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]

Early-stage system-level assessment of emerging technologies is essential for achieving climate neutrality and a circular economy; however, such assessments are often constrained by the lack of representative life cycle inventory data. In thermal energy systems, performance strongly depends on scale, making direct application of laboratory- or bench-scale experimental data potentially misleading in life cycle assessment (LCA). This study investigates the influence of experimental scale on system-level evaluation using a zeolite-based thermal energy storage (TES) system as a case study.LCAs were conducted using performance data from laboratory-, bench-, and pilot-scale experiments and compared with predicted commercial-scale performance derived from numerical simulations. The TES system stores waste heat via water vapor desorption from zeolite and generates pressurized steam using a moving-bed with indirect heat exchanging system. Heat recovery ratios of 36%, 50%, and 61% were obtained... [more]

The aviation sector represents one of the most pressing challenges in the energy transition due to its strong reliance on energy-dense liquid fuels and established fuel infrastructure. Sustainable Aviation Fuel (SAF), particularly from agricultural residues, offers a near-term mitigation pathway; however, large-scale adoption is shaped by policy mandates, infrastructure expansion, market price formation, and passenger demand responses. These coupled dynamics are difficult to capture using aggregate or equilibrium-based models. This study develops an agent-based model to analyze SAF transition pathways and applies it to India's civil aviation system. Results show that SAF adoption emerges from the coordination between infrastructure entry, cost learning, and market responses rather than mandate ambition alone. Even moderate mandates fall short of intended adoption levels without timely infrastructure expansion, while aggressive mandates become infeasible under binding supply and price c... [more]

"Green" hydrogen production via polymer electrolyte membrane (PEM) electrolyzers must overcome significant energy penalties and high costs to become competitive in renewables-based energy systems. Adaptive operating strategies for PEM electrolyzers-by dynamically adjusting current density, pressure, and temperature-have demonstrated efficiency improvements in simple energy systems. However, their effectiveness in the context of complex power-to-X energy systems featuring variable downstream synthesis processes remains unclear. This work shows that integrated optimization of PEM electrolyzer operating parameters in conjunction with downstream methanation processes (MP) delivers substantial system-wide efficiency and cost benefits under dynamic hydrogen demand and pressure conditions. To demonstrate this, an equation-oriented process model of a PEM electrolysis system is embedded within a higher-level energy system model to compare sequential optimization (where the electrolyzer adapts t... [more]

Heat pumps offer the possibility of reducing CO2-emissions in the chemical industry. However, the integration of heat pumps, especially in non-continuous processes, faces several challenges. Energy storage facilitates a way to enhance heat integration by providing a continuous supply of heat flows. By doing so, the question arises as to whether this implementation should be applied to the process or to the utility level. At the process level, there is usually more freedom, as one is not bound by the existing temperature levels of the utility system, which are mostly difficult to retrofit. Therefore, this study presents an approach that generates heat integration concepts at the process level based on two different criteria. These criteria influence which process streams are grouped for a storage implementation and therefore influence the heat integration. The aim is to maintain the heat flows as continuous as possible by integrated heat storages. Finally, the possible heat integration... [more]

The reduction of CO2-emissions in the chemical industry is essential to meet European climate targets. Particularly, the reliance on fossil fuels for process heat supply is a key factor for CO2-emissions. Electrically driven compression heat pumps are a promising option to reduce fossil fuel consumption by upgrading low-temperature waste heat to a higher temperature level, provided that low-carbon electricity is available. However, the integration of heat pumps into chemical utility systems remains a challenge due to economic constraints and the high complexity associated with site-wide heat integration and retrofit of existing structures. This work presents a mixed-integer linear programming (MILP) approach for the optimization of utility systems with integrated heat pumps. To address computational complexity, candidate utility temperature levels are pre-selected, and feasible heat pump coefficients of performance (COP) are precomputed. The framework is applied to both greenfield and... [more]

Flexible, electrified systems for chemical and energy production are promising alternatives to traditional, hydrocarbon-based processes. Flexible systems have the potential to reduce costs and emissions, but the interconnection between design and operation makes these systems challenging to implement. We use an operation-informed design framework to model a flexible, electrified ammonia synthesis system. We examine the levelized cost and carbon intensity of ammonia in response to different grid emissions (0-420 kg/MWh). We find levelized costs from 700-1200 $/ton-NH3 and observe non-monotonicity in carbon-intensity with respect to grid emissions. We rationalize this trend as a design transition from large, grid-reliant systems to smaller, flexible designs that are grid independent. We then study how synergies in demand and unit-operation flexibility can lower both the price and carbon-intensity of ammonia production. We find that for seasonal, or yearly demand (rather than hourly), a f... [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]

Supplementary Material for Estimation of Thermodynamic Properties for Cellulosic Biomass-Derived Compounds: Application to Heat and Work Balances in Process Simulation that will be submitted to Escape36.

