Conference Agenda

In the recent years, sustained and coordinated strategic actions have been established to drastically reduce greenhouse gas (GHG) emissions and to accomplish emission targets and climate change goals established by the European Union (EU) and the United Nations (UN). The European Commission has introduced the "Fit for 55" package, a comprehensive set of legislative proposals aimed at achieving carbon neutrality by 2050, with an interim target of at least a 55% reduction in GHG emissions by 2030 (European Commission, 2021). In this context, green hydrogen has emerged as an energy carrier, and as an alternative fuel and feedstock, produced from renewable energy sources (RES), which could help to decarbonize various sectors.

Among the EU member states, Portugal has unveiled its national energy and climate plans, emphasizing its national hydrogen strategies. Due to its abundant renewable energy sources (i.e. weather and climate conditions), Portugal holds a strategic position in the European green hydrogen landscape. In line with these plans, Portugal has also adopted its National Strategy for Hydrogen (EN-H2), aiming to position the country as a major player in the global hydrogen industry. EN-H2's macro-objectives for 2030 include deploying 2% to 5% of green hydrogen in the industrial energy consumption sector (Presidência do Conselho de Ministros, 2020).

Currently, successful integration of green hydrogen into the industrial and energy sectors hinges on cost reduction and optimized infrastructure development. In all types of renewable and sustainable energy systems, the development of a competitive market requires complex design, planning, and optimization methods. To address the development of the green H₂ economy and overcome significant hurdles, as the current lack of infrastructure and the required capital investments for its expansion, it is necessary to effectively design and plan the green hydrogen supply chain (GHSC), considering its evolving demand.

The concept of the GHSC design is used to study the implementation of hydrogen infrastructures. The GHSC superstructure is a network representation that includes alternatives for feedstock, production, storage, and transport technologies, and requires a deep understanding of the system network, the trade-offs between technologies and the availability of resources. Mathematical modelling approaches, using mixed integer programming (MIP) problems, have been extensively used to model the GHSC. Critical parameters that may significantly influence this superstructure, such as water and RES availability, and its evolution over a long-time, should be implemented in these modelling works. Resources availability has a significant function in the supply chain due to the dependence of hydrogen production on the regionally unique resource characteristics.

This work addresses these challenges by developing a multi-period MILP model for the GHSC design and planning. The objective is to minimize the total network costs, while ensuring that industrial demand is met over time. The developed model considers long-term availability and uncertainty for RES and water, considering evolving hydrogen demand. Diverse scenarios encompass different storage and transportation modes, considering market evolution, economies of scale and penetration rates. The model is applied to the Portuguese Industrial Case, supporting the decision-making process and contributing to a practical, cost-effective and sustainable GHSC.

The conventional disposal methods for sewage sludge, which typically involve concentration and incineration, are compared to hydrothermal gasification (HTG) and anaerobic digestion (AD) coupled with syngas upgrading for methane production. Each process route has been modeled using Aspen Plus V11 software to integrate mass and energy balances, while the MILP Osmose software is employed for energy integration and multi-objective optimization, considering environmental impacts, minimum energy requirements, CAPEX, and OPEX. For HTG operating at 500°C with heat integration via a transcritical CO₂ power cycle and a carbon conversion rate of 80%, the system achieves an exergy efficiency of 50.4%. Various carbon management strategies are also explored, including periodic storage, co-electrolysis for synthetic natural gas production, and carbon sequestration via serpentine mineralization. Given the biogenic origin of the carbon in the sludge, these strategies can potentially support net-zero emissions goals. From an economic standpoint, even with a carbon tax of 166 CHF/t, direct CO₂ emissions remain more cost-effective than sequestration (via mineralization, which is 24% more expensive) or upgrading (via co-electrolysis, which is 20% more expensive). However, when optimizing for environmental impact, particularly in minimizing greenhouse gas emissions, CO₂ mineralization is the preferred configuration. In this context, a Pareto curve was generated to illustrate the optimal configurations balancing costs and impacts. Future work will focus on assessing the integration of these systems within a broader energy framework.

Triethylene glycol (TEG) or mono-ethylene glycol (MEG) absorption are the commercial technologies for natural gas dehydration processes. Nevertheless, the necessity of regenerating solvents under high temperatures results in environmental footprint and complex operation. Membrane with advantages in small footprint and high feasibility operation in hostile conditions is considered as promising technology for natural gas dehydration processes. In this work, system scale design and mesoscale modelling are synchronously adopted to optimize natural dehydration process design. Aspen HYSYS with MemCal extension is used for natural gas dehydration process. Taking pressure ration, stage cut, and multistage as decision variables for minimizing total annual cost (TAC) is optimized through NSGA-II algorithms. The minimum specific cost of < 3.15×10^-3 $/m³ natural gas is estimated to achieve the separation requirement of <120 ppm. Then, the module length, and membrane thickness of the hollow fiber membrane design is investigated using Computational fluid dynamics (CFD), which better configures the simulation results. The system scale engineering design and mesoscale modelling provide an in-depth insight into natural gas dehydration process.

