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The emerging reverse microemulsion technique offers a precise nanofabrication method for intricate nanostructures. Surfactant provides a stable microemulsion, wherein precursors are dissolved. Subsequent resin assisting results in finely patterned nanostructures. This study focuses on creating copper nanoparticles evenly dispersed on titania using varying copper loadings (5%, 10%, and 15% wt) and Brij-35 as the surfactant. Characterization via XRD, FESEM, EDAX, HRTEM, and physisorption reveals CuO/TiO2 nanospheres with different copper loadings. The catalysts were used for the liquid-phase selective oxidation of toluene, producing benzoic acid as the major product. The CuO/TiO2 nanocomposite displayed exceptional performance, with the highest toluene conversion rate (97.02%) and benzoic acid selectivity (98.14%) compared to other nanocomposites. The catalyst's stability and recyclability across five cycles were attributed to the strong copper oxide-titania interaction. The findings suggest this nanocomposite material as a promising candidate for catalytic applications, offering high selectivity and consistent toluene conversion rates. [ABSTRACT FROM AUTHOR]

The rapid growth of technology and uneven demographic population distribution has led to uneven distribution of electrical load demand across the three phases of the electrical distribution network. It has resulted in a steep rise in voltage unbalance scenarios. Such occurrences may have a lasting negative impact on the distribution system in the form of energy loss and extra costs for generation. This paper proposes a Rao optimization-based demand-side management (DSM) model to help the distribution utilities mitigate these issues. The proposed methodology has been designed to effectively distribute the load demand pattern in each node connected across each phase. The proposed DSM methodology has been designed keeping in view to discard any possibility of the need for lateral/load re-phasing. A day ahead load scheduling format has been adopted to design this proposed methodology. It has been implemented on IEEE European 906bus low voltage distribution network. Further, to verify the effectiveness of the proposed methodology, Gauss implicit Zbus load-flow method is performed upon the test system. The test system considered is IEEE European 906bus low voltage distribution system. The entire methodology is designed in the MATLAB 2018a platform. The implementation of the proposed methodology has reduced the phase voltage unbalance rate in the original test system by ~ 50%. Following the application of the suggested technique, the peak to average ratio in the original test system reduced by 6.22%, 38.7%, 3.22%, and 26.46% in each of the three phases, including the total load consumption of the end users. [ABSTRACT FROM AUTHOR]

This paper proposes adaptive voltage control strategies for a three-wire dynamic voltage restorer based on a self-adaptive fuzzy neural network for estimation of fundamental weight components and optimal extreme learning machine control for smooth voltage regulation. The SAFNN overcomes the sequential tuning process of classical ANFIS controllers by simultaneously optimizing the structure and parameters of unknown system dynamics. The construction of SAFNN does not require control base rules in the initial phase and this enhances the learning capability to track the approximated voltage error. The proposed SAFNN employs the linear transformation of the input signal to optimize the required fuzzy rules and speed up the online learning process to achieve the higher accuracy of the control results. The meta-algorithms whale optimization algorithm and Harris hawks optimization is employed for online dynamic behavior identification. The SAFNN-WOA optimizes the membership function to estimate direct and quadrature axis voltage for prediction performance in terms of fast-tracking predefined signals, fewer oscillations, and voltage compensation ability. The dc-link voltage is accurately estimated by employing OELM-HHO and provides improved power quality performance during voltage disturbances. The OELM has the inherent feature of randomly generating the initial weights which effectively evaluates the output weight during the online training procedure and enhances the learning rate. The statistical tools MSE, RMSE, ME, SD, and R during the training stage were evaluated as 168.9052, 12.9964, − 5.5524e−07, 12.9965 and during the testing stage the value are 168.9611, 12.9985, − 0.11566, 12.9986 and 0.78654. These statistical assessment values confirm the goodness-of-fit of the predictive model. The result shows that the proposed hybrid controls SAFNN-WOA and OELM-HHO-based DVR significantly achieves the prediction accuracy for both training and test dataset. Nevertheless, the proposed intelligent DVR model outperforms the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]