FIPSE-7 is organized around three forward-looking themes at the intersection of process systems engineering, AI, and control, spanning physics-informed machine learning (Monday), autonomous/self-driving laboratories (Tuesday), and generative AI (Wednesday). The program is anchored by invited talks from leading academic and industrial voices, including Joel A. Paulson(University of Wisconsin–Madison) on representation, uncertainty, and deployment in physics-informed ML; Mariano Nicolás Cruz Bournazou (TU Berlin) on autonomy from lab to production; Swee-Teng Chin (Dow) on generative AI in chemical manufacturing; Federico Galvanin (UCL) on integrating physics-informed ML with optimal experimental design; Tanja Junkers (Monash University) on closed-loop automated polymerization; and John Kitchin (Carnegie Mellon University) on generative AI in chemical engineering. Together, these sessions highlight both the opportunities and the unresolved challenges in deploying AI responsibly and effectively across modeling, optimization, control, experimentation, and education in chemical engineering.
FIPSE-7 Scientific Program
The Meeting's Three Daily Topics
Monday, 6 July 2026
Physics-informed Machine Learning
Michael Baldea and Mike Doherty
Organizers and Session co-chairs
Session Synopsis:
The use of machine learning (ML) and artificial intelligence (AI) in process systems engineering has been discussed since the 1990s; however, practical applications have been slow to emerge. As noted by Venkatasubramanian (2019), the challenges included the complexity of the problems, the adequacy of existing solutions, and limited computing power. Today, advances in computation and data availability have enabled ML breakthroughs in areas such as e-commerce, computer vision, and generative AI, as well as in materials discovery and property estimation. Applying ML/AI to process systems requires special attention to key issues: prediction accuracy, consistency with material and energy conservation laws, interpretability with respect to governing equations, and continuous model maintenance. Since plant conditions evolve due to equipment changes or gradual degradation, models must be updated regularly. This session highlights open challenges and research directions for advancing AI/ML in multi-scale modeling, optimization, and control of dynamic process systems
Tuesday, 7 July 2026
Autonomous Labs
Claire Adjiman and Victor Zavala
Organizers and Session co-Chairs
Session Synopsis:
Autonomous or self-driving labs (SDLs) that bring together experimental platforms, advanced measurement systems, robotics, automation, and decision-making are ushering a revolution in materials discovery. The integration of these many elements into SDLs that can be used to design sustainable materials and products to be manufactured at scale poses many process systems engineering challenges. These are driven in part by the need to explore poorly understood materials/formulations for which experimental failure rates and uncertainty are high. Beyond product design, questions arise on what roles SDLs, which typically operate at a very small scale, can play in process development and scale-up and how they can enhance existing approaches to process synthesis and design.
Wednesday, 8 July 2026
Generative AI
Srinivas Rangarajan and Qi Zhang
Organizers and Session co-chairs
Session Synopsis:
The integration of Artificial Intelligence (AI) into chemical sciences is revolutionizing research, development, and industrial processes, significantly enhancing our capabilities in various areas, from material discovery and reaction optimization to process operations and business decision-making. Recently, advances in generative AI have shown great potential in driving substantial breakthroughs in innovation and productivity, transforming workflows and solution strategies for complex problems. However, developing generative AI solutions in chemical engineering and related fields presents unique challenges, including the complexity of modeling chemical systems and the necessity for high-quality, domain-specific data. This session will explore the challenges and opportunities of generative AI, providing insights into how to effectively and responsibly harness its transformative power. In particular, we consider the use of generative modeling, large language models, and AI assistants for property prediction, reaction optimization, molecule/material discovery, process design and optimization, supply chain management, scientific discovery in general (e.g., an AI co-scientist), and enhancing educational outcomes in core chemical engineering courses.
