End-of-life tires are a highly promising alternative carbon source for entrained-flow gasification, yet the high-temperature conversion behavior under industrial conditions remains poorly understood. Tire-derived chars were prepared via pyrolysis in a pilot-scale pressurized entrained-flow reactor at 1200–1600 °C and subsequently investigated under both chemically controlled (Regime I) and pore diffusion-controlled conditions (Regime II). Intrinsic gasification kinetics with CO2 and H2O were determined in a pressurized thermogravimetric analyzer up to 5 bar partial pressure. Despite increasing activation energies indicating progressive thermal annealing and structural ordering, chars produced at 1600 °C exhibited the highest overall reactivity, showing that catalytic effects of finely dispersed Na-species can compensate char deactivation at high-temperature conditions. Furthermore, the activation energy in Regime II is determined experimentally and showed strong agreement with theoretical predictions from Regime I. Inverse modeling via the effectiveness factor enabled indirect determination of the char morphology and the transition temperature between Regime I-II. The presented results provide novel high-temperature kinetic data for tire-derived chars under pressurized entrained-flow conditions for reactivity validation and allows for the classification of lab-scale reactivity studies suffering from recondensation of aerosols artefacts during char sampling.

The chemical industry, to this day, remains heavily reliant on fossil carbon. To adhere to climate targets, there is a need to find alternative, circular carbon sources. One key contributor is biogenic residues. This paper determines the spatially explicit technical carbon potential of 23 residue streams from four different origin classes. The goal is to calculate the carbon potential for green methanol production as feedstock for the German chemical industry. While the physical carbon potential is dominated by cereal straw and logging residues, the theoretical carbon potential shows a broader distribution across residue streams. The mobilisable theoretical carbon potential demonstrates that by-products of the wood processing industry and wood shavings, which are currently primarily used for energy purposes, have a high relevance for material applications instead. In terms of the final technical potential, cereal straw (3.72 MtC/a) and green waste (3.67 MtC/a) are leading contributors. The carbon potentials show spatial inhomogeneities across the NUTS3 regions. This information, together with the classification of districts according to their dominant origin class, provides key insights for the siting of large-scale chemical recycling plants. The total technical carbon potential (without economic considerations, dependent on H2 supply) for the mean scenario ranges from 7.20–14.69 MtC/a, which is equal to 90–184% of the expected feedstock carbon demand for green methanol. With increasing competition around biogenic residues, this study provides a spatially resolved basis for assessing their potential role in defossilising carbon supply chains.

The transition of the chemical industry towards carbon neutrality brings complex challenges and can be highly individual at the production-site level. Building on the well-known PyPSA framework, this study presents a linear programming expansion planning model spanning from 2025 to 2050. The model optimizes the energy supply infrastructure by integrating steam-generating technologies, renewable energy sources, and storage technologies. To deepen the understanding of how exogenous modeling assumptions influence the expansion results, electricity price contracts and planning foresight are investigated. Integration of renewable energy sources buffers electricity price volatility, whereas their exclusion leads to higher redundancy of fossil-based technologies. Achieving carbon neutrality necessitates the introduction of electricity price subsidies beyond 2040. Three foresight strategies - perfect, myopic, and rolling horizon - are compared, revealing significant impacts on investment decisions. Especially, technologies that experience a drastic reduction in investment costs or that are not available from the outset of the planning horizon may cause misinvestments. It was found that total system costs may vary by up to 20.35 % depending on the investigated scenario. Finally, a novel two-step global sensitivity analysis using the Morris screening method is conducted to identify the most influential input parameters. The results emphasize the importance of loads and energy carrier costs. By carrying out the global sensitivity analysis with limited foresight, the computational time was decreased by over 90 % while maintaining its information value. The expansion results highlight the importance of direct electrification and strategic foresight for achieving a cost-effective transition.

