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Nickel-rich cathode material, NCA (85:14:1), is successfully synthesized using two different, simple and economical batch methods, i.e., hydroxide co-precipitation (NCA-CP) and the hydroxides solid state reaction method (NCA-SS), followed by heat treatments. Based on the FTIR spectra, all precursor samples exhibit two functional groups of hydroxide and carbonate. The XRD patterns of NCA-CP and NCA-SS show a hexagonal layered structure (space group: R_3m), with no impurities detected. Based on the SEM images, the micro-sized particles exhibit a sphere-like shape with aggregates. The electrochemical performances of the samples were tested in a 18650-type full-cell battery using artificial graphite as the counter anode at the voltage range of 2.7–4.25 V. All samples have similar characteristics and electrochemical performances that are comparable to the commercial NCA battery, despite going through different synthesis routes. In conclusion, the overall results are considered good and have the potential to be adapted for commercialization.

Decarbonizing fossil-fuel usage is crucial in mitigating the impacts of climate change. The burning of fossil fuels in boilers during industrial process heating is one of the major sources of CO2 in the industry. Electrification is a promising solution for decarbonizing these boilers, as it enables renewable energy sources to generate electricity, which can then be used to power the electric boilers. This research develops a user-driven simulation model with realistic data and potential temperature data for a location to estimate boilers’ current energy and fuel usage and determine the equivalent electrical boiler capacity and energy usage. A simulation model is developed using the Visual Basic Application (VBA)® and takes factors such as current boiler capacity, steam temperature and pressure, condensate, makeup water, blowdown, surface area, and flue gas information as input. Random numbers generate the hourly temperature variation for a year for discrete-event Monte Carlo Simulation. The simulation generates the hourly firing factor, energy usage, fuel usage, and CO2 emissions of boilers for a whole year, and the result compares fossil-fuel and electrical boilers. The simulated data are validated using real system data, and sensitivity analysis of the model is performed by varying the input data.

We propose efficient multiple machine learning (ML) models using specifically polynomial and logistic regression ML methods to predict the optimal design of proton exchange membrane (PEM) electrolyzer cells. The models predict eleven different parameters of the cell components for four different input parameters such as hydrogen production rate, cathode area, anode area, and the type of cell design (e.g., single or bipolar). The models fit well as we trained multiple machine learning models on 148 samples and validated the model performance on a test set of 16 samples. The average accuracy of the classification model and the mean absolute error is 83.6% and 6.825, respectively, which indicates that the proposed technique performs very well. We also measured the hydrogen production rate using a custom-made PEM electrolyzer cell fabricated based on the predicted parameters and compared it to the simulation result. Both results are in excellent agreement and within a negligible experimental uncertainty (i.e., a mean absolute error of 0.615). Finally, optimal PEM electrolyzer cells for commercial-scaled hydrogen production rates ranging from 500 to 5000 mL/min were designed using the machine learning models. To the best of our knowledge, we are the first group to model the PEM design problem with such large parameter predictions using machine learning with those specific input parameters. This study opens the route for providing a form of technology that can greatly save the cost and time required to develop water electrolyzer cells for future hydrogen production.

No accepted fluid theory exists for power extraction from unpressurized confined flow. The absence of a valid model to determine baseline uniform power extraction in confined flows creates difficulties in characterizing the coefficient of power. Currently, the primary body of research has been limited to Diffuser Augmented Wind Turbines (DAWTs) and passive fluid accelerators. Fluid power is proportional to the cube of velocity; therefore, passive acceleration is a promising path to effective renewable energy. Hypothetical models and experiments for passive accelerators yield low ideal power limits and poor performance, respectively. We show that these results derive from the misapplication of Betz’s Law and lack of a general theory for confined flow extraction. Experimental performance is due to the low efficiency of DAWTs and prior hypotheses exhibit high predictive error and continuity violations. A fluid model that accurately predicts available data and new experimental data, showing disk specific maximum CP for the confined channel at 38% of power available to disk, is presented. This is significantly lower than the 59% Betz freestream limit yielded by hypothetical models when the area ratio equals one. Experiments and their results are presented with non-DAWT accelerators, where new experimental results exceed CP limits predicted previously and correlate with the proposed predictive model.