Invited talks
Representation, Uncertainty, and Deployment: Making the Most out of Physics-Informed Machine Learning in PSE
Joel A. Paulson
Chemical and Biological Engineering,
University of Wisconsin-Madison
USA
Invited talks
Gaining Autonomy from Lab to Production
Mariano Nicolas Cruz Bournazou
Institute of Biotechnology,
Technical University of Berlin
Germany
Invited talks
Innovative Applications of Generative AI in Chemical Processing and Manufacturing
Swee-Teng Chin
Senior Data Scientist,
The Dow Chemical Company
USA
Integrating Physics-Informed Machine Learning and Optimal Experimental Design for Model Identification: Current Challenges and Future Opportunities
Federico Galvanin
Department of Chemical Engineering,
University College London
UK
Trust the robots: Closed-loop automated flow and batch polymerization
Tanja Junkers
School of Chemistry,
Monash University
Australia
The Generative Turn in Chemical Engineering: From Surrogate Models to Scientific Autonomy
John Kitchin
Department of Chemical Engineering,
Carnegie Mellon University
USA
Short Oral Presentations
(10 min/each)
Structural Self-Healing Online Models
Erdal Aydin and Yaman Arkun, Koc University, Turkey
Adaptive Sampling: A Bottleneck in Feasible Engineering Optimization
Masoud Soroush and Arash Adhami, Drexel University, USA
Physics-Guided Bayesian Optimization via Structured Priors: Models and Constraints
Jay H. Lee, University of Southern California, USA
Toward Robust and Efficient Physics-Informed Machine Learning
Guoquan Wu and Zhe Wu, National University of Singapore, Singapore
A Call to Action: Incorporating PSE Principles into Regulatory Frameworks for Process Models and AI/ML in Pharmaceutical Manufacturing
Salvador Garcia, Eli Lilly and Company, USA
Short Oral Presentations
(10 min/each)
Human-Oriented Reinforcement Learning for Innovation and Novelty Search in Self-Driving Laboratories
Antonio del Rio Chanona and Calvin Tsay, Imperial College London, UK
From Optimal Experiments to Optimal Experimental Campaigns
Calvin Tsay, Imperial College London, UK
Beyond Bayesian Optimization: Decision Architectures for Self-Driving Laboratories
Bhushan Gopaluni, University of British Columbia, Canada
Short Oral Presentations
(10 min/each)
Why Molecule-only Generative Models are not Enough: Toward Context-aware AI for Chemical Designs
Jana M. Weber, Delft University of Technology, The Netherlands
Process Systems Engineering Perspectives on Alignment of AI Models and Agents
Andrew Allman, University of Michigan, USA
Why Process Systems Engineering Challenges Current Paradigms of Monolithic Generative AI
Artur M. Schweidtmann, Delft University of Technology, The Netherlands
From Accuracy to Trust: GenAI Reasoning for Trustworthiness and Adaptation in Process Systems Engineering
Nikolaos Passalis and Michael C. Georgiadis, Aristotle University of Thessaloniki, Greece
Challenges and Opportunities for Vision–Language Models in Optimization and Control of Water Treatment
Minghao Han and Xunyuan Yin, Nanyang Technological University, Singapore
Poster Presentations
Making AI/ML an Everyone’s Game: Overcoming the Hype
Iiro Harjunkoski, Aalto University, Finland
Modeling and Control of Nonlinear Systems with Time-varying Characteristics
Ali Cinar, Mudassir Rashid, Mohammad Ahmadasas, Mate Siket, and Mustafa Bilgic
Illinois Institute of Technology, USA and Obuda University, Hungary
Generative AI in Biopharmaceutical Development and Manufacturing
Seongkyu Yoon, University of Massachusetts Lowell, USA
Feature-Space Engineering for Autonomous Plants: Rethinking Data Topology in Physics-Informed Machine Learning
Isaac Severinsen, Bryan Li, and Brent Young, University of Auckland, New Zealand
Challenges and Opportunities in Direct Lithium Extraction from Emerging Feedstocks: A Process Systems Engineering Perspective
Burcu Beykal, University of Connecticut, USA
Valorizing Historical Data Toward Pharmaceutical Process Development Intelligence
Ulderico Di Caprio and Artur M. Schweidtmann, Delft University of Technology, The Netherlands
Hard Constraints in Machine Learning for PSE: Paradigms and Open Questions
Giacomo Lastrucci and Artur M. Schweidtmann, Delft University of Technology, The Netherlands
Physics-informed Machine Learning on Steam-Turbine Vibrations
Charilaos Kazakos and Ioannis Georgiou, HELLENiQ Energy, Greece
Reflections on Translating PSE Research into Commercial Impact in the Age of AI
Michael Short, University of Surrey, UK
Physics-Informed Neural Networks (PINNs) for Chemical Process Simulation, Parameter Estimation, and Real-Time Operation
Sepehr Aarabi Dahej and Christopher L.E. Swartz, McMaster University, Canada
Towards an Autonomous Multi-Agent LLM Framework to Harmonize Techno-Economic and Life Cycle Data for Supply Chain Optimization
Styliani Avraamidou, Meng-Lin Tsai, Parth Brahmbhatt, University of Wisconsin-Madison, USA
