dragon d3.2 online analysis technologiesfigure 1-3: steinert rtt nir sorter [10] 1.2.3 laser induced...

DRAGON Analysis Technologies

© DRAGON consortium: all rights reserved page I

D3.2 Online Analysis Technologies

(Publishable Part)

Version Date Reason of change 1 08/03/2013 document created 2 23/01/2014 Technologies overview finished 3 10/02/2014 Modifications 4 11/03/2014 Internal review by PE 5 31/03/2014 Document reviewed by the Coordinator

Author(s) and company: INDU, Christian Himmelsbach (HK), Cedric Thalmann, Manuel Petitat

(B+G), Robert Galler, Stefan Barwart, Hartmut Erben (MUL)

Work package: WP3

Work package leader :

Document status:

INDU

final

Confidentiality: Confidential

Keywords: Automation strategies, chemical analysis, physical analysis

Abstract: This document contains the publishable part of the developments for new strategies for automated chemical and physical analysing strategies.

DRAGON D3.2 Online Analysis Technologies (Publishable)

© DRAGON consortium: all rights reserved

Table of Content

1 Research on Commercial Online Analysers and Technologies .................................... 1

1.1 Abstract ......................................................................................................................... 1

1.2 Analyzing technologies ................................................................................................. 1

1.2.1 UV/VIS spectroscopy .................................................................................................................................................1

1.2.2 Near Infrared (NIR) spectroscopy ..............................................................................................................................2

1.2.3 Laser Induced Fluorescence (LIF) spectroscopy .......................................................................................................3

1.2.4 Laser Induced Breakdown spectroscopy (LIBS) ........................................................................................................4

1.2.5 X-ray fluorescence (XRF) spectroscopy ....................................................................................................................5

1.2.6 X-ray diffraction (XRD) ...............................................................................................................................................6

1.2.7 X-ray Transmission (XRT) ..........................................................................................................................................7

1.2.8 Neutron activation analysis ........................................................................................................................................8

1.3 Comparison ................................................................................................................ 11

2 Automated Analysis of Chemical Parameters .............................................................. 12

2.1 Abstract ....................................................................................................................... 12

2.2 Introduction ................................................................................................................. 12

2.3 Online X-ray Elemental Analyser (OXEA 3000) ......................................................... 13

2.3.1 Modification of OXEA 3000 ......................................................................................................................................13

2.3.2 The lifting system .....................................................................................................................................................14

2.3.3 The limit of detection and accuracy of light elements ..............................................................................................14

2.4 Moisture Meter (PMD 2500) ....................................................................................... 15

2.4.1 Development of a novel moisture meter ..................................................................................................................15

2.5 Heavy Elements Analyzer (HEA) ................................................................................ 17

3 Automated analysis of physical parameters ................................................................. 19

3.1 Overview ..................................................................................................................... 19

3.2 Rock strength .............................................................................................................. 19

3.2.1 Disc Cutter Load Monitoring (DCLM) system...........................................................................................................19

3.2.2 Automated Point-Load-Test system .........................................................................................................................33


© DRAGON consortium: all rights reserved

3.3 Grain size and shape distribution ............................................................................... 35

3.3.1 Photo-optical particle analysis ..................................................................................................................................35

3.4 Bulk density ................................................................................................................ 46

3.4.1 Determination of the mass flow rate .........................................................................................................................46

3.4.2 Determination of the volume flow rate ......................................................................................................................49

3.5 Mica content determination ......................................................................................... 51

3.5.1 Introduction ..............................................................................................................................................................51

3.5.2 Selected chemical and physical parameters of mica and quartz .............................................................................53

3.5.3 Sample material and preparation .............................................................................................................................53

3.5.4 Automated microscopic analysis ..............................................................................................................................58

3.5.5 Observations with the polarization light microscope ................................................................................................58

3.5.6 Determination by optical measurements ..................................................................................................................62

3.5.7 Mica separation by ‘slot’ bar sieves .........................................................................................................................65

3.5.8 Mica separation by air classification I .......................................................................................................................66

3.5.9 Mica separation by air classification II ......................................................................................................................67

3.5.10 Mica separation in a water stream ...........................................................................................................................68

3.5.11 Mica separation with Bromoform and Na-tungstate (density separation) ................................................................68

3.5.12 Laboratory tests for the determination of the mica content with the shape separation table (MUL-variant) ............69

3.6 Consistency of excavated material (soft ground) ........................................................ 73

3.6.1 Approach ..................................................................................................................................................................73

4 References ....................................................................................................................... 82

DRAGON D3.2 Analysis Technologies (Publishable)

© DRAGON consortium: all rights reserved page 1

1 Research on Commercial Online Analysers and Technologies

1.1 Abstract

Over the years, numerous methods for mineral identification have been established. Many of these methods are only intended for laboratories, but some methods can also be used in online applications. The following overview shows a list of feasible technologies for online analysers. For a better understanding, the fundamental physical processes are briefly explained and a summary of the mineral identification processes delivers more information on each technology. The presentation of bulky material to the analyzers is different for each analyzing unit. In this context it must be taken in account, if an analyzer can be installed at the main stream (inline) or if a bypass is recommended to optimize the measuring conditions. In addition to the measuring geometry at the bypass preparation of the material is necessary to crush the material down to the ideal particle size. In addition to the measurement characteristics, a thorough evaluation of environmental and health aspects is essential. Some technologies use radiation, such as X-rays, gamma rays, laser or neutron radiation. Suitable shields must be provided and if this is not possible other technologies must be used.

1.2 Analyzing technologies

1.2.1 UV/VIS spectroscopy

1.2.1.1 Physical Principles The physical principle of the UV/VIS (ultraviolet/visible) spectroscopy is the absorption of light in the visual (380 to 750 nm) or near ultraviolet (200 nm to 380 nm) spectrum. The absorption of light depends on the discrete electron transfer from the ground state to higher levels [1]. Usually, the measured values are the transmission in the case of transparent samples, and the reflectance in the case of opaque specimen. For the VIS spectroscopy, the instrumentation consists of a photomultiplier detector, an avalanche photodiode or a CCD camera as well as a Halogen light source or a deuterium lamp for application in the UV spectrum [2] [3].

1.2.1.2 Applications The UV/VIS spectroscopy enables monitoring of the variance of identified minerals. The identification of unknown materials poses a larger challenge, since measuring minerals with this method is influenced by temperature, moisture and surface impurities [4] [5]. It is necessary to have reference samples for each mineral in order to calibrate the measurement system. For mineral identification the UV/VIS spectrum of the measured material is compared to the spectra in the database. Generally speaking analyzing and sorting by VIS spectroscopy is sorting by the color of the material.

1.2.1.3 Market Overview The sensor based sorting is the main application of this analysis technology. In the recycling industry, UV/VIS technology is a well-established method to sort plastic or glass materials. Binder+CO (MINEXX, CLARITY, Figure 1-1), Mogensen (MSort) and Steinert (FSS/KSS) offer a wide range of UV/VIS sorting devices for minerals, glass and plastics.



Figure 1-1: UV/VIS sorting principle, Binder+CO GmbH [6]

1.2.2 Near Infrared (NIR) spectroscopy

1.2.2.1 Physical Principles Compared to the UV/VIS spectroscopy, the infrared spectroscopy is based on the harmonic oscillations of molecules corresponding to the different energy levels. Absorption of radiation in the infrared range is responsible for stimulated molecular movement. The oscillation of the bonding can be measured and is distinctive for a certain material. The used infrared spectrum ranges from 700 nm to 2400 nm, which is referred to as Near Infrared (NIR) [7] [8]. The current method for the NIR spectroscopy is the Fourier Transform Infrared spectroscopy (FTIR) with a Michelson interferometer, which splits up the incoming light of the diffuse reflection into individual wavelengths [9].

1.2.2.2 Applications The application field of the Near Infrared spectroscopy varies from laboratories in the medical sector to agricultural technologies and other industries. In the mining and raw material sector, the NIR technology is used for moisture analyzers and online material characterization. A calibration of the System with the NIR-spectra of each mineral is necessary to identify an unknown material in an online application. To limit the influence of moisture, either a moisture analyzer should be implemented ahead of the NIR equipment or the calibration should be done with a varying water content of the samples.

1.2.2.3 Market Overview In the cement industry the SpectraFlow Analyser-SOLBAS technology [9], produced by ABB Switzerland, is well established to characterize bulk material for raw mix preparation. Other applications within sensor based sorting are Redwave by BTW Binder or the sorting solutions of the Tomra and Steinert company.



Figure 1-2: ABB SpectraFlow (© ABB) [9]

Figure 1-3: Steinert RTT NIR Sorter [10]

1.2.3 Laser Induced Fluorescence (LIF) spectroscopy

1.2.3.1 Physical Principles Luminescence is an emission of light caused by electrons, which switched to unstable energy levels due to an energy impact and subsequently emit photons, while falling back to the original level [11]. The spectrum of this light extends from the UV to the infrared sector. Fluorescence is light emitted simultaneously during energy influence, while phosphorescence is light emitted with time delay. The process of luminescence correlates to particular atoms and is influenced by the molecular and crystal structure of the focused material [4]. The energy output is characteristic for the composition of the material. For the laser induced fluorescence, the analyzer consists of a photomultiplier detector and a laser source, which emits a laser light in the UV range with a frequency of 20 Hz [11].

