Costas Pantelides is the CTO of Siemens Process Automation Software and also a Professor of Chemical Engineering at Imperial College London. He was a co-founder, and subsequently CTO and CEO of Process Systems Enterprise Ltd., one of Imperial’s largest spin-out companies acquired by Siemens AG in 2019.

Costas has extensive experience in the area of digitalisation for the process industries and its application to process design and operation. A key focus of his current work is the role that deep knowledge coupled with plant data can play in the ongoing digital transformation of process operations, and the architecture and design of general software platforms that can support this.

Costas has been the recipient of several awards including the 2007 Royal Academy of Engineering MacRobert Award, the 2016 Sargent Medal of the Institution of Chemical Engineers, the 2019 Computing Practice Award of the American Institute of Chemical Engineers, and an honorary PhD from the Technical University of Dortmund. He is a Fellow of the Institution of Chemical Engineers and of the Royal Academy of Engineering, and an international member of the USA National Academy of Engineering.

Plenary presentation

Process Operations: From Automation to Autonomy

The ability to execute complex calculations based on increasingly accurate process models within online/real-time environments opens new possibilities for process operations, in terms of achieving higher product quality, enhanced profitability and increased sustainability using existing assets with little or no additional capital expenditure.

In this presentation, we will explore recent advancements in model-based process monitoring and control, showcasing examples from various sectors within the process industries. We'll also examine developments in enabling process modeling technologies, driven by both enhanced scientific understanding and breakthroughs in data science and AI.

Additionally, we will discuss the push toward greater autonomy in process operations, focusing on automating tasks traditionally dependent on human operators’ ability to assess situations, devise responses, and implement actions in dynamic environments. We will highlight the impact of recent innovations—ranging from sensor technology to advanced AI techniques—in driving progress toward this goal.