Achieving and sustaining net-zero greenhouse-gas emissions will require the long-term deployment of green hydrogen. Proton exchange membrane water electrolysers (PEMWEs) are attractive for variable renewable electricity (VRE) because of their fast dynamic response; however, they rely on iridium (Ir) anode catalysts, and Ir supply is severely constrained. Here, a Japan-specific dynamic material flow analysis (DMFA) model is developed for 2025-2100 to quantify Ir circularity in PEMWE deployment under a backcasting-oriented hydrogen production pathway. The model tracks Ir in anode catalysts only and represents: (i) Ir demand for new capacity additions and replacements, (ii) end-of-life (EoL) outflows governed by a Weibull lifetime distribution, and (iii) closed-loop recycling characterised by an overall recycling rate across collection, separation/pre-processing, and refining. Sensitivity analyses show that long-term primary Ir requirements are governed by the coupled effects of catalyst... [more]

Existing gas network infrastructure are important national energy assets, transporting mostly fossil-derived natural gas to end-users. Biomethane, methane derived from anaerobic digestion (AD) of organic matter, presents a potential route to replace fossil fuels with home-grown renewable gas. Combined with carbon capture and storage (CCS) of the CO2 in the biogas potentially results in carbon negative energy. This work seeks to understand the feasibility of operating a part of the gas network isolated from the main natural gas network fully on biomethane in Scotland. We present an integrated techno-economic optimisation framework for designing self-sufficient biomethane islands, applied to the Inverness network. The model, implemented as a nonlinear program (NLP), maximises annual net profit from biomethane sales and Green Gas Support Scheme (GGSS) tariffs subject to practical constraints such as GGSS-compliance of =50 % waste-derived biomethane, seasonal supply, land/scale, demand bal... [more]

This paper presents OpenAD-lib, an open-source Python framework for anaerobic digestion (AD) digital twins, unifying mechanistic models, machine learning (ML) surrogates, and model predictive control (MPC) within a modular ecosystem. OpenAD-lib addresses the critical fragmentation in AD digitalisation by bridging mechanistic and data-driven paradigms under explicit uncertainty. By integrating uncertainty-aware feedstock characterisation with robust process control, the platform enables the transition from isolated research tools to fully integrated digital twins, delivering economic and environmental value in AD systems.

This study conducted a life cycle assessment to assess and design regional symbiosis at plant-oil production. These industries face challenges including dependence on fossil fuels and the generation of underutilized by-products, while effective regional symbiosis requires the selection of diverse regional unused resources and assessment based on process models that consider future technological prospects. Mathematical models for plant-oil production were developed using industrial data from literature to calculate inventory data. The case study showed that introducing woody biomass combined heat and power reduced GHG emissions by 8% in the Cradle-to-Grave system boundary, while recycling technology for soap stock using Kolbe electrolysis achieved a 3% reduction. Regional analysis indicated that 33 prefectures in Japan could meet woody biomass demand through sustainable forestry management, potentially reducing GHG emissions in Japan by approximately 0.041%. These results suggest that r... [more]

Regional deployment of bioenergy and bio-based products is often constrained by the seasonality, heterogeneity, and dispersed availability of lignocellulosic biomass. This work demonstrates a computer-aided process engineering (CAPE) workflow that integrates experimental characterization, process modeling, and life cycle assessment (LCA) to support regional symbiosis design using mixed feedstocks from agriculture and forestry. A case study is developed for Tanegashima, a remote Japanese island where unused woody residues and sugarcane bagasse are locally available but temporally mismatched. Torrefaction is modeled in an autothermal configuration: char is the main product, while torrefaction gas and condensables are recovered for internal heat supply and any excess is treated as an energy coproduct. Laboratory measurements (220-400°C, 20°C interval) provide temperature-dependent yields of char, tar, aqueous condensate, and gas, alongside ultimate analysis and heating values of solids an... [more]

Critical metals are indispensable in renewable, low-carbon, and hydrogen technologies due to their unique catalytic and electrochemical properties. They are primarily sourced through mining, which is associated with significant environmental impacts and geopolitical risks due to the uneven global distribution of ore deposits. As a result, efficient recovery of these metals from secondary sources such as electronic waste has become increasingly important. In this context, liquid-liquid extraction (LLE) has emerged as a promising separation technique due to its high selectivity and scalability. The development of intensified, continuous-flow LLE in small channels offers further advantages in terms of mass transfer efficiency, solvent utilization, and process sustainability, making it an attractive approach for the recovery of critical metals. A flow pattern known as segmented flow further enhances mass transfer in LLE in small channels. This work presents a hybrid modelling approach for... [more]

