The thermomechanical response of materials under high-strain deformation is critical to aerospace, defense, automotive, and additive manufacturing (AM) applications [1-3]. However, capturing this behavior remains challenging due to microsecond timescales of high-velocity impact events and reliance on manual, operator-dependent post-processing [4, 5]. These limitations reduce reproducibility and constrain the generation of high-fidelity datasets for constitutive model development. Current approaches are also limited in accessible strain-rate regimes, restricting applicability to AM processes and introducing uncertainty in predictive modeling [2]. This work presents an autonomous, AI-driven framework that transforms raw high-speed impact videos into structured, model-ready material data. The framework integrates two vision foundation models: Grounding DINO for open-vocabulary object detection and the Segment Anything Model (SAM) for high-resolution segmentation, in a zero-shot configurat... [more]

The energy-water nexus emerged in response to global challenges associated with energy and water resources. Along with their existing residential, commercial, and industrial commitments, the intrinsically complex system now faces additional pressure from the rampant rise in demands from data centers. To factor in the impacts of these new circumstances while accounting for the interdependent and interconnected nature of energy and water supply systems, the nexus holistically manages these resources and their corresponding decisions [1]. However, while these decisions are conventionally modeled from a centralized perspective through representative mathematical programs whose optimal decisions can then be optimized, a more realistic perspective models these decisions sequentially. Here, rather than all involved systems making their decision in a simultaneous fashion, decisions are made one after another in sequential order and characterized using multilevel programming. Bilevel programmin... [more]

Monoclonal antibodies (mAbs) have become a cornerstone in the treatment of immunological and oncological diseases. Nevertheless, bringing new antibody therapeutics to market remains a costly and time-consuming process, often requiring more than 10 years of development and investments exceeding 2 billion dollars. Critical stages of the development pipeline include the identification of a robust production cell line, capable of ensuring key quality attributes such as productivity, stability, product quality, and production consistency, as well as the optimization of the culture process (Li et al., 2010). These steps typically rely on extensive experimental campaigns and significant resource allocation. As a result, pharmaceutical companies are increasingly investigating digital and AI-driven approaches to streamline development workflows and accelerate drug time-to-market. In this work, we address two key challenges in mAb process development: i) the automated detection of anomalous cell... [more]

The chemical engineering (ChE) field is becoming increasingly hybrid digital as new advancements in machine learning (ML) integrate with ChE workflows and industrial plant operations. As data scientists and engineers push artificial intelligence (AI) usage, it is important for the engineering workforce and data science methods to be grounded in ChE fundamentals through methods like science guided machine learning (SGML). SGML is a broad term that includes ML architectures that are informed/embedded with physics, chemistry, and thermodynamics first-principles. This presentation highlights and demonstrates selective SGML advances from 2022 to 2026, including multicomponent phase equilibria, surrogate modeling, uncertainty quantification, and agentic large language models (LLMs). Furthermore, we discuss how each of these advancements accelerates the process design and development workflow, and we demonstrate solving novel process design and separation problems using this workflow, pushing... [more]

Artificial intelligence is reshaping what it means to be an engineer and it is changing how we learn, design, and discover. This talk explores the convergence of generative AI, data-driven modeling, and autonomous experimentation, and what that means for engineering education and workforce development. From intelligent tutors and code-generation assistants to self-driving laboratories and agentic research systems, AI is expanding both the cognitive and creative boundaries of the profession. Drawing on examples from open-source educational ecosystems such as pycse, and recent research on agentic science and generative optimization, we will discuss how future engineers can be trained not only to use AI tools, but to think with them, integrating computation, ethics, and domain expertise into continuous, collaborative learning. We will also discuss challenges of buy-in, resources, resistance from both faculty and students, and the need to maintain a balance of traditional learning approach... [more]

Optimal decision-making under uncertainty is a shared challenge across modern chemical, manufacturing, and energy systems that increasingly demand safe, data-driven autonomy. This talk revisits optimal control through the lens of the Bellman equation, emphasizing how optimal control theory and reinforcement learning have developed complementary, yet largely disconnected, perspectives on global optimality. In one view, central to reinforcement learning, the Bellman equation defines a global optimality condition that guides iterative policy learning from interacting with the system, but typically yields opaque control laws that are difficult to interpret, and deploy in safety-critical settings. In another view, widely adopted in model predictive control (MPC), the Bellman equation underpins tractable finite-horizon optimizations that deliver interpretable, constraint-aware, and modular local controllers, yet without explicit guarantees on alignment with global optimality. Building on the... [more]

