An optical-thermal coupled model was developed to study the performance of a typical molten-salt solar power tower, which was proven to be reliable by comparing with testing data. It is found that the mean absolute deviations between simulation and testing data are about 0.8% for the receiver efficiency under full-power condition. The model reveals that the detailed distributions of the solar flux and temperature in the receiver are extremely non-uniform, which resulted in high thermal stress at the tube crown. Moreover, failure analysis of the receiver indicates that the high strain introduced by high flux can result in fatigue failure, but it can be avoided by reasonable aiming strategy. The validated model and results from this work can offer helps for appropriate performance predictions in solar power tower.
This paper provides a performance assessment of different fractions of crude plant digestive juices produced by Nepenthes mirabilis (N. mirabilis) which were used to pretreat mixed agro-waste to determine which fraction produces more holocelluloses while reducing phenolic compounds in the hydrolysate. Fractionation (<3kDa, ˃ 3kDa and <10kDa, ˃ 10kDa) was done with the different fractions being used to pretreat the mixed agro-waste (1:1 ratio, i.e., 25% (w/w) for each; 1 g orange peels, 1 g apples peels, 1 g maize cobs, 1 g grape pomace and 1 g oak plant yard litter) of various particle sizes, i.e., >106µm, >75µm <106µm. The highest results obtained indicated 46,63 (min) to 115,19 (max) g/L for total reducing sugars (TRS) production with a maximum of 6,61 g/L for total residual phenolic compounds (TRPCs) being observed using the <10 kDa/>106µm fraction/particle size of N. mirabilis/agrowaste after 72 hrs, revealing the efficacy of different juice fractions to pretreat the agro-waste. Overall, the digestive enzymes in N. mirabilis pods must be fractionated for agro-waste pretreatment, i.e., using a green chemistry approach for the development of biorefineries, for sustainable renewable energy generation and security.
The pitch system plays a key role in the large offshore wind turbine for regulating pitch angle to the desired one and hence to stabilize the power out. However, due to the uncertainty of time-varying parameters and various unknown disturbances acting on the turbine blades, accurate and rapid regulation of pitch angle can hardly be achieved using the existing available pitch control strategy. Moreover, the actuator faults of the pitch system may occur in the long-term operation of the wind turbine, which greatly reduces the reliability and power generation efficiency. Aiming at the above problems, a nonlinear model of the pitch system considering time-varying parameter uncertainties and unknown disturbances is established, based on which a neural adaptive fault-tolerant control strategy with a rate function is proposed. A co-simulation is developed, and the merits of the proposed method are verified.
Wearable electronics are demonstrating great potential in many areas, but finite capacity of chemical batteries become a bottleneck of their applications. We develop three kinds of wrist-worn energy harvesters that have different power enhancement mechanisms to address this issue. A compact energy harvester with magnetic frequency-up converter is developed to increase energy conversion capacity while a tiny repulsive magnetic spring is introduced to enhance response motion. Besides, a proof-massless energy harvester is investigated to improve motion capture capacity of wrist-worn energy harvester. A general model is constructed to predict the performances of these energy harvesters. The experimental results show that these energy harvesters achieve significant power improvement and their power output and power density can reach ten times that of the counterparts.
The United Nations Industrial Development Organization (UNIDO) implemented the Mediterranean Transfer of Environmentally Sound Technology (MED/TEST) Phase II in the Southern Mediterranean Region between 2016 and 2018. This paper reports the findings of resource efficiency demonstrations with 58 companies in three North African countries (Algeria, Morocco, and Tunisia). The paper draws on the findings of Material Flow Cost Accounting that estimates the full costs of energy, water and rawmaterials used in production processes, and of Resource Efficiency and Cleaner Production Assessments that identify feasible and cost-effective interventions. The combination of these two tools allows for a comparison of the payback periods of a full range of potential resource efficiencymeasures.Not surprisingly, there are severalwater and raw materials measuresthat havereturns on investment similar to those for energymeasures.
