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METE 256 ASSAYING – Ore Reserve Calculations – Control of Processes (Gravity Concentration) – Recovery Calculations – Smelter Schedule Dr

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METE 256 ASSAYING – Ore Reserve Calculations – Control of Processes (Gravity Concentration) – Recovery Calculations – Smelter Schedule Dr 1/5/2017 Kwame Nkrumah University of Science & Technology, Kumasi, Ghana Course Objective • Determination of the constituents of ores and metallurgical products for: – prospecting METE 256 ASSAYING – ore reserve calculations – control of processes (gravity concentration) – recovery calculations – smelter schedule Dr. Anthony Andrews – bullion sales, etc Department of Materials Engineering Faculty of Mechanical and Chemical Engineering College of Engineering www.knust.edu.gh Course Outline Course Assessment • Sampling – Methods of sampling • Quizzes – 10 points – Sampling dividing techniques • Mid Exam – 20 points – Weight of samples relative to size of particles • Final Exam – 70 points • Statistical evaluation of data • Metallurgical testing – Bottle roll test, Column leach test, Acid digestion, Fire assaying, Diagnostic leaching • Characterization and instrumental methods of analyses www.knust.edu.gh www.knust.edu.gh Fire Assaying - Introduction Fire Assaying - Background • Many methods have been developed and refined over the years, but “Fire Assay” remains a favoured method for determining the • The particular fire assay method under discussion is total gold content of a sample. aimed only at measuring • In this method, a pulverised mineral sample is dissolved using heat Gold and Precious Metals and fluxing agents. • Precious metals are extracted from the melted material using • Variations of fire assay can be used for other metals, molten Lead (Pb). however, in most instances other analytical methods are favoured • The precious metals are then separated from the Lead in a secondary process called “cupellation”. • The gold content of the precious metals collected is then determined, using a variety of analytical techniques. www.knust.edu.gh www.knust.edu.gh 1 1/5/2017 Fire Assaying – Applications Traditional Fire Assay Method (After Sample Preparation) • Soil samples 1. Sub-sampling & Catch-weigh • Exploration drill samples 2. Fluxing 3. Firing • Grade control 4. Cooling & Separation 5. Cupellation • Mill solutions 6. Parting & Dissolution 7. Analysis • Tailings www.knust.edu.gh www.knust.edu.gh Sampling Significance of Sampling • A process of taking a portion from a bulk of material and using that portion to represent the bulk of material. • Convenience in size for transportation and testing Or • A sample is a small amount of material removed from a bulk, such that it contains all the components in the • Obtain the desired information at the smallest cost proportion in which they occur in the original lot. • Why Sample??? • Entire bulk may be inaccessible, too massive or too dangerous to deal with. E.g human blood www.knust.edu.gh www.knust.edu.gh Important Considerations in Categories of Sampling Sampling • Representative of the bulk • Exploratory – Samples taken during prospecting, exploration and proving of a • Results from analysis of the sample should be appropriate to mine predict the behaviour of the bulk • No sample can provide absolute information about the bulk • Controlled • Statistical technique – provide an estimate within probability limit – Samples taken to determine the content of specific constituents • All the components in the bulk should have equal chance of in a given environment reporting into the sample • Pre-sampling preparation to reduce biasness www.knust.edu.gh www.knust.edu.gh 2 1/5/2017 Principles of Sampling Methods for Sampling Material in a Lab • The distribution of values in an ore body is never Stratified or Unstratified uniform • When is this sampling • The results of the sampling shall represent as truly as technique used? possible the average metallic content of the ore/bulk • Where will you take a sample material from? • Each single sample must represent a true average of that portion of bulk from which it is taken www.knust.edu.gh www.knust.edu.gh Methods for Sampling Material in a Methods for Sampling Material in a Lab Lab Grab sample Random – chance • Simplest, quickest, and most • Where will you take a sample flexible method from? • It can be carried out on small quantities using spatulas, or on large quantities using shovels Systematic – orderly • This method uses the least Mixing a sample on a rolling mat. equipment, but also is the most • Where will you take a sample Mix by first drawing corner A so that prone to human biases and has a from? the sample rolls towards C, then higher variance between samples drawing corner B to corner D, then than other methods. drawing corner C to corner A, then corner D to corner B, then repeat. www.knust.edu.gh www.knust.edu.gh Methods for Sampling Material in a Sample Dividing Methods Lab Composite sample • The sample does not pass through the sample device and hence prone to error • Individual samples combined as single sample • Sample is taken from the surface where it may not be typical of the mass. Scoop