Systems and methods for characterizing unconsolidated material

Abstract

Apparatus and methods for determining the mass of unconsolidated material including collecting surface measurements of a heap of the material, collecting muon measurements of the heap, and determining a mass of the heap. Apparatus and methods for determining the moisture content of an accumulation of unconsolidated material including collecting surface measurements of the material, collecting muon measurements of the material before and after irrigation, and determining a mass of fluid inserted during irrigation. Apparatus and methods for method for determining an air-filled and a fluid-filled porosity in an unconsolidated material including collecting and analyzing muon attenuation data within an equivalent density model for the unconsolidated material and determining spatial or temporal changes in the amount of air-filled and fluid-filled porosity in the material.

Description

SYSTEMS AND METHODS FOR CHARACTERIZING UNCONSOLIDATED MATERIAL PRIORITY CLAIM

[0001] This application claims priority to United States Provisional Application Serial Number 63 / 728,506 entitled, “SYSTEMS AND METHODS FOR CHARACTERIZING A LEACHING HEAP,” filed on December 5, 2024 and incorporated by reference herein in its entirety.FIELD

[0002] This disclosure relates to monitoring and assessing changes in the volume, density, and volumetric fluid content added to an initially dry or as is granular material in man-made heaps of bulk materials that may occur in the stockpiling of mineral ores, mine waste or residuals, run-of-mine materials, gravel, stones, sand, coal, cement, fly ash, salts, grains, chemicals, clays, and crushed limestone.BACKGROUND

[0003] The monitoring of unconsolidated materials for mechanical stability and fluid accumulation is a significant challenge. Slope failures and landslides of unconsolidated materials, soft soils, and sediments generally occur when the static stress due to the weight of the accumulation of material exceeds the shear strength of the material itself. The literature distinguishes between different failure modes such as translation failure, rotational failure, or wedge failure. Excessive fluid content in the unconsolidated material (e.g. as a result of industrial processes such as heap leaching or natural events such as rainfall and snow melt, etc.) is one of the most common underlying causes for slope stability failure, including potentially the liquefaction of the entire slope. Further, there is no method, direct or indirect, for determining the volume of air inside a material, other than combining volumetric surveys with a measurement of bulk density or total tonnage. Understanding, monitoring, and mitigating the underlying causes or potential for slope stability risks is critical in the mining industrySUMMARY

[0004] Embodiments herein relate to apparatus and methods for determining the mass of unconsolidated material including collecting surface measurements of a heap of the material, collecting muon measurements of the heap, and determining a mass of the heap. Some embodiments may utilize an equivalent density model of the heap, use the mass to calculate how much material is added to, stored, or removed from the heap, or determine an economic value of the heap. Collecting surface measurements may include using a drone or stationarydevice or both or using a laser. In some embodiments, the heap may include materials of varied densities or a stockpile of mineral ores containing iron, aluminum, zinc, copper, or a combination thereof.

[0005] Embodiments herein relate to apparatus and methods for determining the moisture content of an accumulation of unconsolidated material including collecting surface measurements of the material, collecting muon measurements of the material before and after irrigation, and determining a mass of fluid inserted during irrigation. Some embodiments may utilize an equivalent density model of the material or a volumetric analysis to compensate for compaction, slumping, or other volumetric changes of the material over time, determine a location for irrigating, injecting, or inserting leaching fluids or air, or determine an amount of leaching fluids or air to be irrigated, injected, or inserted into the leaching heap. In some embodiments, the location is based on a three-dimensional distribution of bulk density, fluid content, or air-filled porosity or the distribution has been corrected for compaction effects.

[0006] Embodiments herein relate to apparatus and methods for method for determining an air-filled and a fluid-filled porosity in an unconsolidated material including collecting and analyzing muon attenuation data within an equivalent density model for the unconsolidated material and determining spatial or temporal changes in the amount of air-filled and fluid-filled porosity in the material. Some embodiments may collect muon attenuation data over time and revise the changes. Revising the changes may include identifying geometry changes due to compaction or material slumping over time. Some embodiments may use the fluid-filled porosity to estimate the moisture fraction of the material, use the air-filled porosity to estimate the amount of air present in the material, or determine a fluid or air injection strategy for metal recovery from the material.FIGURES

[0007] Figure 1 is schematic to compare heap models.

[0008] Figures 2A, 2B, 2C, and 2D compare natural and equivalent heap models.

[0009] Figure 3 is three dimensional view of data collected by a drone.

[0010] Figure 4 is a three dimensional view of a model built with muon observations and drone data.

[0011] Figures 5 A and 5B are dimensional views of a LIDAR system observing the surface of unconsolidated material resting in a truck bed.

[0012] Figure 6 shows a stockpile scanned by a drone at the top of the figure and an earth based total station system at the right of the figure.

[0013] Figure 7 provides a three dimensional view of a drone flying over a bulk solids handling area that includes a crane, conveyor belt, and bulk solids in storage.DETAILED DESCRIPTION

[0014] Embodiments discussed herein relate to monitoring and assessing changes in the volume, density, and volumetric fluid content added to an initially dry or as is granular material in man-made heaps of bulk materials that may occur in the stockpiling of mineral ores, mine waste or residuals, run-of-mine materials, gravel, stones, sand, coal, cement, fly ash, salts, grains, chemicals, clays, and crushed limestone. Embodiments discussed herein further relate to monitoring and assessing the mechanical stability’ and fluid accumulation in heaps of mining ores, including crushed, milled and / or agglomerated ore, as well as the accumulation of run-of-mine and / or mine waste materials, including dry -stack or filtered materials. This disclosure also relates to indirectly monitoring and assessing the air-filled fraction within the unconsolidated material or a heap of mining ore under leaching, for the purpose of optimizing the efficiency or viability of certain leaching processes (e.g. bioleaching processes) that rely on maintaining viable concentrations of bacteria (e g.Acidophiles) or other oxidizing agents to facilitate the leaching process.

