Online Process Safety Performance Indicators Using Big Data: How a PSPI Looks Different from a Data Perspective

Abstract

This work presents a data-centric method to use IoT data, generated from the site, to monitor core functions of safety barriers on a batch reactor. The approach turns process safety performance indicators (PSPIs) into online, globally available safety indicators that eliminate variability in human interpretation. This work also showcases a class of PSPIs that are reliable and time-dependent but only work in a digital online environment: profile PSPIs. It is demonstrated that the profile PSPI opens many new opportunities for leading indicators, without the need for complex mathematics. Online PSPI analyses were performed at the Syngenta Huddersfield Manufacturing Centre, Leeds Road, West Yorkshire, United Kingdom, and shared with their international headquarters in Basel, Switzerland. The performance was determined with industry software to extract time-series data and perform the calculations. The calculations were based on decades of IoT data stored in the AVEVA Factory Historian. Non-trivial data cleansing and additional data tags were required for the creation of relevant signal conditions and composite conditions. This work demonstrates that digital methods do not require gifted data analysts to report existing PSPIs in near real-time and is well within the capabilities of chemical (safety) engineers. Current PSPIs can also be evaluated in terms of their effectiveness to allow management to make decisions that lead to corrective actions. This improves significantly on traditional PSPI processes that, when reviewed monthly, lead to untimely decisions and actions. This approach also makes it possible to review PSPIs as they develop, receiving notifications of PSPIs when they reach prescribed limits, all with the potential to recommend alternative PSPIs that are more proactive in nature.

Development of Process Safety Cumulative Risk Assessment and Visualization Model/Framework for Petroleum Facilities in Niger-Delta Region, Nigeria

“…The logic/rule is in line with the guidelines which recommended that scoring system should be on a list of safety critical barriers whereby the status of each barrier is indicated using "traffic lights" format [30]. It is also consistent with the view expressed that "defined calculations" (referring to logic/rule) are to be used to perform computations on the extracted safety critical data to place the data in a form that can be presented to plant management, to eliminate variability in human interpretation of process safety risk levels [31]. The logic/rule also aligns with the approach on the design of the "barrier status panel" in Norway using the same "traffic light" format [25], even though the considerations for Green/Amber/Red scoring were a bit different.…”

“…The process safety cumulative risk assessment model/framework therefore represents a transparent and visible way of visualizing cumulative risk in a facility, for decision support in reducing major accident risk potential in daily operation of petroleum facilities. The model/framework will also help to eliminate variability in human interpretation of risk levels [31] and reduce reliance on expert opinion about risk levels in a facility. Due to the changing nature of the barrier impairments in time and space, the need for a web-based software for automation of barrier impairment data collection and real-time visualization of the cumulative risk picture was also established.…”

The BowTie as a Digital Twin: How a BowTie Looks Different from a Data Perspective

“…The second threat, which involves the rate at which the temperature of the reactor increases at reaction initiation, showcases the same barrier as the first threat. The previous analysis completed by Singh et al [30] shows that this barrier is not a barrier; alternatively, however, it can be monitored and displayed as a separate KPI for the process itself. Therefore, the threat and barrier were removed from this BowTie.…”

“…If a derivative function had been used on the original data, many spikes would have been noticed due to the nature of the function built into the software. As described by Singh et al [30], the derivative function is calculated using the current and previous sample points. If there was a significant shift between two simultaneous measurements, then a spike would be observed in the subsequent derivative signal.…”