This paper describes all the procedures and methods currently used at UPV (Universitat Politécnica de Valencia) and UJI (University Jaume I) for the development and use of sensors for multi-phase flow analysis in vertical pipes. This paper also describes the methods that we use to obtain the values of the two-phase flow magnitudes from the sensor signals and the validation and cross-verification methods developed to check the consistency of the results obtained for these magnitudes with the sensors. First, we provide information about the procedures used to build the multi-sensor conductivity probes and some of the tests performed with different materials to avoid sensor degradation issues. In addition, we provide information about the characteristics of the electric circuits that feed the sensors. Then the data acquisition of the conductivity probe, the signal conditioning and the data processing including the device that have been designed to automatize all the measurement process of moving the sensors inside the channels by means of stepper electric motors controlled by computer are shown in operation. Then, we explain the methods used for bubble identification and categorization. Finally, we describe the methodology used to obtain the two-phase flow information from the sensor signals. This includes the following items: void fraction, gas velocity, Sauter mean diameter and interfacial area concentration. The last part of this paper is devoted to the conductance probes developed for the annular flow analysis, which includes the analysis of the interfacial waves produced in annular flow and that requires a different type of sensor.

The control of ventilation systems is often performed by automatic algorithms which often do not consider the future evolution of the system in its control politics. Digital twins allow system forecasting for a more sophisticated control. This paper explores a novel methodology to create a Machine Learning (ML) model for the predictive control of a ventilation system combining Computational Fluid Dynamics (CFD) with Artificial Intelligence (AI). This predictive model was created to forecast the temperature and humidity evolution of a ventilated room to be implemented in a digital twin for better unsupervised control strategies. To replicate the full range of annual conditions, a series of CFD simulations were configured and executed based on seasonal data collected by sensors positioned inside and outside the room. These simulations generated a dataset used to develop the predictive model, which was based on a Deep Neural Network (DNN) with fully connected layers. The model’s performance was evaluated, yielding final average absolute errors of 0.34 degrees Kelvin for temperature and 2.2 percentage points for relative humidity. The presented results highlight the potential of this methodology to create AI-driven digital twins for the control of room ventilation.

Forecasting influent flow in Wastewater Treatment Plants (WWTPs) is critical for managing operational risks during flash floods, especially in Spain’s Mediterranean coastal regions. These facilities, essential for public health and environmental protection, are vulnerable to abrupt inflow surges caused by heavy rainfall. This study proposes a data-driven approach combining historical flow and rainfall data to predict short-term inflow dynamics. Several models were evaluated, including Random Forest, XGBoost, CatBoost, and LSTM, using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²). XGBoost outperformed the others, particularly under severe class imbalance, with only 1% of the data representing rainfall events. Hyperparameter tuning and input window size analysis revealed that accurate predictions are achievable with just 14 days of training data from a 10-year (2012–2022) dataset sourced from a single WWTP and on-site weather station. The proposed framework supports proactive WWTP management during extreme weather events.

CuCrO₂ (mcconnellite) was synthesized using both the solid-state method and microwave dielectric firing. It was characterized as a novel black ceramic pigment for use in various industrial glazes. For the first time, the application of mcconnellite (CuCrO₂) and its coloured glazes as selective solar absorbers (SSA) for integral ceramic solar collectors has been reported. The addition of quartz or anatase as colour modifiers was investigated to prevent the bluing of the pigment in Zn-containing glazes, a phenomenon associated with the exsolution of copper. Furthermore, doping with lanthanide oxides was explored to address two key challenges: controlling the formation of pinhole defects in porcelain glazes, which are linked to the destabilization of Cu⁺, and adjusting the IR cut-off wavelength to improve its performance as SSA.

Aeration is one of the most energy-intensive operations in wastewater treatment plants, with its efficiency strongly affected by the presence of surfactants. This study investigates the impact of Sodium Dodecyl Sulphate (SDS) on oxygen mass transfer using a commercial fine-bubble diffuser. Oxygen transfer experiments were performed under varying air flow rates and SDS concentrations. Key parameters, including the volumetric mass transfer coefficient ($k_{L}a$), gas holdup, bubble size, and interfacial area, were experimentally measured and analysed. SDS reduces the average bubble diameter by up to 50%; above 4 mg/L, further increases in concentration do not change the bubble size. Gas holdup increases by approximately 2% per mg L⁻¹ of SDS, and a new empirical correlation was proposed to predict gas holdup as a function of air flow rate and surfactant concentration, achieving an R² of 0.97 with deviations below 10%. Despite the increase in interfacial area, SDS strongly suppresses interfacial turbulence, reducing the liquid-side mass transfer coefficient ($k_L$) by up to 70%, which ultimately leads to a significant loss of overall oxygen transfer efficiency. The Sardeing model, originally developed for single bubbles, successfully predicted $k_L$ within ±15% of the experimental values, demonstrating its potential as a practical tool for estimating oxygen transfer in aeration systems. These findings highlight the substantial impact of surfactants on fine-bubble aeration performance and underscore the need to account for their effects in the design and operation of industrial aeration systems.