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.

The front matter of the full book of Proceedings of the 36th European Symposium on Computer Aided Process Engineering (ESCAPE 36).
Cover
Title Page
Copyright Page
Table of Contents
Introduction
Peer Review Policy
International Scientific Committee

Contains 335 original peer-reviewed research articles presented at the 36th European Symposium on Computer Aided Process Engineering (ESCAPE 36) in Sheffield, UK. Subject categories include CAPE in Circular Economy, CAPE in Clean Energy Systems, CAPEing with Uncertain Futures, Pharmaceutical & Biotechnological Systems, Modelling & Simulation, Concepts, Methods & Tools, Process Design, Scheduling & Optimisation, Process Control & Operation, Education, and Knowledge Transfer & Entrepreneurship.

A flexible start-stop operating policy that involves full shut-down and start-up may be beneficial for electrified plants under certain grid conditions, such as dispatchable demand response. This paper introduces a multi-period Hamilton-Jacobi reachability framework to explore the space of state trajectories for plant shut-down and start-up. Shut-down is defined in terms of operations leading to a stand-by state with no material flows or energy inputs, and variables within safety constraints. Candidate stand-by states are identified by constructing backwards reachability tubes from the desired steady-state operating point. The candidate shut-down/stand-by state is partitioned in fast and slow regions. Admissible control input trajectories are determined for the fast region, from which the minimum time trajectory is selected as optimal for fast start-up. A proof-of-concept simulation using a reaction/separation/recycle plant is presented.

Mathematical models in process systems engineering (PSE) are widely used to support decision-making in design and operation, but they are mostly limited to deterministic models. For biochemical systems, the biological variability can give rise to stochastic dynamics. This work addresses the question of when to act in such processes, as the stochastic dynamics affect the timing of important events. We consider the case of batch production of malic acid using Ustilago trichophora. The goal is to predict when the substrate concentration falls below a predefined threshold. We extend an existing deterministic model of the process to a stochastic differential equation (SDE) formulation by introducing a Monod-like noise term. Simulations of the SDE model reveal a distribution of substrate depletion times and a deviation between the mean of the stochastic trajectory and the deterministic solution due to nonlinear effects. To determine optimal intervention times under uncertainty, we formulate... [more]

Traditional approaches for mechanistic modelling require in-depth understanding of the underlying chemical and physical phenomena to construct reliable and predictive models. However, at early stages of development, limited experimental data, incomplete expert knowledge, and non-observable states often hinder a full understanding of the underlying mechanisms. Symbolic regression (SR) enables systematic model discovery and offers a practical route to addressing these challenges by automating the identification of interpretable model structures and the estimation of associated parameters from available data. However, structural identifiability and observability (SIO), a critical property of such models, is often overlooked in SR, thereby limiting its broader adoption and effective deployment. To address these limitations, this study proposes a comprehensive framework, which leverages scarce prior knowledge in SR and incorporates SIO analysis, offering a potential solution to capture the... [more]

Centralized monitoring methods experience reduced fault detection sensitivity in large-scale industrial systems due to the masking effect arising from the aggregation of many interconnected variables. Decentralized monitoring, where variables are grouped into subsystems, has been shown to effectively address these limitations. However, the performance of this class of methods critically depends on how the network is partitioned, and the role of its structural factors on fault detection remains poorly understood. This work studies how network topology and causal structure affect decentralized monitoring in high-dimensional systems. Using SimCaNet, a DAG-based data simulator, where large-scale systems with 100-1000 variables were generated, we rigorously compared the performance of centralized and decentralized causal log-likelihood monitoring methods under process perturbations and sensor bias faults. Network partitioning is performed using the Leiden community detection algorithm and c... [more]

As greenhouse gas emissions accelerate global warming, new capture and storage technologies are essential for reducing the industrial CO2 concentration in the atmosphere. This study addresses the urgent need for greenhouse gas capture technologies by developing a detailed dynamic mathematical model for Chemical Looping Process with Integrated Combustion and CO2 capture (CL-ICCC). In the CL-ICCC process configuration, CO2 capture is integrated into the chemical looping combustion system, resulting in a higher-purity, more efficient process. In this work Cu/CuO oxygen carrier material and CaO/CaCO3 sorbent materials were considered in a fixed bed reactor as solid phase to investigate Oxidation and Reduction/Calcination processes under different operating conditions. The simulation results were compared with experimental results from the literature. In case of the oxidation process, a sensitivity study was performed to investigate the behavior of the process for variation of different ope... [more]

A collection of Aspen Plus files for various cases used in the study (see attached report). Simulation cases include a base case, an optimized base case, and variants including cellulose incineration and no decolorization step versions.

