Helen Kilbride and Krisshala Sinanan discuss the benefits and challenges
The Covid-19 pandemic has ushered in the fourth industrial revolution (Industry 4.0) with the increased adoption of digitalisation in many consumer industries, easing the burden on businesses in many ways. But what impact has digitalisation had on process safety in the process industries? Even before the pandemic, promises of better asset utilisation opportunities, operational efficiency improvements, and cost reductions have led to increasing incorporation of digital technologies, but does this translate into improved process safety management?
Process safety in this context is defined as a disciplined framework for managing the integrity of hazardous operating systems and processes by applying good design principles, engineering, and operating practices. It deals with the prevention and control of incidents that have the potential to release hazardous materials or energy1. Improving plant efficiencies and utilisation must also be complemented with advances in the management of process safety. For example, an examination of the largest historical losses in the hydrocarbon industry showed that, in 2018-2019, US refineries reported their highest average utilisation levels since 2005, but were amongst the highest loss contributors for this period2.
Improved process safety through the use of digital technologies such as big data analytics, asset information models (AIMs), artificial intelligence (AI), and smart technology (IIoT and Industry 4.0) offers the potential for a real-time view of risks while reducing operating costs and process safety event-related losses. Digital technologies could be disruptive to process safety by helping manage variables and close the gap between process safety intent and reality.
We often rely on manual approaches to manage process safety through leading and lagging performance indicators for various facets, including tracking process safety events, safety-critical equipment maintenance, and asset integrity inspections. However, these conventional approaches aren’t suitable for machine learning and algorithm-based technologies as the required data is dispersed over many systems and often differently formatted.
Evolving from manual processes to digitalised processes comes with many benefits, but it also comes with its challenges. In this, the fifth article in the DigiTAG series on digitalisation, we present three topics related to the benefits and challenges of digitalising process safety.
Evolving from manual processes to digitalised processes comes with many benefits, but it also comes with its challenges
Big data analytics has the potential to provide actionable insights on operational risk status and trends by discovering patterns and correlations hidden within existing data from process plants. Data analytics can facilitate the real-time manipulation, interpretation and visualisation of the safety-
relevant information contained in valuable process data, performing computations which conventionally may be impossible due to the volume of data required to reach a conclusion, or may take considerable time before becoming actionable. Information from big data analytics can help engineers and management to make better, more informed decisions about where and when to act, what to prioritise, and what to defer if they have a real-time perspective of process safety risk on the plant. It can also be a game-changer in the early detection of potential equipment failures, as well as aid in root cause analysis. The Process Safety and Operational Risk Management Report3 indicated that 91% of survey participants believed that improved access to real-time process safety indicators would improve risk awareness and safety. The need for real-time process safety information is evident!
Access to process data is not an issue. Control systems collect and store thousands of process data points per day from all digital transmitters on process plants. However, process data is not the only available or useful data. It can be significantly enriched when combined with other information. With the transition to digital technologies, many organisations possess digitised documentation and non-process data related to their facilities. For example, process safety information (PSI) such as equipment design and safe operating limits, catalyst deactivation parameters, and heat and material balance data. This data, when analysed alongside real-time process data from the control system, can aid in risk management, but only if the non-process data is correctly structured. The challenge is identifying, selecting and formatting the non-process data so that it can be parsed, combined with and interpreted alongside the relevant process data to enable better decision making, identify focus areas for action, and provide valuable insights for process safety.
There is also the issue of quality and accuracy for process and facility data. Lack of rigorous document control and QA/QC processes throughout construction and operation of the facility can lead to inaccurate data. Malfunctioning or poorly-selected instrumentation, and transient conditions due to startup, shutdown or equipment trips, can also lead to inaccurate process data being recorded in a plant historian. As a result, any strategy for digital technology adoption should thoroughly assess the data available to determine all the essential needs of a new system.
Implementation of improved technologies also leads to the creation of legacy data. Legacy data refers to the data stored in an obsolete or unused system or application. For example, the implementation of improved management of change (MOC) software creates legacy MOC data, which may contain invaluable details of past equipment modifications (eg changes to PSV type or setpoint, modification of vessel internals, etc and their associated manufacturers’ information). Legacy data can also be created with the implementation of improved integrated computerised maintenance management systems (CMMS). Indispensable information such as past work order records and details of maintenance interventions can be crucial for future equipment failure analysis and investigations.
Legacy data is usually incompatible with the newer digital systems, but must be maintained and the information it contains used. International standards require process safety information to be kept for the lifetime of the facility. Therefore, understanding how to integrate legacy data into new systems is crucial for successful implementation. The transition should never prevent safety-critical information that may be stored within legacy data from being accessed. These important requirements emphasise the need to scrutinise proposed digital technology options to ensure a smooth transition and continuous availability of legacy data. Data collection and retention requires proper planning, a robust management system and adequate funding to maximise benefits. Utilisation of an asset information model (AIM) can also minimise legacy data.
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