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Abstract A novel magnesia-stabilized carbide slag (MSCS) was synthesized with carbide slag, magnesium nitrate hydrate and by-product of biodiesel by combustion, which was used as a CO 2 sorbent during the calcium looping process. The effects of preparation condition (combustion temperature, combustion duration, by-product of biodiesel addition and magnesia addition) and CO 2 capture condition (carbonation and calcination atmosphere) on CO 2 capture capacity of MSCS were investigated during the calcium looping cycles. The main compositions of MSCS are CaO and MgO. The addition of by-product of biodiesel in the preparation of the sorbent leads to the uniform mix of MgO and CaO grains in MSCS, which shows an obviously positive effect on its CO 2 capture capacity. Only on the condition of the addition of by-product of biodiesel, MgO derived from magnesium nitrate hydrate improves the cyclic CO 2 capture capacity and durability of MSCS during the multiple cycles. MSCS with a mass ratio of CaO to MgO of 80:20 combusted at 850 °C for 60 min exhibits higher CO 2 capture capacity and greater durability. The CO 2 capture capacity of MSCS can retain 0.42 g/g after 20 cycles, which is 60% higher than that of carbide slag. MSCS calcined under the high concentration of steam displays much higher CO 2 capture capacity and better sintering resistance during the cycles, compared to MSCS calcined under the high concentration of CO 2 . The addition of steam in the carbonation enhances CO 2 capture capacities of MSCS and carbide slag. MSCS consists of CaO–MgO grain groups and the support of MgO sustains the high sintering resistance of the sorbent. MSCS remains much larger surface area and pore volume than carbide slag during the cycles, compared to carbide slag. MSCS appears promising as an effective and low-cost CO 2 sorbent during the calcium looping.

Feature-based machine learning models for capacity and internal resistance (IR) curve prediction have been researched extensively in literature due to their high accuracy and generalization power. Most such models work within the high frequency of data availability regime, e.g., voltage response recorded every 1–4 s. Outside premium fee cloud monitoring solutions, data may be recorded once every 3, 5 or 10 min. In this low-data regime, there are little to no models available. This literature gap is addressed here via a novel methodology, underpinned by strong mathematical guarantees, called ‘path signature’. This work presents a feature-based predictive model for capacity fade and IR rise curves from only constant-current (CC) discharge voltage corresponding to the first 100 cycles. Included is a comprehensive feature analysis for the model via a relevance, redundancy, and complementarity feature trade-off mechanism. The ability to predict from subsampled ‘CC voltage at discharge’ data is investigated using different time steps ranging from 4 s to 4 min. It was discovered that voltage measurements taken at the end of every 4 min are enough to generate features for curve prediction with End of Life (EOL) and its corresponding IR values predicted with a mean absolute percentage error (MAPE) of approximately 13.2% and 2.1%, respectively. Our model under higher frequency (4 s) produces an improved accuracy with EOL predicted with an MAPE of 10%. Full implementation code publicly available.

Abstract The pressure-loss coefficient for a duct junction of square cross-section was determined using computational fluid dynamics (CFD). The predicted junction pressure-loss coefficient for combining flows was generally in good agreement with experimental data from the literature. The junction pressure-loss coefficient was associated with the flow from the side branch to the duct carrying the total flow.

Abstract Liquid desiccant cooling systems are considered a promising technology for accurate humidity control and high energy efficiency. The dehumidifier and the regenerator are the two main components in the system, and their performance directly determines the system performance. This paper is a comprehensive review of the empirical correlations for the determination of mass transfer coefficient and moisture effectiveness in both adiabatic and internal cooling/heating dehumidifier/regenerators, and it further discusses approaches to enhance their mass transfer performance. These methods include structural improvements, such as structural modification, ultrasound atomisation and membrane-based modules, and modification of liquid desiccants, such as the addition of surfactants and nanoparticles. Finally, a brief summary and some suggestions for future work are outlined and addressed.

