Development of Conductivity Sensors for Multi-Phase Flow Local Measurements at the Polytechnic University of Valencia (UPV) and University Jaume I of Castellon (UJI).

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.

Long-term performance and activity study of a two-stage anaerobic EGSB reactors system treating complex and toxic industrial wastewater.

Anaerobic treatment of industrial wastewater using upflow anaerobic reactors is an extended trend due to its high efficiency and biogas production potential, but its implementation in some sectors is limited due to the complexity and toxicity of the wastewaters. In this study, a two-stage expanded granular sludge bed (EGSB) reactors system has been investigated at both bench and pilot scale for the treatment of complex and toxic real wastewater from a petrochemical industry. The effect of different operational parameters including organic loading rate (OLR), hydraulic retention time (HRT) and influent characteristics over COD removal and biogas production and composition have been studied. Additionally, biomass specific methanogenic activity (SMA) and wastewater toxicity have been evaluated after long-term operation. Optimum total HRT of 24 h has been determined resulting in total COD and SO4 2- removal of 56.30 ± 5.25% and 31.68 ± 14.71%, respectively, at pilot scale, and average biogas production of 93.47 ± 34.92 NL/day with 67.01 ± 10.23 %CH4 content and 5210.11 ± 6802.27 ppmv of H2S. SMA and toxicity tests have confirmed inhibitory and toxic effects of wastewater over anaerobic biomass with average maximum inhibition of 65.34% in the unacclimated anaerobic inoculum while chronic toxicity produced a decrease of an order of magnitude in SMA after 600 days of operation. This study demonstrates the feasibility of applying an anaerobic treatment to this wastewater using EGSB reactors between a 0.97-1.74 gCOD/L/day OLR range. Nonetheless, periodic reinoculation would be necessary for long-term operation due to chronic toxicity of the wastewater exerted on the anaerobic biomass. PRACTITIONER POINTS: A two-stage EGSB reactors system has been operated at bench and pilot scale to treat complex and toxic petrochemical wastewater. Optimal total HRT of 24 h resulted in average COD removal ranging from 40% to 60%. SMA and toxicity tests have been performed to study long-term acclimation, detecting an activity depletion of an order of magnitude.

On the use of deep learning and computational fluid dynamics for the estimation of uniform momentum source components of propellers.

This article proposes a novel method based on Deep Learning for the resolution of uniform momentum source terms in the Reynolds-Averaged Navier-Stokes equations. These source terms can represent several industrial devices (propellers, wind turbines, and so forth) in Computational Fluid Dynamics simulations. Current simulation methods require huge computational power, rely on strong assumptions or need additional information about the device that is being simulated. In this first approach to the new method, a Deep Learning system is trained with hundreds of Computational Fluid Dynamics simulations with uniform momemtum sources so that it can compute the one representing a given propeller from a reduced set of flow velocity measurements near it. Results show an overall relative error below the 5% for momentum sources for uniform sources and a moderate error when describing real propellers. This work will allow to simulate more accurately industrial devices with less computational cost.

Experimental and CFD evaluation of ozone efficacy against coronavirus and enteric virus contamination on public transport surfaces.

The limited information about the routes of the transmission of SARS-CoV-2 within the ongoing pandemic scenario mobilized the administration, industry and academy to develop sanitation and disinfection systems for public and private spaces. Ozone has been proposed as an effective disinfection method against enveloped and non-enveloped viruses, including viruses with similar morphology to SARS-CoV-2. Due to this efficacy, numerous gaseous and aqueous phase ozone applications have emerged potentially to inhibit virus persistence in aerosols, surfaces, and water. In this work, a numerical model, a RANS CFD model for ozone dispersion inside tram and underground coach has been developed including the chemical self-decomposition and surface reactions of the ozone. The CFD model has been developed for a real tram coach of 28.6 × 2.4 × 2.2 m (L × W × H) using 1.76 million nodes and the Menter's shear stress transport turbulence model. The model predicts the O3 concentration needed to meet disinfection criteria and the fluid dynamics inside the public transport coach. The effectiveness of the system has been validated with laboratory and field tests in real full-scale coach using porcine epidemic diarrhea virus (PEDV) and murine norovirus (MNV-1) as SARS-CoV-2 and human norovirus surrogates, respectively. Lab-scale experiments on plastic surfaces demonstrated O3 disinfection (100 ppm, 95% RH, 25 min) inactivate > 99.8% MNV-1 and PEDV. Additionally, field tests in real full-scale coach demostrate the efficacy of the system as > 98.6% of infectious MNV-1 and > 96.3% PEDV were inactivated.

