Abstract

With the process of global urbanization, public buildings have been developed on a large scale, and energy conservation and emission reduction in this field are crucial to environmental protection. Passive energy saving through optimizing public building envelope does not need to consume other energy, which has great application potential. Therefore, this study innovatively uses a new type of thermal diode and combines it with the building envelope to form a dynamic building envelope that can passively remove heat and heat insulation, which can reduce the energy consumption of public buildings and promote cross-innovation of different disciplines. Numerical simulation was used to investigate the potential of the dynamic building envelope in public buildings. The results showed that the cooling energy saving potential of the building envelope was between 12.30% and 21.59% in different climate zones, and it had certain climate adaptability. At the same time, the energy saving potential increases with the increase of heat source in the building, reaching a maximum of 27.20%. Compared with the traditional building envelope, this new dynamic building envelope has stronger adjustment ability and better passive energy saving effect, which provides a certain reference for the development of energy saving in the building field.

Abstract

The preparation of phase change materials(PCMs) provides promising solutions for the technology implementation and popularization of renewable energy in both industries of thermal and refrigeration industries. This work focuses on the low temperature PCMs using in energy storage. The cutting-edge PCMs are expected to provide flexible temperature according to the preservation temperature of application systems. Moreover, the microencapsulated PCM (m-PCM) is potential to improve both the energy storage and refrigeration system performance by decreasing the leakage of the PCM and providing a better thermal conductivity.
In this regard, this paper provides a guided technological tour on the materials selection and the preparation methods for the m-PCMs, which remains elusive in the field of low temperature energy storage. Based on the comparative analysis on the existing researches, the effects and potential of different materials and preparation methods on the thermal properties of m-PCMs were summarized and demonstrated. At last, a novel m-PCM with the n-tetradecane as core and polystyrene as shell, as well as its corresponding efficient preparation method was preliminarily proposed and tested to verify its improvement.

Abstract

Solar thermochemical decomposition of CO2 for fuel production is a promising pathway to achieve efficient and stable solar energy applications. Among these methods, Ce-based two-step thermochemical cycles have received increasing attention. To date, a comprehensive thermodynamic assessment of such thermochemical cycle is still lacking, especially in terms of exergy. In this study, a new exergy analysis model for the solar Ce-based thermochemical cycle is developed, with the aim of exploring the potential to improve efficiency according to irreversibility distribution. Through a careful study of the relation between the three independent variables (reduction temperature T_H, Oxidation temperature T_L, and pressure P) and efficiency, we put forth new strategies to reduce various energy and exergy losses. Based on the permutation algorithm, we find the operating conditions with the lowest irreversibility, which results in a 141% improvement in energy efficiency and a 91% improvement in exergy efficiency compared to the highest efficiency case.

Abstract

Methane chemical looping has emerged as a promising avenue to produce blue or green hydrogen. Among the various oxygen carriers investigated for this process, Fe2O3 and NiO have garnered significant attention due to their affordability and non-toxic nature. However, the utilization of such oxygen carriers has exhibited satisfactory yields only at temperatures exceeding 800 °C. This limitation poses challenges for industrial reactor design and application. In this study, we present a novel approach involving the design of nanoscale Fe/Ni bimetallic oxygen carriers. This strategic design can reach a deeper reduction state to FeO while reducing the temperature to 500-600 °C. Moreover, we have achieved scalable production of the proposed oxygen carrier, with a negligible penalty in CH4 conversion (<3%) compared to laboratory-scale samples. A typical 10-kg scale nano oxygen carrier maintained a H2 purity of >78% in the outlet and stable performance over 300 cycles. Such noteworthy findings contribute to the advancement of chemical looping plants for green hydrogen production and refueling.

Abstract

As offshore wind farms (OWFs) evolve to larger scale and longer distance, superconducting technology has an opportunity for application. Liquid hydrogen (LH2) is an ideal cooling medium for superconductors used in OWFs and is a high-quality product. Therefore, an offshore power-LH2 co-production and hybrid delivery system based on superconducting technology is designed. The SEC, EXE, and COP of hydrogen liquefaction and subcooling integration process are 8.82 kWh/kgLH2, 40.6%, and 0.15, respectively. LH2 is produced to cool superconducting generators and transported ashore along with electricity using the hybrid energy pipeline. At a LH2 delivery rate of 1 kg/s, the maximum delivery distance of the hybrid energy pipeline can reach 81.7 km.