1.2.3.2 Applications The laser induced fluorescence is used in online mining applications and the steel industry. It is possible to install a LIF analyzer above a conveyor belt to analyze bulk material. The main field of application is quality control of bulk material. Direct mineral identifications are difficult to perform, because each mineral to be identified needs to be back-referenced by a corresponding sample on the measurement system.

1.2.3.3 Market Overview Online laser induced fluorescence analyzers for conveyor belt applications are produced by Siebtechnik GmbH in Germany (Figure 1-4), Rock ID and IMA Engineering, Finland. The IMA analyzer (Figure 1-5), called OreSpex, is a combination of laser induced fluorescence (LIF) and laser induced breakdown spectroscopy (LIBS) technology.



Figure 1-4: LIF Online Analyser, Siebtechnik GmbH (© Siebtechnik) [12]

Figure 1-5: OreSpex, IMA Engineering (© IMA)

1.2.4 Laser Induced Breakdown spectroscopy (LIBS)

1.2.4.1 Physical Principles Laser induced breakdown spectroscopy (LIBS) is based on the emission spectrum of a laser-induced plasma. A pulsed Nd:YAG laser beam is focused on the sample material and generates a plasma with temperatures up to 30000°K [13]. Inside the plasma, atoms leave the molecular structure and the electrons switch to higher energy levels. When the power supply stops, the electrons fall back to their original shell after a specific relaxation time and emit a characteristic light. A detector measures the spectral emission of each element during the cooling process [14] [15].

1.2.4.2 Applications In fact, the LIBS technology is a method to determine the elemental composition of materials and makes it possible to get all characteristic spectra of each element with several exceptions as Helium and Sulphur. An automated data processing unit can define the mineralogical composition. For conveyor belt applications, a complex target device (Figure 1-6) and an optical shape analysis of the bulk material on the belt is required.

1.2.4.3 Market Overview There are currently three producers of LIBS analyzing systems, IMA Engineering with their LIF/LIBS combination Orespex (Figure 1-5), the Maya analyser manufactured by Laser Distance Spectrometry Israel (Figure 1-7) and the SECOPTA company with their Fiber- and MopaLIBS solutions. All three analysis systems are able to perform measurements on bulk materials on conveyor belts.

Figure 1-6: LIBS Target system [12]

Figure 1-7: Maya Analyser, Laser Distance Spectrometry (© LBS)



1.2.5 X-ray fluorescence (XRF) spectroscopy

1.2.5.1 Physical Principles The X-ray fluorescence (XRF) spectrometry uses the X-rays with energy of typical 10 to 40 keV to excite the atoms of the measured material and ionize the atom by removing an electron of the inner shells, another electron of an outer shell takes its place (L->K, M->K, and M -> L) and emits a characteristic radiation for this atom. Two different methods are used: the energy-dispersive X-ray fluorescence (EDXRF) working with an energy-sensitive detector and the different energies are electronically separated. The second method called wavelength-dispersive X-ray fluorescence (WDXRF) separates the different wavelength (energies) optically as with a prism in the visible range. In most cases the detector is moved to the wanted wavelength. Wavelength-dispersive X-ray spectrometry (WDXRF) has a better signal/noise ratio and consequently a better detection limit by a factor of 10 or even more and a higher energy resolution compared to the EDXRF but a longer measuring time is needed [1] [16].

Laboratory instruments can work with vacuum sample chambers to eliminate the absorption of air. These instruments measure the elements from Boron to Uranium.

1.2.5.2 Applications Online XRF works usually with EDXRF, to measure all the elements in parallel. If the material is continuously presented to the analyser a vacuum-chamber cannot be used. The range of measurable elements is therefore restricted at the low end. With the X-ray fluorescence technology it is possible to determine the elemental composition of bulk materials qualitatively and quantitatively directly on conveyor belts. A calibration on the basis of reference samples allows the analyser to quantitatively determine the elemental composition of the measured material. The reference samples must be consistent with the online measured material. XRF with the exciting energies in the range of 10 to 40 keV is a surface measurement. Therefore the surface of the measured material must be representative. The advantage of the surface measurement is that the measurement is widely independent of the density and independent of the mass flow. Online XRF is ideal for a bypass system with a crusher. The crusher generates fresh clean surfaces and the optimized particle size yields the desired smooth material surface. Protection against ionizing radiation must be observed. However, the prescriptions are usually easy to meet, because the low energy of the used X-rays (typical 10 to 50 keV) is relatively easy to shield.

1.2.5.3 Market Overview Online XRF analyzers are based on the EDXRF. IndutechGmbH. Their OXEA system is protected by several international patents, which allow the detection of light elements from Na to Cl under online conditions with a high accuracy and detection limit. The OXEA models 1000 to 3000 work with short distances of a few mm between the material and the detector. OXEA measures the elements Mg to U. The OXEA models 1000 to 3000 can be installed at a bypass or at the main belt with a varying load and a belt speed of up to 3 m/s with a patented sled construction (Figure 1-8). The particle size is limited to typically <10mm. Installations with up to <50mm have been realized. The OXEA 500 works with distances between 5 and 25 cm and is able to measure at the main belt with a particle size of up to 150 mm. This system is available for the elements K to U.



The Baltic Scientific Instruments (Con-X system – Figure 1-9) works with distances between 6 and 24 cm similar as Indutech´s OXEA 500. Baltic Scientific Instruments offer this system to measure the elements Al to U.

Another supplier is IMA (Finland) with their Quarcon XRF on-belt analyser. IMA offers the system to determine elements from Al to Pb. Steinert provides the sensor based sorting system XSS with an XRF system sensor.

Figure 1-8: Oxea system, Indutech GmbH (© Indutech)

Figure 1-9: Con-X system, Baltic Scientific Instruments (© BSI)

1.2.6 X-ray diffraction (XRD)

1.2.6.1 Physical Principles X-ray diffraction is a common technology to measure mineral composition. The technique involves a monochromatic X-ray beam focused onto a sample and the measurement of its diffraction pattern. The crystalline structure of the sample material is responsible for the reflectance of the X-ray beam in all directions. The deflection depends on the attributes of the basic X-ray beam, the layout of the ring detector (Figure 1-10) and the crystal structure of the material. Due to the different crystal orientations of the sample, areas of deflection occur in certain angles.

The individual layers of the crystalline structure reflect the incident X-ray beam by certain angles, which can either lead to a signal increase or a signal decrease, due to the X-rays being in phase or out of phase. The correlation between the diffracted rays being in phase with the incident X-rays corresponds to the distance between the lattice planes and forms a characteristic pattern for each different mineral. Thus this X-ray diffraction pattern can be compared with a fingerprint of a specific mineral [17] [18].



Figure 1-10: Ring detector, [17]

Figure 1-11: Schematic design of the full automated COSMA Online XRD, [17]

1.2.6.2 Applications X-ray diffraction is an important technology for the identification of the mineralogical composition of crystalline materials and is well established in laboratories. The sample preparation is a basic part of the analysing procedure. The mineral samples have to be crushed and milled down to a grain size of 0.1 µm and furthermore pressed to sample tablets or melted to glass pills with Li-tetraborate. This technology is not suitable for online applications above a conveyor belt, due to the undefined surface of the bulk material. Inline analysis stations are used in the cement industry. These applications use a sample taker to collect the samples from the conveyor belt, process the taken sample to the needs of the analysing device and convey the powder material to the analyser.

1.2.6.3 Market Overview A manufacturer of an online XRD analyser is the FCT ACTech in Pty Ltd, Australia. Their product is called COSMA Online XRD and is fed by a screw sampler with specimen from the conveyor belt (Figure 1-11). Another device is the full automated laboratory QXC/RoboLab made by FL Smith. It is mainly used as industrial robot, which can handle mineral samples throughout different production stages. The main steps are sample crushing, sample pulverization, dosing and powder preparation for the XRD analysis [19]. Siebtechnik is offering a similar system for sample preparation.

1.2.7 X-ray Transmission (XRT)

1.2.7.1 Physical Principles The absorption of X-rays or the radiation of low energy nuclear sources transmitted through a material layer is dependent on the atomic number of the transmitted material and the area weight, the dependence on the atomic number becomes smaller with higher energy. The use of two radiation sources with different energies allows the average atomic number of the transmitted material to be determined. This is described as the “dual energy method”. In a clean two-element mixture the elements can be quantitatively determined. In a multi-element mixture the result is an average atomic number.



Figure 1-12: Principle of operation of X-ray sorting [20]

1.2.7.2 Applications The dual energy method allows the concentration of high elements in a low elements matrix to be determined. This method is very popular for determining the ash content (high elements), in coal (low elements). In this application an Am241 (60 keV) source or an X-ray tube is used as low energy source and a Cs-137 (660 keV) source is used as a high energy source. Dual energy XRT is mainly used in sensor based sorting applications, because only a yes/no answer is needed and not the quantitative measurement. The transmission method can also be used if the material surface is not representative, e.g. if the lumps are contaminated with slurry. Sorting is therefore not dependent on a clean surface and there is no water needed for washing the material before analysing it, which is especially an advantage in dry regions. A typical application for a XRT sorting device is the separation of CaCO3 and SiO2 because of the distinct difference in atomic numbers. [21]

1.2.7.3 Market Overview XRT sorting devices are manufactured by Mogensen (MSort AR1200), Steinert (XSS-T) and Commodas Ultrasort (PRO Tertiary XRT).