[0015] Methods herein determine a total mass of material contained within a stockpile for the purpose of determining its true economic value. Some embodiments may determine a change in the fluid content of a leaching heap for understanding the efficiency for metal recovery’ and economic value. Some embodiments monitor slope stability as well as determine fluid volumes within a large structure such as a stockpile, a mining leaching heap, a pond retaining wall, a man-made dam. an underground tunnel, an open pit, or an access road.

[0016] The systems and methods described herein use a volumetric analysis of the stockpile of materials or leaching heap combined with a direct measurement of the bulk density of the same via muon radiographic techniques. The systems and methods described herein also including determining an excess or increased bulk density due to the accumulation of fluids in the unconsolidated material which can be interpreted in terms of total fluid mass or volume within the unconsolidated material. Embodiments herein relate to monitoring the stability' of the walls of open pit and underground mines, as well as the stability of bulk cargo including stockpiled materials at a loading dock or station, and / or materials under transport to such a loading station, e.g. from a mining operation

[0017] As used herein, the terms ‘‘heap,” “stockpile,” “wall,” or “walls” mean a mass of unconsolidated material.

[0018] As used herein, “unconsolidated material” means sediment that is loosely arranged or unstratified, or whose particles are not cemented together, found either at the surface or at depth or granular material in man-made heaps of bulk materials that may occur in the stockpiling of mineral ores, mine waste, or residuals, run-of-mine materials, gravel, stones, sand, coal, cement, fly ash, salts, grains, chemicals, clays, and crushed limestone. It may also mean a pile of scrap metal, including metal that is going re-smelted or re-cycled, or a metal containing ore feed prior to being loaded onto a smelter, furnace, or other processing unit. The material may include material with varied densities. In some embodiments, the material may include a stockpile of mineral ores. Mineral ores may include ores containing iron, aluminum, zinc, copper, or a combination thereof.

[0019] A measurement of bulk density is determined by way of measurement of the event rate (or flux intensity, particles per second) of atmospheric muons passing through the volume of unconsolidated material under investigation. Atmospheric muons are part of the natural cosmic ray flux and arrive from the upper atmosphere to everywhere on the surface of the earth. The flux of the atmospheric muons is measured by counting the number of particles passing within a solid angle centered around a given direction, per unit of time. Similar to X-ray radiography and for a given incoming muon flux, the attenuation of the muon flux through a given section of a heap, dam wall, walls of an open pit, underground, or the like, is dependent on the average bulk material density integrated along the length of the particle (muon) trajectory' through the volume under investigation. This line integral is also referred to as the opacity of the material. In general, for a given trajectory’ segment of length L of the muon particle in the unconsolidated material, the opacity can be written as 0 = p L where p is the average bulk density along trajectory' L.

[0020] The bulk density so measured is the average density due to all materials within the volume under investigation, including rock grains, fluids, and empty pore spaces. By measuring the muon flux though such an object of interest along different directions, it is possible to form 2D or 3D maps of the bulk density' distribution. Herein, bulk density may reflect the net density of an (heterogenous) unconsolidated material, averaged over its different components, i.e. solid, liquid and air fractions. The average will be along the muon trajectory L for a 2D map, or within a user defined voxel for a 3D tomographic reconstruction of the density.

[0021] Measurement of muon flux is obtained with one or more muon detectors which can be positioned at different view angles with respect to the object or volume under investigation. From the measurements of the muon flux through the volume, a three-dimensional tomographic density map can be formed. In some embodiments, a single measurement point is provided, and the density information remains three dimensional or volumetric in nature but can only be represented by a two-dimensional projection. In other embodiments, multiple measurement points are provided and a 3D reconstruction or tomosynthesis is performed.

[0022] As described herein, each muon detector is able to detect the passage of singleparticle muons (aka muon events) and is also able to determine the direction of each of the single-particle muon events. The muon detectors are not limited and include one or more of scintillator detectors, gas detectors, solid state detectors (e.g. silicon detectors and TFT arrays), transition radiation detectors, and Cerenkov detectors.

[0023] Embodiments of the invention relate to determining a fluid content added to a material in an initial state. This may be entirely dry or partially wet. In the initial phase, the unconsolidated material may include one or more of a dry ore material, a pre-wetted ore material, or an agglomerated ore. Here we will refer to the average density of all solids (and only solids) in the unconsolidated material as the matrix density. It is understood that this matrix density may effectively be an average across the heterogenous mineral composition of all solids in the unconsolidated material. The matrix density of the solid phase in the unconsolidated material may be measured separately via in situ sampling or is a known value or can be realistically assumed to be within a limited range of values. In some embodiments, the matrix density of the unconsolidated material is measured by obtaining and processing multiple samples from different locations in the mining operation / unconsolidated material at hand. The processing required to arrive at an improved determination of the matrix density of all solids in the unconsolidated material may include drying the material or compacting it fully, so as to eliminate any air or fluid filled porosity

[0024] Typical density measurements techniques such as muon radiography, measure a bulk density, i.e. the actual average density of the material, with contributions from solids, fluid, and air-filled porosity.

