the National University of Singapore have developed a system that provides accurate short-term predictions of the state of the machinery in a plant, thus enabling operators to take action before alarms are triggered [1].

“With so many interacting units, a fault can trigger a domino effect, setting off a large number of alarms within a short time, known as an alarm flood. This can confuse and overwhelm an operator, who might then activate the emergency shutdown, which leads to a costly loss of production,” says Adhitya. “Recent studies reveal that operators who are able to predict the evolution of the state of the plant are best able to cope with alarm floods.”

Industrial alarm systems monitor large numbers of process variables — such as the temperature or pressure in boilers — and activate alarms if those variables go outside defined ‘safe’ ranges. Previous methods of dealing with alarm floods have included dynamic adjustments of alarm limits and screening of alarms to remove false or duplicate alarms.

Adhitya and co-workers went further. They combined detailed models of the industrial processes with historical data relating to machine behavior to estimate the rates of change of process variables. With this additional information, operators can assess when each variable is likely to trigger its alarm and can take evasive action.

The researchers tested their system with a case study of a depropanizer plant, which separates hydrocarbons of different sizes in an oil refinery. They simulated several faults, including loss of cooling water and fouling of the condenser, and found that their system predicted all the alarms successfully.

More importantly, the added information provided by their system reduced the diagnosis time for operators by around 35 seconds. The team is hopeful that their system could improve the efficiency of many different processes within and outside the oil industry.

Reference

1. Xu, S., Adhitya, A. & Srinivasan, R. Hybrid model-based framework for alarm anticipation. Industrial & Engineering Chemistry Research 53, 5182–5193 (2014).

Lee Swee Heng | Research SEA News
Further information:
http://www.research.a-star.edu.sg/research/7023
http://www.researchsea.com

Further reports about: A*STAR Industrial Technology diagnosis machinery pressure temperature

More articles from Information Technology:

nachricht RESCAR 2.0: Reliability and Robustness of Electronic Systems in Electrical Cars Improved
30.10.2014 | FZI Forschungszentrum Informatik am Karlsruher Institut für Technologie

nachricht Saving lots of computing capacity with a new algorithm
29.10.2014 | Universität Luxemburg - Université du Luxembourg

All articles from Information Technology >>>

The most recent press releases about innovation >>>

Anzeige

Anzeige

Event News

Registration Open Now: 18th International ESAFORM Conference on Material Forming

28.10.2014 | Event News

Comparing Apples and Oranges? A Colloquium on International Comparative Urban Research

22.10.2014 | Event News

Battery Conference April 2015 in Aachen

16.10.2014 | Event News

 
Latest News

NIST 'combs' the atmosphere to measure greenhouse gases

30.10.2014 | Earth Sciences

First detailed picture of a cancer-related cell enzyme in action on a chromosome unit

30.10.2014 | Life Sciences

High-intensity sound waves may aid regenerative medicine

30.10.2014 | Health and Medicine

VideoLinks
B2B-VideoLinks
More VideoLinks >>>