• Energy Research
• 2021-2025close
• NL close
• CA close
• IN close
• University of Waterloo close

Despite increasing evidence that large northern lakes are rapidly changing due to climate change, descriptive baseline studies of their physicochemical properties are largely lacking, limiting our ability to detect or predict change. This study represents a comprehensive scientific assessment of the limnology of Yukon’s largest lake: Lhù’ààn Mân’ (Kluane Lake), an important waterbody for local and First Nation communities, and key habitat for trout and salmon. Water sample and instrument data generated throughout 2015 describe distinct regions within the lake and their respective seasonal variability. A deep, glacially-influenced southern basin was characterized by cold, turbid, poorly stratified, unproductive, and nutrient-poor conditions; a shallower northwestern region (Tthe Kaala Daagur (Brooks/Little Arm)) was warmer, fully mixed, and more productive; a northeast region (’Ùha K’ènji (Talbot/Big Arm)) was clear and stratified with intermediate depth, temperature, productivity, and nutrient concentrations; and a central region had intermediate physicochemical conditions relative to the other three. This variability demonstrates the need for adequate spatial (within lake) and temporal (between seasons) monitoring of large northern lakes. In 2016, glacier recession within the watershed resulted in diversion of the lake’s primary inflow (‘A’ą̈y Chù’ (Slims River)). Our results, when used together with Indigenous knowledge, form a historical reference that enables assessments of the potential ecological consequences to Lhù’ààn Mân’.

The transformation of the electricity market structure from a monopoly model to a competitive market has caused electricity to be exchanged like a commercial commodity in the electricity market. The electricity price participants should forecast the price in different horizons to make an optimal offer as a buyer or a seller. Therefore, accurate electricity price prediction is very important for market participants. This paper investigates the monthly/seasonal data clustering impact on price forecasting. To this end, after clustering the data, the effective parameters in the electricity price forecasting problem are selected using a grey correlation analysis method and the parameters with a low degree of correlation are removed. At the end, the long short-term memory neural network has been implemented to predict the electricity price for the next day. The proposed method is implemented on Ontario—Canada data and the prediction results are compared in three modes, including non-clustering, seasonal, and monthly clustering. The studies show that the prediction error in the monthly clustering mode has decreased compared to the non-clustering and seasonal clustering modes in two different values of the correlation coefficient, 0.5 and 0.6.

EnvironmentAsia, 16, 1, 59-72

Supercapacitors are fast-charging energy storage devices of great importance for developing robust and climate-friendly energy infrastructures for the future. Research in this field has seen rapid growth in recent years. Therefore, consistent reporting practices must be implemented to enable reliable comparison of device performance. Although several studies have highlighted the best practices for analysing and reporting data from such energy storage devices, there is yet to be an empirical study investigating whether researchers in the field are correctly implementing these recommendations, and which assesses the variation in reporting between different laboratories. Here, we address this deficit by carrying out the first interlaboratory study of the analysis of supercapacitor electrochemistry data. We find that the use of incorrect formulae and researchers having different interpretations of key terminologies are the primary causes of variability in data reporting. Furthermore, we highlight the more significant variation in reported results for electrochemical profiles showing non-ideal capacitive behaviour. From the insights gained through this study, we make additional recommendations to the community to help ensure consistent reporting of performance metrics moving forward.

As climate change becomes a more pressing issue, there is increasing research being performed to investigate greenhouse gas reduction strategies such as carbon capture technology and sustainable fuels. To this end, chemical-looping (CL) technologies such as chemical-looping combustion (CLC) and chemical-looping gasification (CLG) are being explored to improve the sustainability of energy generation. In addition, biomass has recently been studied as a renewable fuel for this process. Various works into CL technologies have been performed, but further investigation is required in the areas of process design and control in order to verify whether this technology can feasibly be implemented for energy generation and to determine the most effective implementation strategies for CL processes. The aim of this thesis is to determine reactor design and control strategies which can be implemented to improve the energy generation, gasification efficiency, and carbon capture effectiveness of packed bed CLC and CLG. In this work, optimal control strategies for large-scale packed bed CLC are obtained by implementing nonlinear model predictive control (NMPC). For NMPC, a multiscale model is developed to simulate the plant behaviour and validated against multiple sources of experimental data, while a pseudo-homogeneous model is used as the internal NMPC model to reduce computational costs for implementation of feedback control. By manipulating the inlet air and inert gas fluxes in the oxidation stage, and the inlet fuel flux in the reduction stage, the outlet temperature and CO2 selectivity could be controlled in order to improve the energy generation and carbon capture effectiveness of the process. Then, a reactor network model was proposed to simulate packed bed biomass-fueled CLG and CLC, and validated using experimental data under both CLG and CLC conditions. Using this model, a variety of oxygen carrier (OC) bed lengths and locations were assessed to evaluate the resulting impact on the performance of CLG and CLC. For CLG, ...

