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Performance simulation of solar-driven absorption heat pump-membrane distillation system for combined desalination brine concentration with feed recirculation and cooling applications

Desalination is the primary choice for securing freshwater provision in water-stressed regions and reduces the gap between rising demand and dwindling natural freshwater resources. However, global desalination plants are dominated by fossil fuel-driven desalination technologies with a 40–50 % recovery ratio. Hence, it is critical to decarbonize desalination and address brine effluent ecological concerns. In this paper, a solar-powered absorption heat pump (AHP)-membrane distillation (MD) system concept was proposed and analysed for small-scale RO plant brine reject management and space cooling applications. The MD subsystem is based on commercial MD modules with batch feed recirculation to reach saturation (from 70 to 260 g/kg salinity). The MD system's heating and cooling consumptions are supplied by the AHP (6.54 MWh and 13.47 MWh, respectively, for a complete batch cycle). The AHP is designed to supply hot water at 85 °C with 701.63 kW heating capacity and co-produced chilled water at 16 °C with a cooling capacity of 857.86 kW, about 67 % is utilized to cool down the brine reject to feed temperature. The thermal and exergy COPs were 1.273 and 0.40 at a driving heat of 135 °C. The solar-powered AHP-MD system is useful for sustainable desalination deployment besides space cooling applications.

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Multi-innovation adaptive Kalman filter algorithm for estimating the SOC of lithium-ion batteries based on singular value decomposition and Schmidt orthogonal transformation

The state of charge (SOC) of lithium battery is a key parameter for effective management of battery management systems (BMS). To address the problems of low precision, complex computation and poor robustness of traditional charging state estimation methods, an enhanced algorithm based on Unscented Kalman filter (UKF) is proposed. The singular value decomposition (SVD) method is adopted to ensure the normal operation of the Unscented Kalman Filter (UKF) algorithm even when the matrix P lacks positive semi-definiteness from a mathematical perspective. This enhancement significantly improves the theoretical robustness of UKF. Schmidt orthogonal transformation is concurrently used to reduce the computational complexity in the sampling point selection process, and the multi-innovation theory is combined with adaptive noise control to further improve the accuracy of SOC estimation. The algorithm is validated using the Urban Dynamometer Driving Schedule (UDDS) condition. The simulation results are excited using Singular value decomposition-multi-innovation adaptive Schmidt orthogonal unscented Kalman filter method (SVD-MIASOUKF). 0.95 % and 1.29 % of maximum errors are obtained at 25 °C and −10 °C, while the maximum errors are 2.46 %–2.99 % using SVD-UKF and UKF. The proposed approach shows faster convergence speed and higher estimation accuracy in comparison to traditional algorithms.

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Improved modelling of low-pressure rotor speed in commercial turbofan engines: A comprehensive analysis of machine learning approaches

Accurate engine thrust modelling offers notable opportunities for reducing fuel consumption, mitigating emission effects, managing air traffic, and optimizing the preliminary design of aircraft. This study focuses on the development of machine learning models to predict the low-pressure rotor speed (N1) parameter, which is closely related to the thrust of turbofan engines, for commercial aircraft during the cruise phase. The analysis utilizes a Flight Data Recorder (FDR) dataset from 1086 actual flights involving twin-engine, narrow-body, short-to-medium haul commercial aircraft equipped with CFM56-7B turbofan engines. To achieve accurate N1 parameter predictions, the study employs machine learning models, including Deep Learning (DL), Random Forest (RF), Gradient Boosted Machines (GBM), and Artificial Neural Networks (ANN). These models are evaluated using statistically significant indicators, demonstrating that machine learning models yield highly accurate results. Among the models tested, the DL model provides the most precise estimates of the N1 parameter. This study not only advances the understanding of engine thrust modelling through machine learning but also provides practical insights for the aerospace industry. The findings underline the potential of machine learning techniques in delivering superior prediction accuracy, which can be integrated into real-time flight management systems, ultimately contributing to more sustainable and efficient aviation operations.

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Comparative life cycle assessment of carbon-free ammonia as fuel for power generation based on the perspective of supply chains

In recent years, there has been a surge in ammonia demand due to growing interest in carbon-free fuel for decarbonization. Thus, it is crucial to identify the methods to fulfill the required demand, particularly for countries with resource scarcity or lacking well-established production infrastructure. The present study compares environmental impact analysis between domestic ammonia production and import ammonia for these countries by using life cycle assessment (LCA) methodology. In these ammonia supply paths, five different production ways were applied: gray (conventional Haber-Bosch process), blue (with carbon capture and storage), green (wind, solar, and nuclear powered water electrolysis with Haber-Bosch process). The assessment showed highest global warming potential (GWP) value at 2.64 ton CO2-eq/ton NH3 for imported gray while domestic nuclear-driven ammonia had the lowest value at 0.81 ton CO2-eq/ton NH3. In addition, contrary to expectations, the human and environment impact of green ammonias were all higher than gray and blue ammonia due to the production of raw materials. This means that environmental improvements of related production process are crucial in preparation for the coming new energy regime, which then lead to the production of environmentally friendly ammonia. Lastly, these results offer valuable guideline for future energy strategy planning.

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Exploring the demand for inter-annual storage for balancing wind energy variability in 100% renewable energy systems

As research in 100% renewable energy systems progresses, closing remaining research gaps, such as quantifying inter-annual balancing requirements, is necessary. For this study, inter-annual variations of wind power yield and the resulting balancing requirements are analysed for the energy transition towards 100% renewable energy of the United Kingdom and the Republic of Ireland. The energy system components are determined and cost implications are quantified comparing two options for inter-annual storage: e-hydrogen and e-methane for a low, medium, and high security case, which are expanded over ten years. The results indicate that more than 300 TWhth,LHV and 500 TWhth,LHV of inter-annual gas storage capacity in 2050 for the low and the high security case, respectively are required. For both investigated cases, the annual system costs increase notably compared to the reference scenario without inter-annual storage. For the case of e-hydrogen, the annualised system costs increase by 19% compared to an increase of 7% for e-methane for medium security cases. The difference in cost growth is due to significantly higher hydrogen underground storage costs that overcompensate additional system costs for e-methane production. This study provides an expansion from seasonal to inter-annual storage and a first estimate of inter-annual balancing requirements and costs.

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Optimization for fuel consumption and TCO of a heavy-duty truck with electricity-propelled trailer

As renewable energy grows in the heavy-duty truck sector, exploration of diverse solutions with varied energy types and powertrain configurations becomes a necessity. While most studies focus on powertrain configurations in tractors, this study takes a different approach: propose and evaluate new plug-in hybrid configurations with multiple power sources for heavy-duty trucks. The tractor retains its engine powertrain, while the trailer is equipped with electric axle(s), forming a fuel-electricity hybrid propulsion system. The specifications of the electric propulsion system are optimized, using a multi-objective particle swarm optimization algorithm and dynamic programming control algorithm to evaluate the energy consumption and total cost of ownership (TCO). The results highlight the potential of the configuration with three electric axles and single reduction gear in regard to vehicle energy consumption. Conversely, the configuration with one electric axle and a two-speed transmission exhibits the lowest TCO. Under the China World Transient Vehicle Cycle, the fuel consumption of the diesel truck is reduced by up to 44.59 % with the use of the optimized hybrid configuration. The truck's TCO can be then reduced by up to 832 thousand CNY in one-million-kilometer operation. For an actual road operation, these figures change to 34.79 % and 284 thousand CNY, respectively.

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