Mineral Processing Design and Operation (eBook)
718 Seiten
Elsevier Science (Verlag)
978-0-08-045461-0 (ISBN)
Provides an orthodox statistical approach that helps in the understanding of the designing of unit processes. The subject of mineral processing has been treated on the basis of unit processes that are subsequently developed and integrated to form a complete strategy for mineral beneficiation. Unit processes of crushing, grinding, solid?liquid separation, flotation are therefore described in some detail so that a student at graduate level and operators at plants will find this book useful.
Mineral Processing Design and Operations
describes the strategy of mathematical modeling as a tool for more effective controlling of operations, looking at both steady state and dynamic state models.
* Containing 18 chapters that have several worked out examples to clarify process operations
* Filling a gap in the market by providing up-to-date research on mineral processing
* Describes alternative approaches to design calculation, using example calculations and problem exercises
Mineral Processing Design and Operations is expected to be of use to the design engineers engaged in the design and operation of mineral processing plants and including those process engineers who are engaged in flow-sheets development.Provides an orthodox statistical approach that helps in the understanding of the designing of unit processes. The subject of mineral processing has been treated on the basis of unit processes that are subsequently developed and integrated to form a complete strategy for mineral beneficiation. Unit processes of crushing, grinding, solid-liquid separation, flotation are therefore described in some detail so that a student at graduate level and operators at plants will find this book useful. Mineral Processing Design and Operations describes the strategy of mathematical modeling as a tool for more effective controlling of operations, looking at both steady state and dynamic state models.* Containing 18 chapters that have several worked out examples to clarify process operations* Filling a gap in the market by providing up-to-date research on mineral processing* Describes alternative approaches to design calculation, using example calculations and problem exercises
Front cover 1
Title page 4
Copyright 5
Preface 6
Table of contents 10
Symbols and Units 16
Chapter 1. Mineral Sampling 26
INTRODUCTION 26
STATISTICAL TERMINOLOGY 26
MINERAL PARTICLES DIFFERING IN SIZE-GY'S METHOD 32
MINERAL PARTICLES OF DIFFERENT DENSITY 36
INCREMENTAL SAMPLING 38
CONTINUOUS SAMPLING OF STREAMS 45
SAMPLING ORES OF PRECIOUS METALS 49
SAMPLING NOMOGRAPHS 50
PROBLEMS 53
REFERENCES 56
Chapter 2. Particle Size Estimation and Distributions 57
INTRODUCTION 57
METHODS OF SIZE ESTIMATION 57
PARTICLE SIZE DISTRIBUTION 70
COMBINING SIZE DISTRIBUTIONS 78
PROBLEMS 84
REFERENCES 87
Chapter 3. Size Reduction and Energy Requirement 88
INTRODUCTION 88
DESIGN OF SIZE REDUCTION PROCESSES 88
ENERGY FOR SIZE REDUCTION-WORK INDEX 90
ESTIMATION OF WORK INDEX FOR CRUSHERS AND GRINDING MILLS 93
PROBLEMS 117
REFERENCES 122
Chapter 4. Jaw Crusher 124
INTRODUCTION 124
DESIGN OF JAW CRUSHERS 124
JAW CRUSHER OPERATION 127
JAW CRUSHER CAPACITY 129
CRITICAL OPERATING SPEED 141
POWER CONSUMPTION 144
PROBLEMS 149
REFERENCES 152
Chapter 5. Gyratory and Cone Crusher 153
INTRODUCTION 153
DESIGN OF GYRATORY CRUSHERS 153
GYRATORY CRUSHER OPERATION 157
GYRATORY CRUSHER CIRCUIT DESIGN 158
CAPACITY 159
POWER CONSUMPTION 161
