Francirley Paz da Silva

This study performs a computationally reproducible multicriteria reanalysis of a four-criterion, UASB-oriented source matrix reported in a 2015 MSc dissertation. Two author-generated numerical scenarios, each containing six WWTP alternatives, are treated as constructed cases rather than verified operating-plant data. The retained criteria are UASB presence, a source-defined flow variable with an unresolved physical unit, CH₄ concentration, and H₂S concentration. Source-rule coding was rechecked, continuous values were ranked with TOPSIS and a weighted sum model (WSM), and sensitivity was examined through threshold perturbation, criterion-weight variation, and an explicit flow-variable test. Four coding inconsistencies were corrected. Under equal weights, WWTP6 ranked first in Case 1 and WWTP1 in Case 2, with 93.3% TOPSIS–WSM pairwise agreement in both cases. Multiplying or dividing the complete flow column by 24 left TOPSIS and WSM scores and ranks unchanged to numerical precision; removing flow preserved the first-ranked alternative in both scenarios but changed some intermediate positions. The results characterize only the documented four-criterion, UASB-oriented matrix and do not estimate absolute gas production, energy output, or economic feasibility.

Carburization is a critical degradation mechanism in HP steel alloys used in pyrolysis furnaces, affecting structural integrity and operational reliability. This study evaluates the feasibility of combining ultrasonic A-scan signal processing and Fourier Transform–based spectral descriptors with machine learning to classify four carburization stages in HP steel tube specimens. A total of 160 A-scan waveforms were acquired under controlled laboratory conditions, each containing 2500 sampled points. Frequency-domain features derived from the Discrete Fourier Transform were used as inputs to decision-tree and k-nearest neighbor classifiers. Model performance was assessed using confusion matrices, accuracy, precision, recall, F1-score, and ROC-AUC in a multiclass framework. Ensemble Bagged Trees achieved the highest within-dataset classification accuracy (>99%) under the adopted cross-validation framework, whereas KNN showed lower classification performance despite higher inference speed. The results indicate strong separability among carburization stages under the evaluated acquisition conditions. Given that multiple acquisitions originated from the same tube specimens, the findings should be interpreted as a feasibility-level assessment. Further validation using independent specimens and expanded datasets is required to assess generalization under industrial conditions.

Educação e tecnologia: ensino da automação por meio do desenvolvimento de um protótipo didático/ Education and technology: teaching automation through the development of a teaching prototype