Fine-bubble aeration is a core process in wastewater treatment plants (WWTPs). However, the physical mechanisms linking bubble plume hydrodynamics to oxygen transfer performance remain insufficiently quantified under configurations representative of full-scale installations. This study presents a local multi-sensor experimental characterization of a multiple bubble plume system using a 4 × 4 array of commercial membrane diffusers in a pilot-scale aeration tank (2 m³), emulating WWTP diffuser density and geometry. Airflow rate was varied to analyze its effects on mixing and oxygen transfer efficiency. The experimental methodology combines three complementary measurement approaches. Oxygen transfer performance is quantified using a dissolved oxygen probe. Liquid-phase velocity fields are then mapped using Acoustic Doppler Velocimetry (ADV). Finally, local two-phase measurements are obtained using dual-tip Conductivity Probe (CP) arrays, which provide bubble size, bubble velocity, void fraction, and Interfacial Area Concentration (IAC). Based on these observations, a zonal hydrodynamic model is proposed to describe plume interaction, wall-driven recirculation, and the formation of a collective plume core at higher airflows. Quantitatively, the results reveal a 29% reduction in Standard Oxygen Transfer Efficiency (SOTE) between 10 and 40 m³/h, driven by a 41% increase in bubble size and an 18% rise in bubble velocity. Bubble chord length also increased with height, by 33%, 19%, and 15% over 0.8 m for 10, 20, and 40 m³/h, respectively. These trends indicate that increasing airflow enhances turbulent mixing but simultaneously enlarges bubbles and accelerates their ascent, thereby reducing residence time and negatively affecting oxygen transfer. Overall, the validated multiphase datasets and mechanistic insights demonstrate the dominant role of diffuser interaction in dense layouts, supporting improved parameterization and experimental benchmarking of fine-bubble aeration systems in WWTPs.

This study explored the Erasmus+ project ’H2OMap: Innovative Learning by Hydraulic Heritage Mapping’, integrating environmental awareness and cultural heritage into secondary education through interdisciplinary, ICT, and STEM-based approaches. Focused on water-related heritage in the Mediterranean, the study pursued three aims: integrate ICT-supported participatory mapping bridging history/geography subjects with digital innovation; identify learning benefits and implementation conditions; and generate transferable outputs and datasets for classroom reuse. Intellectual outputs include a methodological guide, an e-learning course, and an educational multiplatform comprising a mobile mapping app for in situ geocataloguing, an online database, and a geoportal with interactive StoryMaps. Evidence came from classroom testing across age groups, teacher feedback from the e-learning course, student mobilities in Spain, Italy, and Portugal, and platform usage records. More than 390 students and teachers participated, documenting over 100 hydraulic heritage elements. Additionally, dissemination through nine multiplier events and conferences reached over 550 external attendees. Findings show increased student engagement and ICT/GIS skills, clearer cross-curricular integration, and a replicable open workflow supported by structured coordination that strengthens school–university partnerships. Learner experience emphasised hands-on, place-based exploration and collaborative documentation of water heritage. Recommendations include using open geospatial standards, providing teacher training, and maintaining geoportals for classroom reuse.

The expanded granular sludge bed (EGSB) reactor system is a mature anaerobic treatment technology that continues to evolve and expand its applications in wastewater management. This class of reactors provides high treatment efficiencies and significant biogas production rates due to the interaction among the three involved phases: the liquid water matrix, solid granules, and biogas. The present study examines the three-phase flow characteristics of such reactors, which operate under very specific conditions—namely, a reduced density difference between the liquid and millimeter-sized solid particles, relatively low gas hold-up, and moderate bed expansion. To ensure the reproducibility and generality of the results, the solid granules were replaced with acrylonitrile–butadiene–styrene (ABS) particles with a density of ρp = 1050 kg/m3 and two mean diameters, dp = 2.5 mm and dp = 4 mm. These solids were subjected to various combinations of superficial gas (jg) and liquid (jl) velocities in a co-current vertical column of 1 m length and 68 mm inner diameter. Two liquids with different densities, ρw = 1000 kg/m3 and ρw = 1025 kg/m3, were employed to enable a non-dimensional analysis of the results. Unlike previous studies, the results presented here allow for a comprehensive characterization of the flow regimes in terms of the interaction among the three phases under conditions of low density difference and millimeter-sized particles—conditions that lie outside the ranges typically investigated in gas–liquid–solid fluidized bed reactors.

Accurate binarization of phase-detection probe signals (gas vs. liquid) is necessary for the estimation of local void fraction, interfacial velocity, and bubble statistics in gas–liquid flows. Classical threshold based methods—single or double level— performs well on clean laboratory signals but degrades under realistic industrial conditions where noise, baseline drift, and clustered (slug-like) events challenge fixed rules. This work investigates whether deep learning (DL) models trained exclusively on synthetic data can deliver robust, generalizable binarization on real probe measurements. We (i) build a parametric generator of realistic time series from bubbly pulse templates, extended to clusters/slug patterns and perturbed with controlled noise, drift, and oscillatory baselines; (ii) train four lightweight DL architectures—one-dimensional U-Net (UNET-1D), Temporal Convolutional Network (TCN), a minimal one-dimensional Convolutional Neural Network (CNN-1D), and a Bidirectional Long-Short Memory network (BiLSTM)—only on synthetic signals; and (iii) evaluate them against classical threshold methods using event-level and sample-level metrics. On synthetic signal evaluation, UNET-1D and TCN achieve near-perfect event detection and sub-millisecond onset errors. On real bubbly and slug flow sensor data, classical threshold based methods remain highly competitive on clean sensor signals, while DL models retain advantages under non-stationary baselines and clustered events, yielding accurate void and timing with no hand-tuned assumptions. Results support DL as a practical, data-driven complement to fixed algorithms, particularly in noisy or drift-dominated measuring conditions.