The following performance between particle and fluid medium inside hydrocyclone with double vortex finders

In this paper, we used numerical simulations to study the effect of size, density and concentration of particles on the relative motion between two phases in a cyclone separator with double vortex finders, which is different than a traditional separator that has only one overflow pipe

Study on effect of the inner vortex finder length on the flow properties of the hydrocyclone with double vortex finders

The traditional hydrocyclone can only obtain two products: overflow and underflow. In the paper, we propose three-products hydrocyclone with double vortex finders. The hydrocyclone is designed with two coaxial overflow tubes with different diameters. During overflow, light and fine particles exit from the inner overflow tube. The mid-size particles overflow from the outer overflow tube, and the coarse particles through the underflow pipe. Therefore, one classification can obtain three different narrow-grade-classification products. The inner vortex finder length is the important influent factor on the flow performance of the hydrocyclone. This paper is mainly focused on the study of the flow field of both the air and the liquid phase, and of the effects of the inner vortex finder length on the velocity field, pressure field and the air column of the hydrocyclone with double vortex finders

MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pre-trained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries

Sediment-Containing Sewage Separation Using Intermittent-Discharge Columnar Hydrocyclones

Traditional hydrocyclones can be used for the concentration of sewage-containing sediments, but the low underflow concentration and the high content of fine particles result in a large subsequent dehydration workload. This study aimed to investigate the effect of columnar hydrocyclone column height on separation performance and the change in the internal flow field after the underflow orifice of the hydrocyclone was closed, so as to provide a theoretical basis for improving the ability to treat the sewage of the hydrocyclone. Numerical simulation was used to examine the change in the separation performance of the hydrocyclone and the effect of column height on the separation performance of the hydrocyclone in the case of the closed underflow orifice during intermittent discharging. The results indicate that a proper increase in column height was beneficial to improve the separation performance of the hydrocyclone. With the increase in the closing time of the underflow orifice, the particle content at the bottom of the hydrocyclone increased significantly. The experiment proves the feasibility of the intermittent discharge method in practice, and this working method can effectively increase the underflow concentration

Optimal Shut-Down Policy for Air Separation Units in Integrated Steel Enterprises during a Blast Furnace Blow-Down

Optimal shut-down policy for air separation units (ASUs) in the oxygen distribution system is studied for the integrated steel enterprises with a captive oxygen plant during a blast furnace blow-down. A multiperiod mixed-integer linear programming (MILP) model was proposed, which includes the minimum time constraints for ASU shut-down and start-up and the zero vent constraint of gaseous oxygen. When the decision variables are scheduled simultaneously, such as the on/off states of the fixed-load compressors and ASUs, the rate of the variable-load compressors, and the rate of liquefaction units, the model can easily offer the on-site manager a solution to operate the facilities in the oxygen distribution system while avoiding oxygen venting and minimizing the pressure levels of the high-pressure network in the buffer system, thereby reducing the overall energy consumption for oxygen production. The applicability of the model was demonstrated and justified using a real-world case study

Numerical simulation of multiphase flow inside hydrocyclone based on CFD

Abstract This paper applied computational fluid dynamics (CFD) method to investigate the internal multiphase flow filed in a 75 mm hydrocyclone. The Reynolds stress model (RSM) and VOF model were employed in the numerical simulation. This study discussed the velocity and pressure distribution in the hydrocyclone, and analysed the formation and development mechanism of air core. The numerical simulation results showed that the flow field was very unstable in the region of the air core. The axial velocity gradient reached its maxima, and the turbulent fluctuation was strongest in the simulation region. This study provided theoretical basis on further research of the air core effect on separation efficiency and pressure drop

Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework

Explicit spatial information about crop types on smallholder farms is important for the development of local precision agriculture. However, due to highly fragmented and heterogeneous cropland landscapes, fine-scale mapping of smallholder crops, based on low- and medium-resolution satellite images and relying on a single machine learning (ML) classifier, generally fails to achieve satisfactory performance. This paper develops an ensemble ML-based framework to improve the accuracy of parcel-level smallholder crop mapping from very high spatial resolution (VHSR) images. A typical smallholder agricultural area in central China covered by WorldView-2 images is selected to demonstrate our approach. This approach involves the task of distinguishing eight crop-level agricultural land use types. To this end, six widely used individual ML classifiers are evaluated. We further improved their performance by independently implementing bagging and stacking ensemble learning (EL) techniques. The results show that the bagging models improved the performance of unstable classifiers, but these improvements are limited. In contrast, the stacking models perform better, and the Stacking #2 model (overall accuracy = 83.91%, kappa = 0.812), which integrates the three best-performing individual classifiers, performs the best of all of the built models and improves the classwise accuracy of almost all of the land use types. Since classification performance can be significantly improved without adding costly data collection, stacking-ensemble mapping approaches are valuable for the spatial management of complex agricultural areas. We also demonstrate that using geometric and textural features extracted from VHSR images can improve the accuracy of parcel-level smallholder crop mapping. The proposed framework shows the great potential of combining EL technology with VHSR imagery for accurate mapping of smallholder crops, which could facilitate the development of parcel-level crop identification systems in countries dominated by smallholder agriculture

The Study on Numerical Simulation and Experiments of Four Product Hydrocyclone with Double Vortex Finders

A hydrocyclone is an instrument that can effectively separate multi-phase mixtures of particles with different densities or sizes based on centrifugal sedimentation principles. However, conventional hydrocyclones lead to two products only, resulting in an over-wide particle size range that does not meet the requirements of subsequent operations. In this article, a two-stage series, a four product hydrocyclone is proposed. The first stage hydrocyclone is designed to be a coaxial double overflow pipe: under the effect of separation, fine particles are discharged from the internal overflow pipe, while medium-size particles are discharged from external overflow pipe before entering the second stage hydrocyclone for fine sedimentation. In other words, one-stage grading leads to four products, including the first stage underflow, the first stage overflow, the second stage underflow, and the second stage overflow. The effects of structural parameters and operational parameters on flow field distribution in hydrocyclone were investigated via a study of flow field distribution in multi-product hydrocyclones using numerical simulations. The application of four product hydrocyclone in iron recovery shows that the grade and recovery of iron concentrate exceed 65.08% and 86.14%, respectively. This study provides references for understanding the flow field distribution in hydrocyclones and development of multi-product grading instrument in terms of both theory and industrial applications