This work presents a pseudo-2D numerical temperature model for charging and discharging of a latent heat storage fixed-bed reactor filled with spherical solid–solid PCM particles (Na₂SO₄) and operated with thermal oil as heat-transfer fluid (HTF). The model couples a one-dimensional axial HTF energy balance to a radially resolved intra-particle conduction model at each axial position, accounting for temperature-dependent properties and representing the phase transition via an effective heat-capacity method. A Biot-number assessment indicates that lumped particles are not valid in the relevant operating range, motivating radial resolution. The radial discretization is verified against analytical solutions for transient heat conduction in a sphere, demonstrating that the model reliably captures the reactor thermodynamics.

Waste-to-Energy (WtE) power plants recover energy from municipal solid waste (MSW) to produce electricity and heat, reduce disposal volumes, and mitigate potential health hazards. Energy recovery is mainly achieved through grate combustion, where mechanical components ensure adequate mixing and transport of MSW for complete combustion and continuous operation. However, the highly heterogeneous composition of MSW and the complexity of the solid combustion process limit electrical efficiency, motivating efforts to improve overall operational performance. Given the significant economic and technical effort associated with experimental investigations, numerical approaches such as Computational Fluid Dynamics and the Discrete Element Method (DEM) are increasingly employed to improve understanding of the governing physical phenomena and to evaluate potential optimizations. However, numerical methods require detailed input data to realistically represent the studied system. In the case of MSW, direct characterization and conventional calibration approaches are impractical due to the inherent heterogeneous nature of MSW. To address this limitation, the present work investigates an indirect calibration methodology for DEM simulations based on experimental data from a 35 MWth WtE power plant. The proposed approach incorporates internal functions to account for particle shrinkage and to ensure a realistic particle distribution along the grate, combined with a three-stage stochastic evaluation framework. The calibration process identifies parameter sets consistent with experimental observations, enabling a more representative description of MSW particle behavior in grate-based combustion systems. Suitable parameter ranges include a density of 650–750 kg/m3, friction coefficients of 0.6–0.7, a roll resistance of 0.4, and a stiffness fraction of 0.15. Methodological limitations and future opportunities for DEM-based waste modeling are also discussed.

The increasing share of weather-dependent renewable energy sources amplifies the influence of weather uncertainty on the design of urban energy systems. Consequently, selecting representative weather years has become a critical challenge for ensuring resilient and cost-optimal system configurations. However, the impact of different weather years and design approaches on system resilience has not yet been systematically assessed. This study investigates how weather years affect the outcome of deterministic urban energy system design optimization approaches, including perfect-foresight and rolling-horizon optimization. Furthermore, the influence of demand-side flexibility on system resilience is evaluated. An urban energy system optimization model focused on the heating sector, with jointly optimizing district heating network expansion and building-specific supply technologies, is applied. System resilience is assessed using the indicators loss-of-load, loss-of-load duration (stability), robustness, and cost-optimality across 68 historical and future weather years. The results show that robustness increases exponentially with the calculated design peak demand. Demand-side flexibility mainly improves system stability by reducing the 95th percentile of the sequential loss-of-load duration from approximately 100 to below 15 hours. The typical meteorological year performs worse than most actual weather years, yielding the 17th-highest median cumulative loss-of-load with 842 MWh. The design year 2018 results in the most robust system configuration, with zero hours of load loss, but also the highest system costs. However, future weather years derived from IPCC scenarios demonstrate high robustness with improved cost-effectiveness. The best trade-off between cost-optimality and robustness is achieved using the design year 1971. Overall, the findings demonstrate that weather-year selection strongly influences the outcomes of urban energy system design. Furthermore, the proposed rolling-horizon optimization approach enhances system robustness, underscoring the importance of accounting for additional peak capacities in the design process.

