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.

The design of Power-to-Ammonia systems (P2A) is a challenging task. While the technology for all its components, including renewable energy harnessing, electrolysis, Haber-Bosch synthesis, and auxiliary buffers, is mature, assembling such a system to meet the challenges of varying power profiles is not trivial. To ensure resilient, cost-effective designs, careful selection of unit capacities and coordination of all system operations are required. Specifically, this requires modeling system behaviour and enforcing operational constraints to capture system flexibility over a representative time frame. The first steps towards a novel P2A design framework are presented, with a focus on enhanced process operations and exploring new options for process flexibility. A general methodology is proposed, where the full system can be customized, namely by enabling or disabling: (i) multiple renewable energy harnessing sources, (ii) grid operations, and (iii) buffers, including battery and/or an H2... [more]

Pharmaceutical supply chains face increasing pressure to deliver high service levels while meeting environmental and social expectations. Vaccine supply chains amplify these challenges due to strict cold-chain requirements, demand uncertainty driven by acceptance and preferences, and the urgency of public-health objectives. This paper develops a multi-objective mixed-integer linear programming (MILP) framework for national-scale vaccine distribution that explicitly integrates economic cost, service level, greenhouse-gas emissions, and population-level vaccine effectiveness. Behavioral realism is incorporated by modeling vaccine acceptance and brand preferences as operational constraints rather than ex-post indicators. Trade-offs are explored using an e-constraint method that preserves the MILP structure and enables systematic recovery of Pareto-optimal solutions. The framework is applied to a 52-week national case study for metropolitan France during the 2021 COVID-19 vaccination campa... [more]

This work explores Generative Flow Networks (GFlowNets) as a computational approach for sustainable fragrance design, focusing on generating novel molecules that reproduce the scent profile of vanillin while reducing reliance on resource-intensive synthesis and environmentally vulnerable natural sources. An integrated pipeline couples a GFlowNet generator with a fragrance note predictor, which guides learning toward a target odor by rewarding molecules predicted to be aromatically similar to vanillin. Chemical validity and realism are enforced through chemistry filters that penalize unstable or implausible structures and through an odorless-vs-odorant classifier, so only chemically and olfactorily plausible candidates are selected. The agent is trained in a hybrid offline-online regime, implementing reinforcement-based exploration, with hyperparameters tuned via Bayesian optimization. As an independent validation layer, an olfactory receptor docking model estimates binding affinities t... [more]

The unprecedented expansion of Artificial Intelligence is adding increasing electricity demand to Europe's power system. While incumbent plans pursue a net-zero future by 2050, they fail to consider the implications of large-scale AI-based data centres. In this study, a spatially explicit optimisation model is developed to assess how hyperscale data centres may reshape energy infrastructure investment, and emissions trajectories, across different AI demand growth scenarios. The results indicate that, after 2030, AI capacity deployment increasingly shifts toward regions with the ability to expand nuclear and gas-based generation, as firm and flexible power sources are essential for supporting the deployment of high-capacity AI data centres. By 2050, AI-driven electricity demand under high growth scenarios may reach up to 450 TWh, corresponding to 7% of total Europe's demand, with installed AI capacity reaching approximately 85 GW. This additional load leads to an increase of nearly 25 M... [more]

Replacing fossil resources with green hydrogen in industrial production holds tremendous potential for greenhouse gas mitigation. The economic feasibility and greenhouse gas (GHG) mitigation of grid-based electrolytic hydrogen production is highly dependent on the time-variant price and carbon footprint of electricity. In the present contribution, we analyse the economic feasibility of transitioning key carbon-intensive industries, steelmaking, and ammonia production, to green electrolytic hydrogen. Also, we investigate the competitiveness of green electrolytic hydrogen with other environmentally sustainable hydrogen sources derived from biomethane, biogas, and natural gas (associated with carbon capture and storage). We perform process design for steelmaking, ammonia production, and biogas-based steam reforming in order to determine key performance indicators such as costs, conversion factors, and GHG emissions. In particular, we allow for dynamic operation of the industrial processes... [more]

A technical evaluation on the production of sustainable formaldehyde was presented in this report, including process design, advanced simulation, economic analysis, and environmental analysis. Three process configurations to produce formaldehyde were developed: a base-case with no capture of carbon, a post-combustion capture (PCC) process, which utilized 14 wt.% MEA solution-based process, and a direct air capture (DAC) route which used NaOH. Sequestered CO₂ was used as a major feedstock for methanol production via an electrocatalytic reactor (ECR), after which was converted into formaldehyde via a FORMOX process. Large-scale simulations were carried out, demonstrating a yearly methanol production capacity of approximately 62 million kilograms, with a fixed formaldehyde-to-methanol conversion ratio of 1.4 kg per kg of methanol. Economic models were developed using Aspen Process Economic Analyser, indicating that the base-case option (without capture) would involve a capital expenditure... [more]

