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PT Notes

Quality Metrics for Process Hazard Analysis (PHA)

PT Notes is a series of topical technical notes on process safety provided periodically by Primatech for your benefit. Please feel free to provide feedback.

This PT Note is the second of two on the topic of PHA metrics. This PT Note addresses Quality Metrics.

Commonly, metrics used for the hazard identification and risk analysis element in process safety programs focus on PHA recommendations, such as the number of recommendations made per study and the number of recommendations unresolved by their due date. Other more detailed types of metrics can be used for PHA studies that delve into the data contained in a study.

PHA studies contain a wealth of information and quality metrics provide a means of generating insights that are not possible through a manual review of study results. Quality metrics provide insights into how well PHA studies are conducted. They identify inconsistencies in PHA results for different units, processes, technologies, or facilities. The purpose of using such metrics is to monitor and assess the quality of a PHA study performed on a process in order to focus attention on and drive improvements in the quality of PHA studies.

Quality metrics are based on aspects of how hazard scenarios are identified and characterized in PHA studies. They provide insights into the quality of PHA studies by comparison with norms that are derived from the population of other completed studies. Deviations of quality metrics from norms are investigated to determine if they are warranted and any appropriate remedial actions are taken to improve study quality.

For example, quality metrics for hazard and operability (HAZOP) studies include the number of parameters per node and the number of deviations per node. Too few may indicate an incomplete study. Quality metrics for initiating events, that is, the causes of scenarios, include the balance between the types of causes and the number of causes per node. Human failures are fairly common so a small number may indicate that some have been overlooked. Sometimes external events are not considered fully so a small number may be indicative of their omission. Significant deviations from the typical range of the number of causes identified for nodes may indicate that causes may have been missed.

For safeguards, their number per consequence can provide an indication that too much credit is being taken for them or that inappropriate safeguards have been included. Additional quality metrics can be employed using other attributes of PHA scenarios.

Quality metrics can be used during the performance of PHA studies to monitor the quality of the study being performed and to enable any necessary corrections to be made. They should also be used at the conclusion of studies as part of the quality control process for studies.

Use of quality metrics for all PHA studies performed by a company enables the norms used to be refined based on a continually enlarging database of studies whose quality has been managed. This refinement of norms provides for continual improvement in the management of study quality.

Quality metrics provide powerful ways of processing PHA study results to obtain insights that otherwise are not readily discerned. They support companies committed to data-driven decision-making by providing data-based justifications for informed decisions. The use of PHA metrics helps to maximize the return on your company’s investment in PHA studies.

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