[0025] For example, an unconsolidated material was initially dry. The average bulk density pb of the unconsolidated material governs the muon flux. The initial bulk density pb is given by Equation 1. where <[> is the porosity of the matrix of the unconsolidated material andpmis the density of the matrix or which has been determined in the prior step. In Equation 1, no fluid is present and the contribution of air is neglected.ρb= (1 − φ)ρm(1)After both the matrix and bulk density of the initial phase have been measured separately or otherwise ascertained, these can be used in computations to determine fluid content. Any change in the apparent bulk density of the unconsolidated material versus the initial phase, to include spatial changes or changes over time due to an approaching fluid front, can be interpreted as the change due to the presence of fluid volumes in the unconsolidated material.

[0026] For materials with a pore space fully or partially filled by fluids, the resulting bulk density in such wet state is shown by Equation 2, where S is the fraction of empty pore space (i.e. the fraction of porosity) in the material occupied by the fluid, also known as fluid saturation in the material, moisture of the material, and pf is the density of the fluid.ρ = ρb= (1 − φ)ρm+ φSρf(2)

[0027] For the cases mentioned above, including that of a leaching fluid percolating through a heap, pr is well known and generally quite close to the density of water. Thus, knowing the matrix density, a measurement of the bulk density of a representative, well-mixed sample of dry or initial materials of the unconsolidated material provides a direct measurement of its porosity via Eq. 1, whereas a comparison between the apparent bulk density of the unconsolidated material in-situ and under dry or initial condition and the apparent bulk density under saturated condition yields a direct determination of the in-situ fluid saturation S via Eq. 2. In many cases, () is known, estimated, or assumed a priori or otherwise determined beforehand, and therefore any measurement of an excess bulk density distribution in the unconsolidated material is a direct measurement of the fluid that is contained in the unconsolidated material, including a relative measurement of different saturation conditions.

[0028] Adjustments can also be made, as is appreciated by those of skill and as described below, to account for the heap or unconsolidated material compacting or settling by w ay of its own w eight. When this measurement is performed from a single muon detector with the required particle tracking capabilities an accurate 3D analysis of the distribution of fluids is obtained. When multiple detectors are used, further constraints can be placed on the density distribution, or the coverage can be extended.

[0029] As described above, to interpret bulk density or bulk density changes in terms of fluid content or saturation S. we must know the material porosity. However, during the wetting phase, compaction may occur in the unconsolidated material. The net result of this, assuming no net mass loss, is a potential change in porosity between the dry ($o) and wet phases ($1) with (typically) <j)i < $0 due to compaction or slumping. When using the volumetric equation for the bulk density in the initial (dry) and final (wet) states, knowledge of $1 is ultimately required to derive fluid content relative to pore space, that is, fluid saturation, in the wet phase.ρdry= ρmatrix(1 − Φ0)Avef “ AnatHxG ~~ ^1) pfiUidEquations 3 and 4.

[0030] Muon attenuation in matter follows a typical Beer-Lambert law and it is often parametrized in terms of bulk density but in reality, it only depends on the total number of atoms along in the trajectory. Due to its low density, air does not contribute significantly to muon attenuation and how the ordering of materials along the muon trajectory also does not matter.

[0031] Therefore, muon attenuation in the unconsolidated material (e.g. a leaching heap) can be modeled by assuming an equivalent (but shorter) heap, where all the air or empty pore space has been taken out. Figure 1 illustrates equivalent heap density7models 101, 102, 103, 104 that provide the same muon attenuation. The detectors 107 detect p-flux 108 that has endured as the incoming p-flux travels through the heap.

[0032] Equivalent heap models can be built both for dry 102 or wet heaps 103, as shown in Figure 1, where solid shading 104 indicates a solid fraction 104, grey a fluid fraction 105, and white is air 106, which does not contribute to muon attenuation. A natural model of the heap, i.e. a model of the heap as is 101, will have a certain height ho, given by the heap physical dimensions. The height of the equivalent full compact heap model is hi, with hi< ho.

[0033] Further, in the Figures 2A, 2B, 2C, and 2D. the left panels of Figures 2A and 2C provide models of a dry heap (hash marks regions are solids). The right panels of Figures 2B and 2D provide models of a heap in a wet or partially saturated state (thin shading is the fluidfraction). Figures 2A and 2B are natural law models and Figures 2C and 2D are equivalent fully compacted heap models.

[0034] The Figures 2A, 2B, 2C, and 2D indicate four equivalent leaching heaps models from the point of view of muon attenuation and thus measured bulk density. From left to right, we have a heap of height ho where all of the solid and fluid materials are pushed to the bottom and all of the air in its porous volume is at the top. Since air does not contribute to muon attenuation, this heap model is equivalent to any of the other models to the right, i.e. models having no air fraction and a heap height hi, and different ordering or distribution of solids and liquids.

[0035] Using an equivalent model, one can thus determine a change in the material balance of a leaching heap, from an analysis of the changes in the observed muon flux. When this material is known, such as when adding fluid to a leaching heap, we can also perform a volumetric analysis, i.e. determine the volumetric fraction occupied by the added materials.

[0036] Any of the equivalent models of Figures 2A, 2B, 2C, and 2D are not necessarily macroscopic models of the full heap. When this is discretized into a series of finite element voxels, e.g. according to techniques further described below, different equivalent model representations may be used for different groups of pixels or voxels, in order to facilitate the analysis of the heap.

