Abstract

Drying is a critical stage in iron production through pelletization, accounting for a substantial share of the total energy demand. Conventional convective drying methods face limitations in efficiency, particularly with larger pellets and moisture-rich tailings. This study investigates the application of microwave technology as an alternative drying method for iron pellets and tailings. Drying experiments were conducted using both convective oven at 300 °C and a 15 kW industrial microwave system operated at different power levels. The results demonstrate that microwave drying dramatically reduces drying time and energy consumption compared to conventional methods. For iron pellets, microwave drying achieved the target residual moisture content more than six times faster and with nearly 30% lower energy usage. For tailings, the improvements were even greater, with drying more than thirteen times faster and energy consumption reduced by over 50%. These findings highlight the potential of microwave technology to enhance the efficiency and sustainability of pelletization processes in iron production.

Abstract

This paper presents The World Avatar (TWA) as an evolution of connected digital twinning, founded on the idea of a general knowledge representation of the world at large. TWA adopts a unique approach to data integration, analysis, and, most importantly, the inclusion of the space of all possible concepts and world knowledge. More importantly, TWA serves as a dynamic framework for representing complex, evolving systems, supporting seamless human-machine interactions with diverse and dynamic data sources. To demonstrate these capabilities, we present four case studies across German cities, each of which highlights a specific complex urban challenge and demonstrates how TWA can interoperate across spatial, temporal, and real-time data to address these challenges. The case studies highlight the capabilities of TWA in supporting real-time decision-making processes, which are critical for urban sustainability and infrastructure development. This work demonstrates the significant advantages of TWA over conventional digital twin technologies, providing urban planners and policymakers with powerful tools to create more resilient, intelligent, and flexible systems for urban management. TWA shows the potential to equip cities with the capacity to enhance resilience and optimise infrastructure planning. This could enable cities to adopt sustainable, data-driven solutions and build more flexible, resilient, and intelligent urban infrastructure.

Abstract

Small and medium enterprises (SMEs) in Japan face challenges in implementing energy-efficient building operations due to limited resources, knowledge gaps, and the absence of occupant-centric management approaches. This study develops a predictive model for HVAC control tailored to SMEs, drawing on a field experiment conducted in an office building in the Nihonbashi district of Tokyo. Multiple HVAC control patterns were trialled, with systematic data collection including environmental parameters (temperature, humidity, COâ‚‚ concentration), thermal comfort indices (PMV, PPD), and occupant survey responses. These datasets were analysed to evaluate the relationship between zoning strategies, occupant well-being, and energy consumption. Based on these observations, a preliminary optimisation framework for HVAC temperature settings and zoning was proposed to maximise thermal comfort while reducing energy demand. The findings contribute to practical pathways for SMEs to achieve both enhanced occupant well-being and improved energy performance, providing insights for wider urban decarbonisation efforts.

Abstract

The increasing penetration of Power-to-X (PtX) assets introduces new challenges in power system operation, especially when integrating intermittent renewable energy sources. This paper presents the development, integration, and experimental validation of a kW-scale hybrid PV-Battery-Electrolyzer platform at DTU. A rule-based energy management system (EMS) was implemented to coordinate power flows between the photovoltaic (PV) source, lithium-ion battery energy storage system (BESS), and anion exchange membrane (AEM) electrolyzer. Component-level testing quantified key performance constraints, including ramp-rate limits, temperature sensitivity, and system-level round-trip efficiency. The data-driven model for asset performance validated against experimental data was further applied for sensitivity analyses of system sizing and control strategies. Results from hybrid operation show that the BESS buffering significantly improves hydrogen yield with 14.6% and 77.7% under different fluctuating solar profiles and significantly reduces the start-stop cycle from 10–34 times to 3 times per day, which has a great impact on electrolyzer lifetime. The findings highlight critical trade-offs between curtailment, system stress, and hydrogen productivity in such hybrid applications.

Abstract

During drilling COâ‚‚ storage and monitoring wells, maintaining fracture stability is critical for preserving future injectivity and caprock sealing efficiency. Fluctuating bottom-hole pressure can induce cyclic deformation of natural fractures by transient drilling operations. This dynamic behavior destabilizes the plugging layer, making conventional lost circulation materials (LCMs) inadequate for sustained plugging performance. To address this problem, this study experimentally investigates the plugging behavior and formulation design criteria of three elastic LCMs including rubber particles, elastic graphite, and elastic mesh material under both static and dynamically deforming fracture conditions. The effects of particle size distribution, material proportion, pressure-bearing capacity, resealing behavior, and fracture-width adaptability were quantified. Experimental results demonstrate that appropriate coordination among three elastic materials enables controlled migration of the bridging zone within the fracture and significantly increases the fracture reopening pressure. When the particle size distribution of each elastic material is within its respective optimal range and the composite mass ratio satisfies filling: auxiliary bridging: secondary auxiliary bridging = 4:2:1, the optimized ternary formulation preferentially forms a tight, low-permeability, and extraction-resistant plugging layer. The plugging layer exhibits both higher reopen pressure and strong adaptability to dynamic changes in fracture width. These findings provide a scientific theoretical basis for mitigating drilling-induced losses and supporting wellbore stability management in COâ‚‚ storage well drilling.