Modeling of wastewater treatment processes with hydrosludge.

The pressure for Water Resource Recovery Facilities (WRRF) operators to efficiently treat wastewater is greater than ever because of the water crisis, produced by the climate change effects and more restrictive regulations. Technicians and researchers need to evaluate WRRF performance to ensure maximum efficiency. For this purpose, numerical techniques, such as CFD, have been widely applied to the wastewater sector to model biological reactors and secondary settling tanks with high spatial and temporal accuracy. However, limitations such as complexity and learning curve prevent extending CFD usage among wastewater modeling experts. This paper presents HydroSludge, a framework that provides a series of tools that simplify the implementation of the processes and workflows in a WRRF. This work leverages HydroSludge to preprocess existing data, aid the meshing process, and perform CFD simulations. Its intuitive interface proves itself as an effective tool to increase the efficiency of wastewater treatment. PRACTITIONER POINTS: This paper introduces a software platform specifically oriented to WRRF, named HydroSludge, which provides easy access to the most widespread and leading CFD simulation software, OpenFOAM. Hydrosludge is intended to be used by WRRF operators, bringing a more wizard-like, automatic, and intuitive usage. Meshing assistance, submersible mixers, biological models, and distributed parallel computing are the most remarkable features included in HydroSludge. With the provided study cases, HydroSludge has proven to be a crucial tool for operators, managers, and researchers in WRRF.

Design and optimization of a semi-industrial cavitation device for a pretreatment of an anaerobic digestion treatment of excess sludge and pig slurry.

The use of hydrodynamic cavitation (HC) as a wastewater treatment and anaerobic digestion pretreatment is a well-known process. However, most of the experiments have been done at laboratory scale and using a low concentration of total solids in the sludge treated. In this study, the waste-activated sludge has been mixed with pig slurry with the aim of treating two wastes rich in nutrients and organic matter. The HC has been studied not only at laboratory scale but also at industrial scale (up to 500 L), using a novel rotating device consisting of a rotor with multiple teeth that rotate inside a grooved stator. The effectiveness of the process has been calculated using the disintegration degree (DD) and analyzing the volatile fatty acids, while the energy efficiency has been determined with specific energy of the sludge solubilization (SESCOD ) and the specific energy. Results show that both the SESCOD and the specific energy decrease when the cavitation process is scaled from laboratory scale to industrial scale. Specifically, SESCOD decreases from 2.71 × 102 to 0.16 × 102  kJ/g SCOD and specific energy decreases from 3.58 × 104 to 2.85 × 103  kJ/kg TS while DD values show reasonable values up to 17%. PRACTITIONER POINTS: A new industrial hydrodynamic cavitation device has been developed to treat industrial wastewater without chemical additives A volume up to 500 L has been treated at industrial scale experiments. Sludge with 7% of total solids content was satisfactorily disintegrated. The process scale-up lead to an energy efficiency enhancement.

Hindered and compression solid settling functions - Sensor data collection, practical model identification and validation.

Secondary settling tanks (SSTs) are the most hydraulically sensitive unit operations in activated sludge water resource recovery facilities (WRRF). Mathematical models for predicting activated sludge solids settling velocity include parameters that show irreducible epistemic uncertainty. Therefore, reliable and periodic calibration of the settling velocity model is key for predicting activated sludge process capacity, thus averting possible failures under wet-weather flow- and filamentous bulking conditions. The two main knowledge gaps addressed here are: (1) Do constitutive functions for hindered and compression settling exist, for which all velocity parameters can be uniquely estimated? (2) What is the optimum sensor data requirement of developing reliable settling velocity functions? Innovative settling column sensor and full-scale data were used to identify and validate amended Vesilind function for hindered settling and a new exponential function for compression settling velocity using one-dimensional and computational fluid dynamics simulations. Results indicate practical model identifiability under well-settling and filamentous bulking conditions.