[0037] The muon radiography method is able to measure bulk density of the dry material in the heap, or the bulk density of the heap material in its initial state, which may be a material consisting of a dry or wet mix of mineral ores from different stockpiles including mixes that include various agglomerants materials added in order to optimize the porosity7and permeability of the final mixture.

[0038] Using a variety of methods as outlined below, muon radiography is also able to determine a fractional volumetric content (i.e. a saturation) of a fluid, such as a leaching fluid, that is being added to the heap in its initial state. In many cases it is advantageous to convert the measured bulk density of the materials in the heap into a resulting tonnage of materials in the heap. Similarly, it may be advantageous to convert a fractional volumetric content (i.e. a saturation) of a fluid, into a result tonnages of fluids added to the heap. Such conversions require estimates of the volume of the heap.

[0039] The volume of the heap can be measured in a number of ways. In some cases, it would also be advantageous to measure the volume of at least a portion of leaching heap, dump leach, or stockpile repeatedly, as a function of time and as the materials in the heap may shift, compact or sag, for any cause, including also as a result of ongoing leaching operations.

[0040] Volumetric surveys or topographic mapping of a leaching heap, or a portion of a leaching heap or other accumulation of unconsolidated materials, can be done utilizing a variety7of methods. Volumetric surveying is a process that uses surveying techniques to determine the position of a multitude of points on the surface of three-dimensional objects, such as stockpiles, earthworks, or other materials. This data is essential for resource management, project progress monitoring, and financial planning.

[0041] The results of a volumetric survey is a volumetric report with detailed quantitative data about the volume of materials in a given area. This may include a mathematical representation or 3D digital model of the surface of the pile of unconsolidated materials e.g. using raster-based models such as Digital Elevation Models (DEMs), Digital Surface Models (DSMs), Digital Terrain Models (DTMs), as well as vector-based models such as Triangulated Irregular Networks (TINs) or data clouds.

[0042] Models obtained from such volumetric surveys and reports can then be further subdivided in finite element grids or meshes of 3D voxels, thus allowing a discretization of the entire volume of the structure being analyzed.

[0043] Volumetric surveys can be conducted using ground based surveying techniques such as terrestrial laser scanning, wherein a long-range laser scanner is used to capture thousands of data points from the surface of the pile, which can then be used to create a 3D model of the surface of the pile, which can then be used to calculate its volume, or -alternatively - when equivalent data is obtained via survey total stations. Surface measurements such as LIDAR, laser data collection, or photography may be used to build models of the pile of unconsolidated materials which are useful to approximate its volume.

[0044] Volumetric surveys can be also conducted using aerial surveying techniques. Aerial volumetric surveys using drones are an alternative to traditional methods such as laser scanning or surveying total station methods because they can be faster, more efficient, andcan provide accurate results for stockpiles that may be unsafe to stand on. Drones are also controlled remotely, which reduces safety risks.

[0045] Drone scans typically use laser-based methods (such as LIDAR) for scanning the surface of an object and thus determining its volume. A drone-based topographic measurement system utilizes an unmanned aerial vehicle (UAV) equipped with a high-resolution camera or LiDAR sensor to autonomously capture aerial data of terrain. For photogrammetry, overlapping images are processed to generate dense 3D point clouds and orthomosaics. LiDAR directly measures distances to the ground, creating similar point clouds, even through vegetation.

[0046] Precise positioning is achieved through onboard GNSS and IMU, often enhanced by ground control points. Ground control software manages flight planning and real-time monitoring. Post-processing software generates various topographic products, including Digital Elevation Models (DEMs), Digital Surface Models (DSMs), contour maps, volume calculations, and 3D models of the entire asset based on voxels, meshes, or grids. Such systems offer an efficient, flexible, and often cost-effective method for acquiring detailed and accurate topographic data compared to traditional surveying techniques, making it valuable for diverse applications.

[0047] In many cases, combining traditional ground surveying techniques with aerial drone technology ensures that the volumetric surveys are cost-effective and time-efficient while maintaining high standards of precision. Drone based volumetric surveys may include photogrammetric surveys. Lidar or other laser-based surveys, orthomosaic maps, or other methods.

[0048] Figure 6 shows a stockpile, region 601, or leaching heap being scanned by a drone on top 602 and an earth based total station system on the right 603.

[0049] Figure 7 provides a three dimensional view of a drone 701 flying over a bulk solids handling area that includes a crane 703. conveyor belt 704, and bulk solids 705 in storage.

[0050] Any of the above volumetric survey methods may be utilized for performing a volumetric survey of the unconsolidated material in its dry and wet states. This is importantin order to get to an accurate determination of fluid and air-filled porosity', controlling also for compaction effects.

[0051] Figures 2C and 2D illustrate two schematic representations of an accumulation of unconsolidated material, such as a leaching heap, in its initial or (for simplicity) dry state on the left in Figure 2C. and in its final, i.e. the wet state on the right in Figure 2D.

[0052] The granular solid matrix of the material is indicated by the solid regions 201, 202. The empty pore space 203, 204 is left white, whereas the grey indicates the fluid in the system in the final state 205. The two heap models 2 A, 2B are equivalent to the two fully compact heap models 2C, 2D in the bottom panel. Further, we call Vs the total volume occupied by solids, and VF the total volume occupied by fluids.

[0053] One workflow to arrive at fluid content and porosity from a measured bulk density or muon attenuation is as follows.