Abstract

During drilling COâ‚‚ storage and monitoring wells, maintaining fracture stability is critical for preserving future injectivity and caprock sealing efficiency. Fluctuating bottom-hole pressure can induce cyclic deformation of natural fractures by transient drilling operations. This dynamic behavior destabilizes the plugging layer, making conventional lost circulation materials (LCMs) inadequate for sustained plugging performance. To address this problem, this study experimentally investigates the plugging behavior and formulation design criteria of three elastic LCMs including rubber particles, elastic graphite, and elastic mesh material under both static and dynamically deforming fracture conditions. The effects of particle size distribution, material proportion, pressure-bearing capacity, resealing behavior, and fracture-width adaptability were quantified. Experimental results demonstrate that appropriate coordination among three elastic materials enables controlled migration of the bridging zone within the fracture and significantly increases the fracture reopening pressure. When the particle size distribution of each elastic material is within its respective optimal range and the composite mass ratio satisfies filling: auxiliary bridging: secondary auxiliary bridging = 4:2:1, the optimized ternary formulation preferentially forms a tight, low-permeability, and extraction-resistant plugging layer. The plugging layer exhibits both higher reopen pressure and strong adaptability to dynamic changes in fracture width. These findings provide a scientific theoretical basis for mitigating drilling-induced losses and supporting wellbore stability management in COâ‚‚ storage well drilling.

Abstract

This paper presents a systematic study of data-driven modeling of indoor thermal comfort for smart cooling systems, establishing an analytical framework encompassing three main aspects: data collection, modeling analysis, and control execution. The findings indicate that existing studies in data collection exhibit the characteristics of integrating multi-source data, combining objective and subjective data, and employing multi-technology collaborative collection. During the modeling and analysis phase, machine learning algorithms play a dominant role in indoor thermal comfort modeling, achieving higher predictive accuracy than the traditional Predicted Mean Vote model. These models can be divided into two categories: group and personal models, with the latter showing greater potential for personalized predictions. In terms of control execution, the primary strategies can be categorized into two types: model-based optimization control and adaptive control that integrates user feedback. Their effectiveness in improving comfort, reducing energy consumption, and dynamically responding to personalized needs and environmental changes has been preliminarily validated. Furthermore, this paper outlines critical challenges at different stages and systemic levels in this field and proposes directions for future research.

Abstract

Accurate forecasting of electricity load is crucial for the stable operation of power systems and research related to energy systems. Electricity load is influenced not only by long-term socioeconomic factors but also by weather conditions. This study developed a high-resolution hourly electricity load forecasting model for China, utilizing machine learning techniques that integrate meteorological and socioeconomic data. The model demonstrates high accuracy in forecasting. National electricity demand and peak load are projected to rise steadily through 2030 and 2060. The peak-to-off-peak gap is expected to widen, and seasonal fluctuations are expected to intensify. Electricity load shows a positive correlation with extreme temperature, highlighting the impact of heatwaves on demand. These findings provide a solid foundation for further research on power system transformation under carbon neutrality pathways.

Abstract

Seasonal energy storage may represent an important step for the energy transition, reducing both energy consumption and greenhouse gas emissions. This paper presents an analysis of an integrated energy system designed to power a building by combining energy production and storage. The system studied in this paper consists of photovoltaic panels, an electrolyzer to convert excess energy into hydrogen, a metal hydride tank, and a fuel cell for reconversion to electricity. The aim of this paper is to evaluate the energetic and economic performance of this system under various climatic scenarios and for different storage capacities. The results demonstrate that the system can overcome the seasonal mismatch between production and consumption of a 1-apartment building, with a self-sufficiency ratio > 98% and a PV system < 6kW. Economic-wise, the system needs an initial investment of ~120 k€ and a payback period 11÷22 years depending on the storage capacity, thus confirming substantial challenges for the scalability of metal hydrides in residential buildings. Their cost accounts for nearly 38% of the total investment costs.

Abstract

This study investigates the feasibility of cooling on-board hydrogen storage tanks using passive (fins) and active (forced water convection) methods. Two tank types (III and IV), two target pressures (35 and 70 MPa), and two chilled temperatures (-40⁰C and -20⁰C) are analyzed. Results show that Type IV tanks, especially under 70 MPa and 35 MPa , benefit most from cooling. Forced water convection significantly reduces hydrogen gas, liner, and shell temperatures, as well as heat transfer rates, compared to free convection.
Validation against previous studies confirms good agreement, and findings indicate that cooling (particularly forced convection) effectively limits peak temperatures and enhances refueling performance.
Under 70 MPa, -40⁰C, and Type IV tank conditions, forced convection at a water flow rate of 1 kg/s reduces the maximum temperatures of hydrogen, the liner, and the shell by 1.6 °C, 6 °C, and 13 °C, respectively, compared to free convection. In contrast, under 70 MPa, -20⁰C, and Type IV conditions, the corresponding reductions in maximum temperatures are 16 °C for hydrogen, 23 °C for the liner, and 32 °C for the shell walls.