Layer of Protection Analysis (LOPA) is a simplified risk assessment method that provides an order of magnitude estimate of the risk of a potential accident scenario. Humans can be the cause on an accident scenario (the Initiating Event [IE]) or human can serve as an independent protection layer (IPL). In either case, estimating the likelihood of the human error and measuring the human error rate at a site are troublesome tasks within the LOPA framework, which is one of the reasons some companies do not give any credit for an human IPL.
Identifying and sustaining independent protection layers (IPLs) is the heart of LOPA. Each IPL must be
• independent of the initiating event (IE) and the other IPLs
• capable (big enough, fast enough, strong enough, etc.)
• maintained or kept in practice/service
• validated/proven to provide the probability of failure (PFD) chosen
and all of the above must be documented and audited periodically to ensure compliance with these definitions.
There are many types of IPLs, and some are more trustworthy than others, hence the difference in the PFD of IPLs. As just mentioned, one possible type of IPL is a Human IPL. These include preventative steps that may stop a scenario from progressing once it is initiated, but more typically the human IPLs are responses to alerts or alarms or troubling readings and sample results.
This paper discusses the data needed for adequately counting the human in a LOPA (and other risk assessments), and includes discussion of the theory of human factors. The main focus of the paper is on practical means for collecting raw data in a plant setting for substantiating the error rates for the site, and especially for crediting a human IPL. The discussion covers the training requirements that should be met, proof drills for response to alarms, simulations and tests, frequency of proofs, error recovery probabilities, and of course the effect of human factors on human error rates. Actual plant data and tests are included in the paper to provide the reader with some examples of how similar data collection and validation can be set up within their companies.
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