Methanol and ammonia are key energy carriers in a decarbonized society. This study assesses their use in power generation via two pathways: direct utilization as green fuels in fuel cells or as hydrogen carriers. Using these chemicals as hydrogen carriers achieves higher efficiencies (around 40%) due to the maturity of hydrogen fuel cells, resulting in electricity costs around 700 €/MWh compared to 1200 €/MWh for direct utilization. While hydrogen offers lower electricity production costs, efficiency advancements in methanol and ammonia fuel cells could enhance their competitiveness. Additionally, for scenarios involving transportation and power generation, methanol and ammonia prove economically viable, particularly for distances exceeding 3000 km. Consequently, both are crucial for addressing hydrogen-related challenges in the new renewable energy systems.

Decarbonizing industries could significantly increase electricity demand, necessitating strategic grid expansion. This study evaluates the impact of decarbonizing the Pulp and Paper Sector under four 2050 scenarios: carbon capture, biomass-based, direct electrification, and indirect electrification. A bottom-up approach is employed to estimate 2020 final energy demand by heat grade and subsector. Both final and primary energy demand systems are modeled, accounting for the efficiencies of end-use technologies and primary energy transformation processes. The analysis compares primary renewable energy demand (electricity and biomass) normalized per ton of equivalent CO2 avoided against a business-as-usual scenario. It also considers the requirements for wood residues, organic waste, and CO2 storage. The carbon capture scenario, while low in electricity demand, requires significant organic waste for renewable natural gas production and 2.6 Mt of CO2 storage to offset direct and indirect em... [more]

This study optimizes the Sustainable Aviation Fuel Supply Chain Network (SAFSCN) in the Czech Republic, using wheat straw as feedstock. It integrates geospatial data, transportation logistics, and economic feasibility, applying mixed-integer linear programming (MILP) to optimize pyrolysis plant locations and minimize costs. Sensitivity analysis varied wheat production growth by ±0.1% and ±0.2%. Results confirm Sustainable Aviation Fuel (SAF) production is technically and economically viable, with costs projected to decline up to 30.64% and revenues rising 49.07% from 2030 to 2050 due to technological advancements, improved logistics, and economies of scale. The findings underscore the critical role of SAF in achieving EU aviation decarbonization targets and highlight the importance of efficient supply chain planning for scaling SAF production.

Endpoint indicators provide a concise representation of environmental impacts by aggregating multiple midpoint indicators into a single value. Traditional endpoint weighting systems, however, are often limited by biases introduced through panel reviews and a lack of robustness in scientific process models. Additionally, they typically fail to account for the preferences of key stakeholders, including industry, government, and the public. This work addresses these limitations by developing an endpoint indicator that incorporates stakeholder preferences and minimizes dissatisfaction. A multi-stakeholder optimization framework was formulated to achieve this goal, employing distance, downside risk, and conditional value at risk as objective functions. Stakeholder preferences were derived from emissions data for industry, federal spending on environmental issues for government, and public surveys for societal input. Results highlight regional variations in midpoint indicator weightings acro... [more]

In this paper, we present a systematic approach to quantify the safe operating window of a proton exchange membrane water electrolysis (PEMWE) system considering energy intermittency and varying hydrogen demand. The PEMWE model has been developed based on first principles, with the polarization curve validated against a lab-scale experimental setup. The impact of key operational variables is investigated which include voltage, inlet temperature, and water flowrate (utilized for both feed and system cooling). Emphasis is given on operating temperature, a safety-critical variable, as its elevation can pose significant hydrogen safety risks within both the electrolyzer cells and the storage system. The impact of temperature on process safety is quantified via a risk index considering the fault probability and consequence severity. Process operability analysis is employed to assess the achievability of a safe and feasible region for design and operations. This analysis provides a comprehen... [more]

Hydrothermal liquefaction (HTL) has proven to be an appropriate technology for converting sewage sludge into a valuable resource for renewable energy generation. This study focuses on a prospective analysis of various technological scenarios for sewage sludge-to-fuel pathways via HTL, co-located with a wastewater treatment plant, in support of a circular economy perspective. Four technological foreground scenarios and three prospective background scenarios aligned with the Paris agreement’s climate targets REMIND-SSP2-Base (projecting a 3.5°C temperature rise by the end of the century), PKBudg1150 (aiming to limit the rise to below 2°C), and PKBudg500 (targeting a cap below 1.5°C) are analyzed for sewage sludge-to-fuel conversion in 2030, 2040, and 2050. The superstructure problem of the possible combinations of the developed scenarios is solved using the P-graph studio which is based on the branch and bound approach. The goal of this study is to maximize the objective function (OF) by... [more]

Heat pumps play a crucial role in decarbonizing the chemical industry. The integration and sizing of heat pumps in chemical processes is a challenging task in multi-product chemical processes due to the fluctuating waste heat supply and heat demand. Integrating heat pumps may require a retrofit of the utility system. Mathematical optimization is a useful tool to tackle this challenge by enabling the analysis of correlation between relevant system parameters and equipment sizing. This study demonstrates the utilization of mathematical optimization and parameter studies for utility system equipment sizing addressing fluctuating heat supply and demand profiles.

