The detection and diagnosis of power quality (PQ) issues are critical topics in the electrical power system’s generation, transmission, and distribution. Nonlinear loads, power electronic converters, system malfunctions, and switching events are the most common causes of PQ issues. The main purpose of this work is to use an artificial intelligence (AI) technique based on automatic feature extraction to discover and identify PQ difficulties. The AI technique consists of a dedicated architecture of the Long Short-Term Memory (LSTM) network, which is a special type of Recurrent Neural Network (RNN).

Eggshell waste is a biodegradable residue composed of more than 90% CaCO₃, which makes it a great candidate to be converted into functional materials for diverse applications. Herein, domestic hen eggshell waste was dried and calcined in muffle under air at different temperatures (300 to 900 °C) and times (1 or 3 h) to achieve distinctive calcium species, compositions, and solid-phase transformations. The crystal structures achieved were characterized by X-ray diffraction (XRD), evidencing the transformation from CaCO₃ (calcite) to CaO (lime) at high temperatures and the formation of Ca(OH)₂ (slaked lime) due to the hydration of CaO facilitated by ambient water molecules. Considering this preliminary results, prepared solids could be useful as low-cost and metal-free (unsupported) catalysts for different heterogeneous reactions, such as the transesterification of vegetable oil and/or glycerol, where the presence of basic sites are needed.

Mechanochemical activation, by means of high-energy ball milling, was applied to CeO₂ as a strategy to enhance its physicochemical properties. Different milling times were screened to evaluate their effect on ceria. The fluorite-type structure of cerianite was maintained in all cases, no matter the amount of energy introduced by the milling process, as observed via X-ray diffraction (XRD). A decrease in crystallite sizes and a consequent increase in specific surface area (S_BET) were observed through XRD and N₂ sorption (BET method). Pore diameters and total pore volumes were also in line with the duration of CeO₂ milling. Moreover, redox properties and oxygen mobility studied through H₂ temperature-programmed reduction (H₂- TPR) showed an increase in reducibility with milling time, including signals of both bulk and surface ceria, due to the greater number of defects and/or oxygen vacancies achieved by mechanochemical activation. The obtained features could play an essential role in terms of metal–support interaction, reactant adsorption and/or oxygen supply during catalytic reactions. Thus, high-energy ball milling is a useful, simple and green method for the design of materials with catalytic applications.