Abstract

Power consumption from data centers is envisioned to drastically increase in future energy systems. A large share of this power consumption will be, inevitably, converted into heat, proving a huge potential for waste heat recovery opportunities. This work investigates the optimal integration of waste heat recovery from small-scale edge data centers into buildings. A case study using the real-data from NEST building at Dübendorf, Swizterland, is conducted. The results indicate that smaller data centers exhibit better cost optimization. Within the boundary conditions of NEST case study, the self-consumed heat fraction decreases with increasing data center size, and the influence of daily flexibility on heat consumption is negligible.

Abstract

This study focuses on integration of solar thermal evacuated tube collectors for clean energy cooking, aligning with the Sustainable Development Goals. The system is designed to heat oil instead of water, creating a hot reservoir for heat extraction and cooking. The complete system comprises solar thermal collectors, heat transfer oil, a storage tank, a cooking vessel, a control system, and valves. The maximum observed temperature was about 186 oC with an oil-based solar thermal collector whose average efficiency was 29 %. The performance of the solar collectors depends on the global solar irradiance incident on the heat pipes. This paper therefore, presents the experimental results of a heat pipe-evacuated solar thermal collector.

Abstract

The attainment of cost-effective hydrogen production is pivotal for facilitating the increased utilization of hydrogen within future energy systems. This study proposes a mixed integer linear programming approach to simultaneously derive the optimal sizing and operation of hydrogen generation sites. The results showed that a 13.6% reduction in levelised cost of hydrogen can be obtained compared to benchmark solutions. The results highlight the intrinsic interconnection between optimal sizing and operation of hydrogen generation sites, emphasizing that they should not be treated as separate design phases.

Abstract

The conversion of waste through gasification into valuable forms of energy and fuels has gained much interest as part of the transition towards carbon neutrality. Unlike the conventional designs of the gasifier, the rising co-current gasifier combines the advantages of the fixed bed and the fluidized bed reactors in terms of the efficiency and the quality of syngas produced. However, limited research has been conducted on such design. The current study investigates the performance of wood pellets gasification under different operating conditions for combined heat and power (CHP) and biofuels production applications. The system is operated under 50 kW and 40 kW of electrical power generation demands. Further, the gasifier performance is investigated over a wider range of air flow rates (i.e., 50-75 kg/h) and with the change of the reactor bed height. Under these diverse operating conditions, the gasifier exhibited reliable gasification performance, and the resulting syngas compositions estimated in the ranges of (20.26-21.87 vol.% for CO, 18.3-17.5 vol.% for H2, 11.07-9.67 vol.% for CO2, and 2.7-1.8 vol.% for CH4). Correspondingly, the LHV, CGE, CCE varied within the ranges of 5.51-5.3 MJ/Nm3, 84.8-78.95%, and 94.8- 89.9%, respectively.

Abstract

Solid oxide fuel cell (SOFC) is a promising power technology with clean, high efficiency, and exceptionally high fuel flexibility. This research established and experimentally verified a 3D-dynamic model of a planar direct internal reforming (DIR) SOFC. Based on the developed model, the 500-second dynamic response characteristics of the cell fueled by pure hydrogen or 0.3 pre-reformed syngas were investigated after increasing the power density by 20%. The results show that the power density response of the cell, whether hydrogen or syngas-fueled, can be divided into a concentration-dominated fast-response stage with a subsequent temperature-dominated slow-response stage. The fast-response stage is finished in a few seconds, while the slow-response stage takes hundreds of seconds to stabilize. When the initial power density (P0) is relatively low (P0=1500W/m2), the changes in the fast-response stage account for more than 93% of the whole process, indicating that the power density can be stabilized quickly. As the initial power density increases, the proportion of power density change in the slow-response stage gradually increases and reaches more than 50%. When P0=4000W/m2, the power density of the cell fueled by hydrogen or syngas increases by 400 W/m2 and 520 W/m2, respectively, in the slow-response stage, corresponding to 50% and 65% of the whole process.

