Pathway to autonomous operations in refining and petrochemicals
Harnessing advanced technologies to transform industrial operations and usher in a new era of efficiency
Tom Fiske Yokogawa
P etroleum refining and petrochemical companies operate in a highly complex and dynamic environ - ment marked by numerous challenges that affect their efficiency, profitability, and sustainability. As these companies strive to meet global demand for their products, they encounter several significant obstacles, such as: • Dealing with fluctuating raw material and energy costs. • Adapting to changing raw material supplies and qualities. • Complying with ever more stringent environmental regulations. • Keeping pace with rapidly advancing technology. • Managing ageing infrastructure and legacy systems. • Combating an ageing workforce and talent shortages. • Meeting sustainability goals. • Ensuring safety. • Creating resilient supply chains. To meet these challenges, the downstream sector of the oil and gas industry is embracing digital technologies that enable it to significantly transform operations, control costs, and improve profitability. The next wave of efficiency improvements will be ushered in by industrial autonomy. Industrial autonomy Industrial autonomy has multiple benefits that go beyond autonomation. It has proven effective in nearly all aspects of operations by optimising production, reducing energy con - sumption, improving asset reliability, supporting sustainabil - ity goals, improving safety, and providing data-driven insight for continuous improvement and real-time decision-making. The transition from Industrial Automation to Industrial Autonomy (IA2IA) is the next evolution of industrial oper - ations. It is not about employing a single digital technology but rather the use of several advanced technologies in inno - vative ways to improve operations. It involves a combina - tion of ubiquitous connectivity, smart sensors and Internet of Things (IoT), cloud computing, edge devices, drones and robotics, cybersecurity, and advanced data analytics and artificial intelligence (AI) to create self-governing systems that can function with minimal human intervention. Industrial autonomy is the progression from automated systems to self-governing systems. Unlike traditional auto - mation, which relies on pre-programmed instructions with human supervision, industrial autonomy leverages AI and
machine learning (ML) to enable systems to make decisions, adapt to changes, and optimise performance in real-time. Industrial autonomy maturity levels Industrial autonomy (see Figure 1 ) can be applied to a wide variety of functional domains. AI powers industrial auton - omy, serving as an advisor and decision-support system or enabling autonomous operations. There are different levels of industrial autonomy, ranging from manual to completely autonomous: • Level 5 : A highly idealised state that extends autonomy to other functional domains like planning and scheduling, production, and maintenance to achieve complete autono - mous operations. • Level 4 : A system that operates autonomously in certain modes of operation and utilises orchestrated workflows to perform functions across multiple domains. • Level 3 : Select autonomous applications that monitor processes and equipment and enable workers to make bet - ter decisions. • Level 2 : A system where pre-programmed automations conduct most of the production with human supervision. • Level 1 : A system where humans and automation share the workload. • Level 0 : Manual operations.
Autonomous operations Autonomous orchestration
Industrial autonomy
5
4
3
Semi-autonomous
2
Automated
Industrial automation
1
Semi-automated
0
Manual
Figure 1 Autonomous maturity model
65
PTQ Q2 2025
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