Artificial intelligence: a frontier technology powering India’s oil refining revolution
Editor Manoj Sharma editor@refiningindia.com +91 989 9077 595 Managing Editor Rachel Storry
rachel.storry@emap.com tel +44 (0)7786 136440 Editorial Assistant Lisa Harrison lisa.harrison@emap.com Graphics Peter Harper Business Development Director Paul Mason info@decarbonisationtechnology.com tel +44 844 5888 771 Managing Director Richard Watts richard.watts@emap.com
For decades, oil refining was a game of steady-state engineering and rigid schedules. However, as we move through 2026, the industry is shedding its ‘legacy’ skin. The integration of artificial intelligence (AI) is fundamentally rewriting the refinery’s DNA. The
journey from manual, analogue operations to the AI- driven refineries of 2026 has been nothing short of a revolution. This transition was not an overnight switch but a strategic evolution. The trajectory of AI adoption in Indian refining exemplifies a broader industrial transition from mechanical systems to intelligent, data-driven operations. At the heart of refining operations lies complexity, involving hundreds of interconnected units, massive data streams from sensors, and narrow margins for error. Traditional control systems, while reliable, often struggle to adapt to dynamic conditions. AI bridges this gap by learning from historical and real-time data, enabling predictive, adaptive, and autonomous decision-making. This shift marks a move from reactive operations to proactive intelligence. AI implementation in Indian refineries has expanded significantly between 2024 and early 2026, with a clear distinction between the large-scale infrastructure focus of bigger players and a process-driven improvement focus by others. Indian refiners are not only integrating AI into massive hardware and digital infrastructure projects to drive global competitiveness but also focusing on indigenising AI technologies for enhancing operational safety, efficiency, and sustainability. Operational excellence and core refining improvements One of the most significant impacts of AI in refining is in process optimisation. Machine learning models continuously analyse variables such as temperature, pressure, flow rates, and feedstock composition to optimise reactors, distillation columns, and heat exchangers. Refineries use digital twins to simulate ‘what if’ scenarios for complex units like the crude distillation unit (CDU) and fluid catalytic cracking (FCC). The result is higher throughput, improved product quality, and reduced energy consumption – critical factors in an industry where
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Cover Story AI-driven refineries will become more adaptive and efficient
Refining India
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