1.2.8 Neutron activation analysis

1.2.8.1 Physical Principles PGNAA The Prompt Gamma Neutron Activation Analysis (PGNAA) is using the following principle:

Atoms of a sample material exposed to high energy (fast) neutrons absorb a large number of neutrons. As nuclear source of fast neutrons usually Californium 252 is used, which generates neutrons in the MeV range. Due to the impact of neutrons, the nuclei of the atoms become temporarily unstable and emit high energy gamma radiation in the MeV range to reach a stable state. The energy is characteristic for the element. A widely used detector for the PGNAA measurement is a photosensitive crystal made of sodium iodide (NaI) in combination with a photo-multiplier tube NaI-Scintillation counter) [22] [23].

1.2.8.2 Physical Principles PFTNAA The Pulsed Fast Thermal Neutron Activation Analysis (PFTNAA) was developed to replace artificial isotopic sources such as Cf252 in online analysing technologies.



The new technology is based on the interaction between deuterium and tritium ions and is installed inside a tube. The energy range extends up to 14 MeV [24] [25]. Unlike radioactive isotope based units which always emit neutrons, the tube type neutron source can be switched off immediately during non-operation, routine maintenance or in case of emergency.

1.2.8.3 Applications Online analysis, which measures a large volume, is possible using PGNAA or PFTNAA technologies. The usual adjustment for a belt application is mounting the radiation source between the belt and the detectors above the bulk material. This technology is able to detect the elemental composition of the bulk material. As a volume measuring method a compensation of the load on the belt is needed. Furthermore the moderation of the neutrons by hydrogen makes the method dependent on moisture. In addition to matrix effects also these must be compensated and calibrated with reference samples. A protection shield against the high energy neutron as well against the high energy gamma-radiation and a risk management system is necessary for both technologies. Here PFTNAA has advantage compared to PGNAA, because it can be switched off.

There is no significant remaining radioactivity into the material downstream the analyser. The only elements activated are Mn with a period of 2.58 h and Na with a period of 14.95 h. In the cement industry for the raw material analysis, the remaining radioactivity is about 2.8 Bq/t, which is a very low figure, below the recording range of measurement [23] [24].

1.2.8.4 Market Overview The PGNAA Analyser, produced by Thermo SCIENTIFIC USA, is well established for quality control of the production in both the cement and clinker industry. Another manufacturer is Scantech Australia (GEOScan - Figure 1-14). PANalytical is a supplier of analysers with the PFTNAA technology, using neutron tubes produced by EADS Sodern [24].Thermo SCIENTIFIC is also producing PFTNAA equipment.

Figure 1-13: EADS Sodern , PANanlytical (© PANalytical)

Figure 1-14: GeoScan, Scantech (© Scantech)



Figure 1-15: Thermo SCIENTIFIC CB Omni (©TFS) [23]



1.3 Comparison

Table 1: Comparison table of different measurement technologies

Tec

hn

olo

gy

ph

ysic

al

min

eral

/ch

emic

al

com

po

siti

on

el

emen

tal

com

po

siti

on

mai

n c

on

veyo

r b

elt

byp

ass

bel

t

mo

istu

re i

nfl

uen

ce

surf

ace

mea

sure

men

t

mea

suri

ng

ra

te/

dat

a o

utp

ut

rate

envi

ron

men

tal

haz

ard

s

req

uir

emen

ts

COLOR CCD colour line-scanner

colour, brightness

+ - + + + + cont./

1/min UV

NIR

near-infrared spectrometer

absorption of light NIR

+ - + + + + cont./

1/min -

Powder application

LIBS

Nd:YAG-laser, spectrometer

atoms emit spec. light

- + + + - + 20/s laser

LIF laser, sensor

spec. fluorescence

+ - + + - + 20/s

laser

XRT

x-ray source, x-ray sensor

absorption of x-ray,

- + - + + - cont./

1/min X-ray

XRF

x-ray source, x-ray sensor

element spec. fluorescence

- + + + + -

cont./

max 6/min

X-ray Restricted particle size

XRD x-ray source, ring sensor

reflection x-ray diff. angles

+ - - + - +

cont./

1/min X-ray

Powder application

PGNAA

PFTNAA

neutron source, γ-ray sensor

γ-ray emission - + + + + - cont./

1/min

γ-ray

N-ray



2 Automated Analysis of Chemical Parameters

2.1 Abstract

The focus of DRAGON is to develop and adapt the measuring units for online determination of the chemical properties under harsh underground conditions. One of the challenges we faced to adapt all measuring units (OXEA – PMD2450 – HEA) to work on the same bypass belt, all the measuring units have been adapted to work on the same material thickness. OXEA (Online X-ray Elemental Analyser) has also been improved for this project to measure lower elements starting from Z > 11 Sodium (Na), and to have better accuracy on lower elements. The biggest advantage of the XRF (X-ray fluorescence) method is to make the online measurement possible. This method is to use relatively low energy X-rays, which are easy to shield, while at the same time providing high accuracy.

2.2 Introduction

The online analyzer system developed for the installation at the TBM will be installed at the bypass. The bypass stream, taken by a sampling system, will be crushed down to less than 6 mm and conditioned on a sampling belt so that the material has a constant layer thickness of about 3-5 cm. All analyzers will be installed at the bypass belt; as an option a divider with a sample bottle carousel which collects the samples for the laboratory was suggested. These samples can be used for the calibration and a periodic check of the online analyzers. The samples can be synchronized with the online measurement either over a trigger signal or over a time stamp.

At the bypass belt the OXEA, microwave moisture meter and a high sensitive heavy element analyzer are provided. The following developments steps are done within WP3:

Modification of the OXEA 3000

o Development of the lifting system for easy service.

o Improvement of the detection limit and accuracy for light elements.

o Installation of an SQL database as interface to the process.

Development of the novel microwave moisture meter

o Separate modules for the microwave transmitter, the microwave receiver and the evaluation unit.

o Complete new electronics with a symmetric design.

o Extension of the band width from 2.4 - 3 GHz (PMD2450) to 1.4 - 4.4 GHz.

o Modification of the existing evaluation unit (PMD2450) regarding hard- and software.



Feasibility study for the new method to determine low concentrations of rare earth metals and heavy elements (HEA):

o First tests with components from existing XRF equipment.

o Transmission measurement in the 8 KeV range to measure the absorption edge of a Cupper - foil.

o Construction of a transmission measurement device at energies up to 140 KeV.

o Measurement of Iron Ore samples with different Hg concentrations.

o First optimization of the setup and the settings of the parameters.

2.3 Online X-ray Elemental Analyser (OXEA 3000)

2.3.1 Modification of OXEA 3000

The OXEA product line is designed for harsh industrial and mining applications. Modifications in this aspect were not necessary. However, regarding other features of the OXEA 3000, several modifications for the DRAGON project were needed.

The material excavated by the TBM has an upper size of 300 mm and is transported on a conveyor belt, with typical speed of 3 m/s. It is practically impossible to manually take representative samples from such a belt for laboratory testing; this made a sampling system necessary. The advantages of a sampling system are described in detail in [26]. A crusher must be installed at the bypass stream, because of the restrictions in the particle size that can be measured with the OXEA, which is about 10 mm. The bypass belt has a small width and sidewalls are installed to avoid spilling. The OXEA sensor box (OSB) is mounted between the sidewalls; the distance between the material and the detector is only a few mm.

Figure 2-1: OXEA on a bypass measuring magnesite ore



2.3.2 The lifting system

The installation condition on a TBM makes maintenance impossible in case that the analyzer is fixed, as the analyzer must be removed from the working position for any maintenance. To make maintenance easier a pneumatic lift was developed. This allows to lift the OSB pneumatically. In the service position the service door can be opened to get access to the X-ray tube and the detector system. Additionally the OSB can be manually turned by 90 degrees to check the bottom opening for the X-rays, and to clean it, if necessary. Additionally the service position is a safe position, which can be selected in case of abnormal conditions, such as extremely strong vibrations, overload on the belt etc.

2.3.3 The limit of detection and accuracy of light elements

In the past the OXEA has mainly been used to measure the properties of coal in coking plants or power stations. The ash- sulphur content was determined, and in combination with the PMD 2450, the moisture content and the calorific value were also measured. The lowest elements needed for the ash determination are Si and Al, which contributes to the ash content with about 50% and 30%, respectively. However, within the DRAGON project also lower elements as Mg and Na are important. These elements were detected but the detection limit was poor. To detect Al and Si with an acceptable accuracy INDU developed a He purge. This He purge was applied for the Fluorescence X-rays. To fulfil the needs of the DRAGON project, a new construction of the guided beam was developed to allow a He-purge for both beams. Hereby also the absorption of the exciting X-ray beam is reduced. Between the anode of the X-ray tube and the material surface is now the Be window with a thickness of 0.25mm and the free space path with the reduced absorption caused by the substitution of air by He. In a second step INDU developed - in cooperation with the supplier

of the tubes - a new tube with a thickness of only 25 m. The tube is now under a long term test since 10 months. To improve the limit of detection another target was used instead of the standard W target.

Figure 2-2: The influence of a He purge: (a) without He-purge, (b)He-purge for the detector only and (c) a He-purge for the detector and the tube.