Recent years have witnessed a transition in energy structure, where large number of electronic devices and systems are introduced in multiple fields including industry, academia, commerce, and so forth. Safe and efficient operations of these systems are critical to ensure productivity as well as to avoid hazard, which poses strict demands on fault diagnosis. Most of traditional methods tend to focus on algorithm design or certain types of hardware or software flaws under given operational conditions, thus not suitable for modern electronic systems that may suffer from a variety of different faults. In this paper, on top of our previous work on big data, a systematic way of fault diagnosis in electric vehicles is put forward, which covers data processing, feature extraction, model-based diagnosis, and model fusion. The proposed method is trained and validated using data from real-world electric vehicles, which are representative examples of modern complex systems. Results show a diagnosis accuracy over 95% can be achieved with a comprehensive consideration of fault modes under a variety of operational scenarios. The proposed algorithm can also be used to indicate key features leading to faults so that system level upgrade can be performed accordingly. The design criteria and idea of the algorithm is also adaptable to other systems or applications with minor changes.
This work analyzes a real-life dataset of charging records to understand charging behaviors at public charging stations. Data analytics were carried out to understand the statistical distribution of EV charging time, peak charging period, charging duration, seasonal and yearly effects, etc. The study was carried out with charger-centric data of a public car park in England, containing more than 12,000 charging records from 2016 to 2020. The results found that rapid charger (up to 50kW DC) is 3 times more popular than the fast charger (single-phase 7kW) and delivers 4 times more energy than the fast charger. This work will greatly help Charge Point Operators and Power Distribution Networks Operators for EV charging infrastructures planning and operations.
This paper aims to quantify the effect of the Feed-in-Tariff (FiT) rates on the installed capacity of anaerobic digestion (AD) plants in the UK. The paper develops multiple Linear Regression models (LR) to understand the effect of the different factors that drive the diffusion of AD plants. Emerging results suggest that among the different incentives for AD, only the FiT has a significant effect. Secondly, this effect comes to play only after the announcements of revisions to the programme. And third, the results confirm that the FiT has a different effect for each plant size where the medium size plants are the most responsive to the programme, whilst the large plants are the least responsive.
Buildings consume a huge amount of energy and mainly utilize it for occupants’ thermal comfort satisfaction. Real-time thermal comfort assessment can enormously contribute to thermal comfort optimization and energy conservation in buildings. Existing thermal comfort models mainly focus on the real-time assessment of occupants’ current thermal comfort. However, in the transient thermal environment, occupants’ thermal comfort is unsteady and varies from time to time. Therefore, if we only assess occupants’ current thermal comfort, prediction error will be elicited. In order to address this problem, it is principally important to comprehend occupants’ real-time thermal sensation trend in the transient thermal environment. This study investigates a novel thermal sensation index that can directly represent an individual’s current thermal sensation trend. By incorporating the novel thermal sensation index into an ordinary thermal comfort model, a composite thermal comfort model is derived, which can simultaneously address an individuals’ current thermal comfort and current thermal sensation trend. Then, by utilizing a machine learning classification algorithm, we propose its intrusive assessment method using skin or clothing temperatures of ten local body parts measured by thermocouple thermometers and its non-intrusive assessment method using a low-cost portable infrared camera. The novel composite thermal comfort model can provide an early warning mechanism for thermal discomfort and contribute to energy conservation in buildings.
The performance of a proton exchange membrane fuel cell exhibits strong association with liquid transport in a gas diffusion layer. The content and distribution of Polytetrafluoroethylene are key factors that determine liquid transport behaviors in a gas diffusion layer. In this study, by employing a stochastic algorithm, a two-dimensional microstructure of a representative carbon paper type gas diffusion layer was reconstructed. Subsequently, the influence of Polytetrafluoroethylene content and various proposed gradient distributions of Polytetrafluoroethylene in the reconstructed gas diffusion layer on liquid transport behaviors was examined by implementing a two-phase Lattice Boltz-mann method. The results supported the findings that an increased content of Polytetrafluoroethylene in gas diffusion layer favored liquid removal, but an extremely high one could cause a remark decrease of the cor-responding porosity of the gas diffusion layer, hence weakening mass diffusion. An optimal gradient design of Polytetrafluoroethylene could enhance water removal performance of a gas diffusion layer reflected by a reduced liquid water saturation and liquid phase steady-state time, meanwhile could ensure an excellent mass diffusion with a relatively high effective porosity of gas diffusion layer, thereby benefitting fuel cell per-formance. The study here could provide guideline for the design of high performance of a fuel cell with a gradient gas diffusion layer.