No abstract

A gas-lift oil well is an oil well that uses highpressure natural gas to improve oil production. This type of oil well is unstable if not enough gas is available for operation, when a phenomenon called slugging may occur. Besides slugging, other relevant challenges of the problem are unknown internal state and well parameters and unmeasurable disturbances. In this work, a nonlinear model predictive controller (NMPC) for this type of oil well was developed. To build this NMPC, a parameter estimation was performed with data gathered by a four-factor design of experiments. Using the internal states generated during the parameter estimation, a machine learning algorithm was trained to infer the internal state from sensor data. The NMPC was tested against slugging, set point changes, and unmeasurable disturbances and compared with results from both a perfect NMPC and an NMPC with states estimated by extended Kalman filter. Estimation of the internal states by the neural network was accurate enough to allow for proper control. The NMPC actions were aggressive but successfully curbed slugging and kept the controlled variables around their set points. The main contributions of this work are developing a methodology for state inference supported by neural networks and presenting a pair of nonlinear models for model mismatch studies.

(designated as SiAlNb and SiAlTi, respectively), obtained using the sol-gel method were used to immobilize chloroperoxidase. Hydrogen peroxide was quantified using potassium hexacyanoferrate(II) as a redoxmediator and amperometric measurements at 0.0 V vs. Ag/AgCl/Cl À (3 M). The SiAlTi biosensor presented higher sensitivity than the SiAlNb biosensor, however, the first one did not present a good response regarding time. The developed biosensor using the SiAlNb mixed oxide provided good signal levels, good linearity, good stability (retaining approximately 70% of its original response after 6 weeks of usage), a low detection limit (3 mM), good sensitivity, a suitable working range (from 4 to 19 mM), fast response and good repeatability. The recovery of the amperometric method for the detection of hydrogen peroxide in synthetic samples was approximately 100 AE 2%, and for Listerine® Whitening PreBrush Rinse samples fortified with 1, 2 and 3% (v/v) of hydrogen peroxide, it was 100 AE 3%.

The COVID-19 global pandemic is still affecting the world, even considering vaccine applications in most countries, especially due to new variant outbreaks and the possibility that they may present immunological escape. Therefore, mass testing is relevant in infection monitoring and restriction policy evaluations, making low-cost and easy-to-use tests essential. Serological tests might also be useful in monitoring immune response after vaccination. The present work proposes a less-expensive ELISA test route, using a scanner instead of a spectrophotometer and using the saturation of the image as a surrogate for the absorbance of each sample. Images from multiple experiments were selected and correlated with their spectrophotometric absorbance. ELISA plate images were digitized by a simple table scanner and, then, preprocessed using Hue, Saturation, Value (HSV) transformation, aiming to determine which correlates best with the obtained absorbance. Saturation correlated better with absorbance, and the experiments presented R2 consistently above 90% between absorbance and the square of saturation. The new methodology showed similar accuracy, sensitivity, and specificity to the original method, all metrics ranging between 90% and 100% in most cases. An open-source software was also designed to analyze the images, perform the diagnosis, and generate reports.