Abstract Most of the current Stirling-type pulse tube refrigerators (PTRs) adopt inertance tubes with large reservoirs for phase shifting. Recovering the acoustic power dissipated in the inertance tube provides a great potential for improving the efficiency of a PTR. In this study, an inertance tube PTR is modified by replacing the dissipative inertance tube and reservoir with a mass-spring displacer directly coupled to a compression space. Numerical simulations are conducted on both the PTRs based on a validated one-dimensional computational fluid dynamics model. Optimization of the inertance tube PTR shows that the coefficient of performance (COP) is limited within 0.103 at the cooling temperature of 77 K. The simulation of the PTR with the feedback mechanism indicates that COP can be significantly improved due to the extra power recovered by the mass-spring displacer. The parametric analyses of the moving mass, spring stiffness, mechanical resistance, piston diameter, and working frequency of the mass-spring displacer are finally performed. The phase relations at both ends of the regenerator are significantly influenced by the geometric and operating parameters, which further affect the performance. The designing parameters have been optimized, COP reaches about 0.13–0.14 with the relative Carnot COP of around 0.4. It demonstrates that adopting the mass-spring displacer to feed the expansion power back into the compression space is an effective way of improving the performance of PTRs. This work provides comprehensive understanding of the mechanisms and characteristics of the PTRs with the mass-spring displacer. It would be helpful for future designs of such systems.

Abstract Accurate rooftop solar energy potential characterization is critically important for promoting the wide penetration of renewable energy in high-density cities. However, it has been a long-standing challenge due to the complex building shading effects and diversified rooftop availabilities. To overcome the challenge, this study proposed a novel 3D-geographic information system (GIS) and deep learning integrated approach, in which a 3D-GIS-based solar irradiance analyzer was developed to predict dynamic rooftop solar irradiance by taking shading effects of surrounding buildings into account. A deep learning framework was developed to identify the rooftop availabilities. Experimental validations have shown their high accuracies. As a case study, a real urban region of Hong Kong was used. The results showed that the annual solar energy potential of the entire building group was reduced by 35.7% due to the shading effect and the reduced rooftop availability. The reductions of individual buildings varied from 13.4% to 74.5%. In spite of the substantial reductions of the annual solar energy, the shading effect could only slightly reduce the peak solar power. In fact, the annual solar energy reduction could be five times higher than the peak solar power reduction. Further analysis showed that simple addition of the respective solar energy potential reductions, caused by the shading effect and the rooftop availability, tends to highly overestimate the total reduction by up to 26%. For this reason, their impacts cannot be considered separately but as joint effects. The integrated approach provides a viable means to accurately characterize rooftop solar energy potential in urban regions, which can help facilitate solar energy applications in high-density cities.

With increasing air traffic, rising fuel costs and tighter environmental targets, efficient airport ground operations are one of the key aspects towards sustainable air transportation. This complex system includes elements such as ground movement, runway scheduling and ground services. Previously, these problems were treated in isolation since information, such as landing time, pushback time and aircraft ground position, are held by different stakeholders with sometimes conflicting interests and, normally, are not shared. However, as these problems are interconnected, solutions as a result of isolated optimisation may achieve the objective of one problem but fail in the objective of the other one, missing the global optimum eventually. Potentially more energy and economic costs are thus required. In order to apply a more systematic and holistic view, this paper introduces a multi-objective integrated optimisation problem incorporating the newly proposed Active Routing concept. Built with systematic perspectives, this new model combines several elements: scheduling and routing of aircraft, 4-Dimensional Trajectory (4DT) optimisation, runway scheduling and airport bus scheduling. A holistic economic optimisation framework is also included to support the decision maker to select the economically optimal solution from a Pareto front of technically optimal solutions. To solve this problem, a multi-objective genetic algorithm is adopted and tested on real data from an international hub airport. Preliminary results show that the proposed approach is able to provide a systematic framework so that airport efficiency, environmental assessment and economic analysis could all be explicitly optimised.

Abstract An enhanced TRNSYS simulation model, NEM, of the behaviour of a domestic hot-water (DHW) store, with an immersed heat-exchanger (HX), has been developed and validated. This model simulates the dynamic heat-depletion and recovery processes in the immersed HX and predicts the transient temperature-patterns for various DHW draw-off versus time profiles. Realistic daily profiles (RDPs), based on field studies, were developed to provide representative draw-off patterns for the testing of thermal stores and simulation studies. The effects of these RDPs and five other existing profiles on the store’s performance are analysed using the enhanced model. The simulation results indicate the importance of the HX’s recovery, as well as the number, type and time of occurrence of the draw-offs in the profile, on the thermal store’s performance. It is concluded that RDP profiles should be used in the performance testing of thermal stores to obtain results that reflect conditions experienced in the field.