SUPPORTING INFORMATION for the work "Particle Swarm Optimization for simultaneous design and optimization of heat pumps considering Mixed Integer problems", submited to ESCAPE 36.

This document provides digital supplementary material related to the article “Development of a methodology for heat pump-based heat integration in batch processes” which has been submitted to the peer-reviewed proceedings of the 36th European Symposium on Computer-Aided Process Engineering (ESCAPE 2026).

Supplemental information for the article "Multi-Scale Design for Clean Energy Systems: Industrial Electrification and Flexible Operation of Ammonia Synthesis", which has been submitted to 36th European Symposium on Computer Aided Process Engineering. The document includes parametric data and model information.

This document contains digital supplementary material (detailed model description, parameters for different case studies and additional figures) related to the article "Optimization of Site-wide Heat-Integrated Utility Systems with Heat Pumps using MILP" which is submitted to the peer reviewed conference proceeding of the 36th European Symposium on Computer Aided Process Engineering (ESCAPE 36).

The growing size and complexity of energy system optimization models, driven by high-resolution
spatial data, pose significant computational challenges. This study introduces methods to reduce model’s size and improve computational efficiency while preserving solution accuracy. First, a composite-curve-based approach is proposed to aggregate granular data into larger resolutions without averaging out specific properties. Second, a general clustering method groups geographically proximate fields, replacing multiple transportation arcs with a single arc to reduce transportation-related variables. Lastly, a two-step algorithm that decomposes the supply chain design problems into two smaller, more manageable subproblems is introduced. These methods are applied to a case study of switchgrass-to-biofuels network design in eight U.S. Midwest states, demonstrating their effectiveness with realistic and detailed spatial data.

Industries with low-temperature heat demand, such as laundry and syrup sectors, heavily rely on natural gas-fired boilers, posing challenges to achieving net-zero emissions by 2050. Like hard-to-abate sectors, they must explore energy transition strategies, including heat recovery, fuel substitution, or carbon capture, to reduce CO2 emissions. This paper evaluates the potential of energy transition in these sectors through case studies, using a mixed integer linear programming (MILP) approach. The analysis focuses on three key performance indicators (KPIs): specific energy consumption, CO2 reduction, and variable costs. By 2050, the adoption of heat pumps and waste valorization emerge as the most promising solutions for the syrup and laundry sectors. Specifically, the use of heat pumps reduces energy demand by at least 50%, while on-site biofuel production can fully replace natural gas consumption, thus eliminating dependency on external energy sources. The analysis highlights the impo... [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 electrification of industrial heat is a promising pathway for decarbonization, yet challenges persist in balancing capital costs, operating costs, and emissions reduction. While previous studies have assessed electrification through heat integration and graphical methods, these approaches do not inherently determine the optimal hybrid technology configuration. This study introduces an optimization-based framework that systematically evaluates the cost-optimal allocation of electrified and conventional heating technologies. Formulated as a Mixed-Integer Linear Programming (MILP) model and implemented in Gurobi, the framework minimizes Total Annualized Cost (TAC) while satisfying heat demand, technology constraints, and emissions targets. Applied to an industrial case study, the model compares three scenarios: a fully conventional system relying on steam boilers and fired heaters, a fully electrified system utilizing high-temperature heat pumps, electrode boilers, and electric heater... [more]

This work explores Aotearoa-New Zealand’s potential to fully electrify and source industrial process heat demands from renewable energy for 286 industrial sites while exploring the feasibility of green methanol production using excess electricity. Most energy models rely on spatially aggregated supply and demand, which limits the accurate representation of energy value chains. To address this limitation, the model incorporates industrial sites with varied temperature profiles, enabling the use of diverse heating technologies such as heat pumps, electrode boilers, bubbling fluidised bed reactors and biomass boilers. The proposed Mixed-Integer Linear Programming energy model uses the Accelerated Branch-and-Bound (ABB) algorithm, which is implemented within the P-graph framework to optimise the system. The model considers different energy transportation modes, including road transport for biomass and grid infrastructure for electricity. The multi-period design determines optimal heating t... [more]

