The electric power sector is continuously embracing new approaches to improve the reliability and efficiency of the energy system while addressing the growing energy demand and associated technical problems. Recently, the development of artificial intelligence (AI) has provided researchers with powerful tools to deal with various challenges in the power system. One significant advancement in this field is the Voltage Source Converter (VSC), which leverages advancements in power electronics and semiconductor technology. VSC holds immense potential for realizing smart grids, integrating renewable energy, and enabling HVDC transmission systems. Traditionally, the manual tuning of Proportional-Integral (PI) controllers for VSCs depends on a trial-and-error approach or the experience of design engineers, which does not show optimal performance. This process becomes even more complex when dealing with multiple grids, such as VSC-based Multi-Terminal DC (MTDC) grids. In this research article,
Predictive model of oxaliplatin-induced liver injury based on artificial neural network and logistic regression
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Blockage of culverts causes reduction in hydraulic capacity and is one of the main contributors to trigger urban flooding. However, the highly non-linear nature of debris interaction during the flood and lack of blockage-related data from actual flooding events make conventional numerical modelling almost impossible. Literature investigating blockage phenomena reports blockage as a complex hydraulic process, which suggests exploring adaptive solutions using latest technologies. In this context, motivated by the success of data-driven algorithms, in this article, four data driven models (i.e., K-NN, ANN, SVR, 1D-CNN) are implemented to predict the hydraulic blockage at culverts. A new numerical Hydraulics-Lab Blockage Dataset (HBD) is established from a series of lab-scale hydraulic experiments. From the experimental investigations, the ANN model was reported as the best with a R 2 score of 0.95. A potential use-case of presented research for real-world application is also discussed to
An artificial neural network (ANN) was employed to predict biodiesel yield through microwave-assisted esterification of palm fatty acid distillate (PFAD) oil over TiO2‒ZnO mesostructured catalyst. The experimental data of biodiesel content (%) was carried out via changing three input factors (i.e. methanol:PFAD molar ratio, catalyst concentration, and reaction time). The results indicated that ANN is an appropriate approach for modeling and optimizing fatty acid methyl ester (FAME) yield performed over the microwave-assisted esterification process. The network was trained by five different algorithms (i.e. batch backpropagation (BBP), incremental backpropagation (IBP), Levenberg‒Marquardt (LM), genetic algorithm (GA), and quick propagation (QP)). The evaluation disclosed that the QP algorithm gave the least root mean squared error (RMSE), absolute average deviation (AAD), and the highest determination coefficient (R2) for both training and testing data groups. The confirmation test