Solar radiation and human activity generate ubiquitous temperature gradients that could be harvested by thermoelectric generators (TEGs). However, most of these temperature gradients are in the range of very few degrees and, while TEGs are able to harvest them, the resulting output voltages are extremely small (a few hundreds of mV), and DC–DC converters are necessary to boost them to usable levels. Impedance matching between TEGs and DC–DC converter plays a fundamental role in the energy harvesting efficiency. Therefore, it is essential to determine the output power of the system in different configurations, in order to decide on the optimum TEG connection. Here, we present an electronic circuit to measure the maximum power that can be harvested with low-voltage TEGs connected to a DC–DC converter. The developed circuit is an electronic controlled load that drains the maximum current from the output of the DC–DC converter while maintaining its output voltage at the maximum allowed value. Using a mechanical set-up able to apply precise low temperature gradients between the hot and cold side of the TEGs, experimental data using different configurations of TEGs are obtained. The measured results show that, for ultra-low voltages, the TEG ensemble’s output impedance plays an important role not only in the amount of the energy scavenged, but also in the onset temperature of the energy harvesting.

In the oil and gas industry, heat exchangers are subject to loads that cause malfunctioning. These loads are divided into thermal and mechanical stresses; however, most efforts are focused on studying thermal stresses. The present work reduces mechanical stresses by mitigating pressure events in a gasket plate heat exchanger (GPHE). GPHE requires that the hot and cold stream branches have approximately the same pressure. Thus, the work focuses on controlling the pressure difference between the branches. A test bench was used to emulate, on a small scale, the typical pressure events of an oil production plant. A control valve was used in different positions to evaluate the controller. In the experiments, it was observed that the best option to control the pressure difference is to use a hydraulic pump and control valve in the flow of the controlled thermal fluid branch. The reduction in pressure events was approximately 50%. Actuator efforts are also reduced in this configuration.

This article presents the virtual commissioning of a multi-user cyber-physical laboratory for remote access (Multicyber) integrated with Industry 4.0 technologies to promote neoindustrialization. The project incorporates Digital Twins, Virtual and Augmented Reality, Artificial Intelligence, Industrial Robotics, Vision Systems, Autonomous and Collaborative Robots, Cybersecurity, and the Industrial Internet of Things. The objective of the Multicyber project is to create a demonstration center focused on advancing applied research in manufacturing processes. The laboratory features a manufacturing cell with an Autonomous Mobile Robot (AMR), a collaborative anthropomorphic manipulator (cobot), quality inspection via computer vision, a five-axis machine, and a robotic arm for machining. This study presents the simulation of three complete manufacturing routines. Plant Simulation software was used to obtain travel times for the AMR, inspection, and part removal stages, while NX software was used for detailed machining simulations with precision adjustments. The results highlight the benefits of using robotic arms in manufacturing and demonstrate how AMRs and cobots can add value through the planning and integration of routines.

Extended Kalman Filters (EKFs) are widely employed for attitude estimation using Magnetic and Inertial Measurement Units (MIMUs) in battery-powered sensing systems. In such applications, energy availability influences system operation, yet battery state information is commonly treated by external supervisory mechanisms rather than being integrated into the estimation process. This work presents an EKF-based formulation in which the battery State of Charge (SOC) is explicitly included as a state variable, allowing joint estimation of attitude and energy state within a single filtering framework. SOC dynamics are modeled using a low-complexity estimator based on terminal voltage and current measurements, while attitude estimation is performed using a Simplified Extended Kalman Filter (SEKF) tailored for embedded MIMU-based applications. The proposed approach was evaluated through numerical simulations under constant and time-varying load profiles representative of low-power electronic devices. The results indicate that the inclusion of SOC estimation does not affect the attitude estimation performance of the original SEKF, while SOC estimation errors remain below 8% for the evaluated load conditions with power consumption of approximately 0.1 W, consistent with wearable and small autonomous electronic platforms. By incorporating energy state estimation directly into the filtering structure, rather than treating it as an external supervisory task, the proposed formulation offers a unified estimation approach suitable for embedded MIMU-based systems with limited computational and energy resources.

Accurate classification of two-phase gas-liquid flows is essential for reliable monitoring and control in industrial systems. Although several optical and machine-learning studies have been reported, most still focus on single wavelengths or isolated feature domains, lacking an integrated methodology that links sensor physics, feature extraction, and machine learning. This study addresses this gap by proposing the Multi-Feature Multi-Domain (MFMD) framework and its derived Optimal Feature and Method Selection (OFMS) guideline, providing a unified methodology to achieve high accuracy and model parsimony. The methodology was experimentally validated in horizontal air–water flows (five patterns: bubble, plug, slug, stratified, and wavy) using collimated near-infrared (NIR) sensors operating at 850, 980, and 1310 nm, selected for their distinct absorption sensitivities to interfacial, hybrid, and volumetric characteristics. Features were extracted from the time (Probability Density Function, PDF), frequency (Power Spectral Density, PSD), and time-frequency (Discrete Wavelet Transform, DWT) domains. This multi-domain feature set was processed by comparing feature selection (Recursive Feature Elimination, RFE) with transformation methods (Linear Discriminant Analysis, LDA, and Principal Component Analysis, PCA). Classification was performed using Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF), and Artificial Neural Networks (ANN). Results demonstrate that multi-wavelength fusion is essential for reliable flow discrimination. The RFE-based approach achieved 100% accuracy when combining all data sources, while supervised LDA significantly outperformed PCA by enhancing class separability. The resulting OFMS guideline consolidates these findings into a practical tool for selecting the most effective feature–classifier strategy, whether prioritizing maximum accuracy or a more parsimonious machine learning model.

Accurate classification of two-phase gas-liquid flows is critical for the optimization and safety of industrial processes. This work presents the Multi-Feature Multi-Domain (MFMD) methodology, a systematic framework for flow pattern classification in horizontal air-water flows. Five representative patterns-bubble, plug, slug, stratified, and wave-were identified using collimated, non-intrusive nearinfrared (NIR) optical sensors. The approach relies on the fusion of data from three discrete laser wavelengths (850 nm, 980 nm, and 1310 nm), selected for their distinct absorption characteristics in water and sensitivity to interfacial, hybrid, and volumetric features. Features were extracted from the time (Probability Density Function, PDF), frequency (Power Spectral Density, PSD), and time-frequency (Discrete Wavelet Transform, DWT) domains. We compared an interpretable feature selection strategy, Recursive Feature Elimination (RFE), with two transformation methods: supervised Linear Discriminant Analysis (LDA) and unsupervised Principal Component Analysis (PCA). Classification was performed using Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Networks (ANN). Results show that combining sensors at the suggested wavelengths is crucial for high performance. The RFE approach achieved 100% accuracy under the evaluated conditions when merging features from all data sources. Moreover, efficient models were obtained by fusing signals across wavelengths regardless of the reduction method. This work culminates in the Optimal Feature and Method Selection (OFMS) guide, which synthesizes the findings into a practical tool for selecting effective strategies while balancing maximum accuracy and model parsimony.