Overview

This talk from the Autonomous Industrial Systems Laboratory (AISL) explores how to increase autonomy in process industries by combining situational awareness with intelligent decision-making.

We introduce a new framework for safe learning and optimisation that moves beyond traditional methods by using Gaussian-process surrogates to guide safe, guided exploration while optimising for key performance indicators like quality, throughput, and energy efficiency. An industrial case study illustrates the architecture, shows how embedding domain knowledge stabilises early learning, and outlines adversarial robust optimisation to maintain performance under disturbances.

We then discuss how AI can autonomously build models required for modern control, comparing fully data-driven and gray-box (physics+data) identification and highlighting automated model selection. Finally, we present an agentic workflow for abnormal situations: a Large Language Model (LLM) proposes actions, a digital twin agent validates them, and the LLM agent iteratively refines the plan in a closed loop.

Together, through interpretable state estimation, safe learning layered over existing control systems, and human–AI collaboration, we outline a practical path to autonomy that improves performance and resilience without compromising safety.

Speaker

Mehmet Mercangoz, ABB Associate Professor in Autonomous Industrial Systems, Imperial College London

Mehmet is an associate professor in the department of chemical engineering at Imperial College London, where he leads the autonomous industrial systems laboratory (AISL). His research focuses on the development of intelligent, adaptive, and autonomous systems for industrial operations, combining methods from optimisation, control theory, machine learning, and artificial intelligence to address challenges in process systems engineering, with applications spanning energy systems, gas compression, manufacturing, and industrial decarbonisation.

Prior to joining Imperial, he worked at ABB in various R&D roles, developing model predictive control solutions for gas compressors — a technology that today underpins the reliable transport of natural gas from Norway to many European countries — and contributing to the development of advanced energy storage technologies and numerous optimisation and control applications. He holds a PhD in chemical engineering from the University of California, Santa Barbara.

The material presented has not been peer-reviewed. Any opinions are the presenters' own and do not necessarily represent those of IChemE or the Process Management and Control Special Interest Group. The information is given in good faith but without any liability on the part of IChemE.

Time

10:00–11:00 GMT.

Software

The presentation will be delivered via Microsoft Teams. We recommend downloading the app from the Microsoft website, rather than using the web portal.