1,721,010 research outputs found
Redi F, Alberini F, Montagnetti R, Siena E (2017). Colle di Casciano nel territorio di Barete (AQ): note di topograia tardoantica e altomedievale dell’alta valle dell’Aterno. ARCHEOLOGIA MEDIEVALE, vol. XLIV, p. 219-235, ISSN: 0390-0592
No full textIn this paper it is presented the work of Airborne remote sensing by UAV and the consequent aero-photogrammetric survey of the area immediately to the north of Colle di Casciano, between the districticts of Basanello and Teora, both in municipal territory of Barete (AQ) - Italy. The aerial remote sensing by UAV of that area is one of the great research project of the L'Aquila University aimed at deepen of the the knowledge and study of the site where they were found the ostrogote belt buckles today preserved in the Museum of Villa Giulia in Rome. The remote sensing of that area has been finalized to the elaboration of some orthophoto for a better identification of several cropmarks and soilmarks that we have already identified on the site thanks to satellite images
A step towards the live identification of pipe obstructions with the use of passive acoustic emission and supervised machine learning
No full textA single passive acoustic emission sensor was used to collect signals coming from an obstructed pipe in a water recirculation system. Four geometrically different obstructions were investigated. The flow field of water around each obstruction was visualised with the use of 2D particle image velocimetry (PIV) to identify the different flow features. In parallel, the acoustic emission signals were acquired by locating a piezoelectric sensor on the outer wall of the pipe at the tip of the obstruction. The acoustic emission signals were then pre-processed and the frequency domain was extracted for 100 recordings in each case. Signals were processed further by using principle component analysis and a matrix is created for supervised machine learning algorithms. This methodology was applied over a range of four flow rates, all in fully developed turbulent flow. Results showed that different obstructions generated different acoustic signals and flow fields, which reflected the different flow fields observed with PIV. The average velocity and amplitude of the acoustic signals increased in magnitude with increasing flow rate. The machine-learning algorithm with highest prediction values was quadratic support-vector machine with predictions in the area of 95% accuracy or above. This makes the combination of machine learning and a single passive acoustic sensor a viable option to predict pipe obstructions and the type of obstruction. This may lead to a useful application for urban water supply or sewage systems as well as agricultural practice for field irrigation or the detection of nozzle blockages
Identification of suspension state using passive acoustic emission and machine learning in a solid–liquid mixing system
Full text linkSolid–liquid mixing is a core operation in many manufacturing processes in the food, cosmetics, pharmaceutical and chemical industries. This work aims to develop an accurate and reliable sensing methodology using passive acoustic emission (PAE) coupled with supervised machine learning (ML) algorithms, to allow identifying and predicting solid–liquid suspension state. Using PAE in process monitoring is beneficial because it is affordable, sensitive, non-intrusive, and suitable for on-line applications. PAE equipment includes a piezoelectric sensor, placed in contact with the system, an amplifier, a filter, an oscilloscope to record the signal and a computer. Experiments were carried out in a fully baffled, flat bottom glass vessel equipped with a PBT impeller. Acoustic signals were recorded with sampling frequency of 750 kHz, impeller speed range 50–1000 rpm and varying solid features, i.e., particle size (dp range 0.250–6 mm), solid loading and solid density (acryl-glass particles). For each classification run, sampled data were pre-processed using Fast Fourier Transform (FFT) to reveal any detailed spectral characteristics of the signal in the frequency domain. Spectra have been filtered and then reduced by selecting the highest variance frequencies. As labelling, established optical measurements were used to classify the acoustic frequency spectra. The frequency data set has been split in training (60%), cross validation (20%) and test (20%) sets and were used, respectively, to build the model, identify the best model parameters (optimisation step), and finally to check the accuracy (test step). The developed technique has shown excellent results in recognizing spectra corresponding to different classes with observed accuracy greater than 99.72%
Quantitative Measurements of the Critical Impeller Speed for Solid-Liquid Suspensions
No full textA quantitative methodology for particle suspension assessment is presented. A new parameter, fmov/tot, the ratio of the mean number of moving particles to the total number of particles, is introduced to evaluate the minimum speed required to just suspend solids. This approach is tested to investigate the impact of impeller clearance on the minimum impeller speed, Njs, in a vessel when using a radial flow Rushton turbine. Flow patterns and power numbers obtained experimentally and computationally support the suspension findings. Image analysis is an appropriate method for determining Njs. Lowering the impeller clearance reduces the speed required for particle suspension with a change of flow pattern from a radial discharge with two loops to a single loop scouring the vessel base. The power number also falls markedly at the two-to-one loop transition as does the strain rate near the base
Euler-Lagrange CFD modelling of unconfined gas mixing in anaerobic digestion
No full textA novel Euler-Lagrangian (EL) computational fluid dynamics (CFD) finite volume-based model to simulate the gas mixing of sludge for anaerobic digestion is developed and described. Fluid motion is driven by momentum transfer from bubbles to liquid. Model validation is undertaken by assessing the flow field in a labscale model with particle image velocimetry (PIV). Conclusions are drawn about the upscaling and applicability of the model to full-scale problems, and recommendations are given for optimum application
Effect of residence time and energy dissipation on drop size distribution for the dispersion of oil in water using KMS and SMX+ static mixer