The aluminium industry emits approximately 1.1 billion tonnes of CO2-eq annually, contributing about 2% of global industrial emissions. Decarbonization pathways aim to achieve net-zero emissions by 2050, but this requires making decisions today for technologies having lifetimes of 20 – 25 years, based on uncertain economic assumptions, particularly given the volatility of energy prices. Traditional price forecasting models often fail to anticipate major disruptions, such as the 2022 energy crisis. This work applies Monte-Carlo Analysis (MCA) to evaluate the financial stability of decarbonization pathways under energy crisis scenarios and report on the resilience of the alternative solutions. In the modelled secondary aluminium production facility, direct electrification is assumed for lower temperature furnaces of annealing heat treatments or preheating, while the study defines the decarbonization options based on the melter furnace technology, a key bottleneck in terms of load and via... [more]

Energy storage is essential for transitioning to a renewable system based on renewable sources. To meet this challenge, Power-to-X technologies are attracting more attention. This work explores converting the excess of electric energy obtained from wind or solar sources into hydrogen and then into methane leveraging existing natural gas infrastructure for easier storage and transport. The process involves two stages: Firstly, the methane production step using Power-to-X technologies during excess renewable energy periods and, secondly, the electricity generation step during high demand with CO2 capture for reuse in methane synthesis, forming a closed carbon loop. In this way the Power-to-X process is integrated with repurposed combined cycle power plants (CCPPs) creating a Power-to-methane-to-power system. Two approaches are evaluated: oxy-combustion, which simplifies process CO2 purification and air combustion, which needs a more complex CO2 purification, such as amine absorption or P... [more]

The reduction and recovery of organic fraction of municipal solid waste is a major challenge for contemporary society. It requires the establishment of regional strategies with minimized environmental impact. This study employs life cycle assessment to evaluate the respective environmental performances of the current French system based on incineration, and those of alternative systems including (i) anaerobic digestion with composting and (ii) composting for biowaste treatment under different energy scenarios. The environmental impacts of Parisian biowaste are calculated by considering incineration technologies in the area, the French energy mix in 2022, the average European energy mix in 2022 and the projected French energy mix for 2030. The results show that the proportion of fossil-based sources in the energy mixes significantly influences the environmental performance of waste management systems. Energy mixes based in high-carbon fossil sources dependency tend to favour incineratio... [more]

Reduction of greenhouse gas emissions is an important environmental element to actively combat the global warming and climate change. In view of reducing the CO2 concentration from the atmosphere, the Direct Air Capture (DAC) options are promising technologies in delivering negative carbon emissions. The integration of renewable-powered DAC systems with the CO2 utilization technologies can deliver both negative carbon emissions as well as reduced energy and economic penalties of overall decarbonized processes. This work evaluates the innovative energy- and cost-efficient potassium - calcium looping cycle as promising direct air capture technology integrated with various CO2 catalytic transformations into basic chemicals / energy carriers (e.g., synthetic natural gas, methanol etc.). The integrated system will be powered by renewable energy (in terms of both heat and electricity requirements). The investigated DAC concept is set to capture 1 Mt/y CO2 with about 75 % carbon capture rate.... [more]

Hydrogen is increasingly recognized as a crucial energy carrier for a low-carbon future. However, most studies on clean hydrogen production devote limited attention to the entire supply chain. This study evaluates the sustainability of 800 combinations of hydrogen production and transportation methods, comparing their environmental impacts against the geophysical limits defined by the Planetary Boundaries framework. Findings reveal that no supply chain alone can make the current economy sustainable, yet powering water electrolysis with bioenergy and carbon capture and storage can meet the CO2-based planetary boundaries. The analysis also underscores the need for decarbonization efforts in the hydrogen transportation sector, as certain options could offset the benefits of clean hydrogen production.