To check, if it is possible to detect sodium with the modified analyzer a sample of salt was measured. For the process control the parameters must be measured accurately and reliably. Online analyzers generally do not comply with ISO standards. This is mostly due to the slow testing methods. Online analyzers are designed to measure indirectly. The measured variables are mainly influenced by the quantity of interest (measuring parameter), but in some extent by other quantities, which disturb the measurement. The selected principle of an online analyzer for a specific application should be most sensitive for measuring parameters with negligible sensitivity for the disturbing parameter. Because of the influence of the disturbing parameters a product-specific calibration and a check of the analyzer from time to time is necessary. This is accomplished by comparing the analyzer data, taken directly from the measured material stream, with the verified laboratory values of samples taken from the measured material stream at the same time. Both requirements can be met using an automatic sampling system, which is a necessary part of a modern online analyzing system [26].

Especially for light elements, the spectra are influenced by the absorption of a water film layer on the material. This effect must be compensated and a moisture meter is an essential part of the OXEA. One modification of the OXEA is therefore the development of a suitable moisture meter. The infrared method has several disadvantages, e.g. the dependence on particle size and colour of the material. Therefore according to the experience the microwave option is the better solution.

2.4 Moisture Meter (PMD 2500)

2.4.1 Development of a novel moisture meter

The strategy for designing an online analyzer is to ensure that the long term drift of the analyzer is negligible compared to the influence of the disturbing parameters, which cannot be compensated. For a moisture meter based on the measurement of the attenuation and the phase-shift of microwaves, transmitted through a material layer, the largest disturbing parameter is the particle size. To reduce the influence of the particle size variations, it is advantageous to install the moisture meter at the bypass-belt to measure the material, which is crushed down to < 6mm.

In general, for any transmission analyzer the measuring signals increase with increasing layer thickness. Therefore at thin layers the measuring effect of the microwave transmission type moisture meter becomes small and the long term shift of the analyzer becomes larger compared to the measuring signal. For the microwave transmission type moisture meter available on the market this breakdown is at about 100 mm. To reduce this breakdown to about 30 mm the long term stability of the moisture meter must be improved by a factor of > 3. For an installation at a bypass-belt a novel microwave moisture meter with the improved long term stability must be developed. INDU found a way to improve the accuracy of a microwave transmission type moisture meter. To realize the novel microwave moisture meter, the microwave part must be completely new developed. The hard- and software of the existing evaluation unit of INDU´s PMD 2450 must be modified. The long term drift of the existing microwave transmission type moisture meter is caused by the shift of the electronics and the shift caused by other components of the moisture meter.



Here the effect of the microwave cables is dominant. To reduce this effect, the length of the cables must be as short as possible. At the existing moisture meters the typical cable length between the compact microwave evaluation unit and the antennas is 1.5 - 2.5 m at the main belt, if the electronics are optimally positioned. However, this is usually not the ideal ergonomic installation. At a small bypass belt the typical length for the cables for the upper and lower antenna is 1m and 1.5m, respectively. The new analyzer has separate units for the microwave transmitter and the microwave receiver which can be installed close to the transmitting antenna and the receiving antenna respectively. Hereby the length of the antenna cables is reduced to 25 cm each, this means the cable length is reduced by a factor of 6. The disturbing influence of the microwave cables is proportional to the length of the cables and is hereby also reduced by a factor of 6. The microwave unit of the existing PMD was developed in 2000. Modern designs and the use of new microwave components improve the accuracy of the instrument dramatically. The technical data of the PMD 2450 and the novel microwave moisture meter PMD 2500 in table 2 show the improvements.

Table 2: Comparison of the technical data PMD 2450 and PMD 2500

PMD 2450 Novel Microwave moisture meter PMD 2500

Specifications:

-attenuation: 0,2 dB -attenuation: 0,1 dB Improvement (Factor 2)

-phase shift: 9° (3°/GHz) -phase shift: 3° (1°/GHz) Improvement (Factor 3)

-dynamic range: 80 dB -dynamic range: 110 dB Improvement ( Factor 300 for voltages)

Multifrequency technique:

2,4-3 GHz 1,4-4,4 GHz Improvement (Factor 5)

Compact Microwave unit with: Separate units for:

-Microwave transmitter -Microwave transmitter at the transmitting antenna

-Microwave receiver -Microwave receiver at the receiving antenna

-Evaluation unit -Evaluation unit installed at an ergonomic place

Microwave cables 1,5 m – 2,5 m – 4 m Microwave cables 0,3 m Improvement (Factor 7)

The long term stability of the PMD 2500 is improved by a factor of 4 and performance exceeds the required one. Additionally the PMD 2500 exceeds the specifications for frequency range and dynamic range. Another aspect of the development is the need to make the cabinet ragged enough for underground use. The existing PMD 2450 has an IP 65 alumina cabinet with a plexiglass door, and has no Atex certification. The novel microwave moisture meter has now three cabinets optional in steel or stainless steel and additionally an Atex version for zone 22 .



2.5 Heavy Elements Analyzer (HEA)

A feasibility study for the new method to determine low concentrations of heavy elements (HEA) was done. The actual status is as follows:

First test with components from existing XRF equipment were done.

Transmission measurement in the 8 KeV range to measure the absorption edge of a cupper foil was done.

Construction of a transmission measurement device at energies up to 140 KeV is finalized.

Measurement of Iron Ore samples with different Hg concentrations was done.

First optimization of the setup and the settings of the parameters was done.

Figure 2-3: XRF-measurement of iron ore with 950 ppm Hg – L-lines at 10.0 keV and 12.8 keV are just above the noise level



Figure 2-4: HEA measurement of Iron ore with 950 ppm Hg



3 Automated analysis of physical parameters

3.1 Overview

Table 3: Listing of relevant physical parameter

3.2 Rock strength

3.2.1 Disc Cutter Load Monitoring (DCLM) system

The rock strength is one of the major assessment criteria for the classification and recycling of the excavated material from TBM’s. The characterisation of the rock properties by online monitoring of the disc cutter load seems to be feasible.

Theoretical consideration:

Figure 3-1 shows the basic idea how to realize the automated online determination of the rock strength. Generally the CSM-calculation model is used for the dimensioning of the cutter-head design and main drive of the TBM on the basis of a geotechnical report. Therefore an iterative process is started by the variation of the input parameter setting. This method allows the optimization of the driving parameters.

In the opposite way the driving parameters, especially the disc cutter load, are online monitored and are continuously provided by the evaluated TBM-data. Having this as a basis it should be possible to recalculate the actual compressive strength at the face.



In this context it needs to be mentioned that the CSM-calculation method is based on certain premises like rock strengths of > 120 MPa and homogenous, isotropic geological conditions.

Figure 3-1: Theoretical correlation between DC-load and rock strength

Practical consideration:

Factors which are influencing the strength of the rock at the face:

TBM-data comprise the impact of all hard rock properties => Unconfined Compressive Strength (UCS) *) + Brazilian Tensile Strength (BTS) => discontinuities within the rock mass *) (rock cleavage, faulting zones, …) => primary stresses in the excavation face *) => water flow conditions in the rock mass *): High impact on the penetration, thrust force (DC) and torque

Averaging of the values for the rock strength if different rock materials are existing in the face

Nevertheless in coordination within the DRAGON-consortium the following approach regarding the automated online determination of the rock strength seems to be feasible. Even if the directly correlation between the disc cutter load and the compressive strength is practically not realistic, the specific load progression on the disc cutter in cutting hard rock material could be an indicator for the characterisation of the rock properties. Therefore the development of a Disc Cutter Load Monitoring System was started. Furthermore there might be a chance to calibrate this system by a standard method (e. g. PLC-Point Load Test).

In WP3 a design concept for the prototype of a Disc-Cutter-Monitoring-System (DCLM-system) has been worked out. This development is subdivided in the following 3 tasks:

Sensor technology Telemetry system Data processing and visualisation

DRAGON D3.2 Analysis Technologies (Publishable)


3.2.1.1 DCLM - sensor technology

Figure 3-2: Basic concept of the disc cutter load monitoring system (DCLM)

The basic concept of the disc cutter load monitoring system (DCLM) is measuring the load of each disk and providing data in real time on the driver´s console while simultaneously storing the information in a database. In addition, the general TBM information, for example the torque of the cutter head and the penetration rate is saved in the database in accordance to the measured data. One of the main challenges during the development of the DCLM system was the implementation of a suitable sensor technology and identifying the right position for the sensor. The sensor design is closely connected to the geometry of the cutting tool and the fixation of the tools in the cutter head. An overview of the installation situation is shown in Figure 3-3.

Figure 3-3: Disc cutter assembly

An important design tool is the FEM simulation of the cutter housing, respectively cutter box, which shows the highly stressed regions in the structure. Areas with explicit deformations caused by external disk loads are suitable for the implementation of the sensor or a load cell. The simulation shows (Figure 3-4) that the insert, the connection bolt of the insert and the wedge are adequate parts to include a sensor.

F

F F

F

F

F

disc load

[kN

]

time [s] or angle [°]

Disc cutter

housing

Disc cutter

1 2

3

Fastening set:

1: Insert

2: Wedge

3: Clamping block



Figure 3-4: FEM simulation of the cutter box, location of highly stressed areas

There are three different technology options for the disc cutter load sensor which could be integrated to the fastening set of the disc cutter. All these options are investigated and evaluated in detail by the DRAGON-partner MUL. The DCLM sensor option 1 is provided by HK and is based on a piezo-electric load cell, which is located between the insert and the cutter housing (Figure 3-5). The DCLM option 2 was developed by Entacher [30] and is based on the pre-stress reduction of the fixing bolts caused by external disc loads (Figure 3-6). Due to the given position of the measurement bolts, the signal behaviour and especially the amplification of the load signal, further investigations are necessary. A redesign of a standard cutter housing to get an optimal position of the bolts for a better signal amplification was no option from the manufacturer’s side.