No full textThe Planar Laser Induced Fluorescence technique was used to determine the drop size distribution of oil dispersed in water at the inlet and outlet of two static mixer geometries (KMS and Sulzer_SMX+) equipped with either 6 or 12 elements. A mineral oil (Lytol®), three times more viscous than the water continuous phase, was used as the dispersed phase. The oil flow rate was kept constant through all experiments forcing the drop detachment from the secondary inlet. The L–L system was very dilute ( ̃0.05–0.0007% v/v O/W) to avoid coalescence phenomena. The flowrate of the continuous phase (water) was altered giving values of Reynolds number from 2000 to 12,000, covering high transitional and turbulent flow regimes. Increasing the flow rate of the continuous phase, the detached oil drops from the secondary inlet decreased in size as expected. However, same drops after flowing a length of 0.4 m of an empty pipe reached a constant size. To investigate a wider range of energy dissipation and residence time, the presence of static mixers has been investigated. Pressure drops, hence energy consumed, were measured to compare the different set ups and drop size distributions. The results show that by increasing the flow rate, the drop size decreased up to a critical point, beyond which oil droplet size reduction became inefficient. The collected data were then used to derive a methodology to identify the optimal flow conditions and choice of static mixer device to achieve best drop size reduction with less energy per unit mass
A New Approach to Evaluate 3D Flow Fields Using an Off-Axis 2D PIV System: Investigation of a Tubular Reactor Equipped with Kenics Static Mixers
Full text linkIn this work, an off-axis 2D Particle Image Velocimetry system is used to obtain the 3D flow field at the outlet of a tubular reactor equipped with Kenics static mixers. The 3D flow fields are obtained exploiting the out-of-plane velocity component and considering the symmetrical features of the flow generated by the static mixers. The raw results show that the velocity vectors, measured on a cross section perpendicular to the tube axis by 2D-PIV with the camera located at 24° from the measurement plane, are affected by the axial component of the flow. However, taking into account the symmetry of the flow f ield with respect to the tubular reactor axis and evaluating the effect of the out of plane velocity component, the correct 2D velocity vectors on the plane and also the velocity component in the axial direction can be calculated from the raw 2D PIV data. The consistency of the methodology is demonstrated by comparison of the results with the flow field measured in a smaller tubular reactor of similar geometry and Reynolds number with a symmetrical 2D-PIV system, with the camera located perpendicularly to the laser plane. Then, the 3D features of the flow are analyzed to characterize the effects of the different combinations of static mixer configurations on the fluid dynamics of the system in turbulent conditions. The results show that, as the pressure drop increases, a more uniform velocity distribution is achieved
Using transient energy release measurements for the in‐line characterization of non‐Newtonian fluids and fluid state in pipe flow
Full text linkAlberini et al. have developed a new technology based on a passive acoustic emission (AE) sensing system that uses only a single sensor, with the goal of providing live and in‐situ measurement of rheology. For this study, three different types of fluids were selected to represent common rheological behaviours: Newtonian behaviour, non‐Newtonian behaviour with power law, and non‐Newtonian behaviour with Herschel–Bulkley relationship. By analyzing the transient energy released during the interaction between the probe and the fluid, distinct acoustic fingerprints were identified in the frequency domain. These acoustic fingerprints were found to be characteristic of the different fluids and their rheology, and were validated in triplicate. Furthermore, the results showed that the intensity of the acoustic emissions increased with higher flow rates (30 to 50 L/min). To test the correlation between flow rate and acoustic response, a neural network regression test was conducted, which demonstrated a direct correlation between AE peaks and flow rate. The neural network used was nonlinear autoregressive network with exogenous inputs (NARX), and the test involved a stepwise regression with 70% training and 30% network validation. The study also introduced the Rheology‐AE quotient, which maps fluid constituents against the acoustic signal. Results showed that this was a reliable means of deriving live rheology from a fluid's frequency domain. Finally, the results obtained from this study were validated using an offline rotational rheometer
Validation of a procedure for the numerical simulations of gas–liquid stirred tanks by means of a computational fluid dynamics approach
No full textImpellers with concave and vertically asymmetric blades proved superior gas dispersion capabilities and power consumption characteristics under gassed turbulent conditions with respect to traditional flat-blade turbines in aerated fermenters. In this study, a pilot-size gas–liquid tank stirred with an asymmetric blade disk impeller is numerically investigated by means of a Reynolds averaged two-fluid model combined with a simplified population balance model without adjustable parameters. This work aims at increasing the predictive capabilities of computational fluid dynamics multiphase modelling by validating a computational approach for the realistic simulation of industrial aerated fermenters and thus allowing for a more reliable scale-up. A methodology for achieving fully predictive results on fundamental variables for gas–liquid stirred tanks such as gassed power consumption, overall gas hold-up, and volumetric mass transfer coefficient, with affordable computational requirements at pilot and industrial scale is presented. Two-phase results are compared with the experimental data collected in a geometry matching the computational domain equipped with 3 planes of 16 sensors to enable electro-resistance tomography measurements and with suitable correlations from the literature. The limits and strength of the numerical procedure are discussed, starting from the comparison between computational predictions and experimental measurements
Inline monitoring of lactobionic acid production from cheese whey by Pseudomonas taetrolens in a stirred bioreactor using electrical conductivity
Full text linkIn this study, we introduce a novel experimental approach and present a simplified mathematical model for a quick monitoring of a biotec process producing lactobionic acid (LBA). It relies on monitoring the electrical conductivity of the fermentation broth and it is designed to predict the concentration of LBA throughout the microbial cheese whey valorization via LBA production. Following a systematic series of experiments conducted to refine the mathematical model, we performed conductivity monitoring during LBA production from "caciotta" and "squacquerone" wheys by Pseudomonas taetrolens in a 3 L stirred tank bioreactor. Throughout the bioproduction process, the conductivity values exhibited an upward trend corresponding to the increase in LBA concentration. Our findings underscore the feasibility and advantages of employing inline conductivity monitoring during LBA production from various cheese wheys. The results emphasize that conductivity measurements can effectively estimate product concentration in a fermentation process, particularly when there is a shift in ionic concentration. Furthermore, these conductivity measurements offer valuable insights for monitoring and optimizing the working conditions in a fermentation process
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