Self-driving laboratories promise to compress materials-discovery timelines from years to weeks by replacing trial-and-error experimentation with closed-loop, algorithm-guided campaigns. Yet, despite the rapid proliferation of robotic and automation hardware, today's autonomous labs rely almost exclusively on Bayesian optimization (BO) to decide what experiment to run next. BO is a sensible approach to low-dimensional optimization problems with smooth response surfaces, but it struggles in precisely the regimes that matter most for real materials campaigns: tight experimental budgets, dozens of process parameters, mixed-integer choices, hard physical constraints, and noisy expensive measurements. In this talk, I will show how moving from BO to partitioning-based algorithms can substantially improve data efficiency, scale gracefully to dozens of process variables, and handle the constraints and noise that characterize realistic experimental campaigns. I will summarize a recently complet... [more]

Artificial intelligence is increasingly delivering measurable impact in the chemicalindustry, moving from isolated pilots toward embedded analytics across operations,supply chain, and Research & Development. At the same time, a clear industry shift isemerging: while developing accurate models remains important, there is a growingemphasis on realizing sustained value from AI investments. This talk presents apractitioner's perspective on applied AI in the chemical industry, focusing on what isworking, what is challenging, and where future opportunities lie. We begin with examples of AI applications that have demonstrated value in industrialsettings, including process monitoring, demand forecasting, and computervision-enabled inspection and automation. These successes highlight a criticalprinciple, i.e. value is not created by models alone, but by extracting actionable insightsfrom data and enabling datadriven decisions informed by those insights. It is ultimatelythe decisions that dr... [more]

In the last decade, the adoption of sophisticated modeling, machine learning, and artificial intelligence, combined with process analytical technologies like Raman and NIR, has become commonplace in Johnson & Johnson's manufacturing landscape. This transformation has driven groundbreaking achievements once considered pure science fiction-such as real-time release of oral solid dosage forms, central & automated process control, and remote fault detection that surpasses human capabilities, demonstrating tangible value and opening new horizons for innovation. This presentation will reflect on our manufacturing & supply chain journey, sharing key lessons learned and focusing on next steps. As we relentlessly pursue new cures, these technological innovations form a vital part of a comprehensive system dedicated to ensuring the high-quality, reliable supply of complex medicines. With the advent of Cell and Gene Therapies, which introduce substantial patient-specific variability, existing ML... [more]

Direct air capture (DAC) with porous adsorbents has the potential to aid large-scale decarbonization, but identifying useful sorbents for capturing CO₂ from humid air remains a formidable challenge given the vast chemical space of candidate materials such as metal-organic frameworks (MOFs). This talk surveys how AI and machine learning, powered by large, high-fidelity computational datasets, are reshaping the discovery pipeline for DAC sorbents using the Open DAC (ODAC) project as a central example. The earlier Open DAC 2023 (ODAC23) dataset established the approach with roughly 38 million density functional theory (DFT) calculations of CO₂ and H₂O adsorption across more than 8,000 MOFs. The new Open DAC 2025 (ODAC25) dataset comprises nearly 60 million DFT single-point calculations for CO₂, H₂O, N₂, and O₂ adsorption in more than 15,000 sorbent structures, introducing chemical and configurational diversity through functionalized MOFs, high-energy GCMC-derived placements, and syntheti... [more]

Molecular crystals are bound by dispersion (van der Waals) interactions, whose weak nature gives rise to polymorphism, the ability of a compound to crystallize in different structures. Crystal structure profoundly influences the physical and chemical properties, and hence the functionality of molecular solids in applications including pharmaceuticals, electronic devices, and energetic materials. Therefore, the ability to predict the structure and properties of molecular crystals is of paramount importance. To this end, we combine first principles simulations with machine learning. Molecular crystal structure prediction (CSP) is challenging because it requires searching a high-dimensional configuration space with high accuracy. CSP workflow have two main components, structure generation and stability ranking. For structure generation, we develop the Genarris code [1], which generates random structures in all compatible space groups with physical constraints on intermolecular distances.... [more]