High-temperature heat pumps (HTHP) are a promising technology to cover the peak demand in geothermal heating plants (GHP) during winter, but are often not economical due to few operational hours. Reversible HTHPs (RHP), which can also operate as an ORC for power generation, could tackle this issue. Therefore, this work investigates the use of RHPs for peak load coverage in GHPs by means of annual plant simulations. The subsequent techno-economic analysis compares the RHP to conventional peak load technologies such as HTHPs and gas boilers. By introducing a physical model for the air-cooled condenser (ACC), part load effects during ORC operation and the impact of different ACC sizes are investigated. The results show that the ACC size can be reduced by 50% of its initial value without relevant reductions in annual net electricity generation. With a 50% reduced ACC size, the RHP becomes competitive, yielding a levelised cost of heat (LCOH) of 145.4 /MWh, and replaces the HTHP (LCOH of 150.4 /MWh) as the second-best peak load system after the gas boiler (LCOH of 99.9 /MWh) for 2024 energy prices. Finally, a sensitivity analysis is conducted to investigate the impact of fluctuating energy prices, different ACC sizes and varying electricity price ratios (i.e. the ratio of sales and purchase price). The results indicate that RHPs and HTHPs are most competitive for higher gas prices and the RHP system is well suited to lower the investment risk, as it shows a high resilience towards fluctuating electricity prices.

The transition to decarbonized energy systems has fueled a controversial debate over the necessity of traditional “baseload” power. Skepticism remains regarding the reliability and economic feasibility of power systems relying mainly on cheap variable renewable energy (VRE) sources. Addressing this, the German Academies' project “Energy Systems of the Future” (ESYS) analyzed the role of baseload power plants within a decarbonized, continental-scale energy system. Their findings indicate that a secure, net-zero European electricity system is technically robust and economically viable when based on VRE paired with extensive flexibility, storage, and grid interconnections, without requiring new baseload capacity. The integration of new low-carbon baseload technologies, such as nuclear fission or fusion, natural gas with carbon capture and storage (CCS), or geothermal energy, has a marginal impact on overall system costs. While low-cost baseload technologies could be efficiently integrated to achieve high utilization, their future role is contingent on achieving cost reductions beyond current realities.

Controlling NOX emissions from solid fuel combustion remains a challenge. This study presents an experimental investigation into the factors influencing NOX formation during biomass combustion in an entrained flow reactor. Eight different biogenic fuels, including pre-treated samples, were tested alongside two additional fuels doped with KCl and coal fly ash. The experiments examined the combined effects of temperature, overall stoichiometric ratio, fuel-bound nitrogen content, and additive presence on NOX emissions under both non-staged and air-staged combustion. Results show that increasing temperature, stoichiometric ratio, and fuel-N content led to higher NOX formation, whereas the addition of specific inorganic additives significantly reduced emissions, with KCl lowering NOX by up to 49.4%. Compared with the used fuels, bark pre-treated through steam explosion displayed an enhanced NOX formation. Air-staging experiments demonstrated that temperature, stoichiometry, and residence time in the reduction zone strongly affected NOX levels, with the lowest emissions achieved at stoichiometric ratios between 0.7 and 0.9. Extended residence times further decreased emissions and shifted the optimal lambda towards 1, while double air staging yielded an additional 43.8% reduction compared with single-stage operation. The conversion of fuel-N to NO decreased with increasing nitrogen content for both staged and non-staged conditions. Overall, the results highlight that both combustion conditions and fuel characteristics impact NOX emissions and that optimised air staging, in conjunction with suitable fuel selection and additive use, enables substantial primary reduction of NOX emissions from pulverised biomass combustion.

