Tools for Mining: Techniques and Processes for Small Scale Mining (GTZ, 1993, 538 p.) A. Analysis A.1. Definition A.2. Initial conditions and problem areas

Tools for Mining: Techniques and Processes for Small Scale Mining (GTZ, 1993, 538 p.)

A. Analysis

A.1. Definition

The section on analysis includes the determination of the chemical and physical properties of soil, rock and ore samples as well as of concentrates, middlings and tailings from beneficiation processing. The analytical procedure used here consists of the following four steps:

1. sampling,
2. chemical or physical analysis of sample material,
3. classification and statistical analysis of data, and
4. interpretation of the results.

The application of analytical procedures in the small-scale mining industry are particularly significant for prospecting, exploration, quality control during mining, beneficiation, marketing and environmental protection.

A.2. Initial conditions and problem areas

Small-scale mining in developing countries suffers from a lack of knowledge concerning crude ore reserves as well as product composition. The situation is worsened by the fact that ihomogeneous mineralization exists, especially in deposits of sub-volcanic genesis as are characteristic of the Andean region. As a result, variations in mineralization occur within small proximities with regard to both geological relationships and mineralogical and geo-chemical compositions. A good example of this can be seen in Bolivian tin deposits, where the tin source can be Cassiterite (for chemical composition see Table), Cylindrite, Teallite or Frankeite (three Sulfostannates) or Stannite. Knowledge of the entire geological relationship is critical for planning. not only the mining procedure but especially the beneficiation processing.

The composition of concentrates is frequently not known by the small-scale mining operators, which can be disadvantageous for selling the products. Impurities in the concentrates result in lower prices for the product following high penalty deductions assessed by the buyers or the beneficiation plant, and further impairing the marketing of profitable by-products. The Cascabel Mine in Bolivia (Dept. La Paz) serves as an example, which, despite higher lead, silver and tin contents in its concentrates, is only able to market its products with great difficulty, suffering large penalty assessments (price discounts) due to abnormally high levels of mercury contamination. These mineralization problems occur not only in the primary vein ore deposits, but are also present in placer deposits; deficient product knowledge is the reason why valuable platinum contents in alluvial gold deposits (e.g. in Colombia) are not being mined and consequently not being separately marketed.

Another characteristic problem of small-scale mining in developing countries is the questionable credibility of the analyses, which, as a rule, are performed by the buyer himself. Control checks have shown that results of the analyses are being manipulated to the advantage of the buyer and to the disadvantage of the small-scale mine operators. Primarily, the silver contents were given as too low, and the residual moisture levels as too high, which is difficult to prove in the absence of control measures.

The resulting conclusion is that the small-scale mining industry needs to implement its own control program. In addition to quality control during mining (grade control) and beneficiation and marketing planning, analytical procedures suitable for small-scale mining are also important for prospecting and exploration activities.

The use of centralized analytical methods becomes inconvenient or even impossible for small-scale mining due to the location-dependency of stationary analytical techniques, and the lack of infrastructure in the remotely-located, isolated small-scale mining operations.

The need exists within the small-scale mining industry for a simple, portable analyitical procedure. The main criteria should include low cost and quick performance with limited equipment and time requirements while avoiding unnecessary measuring precision. The extent to which the analytical results are representative and are reproducable is determined more through the quality and preciseness of the sampling rather than the application of the most optimal method of analysis. An analysis which is precise to several places behind the decimal point is worthless when an improper sampling procedure results in inaccurate figures in front of the decimal point.

The lack of simple analysing procedures for smallscale mining is not limited just to developing countries; this is an area calling for research-anddevelopment efforts.

A.2.1 SAMPLING

The sampling procedure is of primary importance for the technical planning of mining and beneficiation operations. However, a very precise and exact analysis is of no value if the sample being analyzed is not representative. A sample is representative of its original geologic environment only when the same chemical, mineralogical and physical relationships characteristic of the specific geologic area are exhibited in the sample. These relationships are defined by the mineral or element distribution, humidity, granulation and grain-size distribution, permeability, etc. When a waste dump, mineral deposit or beneficiation product is analyzed, it is not possible to examine the entire dump or deposit, or the total product quantity, but only portions of the whole. Proper conclusions can only be made from these sampled portions when they are representative of the whole.