The development of a highly sensitive load cell in connection with an optimized position in the cutter house or in the insert allows the use of different existing cutter house constructions to get a highly accurate disc load measurement system (Figure 3-7). The current load cell design of the DCLM sensor 3 depends on the established strain gauge technology and is completely protected against the rough conditions during the cutting process at the tunnel face. The implementation of a Wheatstone full bridge in the load cell provides a completely temperature compensated signal for the following data acquisition process. The load cell itself is designed as a cartridge which is force fitted into the existing steel construction. The concept of the data transfer from the cutter head to the TBM back up system depends on the operating mode of the tunnel boring machine; wireless as well as wire connected data transmissions are available.

FNormal

FCut



DCLM-sensors (Option 1)

Figure 3-5: Insert of the disc cutter fastening set equipped with piezo-elements


Figure 3-6: Fixing screw of the insert equipped with strain gauge

Disc cutter housing

Option_1:

Piezo element (insert)

Insert

Cable

connection

Disc cutter housing

Option_2:

Strain gauge (screw)

Insert

Cable

connection




Figure 3-7: Insert of the disc cutter fastening set equipped with strain gauge bushing

3.2.1.2 Laboratory tests regarding the DCLM-sensors Set-up and test program:

The design procedure of the DCLM system and the current tests of each stage are shown in Figure 3-8. The V-model of the construction includes three stages from the overall system to the detailed construction of the load cell and their test runs.

Figure 3-8: V model of the DCLM concept design and test runs

Disc cutter housing

Option_3:

Strain gauge (insert) Insert

Cable

connection

Inductive

interface



The load cell test is the initial test of the developed load cells. The load cell is force fitted into a thick-walled cylindrical profile with an outside-diameter of 50 mm (Figure 3-9). The test cylinder with the sensor cartridge is supported by two half-shells between the pressure pads of the MTS testing machine.

Figure 3-9: Load cell test (third test stage)

The target of this test is the characterization of linearity and hysteresis behaviour of the signal and further the assembly angle position of the load cell. The force path of the load cell test is shown in Figure 3-10. The test set-up is loaded with an initial force of 10 kN and a main load of up to 100 kN, after a short hold time of the maximum force, the external load is reduced to the initial force of 10 kN.

Figure 3-10: Test program of the load cell test (third test stage)

load cell force path



The second test is the insert test, which includes a real insert of the fastening set. The geometry of the insert demands a special pressure pad for the test procedure (Figure 3-11).

Figure 3-11: Insert test with the special pressure pad (second test stage)

The insert test consists of alternating sequences of increasing force ramps of 50 kN and a hold time at a maximum load of up to 500 kN. The maximum load of 500 kN represent a disk load of 1000 kN on the disk cutter and equals four times the nominal disk load. The insert test will provide information about the linearity and the hysteresis behaviour of the assembled insert. A further result is the influence of the boundary conditions of the test rig compared to the boundary conditions of the load cell test.

Figure 3-12: Test program of the insert test (second test state)

Insert test force



The main test stage is the cutter box system test. In this test a completely assembled cutter box, as installed on the Bossler machine S-833 produced by the Herrenknecht AG, is placed in the servo hydraulic test rig. This complex construction is developed and produced especially for this test stage (Figure 3-13). The cutter box of the test equipment contains a real 17 inch disc and is fixed under real conditions.

Figure 3-13: Assembling of the cutter box system test in the servo hydraulic test rig, University of Leoben (first test stage)

The external disc load of the cutter box system test consists of alternating sequences of increasing force ramps of 50 kN and a hold time at a maximum load of up to 500 kN, such as the initial test force path (Figure 3-14). The maximum load represents two times the nominal disk load. This test supplements the information of linearity, hysteresis behaviour of the signal and the influences generated by the surface contacts and the pre loads of the fixing bolts of the insert and the wedge.

Figure 3-14: Test program of the cutter box system test (first test stage)

System test force

F



Results and evaluation:

Figure 3-15 shows the amplification and the linearity behaviour of the signal. Another quality criterion is the hysteresis of the signal, which can be illustrated by plotting the signal (mV/V) against the external load (kN). The fitting position angle has a significant influence on the amplifying and linearity of the signal with a constant hysteresis.

Figure 3-15: Signal behaviour of the load cell with various swiveling angles (third stage test)

The insert test (second stage) illustrates the influence of the improved insert. Figure 3-16 shows the signal behaviour of different tested load cells in the insert. The representation of the test results of the second test stage is consistent with the diagram in Figure 3-14. The criteria of the test are primarily the hysteresis behaviour of the signals and furthermore the signal amplification. The linearity of the signal can be recalculated in a following data processing step. Continued test runs with different boundary conditions show a significant influence of the contact properties between the insert and the pressure pads. The effect is shown in Figure 3-17 with a linearization of the signal.

The contact properties were changed from a steel to steel contact with a high roughness to a sliding contact by using a PTFE film. Based on these investigations a dependence of external contact conditions on the insert and the linearity of the signal are deduced. The amplification, drift and hysteresis behaviour of the signal do not primarily depend on the external contact conditions but on the load cell properties which are specially tested in the load cell test. The cutter box system test generates a direct transferability of the test results to the DCLM system on the real tunnel boring machine. Through the use of the original S-833 cutter box in the test equipment, the measurement principles can be completely designed in the laboratory to get a fast and modular implementation of the DCLM system on a real TBM at the job site. Furthermore the influence of the fixing bolts and the disc fastening are represented in the results. In Figure 3-18 the comparison of two different measurement devices are shown in the diagram.



In one test run, consisting of three sequential arrangements of the test load shown in Figure 3-14, the DCLM option 2 (measurement bolts) and the DCLM Option 3 (load cell positioned in the insert) are tested. Simultaneously, two different kinds of load cells in the inserts are used. Referring to the results in Figure 3-18, the DCLM Option 3 will get a higher performance in the signal amplification with a good linearity and hysteresis behaviour of the signal.

Figure 3-16: Signal behaviour of the different load cell designs (second stage test)

Figure 3-17: Signal behaviour of the different boundary condition (second stage test)



Figure 3-18: Signal behaviour of the different load cell designs and measurement options (first stage test).

Furthermore the test of the DCLM-Telemetry system in combination with the new DCLM system Option 3 will be done with the equipment of the cutter box system test.

3.2.1.3 DCLM-Telemetry-system Application OPEN MODE

In Figure 3-19 the power supply and also the data transfer of the DCLM-system for the application on a TBM in open operational mode is illustrated. Open operational mode means that typically for hard rock TBM’s the heading face is under stable conditions and the excavation chamber is not filled and also not pressurised. This allows the online data transmission by radio signaling.

Procedure:

The insert of the disc cutter axis bearing, which is a shape-related so-called C-part, is equipped with strain gauge load sensors. They are connected to a data acquisition part to convert the analog signals for the later transmission by the specific inductive coupling. This inductive coupling is required to avoid a plug-and-socket connection under the rough operating and mounting conditions in tunneling. When the measured data have been inductively transmitted to a central monitoring box mounted at the cutter head, it is reproduced and amplified in line with the LAN acquisition. At the same time the rotary position of the cutter head is additionally detected by an acceleration sensor, in order to relate the measured data of the load to the corresponding surface area of the heading face. Then the measured data is temporarily logged by a data logger and radio transmitted by WLAN. The before mentioned data acquisition and transmission is powered by the rechargeable battery as an additional part of the overall equipment installed in the monitoring box. Finally the DCLM-data and if required also TBM-data is sent into the control cabin for its processing, evaluation and display by visualisation software installed on an industrial PC (IPC).



Figure 3-19: Schematic of the DCLM-telemetry (open mode)

Application CLOSED MODE:

In Figure 3-20 the power supply and also the data transfer of the DCLM-system for the application on a TBM in closed operational mode is illustrated. Closed operational mode means that typically for shield machines in soft-ground the excavation chamber is filled and also pressurised for heading face support. This requires a wired power and online data transmission.

Procedure:

In comparison to Figure 3-19 the WLAN data transmission is replaced by a rotary coupling with additional equipped FEC (Forward Error Correction) features to ensure a proper data transmission from the rotary part to the stationary part. In this context the data logger is not required anymore. Furthermore an encoder is directly installed to the rotary coupling for the separate detection of the rotary position of the cutting wheel and replaces the acceleration sensor. The data from the encoder is processed and provided in line with the TBM-data evaluation. Apart from the data transmission the rotary coupling also allows the stationary power supply of the overall DCLM-system, so there is no rechargeable battery in the monitoring box required anymore.



Figure 3-20: Schematic of the DCLM-telemetry (closed mode)DCLM-data processing and visualization

3.2.1.4 DCLM-data-processing and visualization The intent of the visualization should be supplying the disk load data in real time on a display in the control cab. Entacher et al. [31] show a possible concept of the graphical representation by radar plotting. An enhancement of the cutter force visualization is the implementation of geological properties on the display, as shown as in Figure 3-21.

Figure 3-21: (a) Radar plotting design of the disk load, (d) disk load corresponding with the geological mapping [31]



3.2.2 Automated Point-Load-Test system

At the moment there is no accurate on-line measurement system to determine the rock strength of the excavated material available on the market. Most of the methods are only operating under laboratory conditions and need specially prepared specimens, such as core samples. A field test, which can be correlated with the uniaxial compressive strength of rock, is the point load test. The following chapter will give an overview of the necessary improvements to be able to mount an automated version of the point load test on a TBM backup system.

