Industrial processes operated at plants often present hazards to the people that work there. Plant design aims to address many of the issues that could compromise safety.
Process safety and personal safety are directly affected by the activities undertaken throughout the lifecycle of the plant, beginning with design.
A Safety Instrumented System (SIS) is utilized to resolve the delicate balancing act between efficient and safe operation of the plant. It is used when other protection methods can’t reduce the probable frequency of a hazard resulting in harm, to a tolerable level.
An SIS is engineered per IEC 61511 to perform specific control functions to failsafe or maintain safe operations of a process when unacceptable or dangerous conditions occur.
The specific control functions performed by an SIS are called Safety Instrumented Functions (SIF). SIFs provide an active protection method, i.e., they have to function automatically and on-demand to detect, decide and act based on input conditions to mitigate the consequences of an industrial hazard by moving the system into a safe state. In contrast, passive protection measures mitigate risk without active functioning or intelligence. Gas analyzers are often used as one active protection component within a SIF.
The gap between tolerable hazard frequency and probable pre-SIS hazard frequency determines the required risk reduction, which directly translates to what is known as the Target Safety Integrity Level (SIL).
Each SIF must be designed to meet the requirements at the Target SIL level and thus demonstrate the required level of risk reduction. The SIL calculations required for this are somewhat complex and time-consuming, but essentially, the process is to gather failure rate data for the SIF components and account for factors such as test frequency, redundancy, voting arrangements, etc. The result is that for each SIF, an overall Probability of Failure on Demand (PFD) is calculated.
Software packages, such as exSILentiaTM or SIL-SolverTM, are increasingly used to make SIF design and SIL verification easier. They allow modelling of a SIF and use data from integrated device databases to run calculations to arrive at the probability of failure on demand (PFDavg) and mean time to spurious failure (MTTFS).
These tools provide cost and time savings, but could they provide increased benefits as a result of digital transformation?
Rather than using generalized failure rate values for each technology type, it may be that, in the future, they use specific data shared in an agreed standard digital format from the equipment suppliers. Specific equipment failure data would provide more accurate results.
For complex products where the market has a diverse range of quality, performance capability, and gas measurement technologies, the change in results could be significant, possibly reducing SIF cost or reducing the frequency of manual and production invasive proof test intervals.
Furthermore, if gas analyzer data (such as measurement, health, and event logs) were available and linked with the application and environmental context, the resulting real-world failure data would provide a sound basis for SIL verification, providing greater confidence in safety margins.
Additionally, of course, the data would provide new insight into the causes of failure in the field, allowing product design to become increasingly reliable, leading to safer operation.