In the testing of a pile of crude ore, for example, it is not sufficient to take only one chunk of ore from the pile, which may be representative of only the country-rock or the mineralization itself. An analysis of this sample alone would result in erroneous conclusions concerning the metal-content of the deposit in general. Several sampling techniques which consistently produce representative results are discussed below:

Bulk sampling is employed for the sampling of loose fine-to-coarse-grained materials, such as in the analysis of tailings, waste dumps, products, and crude ores. Numerous smaller samples are taken from a number of various arbitrary locations throughout the material pile without preference to any particularly richer or poorer regions. The sampling procedure should not only include numerous different sample locations but should also ensure that the grain-size of the samples also vary, in that samples of the finer fractions and the fines are also collected along with the large pieces of ore. In so doing, the sample volume or quantity should always be at least ten times greater than that of the largest individual sample in order to assure that the effects of classification, whether from deposition in the pile or from selectivity during blasting, are statistically compensated.

Channel sampling is a method of sampling exposed in-situ ore-bodies. In this procedure, sample material is obtained from a groove, constant in width and depth, cut into the rock over a specific length, for example from the hanging wall to the foot wall across the width of the face during drifting; for example, a slit 10-cm in height and 5-cm in depth is cut out along the entire stope width with the sample material being collected on a tarp spread on the roadway floor below.

In-situ ore bodies can also be tested by grab sampling. Over the affected sample area, for example the area of the face, numerous equally-sized samples are randomly taken by hammering, digging or prying off loosened chunks without any locational preference for richer or poorer zones of mineralization. This sampling method is considerably easier to carry out than channel sampling, especially in hard ore bodies.

Cuttings-sampling recovers sample material from the washed drill dust or drill cuttings which are produced during drill-and-blast drifting and mining. As a result, the collected sample material originates not just from exposed surfaces, but rather represents a three-dimensional sampling area when an entire drilling-grid is sampled. An additional advantage of this method is that the sample material already exists in a finely-comminuted form.

In order to avoid systematic causes of errors, sampling should always be conducted by only one and the same person.

Fig.: Manual quartering of sample material by mixing, coning, mixing, flattening, quartering, and discarding of two opposite-lying quarters. Source: Schroll

For further treatment larger-sized samples are crushed and subsequently quartered. This is performed by heaping the crushed sample material into a cone, thoroughly mixing it several times via shovelling, and again heaping it into a cone by pouring. The cone is then pressed or stomped flat, and the resulting flat cone base is divided into four equal segments. Two of the quarters, located opposite one another, are analogously processed further, while the remaining two quarters are discarded. This procedure is repeated as often as required until the desired sample quantity has been reached. The conical pouring of the sample material assures the homogeneity of the sample.

The homogeneity of ores, alluvial deposits, tailings or other mined waste, and the degree to which the samples are representative, is particularly important where the element or mineral-content is low. This occurs in discrete aggregates, where the valuable mineral exists as separate grains independent of the mineralized matrix. An important example is gold. Gold analysis demands values of a magnitude of less than 1 g/t. Gold particles can appear as gold glitters or nuggets with individual weights of up to more than 1 9. If, for example, one ton of crude ore which has only a single 1-9 gold nugget is analyzed by using 100kg of sample material which happens to contain this nugget, the results will indicate 10 9 of gold/l, which of course is too high. Statistically, in 9 out of 10 cases the nugget would not be contained in a 100-kg crudeore sample, so that the gold content is then assessed at 0 g/t. This effect is known as the nugget effect and requires, in such cases, multiple samples of large quantities. The more nonhomogeneous the sample material, the higher the number of samples required in order to obtain a statistical median value which approaches the true value for the material as a whole.

A.2.2 MINERALOGICAL EXAMINATION

Under certain conditions, an optical measuring or visual estimation of the ore content underground can serve as a substitute for an analysis. The prerequisites for this are:

- a relatively high proportion of ore minerals in the total material, since only then is the measured or estimated value of sufficient accuracy, and

- high visibility of the ore mineral under the conditions of examination. This requires clean working faces or sampling surfaces, perhaps involving the use of artificial methods to improve visibility, for example with ultra-violet lamps to detect mineral luminescence.

