As in any human-driven environment, safety is paramount in the energy industry. The complexity of our day-to-day operations, however, requires operators to implement very robust processes for preventing and mitigating hazardous events. Robust, in this case, means that our safety systems rely on both instrumented and noninstrumented barriers to properly manage risks. 

• Instrumented safety barriers directly monitor and respond to process parameters. Examples include instrumented systems like alarms, control loops, and both hardware and software that shut down automatically when hazards are detected (literally called safety instrumented systems or SIS).
• Noninstrumented safety barriers are passive barriers like physical containment structures, relief valves, or manual procedures.

While noninstrumented barriers play a crucial role, we’re going to focus on instrumented safety barriers, which also qualify as independent protection layers (IPLs). IPLs are not only known for their independence but also their reliability and auditability when it comes to mitigating risks associated with hazardous scenarios. Needless to say, today’s digital tools are further enhancing the management of these instrumented safety barriers, making them even more effective in maintaining safety and optimizing performance.

The role of digital tools in safety barrier management

Digital tools like data analytics, smart devices, and solutions based on AI and machine learning (ML) are transforming the way our industry manages instrumented safety barriers. Operators are now able to continuously monitor, assess, and enhance their safety systems’ performance, enabling a more proactive approach to identifying potential failures before they escalate into major incidents. 

Let’s break down how digital tools enhance the management of specific safety barriers.

Basic process control system loops

A basic process control system (BPCS) maintains process parameters within operational limits. In some cases, control loops within the BPCS can act as IPLs if they meet specific criteria, such as independence and reliability. For an IPL control loop to remain effective, it should operate in automatic mode more than 95% of the time.

Operators can track how well these control loops perform using digital tools such as monitoring software. These tools proactively highlight performance degradation, monitoring deviations from set points or periods when the control is in manual mode and allowing timely corrective actions—timely always meaning before a safety incident occurs.

Safety-related alarms

Alarms act as the first line of defense, alerting operators to deviations from normal operating conditions. Safety-related alarms (SRAs) are alarms that meet IPL criteria and provide a limited risk reduction (i.e., a risk reduction factor of 10 or probability of failure on demand of 0.1).

Digital tools for alarm management are critical. By monitoring key performance indicators, such as the number of annunciated alarms per hour, and identifying “chattering” or “stale” alarms, operators can reduce alarm overload and ensure that SRAs maintain their intended functionality.

Safety instrumented functions

A safety instrumented function (SIF) is a critical protection system designed to detect dangerous conditions and take predefined actions to bring a system back to a safe state. Each SIF is assigned a safety integrity level (SIL), which defines its ability to reduce risk.

Effectively managing an SIF’s lifecycle requires

• SIF demand rate monitoring—Tracks how often an SIF is called upon and evaluates its reliability.
• Failure rate analysis—Monitors failure frequency and highlights patterns for proactive maintenance.
• Override and bypass monitoring—Tracks the time that an SIF spends in a bypassed state, thereby reducing its time at risk.
• Proof testing compliance—Ensures timely proof tests, which are crucial for maintaining an SIF’s integrity. 

Furthermore, AI-based predictive models can be used to monitor and predict when an SIF might fail or require maintenance. Smart diagnostics, for example, are integrated into SIF components to provide real-time health assessments, again allowing operators to identify issues before they escalate.

And these algorithms are continuously evolving. The more operational data they process, the better they become at predicting failures and optimizing maintenance schedules. Take digital twin technology, which creates a virtual model of a system. It can simulate different failure scenarios and analyze the impact of SIF interventions, leading to even more improvements in safety management.

Last but not least, equipping maintenance technicians with smart handheld devices , connected to central databases, enables them to conduct proof tests efficiently in the field. These devices provide access to digitalized procedures, track compliance, and log any deviations or failures on the spot, ultimately improving the accuracy and reliability of overall SIF proof testing. 

Proof testing is crucial for ensuring that SIFs are functioning as intended and that hidden failures are revealed. Using digital testing tools, technicians can ensure tests follow predefined procedures and gather data in real time.

The deferral of proof testing should be managed with care. By using digital tools to track the frequency of deferrals and assess the risk associated with extending proof testing intervals, decision-makers are able to better balance their safety and operational demands.

What does this mean for safety barrier management?

At this point, it’s clear that incorporating digital tools for enhanced safety barrier management provides a proactive approach to maintaining process safety in the energy industry. By leveraging AI- and ML-based diagnostics, real-time monitoring, and smart devices, operators can continuously assess and improve the performance of instrumented safety barriers such as BPCS loops, SRAs, and SIFs.