Different types of classifiers for acoustic partial discharge (PD) pattern classification have been widely discussed in the literature. The classifier performance mainly depends on the measurement conditions (location and type of the PD, acoustic sensor position and frequency response) as well as extracted features. Recent research posits that features extracted by singular value decomposition (SVD) can exhibit the natural characteristics and energy contained in the signal. Though the technique by itself is not novel, in this paper, SVD is employed for PD classification in a revised way starting from data arrangement in Hankel form, to embedding the hypergraph-based features and finally to extracting the required set of optimal features. The algorithm is tested for various measurement conditions that include the influences of various PD locations and oil temperatures. The robustness of the algorithm is also tested using noisy PD signals. Experimental results show the proposed feature extraction method supremacy.

Two different classes of hairy self-suspended nanoparticles in the melt state, polymer-grafted nanoparticles (GNPs) and star polymers, are shown to display universal dynamic behavior across a broad range of parameter space. Linear viscoelastic measurements on well-characterized silica-poly(methyl acrylate) GNPs with a fixed core radius (Rcore) and grafting density (or number of arms f) but varying arm degree of polymerization (Narm) show two distinctly different regimes of response. The colloidal Regime I with a small Narm (large core volume fraction) is characterized by predominant low-frequency solidlike colloidal plateau and ultraslow relaxation, while the polymeric Regime II with a large Narm (small core volume fractions) has a response dominated by the starlike relaxation of partially interpenetrated arms. The transition between the two regimes is marked by a crossover where both polymeric and colloidal modes are discerned albeit without a distinct colloidal plateau. Similarly, polybutadiene multiarm stars also exhibit the colloidal response of Regime I at very large f and small Narm. The star arm retraction model and a simple scaling model of nanoparticle escape from the cage of neighbors by overcoming a hopping potential barrier due to their elastic deformation quantitatively describe the linear response of the polymeric and colloidal regimes, respectively, in all these cases. The dynamic behavior of hairy nanoparticles of different chemistry and molecular characteristics, investigated here and reported in the literature, can be mapped onto a universal dynamic diagram of f/[Rcore3/ν0)1/4] as a function of (Narmν0f)/(Rcore3), where ν0 is the monomeric volume. In this diagram, the two regimes are separated by a line where the hopping potential ΔUhop is equal to the thermal energy, kBT. ΔUhop can be expressed as a function of the overcrowding parameter x (i.e., the ratio of f to the maximum number of unperturbed chains with Narm that can fill the volume occupied by the polymeric corona); hence, this crossing is shown to occur when x = 1. For x > 1, we have colloidal Regime I with an overcrowded volume, stretched arms, and ΔUhop > kBT, while polymeric Regime II is linked to x < 1. This single-material parameter x can provide the needed design principle to tailor the dynamics of this class of soft materials across a wide range of applications from membranes for gas separation to energy storage.

AbstractRecent development in using wind turbines for urban areas results in inserting turbines inside buildings. As buildings' walls may act as a duct for the turbine, this study focuses on a ducted wind turbine with a fixed duct geometry. A method is organized for achieving the improved generated power and the wind speed augmentation with fixed geometry of duct regardless of the type of the turbine, which is the aim of building designers. Using a porous disc (PD) instead of a wind turbine rotor makes the study cost and time effective. PDs within a duct help estimate any given duct's maximum available power extraction capability. In addition, experimental and numerical tests examine the effect of PDs solidity on the performance of diffuser augmented wind turbines and the corresponding economic analysis. Both experimental and numerical results agree that the power coefficient highly depends on the solidities of the PD. The power coefficient of a ducted PD with a solidity of 0.3 is augmented by up to 30%. Nevertheless, in some cases, employing a duct can contribute to the power reduction if the solidity exceeds a critical value. A smoke visualization technique helps vortex study. Economic assessment of a ducted turbine for three scenarios belonging to Germany and Italy shows a 15.3% decline in cost per electricity production. The payback period decreases by 3.42 years, 7.68 months, and 6.36 months for Scenarios 1, 2, and 3.