The visual evaluation is significant in lead-zinc mining in hydrothermal deposits with classic vein patterns. As a rule, testing is combined in this case with geological mapping of the hanging or footwalls. In so doing, the total width of the wall and the width of the ore veins are measured onto one profile. The profile must run vertically along the strike and dip of the vein; otherwise the values appear unrealistically high. If the profile is divided into several fragments, the sum of the individual vein thicknesses can be determined (for example, 25 cm galena, 15 cm sphalerite over a thickness of 175 cm). From this information, the volumetric proportion of the various ore minerals can be calculated. When the densities of each ore mineral and host rock are included, the total weight proportion of the ore minerals can then be established. With this information, and the additional knowledge of the metal content in each ore mineral, the metal-content distribution (% by weight) of the sampled profile can be determined. The incorporation of correction factors to account for mineral intergrowths, etc., can increase the accuracy of this method. This form of sampling or testing has proven itself even in highly mechanized operations in industrialized countries where it competes against modern procedures, such as portable X-ray fluorescent analysis.

Another mining sector which employs optical evaluation is scheelite mining, where the mine face is irradiated with an ultra-violet lamp which induces fluorescence of the scheelite.

As is true for sampling procedures, a high degree of accuracy in the optical test results can only be attained through disciplined work procedures and a great deal of experience.

A.2.3 ADVANTAGES OF MINIMIZING ACCURACY

All analysing procedures and evaluation methods exhibit a linear relationship between degree of accuracy and the cost of analysis, or, in other words, the more accurate the analysis, the more complex the equipment and the higher the costs. The lower the detection limit of the analytical method, i.e. the smaller the analyzed value is, the more expensive the analysis will be. Looking at this fact, it is absolutely necessary from an economical standpoint that the small-scale mining industry employs the cheapest method of analysing available within the desired accuracy and metal-content limits.

A.2.4 DETERMINATION OF ELEMENT DISTRIBUTION IN RAW ORE AND CONCENTRATES

Lack of knowledge about the contents of the different elements in raw ore, mine waste and concentrates is frequently the cause for the inefficient or uneconomic performance of small-scale mining operations. As a rule, only the contents of the desired metals in the ore and concentrate are examined. Consequently, the causes for undesired metal contents in the products, and subsequent penalty assessments, are not known by the small-scale miner. Additionally the accounting statements from the ore buyer do not explicitly indicate the reasons for penalty deductions. Commercially marketable byproducts also remain unidentified.

A number of various contaminating metals and elements which may be present in the mine products lead to penalties, assessed by the smelters in the form of price reduction, when the content of these metals exceed a maximum tolerance level. These elements, their maximum tolerance limits, and the penalty amounts are established by the smelting standards, varying according to smelting process, market situation and buyer. Consequently, a definite statement concerning these elements and tolerance limits cannot be made; however, as a general reference, the following table lists some critical elements which are deleterious to non-ferrous metal ore concentrates:

As a rule, non-ferrous metal smelters penalize:
Bi	In almost all concentrates
Hg	in almost all concentrates
S	in concentrates of valuable oxide minerals
As	in Pb-Ag-concentrates
Cu	in Pb-Ag-concentrates
Cd	in Pb-Ag-concentrates
Se	in almost all concentrates

Penalties can lead to a considerable decrease in profit for small-scale mining operations. Therefore, knowledge of the element and trace-element distribution should be obtained, as much as possible, before initiating any mining activities or planning the beneficiation plant in order to establish a marketing strategy.

Similar to the deleterious metals, element contents which would be worth recovering and marketing in the form of by-products are also often overlooked; for example, zircon sand from alluvial deposits, gold-containing pyrite and arsenopyrite from complex sulphide veins. Here, as well, a knowledge of the element distribution prior to the start of any mining activities is crucial in order to formulate an optimal marketing strategy.

The practice of performing complete analyses on a concentrate sample and on a mixed raw-ore sample, conducted by a competent laboratory for the purpose of determining the contents of all relevant metals, trace-elements and cations, should become standard procedure for the small-scale mining industry. Governmental support of these needs, for example by providing inexpensive analyses, would significantly contribute to promoting the small-scale mining industry.

The performing of mineral analyses also serves an important function from an environmental-protection viewpoint by identifying environmentally-damaging components such as sulfur in coal, residual mercury in gold tailings, cyanide and arsenic contents in mining wastes, etc.