Abstract

The surge in methanol demand as a fuel source, coupled with global resource competition, necessitates innovative routes for bulk chemical production, including methanol. China, a prominent methanol producer and consumer, drives industry expansion, raising substantial environmental concerns. This article compares the stateof-the-art “liquid sunlight” methanol production technology (CCTM) with traditional pathways (CGTM, COTM, NGTM, BOTM) through a cradle-to-gate life cycle assessment (LCA). Employing CML2001 and EI99 methodologies, we analyze ten environmental impact categories and two endpoint categories. Our study reveals the NGTM path as the cleanest for methanol production, while the CGTM route poses severe cumulative environmental impacts, 2.0 to 3.6 times worse than other methods. Environmental harm constitutes 73% to 81% of the five technology approaches’ impacts, with human health impacted 10% to 12% of the time.

Abstract

When fast and accurate detection is crucial, such as in cases of pipeline leakage accidents, machine learning is a powerful tool for integrating real data with artificial intelligence. In this context, this study introduces reliable artificial neural network (ANN) models that are intended to precisely locate single or several leaks in undersea gas pipelines. Using OLGA multiphase software, thorough data for numerous leak scenarios are generated under multiphase flow conditions and based on an actual offshore pipeline profile. These data cover measurable flow variables (mass flow rate, temperature, and _x000D_
pressure) at the inlet and outlet of the monitored pipeline. The developed ANN models were trained to estimate the leak’s location(s) and size(s) under single leak as well as multiple leak conditions, which have not been investigated before in open literature. The findings show that for single leak scenarios, the ANN models can detect and identify the leak size and location with an error of less than 1.0% across all phase flow scenarios. The leak localization error, however, is higher than 8.0% under multiple leak conditions. The outcomes of this study also show that the accuracy of the ANN models is significantly influenced by the phase flow conditions and number of ANN input parameters. To improve ANN performance and minimize the impact of noise signals, it is suggested to employ a multi-stage leak identification technique. To do this, an ANN model with two inputs must be utilized first, and subsequently, a model with six inputs must be progressively added. In this study, our approach and methods for generating leak-based data and identifying leaks in multiphase flow and various leak conditions can serve as a foundation for enhancing the efficiency and reliability of future machine learningbased leak detection systems.

Abstract

This study presents an innovative LH2 productionsystem using dual mixed refrigerant (DMR)-basedcryogenic processes. New dual-refrigeration loops aredeveloped for the precooling and liquefaction of thegaseous hydrogen with two distinguished mixed refrigerants for each process. Thorough energy, exergy, economic, and environmental (4E) analyses are conducted for the proposed LH2 system in this study. The proposed system produces LH2 with an energy consumption of 3.732 kWh/kgLH2, 48% lower than the single mixed refrigerant SMR-based systems and 70% lower than the current commercial plants. Furthermore, the exergy efficiency (59.65%) of the present system is enhanced by 33% compared to the SMR-based systems (44.89). In addition, the present system produces LH2 with a levelized cost of 1.89 $/kgLH2, which is 21% lower than that for SMR-based large-scale systems and 70% lower than that of small-scale systems. Environmentally, the proposed DMR-LH2 system reduced CO2 emissions by 29-69% compared to large-scale SMR-based systems. Moreover, the DMR-LH2 system is characterized by more flexibility in the design of the process equipment and eliminates the potential for freezing problems. This study’s dual mixed cryogenic refrigeration approach provides guidelines for future research to improve the technical and economic feasibility of LH2 production, making it competitive with other energy storage and transportation options.

Abstract

Global warming has been a glowing problem. At the same time, natural disaster such as earthquake and rainstorms are becoming more frequent. Therefore, decarbonization and increase of resilience are needed to all energy systems. Practically, PEB and PED has been positively introducing in the world. To show resilient systems, this study conducted several pattern of case studies which can evaluate the resilience quantitatively. Through this case study, it revealed that three-day evaluation is more effective than one-day evaluation.

Abstract

The integration of photovoltaic systems into the power grid is becoming increasingly important for sustainable energy production. The efficiency of PV systems is heavily dependent on weather conditions, making accurate weather classification crucial for system performance prediction and optimization. In this study, we utilize a threshold-based weather classification method for PV systems and compare it to K-means clustering classification. The effectiveness of both methods was evaluated using real-world data from a PV system in a temperate climate region. This comparative analysis highlights the strengths and limitations of each approach, demonstrating their combined utility in enhancing the accuracy and reliability of weather classification, thereby offering a comprehensive tool for optimizing PV system performance across diverse meteorological conditions.