The accumulation of blended-fiber textile waste poses a significant environmental challenge. Conventional recycling processes are often unable to efficiently process textile blends due to their heterogeneity. Blended fibers are typically composed of a wide variety of synthetic and natural fibers, dyes, and coatings. The inability to separate and reuse valuable materials such as polyester, nylon, and cellulose fibers limits the potential for a sustainable circular economy. In this work, this challenge is addressed by designing and simulating a chemical recycling route for mixed textile waste using Aspen Plus®. The overall process concept is selected based on a systematic screening of existing valorization approaches and implemented based on results from a lab-scale study. In a glycolysis reaction, polyester is depolymerized into bis(2-hydroxyethyl) terephthalate (BHET), which is the main product of the process. Nylon and cotton fibers are retrieved as intact fibers. Selected process var... [more]

Aspen Plus models of an acetic acid production process from CO2 via electrolysis. See linked report for full details.

Electrochemical reduction of CO2 (eCO2RR) is a potential pathway for the defossilization of the process industry, offering the possibility of using captured carbon to produce a wide variety of green chemicals, such as CO, formic acid, acetic acid and ethanol, while simultaneously electrifying the chemical industry. However, liquid products from eCO2RR still face major challenges with respect to the downstream processing due to their high dilution in aqueous streams, the presence of electrolytes, and the formation of pinch points and azeotropes. This work investigates the integration of a CO2 electrolysis setup producing acetic acid at high selectivity with the downstream processing section. The process has been simulated in Aspen Plus with particular emphasis on the integration of electrolyzer waste heat via heat pumps and the recovery and recycling of electrolytes using established separation technologies. In contrast to commonly proposed approaches based on electrochemical separatio... [more]

Aspen Plus files that accompany the report Utilisation of Cement Flue Gas for Green Methanol Production: Process Design, Simulation, and Techno-Economic Assessment. See link.

This work valorises two industrial waste streams from Hope Cement Works—flue gas CO2 and low-grade waste heat—converting them into green methanol via catalytic hydrogenation with renewable hydrogen, while returning by-product O2 to the kiln for oxygen-enriched combustion. The plant captures 42.9 t/hr CO2 (88.7% efficiency) via monoethanolamine (MEA) absorption with rate-based RadFrac columns and produces 213 000 t/yr methanol over Cu/ZnO/Al2O3 at 230 ◦C/70 bar. Aspen Plus V14 simulation achieves 62.9% per-pass CO2 conversion and 99.7% overall via a 2.76:1 recycle loop. Six heat exchangers recover 66.5MW. Multi-objective ε-constraint optimisation reveals that the levelised cost of methanol (LCOM) and CO2 utilisation are positively correlated: the cost-optimal design achieves 99.5% utilisation because hydrogen (65–78% of operating expenditure, OPEX) is co-lost with CO2 in the purge. LCOM ranges from £779/t (£30/MWh) to £1303/t (UK grid); progressive integration of cement waste heat (£62/... [more]

The transition toward circular economy principles in water treatment requires advanced process systems engineering tools to evaluate the trade-offs between environmental sustainability and economic viability, particularly for energy-intensive Zero Liquid Discharge (ZLD) systems. While classic ZLD systems treat concentrated brine as waste, circular ZLD (CZLD) systems incorporate salt recovery technologies that generate marketable salt product. This study presents a comprehensive technoeconomic assessment framework for CZLD systems integrated with renewable energy. The framework is developed to evaluate different CZLD configurations that generate saleable sodium chloride. The assessment methodology integrates solar photovoltaic systems with increasing capacities (100-1400 kW) to analyze renewable energy penetration and energy storage requirements. The renewable energy integration model incorporates hierarchical energy dispatch algorithms prioritizing direct solar utilization, battery sto... [more]

The integrated recovery of heat and mechanical work has gained increasing importance in process integration due to the strong thermodynamic coupling between temperature and pressure changes in many industrial systems. This work presents a rigorous framework for the simultaneous synthesis of Work and Heat Exchanger Networks (WHEN), in which heating, cooling, compression, expansion, throttling, and pumping are optimized in a coordinated manner. The problem is formulated using Generalized Disjunctive Programming (GDP), allowing the explicit representation of alternative thermodynamic paths, phase-dependent behavior, and logical equipment choices. Process streams are defined by supply and target states, while only bounds are imposed on intermediate pressures, temperatures, and flow rates. Streams may change classification between hot and cold multiple times and may undergo several phase transitions.Rigorous thermodynamic correlations obtained from Aspen HYSYS are embedded in the optimizati... [more]

In the field of biochemical process design, the accurate modeling of microbial growth is essential for the development and optimization of biological reactors used in the production of high-value compounds. Achieving this objective requires a detailed understanding of how environmental factors-such as pH and nutrient availability-influence microbial dynamics across the four distinct growth phases: lag, exponential, stationary, and death. Traditionally, reactor design relies heavily on the Monod model, which provides a simplified representation of microbial growth, focusing primarily on the exponential phase under constant operating conditions (1). However, this model presents substantial limitations when applied to dynamic environments where key parameters vary over time. To overcome these constraints, the present study proposes a data-driven modeling approach using a multilayer perceptron (MLP) artificial neural network for the prediction of microbial growth trajectories under varying... [more]