In wells that operate by electrical submersible pump (ESP), the use of automation tools becomes essential in the interpretation of data. However, the fact that the wells work with automated systems does not guarantee the early diagnosis of operating conditions. The analysis of amperimetric charts is one of the ways to identify fail conditions. Generally, the analysis of these histographics is performed by operators who are often overloaded, generating a decrease in the efficiency of observing the well operating conditions. Currently, technologies based on machine learning (ML) algorithms create solutions to early diagnose abnormalities in the well’s operation. Thus, this work aims to provide a proposal for detecting the operating conditions of the ESP pump from electrical current data from 24 wells in the city of Mossoró, Rio Grande do Norte state, Brazil. The algorithms used were Decision Tree, Support Vector Machine, K-Nearest Neighbor and Neural Network. The algorithms were tested without and with hyperparameter tuning based on a training dataset. The results confirm that the application of the ML algorithm is feasible for classifying the operating conditions of the ESP pump, as all had an accuracy greater than 87%, with the best result being the application of the SVM model, which reached an accuracy of 93%.

A statistical optimization of Reactive Red 141 (RR141) removal by heterogeneous photo–Fenton reaction using ZnFe₂O₄ particles was performed. ZnFe₂O₄ oxide was synthesized by microwave-assisted solvothermal route and characterized by X–ray diffraction, Fourier transform infrared spectroscopy, and N₂ adsorption–desorption isotherms. This material was used as a catalyst for the removal of RR141 dye from aqueous solution. Statistical optimization for the RR141 removal was performed using a 2⁴⁻¹ fractional experimental design, followed by the response surface methodology. Effects of pH, reaction time, dye concentration, and H₂O₂ concentration were evaluated. ZnFe₂O₄ particles presented a mesoporous structure with surface area of 65 m² g⁻¹. It was found that the more adequate conditions for the RR141 dye removal were pH 2.0, reaction time of 60 min, dye concentration of 60 mg L⁻¹, and H₂O₂ concentration of 10 mM. In these conditions, 90% of color removal was achieved. The results demonstrate that the ZnFe₂O₄ oxide is an efficient catalyst for the removal of RR141dye from aqueous solution through the heterogeneous photo–Fenton reaction.

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O presente artigo teve como objetivo analisar os parâmetros termodinâmicos, dados calorimétricos e volumétricos, do etanol (CH2COH), uma substância amplamente utilizada na indústria. A metodologia incluiu a coleta de dados de artigos usando ferramentas NIST e Periódicos Capes, seguida pela inserção no Excel para cálculos de predição e estatística. Softwares como Peng-Robinson e DDB foram utilizados para obter dados experimentais. O aplicativo Origin foi empregado para calcular erros e discrepâncias em relação às referências. Além disso, modelos como a equação de Antoine, equação Rackett e regressão polinomial foram usados para determinar coeficientes e calcular capacidades caloríficas e volumétricas. Os resultados indicaram boa adaptação dos modelos propostos aos dados experimentais, com erros médios variando entre 0,039% e 22,8%. Concluiu-se que a determinação dos aspectos termodinâmicos é crucial para a indústria e formação acadêmica, prevendo o comportamento de substâncias químicas.