TY - JOUR
T1 - Online Process Safety Performance Indicators Using Big Data
T2 - How a PSPI Looks Different from a Data Perspective
AU - Singh, Paul
AU - van Gulijk, Coen
AU - Sunderland, Neil
N1 - Funding Information:
Authors acknowledge the support provided by colleagues from the University of Huddersfield and Syngenta Huddersfield Manufacturing Centre.
Publisher Copyright:
© 2023 by the authors.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - 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.
AB - 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.
KW - barrier
KW - PSPI
KW - real-time
UR - http://www.scopus.com/inward/record.url?scp=85172904127&partnerID=8YFLogxK
U2 - 10.3390/safety9030062
DO - 10.3390/safety9030062
M3 - Article
AN - SCOPUS:85172904127
VL - 9
JO - Safety
JF - Safety
SN - 2313-576X
IS - 3
M1 - 62
ER -