[0054] 1) Measure the muon flux at depth, under the unconsolidated material in its initial state

[0055] 2) By comparing the measured vs the expected muon flux, obtain a first bulk density’ of the heap, i.e. p0. Note that p0= pbulk= pm(1 -Φ0) .

[0056] 3) Knowing or assuming a value for the matrix density, pm, determine a value for the initial porosity of the material, Φ0.

[0057] 4) Determine the volume of the solid fraction of materials in the heap, Vs, by combining information about the total volume of the heap - Fo- and its porosity, i.e. Vs= V0(1−Φ0).

[0058] 5) Measure the muon flux at depth, under the unconsolidated material in its final state. By comparing the measured vs the expected muon flux, obtain a second bulk density of the heap, i.e. ρ1.ρ1= ρbulk= ρm(1 − Φ1) + Φ1S ρfluid

[0059] 6) By analyzing the difference between the two bulk densities, using an equivalent heap module, and knowing the density of fluids added to the heap, determine the volume of fluid VFadded to the heap. In some cases, the volume of fluids may be determinedindependently from information about the irrigation or injection rate of fluids into the heap, or atmospheric precipitation data.

[0060] 7) Obtain a new measurement of the total heap volume in its final state, i.e. V1

[0061] 8) Determine the final state porosity! by utilizing the previously determined volume of the solids and fluids present in the heap, and the total heap volume in its final state, i.e. Φ1= 1 − VS / V1

[0062] 9) From the measured bulk density and final state porosity (after compaction) determined above, obtain an improved value for the fluid saturation content S, via the volumetric equation at step 5)

[0063] In this analysis, one first determines the initial dry porosity of the heap o. That information, combined with a measurement of the total volume Vo of the heap via volumetric survey, allows one to determine the volume of solids Vs(step 4).

[0064] In the final state of the heap, the amount of solids and thus their volume has not significantly changed. However, there is now a certain volume of fluid VFthat has been added to the heap. Since the density of this fluid is generally known, we can determine its volume via a comparison of the muon attenuation before and after, e.g. utilizing the equivalent heap model of Figure 2D, bottom right panel.

[0065] Consider aleaching heap with a volume Vo = 100x100x10 m3= 105m3. Let us assume the matrix density, i.e. the density averaged across all materials in the solid phase, is pm=2.6 g / cm3. An initial measurement of the bulk density of the heap with muons gives p0= 1.69 g / cm3, and, thus, an initial porosity of Φ0= 35 percent and equivalent volume of solids of VS= V0(1−Φ0) = 6.5 104m3are inferred. The mass of solids is Ms= pm. Vs= 1.0985 105tons.

[0066] After a certain amount of irrigation with a fluid with a density Pf = 1.05 g / cm3, the muon flux measured at depth decreased by 5 percent. For simplicity’, we neglect small corrections due to different chemical composition between the fluid and the solids in the heap.

[0067] An equivalent model analysis indicates that the observed -5 percent attenuation of the muon flux corresponds to the effect of having added 0.7 m of material to the top of the 10 m tall heap. However, 0.5 m of material with a bulk density of 1.69 g / cm3has the same opacity and therefore is equivalent to having added 1.127 m tall layer of fluid with the above density to the top of the heap.

[0068] With this information we can determine the volume of fluids by multiplying by the area of the heap (100 x 100 m2), i.e. VF = 1.127 104m3. An aerial survey of the heap indicates that this has also lost 2 percent of its volume due to compaction, therefore now V1= 9.8 104m3.

[0069] Next, we may calculate the final state porosity Φ1= 1 − VS / V1= 1 −6.5 / 9.8 = 33.67 percent.

[0070] The newly measured bulk density is indeed 7 percent higher, or p = 1.766 g / cm3. Now, the fluid saturation can be determined by inverting the volumetric equation in step 5 for S. In this case, we obtained S = 11.7 percent. Neglecting the effects of compaction, i.e. the change in porosity, would have led to an estimate of S = 20.7 percent which is significantly different.

[0071] In certain cases, the amount of fluid added to a leaching heap may be obtained from irrigation and / or fluid injection data, as well as from atmospheric precipitation data (rain, snow).

[0072] Once the volume of solids and fluids is known, then the total porosity in the final state - Φ1-can be readily obtained from a measurement of the new total volume of the heap V1 (after compaction, if any) via the expression in step 8). Knowing this, the fluid saturation or fractional fluid content S, which is the quantity of interest in leaching operations, can be derived via the volumetric equation (step 5).

[0073] If S is the volume fraction of liquids hosted in the final porosity Φ1, then Oi (1 — S) is by definition the volume fraction of air in the final porosity Φ1. The air-filled porosity is of great interest for leaching operations, particularly of sulphide ores where oxygen must be present for the ore to oxidize prior to being leaching via the lixiviant reagent (e.g. sulphuric acid in the case of copper). When not enough oxygen is present, oxidizing agents such as bacteria used in bioleaching are not able to perform their function efficiently whichultimately reduces the amount of metal recovered (per unit time) as sulphide ore leaches at much reduce rates than oxide ore.

[0074] One other workflow to arrive at fluid content and porosity from a measured bulk density or muon attenuation is as follows.

[0075] 1) Measure the muon flux at depth, under the unconsolidated material in its initial state

[0076] 2) By comparing the measured vs the expected muon flux, obtain a first bulk density of the heap, i.e. p0. Note that p0= pbulk= pmatrix(1 - Φ0).

[0077] 3) Knowing or assuming a value for pmatriX, determine a value for the initial porosity of the material, Φ0.