Industrial operations need AI systems that can reason across live process data, engineering knowledge, and operator workflows. Yet conventional machine learning models often remain narrow predictors, while large language models lack grounding in plant behaviour, constraints, and real-time operating context. This talk presents Orbital, a grounded multi-agent system for decision support in industrial operations. Orbital combines three complementary layers: a time-series model for multivariable process dynamics and uncertainty-aware forecasting; a constraint-learning layer that extracts engineering relationships from plant documentation, including P&IDs, datasheets, mass and energy balances, and operating manuals; and a language-fusion layer that aligns process behaviour with engineering descriptions. These components are coordinated through specialist agents for planning, tool execution, verification, memory, and response composition. The system moves beyond prediction toward interpretab... [more]

Decision-making in the chemical industry is difficult because operators must choose actions under uncertainty: plant behavior is nonlinear and time-varying, measurements are noisy and incomplete, operating constraints and safety margins are strict, and economic objectives often conflict with each other. Data-driven AI methods can help turn high-frequency process data into predictions and recommendations, but in highstakes settings they are most useful when they leverage prior domain knowledge rather than treating the plant as a black box. In this work, prior knowledge is incorporated in two general ways. First, we use hybrid modeling, where deterministic structure from first principles (e.g., balances, thermodynamics, kinetics) is combined with data-driven learning to improve extrapolation, robustness, and interpretability. Second, we use preference-based learning, where expert judgment is captured qualitatively through pairwise comparisons or rankings of candidate options, enabling op... [more]

Industrial process data analytics is fundamentally constrained by data scarcity. Process data are strongly dependent on operating regimes, product grades, control policies, equipment, and scale. These characteristics make it difficult to build reliable models for new plants, new products, abnormal conditions, or future operating regimes. This talk discusses emerging approaches for learning process models under such data-scarce conditions. Relevant sources of transferable knowledge include mechanistic models, laboratory experiments, previous products and plants, and pretrained time-series representations. I will discuss how such knowledge can be exploited through grey-box or hybrid modeling, physics-informed machine learning, transfer learning, and time-series foundation models. The focus will be on these methodologies and their applications to industrial process monitoring and optimization. Particular attention will be paid to source selection and negative transfer.

Recent years have seen the rapid proliferation of generative AI tools, from chatbots to autonomous "agents." These tools have transformative potential in all parts of society, including the chemical process industry. However, deploying these tools to their full effect will require careful consideration of the areas where generative AI can enhance, rather than replace existing chemical engineering knowledge, and how it can be deployed in a safe manner given the current limitations in generative AI technology. The first half of this talk reviews some promising use cases of generative AI in process systems engineering emphasizing areas where first principles knowledge is insufficient for the task. In particular, efforts utilizing AI for generating new decompositions of large scale optimization problems, explaining optimization outcomes, and incorporating hard-to-quantify sustainability objectives into decision making frameworks are discussed. In the second half of the talk, I transition t... [more]

The med-tech value chain is being re-shaped by enterprise-grade AI technologies that deliver measurable gains while meeting the bar for safety, quality, and compliance. In this presentation, I'll share how GE HealthCare applies machine learning and GenAI-end to end-to improve manufacturing performance, reduce cost and improve customer service. We'll walk through end-to-end use cases including computer-vision applications to reduce scrap and variation; touchless demand and revenue forecasting that has improved accuracy with corresponding gains in on-time delivery and working-capital reductions; scheduling and logistics optimization; and GenAI-assisted quality applications. Beyond the algorithms, I'll highlight the socio-technical pieces that make these systems stick: robust MLOps, human-in-the-loop workflows, and alignment to regulatory and safety expectations. The session is designed for a diverse audience-researchers, practitioners, and leaders across industries-offering concrete exam... [more]

This is a book of abstracts from FOPAM 2026, containing 46 submissions from both oral and poster presentations. Key topics include: Emerging Methods in Generative AI, Industrial AI and Machine Learning, AI and Machine Learning for Sustainable Process and Product Chemistry, AI and Machine Learning for Processes and Control, AI and Machine Learning for Education and Workforce Development, and Responsible AI.

