Chemical looping steam methane reforming (CLSMR) using metal oxide as oxygen carrier is regarded as an promising approach for hydrogen production, offering reduced costs and lower CO2 emissions compared to conventional steam methane reforming process. In this study, we proposed a multi-step chemical looping steam methane reforming process using NiFe2O4 as oxygen carrier (OC) for hydrogen production. Simulation model of the proposed cycle was developed using Aspen Plus. Experiments on fixed-bed reactor have been conducted to validate the reliability of simulation model. The effect of key process parameters has been evaluated. We found that the presented CLSMR process realized over 85% CH4 conversion in reduction step at 700 Â°C and more than 1.6 times of total product generation rate than that of FeO/Fe3O4 system at 900 Â°C in experiments. In terms of simulation model, 86.5% of methane to fuel efficiency and 66.9% of net efficiency could be obtained. The results demonstrate the proposed process has the potential to make advances in energy-efficient hydrogen production.
In this study, a simple framework was developed that can help identify and quantify peak load at sports facilities called Rocklunda Fastigheter AB. By analysing the electricity demand profiles and electricity prices from the Nord pool market, we characterize the equipment contributing most to a particular peak load. In addition, we quantified peak loads that occur during high electricity prices. This framework is beneficial in choosing
an appropriate demand-side management strategy for reducing peak loads and electricity costs for both academic and public end-users. Finally, a load-shifting strategy based on Mixed Integer Linear Programming (MILP) was developed to minimize the total annual
electricity cost. This approach suggests shifting the electricity demand to the early morning hours while reducing it in the evening when the electricity prices are higher. Finally, a cost-benefit analysis revealed the potential for savings of up to 9.5% when implementing a flexibility factor of 30%.
The transient behavior of the Pump as Turbine (PAT) coupled to Self-Excited Induction Generators (SEIGs) under variable load conditions is one of the critical aspects in small-scale hydro generation systems. This paper investigates the behavior of the PAT-SEIG system during load variations and explores the impact of transient phenomena on system performance. Simulation studies are conducted using MATLAB/SIMULINK and the effects of various loading conditions are investigated and the results are analyzed and discussed. The research findings contribute to a deeper understanding of the transient behavior of PAT-SEIG operating under variable load conditions. The study offers valuable insights for optimizing the performance and stability of PAT-SEIG system applied in small-scale stand-alone hydropower systems.
The pipe network fuel supply system plays a crucial role in guaranteeing the secure and uninterrupted operation of the airport. The issue of high energy consumption in parallel pump systems has attracted much attention and concern. A predictive scheduling method based on airport gate assignment is proposed to achieve closed-loop control. Serving as the foundation for supply optimization, airport gate assignment problem is solved to determine corresponding gates for flight refueling. The hydraulic steady state simulation is carried out to identify the position of the lowest-pressure fuel hydrant. In order to reduce operational energy consumption, the dichotomy method is employed to determine the minimum allowable pressure at the inlet of the pipe network. Taking into account pump switch times as an indicator for pump maintenance, the optimal scheduling strategy is finally achieved through dynamic programming. Operational and maintenance costs are proved to be significantly reduced with the application of the proposed predictive scheduling strategy.
The working condition data of lithium-ion battery can vary significantly due to factors such as battery type, production processes, and usage conditions. These data differences pose a challenge to accurately predicting the state of charge (SOC) , leading to various scenarios where the model exhibits low training accuracy, high training accuracy and low prediction accuracy, and so on. To investigate the impact of data differences on the training results, it is crucial to study the influence of distribution diversity of large-scale data on the generalization of the prediction model of SOC. Therefore, 32 operational data sets of actual lithium batteries were studied in this paper. Considering the demand of advanced battery management technology, random forest (RF) was combined with MIMO strategy to predict multi-step SOC, and prediction models were established for 32 operational data sets respectively. The application effect of RF is studied and the effect of data set properties on multi-step prediction model of SOC is analyzed. The results indicate that, for large-scale lithium-ion battery data, excluding a small amount of data, the RF-MIMO model achieves an R2 training accuracy of approximately 0.95 or higher for predicting future SOC with a time step of 180 intervals. The median R2 accuracy of each model to predict other data sets remains about 0.9. When the dataset meets the requirements of a wide distribution range of SOC, a left-skewed tendency in the kernel density curve, and a relatively uniform distribution, the model training can obtain high precision.