[0078] 4) Determine the volume of the solid fraction of materials in the heap, Vs, by combining information about the total volume of the heap - Vo- and its porosity, i.e. Vs= Vo (1 - d’o)

[0079] 5) Measure the the muon flux at depth, under the unconsolidated material in its final state. By comparing the measured vs the expected muon flux, obtain a second bulk density of the heap, i.e. with ρ1= ρbulk= ρmatrix(1 − Φ1) + Φ1S ρfluid.

[0080] 6) As discussed above, determine first an initial dry porosity of the heap Φ0and estimated the initial volume of solids Vs using an equivalent heap model (i.e. a fully compacted heap model).

[0081] 7) Determine the volume of air in the initial state. When the heap is in its initial dry state, the initial volume of air is simply given by the product of porosity and the total initial volume of the heap from the volumetric survey, i.e. V0air= Φ0V0. If in the final state, the heap has undergone any compaction, its new volume Vi will be Vi < Vo. Since the granular matrix in the unconsolidated material is generally incompressible, the difference between the two volumes must be due to a reduction in the volume of air. If AV is such volumetric difference, then the final volume of air is given by V1air= Voair— AV — VFwhere VF can be obtained from an equivalent heap model analysis of the reduction in the muon flux.

[0082] 8) Knowing the final air volume, the fluid volume fraction Vs, and the final heap volume Vi. one can determine the final porosity cPt = (VF+ 7“"') / ty Using the numerical example of above, we have Φ0=35% and Vo= 105m3. Thus, we estimate Uoa"’=3.5 104m3. After having observed a volumetric compaction of 2% for the overall heap in the final state, we will assume Vi = 9.8 104m3. Using VF = 1.127 104m3from the previous analysis we obtain V1air= (3.5 - 0.2 - 1.127) 104m3= 2.173 104m3and we can now calculate the new value for the final state porosity, i.e. Φ1=33.67%

[0083] The approaches outlined above will also work when the heap is not initially fully dry, as i) under wetting conditions, the change in the muon flux attenuation can be described as an additional layer of water in an equivalent heap model, in accordance with the Beer-Lambert behavior of the attenuation of cosmic ray muons travelling in unconsolidated materials, and ii) any volumetric changes in a leaching heap as a result of wetting conditions effectively pushes air out, thus we can neglect changes in the volume of solids and fluids.

[0084] The volume of fluids is indeed constant at equilibrium, such as during heap leaching operations. When this is strictly the case, correction can be applied by measuring any changes in effluent from the leaching heap.

[0085] Without loss of generality, when the heap is not fully dry. then the volume of solids or fluids determined above becomes an effective volume of solids, fluids, or both.

[0086] Both of these workflows, combining a volumetric survey with a bulk density measurement, give operators access to two key parameters for predicting the efficiency and safety of leaching operations, namely fluid and air-filled porosity.

[0087] Such an analysis of the heap maybe done sector by sector or, when a 3D representation of the heap is available, for any group of vertically stacked voxels between the muon sensor and a given point or pixel on the surface of the heap, as well as any group of voxels stacked along a slanted direction connecting the same.

[0088] When two or more muon sensors are utilized, standard tomographic reconstruction techniques can be used to further analyze the heap in the vertical direction, i.e. providing independent density values to each voxel in the 3D representation, including voxels midway between the detector and given point on the surface of the heap. It should be clear that the above analysis for determining fluid content and air-filled porosity (with or withoutthe use of an equivalent model) can also be performed for such voxels, and indeed across any voxel part of a 3D representation of the heap.

[0089] Having a 3D distribution of air-filled porosity allows the determination of areas of relative compaction or high porosity' which is important for determining optimal irrigation strategies

[0090] High porosity is deleterious to leaching heaps, as it may favor the formation of preferential flow channels, which hinder a uniform irrigation of the asset and, thus, metal recoveries. Excessive compaction or high porosity’ also affect the economic value of a stockpile of materials.

[0091] Accurate quantification of fluid content, in the presence of compaction, is also key for the determination of the economic value of stockpiles, e.g. of mining ores or mined commodities, during custody transfer operations at loading docks and yards.

[0092] For leaching heaps, a determination of fluid content in situ is also important for defining appropriate re-leaching strategies, including optimally planning for how much fluid needs to be injected and where.

[0093] When the heap is in an initial dry stage, such optimal fluid delivery strategies may dose the delivery of fluids based on the estimated porosity across a column of material below the injection or irrigation point. Volumes with reduced porosity are by definition more compacted, and this may result in a slower fluid percolation and potentially dangerous clogs or fluid backups, as well as unwanted surface pooling and unnecessary fluid loss by evaporation

[0094] For similar reasons, when the heap is already in a wet or leached stage, but nonetheless undergoing secondary leaching or re-leaching (including injection leaching) to recover more metal, optimal fluid delivery strategies may dose the delivery of fluids based on the estimated amount of fluid already present across a column of material below the injection or irrigation point.

[0095] Accurate knowledge of fluid content is also important for a slope stability’ analysis of the unconsolidated material. Strategies for reducing or eliminating slope stabilityrisk are based on ensuring that the fluid saturation in the porous space of the material does not exceed limit values, i.e. Attenberger limits or other empirically determined limit values.

[0096] Ensuring sufficient air-filled porosity is key to the leaching of sulphide ores. Insufficient air or oxygen content in such leaching heaps hampers ore oxidation and may result in slower production rates and overall lower recoveries. An in-situ measurement of air and fluid filled porosity may be used for validating any hydro-metallurgical models and thus strengthen the value or narrow the uncertainty of their outputs and predictions, including about the net present economic value of the operation and its future cash flows.