This document serves as the digital supplementary material for a publication titled "Spatio-temporal Framework for Energy Systems Network Design"

Chemical recycling has emerged as a promising pathway for increasing plastic circularity by re-covering value from mixed and contaminated waste streams that are unsuitable for mechanical recycling. Because chemically recycled products become indistinguishable from their fossil-derived counterparts, recycled-content (RC) certification relies on Chain-of-Custody (CoC) ac-counting frameworks, primarily Mass Balance (MB) and Book-and-Claim (BC). While these ac-counting approaches determine how recycled content is attributed to products, their implications for plastic supply chain design remain poorly understood. This work develops a mixed-integer optimization framework for the design of integrated plastic supply chains that explicitly incorporates alternative MB allocation methods and BC accounting for RC tracking while capturing competition between fossil and recycling pathways. The model simultaneously optimizes technology selection and capacity, facility location, transportation, and ma... [more]

The electrification of regional aviation requires coordinated planning of airport energy systems that integrate renewable generation, energy storage, and hydrogen technologies in a cost-efficient and resilient manner. This paper presents a scalable techno-economic optimization framework that models multiple airports as collaborative energy hubs. An object-oriented mixed-integer linear programming (MILP) formulation is combined with a genetic algorithm (GA) to optimize infrastructure sizing and energy dispatch. The framework is applied to three Swedish regional airports-Västerås, Jönköping, and Visby. A set of scenarios, including parties operating under shared wind-energy contracts using power purchase agreements (PPAs) and dynamic pricing (DP), was studied. Detailed representations of battery energy storage, hydrogen production and storage, and market interactions are included. Results show that coordinated operation and airport collaboration under a smart energy management system can... [more]

Supplementary Information for FOCAPO-CPC 2027 paper submission titled: "Acquisition Functions for Gaussian Process-Based Model Predictive Control"

The conceptual design and performance of a novel Methylcyclohexane-Toluene-Hydrogen based chemical heat pump was studied using steady state simulations. The distillation operating parameters of the chemical heat pump were optimized to maximize the Coefficient of Performance based on heat quantity (COP) and its corresponding Coefficient of Performance based on electric work input (COPW) was calculated. The best operating temperature ranges of the endothermic and exothermic reactor are 200°C-225°C and 250°C-275°C respectively. An endothermic temperature of 200°C and an exothermic temperature of 250°C results in a COP of 0.1357 and a COPW of 13.3. By integrating this chemical heat pump with a vapor compression heat pump COP increased to 0.1445 while COPW reduced to 4.9.

Nanoparticle (NP) synthesis has been extensively studied since the mid-1800s and are utilized across numerous fields due to their unique microscopic properties that collectively yield macroscopic benefits. Of particular interest are silver (Ag) NPs, whose controllable size and morphology impart distinct catalytic, electronic, and optical properties advantageous for environmental and energy-related applications. The theoretical understanding of NP nucleation and growth has advanced considerably starting with classical nucleation theory, evolving into the LaMer model centering on burst nucleation and diffusion-limited growth and resulted in near monodispersed hydrosols. Finke and Watzky later introduced the autocatalytic model considering a slow and continuous nucleation and autocatalytic surface growth not limited by monomer diffusion. However, the precise mechanisms remain the subject of active debate for the different homogeneous and heterogenous nucleation systems. In this study, si... [more]

The reliable identification of feasible and optimal operating conditions is a key challenge in the design and optimization of thermochemical conversion processes, where kinetics, limited data availability, and strict physical constraints coexist. In this work, a novel data-driven strategy based on Physics-Informed Neural Networks (PINNs) is proposed to explore the operability space of a bubbling fluidized bed (BFB) plastic pyrolysis process. The approach integrates mechanistic knowledge through explicit mass balance constraints with data-driven learning, enabling accurate prediction of and feasibility boundaries. An adaptive sampling framework is employed to iteratively augment the training dataset. The trained PINN surrogate is then used to predict feasible regions and perform constrained optimization aimed at minimizing tar production, which is one of the most problematic byproducts in plastic pyrolysis processes. Beyond classical optimality, a robustness-oriented uncertainty quantif... [more]

Poster illustrating the work presented at ESCAPE-36 conference. Starting from introducing what energy microgrids are and why their efficient management is relevant nowadays, this poster guides through the application of Reinforcement Learning to the optimal control of distributed energy resources in microgrids, highlighting how incorporating classical control priors into the learning process improves performance during both training and inference.