PROBLEMS 163
REFERENCES 166
Chapter 6. Roll Crushers 167
INTRODUCTION 167
DESIGN OF ROLL CRUSHERS 167
OPERATION OF ROLL CRUSHERS 172
CAPACITY OF ROLL CRUSHERS 173
POWER CONSUMPTION OF ROLL CRUSHERS 173
HIGH PRESSURE GRINDING ROLLS (HPGR) 174
OPERATION OF HPGR 175
CAPACITY OF HPGR 179
POWER CONSUMPTION OF HPGR 181
PROBLEMS 182
REFERENCES 184
Chapter 7. Tubular Ball Mills 186
INTRODUCTION 186
DESIGN OF TUBULAR MILLS 187
OPERATION OF TUBULAR BALL MILLS 189
ESTIMATION OF MILL CAPACITY 209
MILL POWER DRAW-MECHANICAL METHODS 212
PROBLEMS 233
REFERENCES 235
Chapter 8. Tubular Rod Mills 237
INTRODUCTION 237
DESIGN OF ROD MILLS 237
OPERATION OF ROD MILLS 240
ROD MILL CAPACITY 242
ROD MILL POWER DRAFT 244
MILL DRIVE 253
PROBLEMS 254
REFERENCES 258
Chapter 9. Autogenous and Semi-Autogenous Mills 259
INTRODUCTION 259
DESIGN OF AG/SAG MILLS 259
OPERATION OF AG/SAG MILLS 264
AG/SAG MILL POWER 268
CHOICE OF OPTIONS BETWEEN AG AND SAG MILLS 274
PROBLEMS 276
REFERENCES 278
Chapter 10. Mathematical Modelling in Comminution 280
INTRODUCTION 280
BASIS FOR MODELLING COMMINUTION SYSTEMS 281
MATHEMATICAL MODELS OF COMMINUTION PROCESSES 288
MODELLING CRUSHING AND GRINDING SYSTEMS 293
PROBLEMS 313
REFERENCES 316
Chapter 11. Screening 318
INTRODUCTION 318
BASIC DESIGN FEATURES IN SCREENS 318
OPERATION OF STRAIGHT SCREENS 330
CAPACITY AND SCREEN SELECTION OF STRAIGHT SCREENS 343
OPERATION OF CURVED SCREENS 356
MODELLING OF THE SCREENING PROCESS 357
SCREENING AND CRUSHING CIRCUITS 373
PROBLEMS 373
REFERENCES 378
Chapter 12. Classification 379
INTRODUCTION 379
DESIGN FEATURES OF MECHANICAL CLASSIFIERS 379
DESIGNING THE POOL AREA OF MECHANICAL CLASSIFIERS 384
DESIGN FEATURES OF CENTRIFUGAL CLASSIFIERS 390
OPERATION OF MECHANICAL CLASSIFIERS 394
CAPACITY OF MECHANICAL CLASSIFIERS 402
OPERATION OF CENTRIFUGAL CLASSIFIERS 403
HYDROCYCLONE MODELS 411
HYDROCYCLONE CAPACITY 413
HYDROCYCLONE CIRCUITS 417
PROBLEMS 420
REFERENCES 424
Chapter 13. Solid - Liquid Separation 426
INTRODUCTION 426
DESIGN FEATURES OF THICKENERS 426
THICKENER DESIGN-BATCH PROCESS 429
THICKENER DESIGN-CONTINUOUS THICKENERS 431
OPERATION OF THICKENERS 456
THICKENERS IN CIRCUITS 456
PROBLEMS 458
REFERENCES 461
Chapter 14. Solid Liquid Separation - Filtration 463
INTRODUCTION 463
DESIGN FEATURES OF FILTERS 464
OPERATION OF FILTERS 477
CAPACITY OF CONTINUOUS VACUUM FILTERS 495
WASHING OF DEPOSITED CAKE 498
DRYING OF DEPOSITED CAKE 503
OPTIMUM THICKNESS OF CAKE 511
FILTERING MEDIA 511
FILTERING AIDS 512
FILTRATION IN MINERAL PROCESSING CIRCUITS 512
PROBLEMS 513
REFERENCES 517
Chapter 15. Gravity Separation 519
INTRODUCTION 519
PARTICLE SETTLING RATES 521
GRAVITY SEPARATION OPERATIONS 532
JIGS 533
DIFFERENTIAL MOTION TABLE SEPARATORS 540
FLOWING FILM CONCENTRATORS 546
DENSE (OR HEAVY) MEDIA SEPARATION 552
GRAVITY SEPARATION PERFORMANCE 562
PROBLEMS 574
REFERENCES 579
Chapter 16. Flotation 580
INTRODUCTION 580
FLOTATION REAGENTS 582
FLOTATION EQUIPMENT 584
FLOTATION CIRCUITS 588
FLOTATION KINETICS 589
FACTORS AFFECTING THE RATE OF FLOTATION 609
OTHER FLOTATION MODELS 619
PROBLEMS 623
REFERENCES 627
Chapter 17. Metallurgical Process Assessment 629
INTRODUCTION 629
ANALYSES OF CONSTITUENTS 629
DEFINITION OF TERMS 631
MATERIAL BALANCE 633
CIRCULATING LOAD 641
PROBLEMS 645
REFERENCES 646
Chapter 18. Process Control 647
INTRODUCTION 647
CONTROLLER MODES 648
SIGNALS AND RESPONSES 651
INPUT AND OUTPUT SIGNALS OF CONTROLLERS 654
INTEGRATION OF PROCESSES AND BLOCK DIAGRAM 657
SETTING AND TUNING CONTROLS 659
