Scale-up of batch grinding data for simulation of industrial milling of platinum group minerals ore

Chimwani, Ngonidzashe; Mulenga, François K.; Hildebrandt, Diane; Glasser, David; Bwalya, Murray M.

doi:10.1016/j.mineng.2014.01.023

Cited by 22 publications

(21 citation statements)

References 21 publications

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“…The initial scale-up was successfully done and the scaled up parameters were validated against real plant data before various factors that affect the milling kinetics were explored by Chimwani et al (2013). This was followed by the optimisation of mean residence time by Mulenga and Chimwani (2013) using the same scale-up programme.…”

Section: Methodsmentioning

confidence: 99%

Use of the attainable region method to simulate a full-scale ball mill with a realistic transport model

Chimwani

Mulenga

Hildebrandt

et al. 2015

Minerals Engineering

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Section: Methodsmentioning

confidence: 99%

Use of the attainable region method to simulate a full-scale ball mill with a realistic transport model

Chimwani

Mulenga

Hildebrandt

et al. 2015

Minerals Engineering

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“…Final equations in terms of actual factors are shown in Eqs. (12) and (13): Equations (12) and (13) reveal how the individual variables affected specific energy requirement during bio-fiber comminution process. A comparable pattern was noticed for both banana and coir fibers.…”

Section: Modelling By Response Surface Methodsmentioning

confidence: 99%

“…Generally specific energy requirement in comminution processes is controlled by factors such as mill speed, volume of mill chamber, size reduction mechanism, moisture content, drying temperature, fiber treatment conditions, expected particle size and shape distribution [3,11]. To identify optimal design arrangements, designers have considered laboratory scale milling experiments to establish particle size distribution functions, size parameters and independent variables which could provide insight into optimal operating conditions [12].…”

Section: Introductionmentioning

confidence: 99%

Predictive Energy Requirement Models in Bio-fiber Comminution Process

2020

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Comminution of bio-fibers is necessary for their effective application in development of reinforced composite and feedstock; however, comminution is an energy intensive process and minimization of specific energy required in the size reduction of bio-fibers is an optimization task in design of various particulate based materials. The objective of this study was to model specific energy requirement in bio-fiber comminution process. The banana and coir bio-fibers used in the study were respectively extracted from post-harvest banana stem and outer shell of a coconut. Characterization of treated bio-fibers was done using Fourier Transform Infrared Spectroscopy and comminution process was carried out in a laboratory-scale knife mill. Electrical energy was recorded using a plug-in power energy meter while Kick's, Rittinger's and Bond's energy laws provide insight into the relationship between particle size reduction and energy consumption during comminution process. The Rosin Rammler-Bennett (RRB) and Gaudin-Schuhmann (GS) models were fitted with MATLAB ® R2014a. The Artificial Neural Networks (ANN) and Response Surface Method (RSM) was utilised in predicting specific energy requirements as controlled by fiber treatment conditions, milling speed, initial fiber length and drying temperature. The predictive accuracy of the RSM and ANN models were adjudged adequate in terms of the process independent variables. A global optimal condition was found at 2% NaOH concentration, 29 mm fiber length, 1981 rpm mill speed and 41 °C drying temperature. At these conditions, specific energy was 1.430 kJ/kg for banana fiber and 1.109 kJ/ kg for coir fiber having desirability of unity.

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“…This finding of J = 40% being the optimal ball filling has also been argued in previous research articles: Mulenga and Chimwani. [19]; Chimwani et al [15]; and Chimwani et al [18]. Chimwani et al [18] then investigated the implications of higher ball filling such as J = 40% on power consumption for the same mill ore, as studied in this work.…”

Section: Mass Fraction In %mentioning

confidence: 99%

“…Laboratory and Industrial mill parameters[15].Laboratory tests parameters Industrial mill parameters Flow chart of the algorithm for the maximisation of M 2 .…”

mentioning

confidence: 99%

Ball size distribution for the maximum production of a narrowly-sized mill product

2015

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Scale-up of batch grinding data for simulation of industrial milling of platinum group minerals ore

Cited by 22 publications

References 21 publications

Use of the attainable region method to simulate a full-scale ball mill with a realistic transport model

Use of the attainable region method to simulate a full-scale ball mill with a realistic transport model

Predictive Energy Requirement Models in Bio-fiber Comminution Process

Ball size distribution for the maximum production of a narrowly-sized mill product

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