To achieve long-term greenhouse gas neutrality in aviation, replacing fossil aviation fuels with Sustainable Aviation Fuels (SAF) from renewable sources is essential. A SAF production process from renewable hydrogen and carbon dioxide, was designed using Aveva Process Simulation, followed by comprehensive economical assessments. The designed process leads to an annual production of 37kt of SAF, with 97% of the molecules featuring a carbon chain length between 8 and 16. This output indicates a robust and targeted production capability. With an in-depth optimization of the methanol reactor, it was found that the profitability of the plant aligns with other SAF studies, demonstrating a Minimum Selling Price of Product of $2.46/kg after Heat Integration. In terms of economic profitability, the production of SAF using the methanol pathway appears to be an alternative to other SAF production pathways such as Fischer-Tropsch process but resides dependent on the evolution of H2 production tech... [more]

Climate change, driven by increasing CO2 emissions, necessitates innovative mitigation strategies, particularly for hard-to-abate industries. Carbon Capture and Utilization technologies offer promising solutions by capturing CO2 from industrial flue gases and converting it into value-added products. Among capture methods, membrane separation stands out for its compact design, energy efficiency, and scalability. Following capture, CO2 can be converted into chemicals like formic acid using electrocatalytic processes, enabling energy storage from renewable sources. This study proposes the design of an industrial demonstrator for a CO2 recycling plant targeting hard-to-abate sectors such as textile and cement industries. The system integrates polymeric membranes for CO2 capture and a 100 cm² electrochemical reactor for CO2 electroreduction into formic acid. Experimental data from both stages are used to develop predictive models based on artificial neural networks (ANN), optimizing system... [more]

A comprehensive model has been developed to design heat exchanger networks integrated with organic Rankine cycles (ORCs) and heat pumps, aiming to optimize energy efficiency. The model focuses on two key objectives: first, using heat pumps to reduce dependency on external services by enhancing heat recovery within the system; second, utilizing ORCs to recover residual heat or generate additional energy. To achieve optimal performance, the model requires careful selection of fluids for both ORCs and heat pumps, and the determination of optimal operating temperatures for maximum efficiency. The heat exchanger network is designed to be flexible, with non-fixed inlet and outlet temperatures, while simultaneously optimizing the number and operating conditions of ORCs and heat pumps. This approach reduces costs related to external services, electricity, and equipment such as compressors and turbines. Ultimately, the model facilitates the design of a heat exchanger network that efficiently ut... [more]

In this work, we propose a mixed-integer linear programming (MILP) model that optimizes economic and environmental objectives by retrofitting fuel stations for the case study of Spain. The model contains set-covering constraints that ensure that there is at least one retrofitted fuel station within a radius of 20 kilometers of each retrofitted fuel station. The results indicate that by retrofitting fuel stations to allow for electric vehicles, both economic and environmental objectives improve, while showing which power plants would be tasked with the increase in electricity production to satisfy the increased electric demand.

The electrification of chemical processes and carbon capture and utilisation represent two promising approaches to improve efficiency and decrease carbon emissions of the process industry. The development of electrolyzers has gathered momentum in the last decades, allowing for the possible introduction of renewable electrons into carbon dioxide-based chemicals manufacture. While the performance of the electrolyzers is subject to improvements driven by the experimental community, the generation of waste heat is unavoidable due to the electrical resistances and process inefficiencies within the electrochemical cells. The possibility of re-using this waste heat has not been investigated within the realm of carbon dioxide electrolyzers. Here we show the potential of upgrading this waste heat by means of a heat pump, for its utilisation in the downstream processing of formic acid obtained from carbon dioxide electroreduction. We found that the waste heat represents roughly 62% of the power... [more]

The separation of 1,3-butadiene from C4 hydrocarbon mixtures is a crucial step in the production of synthetic rubbers and plastics. Conventional extractive distillation methods using solvents, like N,N-dimethylformamide (DMF), have proven effective but presents significant health and environmental challenges. This study explores the feasibility of using propylene carbonate (PC) as a green solvent alternative for butadiene extractive distillation, leveraging its environmentally friendly properties and industrial compatibility. Simulations were conducted using Aspen Plus®, employing the Non-Random Two-Liquid (NRTL) model coupled with the Redlich-Kwong equation of state to describe phase equilibrium. Results indicate that PC integrates seamlessly into existing processes, achieving comparable operational stability and butadiene separation efficiency with minimal modifications. A significant design improvement was the elimination of the methylacetylene separation column in the PC process, w... [more]

Space exploration demands the integration of multiple scientific and engineering disciplines, with chemical engineering and process systems engineering playing pivotal roles. This paper examines their critical contributions to propulsion systems, life support mechanisms, and advanced materials essential for space missions. Recent advancements in chemical propellants and rocket fuels, illustrated by SpaceX and NASA missions, have significantly improved propulsion efficiency and safety. Chemical engineering is vital in developing air purification, water recycling, and bioregenerative life support systems, ensuring astronaut survival and mission sustainability. Additionally, creating heat-resistant, lightweight materials enhances spacecraft durability under extreme space conditions. Process systems engineering (PSE) complements these efforts by integrating, simulating, and controlling complex systems. PSE ensures reliable subsystem integration and uses predictive analytics and advanced mo... [more]