3.2.2.1 State of the art

The basic procedure of the point load test is the loading of a piece of rock between two steel points to reach the failure criterion (Figure 3-22) and detecting the maximum load belonging to a defined surface. Figure 3-23 shows the possible geometries of the rock specimens such as cylindrical cores, cut blocks or irregular nuggets. Point load tests are usually used as a quick method to get information regarding the strength of the excavated rock mass. The actually suggested method was published by Thuro K. [32] with a modified shape analysis of the rock sample and three options for the evaluation of the test result. The test equipment currently available on the market is operated manually or semi-automated. The piece of rock is positioned by hand between the steel points and is loaded by a hydraulic device or a mechanical system.

Figure 3-22: Principle of the point load test [33]

Figure 3-23: Specimen shape requirements for the point load test [33]



3.2.2.2 Automation of the point load index test The automation of the PLI test (Point Load Index) can be divided into three categories:

1. Material handling 2. Test rig automation 3. Shape detection

The first category includes the material flow of the rock pieces and the handling of each sample. The second category governs the automation of the main movement of the test equipment and the test sequence. The third and main category deals with the automated detection of the shape, size and the position of the sample as well as combining the information of the shape detection with the movement and load on the testing steel points. Figure 3-24 shows a possible test sequence of the PLI test equipment. First the specimen is centered between the steel points with a conveyor. After positioning the load frame dips on the specimen with a low pre load. The next step includes main movement of the hydraulic piston until the specimen breaks. In the test sequence the position of the hydraulic piston, the point load force and the hydraulic pressure are measured and recorded.

Figure 3-24: Sequences of the automatized point load test device

Binocular vision systems are state of the art for obtaining geometrical information on irregular surfaces. Other possible technologies are the laser light section method or the stripe projection method. To get the complete 3D information of the specimen, several cameras are positioned around the point load test rig (Figure 3-25). The isochronous shot of each camera and the following image processing generate a point cloud and further the geometrical information of the specimen.



Figure 3-25: Schematic of the positions of the optical measurement system

First experiments are done with the ENSENSO N10 3D camera system. Figure 3-26 shows the test installation consisting of three ENSENSO 3D camera systems and three colour CCD cameras. The experiment delivers first feasible geometries in spite of the large gaps in the point clouds, which are based on the unpropitious position of the cameras. Further investigations should give a better performance of the point cloud without shading effects in the 3D model.

Figure 3-26: Schematic sequences of the automatized point load test device

3.3 Grain size and shape distribution

3.3.1 Photo-optical particle analysis

3.3.1.1 State of the art The determination of the grain size and shape distribution is required for the evaluation and classification of the excavated material as useable material.



The following already existing measuring technology from the company Haver & Boecker [29] is the most feasible method in the automated analysis of the grain size and shape distribution. This photo-optical particle analysis system is tested with a representative sample from job-site in the technical centre at Haver&Boecker.

PHOTO-OPTICAL ANALYSIS:

The principle of the CPA (Computerized Particle Analysis) measurement:

HAVER CPA measuring instruments are based on digital image processing. A high-resolution digital line scan camera scans the particles in free-falling bulk materials against the background of a LED lighting array with a recording frequency of up to 28,000 line scans per second. The scanned lines are combined by the CPA to form an endless data record and the shadow projections of the particles are evaluated in real time (HAVER REAL TIME) in parallel with the measuring process. Up to 10,000 particles can be detected and analysed every second. Due to a GigE camera interface the CPA devices can be operated using a notebook without additional hardware module (camera card). The GigE technology has a high transfer rate of up to 1,000 Mbit/s. All HAVER CPA units feature the HAVER REAL TIME function and work with only one line scan camera. Every particle in the grain size measuring range can thus be measured and the result used in the size and shape analysis. Double detection due to individual images overlapping is ruled out, as are partial range detection and mis-measurement of truncated particles. In addition, the HAVER REAL TIME function also enables all CPA units to be used as particle counting devices [29].

In Figure 3-27 and Figure 3-28 the simplified assembly of the relevant system components and the functional principle of the photo-optical analyser by the free falling particle scanning is shown. In order to realize an optimized analysis regarding time requirement and accuracy the continuous particle feeding by vibrating conveyor is automated controlled.



Figure 3-27: Functional principle of CPA [29]

Figure 3-28: free-falling particle scanning [29]



Figure 3-29 shows the shadow projections of the particle scanning, so every single grain is measured and evaluated in terms of the grain size and shape distribution.

Figure 3-29: Online shadow projection of the particles [29]

All grains with the respective profiles and dimensions are listed in a specific data-base for the directly evaluation in real time and also for the later individual evaluation by variation of the different parameter settings. In Figure 3-30 the particle list with additional data filtering functions is illustrated. It also enables the further individual data processing.

The data of the measured particles are generated and processed by the evaluation software. In this context a variety of different 1D-, 2D- and 3D-related data are available and will be individually displayed in tables and diagrams. Figure 3-31 illustrates a combined visualization of the grain size distribution curves for different samples. In comparison to the conventional sieve analysis the photo-optical analysis enables the following advantages: [29]

• Automated online measurement and evaluation in real time

• 1D-, 2D- and 3D-related data processing

• Individual data evaluation and visualization

• Comparative evaluation of different measurements

• Calibration of the analyzing system by weighing

In the portfolio of Haver&Boecker there are several types of photo-optical analyzers already existing. The CPA 4 type is the standard analyzer for the laboratory use and is mainly used for granulated materials.



Figure 3-30: particle list [29]

Figure 3-31: Visualization of the grain-size and -shape distribution by HAVER CPA software [29]



The CPA 4 CONVEYOR type, see Figure 3-32 has been especially developed for analysing elongated materials, the measuring results of which tend to be falsified due to overlaying and rotation of particles while the image is being analysed. In this process, the material sample is fed via a vibrating conveyor from where it passes onto a faster running belt conveyor. The resulting difference in speed separates the particles and brings them into a stable orientation (maximum length to maximum width) before the digital image analysis takes place. The HAVER CPA CONVEYOR measuring principle virtually rules out random rotation of the particles at the instant they are measured. [29]

Figure 3-32: HAVER CPA 4 CONVEYOR [29]

Apart from the laboratory types CPA 4 and CPA 4 conveyor, the CPA 5 type illustrated in Figure 3-33 is designed for the in-situ measuring and directly implemented at the head of a belt conveyor. The dimensions of the CPA 5 type are adapted to the respective belt conveyor [29].

Figure 3-33: HAVER CPA 5 [29]



PERIPHERAL EQUIPMENT

Modular peripherals, containers

The product range of HAVER CPA units is complemented in practice by various peripheral devices. Moist materials, for example, must be first dried in order to be able to measure them photo-optically. For this purpose, the HAVER HSD High Speed Dryer illustrated in Figure 3-34 has been developed, which dries mineral bulk material within a minimum amount of time. Conveyor speed and heating temperatures can be adjusted to suit the material. The high heating capacity and air quantity guarantee high flow rates. The HAVER HSD is frequently used for pre-drying wet gravel before analysing with the HAVER CPA 4-1. It can be used with all HAVER CPA units as well as separately for the fast drying of bulk material [29].

Figure 3-34: HAVER HSD for fast drying of moist bulk material [29]

The HAVER DMS shown in Figure 3-35 is a double deck screening machine for pre-screening with two separating cuts. In doing so, the feed material can be divided into three fractions. The two screens can be individually designed and tensioned by the Haver screening service. Rubber balls or special ultrasonic modules can be used to implement even super fine separating cuts and to enable automatic cleaning of the screens to be achieved. The prescreening function of the HAVER DMS is used in combination with a HAVER CPA particle measuring unit to screen out the fine content of a material sample, which will dramatically reduce measuring times [29].



Figure 3-35: HAVER DMS for screening out the fine contents of bulk materials [29]

To achieve optimum utilization of the classification area, Haver&Boecker recommends additional pre-dosing with the magnetically driven HAVER EMZ feed trough. Its large material hopper makes it the ideal buffer storage for downstream processes. In step mode, it conveys all kinds of bulk material [29].

Figure 3-36: HAVER EMZ for conveying and dosing of bulk materials [29]



Haver&Boecker has developed HAVER AS auto sampler for automatically feeding HAVER CPA devices with material samples. Six, twelve or twenty-four samples (AS 6, AS 12, AS 24) can be automatically measured around the clock without tying up capacity. The HAVER AS is controlled by the HAVER CPA software via an interface. It can be used as a laboratory or technical centre unit and also for collecting retain samples [29].

Figure 3-37: Haver AS 12 [29]

For the practical application the relevant analyzing equipment and peripheral devices can be systematically combined and separately installed in a container. This allows complex analysis in hard ambient conditions [29].