Abstract

In this research, a hydrogen and electricity co-generation system based on chemical recuperation enabled by complementary utilization of natural gas and electricity is proposed. The system uses an electrolysis for hydrogen generation and its byproduct oxygen for oxy-fuel burning to achieve near-zero CO2 emissions. To increase the power generation, flue gas from turbine is used to reform with natural gas, decreasing irreversible losses in natural gas direct combustion. The thermodynamic analyses are conducted to analyze the performances of the proposed and reference systems. The results show that the energy and exergy efficiencies are 54.12% and 51.69%, respectively, improving by 2.96 and 2.93 percentage points compared to reference system.

Abstract

Solid oxide electrolysis cells (SOEC) can be used for efficient hydrogen production. However, the SOEC needs both high-temperature steam and hydrogen as feed gas. This paper proposed a new system that combines a solar thermochemical reactor and SOEC together. The simulation results indicate that although the combined system can avoid additional hydrogen sources, it also lowers the hydrogen yield by 61.41% due to the discontinuous H2O/H2 supply. Multiple solar reactors can provide mixed H2O/H2 alternatively thus extending the gas feeding period for SOEC. A combined system with double reactors can increase the hydrogen yield by 94.67% compared with a single-reactor SOEC system. Compared with the traditional SOEC system, the single-reactor SOEC system and double-reactor SOEC system can save heating electricity by 25.69% and 42.2% respectively.

Abstract

Oil and gas drilling, essential for exploring and exploiting petroleum resources, involves significant time, labor, and costs, often exceeding $300,000 daily. Predicting the drilling rate (Rate of Penetration, ROP) accurately and promptly is crucial for improving efficiency and reducing expenses. In drilling, physics-based and machine learning models are typically used for ROP forecasting. Physics-based models, while intuitive, often lack precision in complex conditions. Machine learning models, though precise, face challenges with data availability and training costs in real-time settings. This paper introduces a novel approach combining a physics-based model with a particle filter algorithm for real-time ROP prediction. It adapts the Bourgoyne-Young ROP model and Markov assumptions into a state space model, using the particle filter to estimate elusive coefficients through probability theory. This enables real-time data updates for more accurate ROP predictions. The proposed framework is evaluated against traditional models using open-source and field drilling datasets in post-drilling and real-time scenarios. Results show conventional physics-based models fall short in both scenarios, while machine learning and the new particle filter model show significant improvements. In post-drilling analysis, these models achieve under 5% mean relative error. For real-time predictions, machine learning models have over 20% error, but the particle filter model reduces this to approximately 15%. This highlights the particle filter model’s superiority in accuracy and cost-effectiveness under dynamic and uncertain drilling conditions. This paper presents a robust, efficient solution for ROP prediction and optimization, marking a significant advancement in the drilling field.

Abstract

Energy-related investment decision-makers in the residential sector are heterogeneous in the sense that their preferences are rooted in socioeconomics, values, and perceptions, even when an ostensibly identical decision task is at hand. This study aims to address one of the challenges associated with long-term energy transition modelling by capturing the human dimension in energy system modelling. A novel multi-agent modelling approach, the MUSE-RASA model [1], is used to evaluate heterogeneous consumer-investors in the residential sector. Survey-based agent characterisation is used in the MUSE-RASA model to illustrate the difference between a single representative agent model and a multi-agent system. A survey of 1,500 Chinese families is used to generate six agents with varying investment goals, technology search strategies, and decision methods for purchasing and upgrading heating systems. The agent-based approach integrates high-resolution gridded data to geographically target the agents on a map. The results suggest that market fragmentation achievable with agent-based approaches are more powerful as closer to the real world.

Abstract

This study introduces a performance model for a single-effect evaporation desalination process integrated with a condenser unit. Herein, the proposed desalination unit is developed, designed, and assessed in terms of its performance. The key input parameters used to evaluate system performance conditions include salinity, flow rate, temperature, and evaporator area. Based on the calculations, the model determines the steam temperature and flow rate, which influence the evaporation rate and energy consumption. The end condenser plays a crucial role in condensing vaporized water and maintaining the required temperature and pressure levels. Furthermore, the desalination system was developed using advanced modeling in the MATLAB/Simulink toolbox, which effectively integrates distinct mathematical relations. Sensitivity analysis was performed to assess the impact of brine blowdown temperature and feed mass flow rate on the evaporator performance ratio and productivity, as well as on the condenser power and effectiveness. It provides valuable insights into single-effect evaporation desalination operation, allowing the evaluation of efficiency, optimization of design and operating conditions, and informed decision-making regarding desalination plant performance.