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Multi-Agent Based Process Supervisory System for Monitoring Large Scale Chemical Processes
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Abstract

Fault Detection and Identification in chemical process industries has been an active area of research for over three decades. Early and precise detection of process faults is essential to prevent off-spec products and also in many cases to prevent explosions and other serious accidents. The focus from academia has by and large been on developing a single monolithic monitoring strategy for process industries. But the sheer size and nature of chemical processes make the application of these monolithic strategies, which are computationally intensive, to real plants difficult. Moreover, modern sensors (Rosemount wireless sensors etc), instruments and equipments have some built-in diagnostic capabilities which are capable of determining faults within themselves, but current monolithic process monitoring strategies do no take advantage of such information. Also, a single method may not be capable of handling all the fault scenarios as each monitoring algorithm has its own inherent advantages and disadvantages. Improvement and robustness in monitoring chemical processes could be achieved by using multiple methods and by combining their results in a meaningful manner

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