COMPLEX ADVANCED CONTROLLERS 662
DEAD TIME COMPENSATION 664
INSTRUMENTATION AND HARDWARE 665
CONTROLS OF SELECTED MINERAL PROCESSING CIRCUITS 669
ADVANCES IN PROCESS CONTROL SYSTEMS 680
EXPERT SYSTEMS 686
MECHANICS OF DIGITAL PROCESS CONTROL SYSTEMS 691
PROBLEMS 693
REFERENCES 695
Appendix A 697
A-1 Average Work Index of Selected Minerals 697
A-2 Abrasive Index of Selected minerals 698
A-3 Converison of Material Size to 80% Passing Equivalent 699
A-4 Bulk Density of Steel Rods Charged in Tumbling Mills 700
A-5 Specifications of New Grinding Steel Rods 701
A-6 Pulp Properties 702
A-7 Standard Sieve Sizes 704
Appendix B 705
B-1 Bond Work Index Test Procedure for Determination of the Bond Ball Mill Work Index 705
B-2 Bond Work Index Test Procedure for Determination of the Bond Rod Mill Work Index 708
B-3 Rod Mill Power at Mill Pinionshaft (Horsepower) 711
Appendix C 712
C-1 Laplace Transformation 712
C-2 Laplace Transforms of Common Hardware 713
Appendix D 714
D-1 Common Conversion Factors 714
D-2 Viscosity of Pure Water 0-100° 715
Index 716
Mineral Sampling
1 INTRODUCTION
A processing plant costs many millions of dollars to build and operate. The success of this expenditure relies on the assays of a few small samples. Decisions affecting millions of dollars are made on the basis of a small fraction of the bulk of the ore body. It is therefore very important that this small fraction is as representative as possible of the bulk material. Special care needs to be taken in any sampling regime and a considerable effort in statistical analysis and sampling theory has gone into quantifying the procedures and precautions to be taken.
The final sampling regime adopted however is a compromise between what theory tells us should be done and the cost and difficulty of achieving this in practice.
1.1 Statistical Terminology
A measurement is considered to be accurate if the difference between the measured value and the true value falls within an acceptable margin. In most cases however the true value of the assay is unknown so the confidence we have in the accuracy of the measured value is also unknown. We have to rely on statistical theory to minimise the systematic errors to increase our confidence in the measured value.
Checks can be put in place to differentiate between random variations and systematic errors as the cause of potential differences. A random error (or variation) on average, over a period of time, tend to zero whereas integrated systematic errors result in a net positive or negative value (see Fig. 1.1).
The bias is the difference between the true value and the average of a number of experimental values and hence is the same as the systematic error. The variance between repeated samples is a measure of precision or reproducibility. The difference between the mean of a series of repeat samples and the true value is a measure of accuracy (Fig. 1.2).
A series of measurements can be precise but may not adequately represent the true value. Calibration procedures and check programs determine accuracy and repeat or replicate/duplicate measurements determine precision. If there is no bias in the sampling regime, the precision will be the same as the accuracy. Normal test results show that assays differ from sample to sample. For unbiased sampling procedures, these assay differences are not due to any procedural errors. Rather, the term “random variations” more suitably describes the variability between primary sample increments within each sampling campaign.
