Rising electricity demand and the growing integration of renewables are intensifying congestion in transmission grids. Grid topology optimization through busbar splitting (BuS) and optimal transmission switching can alleviate grid congestion and reduce the generation costs in a power system. However, BuS optimization requires a large number of binary variables, and analyzing all the substations for potential new topological actions is computationally intractable, particularly in large grids. To tackle this issue, we propose a set of metrics to identify and rank promising candidates for BuS, focusing on finding buses where topology optimization can reduce generation costs. To assess the effect of BuS on the identified buses, we use a combined mixed-integer convex-quadratic BuS model to compute the optimal topology and test it with the non-linear non-convex AC optimal power flow (OPF) simulation to show its AC feasibility and generation cost reduction compared to the AC-OPF simulations. By testing and validating the proposed metrics on test cases of different sizes, we show that they are able to identify busbars that reduce the total generation costs when their topology is optimized. Thus, the metrics enable effective selection of busbars for BuS, with no need to test every busbar in the grid, one at a time.
Urbanization is a suspected contributor to insect declines worldwide. As the world becomes more urban, it is critical that we understand how urbanization affects insect abundance and biomass. We explore how within-city variation in urban land use affects total insect biomass and herbivorous insect biomass, with a focus on zoning and two metrics of vegetative productivity: canopy cover and normalized difference vegetation index (NDVI). We ask whether energy flows through urban systems as we expect it to flow in non-urban systems, such that more vegetative productivity predicts more herbivorous insect biomass and more total insect biomass. We found that zoning was a good predictor of total insect biomass; commercial areas consistently had the lowest insect biomass, while parks and greenways each had the highest biomass in different seasons. We unexpectedly found that herbivore biomass declined in areas with higher NDVI, particularly during the late summer in certain zoned land uses. Zoning may be a decent initial predictor of insect biomass that urban planners can use to help identify and prioritize areas for insect conservation in cities. However, given that we did not find a positive link between vegetative productivity (NDVI) and insect biomass, more research is needed to determine how habitats within different zoned land uses modulate the flow of energy from vegetation to insects and to higher trophic levels.
Hydrogen (H2) interfacial tension (IFT) predictive models assist in optimizing underground hydrogen storage (UHS), thereby improving its efficiency, safety, and scalability. Standalone deep learning (DL) and hybrid deeplearning-optimization (HDL) algorithms were developed to predict H2 interfacial tension (H2 IFT). 2676 laboratory data points, compiled from published studies, form the dataset evaluated. 27 anomalous data points (outliers) were removed from the training data subset. K-fold cross-validation was then applied to provide statistically robust validation of the trained models. To assess reproducibility, each algorithm was trained, validated, and tested five times. The results demonstrate that HDL algorithms significantly improve H2 IFT prediction performance and reproducibility compared to standalone DL models. The long-short term memory - cuckoo optimization algorithm (LSTM-COA) model achieved the lowest RMSE values with training (0.6029 mN/m), validation (0.9851 mN/m), and testing (1.1527 mN/m) subsets. The LSTM-COA model exhibited the best balance between prediction precision, uncertainty, and model complexity, confirming its robustness. The model's generalizability was rigorously validated through a blind test on an independent dataset, achieving an R2 of 0.916 and an AARE of 1.85%. Furthermore, it demonstrated superior predictive accuracy compared to an established empirical correlation, reducing the average absolute relative error by over 50% compared to the correlation's result. Evaluation of the LSTM-COA model applied to various gas systems revealed a weaker performance for the H2-CO2 systems, but prediction errors remained within acceptable limits. Feature influence analysis identified pressure as the most influential input variable and salinity as the least influential. The results highlight that the LSTM-COA model's reliable H2 IFT prediction performance can be beneficial in optimizing the performance of underground hydrogen storage systems and reducing their operational uncertainties.
In this paper, we study the well-posedness and boundary stabilization of the initial-boundary value problem for the complex Ginzburg-Landau (CGL) equation on a finite interval. First, we establish a local well-posedness theory for the open loop model in L^2-based fractional Sobolev spaces in the case of Dirichlet-Neumann type inhomogeneous mixed boundary conditions. This local well-posedness result is based on linear estimates derived by using the weak solution formula obtained via the unified transform (also known as the Fokas method). Next, we study the global well-posedness properties of the open loop model in presence of inhomogeneous boundary conditions. Then, we turn our attention to the rapid boundary feedback stabilization problem and design a nonlocal controller which uses a finite number of Fourier modes of the state of solution. This design relies on the fact that solutions of the CGL equation can be separated into a slow, finite-dimensional component and a rapidly decaying tail, with the former primarily governing long-term behavior. We determine the necessary number of modes required to stabilize the system at a specified rate. Additionally, we identify the minimum number of modes that ensure stabilization at an unspecified decay rate. These theoretical results are validated by numerical simulations. The spatiotemporal estimates established in the first part of the paper are also employed to obtain local solutions of the controlled system. The existence of global energy solutions follows from stabilization estimates, while uniqueness follows from the uniqueness of an associated initial-boundary value problem with homogeneous boundary conditions whose solutions are in correspondence with the solutions of the original system through a bounded invertible Volterra-type integral transform on Sobolev spaces.
Although several chemical processes have been developed to convert waste polyolefins, their long carbon chain and high viscosity when molten result in transport-limiting kinetics. In this work, the potential of compressed CO2 as a reaction medium was evaluated for the catalytic hydrocracking of polyethylene (PE) into hydrocarbon fuels using Ru-loaded zeolites. The results showed that the addition of compressed CO2 (3 MPa H-2 + P CO2 = 6 MPa) to the catalytic hydrocracking of PE led to a relative increase of 27% and 64% in PE conversion and liquid product yield, respectively, compared to the H-2-only control reaction (3 MPa H-2 pressure) using a 5 wt % Ru/H-beta catalyst at 200 degrees C for 4 h. Similar trends were observed with a 5 wt % Ru/H-Y catalyst. However, the presence of compressed CO2 did not enhance the hydrocracking performance when a less viscous substrate (i.e., n-hexadecane), relative to the PE melt, was used at 200 degrees C for 1 h with 5 wt % Ru/H-Y. These results demonstrate the potential of compressed CO2 as a reaction medium to enhance the chemical transformation (e.g., hydrocracking and hydrogenolysis) of highly viscous molten plastic wastes.
