Development of a New Solver to Model the Fish-Hook Effect in a Centrifugal Classifier

entrifugal air classifiers are often used for classification of particle gas flows in the mineral industry and various other sectors. In this paper, a new solver based on the multiphase particle-in-cell (MP-PIC) method, which takes into account an interaction between particles, is presented. This makes it possible to investigate the flow process in the classifier in more detail, especially the influence of solid load on the flow profile and the fish-hook effect that sometimes occurs. Depending on the operating conditions, the fish-hook sometimes occurs in such apparatus and lead to a reduction in classification efficiency. Therefore, a better understanding and a representation of the fish-hook in numerical simulations is of great interest. The results of the simulation method are compared with results of previous simulation method, where particle–particle interactions are neglected. Moreover, a validation of the numerical simulations is carried out by comparing experimental data from a laboratory plant based on characteristic values such as pressure loss and classification efficiency. The comparison with experimental data shows that both methods provide similar good values for the classification efficiency d$_{50}$; however, the fish-hook effect is only reproduced when particle-particle interaction is taken into account. The particle movement prove that the fish-hook effect is due to a strong concentration accumulation in the outer area of the classifier. These particle accumulations block the radial transport of fine particles into the classifier, which are then entrained by coarser particles into the coarse material

Soft sensor development for real-time process monitoring of multidimensional fractionation in tubular centrifuges

High centrifugal acceleration and throughput rates of tubular centrifuges enable the solid–liquid size separation and fractionation of nanoparticles on a bench scale. Nowadays, advantageous product properties are defined by precise specifications regarding particle size and material composition. Hence, there is a demand for innovative and efficient downstream processing of complex particle suspensions. With this type of centrifuge working in a semi-continuous mode, an online observation of the separation quality is needed for optimization purposes. To analyze the composition of fines downstream of the centrifuge, a UV/vis soft sensor is developed to monitor the sorting of polymer and metal oxide nanoparticles by their size and density. By spectroscopic multi-component analysis, a measured UV/vis signal is translated into a model based prediction of the relative solids volume fraction of the fines. High signal stability and an adaptive but mandatory calibration routine enable the presented setup to accurately predict the product’s composition at variable operating conditions. It is outlined how this software-based UV/vis sensor can be utilized effectively for challenging real-time process analytics in multi-component suspension processing. The setup provides insight into the underlying process dynamics and assists in optimizing the outcome of separation tasks on the nanoscale

Effects of flow baffles on flow profile, pressure drop and classification performance in classifiers

This paper presents a study of the use of flow baffles inside a centrifugal air classifier. An air classifier belongs to the most widely used classification devices in mills in the mineral industry, which is why there is a great interest in optimizing the process flow and pressure loss. Using Computational Fluid Dynamics (CFD), the flow profile in a classifier without and with flow baffles is systematically compared. In the simulations, turbulence effects are modeled with the realizable k–ε model, and the Multiple Reference Frame approach (MRF) is used to represent the rotation of the classifier wheel. The discrete phase model is used to predict the collection efficiency. The effects on the pressure loss and the classification efficiency of the classifier are considered for two operating conditions. In addition, a comparison with experimental data is performed. Firstly, the simulations and experiments show good agreement. Furthermore, the investigations show that the use of flow baffles is suitable for optimizing the flow behavior in the classifier, especially in reducing the pressure loss and therefore energy costs. Moreover, the flow baffles have an impact on the classification performance. The impact depends on the operation conditions, especially the classifier speed. At low classifier speeds, the classifier without flow baffles separates more efficiently; as the speed increases, the classification performance of the classifier with flow baffles improves

Development of Prediction Models for Pressure Loss and Classification Efficiency in Classifiers

This paper presents the development of prediction models for pressure loss and classification efficiency in classifiers. Classifiers belong to one of the most important classification devices in gas particle processing and a fast and accurate determination of pressure loss and cut size is of great interest. The first model developed in this work allows the calculation of pressure loss as a function of geometric and operational parameters. It is based on a number of measured values that are obtained from previous numerical simulations (CFD). The maximum deviation of the model is less than 20% and the model operates in real time. However, the model requires calibration for each type of classifier. The second model for classification efficiency is based on a simplified two-dimensional approach in which the flow profile and particle trajectories are determined exclusively for the area between two classifier blades. The model is applicable for all geometrical and operational parameters and calculates the desired parameters within a few minutes, with a maximum error rate of 25%. In combination, the two models allow for the process optimization of classifiers in complete systems

Quasi-Continuous Production and Separation of Lysozyme Crystals on an Integrated Laboratory Plant

Vacuum crystallization with subsequent solid–liquid separation is a suitable method to produce and separate the temperature-sensitive protein lysozyme. The conventional process is performed batch-wise and on different devices, which in turn leads to disadvantages in terms of energy efficiency, contamination risk and process control. This publication therefore focuses on the application of the previously multistage process to a quasi-continuous, integrated single plant. The transfer occurs successively and starts with the substitution of the batch vessel by a process chamber. Afterwards, the filtration scale is increased and the formerly deployed membrane is replaced by an industrial filter cloth. Based on the results of these experiments, the complete process chain is successfully transferred to an integrated laboratory plant