The increased use of pharmaceuticals globally over the past two decades has contributed to an increase in unused pharmaceuticals and a corresponding surge in pharmaceutical waste. Thus, there is an impetus for the development of processes for the recovery of the active pharmaceutical ingredients (APIs) from these unused drugs. This study introduces a decision framework for solvent selection to enable the recovery of APIs using a general separation train where cooling crystallization is the final step. The framework is designed to base solvent selection not just on the solubilities of the formulation contents but also considers the overall recovery that can be achieved in the process. In addition, the environmental sustainability of the framework is analyzed using the process mass intensity metric (PMI). The effectiveness of this framework is demonstrated by using paracetamol (PA) as a model API in a formulation consisting of five of the excipients commonly found in PA formulations. The... [more]

Precision modeling and forecasting of soil moisture are essential for implementing smart irrigation systems and mitigating agricultural drought. Most agro-hydrological models are based on the standard Richards equation, a highly nonlinear, degenerate elliptic-parabolic partial differential equation (PDE) with first order time derivative. However, research has shown that standard Richards equation is unable to model preferential flow in soil with fractal structure. In such a scenario, the soil exhibits anomalous non-Boltzmann scaling behavior. Incorporating the anomalous non-Boltzmann scaling behavior into the Richards equation leads to a generalized, time-fractional Richards equation based on fractional time derivatives. As expected, solving the time-fractional Richards equation for accurate modeling of water flow dynamics in soil faces extensive computational challenges. To target these challenges, we propose a novel numerical method that integrates finite volume method (FVM), adaptiv... [more]

Operational decision-making in Process Systems Engineering (PSE) has achieved high proficiency at specific levels, such as supply chain optimization and unit-operation optimization. However, a critical challenge remains: integrating these layers of optimization into a cohesive, hierarchical decision-making framework that enables sustainable and automated operations. Addressing this challenge requires systems capable of coordinating multi-level decisions while maintaining interpretability and adaptability. Multi-agent frameworks based on Large Language Models (LLMs) have demonstrated significant promise in other domains, successfully simulating traditional human decision-making tasks and tackling complex, multi-stage problems. This paper explores their potential application within operational decision-making for PSE, focusing on sustainability-driven objectives. A realistic Gas-Oil Separation Plant (GOSP) network is used as a case study, mimicking a hierarchical workflow that spans from... [more]

This work approaches a detailed characterization of the aspects inherent to the innovative paradigm of Water and Energy Integration Systems (WEIS). These consists in conceptual physical systems which consider all potential energy-using and water-using processes in a site, all potential recirculation of material and energy streams between these and the integration of several categories of state-of-the-art technologies. The WEIS have the ultimate aim to promote the sustainability character associated to existing installations (through the reduction of energy and water input and contaminants output). The specific characteristics of WEIS are compared to existing similar process integration methodologies and a set of performance indicators are determined, having as a basis two previous case-studies approached for the Engineering project of WEIS. The performed analysis in this work revealed that the innovative paradigm is able to constitute Engineering projects with associated sustainability... [more]

Data-driven decision-making is crucial in the transition to a low-carbon economy, especially as global industries strive to meet stringent sustainability goals. Traditional life cycle assessments often rely on static emission factors, overlooking the dynamic nature of the energy grid. As renewable energy penetration increases, grid carbon intensity fluctuates significantly across time and regions, due to the inherent intermittency of renewable sources like wind and solar. This variability introduces discrepancies in emission estimations if time-averaged factors are applied, leading to sub-optimal process operations and unintended environmental consequences. To this end, we present a real-time emission-aware optimization framework, which is implemented through a mixed-integer linear programming formulation that can determine optimal design configurations and operation schedules while simultaneously mitigating emissions by utilizing electricity price forecasts, time-varying emission fact... [more]

A generally applicable framework for the evaluation of the sustainability of distillation processes is proposed by aligning indicators directly to selected sustainable development goals (SDGs) created by the United Nations. The indicators are related to the goals good health and well-being (SDG 3), clear water and sanitation (SDG 6), affordable and clean energy (SDG 7), decent work and economic growth (SDG 8), industry, innovation and infrastructure (SDG 9), responsible consumption and production (SDG 12), climate action (SDG 13) and life below water (SDG 14). A total of 12 sustainability indicators, including human toxicity potential, wastewater generation, water consumption, renewable energy share, energy demand, material footprint, profit, waste generation, recycling ratio of waste, greenhouse gas emission, eutrophication potential and acidification potential are assigned to selected SDGs. The application of the indicators is illustrated by two case studies: a batch (BD) and a conti... [more]