sampling • Shake sample before sampling. www.knust.edu.gh www.knust.edu.gh 3 1/5/2017 Sample Dividing Methods Sample Dividing Methods Chute-Type Riffle Sampler Coning and quartering www.knust.edu.gh www.knust.edu.gh Sample Dividing Methods Comparison of Lab Sample Devices Rotary Riffle Splitter Standard Deviation of Sampling Method Samples (%) Cone & Quarter 6.81 Grab Sampling 5.14 Chute-Type Sample Splitter 1.01 Rotary Riffle 0.125 www.knust.edu.gh www.knust.edu.gh Sampling Problems and Sampling Problems and Requirements Requirements • Degree of representativeness is based on heterogeneity • Problems in sampling centers on: • Issues with variations in the distribution of components within the – Nature and efficiency of sampling process bulk such as: – Weight reduction in the lab – Size segregation – Correctness in the interpretation of data – Mineralogy – Reliability of results – Chemical composition – Accuracy of results – Grade – Precision of results – Moisture content – Biasness in sampling and measurement – Weight – Shape • Incorrectness of the above will result in sampling error www.knust.edu.gh www.knust.edu.gh 4 1/5/2017 Size effect on sample integrity Sampling Calculations using Gy’s Method • Mineralogy, grade and moisture content may vary with size • This method is a general-purpose calculation to determine the minimum size of sample needed to ensure that it will be • Bulk material …Gross sample…Lab…Measurement representative of the whole lot, within specified limits. – Samples for lab measurement are obtained by standard techniques – Samples for lab measurement can be size-biased Before using, approximate estimates of the following will be needed: • Coarse samples presents challenges in size volume reduction • The content of the species of interest in the lot (assay) • Smaller volume samples are more representative when particle • The general shape of the particles size is fine • The densities of the various species and phases present • The particle size distribution • The degree of liberation, and the grain size www.knust.edu.gh www.knust.edu.gh Sampling Calculations using Gy’s Sampling Calculations using Gy’s Method Method Basic Equation: Basic Equation: When W is much larger than M, the equation is simplified to: www.knust.edu.gh www.knust.edu.gh Gy’s Equation – Working out C Calculating with incomplete information 1 1 푆2 = 푓푔푙푚퐷3 − Make the following conservative assumptions: 푀 퐿 • f = 0.5 (normal blocky particles); Where C is fglm • g = 0.75 (narrow size distribution. Use g = 1 if the sample is • f= particle shape factor (describes the shape of the particles) obviously monosized and 0.25 for broad size distribution); • g= granulometric factor (describes how much variation there is • l = 1 (grains are as large as the particles) in the size of particles) • l = liberation factor (how close to liberation the material has • The value of m will still need to be calculated, based on your best been ground) estimate of the assay of the sample and the densities of the • m = mineralogical composition factor (describes how much of components of interest. a rock is made up of the element of interest at a given grade) www.knust.edu.gh www.knust.edu.gh 5 1/5/2017 Calculating with incomplete Calculating with incomplete information information • The liberation factor, l, is a measure of the degree of dispersion • The composition factor (m), is calculated from the formula: of the valuable material through the bulk, and of the homogeneity of the material. 1 − 푎 푚 = 1 − 푎 푟 + 푎푡 • It is calculated from the expression: 푎 퐿 푙 = 푑 Where: r = specific gravity of the valuable component t = specific gravity of the remainder of the material Where: a = fractional average assay of the valuable substance L = the size where the values are essentially completely liberated (grain size), cm d = sieve size www.knust.edu.gh www.knust.edu.gh Gy’s Equation Work Example • A sample of 200 g is to be taken and used for fire • Simplified version of Gy’s equation: assaying from a bulk sample of weight 5 kg with 푊 ≥ 125000푑3 average particle size 10 mm. How fine should the material be crushed before a representative sample can W = weight, g be taken? d = diameter of the largest particle (cm) www.knust.edu.gh www.knust.edu.gh Home Work Important Terminologies Bulk Materials Parameters: • Materials of Interest: CuFeS2 in a silica matrix, 1.5% Cu • Replicates: - samples of the same size that are carried through an (4.3318% CuFeS2); Top Size = 1.5 cm; CuFeS2 grain size = 0.01 analysis in exactly the same way. cm. • Precision: - the closeness of data to other data that have been obtained in exactly the same way. • Desired sampling accuracy: ±0.02% Cu, certainty of 0.99 (2.576 • Accuracy: - the correctness of measurement or closeness of a result standard deviations) to its true or accepted value. • Outlier: - an occasional result in replicate measurements that obviously differs significantly from the rest of the results. • CuFeS2 specific gravity = 4.2; Overall specific gravity = 2.8; Broad size distribution. • Bias: - a measures of the systematic error associated with an Determine the minimum sample weight (in grams) needed for analysis.
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