This study develops three heuristics for district heating network design, each capable of handling multiple design periods and heat sources, and systematically benchmarks them alongside deterministic global optimization methods on 56 real-world districts with up to 10000 heat sinks. The benchmarked models include mixed-integer linear programs (MILPs) with affine and constant regression models, a linear programming model with a linear regression, minimum and prize-collecting Steiner tree heuristics, and a constrained Steiner tree. Two design scenarios are considered: forced design, in which all sinks must be connected, and economic design, in which profitability determines which sinks under a given heat price are connected. A key focus of this study is investigating how MILP optimizations for district heating networks can be warm-started using heuristic solutions and the resulting impact on computational time. In the forced design scenario, warm-starting reduces the time to solve the constant regression model by approximately 24% on average, extending the maximum solvable district size from 4750 to 7750 sinks. In the economic design scenario, warm-starting proves especially effective for refining solutions in smaller districts while still yielding improvements in larger districts with more than \num{4000} heat sinks. Across both scenarios, the affine and constant regression MILP models consistently deliver the best network designs, while the linear regression model performs the worst compared to the investigated heuristics. Overall, fast heuristics are recommended for large districts or early-stage planning, while warm-started MILP optimization is recommended for later planning stages.

Understanding heat transfer under near-critical pressure conditions is essential for the safe design of thermal-hydraulic systems such as future supercritical water reactors. While these systems operate under supercritical conditions during normal operation, subcritical states may occur during start-up, shutdown, or loss-of-pressure accidents. Under such conditions, a boiling crisis may develop if the critical heat flux (CHF) is exceeded, resulting in a sudden deterioration of heat transfer and a sharp increase in heating surface temperature. This behavior poses a significant safety concern, as excessive wall temperatures can result in fuel cladding degradation or failure and compromise overall system integrity. Experimental investigations on the boiling crisis, the associated critical heat flux, and heat transfer under post-CHF conditions have been conducted for several decades, leading to the development of various predictive approaches. However, most studies have focused on pressure ranges relevant to conventional pressurized water reactors. In contrast, experimental data at reduced pressures in the range 0.7<pr<1 remain scarce. To address this gap, the present study provides a comprehensive dataset on CHF and post-CHF heat transfer obtained from an industrial-scale test facility operating in this pressure regime. The dataset comprises 176 fully documented experiments using water as working fluid. The systematic variation of experimental parameters enables an interpretation of the contributions of the physical mechanisms governing the onset of the boiling crisis and post-CHF heat transfer with respect to pressure, mass flux, inlet temperature, and heat flux. This approach allows the identification of parameters that favorably or unfavorably influence CHF and characterize their impact on post-CHF heat transfer, thereby supporting the development and validation of improved safety-relevant prediction methods for next-generation nuclear reactor concepts.

Entrained-flow gasification of waste allows for high recycling rates towards CO2-neutral chemical building blocks. In order to engineer such gasifier facilities, experimental analysis of the reaction rates is of great importance. Solid-recovered material (SRM) from commercial mixed plastic waste is prepared cryogenically to a conveyable powder achieving a characteristic particle diameter, suitable for subsequent analysis at pressurized conditions in a wire-mesh reactor, a high-temperature entrained-flow reactor and thermogravimetric analyzer. The pyrolysis behavior of SRM is investigated via a parameter study on temperature, pressure, heating rate and holding time. Kinetic devolatilization data is derived from a wire-mesh reactor, mimicking the reaction conditions of an entrained-flow gasifier at lab-scale. Full devolatilization within 400 ms at 1600 °C despite a particle size of 600 µm is achieved. A representative SRM pyrolysis char is prepared in a unique entrained-flow reactor under relevant near-industrial conditions at 1400 °C, 10 bar and 2.4 s for the first time in literature. The char sample is characterized in its properties and intrinsic reactivity towards O2, CO2 and H2O in a thermogravimetric analyzer at 10 bar. A Power Law model allows for accurate representation of the experimental results, providing a reliable data set for gasifier CFD simulation. Here, the reactivity of SRM is driven by the Alkali-Index, and greater than bituminous coal, lower than lignite, but similar to pine and waste wood. Highly necessary performance indicators like conversion, cold gas efficiency and syngas quality can be determined towards thermo-chemical recycling of plastic waste allowing for stand-alone or co-gasification strategies.