While the transition from traditional energy sources to renewable energy is necessary to reduce greenhouse gas (GHG) emissions, it introduces new challenges related to material use, both in quantity and type, potentially leading to resource scarcity, biodiversity loss, and waste accumulation. Therefore, incorporating circular economy (CE) principles into the design and planning of energy systems becomes essential. Despite the growing recognition of circularity, current assessments in energy systems focus on economic performance and GHG emissions. In this work, we propose a metric for quantifying circularity of energy systems based on the CE assessment framework MICRON, addressing the gap between CE metrics and energy systems planning. The framework is adapted to energy systems by accounting for the specific characteristics of energy technologies and by incorporating metrics associated with critical material use, scarcity, and durability. Its applicability is demonstrated through a case... [more]

The biopharmaceutical sector has traditionally focused on cost-efficient process design and capacity planning to meet rising demand. Recently, sustainability pressures have increased, driving efforts to reduce the environmental footprint of manufacturing and supply chains; however, strict quality and sterilization requirements can limit the implementation of fully circular resource-use strategies. In this space, adopting an industrial-cluster systems view could unlock opportunities to improve sustainability of industrial clusters through coordinated material and energy exchange, supporting resource efficiency at cluster level and still meet sector-specific quality/sterilization requirements. In this work, we present life cycle assessment (LCA)-based comparative analyses to investigate the potential of industrial symbiosis within monoclonal antibody (mAb) manufacturing, whereby LCA process models are based on comprehensive techno-economic analyses that quantify resource inputs and waste... [more]

The accumulation of automotive plastic waste poses a growing environmental threat; while recycling has the potential to address this, its use remains limited by the complexity of the materials used in vehicle components. Specifically, the presence of mixtures of polypropylene (PP), polyethylene (PE), and polyoxymethylene (POM) in the materials makes mechanical recycling challenging due to the difficulty of separation. To address the inefficiency of current end-of-life management, we present a systematic computational framework integrating computer-aided molecular and process design (CAMPD) with technoeconomic assessment (TEA) and life cycle analysis (LCA) to design a solvent-based recycling process capable of producing near-virgin quality resins. This framework involves utilizing the SAFT-?? Mie equation of state to predict thermodynamic properties and employing nonlinear programming (NLP) to perform process optimization. From an evaluation of 875 solvent candidates, we identify 72 fea... [more]

Gallium is a critical material with increasing demand driven by compound semiconductors such as gallium nitride (GaN) and gallium arsenide (GaAs) used in power electronics and optoelectronics and a highly concentrated supply chain, with 98 % of refined production occurring in China. While recycling remains limited, GaAs semiconductor manufacturing generates wastewater that can contain gallium concentrations ranging from 1-35 mg·L?¹, representing an underutilized secondary resource. This study evaluates the technical and economic feasibility of recovering gallium from GaAs semiconductor wastewater across an input range of 10-100 m³·d?¹ using process modelling and a techno-economic analysis comparing two alternative separation routes: ion exchange (IX) and solvent extraction (SX). Using a real-world industrial wastewater composition, IX achieves a higher overall recovery than single-stage SX (80.40 % vs. 62.54 %), which translates into consistently lower levelized costs of gallium. The r... [more]

The wine industry generates large volumes of organic effluents, whose inadequate management poses significant environmental challenges but also offers opportunities for resource recovery. In this work, an integrated biorefinery scheme for the valorization of winery effluents is proposed and evaluated through steady-state simulation in Aspen Plus®. The biorefinery converts winery wastewater into a portfolio of value-added chemicals and biofuels, including levulinic acid, propylene glycol, formic acid, light gases, naphtha, sustainable aviation fuel, green diesel, and bioethanol, while enabling water recovery and carbon dioxide management. Two alternative CO2 capture routes are analyzed and compared: a conventional CaO-based carbonation-calcination process and an innovative absorption system using deep eutectic solvents (DES), specifically choline chloride-urea. Technical performance is assessed through chemical oxygen demand (COD) removal, recovery, conversion, yield, and product mass r... [more]

The increasing popularity of electric vehicles (EVs) leads to an expected rise in the quantity of end-of-life lithium-ion batteries (LIBs) that require efficient management. This paper presents a State Task Network (STN) based optimization model to analyze and optimize the supply chain for LIBs, allowing for the selection of optimal processing routes, facility locations, capacities and reintegration of recovered materials, as well as analyzing the possible trade-offs between different end-of-life management strategies. Based on available data from the literature, the model is demonstrated with the LIB supply chain considering both primary production and different end-of-life strategies for spent LIBs (recycling and reuse). The case study reveals that mechanical pretreatment followed by hydrometallurgical recycling is the optimal pathway and it outperforms the linear supply chain in both costs and emissions. The cost optimal solution opts for more centralized collection and disassembly,... [more]