Scale-up of decanter centrifuges for the particle separation and mechanical dewatering in the minerals processing industry by means of a numerical process model

Decanter centrifuges are frequently used for thickening, dewatering, classification, or degritting in the mining industry and various other sectors. Their use in an industrial process chain requires a sufficiently accurate prediction of the product and the machine behaviour. For this purpose, experiments on a smaller pilot-scale are carried out for scale-up of a decanter centrifuge, which is usually a major challenge. Predicting the process behaviour of decanter centrifuges from laboratory tests is rather difficult. Basically, there are two common ways of scale-up: First, via analytical methods and the law of similarity, which often requires an enormous experimental effort. Second, using numerical models, which demands a mathematically and physically precise description of the multiple processes running simultaneously in such machines. This article provides an overview of both methods for scale-up of a decanter centrifuge. The concept of a previous developed numerical approach is introduced. Pros and cons of both scale-up methods are compared and further discussed. Experiments on lab-scale, pilot-scale, and industrial-scale decanter centrifuges with two different finely dispersed calcium carbonate water suspensions were carried out and simulations were done to investigate and prove the scale-up capability and transferability of the numerical approach

Grey box modelling of decanter centrifuges by coupling a numerical process model with a neural network

Continuously operating decanter centrifuges are often applied for solid-liquid separation in the chemical and mining industries. Simulation tools can assist in the configuration and optimisation of separation processes by, e.g., controlling the quality characteristics of the product. Increasing computation power has led to a renewed interest in hybrid models (subsequently named grey box model), which combine parametric and non-paramteric models. In this article, a grey box model for the simulation of the mechanical dewatering of a finely dispersed product in decanter centrifuges is discussed. Here, the grey box model consists of a mechanistic model (as white box model) presented in a previous research article and a neural network (as black box model). Experimentally determined data is used to train the neural network in the area of application. The mechanistic approach considers the settling behaviour, the sediment consolidation, and the sediment transport. In conclusion, the settings of the neural network and the results of the grey box model and white box model are compared and discussed. Now, the overall grey box model is able to increase the accuracy of the simulation and physical effects that are not modelled yet are integrated by training of a neural network using experimental data

Real-Time Modeling of Volume and Form Dependent Nanoparticle Fractionation in Tubular Centrifuges

A dynamic process model for the simulation of nanoparticle fractionation in tubular centrifuges is presented. Established state-of-the-art methods are further developed to incorporate multi-dimensional particle properties (traits). The separation outcome is quantified based on a discrete distribution of particle volume, elongation and flatness. The simulation algorithm solves a mass balance between interconnected compartments which represent the separation zone. Grade efficiencies are calculated by a short-cut model involving material functions and higher dimensional particle trait distributions. For the one dimensional classification of fumed silica nanoparticles, the numerical solution is validated experimentally. A creation and characterization of a virtual particle system provides an additional three dimensional input dataset. Following a three dimensional fractionation case study, the tubular centrifuge model underlines the fact that a precise fractionation according to particle form is extremely difficult. In light of this, the paper discusses particle elongation and flatness as impacting traits during fractionation in tubular centrifuges. Furthermore, communications on separation performance and outcome are possible and facilitated by the three dimensional visualization of grade efficiency data. Future research in nanoparticle characterization will further enhance the models use in real-time separation process simulation

About Modeling and Optimization of Solid Bowl Centrifuges

Many processes involve solid bowl centrifuges as a solid–liquid separation step, typically used for clarification, thickening, classification, degritting, mechanical dewatering, and screening. In order to operate solid bowl centrifuges safely, with minimum resource consumption and reduced setup times, modeling and optimization are necessary steps. This is a challenge due to the complex process behavior, which can be overcome by developing advanced physical models and process analysis. This review provides an overview of solid bowl centrifuge applications, their modeling, and addresses future optimization potentials through digital tools. The impact of dispersed phase properties such as particle size, shape, surface roughness, structure, composition, and continuous liquid phase is the reason for the lack of generally applicable models. Laboratory-scale batch sedimentation centrifuges are used to predict material behavior and develop material functions describing separation-related properties such as sedimentation, sediment build-up and sediment transport. The combination of material functions and modeling allows accurate simulation of solid bowl centrifuges from laboratory to industrial scale. Since models usually do not cover all influencing variables, there are often deviations between predictions and the real process behavior. Gray-box modeling and on-line or in-situ process analytics are tools to improve centrifuge operation

Development of Prediction Models for Pressure Loss and Classification Efficiency in Classifiers

This paper presents the development of prediction models for pressure loss and classification efficiency in classifiers. Classifiers belong to one of the most important classification devices in gas particle processing and a fast and accurate determination of pressure loss and cut size is of great interest. The first model developed in this work allows the calculation of pressure loss as a function of geometric and operational parameters. It is based on a number of measured values that are obtained from previous numerical simulations (CFD). The maximum deviation of the model is less than 20% and the model operates in real time. However, the model requires calibration for each type of classifier. The second model for classification efficiency is based on a simplified two-dimensional approach in which the flow profile and particle trajectories are determined exclusively for the area between two classifier blades. The model is applicable for all geometrical and operational parameters and calculates the desired parameters within a few minutes, with a maximum error rate of 25%. In combination, the two models allow for the process optimization of classifiers in complete systems