[0097] In some embodiments, hot air is injected into sulphide leaching heaps for the purpose of accelerating ore oxidation and leaching dynamics. Information about the distribution of actual air-filled porosity in-situ can help drive decisions on how to optimize an oxygen or hot air injection process, which is typically very energy consuming and costly to operate at the scale of a full leaching heap.

[0098] Figure 3 visualizes drone data 301 and Figure 4 provides a model 401 that relies on drone data and muon detector data. This combined model 401 allows for more reliable information about a leaching heap's compaction or shrinkage. This may be up to 5 or 10 percent of the total volume. This methodology may be used for optimizing a fluid content determination of a stockpile / heap in the presence of compaction. The density- and volumetric measurements can also be combined for determining an economic value or a total mass (or an effective total mass) of the material within the acceptance volume of the muon detector.

[0099] For determining either i) the total mass of material in a stockpile, or ii) the total mass of solids or solids and fluids in a leaching heap in its initial state, including also iii) determining a fraction of solids V_S according to the the analysis outlined above, one workflow is the following.1) Perform a volumetric scan, survey or topographic mapping of the heap or stockpile using aerial drones or ground based measurements.2) Determine a continuous numerical representation of the external surface of the accumulated materials by using raster-based models or averaging discrete data cloud points.3) Represent the volume of the accumulation of materials numerically, e.g. via a finite element model using a refined mesh to approximate the surface of the pile at the cmscale. The mesh elements or voxels do not have to be of equal size and their geometric shape (cubic, rectangular, polyhedric) may be optimized to capture more of the complex surface features that a distribution of accumulated materials may have.4) Considering the position of the muon sensor, and assuming a nominal density of the pile determine a minimum threshold energy required for muons to be able to fully traverse the asset and arrive at the detector for each position on the surface of the pile.5) Determine a calculated muon flux at the detector by integrating the expected or measured atmospheric muon flux (outside the pile) along the multiple directions within the acceptance the detector.6) The integration of the muon flux above the actual energy threshold is required in order to compare with the measured muon data but this task is complex and non analytical in nature.7) One may use MonteCarlo methods for this task, wherein i) muon trajectories are generated over a energy range starting at a conservative initial estimate of the energy threshold, to be then ii) propagated to the detector using a finite element nuclear transport model (such as Geant4, MCNP or other codes) with realistic accounting of discrete energy loss in the stepping forward of each muon trajectory.8) In step 7. the initial or starting energy threshold should be chosen such as to be always less than the actual energy threshold determined from calculations of muon energy loss in a continuous slowing down approximation, such as with standard muon range and energy loss tables.9) Compare the expected muon flux to the muon flux measured at each of the multiple directions, after correcting for detector or DAQ inefficiency,10) Iteratively adjust the density in each of the voxels of the finite element model until it matches the measured muon flux.11 ) Multiply the density found in each voxel of the above 3D density map by the volume of the voxel itself and sum across all voxels. This volumetric integration is then used to obtain the mass of the material in the stockpile.

[0100] A similar approach may also be used to determine a 3D map of not bulk density but also of fluid content, total porosity or air filled porosity. In certain implementations and in order to reduce the overall computational time, the iterative adjustment of the density in eachvoxel of the finite element model to match the measured muon flux along the multiple muon directions may also be done using machine learning techniques. These may include multiple layers of neural networks that have been previously trained utilizing predictions of the above nuclear transport model for different distributions or arrangements of bulk density values, fluid saturation, or air-filled porosity across the 3D voxel representation of the stockpile.

[0101] The above workflow for determining a total mass may also be used when material is being removed from a stockpile in order to obtain a more accurate, faster measurement of the true economic value of the material being removed and transported away from the facility, e g. for ship loading, in the process of executing sales, or other custody transfer applications. The sensor should be positioned to measure a quiet or calm portion of the stockpile, i.e. without too much materials removal or addition.

[0102] When the stockpile consists of 2 materials with different densities and economic values (e.g. goethite and smectite in iron ore) including when one such material is a solid and the other one is water or rain or other forms of moisture trapped in the pile, the above method can be used to obtain a precise determination of the combined bulk density, which is the average density weighing the different components according to their relative concentration. From this, knowing the densities of the two end points, an estimate of the relative fraction of the two components, and thus an indicator of the true economic value of the pile, can be obtained. Thus, the muon and drone system measure both how much material is being added, stored, or removed from the stockpile, as well as its economic value.

[0103] For instance, both goethite and smectite appear in iron ore and iron ore stockpiles, but have significantly different values of density’, i.e. 3.3 to 4.3 g / cm3for goethite and 2.1 to 2.3 g / cm3for smectite. This difference is indeed relative to different iron content, which is lower in smectite, and thus economic value (which again, is lower in smectite. Whe the muon systems above measure the combined density of the iron ore stockpile, not only the total mass present, added or removed from the stockpile can be determined, but from the bulk density one can also obtain an indicator of the grade of the iron ore and thus its economic value.

[0104] A similar analysis could be useful for determining the value of a pile of scrap metal, including metal that is going re-smelted or re-cycled, or the value of a metal containing ore feed prior to being loaded onto a smelter, furnace, or other processing unit.Grains, sand and aggregates (e..g. concrete) are other unconsolidated material that may benefit from embodiments herein.