Figure 3-38: Haver Graviopt [29]



3.3.1.2 Laboratory tests at Haver&Boecker

Figure 3-39: HAVER CPA 4 in operation [29]

Figure 3-40: HAVER CPA 4 conveyor in operation [29]

First tests in the laboratory of Haver&Boecker with samples from the job-site (project S-765 / S-766) have been carried out. The test program comprises the measuring of the samples in dry and wet condition with the two different types of photo-optical analyzers HAVER CPA 4 and HAVER CPA 4 conveyor. The samples were previously prepared by sieving the insignificantly fine particle fraction < 3 mm. In Figure 3-39 and Figure 3-40 the operating photo-optical analyzer HAVER CPA 4 with the measuring range of 96 µm to 220 mm is illustrated. The measuring process for a sample mass of 12 kg takes approximately 4 to 5 minutes. [29] This type is equipped with an additional belt conveyor for grain singling to enable an optimized measuring process. In terms of the prototype development in WP 5 the photo-optical analyzer HAVER CPA 4 conveyor is implemented for the automated grain size analysis. [29]



Figure 3-41 shows the diagram with the grain size distribution curves of the photo-optical analysis in comparison to conventional sieve analysis in the laboratory at MUL. According to that the resulting deviations of absolute ca. 10 % in using the existing technology of the different photo-optical analyzer are in a good range, so the automated analysis for the relevant grain sizes (> 8 mm) is feasible. [29]

Figure 3-41: comparison of the measurement results and the sieving curve with samples from Chute de Gavet (S-765)

In this context the moisture content needs to be limited in order to avoid agglomeration effects of the gravel. The critical values of the moisture content are going to be investigated in line with the development activities of the prototype in WP5

essential measuring time measuring ranges 3D-related analyzing results.

3.3.1.3 Tunnelling-related modification of the evaluation software In comparison with the previously measured grain-size distribution curve by the sieve analysis standard method in the laboratory at MUL, the 3D-related data needs to be corrected by a grain-shape-specific computer model. This is going to be tested in line with the development activities of the prototype in WP5.

Haver&Boecker implements a volume calculation algorithm to generate 3D informations out of the measured 2D grain size shown in Figure 3-30. The 2D grain contour is sectioned in several parallel strips. Afterwards each section represents the cross section of a cylindrical volume. By summing up of all particular cylinders of one grain, the grain volumes are generated and provide the volume information of the grain size distribution.



Figure 3-42: Haver&Boecker volume model

3.4 Bulk density

If the mass flow rate and the volume flow rate of conveying material are measured at the same time, the bulk density is calculated by the following equation.

: Bulk density – kg/m3

: Mass flow rate – kg/s

: Volume flow rate – m3/s

3.4.1 Determination of the mass flow rate

The belt weighing system is a proven technology for measuring the mass flow rate of discharged excavated material on TBM’s.

In Figure 3-43 the functional schematic of the belt weighing system is shown. The speed measuring in meter per second and weight measuring in kg per meter are input parameters for the evaluation of the mass flow rate in kg per second and conveying capacity in tons per hour.

The material flow on the conveyor belt generates a vertical load on the idlers and on the idler bracket. The idler bracket is mounted to a weighing frame, so the vertical load is directly measured by the implemented load cells. Additionally the conveying speed is measured by a measuring wheel, which is running on the lower belt. Then the measured signals from the load cells and speed sensor are online processed by the evaluation unit. So the respective data for the mass flow rate is directly displayed and provided as an analog output signal.



Figure 3-43: Schematic of the belt weighing system [36]

Figure 3-44: assembly of the belt scale [36]



Figure 3-45: Siemens milltronics belt scale [36]

In order to enable a proper measuring process with an accuracy of ≤ 5 % for the mass flow rate the following aspects needs to be strictly considered:

Alignment of the belt scale must be checked

Measuring range of the belt scale must be checked

Belt scale must be calibrated in operation Zero calibration over the complete length of the endless belt (time duration at a belt velocity of 2,8 m/s is about 3 min @ 500 m length) Calibration with defined test weights simply connected at the belt scale

Check measurement by the load of a defined amount of material

Figure 3-46: Belt scale with additional test weights [36]



3.4.2 Determination of the volume flow rate

The online measuring of the volume flow rate for the conveyed excavated material by a bulk scanning system is also a proven technology. In Figure 3-47 the functional schematic of the volume flow rate is shown. The surface area of the bulk solids on the running belt conveyor is scanned by laser beams. On the basis of the previously scanned conveyor belt profile the measured data for the bulk solids profile from the bulk scanner are processed by the evaluation unit and the resulting volume flow rate is provided via specified interface. [37]

Operational principle:

The surface contour of the bulk goods is scanned by a laser scanner with a high angle definition in two dimensions at very short intervals of time. The laser pulse transmitted is reflected by the object measured (the bulk goods) and then evaluated in the receiver module of the scanner (light transit time measurement). The laser impulses are deflected in a defined manner by means of an internal rotating mirror. This produces a scan shaped to the profile of the bulk goods. The two dimensional contour data is transferred to the evaluation unit in digitalized form. By comparing the bulk goods profile with the profile of the empty conveyor belt, the respective bulk goods area can be calculated and with the help of data on the belt velocity, bulk goods density and time, the requested volume or mass flow can be determined. These values are totaled by an electronic counter to the total volume or total mass. [37]

Figure 3-47: Schematic of the bulk scanning system [37]

Components

Laser scanner:

The laser scanner is the sensor for the system. Laser light pulses are transmitted with an internal rotating mirror through an angle of opening which can be set using the operating program. The transit time of the light signals reflected by the bulk goods or unloaden conveyor belt are measured in the scanner and converted into length vectors. Therefore the profile of the bulk goods can be determined across the entire angle of opening. [37]



Evaluation unit:

The evaluation unit carries out the system control and the processing of the length vectors determined by the laser scanner into the desired output variable. The output variables are available as analogue signal 0…20mA. The conversion to the desired output variable can be done by entering constant values of by connecting external transmitters for belt velocity or bulk goods mass (belt scales). The evaluation unit contains a two-line LC-screen for displaying the measuring variable. The following can be selected:

i. Volume flow (with a constant value for time or belt velocity or connection of an impulse or analogue value transmitter for belt velocity)

ii. Mass flow (with a constant value for bulk goods density and current volume flow)

iii. Bulk goods density (if an analogue value transmitter for bulk goods mass and current volume flow is connected). [37]

Figure 3-48: 3D-illustration of the bulk solids profile scanning [37]

Figure 3-49: SICK bulkscan 210 [37]



3.5 Mica content determination

3.5.1 Introduction

Mica has a negative impact on fresh and hardened concrete [43] [38] [50] [46]. In the case of the AlpTransit projects the main part of the tunnels were bored in the central Alpine region which consist of crystalline rocks. They possess a high content of layer silicates (mica) which are frequently <2mm in diameter, and these enrich the finer fractions during the preparation process. Free mica (not bonded into the rock mass), which comes into contact with combined water and cement, has a negative effect on fresh concrete and the properties of set concrete. As the mica content increases, the water quantity for constant workability also increases. Laboratory mortar tests have shown that not only the quantity but also the grain size and type of free mica influence the parameters [51]. They have also shown that mainly mica greater than 0.125mm exercises a negative influence. Fine mica (< 0.125mm) did not influence the mortar properties negatively; on the contrary, the slump and the tensile strength were even increased. With regard to the effect of the mica type on the mortar quality, the tests indicate that muscovite is more harmful than biotite or chlorite. Evaluation of the free mica content in the entire sand fraction 0/4mm is a relatively time consuming analysis. The mortar tests with aggregates from the AlpTransit project showed that the sand investigations can be done on the sand sub-fraction 0.25/0.5mm; this is representative of the entire sand 0/4mm (Figure 3-50).

Figure 3-50: Free mica content in the sand 0/4mm compared with the sub-fraction 0.25/0.5mm

The AlpTransit project accepted sand (0/4mm) with a total free mica content of less than 14 particle-%. This means that maximum mica content in the sand sub-fraction 0.25/0.5mm must be less than 35 particle-%. Currently, the amount of mica present in excavated material is determined by counting 200 mineral particles from the fraction 0.25 to 0.5 mm and 1 to 4 mm, respectively, under a stereomicroscope and to identify the particle-% of mica present. There is no uniform standard within the European Union regarding the maximum amount of mica within concrete and mortar. With the exception of the Swiss standard SN 670 115 which fixes the maximal content of free phyllosilicates in concrete and mortar to maximal 2 mass percent, there are no other available standards on this topic.



The amount of free mica in the crushed sand can be reduced by flotation technique. During the first step of this process, mud (<0.063 mm) is removed from the sand fraction in a cyclone unit. The resulting sand (0/1mm) is then washed with water and a chemical product (collector) in the flotation plant. The unit includes three basins, or flotation cells, linked together (Figure 3-51). These compartments are agitated and ventilated to enhance the selective mica adsorption on the collector which flocculates and produces foam on the water surface (Figure 3-52). This supernatant is then removed with paddles before being dewatered. In parallel, the washed sand is discharged in a separate sand dewatering unit. The flotation process is an environmentally friendly process, which only uses one chemical product (the collector). Since the collector is almost completely bio-degradable (> 90%), it doesn’t contaminate the flotation wastewater. The latter can be re-used as processing water without concerns. Thus flotation runs on an almost closed water cycle.

Figure 3-51: The flotation unit contains 3 cells linked together.

Figure 3-52: Paddles remove the foam containing the adsorbed mica particles.

As shown in Figure 3-53, the flotation process can remove up to 50% of the mica present in the crushed sand (0/1 mm). In addition, the foam (flotation product) contains 80 to 90% of the mica particles.

Figure 3-53: Results of the mica reduction by flotation technique



The aim of the following chapters is to test several possibilities to automate the mica content determination. The focus is set on the 0.25-0.5mm fraction.