The transition to renewable energy sources, such as biogas, requires purification processes to separate methane from carbon dioxide, with adsorption-based methods being widely employed. Accurate simulations of these systems, governed by coupled PDEs, ODEs, and algebraic equations, critically depend on precise parameter determination. While traditional approaches often result in significant errors or complex procedures, optimization algorithms provide a more efficient and reliable means of parameter estimation, simplifying the process, improving simulation accuracy, and enhancing the understanding of these systems. This work introduces an Artificial Intelligence-based methodology for estimating the isotherm parameters of a mathematical phenomenological model for fixed-bed experiments. The separation of CO2 and CH4 is used as case study. This work develops an algorithm for parameter estimation for the system's mathematical model. The results show that the validated model has a close fit... [more]

Optimizing the operational conditions of electrochemical separation systems to achieve higher separation efficiency remains a complex challenge due to their nonlinear and dynamic nature. In this work, we proposed a Reinforcement Learning (RL)-based control framework to address this challenge. By applying various RL algorithms, we trained an RL-based controller that adapts to different system configurations and conditions. Also, the trained model learns the optimality between the removal efficiency and energy consumption. Overall, this approach autonomously learns the optimal operational parameters, significantly improving ion removal efficiency. The proposed RL-based control system enhances the performance of electrochemical system, providing a versatile and adaptive solution for optimizing separation across multiple electrochemical technologies. This work demonstrates the potential of RL in advancing the design and control of sustainable water purification systems.

A reinforcement learning framework is developed for the synthesis of distillation trains. The rigorous Naphtali-Sandholm algorithm for equilibrium separation modeling was implemented in JAX and coupled with the benchmarking Jumanji RL library. The vanilla actor-critic agent was successfully trained to build distillation trains for a seven-component hydrocarbon mixture. A transformer encoder structure was used to apply self-attention over the agent’s observation. The agent was trained on minimal data representation containing quantitative component flows and relative volatility parameters between present components. Training sessions involving 5·104 episodes (3·105 column designs) were typically run in under 60 minutes. While training was fast and reliable with appropriate tuning of the hyperparameters, further improvements are needed in the generalizability performance for similar separation problems.

Sustainable development goals have spurred advancements in bioprocess design, driven by improved process monitoring, data storage, and computational power. High-fidelity models are essential for advanced process system engineering, yet accurate parametric models for bioprocessing remain challenging due to overparameterization, often resulting in poor predictive accuracy. Hybrid modeling, combining parametric and non-parametric methods, offers a promising solution by enhancing accuracy while maintaining interpretability. This study explores hybrid models for succinic acid fermentation by Escherichia coli, a critical process for sustainable bio-based chemical production. The research presents a structured exploration of hybrid model architectures and their robustness under varying conditions. Experimental data were preprocessed to remove noise and outliers, and hybrid model structures were developed with differing levels of hybridization (from one to all reaction rates). Kinetic paramete... [more]

Surface tension is a fundamental property at the liquid/gas interface, influencing phenomena such as capillary action, droplet formation, and interfacial behavior in chemical engineering processes. Despite its significance, experimental determination of surface tension is time-intensive and impractical for in silico-designed compounds. Predictive models are essential for bridging this gap. This study expands on Gaudin's COSMO-RS-based model, which assumes uniform molecular orientation at the surface, by testing its predictive capability across broader temperatures (5-50°C) and developing a hybrid model combining first-principle and machine learning insights to improve Gaudin's model predictions. The HM employs a serial configuration where COSMO-RS predictions serve as inputs alongside molecular descriptors, derived using the Mordred library. SHAP analysis guides feature selection, enhancing model interpretability. An artificial neural network refines predictions, optimized via Bayesian... [more]

Developing ultra- or high-performance liquid chromatography (HPLC) methods for analysis or purification requires significant amounts of material and manpower, and typically involves time-consuming iterative lab-based workflows. This work demonstrates in two case studies that an autonomous HPLC platform coupled with a mechanistic model that self-corrects itself by performing parameter estimation can efficiently develop an optimized HPLC method with minimal experiments (i.e., reduced experimental costs and burden) and manual intervention (i.e., reduced manpower). At the same time, this HPLC platform, referred to as Smart HPLC Robot, can deliver a calibrated mechanistic model that provides valuable insights into method robustness.