To achieve the chemical industry's transition to net-zero emissions and overall sustainability, the next generation of chemical processes must be both environmentally and economically viable. However, many emerging sustainable processes remain economically unfeasible, often due to technical challenges such as low selectivity or yields, resource competition, or high energy demands [1]. In today’s linear economy, where processes typically operate in isolation, potentially crucial synergies between chemical processes could boost their economic and environmental performance. The integration of multiple chemical processes, sometimes referred to as chemical clustering, has been explored in several studies that showed that the economic and environmental performance of the cluster often outperforms that of the individual processes [2]. However, these studies predominantly focus on mature, high-TRL (Technology Readiness Level) technologies and are largely limited to the production of high-production volume products, such as methanol, olefins, or fuels. Furthermore, these investigations do not systematically explore and identify candidates with significant synergies, but rather manually select the candidates only via heuristics or previous knowledge. As a result, there is a significant gap in the literature concerning the identification of synergies between chemical processes involving low- to mid-TRL processes, and the impact on economic and environmental feasibility through process integration remains underexplored.

In this work, we explore the rigorous optimization and integration of chemical clusters by developing a methodology that systematically identifies synergies and selects high-, mid-, and low-TRL chemical reactions using superstructure optimization. A chemical reaction network (CRN), generated by querying reactions from Reaxys, serves as a “low fidelity” proxy for fully developed chemical processes. The CRN is represented as a directed bipartite graph that connects product and reactant chemicals to model possible mass and energy exchanges between chemical processes within the chemical industry. Optimization and property estimation techniques are employed to address critical data gaps for heat and mass integration — such as reaction stoichiometry, process energy requirements, and key performance indicators related to Green Chemistry, Life Cycle Assessment, and Techno-Economic Analysis. We formulate a multi-objective superstructure optimization problem that maximizes both economic and environmental objectives to select and integrate both the mass and energy flows of candidate chemical reactions. This optimization produces a Pareto front of non-dominated chemical clusters with high synergy, which are further analyzed to uncover additional insights, such as the identification of key reactions that enable efficient clustering. Overall, this work highlights the significant potential of process integration, particularly through the inclusion of low- and mid-TRL technologies, to pave the way for the next generation of sustainable and economically viable chemical processes.

References

(1) Gwehenberger, G.; Narodoslawsky, M. Sustainable Processes—The Challenge of the 21st Century for Chemical Engineering. Process Safety and Environmental Protection 2008, 86 (5), 321–327. https://doi.org/10.1016/j.psep.2008.03.004.

(2) Demirhan, C. D.; Tso, W. W.; Powell, J. B.; Pistikopoulos, E. N. A Multi-Scale Energy Systems Engineering Approach towards Integrated Multi-Product Network Optimization. Applied Energy 2021, 281, 116020. https://doi.org/10.1016/j.apenergy.2020.116020.

This work aims to develop an energy storage system that allows fluctuating energy inputs (i.e. from process sections driven by renewable sources) to power two process units that are operated continuously at different temperatures. The system consists of two vessels storing diathermal mediums: one for the hotter- and the other for the colder-energy fluxes. The investigated solutions include sensible-heat-, latent-heat-, and thermochemical-TES (Thermal Energy Storage). Also, Organic Rankine Cycles with lithium-ion batteries and thermoelectric generators were assessed. Indeed, all these technologies allow the exploitation of low-temperature thermal energy to supply the high-temperature unit during periods of energy scarcity. Both vessels aim for total self-sufficiency; however, the option to rely on external utilities has been included to meet the energy demand of both units when not sufficient process-side power is available. To assess the performance of the proposed storage systems, two energy profiles were investigated: one showing high energy inputs (i.e. optimistic scenario) and the other featuring low energy inputs (i.e. pessimistic scenario). Finally, an optimization problem was formulated to estimate the optimal size of both storage vessels. Increasing their capacity would lead to higher capital expenses (CAPEX) and lower overall operating expenses (OPEX). The investigated storage systems exhibited significant cost reductions when compared to non-integrated solutions (i.e. those relying on external utilities only).