Biomass conversion to chemical derivatives and essential intermediates is regarded as a long-term strategy for the chemical sector. Among the numerous valuable chemicals obtained from biomass, 5-hydroxymethylfurfural (HMF) is considered an industrially relevant compound due to its capacity to be converted into a variety of value-added chemicals. Compared to conventional catalytic synthesis, bio-catalysis has emerged as a potential greener substitute for HMF conversion to value-added compounds. HMF conversion through bio-catalysis, although more sustainable, seldom leads to the production of a single derivative. Thus, the development of efficient purification and separation processes of several products are crucial to scalability. The downstream process for the novel enzymatic conversion of HMF to high value-added chemicals (i.e., 1-phenylethylamine, 2,5-bis(hydroxymethyl)furan, 1-phenylethylalcohol, and 5-(aminomethyl)-2-furanmethanol) was designed by means of rigorous simulations in A... [more]

This document contains digital supplementary material (characterization of the case-studies, developed simulation models (and final configurations), optimisation models and post-processing assessments) related to the article entitled “The Paradigm of Water and Energy Integration Systems (WEIS): Methodology and Performance Indicators”, which is part of the peer reviewed conference proceeding of the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35). The present content has been adapted from the PhD thesis entitled "Simulation and Optimisation of Water and Energy Integration Systems (WEIS): An Innovative Approach for Process Industries".

As interest in additive manufacturing (AM) continues to increase, it has become more important to have a robust method to help potential users select the AM process that best suits their technological needs while providing the greatest potential benefits in terms of sustainability and its effect on people. This paper presents the development of a framework for selecting the best AM process for a given application by considering both sustainability and human factors through the combination of axiomatic design and the analytic hierarchy process. Thirty-one participants with varying levels of expertise (novice and advanced users) were involved in the study, considering the frequency of 3D printer usage (novice users: never, rarely; expert users: sometimes, almost always, always) for prototyping parts. They employed fused deposition modeling (FDM) and stereolithography (SLA) (both 3D desktop printers) and collected data on five evaluation criteria. The participation of experts helped estab... [more]

This study aims to evaluate the emissions of greenhouse gases (GHGs) stemming from the sludge treatment sector in China and to investigate the feasibility of novel technologies in curtailing these emissions, with the aim of fostering sustainable sludge management methodologies. Employing a life-cycle assessment (LCA) methodology, the research computed the comprehensive GHG emissions resulting from sludge treatment, taking into consideration diverse elements such as treatment techniques (e.g., landfills, incineration, and land application) and the geographical variations among China’s 660 municipalities. Findings indicate that the total amount of GHG emissions from sludge treatment amounted to 18.54 Mt CO2-eq in 2017, with incineration registering the highest emissions (10,011.53 kg CO2-eq/t dry sludge (DS)), followed by landfills (717.51 kg CO2-eq/t DS) and land application (276.41 kg CO2-eq/t DS). The geographical dispersion of emissions characteristics reveal notable regional dispari... [more]

Currently, the development of innovative materials for the treatment of various diseases is highly interesting and effective. Additionally, in recent years, environmental changes, including the search for a sustainable world, have become the main goal behind developing sustainable and suitable materials. In this context, this research produced innovative hydrogels that incorporate cellulose nanocrystals and nanofibres from underutilised fibres from a semiarid region of Brazil; the hydrogels were loaded with vitamin D to evaluate controlled drug release for the treatment of diverse diseases. Spectroscopic (FTIR, Raman, UV−VIS), X-ray diffraction, zeta potential and morphology (SEM, TEM) analyses were used to characterise these hydrogels. In addition, biocompatibility was assessed using a resazurin assay, and the in vitro kinetic accumulative release of vitamin D was measured. The results showed that nanocrystals and nanofibres changed the structure and crystallinity of the hydrogels. In... [more]

Chemical Process Industries must navigate a series of changes in their operations to comply with increasing sustainability targets. These changes may involve the use of electricity-based operations, the implementation of carbon capture strategies, and the use of biomass or end-of-life carbon-containing waste as feedstocks. De-carbonizing oil refineries is particularly challenging as they possess highly valuable infrastructure. Discarding this infrastructure before the end of its life to build entirely new electric and biomass-based operations does not seem to be an economical or even a sustainable solution. This presentation will cover recent work in my group related to the decarbonization of oil refineries, focusing on proposing solutions that could be integrated with existing plants... (ABSTRACT ABBREVIATED)