Random variations are an intrinsic characteristic of a random process whereas a systematic error or bias is a statistically significant difference between a measurement, or the mean of a series of measurements, and the unknown true value (Fig. 1.1). Applied statistics plays an important role in defining the difference between random variations and systematic errors and in quantifying both.
1.1.1 Mean
The most important parameter for a population is its average value. In sampling and weighing the arithmetic mean and the weighted mean are most often used. Other measures for the average value of a series of measurements are the harmonic mean, and the geometric mean. Mode and median are measures of the central value of a distribution. The mode forms the peak of the frequency distribution, while the median divides the total number of measurements into two equal sets of data. If the frequency distribution is symmetrical, then its mean, mode and median coincide as shown in Figs. 1.3 and 1.4.
For a binomial sampling unit of mixed particles the average percentage of mineral A is calculated by adding up all measurements, and by dividing their sum by the number of measurements in each series.
¯=∑ xin
(1.1)
The weighted percentage is calculated, either from the total number of particles in each series, or by multiplying each incremental percentage with the mass in each corresponding increment, and by dividing the sum of all products by the total mass for each series. However, the small error that is introduced by calculating the arithmetic mean rather than the weighted average, is well within the precision of this sampling regime. The following formula is used to calculate the weighted average for a sample that consists of n primary increments:
¯ = ∑ (ΔMi.xi)M
(1.2)
Due to random variations in the mass of each primary increment the weighted average is a better estimate of v, the unknown true value, than the arithmetic mean.
1.1.2 Variance
The variance, and its derived parameters such as the standard deviation and the coefficient of variation, are the most important measures for variability between test results.
The term range may be used as a measure of variability.
Example 1.1
Consider a binary mixture of quartz and hematite particles with approximately 10% hematite. Samples are taken and the number of hematite particles are counted to obtain the percentage of hematite in the sample. Table 1.1 gives the result of ten samples. For a binomial sampling unit the range is (maximum value – minimum value) = 12.6 − 5.7 = 6.9%.
Table 1.1
Sampling with a Binomial Population (Quartz and Hematite).
| 1 | 105 | 11 | 116 | 9.5 |
| 2 | 132 | 19 | 151 | 12.6 |
| 3 | 99 | 10 | 109 | 9.2 |
| 4 | 98 | 7 | 105 | 6.7 |
| 5 | 83 | 5 | 88 | 5.7 |
| 6 | 87 | 11 | 98 | 11.2 |
| 7 | 91 | 12 | 103 | 11.7 |
| 8 | 86 | 8 | 94 | 8.5 |
| 9 | 98 | 12 | 110 | 10.9 |
| 10 | 113 | 14 | 127 | 11.0 |
If each series of measurements is placed in ascending order, then the range is numerically equal to xn − x1 so that the range does not include information in increments x2, x3, …, xn−1. For a series of three or more measurements the range becomes progressively less efficient as a measure for variability as indicated in Fig. 1.5.
For two samples, the range is the only measure for precision but this is not sufficient to estimate the precision of a measurement process. The precision of a measurement process requires the mean of absolute values of a set of ranges calculated from a series of four or more simultaneous duplicates. This is the variance.
The classical formula for the calculation of the variance is:
(x)=∑ (x¯−xi)2n−1≈∑ xi2−(∑ xi)2nn−1
(1.3)
The standard deviation, σ, is the square root of the variance. The coefficient of variation (CV), is a measure of precision and is numerically equal to:
(%)=100σx¯
(1.4)
Example 1.2
Variance values from a sampling procedure with a binary mixture of mineral particles is given in Table 1.2.
Table 1.2
Variance values from the sampling example with a mixture of quartz and...
| Erscheint lt. Verlag | 26.6.2006 |
|---|---|
| Sprache | englisch |
| Themenwelt | Naturwissenschaften ► Chemie |
| Naturwissenschaften ► Geowissenschaften ► Mineralogie / Paläontologie | |
| Technik | |
| ISBN-10 | 0-08-045461-5 / 0080454615 |
| ISBN-13 | 978-0-08-045461-0 / 9780080454610 |
| Haben Sie eine Frage zum Produkt? |
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