[0105] The estimate of the water or moisture fraction is also important as - for safety reasons - shipments of bulk materials by ship much follow standards such as standard transportable moisture limits set by, e.g., the International Maritime Commission or an insurance policy. When too much moisture is inadvertently loaded onto a ship, it may result in stability issues affecting the seaworthiness of the vessel.

[0106] The above workflow could also be used to determine the tonnage of ore being transported by a mining truck or rail car. The tonnage information so obtained can be further utilized to divert the ore to an optimal processing or storage unit or to determine the economic value of the ore itself. High density ore tends to be richer in metal content, and high density is an indicator of hard rock mineralization. Herein, the muon sensor may be located at the base of the rail car or on its side or undercarriage. A similar alignment may be selected for a truck. A drone or a portal may be used to provide similar information. In this case, the volumetric information can be determined by a portal system, instead of an autonomously flying drone or a surface-based survey system. Embodiments herein may be used to precisely account for the true economic value of an ore pile, stockpile, feed pad, etc., as well as to validate commercial transactions and offer a more accurate result, and to comply with shipping safety regulations.

[0107] Similarly, it is in many cases not practical or economic to determine the true volumetric density of a large accumulation of materials using scales, load cells, samples, or borehole measurements. Muon attenuation radiography solves this problem altogether due to the distributed and penetrating nature of the atmospheric muon flux, and the ability to instrument a muon sensor that can analyze the directional information of the muon flux. However, muography alone measures the combined effect of density x length, and thus cannot easily distinguish differences due to geometrical effects or intrinsic material properties.

[0108] Ore sorting is a secular challenge in mining operations. Separating gangue from valuable materials would allow one to significantly reduce costs. At the same time, ores with different grades can be optimally processed in different ways, if the ore could be sorted accordingly. Ore sorting is particularly challenging because of the scale at which the industryoperates. Large mining operations may transport up to several 100,000's of tons of material per day. It is simply impossible to analyze such volumes with conventional analytical equipment in a low-cost, inline, and automated fashion. Systems have been placed on truck shovels to provide some initial screening of the materials based on electrical or spectroscopic methods such as XRF. However, such methods struggle with being quantitative or representative enough (i.e. being able to sample the full load under question). On the other hand, density is in some cases an effective proxy for metal content or economic value of mineral ores. Iron ore is an illustrative example as it is often associated and predominantly consists of two different minerals, i.e. smectite and goethite which have different iron content and also different densities. In a simplified two component system, a measurement of the bulk density will be able to determine the true iron content of the pile, i.e. its actual economic value. More, in general, a measurement of the bulk density of an accumulation of ore is a measurement of its dilution. The dilution may be due to the presence of gangue or other low-grade materials. Or it may be due to an accumulation of fluid due to, e.g. atmospheric events. In the case of a coal stockpile, wet or dry coal stockpiles (or portions thereof) clearly have different caloric content and economic value. Ore reconciliation and accounting is particularly important at a smelter, blast or electric furnace, as materials with different properties, origin and owners may be commingled onto a common feed pad.

[0109] Information about the economic value of the transported ore could be used to compare and update mine plans, a mine block model, or estimates of the net present value of a mining operation. Some embodiments may divert a mining truck depending on the mass it is transporting or the density of the material.

[0110] Figures 5A and 5B provide three dimensional views of a laser-based device 501 to estimate the bulk material 502 in the bed of a truck 503. That is, it measures the shape, not the density. A portal 504 here is a frame or structure, typically elevated and spanning across a defined area (like a roadway, a railways, conveyor belt, or stockpile), that houses and supports one or more measuring devices (such as laser scanning devices 501) specifically designed to perform non-contact, three-dimensional measurement of the volume of bulk materials, such as ores, aggregates, wood chips, grains, scrap metal, etc., whether they are on trucks, conveyor belts, in stockpiles, in heaps, or in combination.

Claims

I claim:

1. A method for determining the mass of unconsolidated material, comprising:collecting surface measurements of a heap of the material;collecting muon measurements of the heap; anddetermining a mass of the heap.

2. The method of claim 1, further comprising utilizing an equivalent density model of the heap.

3. The method of claim 1, wherein collecting surface measurements comprises using a drone or stationary device or both.

4. The method of claim 1, wherein collecting surface measurements comprises using a laser.

5. The method of claim 1, further comprising using the mass to calculate how much material is added to, stored, or removed from the heap.

6. The method of claim 1, further comprising determining an economic value of the heap.

7. The method of claim 1, wherein the heap comprises materials of varied densities.

8. The method of claim 7, wherein the heap is a stockpile of mineral ores comprising iron, aluminum, zinc, copper, or a combination thereof.

9. A method for determining the moisture content of an accumulation of unconsolidated material, comprising:collecting surface measurements of the material;collecting muon measurements of the material before and after irrigation; anddetermining a mass of fluid inserted during irrigation.

10. The method of claim 9, further comprising utilizing an equivalent density model of the material.

11. The method of claim 9, further comprising utilizing a volumetric analysis to compensate for compaction, slumping, or other volumetric changes of the material over time.

12. The method of claim 9, further comprising determining a location for irrigating, injecting, or inserting leaching fluids or air.

13. The method of claim 12, wherein the location is based on a three-dimensional distribution of bulk density, fluid content, or air-filled porosity.

14. The method of claim 13, wherein the distribution has been corrected for compaction effects.

15. The method of claim 12, further comprising determining an amount of leaching fluids or air to be irrigated, injected, or inserted into the leaching heap.

Images