Slagging entrained-flow gasification is gaining attention due to its potential to produce CO2-neutral (net-zero) chemicals and liquid fuels from waste. This study evaluated the slagging behavior of phosphorus-rich biogenic residues such as solid digestate, pine wood, sewage sludge, and refuse-derived fuels from food packaging, mixed plastic, and automotive tire waste. Thermodynamic phase modeling by FactSage indicated that phosphorus-rich digestate and refuse-derived fuels from mixed plastic waste are promising feedstocks for entrained-flow gasification when enriched with SiO2. Experimental studies at the laboratory scale focused on the model validation and the effect of SiO2 and P2O5 on crystal formation and viscosity. This included the preparation of synthetic slags, respective viscosity measurements, as well as structural analysis by EPMA, SEM and XRD. This work highlights the key role of P2O5 at concentrations up to 15 wt% and its interplay with K2O. A compensated impact of P2O5 on the slag viscosity is concluded. The results were compared to viscosity models, where an artificial neural network model (ANNliq) showed the best overall performance for predicting phosphorus-rich slags. To validate the laboratory results and to cover refractory interactions, digestate slags were prepared under relevant entrained-flow gasification/combustion conditions. The results reveal the influence of oxidizing and reducing atmospheres on slag structure and emerging crystal formation.

Reversible Organic Rankine Cycle (ORC) systems (rORC) alternate between high-temperatureheat pump (HTHP) and power-generation operation, making them a promising technologyfor geothermal combined heat and power plants. While rORC systems have been studiedat steady state, component-level dynamic models that resolve transients are absent from theliterature. This work presents and experimentally validates such a model for a fully reversibleHTHP/ORC test rig. The model is implemented in APROS and coupled to external Pythonsub-models for the reversible twin-screw machine, namely a polynomial expander model withempirical corrections, a neural-network power prediction and an empirical pump correlation.In the stationary validation, the ORC net electric thermal efficiency is reproduced with amean absolute error (MAE) of 0.27 to 0.32 percentage points and the control-relevant HTHPtemperatures within 1 K, including the district-heating supply temperature (MAE 0.80 K). Inthe dynamic validation, the ORC pump power, expander power and expander speed follow themeasurements in timing andamplitude,andtheHTHPloadtransitionsarereproducedaccurately.The mechanical coefficient of performance (COP), the most demanding validation target, isreproduced with an MAE of 0.31 to 0.44 in both validation stages. The absolute agreementof individual HTHP process variables remains limited by a systematic overprediction of therefrigerant mass flow, which is caused by residual lubricating oil outside the pure-refrigerantmodel. This bias largely cancels in the COP. The validated model provides a basis for automated mode-switching, advanced control, synthetic operating data and a future digital twin.

Heterogeneity in municipal solid waste (MSW) properties remains a major challenge for thermochemical processing applications in both energy recovery and recycling sectors. The inherently heterogeneous nature of waste directly affects its characteristics as feedstock, complicating process design, operational control, and numerical modeling. The present study develops a probabilistic framework for the hierarchical classification and aggregation of waste properties. Waste is assumed to be composed of four main groups: Paper, Organic, Plastic, and Inert. Each of them is divided into representative subgroups. Thermophysical properties, including specific density, heat capacity, and thermal conductivity, together with compositional data as proximate and elemental analyses, are evaluated to quantify the influence of the different main groups on the overall waste mixture behavior. The framework is based on an extensive database gathered from literature considering the four main groups and 33 subgroups, including 1259 data points for thermophysical properties, 346 for proximate analysis, 469 for elemental analysis of combustible fractions, and 120 for chemical characterization of inert components. The method conserves the intrinsic variability of the dataset, restricts the introduction of additional parametric assumptions during aggregation, and ensures a consistent and traceable transfer of uncertainty across the different levels. Main group influence on the generated waste samples is analyzed and discussed. The resulting probabilistic description supports more realistic numerical simulations, facilitates sensitivity analyses, and enhances robustness in thermochemical process design, operation and optimization.