Transportation safety, as a critical component of an efficient and reliable transportation system, has been extensively studied with respect to societal economic impacts by transportation agencies and policy officials. However, the embodied energy impact of safety, other than induced congestion, is lacking in studies. This research proposes an energy equivalence of safety (EES) framework to provide a holistic view of the long-term energy and fuel consequences of motor vehicle crashes, incorporating both induced congestion and impacts from lost human productivity resulting from injury and fatal accidents and the energy content resulting from all consequences and activities from a crash. The method utilizes a ratio of gross domestic product (GDP) to national energy consumed in a framework that bridges the gap between safety and energy, leveraging extensive studies of the economic impact of motor vehicle crashes. The energy costs per fatal, injury, and property-damage-only (PDO) crashes in gasoline gallon equivalent (GGE) in 2017 were found to be 200,259, 4442, and 439, respectively, which are significantly greater than impacts from induced congestion alone. The results from the motor vehicle crash data show a decreasing trend of EES per crash type from 2010 and 2017, due primarily in part to a decreasing ratio of total energy consumed to GDP over those years. In addition to the temporal analysis, we conducted a spatial analysis addressing national-, state-, and local-level EES comparisons by using the proposed framework, illustrating its applicability.

The electric power systems together with the entire energy sector are rapidly evolving towards a low-carbon, secure, and competitive economy facing revolutionary transformations from technical structure to economic value chain. Pathways to achieve sustainability led to the development of new technologies, accommodation of larger shares of unpredictable and stochastic electricity transfer from sources to end-users without loss of reliability, new business models and services, data management, and so on. The new technologies and incentives for local energy communities along with large development of microgrids are main forces driving the evolution of the low voltage energy sector changing the context and paradigm of rigid contractual binding between utilities and end-user customers (now progressing to flexible prosumers with generation and storage capabilities). The flexibility and operation of a prosumer can be enhanced by a non-intrusive time-frequency analysis of distorted power quality waveforms for both generation and demand at the point of common connection. Therefore, it becomes of importance to discriminate among successive quasi-steady-state operation of a given local system using only the aggregated waveforms information available in the PCC. This paper focuses on the Hilbert–Huang method with modifications such as empirical mode decomposition improved with masking signals based on the Fast Fourier Transform, Hilbert spectral analysis, and a post-processing method for separating components and their amplitudes and frequencies within distorted power signals for a low-voltage prosumer operation. The method is used for a time-frequency-magnitude representation with promising localization capabilities enabling efficient operation for prosumers.

Environmental considerations are motivating the adoption of low global warming potential refrigerants. Most of these are mildly flammable, i.e., A2L. Their susceptibility to ignition from various ignition sources is poorly understood, particularly for the stoichiometric and quiescent mixtures that are emphasized here. The viability of fifteen residential ignition sources to ignite four A2L refrigerants is considered. Tests are performed in a windowed chamber with a volume of 26 L. The refrigerants are R-32 (difluoromethane); R-452B (67% R-32, 26% R-1234yf, and 7% pentafluoroethane); R-1234yf (2,3,3,3-tetrafluoropropene); and R-1234ze (1,3,3,3-tetrafluoropropene). Two types of ignition sources are confirmed here to be viable: a resistively heated wire at 740 °C and open flames. When the refrigerant concentration was increased slowly, candle flames and butane flames extinguished before initiating any large deflagrations. Eleven other sources were not viable: a smoldering cigarette, a butane lighter, friction sparks, a plug and receptacle, a light switch, a hand mixer, a cordless drill, a bread toaster, a hair dryer, a hot plate, and a space heater. The difficulty to ignite these refrigerants in air is attributed to their long quenching distances (up to 25 mm). Under some conditions the refrigerants were observed to act as flame suppressants.

The energy cost for producing electricity via wave energy converters (WECs) is still not competitive with other renewable energy sources, especially wind energy. It is well known that energy maximising control plays an important role to improve the performance of WECs, allowing the energy conversion to be performed as economically as possible. The control strategies are usually subsequently employed on a device that was designed and optimized in the absence of control for the prevailing sea conditions in a particular location. If an optimal unconstrained control strategy, such as pseudo-spectral optimal control (PSOC), is adopted, an overall optimized system can be obtained no matter whether the control design is incorporated at the geometry optimization stage or not. Nonetheless, strategies, such as latching control (LC), must be incorporated at the optimization design stage of the WEC geometry if an overall optimized system is to be realised. In this paper, the impact of device motion and force constraints in the design of control-informed optimized WEC geometries is addressed. The aim is to verify to what extent the constraints modify the connection between the control and the optimal device design. Intuitively, one might expect that if the constraints are very tight, the optimal device shape is the same regardless of incorporating or not the constrained control at the geometry optimization stage. However, this paper tests the hypothesis that the imposition of constraints will limit the control influence on the optimal device shape. PSOC, LC and passive control (PC) are considered in this study. In addition, constrained versions of LC and PC are presented.