scholarly journals Ultrasonic Transmission Tomography Sensor Design for Bubble Identification in Gas-Liquid Bubble Column Reactors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4256 ◽  
Author(s):  
Nan Li ◽  
Mingchen Cao ◽  
Kun Xu ◽  
Jiabin Jia ◽  
Hangben Du
Keyword(s):  
Bubble Column ◽  
Excitation Frequency ◽  
Gas Bubbles ◽  
Gas Bubble ◽  
Transmission Tomography ◽  
Bubble Distribution ◽  
Bubble Column Reactors ◽  
Imaging Results ◽  
Liquid Bubble ◽  
And Diffusion

Scientists require methods to monitor the distribution of gas bubbles in gas-liquid bubble column reactors. One non-destructive method that can potential satisfy this requirement in industrial situations is ultrasonic transmission tomography (UTT). In this paper, an ultrasonic transmission tomography sensor is designed for measuring bubble distribution in a reactor. Factors that influence the transducer design include transmission energy loss, the resonance characteristics and vibration modes of the transducer, and diffusion angles of the transducers, which are discussed. For practical application, it was found that an excitation frequency of 300 kHz could identify the location and size of gas bubbles. The vibration mode and diffusion also directly affect the quality of the imaging. The geometric parameters of the transducer (a cylinder transducer with a 10 mm diameter and 6.7 mm thickness) are designed to achieve the performance requirements. A UTT system, based on these parameters, was built in order to verify the effectiveness of the designed ultrasonic transducer array. A Sector-diffusion-matrix based Linear Back Projection (SLBP) was used to reconstruct the gas/liquid two-phase flow from the obtained measurements. Two other image processing methods, based on SLBP algorithm named SLBP-HR (SLBP-Hybrid Reconstruction) and SLBP-ATF (SLBP-Adaptive Threshold Filtering), were introduced, and the imaging results are presented. The imaging results indicate that a gas bubble with a 3 mm radius can be identified from reconstructed images, and that three different flow patterns, namely, single gas bubble, double gas bubble with different diameters, and eccentric flow, can be identified from reconstructed images. This demonstrates that the designed UTT sensor can effectively measure bubble distribution in gas-liquid bubble column reactors.

Gas holdup in homogeneous and heterogeneous gas-liquid bubble column reactors

1999 ◽  
Vol 77 (1) ◽  
pp. 11-21 ◽  
Author(s):  
Amir Sarrafi ◽  
Hans Müller-Steinhagen ◽  
John M. Smith ◽  
Mohammad Jamialahmadi
Keyword(s):  
Bubble Column ◽  
Gas Holdup ◽  
Bubble Column Reactors ◽  
Liquid Bubble

Prediction of the Gas Holdup in Industrial-Scale Bubble Columns and Slurry Bubble Column Reactors Using Back-Propagation Neural Networks

2005 ◽  
Vol 3 (1) ◽  
Author(s):  
Arsam Behkish ◽  
Romain Lemoine ◽  
Laurent Sehabiague ◽  
Rachid Oukaci ◽  
Badie I Morsi
Keyword(s):  
Neural Networks ◽  
Bubble Column ◽  
Back Propagation ◽  
Gas Holdup ◽  
Bubble Columns ◽  
Catalyst Loading ◽  
Gas Bubble ◽  
Gas Velocity ◽  
Bubble Column Reactors ◽  
Superficial Gas Velocity

The total gas holdup and the holdup of large gas bubbles were predicted in bubble column reactors (BCRs) and slurry bubble column rectors (SBCRs) using two Back-Propagation Neural Networks (BPNNs). Over 3880 and 1425 data points for gas holdup and Large gas bubble holdup respectively, covering wide ranges of gas-liquid-solid physical properties, operating variables, reactor geometry, and gas sparger type/size, were employed to develop, train and validate the two neural networks. The developed BPNN for gas holdup has a topology of [14,9-7,1] and was able to predict the trained and untrained data with an average absolute relative error (AARE), standard deviation, and regression coefficient (R2) of 16, 19 and 90%, respectively. The developed BPNN for large gas bubble holdup has a topology of [14,8,1] and was capable of predicting the trained and untrained data with AARE, standard deviation, and R2 of 10, 14 and 93%, respectively. The BPNNs were then used to predict the effects of pressure, superficial gas velocity, temperature and catalyst loading on the total syngas holdup for Low-Temperature Fischer-Tropsch (LTFT) synthesis carried out in a 5 m ID SBCR. The predicted total syngas holdup appeared to increase with increasing reactor pressure, superficial gas velocity and the number of orifices in the gas sparger. The predicted syngas holdup, however, was found to decrease with increasing catalyst loading and reactor temperature. Also, under similar LTFT operating conditions (P = 3 MPa, T = 513 K, CW = 30 and 50 wt%), the total syngas holdup values predicted for H2/CO ratio of 2:1 and cobalt-based catalyst are consistently lower than those obtained for H2/CO ratio of 1:1 and iron oxide catalyst in the superficial gas velocity range from 0.005 to 0.4 m/s. These predictions are in perfect agreement with reported literature trends, which underscore the reliability and validity of the developed BPNNs in predicting the total syngas holdup and the holdup of large gas bubbles in large-scale bubble columns and SBCRs operating under industrial conditions.

Gas bubble induced scalings in geothermal systems

2021 ◽  
Author(s):  
Lilly Zacherl ◽  
Thomas Baumann
Keyword(s):  
Ph Value ◽  
Saturation Index ◽  
Gas Bubbles ◽  
Gas Bubble ◽  
Geothermal Systems ◽  
Injection Wells ◽  
Term Operation ◽  
Maintenance Schedule ◽  
Local Experience ◽  
Maintenance Schedules

<p>Scalings in geothermal systems are affecting the efficiency and safety of geothermal systems. An operate-until-fail maintenance scheme might seem appropriate for subsurface installations where the replacement of pumps and production pipes is costly and regular maintenance comprises a complete overhaul of the installations. The situation is different for surface level installations and injection wells. Here, monitoring of the thickness of precipitates is the key to optimized maintenance schedules and long-term operation.</p><p>A questionnaire revealed that operators of geothermal facilities start with a standardized maintenance schedule which is adjusted based on local experience. Sensor networks, numerical modelling and predictive maintenance are not yet applied. In this project we are aiming to close this gap with the development of a non-invasive sensor system coupled to innovative data acquisition and evaluation and an expert system to quantitatively predict the development of precipitations in geothermal systems and open cooling towers.</p><p>Previous investigations of scalings in the lower part of production pipes of a geothermal facility suggest that the disruption of the carbonate equilibrium is triggered by the formation of gas bubbles in the pump and subsequent stripping of CO<sub>2</sub>. Although small in it's overall effect on pH-value and saturation index, significant amounts of precipitates are forming at high volumetric flow rates. To assess the kinetics of gas bubble induced precipitations laboratory experiments were run. The experiment addresses precipitations at surfaces and at the gas bubbles themselves.</p>

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