Characterization of solid–liquid suspensions (real, large non-spherical particles in non-Newtonian carrier fluid) flowing in horizontal and vertical pipes

2007 ◽  
Vol 78 (1) ◽  
pp. 345-355 ◽  
Author(s):  
A. Legrand ◽  
M. Berthou ◽  
L. Fillaudeau
Keyword(s):  
Spherical Particles ◽  
Carrier Fluid ◽  
Liquid Suspensions ◽  
Solid Liquid

scholarly journals A Finite Element/Neural Network Framework for Modeling Suspensions of Non-spherical Particles

2021 ◽  
Author(s):  
Martyna Minakowska ◽  
Thomas Richter ◽  
Sebastian Sager
Keyword(s):  
Neural Network ◽  
Particle Interaction ◽  
Hydrodynamic Forces ◽  
Analytical Models ◽  
Spherical Particles ◽  
Complete Set ◽  
Liquid Suspensions ◽  
Solid Liquid ◽  
New Framework ◽  
Specific Particle

AbstractAn accurate prediction of the translational and rotational motion of particles suspended in a fluid is only possible if a complete set of correlations for the force coefficients of fluid-particle interaction is known. The present study is thus devoted to the derivation and validation of a new framework to determine the drag, lift, rotational and pitching torque coefficients for different non-spherical particles in a fluid flow. The motivation for the study arises from medical applications, where particles may have an arbitrary and complex shape. Here, it is usually not possible to derive accurate analytical models for predicting the different hydrodynamic forces. The presented model is designed to be applicable to a broad range of shapes. Another important feature of the suspensions occurring in medical and biological applications is the high number of particles. The modelling approach we propose can be efficiently used for simulations of solid-liquid suspensions with numerous particles. Based on resolved numerical simulations of prototypical particles we generate data to train a neural network which allows us to quickly estimate the hydrodynamic forces experienced by a specific particle immersed in a fluid.

scholarly journals Ultrasonic Time-of-Flight Computed Tomography for Investigation of Batch Crystallisation Processes

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 639
Author(s):  
Panagiotis Koulountzios ◽  
Tomasz Rymarczyk ◽  
Manuchehr Soleimani
Keyword(s):  
Spatial Information ◽  
Injection Rate ◽  
Ultrasound Tomography ◽  
Measurement Systems ◽  
Process Dynamics ◽  
Tomography System ◽  
Ultrasonic Time ◽  
Crystallisation Process ◽  
Liquid Suspensions

Crystallisation is a crucial step in many industrial processes. Many sensors are being investigated for monitoring such processes to enhance the efficiency of them. Ultrasound techniques have been used for particle sizing characterization of liquid suspensions, in crystallisation process. An ultrasound tomography system with an array of ultrasound sensors can provide spatial information inside the process when compared to single-measurement systems. In this study, the batch crystallisation experiments have been conducted in a lab-scale reactor in calcium carbonate crystallisation. Real-time ultrasound tomographic imaging is done via a contactless ultrasound tomography sensor array. The effect of the injection rate and the stirring speed was considered as two control parameters in these crystallisation functions. Transmission mode ultrasound tomography comprises 32 piezoelectric transducers with central frequency of 40 kHz has been used. The process-based experimental investigation shows the capability of the proposed ultrasound tomography system for crystallisation process monitoring. Information on process dynamics, as well as process malfunction, can be obtained via the ultrasound tomography system.

scholarly journals Rheological Characterization of a Concentrated Phosphate Slurry

Fluids ◽  
2021 ◽  
Vol 6 (5) ◽  
pp. 178
Author(s):  
Souhail Maazioui ◽  
Abderrahim Maazouz ◽  
Fayssal Benkhaldoun ◽  
Driss Ouazar ◽  
Khalid Lamnawar
Keyword(s):  
Flow Behavior ◽  
Continuous Phase ◽  
Raw Material ◽  
Rheological Characterization ◽  
Phosphate Ore ◽  
Insoluble Particles ◽  
Experimental Protocols ◽  
Solid Liquid

