Effects of grinding parameters on product fineness in jet mill grinding

Tuunila, Ritva; Nyström, Lars

doi:10.1016/s0892-6875(98)00095-8

Cited by 46 publications

(23 citation statements)

References 5 publications

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“…In addition to the cut size equation aligning with previously reported data for variation in solids feed rate and gas mass flow rate, the variation in classifier geometry and grind chamber height also matches previously reported data in the academic literature [17,18]. Tuunila and Nyström [17] showed that for a fixed grind chamber height, a reduction in the gap h1 results in a coarsening of the output PSD, matching the prediction of Eq.…”

Section: Fig (19) Predicted Cut Size Against Esp For Various Motive supporting

confidence: 86%

“…Tuunila and Nyström [17] showed that for a fixed grind chamber height, a reduction in the gap h1 results in a coarsening of the output PSD, matching the prediction of Eq. (29).…”

Section: Fig (19) Predicted Cut Size Against Esp For Various Motive supporting

confidence: 68%

“…Both Midoux et al [15] and Zhao and Schurr [16] suggested that Esp could be used for scale up without presenting supporting evidence and in contrast to detailed experimental studies on mill geometry by Tuunila and Nyström [17] and Katz and Kalman [18]. These two research papers [17,18] showed experimentally that varying geometrical parameters leads to differences in particle size for identical combinations of gas flow rate and solids feed rate.…”

Section: Empirical Correlation With Specific Energymentioning

confidence: 99%

“…These two research papers [17,18] showed experimentally that varying geometrical parameters leads to differences in particle size for identical combinations of gas flow rate and solids feed rate. Some mill designs are more or less efficient than others, leading to differences in size at the same Esp value.…”

Section: Empirical Correlation With Specific Energymentioning

confidence: 99%

“…Some mill designs are more or less efficient than others, leading to differences in size at the same Esp value. Katz and Kalman [18] for example showed that a grind angle of 45º was optimal compared to larger angles, and Tuunila and Nyström [17] reported that some classifier settings are more efficient than others.…”

Section: Empirical Correlation With Specific Energymentioning

confidence: 99%

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The spiral jet mill cut size equation

et al. 2016

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Micronisation, or fine grinding, is a key unit operation for many industries, with the spiral jet mill being popular as it is robust and reliable. Within this paper an analytical derivation for spiral jet mill cut size as a function of micronisation settings, gas thermodynamic properties and empirically derived constants for the material and mill is presented for the first time. This has been corroborated by experimental evidence and previously reported data in the academic literature and provides an insight into the interaction between aerodynamic particle classification and fine grinding. A scale up methodology is proposed for a high value material by using a small scale mill to determine the material specific constants of the high value material and a cheaper surrogate material to determine mill specific constants at increased scale.

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Section: Fig (19) Predicted Cut Size Against Esp For Various Motive supporting

confidence: 86%

“…Tuunila and Nyström [17] showed that for a fixed grind chamber height, a reduction in the gap h1 results in a coarsening of the output PSD, matching the prediction of Eq. (29).…”

Section: Fig (19) Predicted Cut Size Against Esp For Various Motive supporting

confidence: 68%

Section: Empirical Correlation With Specific Energymentioning

confidence: 99%

Section: Empirical Correlation With Specific Energymentioning

confidence: 99%

Section: Empirical Correlation With Specific Energymentioning

confidence: 99%

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The spiral jet mill cut size equation

et al. 2016

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Modeling of continuous self‐classifying spiral jet mills part 1: Model structure and validation using mill experiments

et al. 2014

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The objective of this work is to develop a milling model for a continuous self-classifying spiral air jet mill. Its foundation is a population balance model with selection and breakage distribution functions that have been related to a minimal number of mill-dependent and powder-dependent parameters. Initially, experimentation is required to determine the mill-dependent parameters for a specific mill, by milling a "base" powder at multiple operating conditions. Powderdependent parameters can be determined from either mill experiments or from material characterization measurements that require small amounts of powder (presented in Part 2). Ultimately, the milling model presented successfully predicts the product particle size using as inputs the feed particle-size distribution and mill operating conditions. Three crystalline powders, sodium bicarbonate, lactose monohydrate, and sucrose, have been used to test the proposed milling model.

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Optimization of the Classifying Efficiency of Spiral Jet Mills by Investigating the Flow Conditions and the Grinding Performance

Radeke

Lindner

Ulbricht

et al. 2023

Chemie Ingenieur Technik

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The achievable grinding fineness of spiral jet mills does not only depend on the comminution efficiency in the region of the nozzle jets but is also significantly affected by the separation efficiency in the region of the product outlet. Therefore, the aim of this study is to investigate the influences of the product outlet diameter and different particle trap rings on the flow conditions and the grinding performance, optimizing the classifying efficiency of spiral jet mills. The velocity fields analyzed by particle image velocimetry and the experimental grinding results show that the classification process can be optimized by both reducing the product outlet diameter and installing a particle trap ring.

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Effects of grinding parameters on product fineness in jet mill grinding

Cited by 46 publications

References 5 publications

The spiral jet mill cut size equation

The spiral jet mill cut size equation

Modeling of continuous self‐classifying spiral jet mills part 1: Model structure and validation using mill experiments

Optimization of the Classifying Efficiency of Spiral Jet Mills by Investigating the Flow Conditions and the Grinding Performance

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