In the last decades, the growing awareness of climate change and the high political sensitivity of critical resources such as energy have emphasized a need for local, renewable and optimized energy mixes at various scales. Several studies have therefore aimed to optimize renewable energy technologies and plant locations to develop more renewable and efficient Energy Mixes. Following this trend, this paper applies and compares Goal Programming, Branch-and-Cut and NSGA-II to a multi-objective combinatorial optimization problem focused on the energy mix of Oise, France. Results show more optimality for Goal Programming and Branch-and-Cut, accompanied by a high sensitivity to constraints, while NSGA-II provides more technological diversity in the computed solutions.

The unit commitment problem determines the optimal strategy to meet the electricity demand at minimum cost by committing power generation units at each point of time. Solving the unit commitment problem gives rise to a challenging optimization problem due to its combinatorial complexity and potentially long solution time requirements. Our proposed solution approach utilizes a decomposition method in conjunction with alternative models from the EGRET library. Results of this decomposition approach tested against four benchmarking systems show that significant computational speed ups are achieved.

Green hydrogen will play a key role in the decarbonization of the steel sector via the direct reduction path [1]. To meet the demand side, both a highly efficient numbering-up based scaling strategy for water electrolysis is needed as well as flexible operation strategies that follow the fluctuating electricity load. This paper presents a modularization approach for electrolysis systems that addresses both aspects by combining different electrolysis technologies into one heterogeneous electrolysis system. We present a modular design of such a heterogeneous electrolysis system that can be scaled for large-scale applications. The impact of different degrees of technological and production capacity-related heterogeneity is investigated using system co-simulation to find an optimal solution for the goal-conflict, that the direct reduction of iron for green steel production requires a constant stream of hydrogen while the renewable electricity profile is fluctuating. For this use-case the d... [more]

Real-time carbon accounting is crucial for advancing policies that effectively meet sustainability objectives. This work introduces a carbon tracking tool specifically designed for the European electricity grid. The tool collects hourly data on electricity consumption and generation, cross-border power exchanges, and weather information to assess the real-time environmental effects of electricity use, employing locally-specific emission factors for the generation sources. It utilizes weather data from various stations across Europe to produce week-ahead forecasts of carbon intensity in the grid. Predictions are created using a random forest regressor, integrated within the optimal controller of an operational industrial batch process. This prediction-based optimizer seeks to reduce total emissions tied to the process schedule's electricity consumption by implementing a rolling horizon strategy. By leveraging enhanced energy flexibility, the controller provides significant opportunities... [more]

This document contains digital supplementary material (detailed model description, parameters for different case studies and figure of exemplary waste heat supply and heat demand) related to the article "A Data-Driven Conceptual Approach to Heat Pump Sizing in Chemical Processes with Fluctuating Heat Supply and Demand" which is submitted to the peer reviewed conference proceeding of the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35).

Achieving sustainability in the energy sector re-quires an economically viable path with a balanced tran-sition that does not aggravate environmental and socio-logical problems associated with current fossil-based power production. Increasing the grid penetration of intermittent renewables to realize a sustainable energy future without consideration of the balanced transition may result in devastating economic and societal impacts [1]. As we press for the minimization of renewable power curtailment, current fossil-based technology struggles to meet demand under extreme transient and part-load conditions. This results in dramatic reduction of efficiency and a corresponding increase in emissions of not only carbon, but far more devastating pollutants... (ABSTRACT ABBREVIATED)

The United States’ Inflation Reduction Act (IRA) of 2022 has established incentives to facilitate the energy transition. While these policies provide economic incen-tives that encourage investment and may reduce financial risk for the private sector on the supply side, transitioning to a lower carbon or net-zero economy by 2050 presents several challenges. These include designing flexible production systems that can interact with inter-mittent renewable energy resources, ensure process safety, redesigning existing energy infrastructure to support new energy carriers like hydrogen or ammonia, and making long-term investment decisions in an uncertain and evolving market... (ABSTRACT ABBREVIATED)