The carbon dioxide heat pump for simultaneous heating and cooling is an exceptional technology; however, current research tends to excessively prioritize the overall system efficiency improvement, neglecting the alignment of the system with heating and cooling supply and demand. This oversight leads to the wastage of redundant heat or cold in practical applications, resulting in energy loss. Therefore, addressing this from a supply-demand perspective, this study proposes a model predictive control based on demand. It integrates a novel carbon dioxide heat pump structure to mitigate the loss of redundant energy. Furthermore, this approach utilizes neural network identification to reduce the online computational load of the model predictive control in the carbon dioxide heat pump system.
Molten salts have the advantages of a wide range of liquid temperatures and high heat storage capacity, which have been widely used in the field of solar thermal utilization. The significant disadvantage of molten salts is their low thermal conductivity, and the addition of nanoparticles can effectively enhance the heat transfer ability of molten salts. In this paper, novel composite molten salt materials are prepared by adding zero-dimensional Al2O3 nanoparticles, one-dimensional multi-walled carbon nanotubes, and two-dimensional graphene nanosheets with different combinations of multidimensional nanoparticles, respectively, using ternary carbonates as the base salt. The thermal diffusivity of the composite carbonates in the liquid state was measured by the laser flash method at different temperatures to analyze the effect of multidimensional nanoparticles on the thermophysical properties of ternary carbonates. The experimental results show that zero-dimensional alumina nanoparticles and two-dimensional graphene sheets have a synergistic strengthening effect. With the addition of zero-dimensional alumina nanoparticles and an additional 0.5% mass fraction of two-dimensional graphene nanosheets, the thermal diffusivity of the composite carbonate can be enhanced by a maximum of 54.08%, and the prepared composite carbonate has a better stability.
In response to the European Commission’s aim of cutting carbon emissions by 2050, there is a growing need for cutting-edge solutions to promote low-carbon energy consumption in public infrastructures. This paper introduces a Proof of Concept (PoC) that integrates the transparency and immutability of blockchain and the Internet of Things (IoT) to enhance energy efficiency in tangible government-held public assets, focusing on curbing carbon emissions. Our system design utilizes a forecasting and optimization framework, inscribing the scheduled operations of heat pumps on a public sector blockchain. Registering usage metrics on the blockchain facilitates the verification of energy conservation, allows transparency in public energy consumption, and augments public awareness of energy usage patterns. The system fine-tunes the operations of electric heat pumps, prioritizing their use during low-carbon emission periods in power systems occurring during high renewable energy generations. Adaptive temperature configuration and schedules enable energy management in public venues, but blockchains’ processing power and latency may represent bottlenecks setting scalability limits. However, the proof-of-concept weakness and other barriers are surpassed by the public sector blockchain advantages, leading to future research and tech innovations to fully exploit the synergies of blockchain and IoT in harnessing sustainable, low-carbon energy in the public domain.
Physicochemical properties of synthetic fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning (ML) models are constructed to discover intrinsic chemical structure-properties relationships. The models are trained using data from molecular dynamics (MD) simulations. The fuel structure is represented by molecular descriptors. Such a symbolic representation of the fuel molecule allows to link important features of the fuel composition with key properties of fuel utilization. The results show that the present approach can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
The number and penetration of electric vehicles(EVs) are increasing. Electric vehicle charging load has the two characteristics of power load and energy storage because electric vehicles are becoming a new flexible resource to participate in the auxiliary services of power systems, which can improve the operation of power systems. As the basis of electric vehicle flexibility application, the flexibility characterization of electric vehicles has become the primary problem to be solved. Therefore, an electric vehicle flexibility characterization method based on the behavior data of users is proposed. Firstly, the original data is cleaned and reconstructed, and the behavior data set of electric vehicle users is extracted. Then, based on the electric vehicle user behavior data set, an electric vehicle user flexibility potential evaluation index system is proposed, which characterizes the electric vehicle flexibility potential from the three dimensions of capacity, charging time, and charging power. Secondly, an electric vehicle flexibility controllable region construction method based on an evaluation index is proposed to describe the flexibility of electric vehicle users with different charging habits. Finally, using real user data for verification, the results show that the proposed method can accurately describe the flexibility of different electric vehicle users. The results can provide a basis for electric vehicle aggregators (EVA) to participate in power grid auxiliary services.