The transition from a linear, fossil-based polymer economy to a circular bio-economy is critical for mitigating resource depletion and greenhouse gas emissions. This study provides a rigorous comparison of two biomass-to-plastic pathways: a biochemical route (PLA via enzymatic hydrolysis) and a thermochemical route (bio-PE via gasification and MTO). Based on Aspen Plus simulations and a "lifetime-adjusted" lifecycle assessment framework, we evaluate the environmental performance of these routes in the transition from linear to circular systems. Unlike standard "cut-off" methods, the lifetime-adjusted model accounts for virgin make-up and molecular retention across multiple recycling cycles. Results indicate that at current 15% recycling rates, PLA exhibits the lowest global warming potential due to significant biogenic carbon sequestration. However, as recycling rates reach 75%, process efficiency becomes the dominant factor; the precise biochemical recycling of PLA continues to outper... [more]

This work presents a hybrid modelling approach for a downdraft sewage sludge gasifier within the Shit2Power (S2P) process. The gasifier is represented in CHEMCAD NXT by a series of four standard reactors that combine stoichiometric and equilibrium models with a data-driven correction step to account for deviations from ideal Gibbs equilibrium. Reaction conversions in the correction reactor are fitted to experimental synthesis gas compositions reported by Werle (2014) [7] for 30 operating points with varying equivalence ratios and reactor inlet air temperatures. The calibrated hybrid reactor model is evaluated against these data and shows conservative agreement for the combustible gas components of the synthesis gas. To overcome the limitations of linear interpolation between fitted operating points, several machine learning approaches are evaluated to predict the reaction conversions, and boosted neural networks are selected as a compromise between prediction accuracy and smooth behavi... [more]

An integrated multiproduct facility for the valorization of apple pomace in the green production of high value-added chemicals such as phenolic compounds and pectin, bioethanol, as well as apple juice, was optimally designed. The process includes green extraction technologies relying on subcritical water extraction and ethanol produced on-site through fermentation of residues. Two scenarios were evaluated: one based on purchasing the ethanol and another with on-site ethanol production. The units of the process were modeled using first principles. The process superstructure was formulated as a mixed-integer nonlinear programming problem, solved by decomposition. Investment and production costs of both alternatives were similar, with unit production requirements ranging from 1.09 €/L to 1.13 €/L. The discounted payback periods were 6.7 years and 6.4 years for the on-site ethanol production and purchased ethanol scenarios, respectively, while the internal rates of return were 36.4 and 37.... [more]

The utilization of low-quality vegetable oils as raw materials helps to reduce the production costs of biodiesel. Waste cooking oils are examples of this type of raw material, having a high content of free fatty acids. While biodiesel is a sustainable alternative to fossil fuels, conventional production methods face challenges due to low reaction rates and high energy demands. This study investigates pathways for biodiesel production from waste cooking oil using process intensification technologies in combination with biowaste-derived heterogeneous catalyst. Conventional and intensified (reactive distillation-based) processes are compared using Aspen Plus simulations as an analysis tool, focusing on the production of biodiesel from waste cooking oil (WCO) using CaO as a catalyst. According to the results, the conventional method at 60°C and 6:1 methanol-oil ratio achieves 95% conversion but suffers from high methanol use and long reaction times (65 min). The intensified process at 65-7... [more]

Conventional urea production is a centralized and fossilintensive process associated with significant greenhousegas (GHG) emissions and limited flexibility for deep decarbonization. As an alternative, the Integrated COnversion of NItrate and Carbonate steams (ICONIC) project is developing innovative electrochemical urea (eurea), via the co-electroreduction of nitrogen and carbon sources using renewable power. While recent research advances in electrocatalysis have demonstrated promising Faradaic efficiencies (FE) toward urea, the design of electrochemical systems involves inherent tradeoffs between key performance indicators (KPIs) such as current density, cell voltage, and FE. Crucially, the implications of electrolyzerlevel performance on plantlevel economics and environmental impacts remain poorly understood. To address this gap, we integrate process modelling with technoeconomic and lifecycle assessment (TEA-LCA) to evaluate the trade-offs of KPIs from a process systems per... [more]