Char gasification kinetics of biogenic feedstocks like digestate, sewage sludge, pine wood and rhenish lignite as a fossil benchmark are investigated to identify the most suitable feedstock for entrained-flow gasification. By replicating valid char properties to those of commercial gasifiers and to ensure a robust reactivity assessment, the pyrolysis chars are prepared under the same conditions of 1400 degrees C, 10 bar, 2.4s in a pilot-scale, high-pressure, high-temperature entrained-flow reactor. The obtained chars are characterised in their surface-specific intrinsic reactivity with O2, CO2 and H2O. The Power Law and the Langmuir-Hinshelwood approach are applied for kinetic modelling and evaluated towards their goodness of fit. The broad range of char properties among the feedstocks allowed to directly link the kinetic parameters to the fixed carbon-to-ash ratio and the specific AlkaliIndex. The results indicate a strong dependency and demonstrate that inorganic components have a decisive influence on intrinsic reactivities. High-temperature char reaction rates are estimated via measured effectiveness factor, and show that digestate and sewage sludge achieve similar reactivities compared to coal, compensating for the lack of surface area through catalytic effects. The derived reaction rates are crucial for the modelling and design of industrial gasifiers, supporting the optimisation of operating conditions, cold gas efficiency and syngas quality.

Containing essential plant nutrients like P, sewage sludge is a potential raw material for recovering nutrients. However, sewage sludge contains organic pollutants and heavy metals. Thermal treatment can crack organic pollutants and reduce heavy metals. This study investigated whether entrained-flow gasification, despite its short residence time, enables the evaporation of heavy metals to be a suitable thermal treatment method for sewage sludge. Thermodynamic equilibrium calculations predicted how the reaction atmosphere, temperature, and additives affect the evaporation of heavy metals during thermal treatment. Sewage sludge was gasified under different atmospheres, temperatures, and with seventeen additives. Remaining ash was analyzed for its Cd, Cu, Cr, Hg, Ni, and Pb content. Using different types of sewage sludge, one high in Fe and one high in Al, findings were reproduced, while including As, Tl, and Zn in the investigation. In line with thermodynamic equilibrium calculations, halogen-containing additives promoted the evaporation of some heavy metals. The most significant factor influencing heavy metal evaporation was an increase in gasification temperature. At studied conditions, entrained-flow gasification evaporated over 90 Entrained flow gasification of three kinds of sewage sludge for P recovery Influence of atmosphere, temperature and up to seventeen additives on the ash Thermodynamic equilibrium calculations to estimate influences on the evaporation Halogen containing additives support the evaporation of heavy metals in gasification Gasification temperature increases the evaporation of heavy metals most significantly

District heating networks are a key enabler of renewable energy integration in urban areas. However, their deployment is often constrained by high upfront investment costs, making cost-efficient design essential. This work applies a global sensitivity analysis to systematically assess how technical and economic parameters influence the levelized cost of heat, with a focus on piping network design. First, a comprehensive characterization of the design optimization model's input parameters is performed, including load profiles of three consumer types and detailed piping cost structures. A two-stage global sensitivity analysis is then applied to a representative urban district with 4250 heat sinks and a total energy demand of 174 GWh/year, with uncertain peak thermal power demand, to quantify the variability in design outcomes due to parametric uncertainty. The variability in heating costs attributed to piping ranges between 20-170 e /MWh, primarily driven by the depreciation rate (S-T = 0.62), specific piping investment cost (S-T = 0.30), and piping lifetime (S-T = 0.04), in descending order of influence. Insulation standards and their cost uncertainty emerge as secondary sensitivity drivers (S-T = 0.02), while the maximum specific pressure drop and the temperature difference between the supply and return lines play a comparatively minor role, collectively accounting for S-T < 0.01. The results highlight insulation standards as a high-leverage decision for cost-efficient designs, while policymakers must incentivize capital-cost-reduction mechanisms and large-scale deployment to lower centralized heating costs.