Phosphate ore slurry is a suspension of insoluble particles of phosphate rock, the primary raw material for fertilizer and phosphoric acid, in a continuous phase of water. This suspension has a non-Newtonian flow behavior and exhibits yield stress as the shear rate tends toward zero. The suspended particles in the present study were assumed to be noncolloidal. Various grades and phosphate ore concentrations were chosen for this rheological investigation. We created some experimental protocols to determine the main characteristics of these complex fluids and established relevant rheological models with a view to simulate the numerical flow in a cylindrical pipeline. Rheograms of these slurries were obtained using a rotational rheometer and were accurately modeled with commonly used yield-pseudoplastic models. The results show that the concentration of solids in a solid–liquid mixture could be increased while maintaining a desired apparent viscosity. Finally, the design equations for the laminar pipe flow of yield pseudoplastics were investigated to highlight the role of rheological studies in this context.

scholarly journals Preparation and Characterization of the Sulfur-Impregnated Natural Zeolite Clinoptilolite for Hg(II) Removal from Aqueous Solutions

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 217
Author(s):  
Marin Ugrina ◽  
Martin Gaberšek ◽  
Aleksandra Daković ◽  
Ivona Nuić
Keyword(s):  
Chemical Modification ◽  
Aqueous Solutions ◽  
Specific Surface ◽  
Surface Area ◽  
Natural Zeolite ◽  
Contact Time ◽  
Liquid Ratio ◽  
X Ray ◽  
Solid Liquid

Sulfur-impregnated zeolite has been obtained from the natural zeolite clinoptilolite by chemical modification with Na2S at 150 °C. The purpose of zeolite impregnation was to enhance the sorption of Hg(II) from aqueous solutions. Chemical analysis, acid and basic properties determined by Bohem’s method, chemical behavior at different pHo values, zeta potential, cation-exchange capacity (CEC), specific surface area, X-ray powder diffraction (XRPD), scanning electron microscopy with energy-dispersive X-ray analysis (SEM-EDS), Fourier transform infrared spectroscopy (FTIR), as well as thermogravimetry with derivative thermogravimetry (TG-DTG) were used for detailed comparative mineralogical and physico-chemical characterization of natural and sulfur-impregnated zeolites. Results revealed that the surface of the natural zeolite was successfully impregnated with sulfur species in the form of FeS and CaS. Chemical modification caused an increase in basicity and the net negative surface charge due to an increase in oxygen-containing functional groups as well as a decrease in specific surface area and crystallinity due to the formation of sulfur-containing clusters at the zeolite surface. The sorption of Hg(II) species onto the sulfur-impregnated zeolite was affected by the pH, solid/liquid ratio, initial Hg(II) concentration, and contact time. The optimal sorption conditions were determined as pH 2, a solid/liquid ratio of 10 g/L, and a contact time of 800 min. The maximum obtained sorption capacity of the sulfur-impregnated zeolite toward Hg(II) was 1.02 mmol/g. The sorption mechanism of Hg(II) onto the sulfur-impregnated zeolite involves electrostatic attraction, ion exchange, and surface complexation, accompanied by co-precipitation of Hg(II) in the form of HgS. It was found that sulfur-impregnation enhanced the sorption of Hg(II) by 3.6 times compared to the natural zeolite. The leaching test indicated the retention of Hg(II) in the zeolite structure over a wide pH range, making this sulfur-impregnated sorbent a promising material for the remediation of a mercury-polluted environment.

scholarly journals Deep Learning Based Instance Segmentation of Titanium Dioxide Particles in the Form of Agglomerates in Scanning Electron Microscopy

Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 968
Author(s):  
Paul Monchot ◽  
Loïc Coquelin ◽  
Khaled Guerroudj ◽  
Nicolas Feltin ◽  
Alexandra Delvallée ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Titanium Dioxide ◽  
Learning Community ◽  
Spherical Particles ◽  
Average Value ◽  
Size Characterization ◽  
Scanning Electron

The size characterization of particles present in the form of agglomerates in images measured by scanning electron microscopy (SEM) requires a powerful image segmentation tool in order to properly define the boundaries of each particle. In this work, we propose to use an algorithm from the deep statistical learning community, the Mask-RCNN, coupled with transfer learning to overcome the problem of generalization of the commonly used image processing methods such as watershed or active contour. Indeed, the adjustment of the parameters of these algorithms is almost systematically necessary and slows down the automation of the processing chain. The Mask-RCNN is adapted here to the case study and we present results obtained on titanium dioxide samples (non-spherical particles) with a level of performance evaluated by different metrics such as the DICE coefficient, which reaches an average value of 0.95 on the test images.

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