Plastic waste represents an abundant and underutilized resource that can be converted into valuable products through microwave-assisted pyrolysis. In this research, a novel microwave-assisted processing plant that converts mixed plastic waste to olefins and aromatics is developed and simulated on Aspen Plus (v.14) guided by laboratory-scale experimental data. The experimental results show that at a bulk temperature of 400°C and ambient pressure, approximately 95% of a solid waste plastic feed comprised of equal portions of polypropylene and polyethylene is converted to gases, with nearly two-thirds of the resulting effluent gas composed of olefins. Simulation results show that 2889.1 kg/h propylene, 2088.0 kg/h ethylene and 96.3 kg/h aromatics (benzene and toluene) are produced as main products from 8000 kg/h of mixed plastic feed. High-purity propane and ethane streams were also recovered and sold as byproducts. A technoeconomic analysis is subsequently conducted, revealing that the p... [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 escalating urgency to address climate change has driven carbon capture (CC) technologies into the spotlight, particularly for large-scale emitters, which benefit from economies of scale. However, small-scale emitters account for a significant share of CO2 emissions, yet such applications remain largely overlooked in the literature. While CC cost is often used as a key performance indicator (KPI) for CC technologies, the lack of standardized cost estimation methods leads to inconsistencies, complicating comparisons, and hindering the deployment of CC systems. This study addresses these challenges by developing flexible short-cut correlations for selected CC technologies, providing estimates of the total equipment cost (TEC) and energy consumption specific to small-scale applications across various CO2 inlet concentrations (mol%) and capture scales (10 – 100 kt/y). The flexibility of the correlations enables the integration of various cost estimation methods available in the literatu... [more]

Job Safety Analysis (JSA) is critical for proactively identifying workplace hazards, assessing their potential consequences, and implementing effective control measures. However, traditional JSA methods can be inefficient and prone to errors, particularly in complex industrial environments. This paper introduces AutoJSA, a knowledge-enhanced framework that leverages large language models (LLMs) to automate and optimize the JSA process. We collected 73 high-quality JSA reports from a chemical engineering company and divided the JSA workflow into three key tasks: hazard identification, consequence identification, and control measure generation. Two approaches - fine-tuning and retrieval-augmented generation (RAG) - were employed on a base LLM (GLM-4-9B-Chat) to adapt it for these domain-specific tasks. Experimental results demonstrate that both fine-tuning and RAG significantly improve task performance relative to the unmodified model, with fine-tuning generally providing larger gains. W... [more]

Graph neural networks (GNNs) have proven state-of-the-art performance in molecular property prediction tasks. However, a significant challenge with GNNs is the reliability of their predictions, particularly in critical domains where quantifying model confidence is essential. Therefore, assessing uncertainty in GNN predictions is crucial to improving their robustness. Existing uncertainty quantification methods, such as Deep ensembles and Monte Carlo Dropout, have been applied to GNNs with some success, but these methods are limited to approximate the full posterior distribution. In this work, we propose a novel approach for scalable uncertainty quantification in molecular property prediction using Stochastic Gradient Hamiltonian Monte Carlo (SGHMC). Additionally, we utilize a cyclical learning rate to facilitate sampling from multiple posterior modes which improves posterior exploration within a single training round. Moreover, we compare the proposed methods with Monte Carlo Dropout a... [more]

Distillation processes account for a substantial share of the industrial energy demand. Yet, these energy requirements can be reduced by a variety of energy integration methods, including various forms of direct heat integration, multi-effect distillation, thermal coupling and vapor recompression. Consequently, these intensification methods should be evaluated quantitatively in comparison to each other for individual separation tasks, instead of benchmarking single options with conventional sequences or relying on simplified heuristics. In order to overcome the computational burden of a broad assessment of a large number of process alternatives, a computationally-efficient framework for the energetic and economic evaluation of such energy integrated distillation processes is presented, which builds on thermodynamically-sound shortcut models that do not rely on constant relative volatility and constant molar overflow assumptions.

A new methodology for the assessment of the performance loss in catalytic reactors due to deactivation was developed and applied to fixed- and fluidized-bed CO methanation, with catalyst subject to coking. The methodology is based on the solution of heat and mass balances, by decoupling the reactor and deactivation dynamics. This is possible by using consecutive 1D, steady-state calculations for the characterization of the reactor performance. In this way, the progressively lower values of catalyst activity along the time on stream are computed with the integration of a dedicated dynamic model. This method has shown promising results in the characterization of the loss of performance of the reactor over time. The model correctly describes a progressive deactivation of the catalyst in fixed-bed reactors, while it shows that the decrease in activity is sudden for the whole reactor volume in fluidized bed reactors and occurs after a critical time-on-stream. Besides, it was observed that t... [more]

Hybrid modelling is widely employed in chemical engineering to generate highly accurate predictions. Such an approach merges first-principle modelling with machine learning techniques to identify and model the epistemic uncertainty from experimental data. Despite its advantages, this still requires cross-domain competencies that are difficult to find in the chemical industry and high human involvement. The possibility of automating the identification and training model would be significantly beneficial for the widespread adoption of hybrid modelling methodology within the chemical industry. This work presents a novel algorithm for the automatic identification of hybrid models (HMs) starting from the first-principle representation of the system, described by differential equation sets. The methodology formulates the problem as mixed-integer programming, identifying the equation running under uncertainty, identifying the machine learning model hyperparameters, and training the latter. Th... [more]