The study of sustainable design has gained prominence in response to the growing emphasis on environmental and social impacts of critical infrastructure. Addressing the different dimensions inherent in sustainability issues necessitates the application of many-objective optimization techniques. In this work, an illustrative four-objective design system is formulated, wherein uncertainties lie within two different socially-oriented objectives. A stochastic community detection approach is proposed to identify robust groupings of objectives. The findings reveal that the modularity of the optimal solution surpasses that of the average graph, thus demonstrating the efficacy of the proposed approach. Furthermore, a comprehensive exploration of the Pareto frontiers for both the robust and single-scenario best groupings is undertaken, demonstrating that using the robust grouping results in little to no information loss about tradeoffs.

Most studies in process systems engineering are applying incomplete methods when incorporating sustainability. Including sustainability is a laudable goal, and practitioners are encouraged to develop systems that promote economic, environmental, and social aspects. Ten methods that are often overlooked in performing sustainable process systems engineering are listed in this effort and discussed in detail. Practitioners are encouraged to create designs that are inherently safer, to be more complete in their identification of process chemicals used and released, to be complete in their definitions of supply chains, and to apply additional environmental impact categories. Other methods point to items that are factors in process systems engineering such as disruptive recycling, robust superstructures for optimizations, and employing complete sets of objectives. Finally, users should be aware that sustainability tools are available, which might have been outside of their awareness.

One of the main challenges to support life in space is the development of sustainable, circular processes that reduce the high cost of resupply missions. Space biomanufacturing is an emerging paradigm that aims to reduce the need for resources, enabling on-demand manufacture of products. The cost of installing biomanufacturing systems in space depends on the cost of transporting the system components, which is directly proportional to their mass/weight. From this perspective, the system mass is a critical factor that dictates process design, and this has important implications in how we can approach such design. For instance, mass constraints require circular use of resources and tight process integration (to minimize resupply) and restricts the type of resources and equipment needed. In this work, we evaluate the lactic acid bioproduction design using Escherichia coli, Saccharomyces cerevisiae, and Pichia pastoris. We use the Equivalent System Mass (ESM) metric as a key design measure... [more]

Circular systems design is an emerging approach for promoting sustainable development. Despite its perceived advantages, the characterization of circular systems remains loosely defined and ambiguous. This work proposes a network optimization framework that evaluates three objective functions related to economic and environmental domains and employs a Pareto analysis to illuminate the trade-offs between objectives. The US polyethylene terephthalate (PET) value chain is selected as a case study and represented via a superstructure containing various recycling pathways. The superstructure optimization problems are modeled as a mixed integer linear program (MILP) and linear programs (LPs), implemented in Pyomo, and solved with CPLEX for a one-year assessment horizon. Solutions to the circular economy models are then compared to the corresponding solutions of linear economy models. Preliminary results show that the optimal circular network is advantageous over the optimal linear network fo... [more]

Solid oxide cells (SOCs) are a promising dual-mode technology that generates hydrogen through high-temperature water electrolysis and generates power through a fuel cell reaction that consumes hydrogen. Reversible operation of SOCs requires a transition between these two modes for hydrogen production setpoints as the demand and price of electricity fluctuate. Moreover, a well-functioning control system is important to avoid cell degradation during mode-switching operation. In this work, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional integral (PI) control strategies, while ramping between the modes of hydrogen and power production. While both control methods provide similar performance in many metrics, NMPC significantly reduces cell thermal gradients and curvatures (mixed spatial temporal partial derivatives) during mode switching. A dynamic process flowsheet of the reversible SOC syste... [more]

The (bio-)pharmaceutical industry is facing crossroads in an effort to ramp up its global capacity, while working to meet net-zero targets and to ensure continuous drug supply. Beyond geopolitical challenges faced worldwide, (bio-)pharmaceutical processes have been historically very complex to design, optimise and integrate in a global distribution network that is resilient and adaptable to changes. In this paper we offer a perspective of how Process Systems Engineering (PSE) tools can support and advance (bio-)pharma practices with an outlook towards 3-fold sustainability. The latter is considering three main pillars, namely social (drug supply), economical and environmental sustainability. We discuss PSE contributions that have revolutionised process design in this space, as well as the optimisation of distributions networks in pharmaceuticals. We do this by means of example cases: one on model-based unit operation design and a second one on sustainable supply chain networks in the... [more]