A.2.5 DETERMINATION OF THE VALUABLE-MINERAL SOURCE

In addition to a purely geochemical examination of the raw materials, a mineralogical knowledge, especially of the valuable-mineral sources, is of major priority in small-scale mining. Since beneficiation processing in small-scale mining usually leaves the material components of the minerals unchanged, this identification of the valuable-mineral source is particularly important for planning and marketing. This can be accomplished through microscopic examination of polished sections, which enables experienced microscope analysts to quickly and easily semi-quantitatively recognize segregations, trace element minerals, etc.

The question, for example, of whether silver appears as a silver mineral or as a lattice element of lead or zinc minerals can strongly influence the beneficiation, marketing and profitability of a mining operation.

Equally important is the mineralogical composition of the raw material in primary gold deposits, in which the gold can occur as free gold or bound to pyrite or arsenopyrite as "refractory ore".

Whatever the situation, it is essential that the major ore minerals can be marketed. Some ore deposits produce main valuable minerals which are sellable only with great difficulty, if at all; such as the complex ore deposits with spienles sulfades (antimony and arsenic) as the metal source.

One example is the Taricoya Mine in Bolivia, whose raw ore reserves are relatively promising according to FONEM, as here shown in the Table:
Pb: 3.45 %	Ag: 379 g/t
Sb: 6.48 %	Au: 7 g/t
However, because the main ore mineral is composed of specular jamsonite (Pb4FeSb6S14) selling the concentrates is very difficult.

The above example shows that the results of mineralogical studies play an important role in determining whether or not an ore deposit can be mined profitably using the simple mining methods characteristic of small-scale mining.

A.2.6 OTHER RAW MATERIAL STUDIES

In addition to chemical and mineralogical composition, other characteristic data are also important, depending upon the material, for the analysis of raw mineral reserves. Examples are:

- ash content' thermal value, sulfur content, caking capacity, etc. for fossil fuels (coal, peat);
- compressive strength of a cube, cleavability and permeability for construction materials;
- swelling characteristic for certain clays (vermiculite);
- weaving characteristic for asbestos;
- coloration for pigment raw materials (barite, kaolin);
- grain sizes for many raw materials (large grain size for graphite and mica, fine grain size for kaolin;
- hardness for grinding material (corundum, garnet).

The following table presents a list of essential ore minerals including primary physical characteristics and types of veins and host rocks.

Table: Characteristics of Ore Minerals including Vein Types, Gangue or Matrix, Asociated Minerals and Host Rocks:

Name	Composition	Content of valuable minerals	Density	Tennnacity 1)
Ordinary lead-zinc mineralization:
galena	PbS	Pb: 86.6 %	7.2-7.6	4
sphalerite	ZnS	Zn: 67.0 %	3.9-4.1	2
wurtzite	ZnS	Zn: 67.0 %	4.0-4.1	2
greenockite	CdS		4.8	*
cerussite	PbCO3	Pb: 77.5 %	6.4-6.6	1
anglesite	PbSO4	Pb: 68.3 %	6.3-6.4	1
smithsonite	ZnCO3	Zn: 52.1 %	4.0-4.5	2
Mixed lead-silver-zinc-gold mineralization:
bournonite	CuPbSbS3	Pb: 42 %	5.75.9	3
boulangerite	Pb5Sb4S11	Pb: 55 %	5.9-6.5	2
jamesonite	Pb4FeSb6S14	Pb: 40 %	5.6	4
tetrahedrite	Cu12Sb4S13	Ag: up to 19 %	4.6-5.1	2
free silver	Ag	Ag: up to 100 %	10.1-11.1	6
stephanite	Ag5SbS4	Ag: 68 %	6.2-6.4	2-4
argentite	Ag2S	Ag: 87 %	7.2-7.4	6
proustite	Ag3AsS3	Ag: 65 %	5.6	2
pyrargyrite	Ag3SbS3	Ag: 60 %	5.8	2
petzite	Ag3AuTe2	Ag: 41.8 %
		Au: 25.4 %	8.7-9.1	5
free gold	Au	Au: up to 100 %	15.5-19.3	6
copper minerals:
free copper	Cu	Cu: up to 100 %	8.5-9.0	6
covellite	CuS	Cu: 66.5 %	4.6-4.8	4
chalcocite	Cu2S	Cu: 79.9 %	5.5-5.8	4
bornite	Cu5FeS4	Cu: 63 %	4.9-5.3	2-4
chalcopyrite	CuFeS2	Cu: 34.7 %	4.1-4.3	3
enargite	Cu3AsS4	Cu: 48 %	4.4-4.5	2
cuprite	Cu2O	Cu: 88.8 %	6.1	2
malachite	Cu2(OH)2CO3	Cu: 57 %	4.0-4.1
azurite	CU2(OH/CO3)2 Cu: 55 %	3.8	2
tin minerals:
cassiterite	SnO2	Sn: 78.1 %	6.8-7.1	2
teallite	PbSnS2	Sn: 30%	6.4	4
franckeite	Pb5Sn3Sb2S14 Sn: 17 %	59	4
stannite	Cu2FeSnS4	Sn: 27.5 %	4.3-4.5	2
antimony minerals:
antimonite	Sb2S3	Sb: 71.4 %	4.6-4.7	4
antimonochre	Sb2O3(H2O)	Sb: var.	5.6-6.6	**
bismuth minerals:
free bismuth	Bi	Bi: up to 100 %	9.7-9.8	2
bismuthinite	Bi2S3	Bi: 81 %	6.8	4
bismuthochre/bismite	Bi2O3	6.7-7.5	**
tungsten minerals:
scheelite	CaWO4	W: 63.8 %	6.1	2
wolframite	(Fe,Mn)WO4	WO3: 76 %	7.1-7.5	2
ferberite	FeWO4	WO3: 76.4 %	7.5	2
huebrerite	MnWO4	WO3: 76.6 %	7.1	2
tungstic ochre/tungstite	WO2(OH)2	4.0-4.5	**
additional and accompanying minerals:
realgar	As4S4	As: 70 %	3.6
orpiment	As2S3	As: 61 %	35
molybdenite	MoS2	Mo: 60 %	4.6-5.0
pyrite	FeS2		5.0-5.2
pyrrhotite	FeS		4.6-4.8
haematite	Fe2O3		4.9--5.3
arsenopyrite	FeAsS		5.9-6.2
limonite	FeOOH		aprooox.4
jarosite	KFe3((OH)6/(SO4)2)	3.1-3.3
argentojarosite	AgFe3((OH)6/(SO4)2)	?
plumbojarosite	PbFe6((OH)6/(SO4)2)	?

¹⁾Tenacity characterizes brittleness or breaking characteristics of the mineral

Explanation of tenacity/Remarks:

1	very brittle
2	brittle
3	less brittle
4	mild
5	ductile
6	very ductile
*	exists as fine intergrowths
**	exists in pulverized form due to weathering
1)	Tenacity characterizes brittleness or breaking characteristics of the mineral

Table: Characteristics of Ore Minerals including Vein Types, Gangue or Matrix, Associated Minerals and Host Rocks:

Name	Composition	Density
quartz	SiO2	2.6-2.7
calcite	CaCO3	2.6-2.8
siderite	FeCO3	3.7-3.9
dolomite	CaMg(CO3)2	2.8-2.9
fluorite	CaF2	3.1-3.2
barite	BaSO4	4.3-4.7
vivianite	Fe3PO4 8H2O	2.6-2.7
apatite	Ca5(F,Cl,OH)(PO4)3	2.9-3.1
epidote	(Ca2Fe)(AI2O)(OH)Si2O7SiO4	3.4-3.5
tourmaline	Complex boron-hydroxylic silicate	3.0-3.1
orthoclase	(K,Na)AISi3O8	2.5-2.7
plagioclase	(Ca,Na)(Al,Si)4O8	2.6-2.7
alunite	KAI3(OH)6(SO4)2	2.6-2.9

HOST ROCK

Name	Density
granite	2.6-2.7
diorite	2.8-2.9
syenite	2.6-2.8
dacite	2.6-2.7
andecite	2.5-2.6
trachyte	2.6-2.8
basalt	2.7-3.2
porphyry	2.7-2.9
gneiss	2.4-2.7
quartzite	2.3-2.6
sandstone	2.2-2.5
clay shale	2.6-2.7