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]

This supplemental material accompanies the paper “Supervised Dispatching for Identical Parallel Machine Total Tardiness Scheduling.” It provides additional details, computational results, and supporting materials related to the methods and experiments presented in the main paper.

Supplementary Information for full paper submission for FOCAPO-CPC 2027

Digital competency is now core to chemical engineering practice, yet the extent and coherence of digitalisation skills provision across undergraduate curricula remain uneven. This study maps qualitatively and qualitatively digital learning outcomes across undergraduate chemical engineering programmes at the University of Sheffield, against a digital skills framework (data analysis, process simulation, process automation & control, reproducible workflows, programming, data governance). In recent years, digital skills education within chemical engineering education has advanced considerably, driven by the broader industrial shift toward Industry 4.0 and reinforced by the global challenges. Academic institutions have begun to integrate digitalisation-related content more deliberately within syllabus, in alignment with degree programme accreditation requirements and industry needs. Beyond introductory spreadsheet manipulation and basic programming, many courses are now embedding more advan... [more]

The accelerating social, environmental, and economic challenges of the twenty-first century call The growing complexity of sustainability challenges calls for educational approaches that integrate technical analysis with multi-stakeholder decision-making. Industrial ecology (IE) provides a relevant framework by combining systems thinking, resource flow analysis, and socio-environmental considerations. However, it is still predominantly taught through traditional lecture-based methods, limiting students' ability to engage with real-world complexity. This paper proposes and evaluates an experiential pedagogical framework based on industrial ecology, combining stakeholder role-play, industrial symbiosis scenario design, and multi-criteria decision analysis (MCDA). Implemented in a semester-long course, the framework enables students to collaboratively design and evaluate resource-exchange networks while representing different stakeholder perspectives. Results show significant improvements... [more]

Advancing student understanding of process design requires a balanced integration of theoretical knowledge with real-world industrial applications. This study introduces system design thinking-based learning through an engineering clinic approach that bridges the gap between classroom concepts and chemical engineering practice. Using an industrial multiproduct oil pipeline operation as a case study, students are exposed to real-world industrial systems, identify bottlenecks in a process, draw similarities between systems at different scales, and implement control strategies to address a practical industrial problem. In this study, we highlight the collaborative efforts between faculty, students, and industry partners to provide experiential learning in process design, modeling, and control to address the challenge of minimizing product loss during flushing operations in multiproduct petroleum pipeline systems.

The implementation of processes that use renewable energy requires that a techno-economic analysis be performed beforehand to determine its economic and technical feasibility. A techno-economic analysis was performed proposed wind farms in Trinidad and Tobago using the System Advisor Model simulation software. Metrics included the annual energy production in kWh, capacity factor, net present value in US$ and internal rate of return. From the above, the number of households that can be powered each month by the farms were calculated. The results showed that rotor diameter, which defines the swept area has a significant impact on annual energy production as a 33 m difference translated into a 27.3 GWh and 22.9 GWh difference in output. The results are promising and show that the oil and natural gas-based economy can be diversified.

As high-throughput experimentation (HTE) becomes a cornerstone of modern materials research, undergraduate and postgraduate curricula increasingly require students to possess Python programming skills to operate automated liquid-handling robots, such as the Opentrons OT-2 and Flex. However, the high cost of this hardware often necessitates shared equipment use during hands-on lab sessions, creating a significant pedagogical barrier: students lack the individual time required to iteratively test and debug their protocols on physical robotic platforms for automated material synthesis. Furthermore, we observe that the scarcity of robotic platforms creates an imbalance in group dynamics, where students with more coding experience often lead protocol development, while those with less experience remain disengaged. To address these challenges, we developed an interactive simulator that translates Python protocols into 2D animations on personal laptops. Using gold nanoparticle (AuNP) synthesi... [more]