Figure 3-54: Intergrowth of biotite and quartz

3.5.2 Selected chemical and physical parameters of mica and quartz

The chemical composition of muscovite is KAl2(AlSi3O10)(OH)2. Minor substitution of Na, Rb, Cs can occur for K; Mg, Fe, Li, Mn, Ti, Cr for Al and F for OH [44]. The density of Muscovite is 2.76-2.88 g/cm3. Muscovite has a perfect cleavage and can therefore be split into very thin, flexible and elastic sheets. It has a light color [44]. Electrostatic shape separation is used to separate muscovite from other minerals (e.g. Yuga et al. [48]).

The chemical composition of biotite is K(Mg,Fe)3(AlSi3O10)(OH)2. Substitution occurs of Fe2+ for Mg, Fe3+ and Al for Mg and from Al for Si. Na, Ca, Ba, Rb and Cs can substitute for K [44]. The density of biotite is 2.8 to 3.2g/cm3. Like muscovite, biotite has a perfect cleavage and forms flexible and elastic sheets. It usually has a dark color from dark green, brown to black and more rarely light yellow [44]. To separate biotite from other minerals magnetic separators are commonly used (e.g. Central European Ar-Laboratory [49]).

The chemical composition of quartz is SiO2 and its density is 2.65 g/cm3 (Klein, 2002). Quartz has a conchoidal fracture. Thus, unlike muscovite and biotite, quartz mainly has a rounded shape. It is usually transparent or white but due to frequent chemical impurities it can be of any colors. Quartz is strongly piezoelectric and pyroelectric [44]. From the selected characteristics above it is apparent that mica (bitotite and muscovite) differ from quartz by its often thin flaky appearance. Mica has also a slightly higher density than quartz, calcite or feldspars. Several different methods to separate mica from the other minerals in the 0.25 to 0.5mm fraction are presented here. Artificial samples were prepared especially for this reason and also natural samples were used. The focus has been set to choose fast methods that do not have any negative impact for the environment.

3.5.3 Sample material and preparation

There are two kinds of sample materials: Artificially prepared samples and natural samples. The natural samples derive from different gneiss rocks from southern Switzerland. For preparation the artificial samples quartz porphyry, muscovite and biotite were prepared separately and then mixed. The mica material that was used to prepare the artificial samples originates from Kremer Pigmente, Aichstetten (Germany). The quartz porphyry is from the building contractor Carlo Bernasconi AG in Bern (Switzerland). It was present as cobblestones measuring approximately 6x6x6cm.

First, the cobblestones were broken with a hammer into about 4 pieces per cobblestone and then fed into a laboratory jaw crusher (walter & bai ag, type A092).



The output of the crusher was washed, sieved and the fraction 0.25-0.5mm was collected while the fraction smaller than 0.25 was rejected. The fraction larger than 0.5mm was passed through the crusher again. The whole procedure was repeated about four to five times. The muscovite and biotite material were both available as fraction 0.25 to 1.0mm. They were sieved wet and separated to retrieve the 0.25 to 0.5mm fraction. In a first test, 20g of biotite and 80 g of quartz porphyry were mixed together and 200 pieces of this mixture were counted by hand to determine the particule-% of biotite. The hand counting resulted in 80 number percent of biotite. A new mixture of 6g and 54g of quartz porphyry resulted in 60 number percent of biotite. All other biotite and muscovite quartz porphyry mixtures where prepared in the same trial and error manner. The most challenging was to create a homogeneous mixture and to avoid particle segregation when using a spatula to distribute the sample on the sample holder for counting. This was specially the case for test samples 9 and 10 which contain 20% of muscovite or 0.5 and 0.2g of muscovite each, respectively (Table 4). The samples used for analysis are given in Table 5.

Table 4: Preparation of the artificial test samples. Abbreviations: Bt=biotite, M=muscovite.

Test mixture weight (g)

number count (%) number count (%) rounded

1 20 g biotite + 80 g quartz porphyry 100g 79.8% biotite 80% biotite



4 0.5 g biotite + 59.5 g quartz porphyry 60g 10.7% biotite 10% biotite

5 1.5 g biotite + 58.5 g quartz porphyry 60g 28.8% biotite 30% biotite


7 1 g muskovite + 59 g quartz porphyry 60g 28.9% muscovite 30% muskovite

8 2 g muskovite + 58 g quartz porphyry 60g 41.2% muscovite 40% muskovite

9 0.5 g muskovite + 59.5 g quartz p. 60g 20.8% muscovite 20% muskovite



12 1 g biotite + 1 g muskovite + 58 g quartz p. 60g 17% Bt+31.5% M=48.5% mica 50% mica

13 0.4 g biotite + 0.1 g muskovite + 59.5 g quartz p. 60g 7.5% Bt+2.3% M=9.8% mica 10% mica

14 0.6 g biotite + 0.4 g muskovite + 59 g quartz p. 60g 16% Bt+14.1% M=30.1% mica 30% mica

15 0.5 g biotite + 0.2 g muskovite + 59.3 g quartz p. 60g 10.2% Bt+9.6% M=19.8% mica 20% mica

16 1 g biotite + 0.5 g muskovite + 58.5 g quartz p. 60g 17.6% Bt+22.3% M=39.8% mica 40% mica



Table 5: The artificial test samples.

sample name number percent

Q10 100% quartz porphyry

Bt10 100% biotite

M10 100% Muskovit

Bt1 10% biotite + 90% quartz porphyry




M1 10% muscovite + 90% quartz porphyry




G1 10% mica (7.5% Bt + 2.3%M) + 90.2% quartz porphyry

G2 20% mica (10.2% Bt + 9.6% M) + 80.2% quartz porphyry

G3 30% mica (16% Bt + 14.1% M) + 69.9% quartz porphyry

G4 40% mica (17.6% Bt + 22.3% M) + 60.1% quartz porphyry

G5 50% mica (17% Bt + 31.5% M) + 51.5% quartz porphyry

Some of the natural samples were analyzed by X-ray diffraction (XRD) at the University of Bern to improve the calibration of the XRF based OXEA from INDU. The method of X-ray diffraction uses the interaction of a primary X-ray beam with the crystal lattices to determine the type and the amount of minerals present in a sample. The samples were milled for about 6 minutes in a tungsten carbide container. 1.5 g of the sample material was mixed with the internal standard LiF (Lithium fluoride) and homogenized for 10 minutes. The material was transferred into a sample holder. Due to their platy habitus, mica minerals tend to align. Thus a piston was used to disorientate the samples. XRD measurements were carried out on a CubiX3 from Panalytical using Cu-radiation at 45kV/40mA and a secondary monochromator. The results were analyzed with the software HighScorePlus from Panalytical. Single phases were identified and quantified by Rietveld refinement. Rietveld refinement of sheet silicates like mica that have their main peak below 20°2 theta is difficult and imprecise. Thus another method was used to quantify the different phases. Different mixtures of a sample and pure mica (chlorite and muscovite) were prepared. The sample B-R-10 was used as starting material since its natural mica content is very low. Sample mixtures of B-R10 with addition of 10, 30, 50 and 70 weight percent of muscovite and mixtures with 10 and 30 weight percent of chlorite were prepared and measured.



The intensity and area of the peaks of muscovite/biotite at 8.7 and 8.8° 2 theta were measured and correlated with the weight percent muscovite/biotite. This resulted in an almost linear trend line.

Also the intensity and area of the chlorite peaks at 6.1 and 12.4° 2 theta were measured and correlated with the known weight percent. Like with muscovite/biotite the result is an almost linear correlation. Based on this almost linear correlation the chlorite and muscovite/biotite content of all 15 samples was calculated and compared with the results achieved by hand counting (Table 6).

Table 6: Natural samples comparison hand count and XRD results.

sample XRD (weight-%) hand counted (number %) difference (n-w)

B-R-05 43 33 -10

B-R-06 42.7 19 -23.7

B-R-07 22.1 12 -10.1

B-R-08 65.1 58 -7.1

B-R-09 23.3 18 -5.3

B-R-10 1.5 1.4 -0.1

B-S-02 22.1 24 1.9

B-S-25 20.3 20 -0.3

C-R-07 40.2 39 -1.2

C-R-08 12.4 9 -3.4

C-R-14 19.6 11 -8.6

C-R-17 4.7 10 5.3

C-R-19 46.2 40 -6.2

C-R-20 70.4 70 -0.4

C-R-26 45.3 36 -9.3

The difference is given as hand count-XRD results.

Table 7: Comparison of artificial biotite samples evaluated by hand counting and XRD.

sample hand counted

XRD difference

number-% weight-% (n-w)

Bt1 10 9.1 0.9

Bt1 10 7.9 2.1

Bt2 20 10.7 9.3

Bt2 20 11.0 9.0

Bt3 30 23.0 7.0

Bt3 30 18.4 11.6

Bt6 60 40.4 19.6

Bt6 60 45.2 14.8

Bt8 80 53.9 26.1

Bt8 80 53.7 26.3



The difference is given as hand count minus XRD results. The XRD results are based on the linear correlation from the natural samples.

Figure 3-55: The correlation between XRD-weight-% and hand count number-%.

When using the linear correlation from the B-R-10 samples above the biotite content in the artificial samples is underestimated by up to 30% in comparison to hand counting (Table 8). However, there is a good linear correlation between XRD-weight-% and hand count number-% (Figure 3-55). The artificial samples from Table 5 did not yield evaluable results. The muscovite samples M1 to M4 and the mixed samples containing muscovite G1 to G5 did not produce useful results at all. The muscovite crystal structure could not be resolved by XRD even when measured separately. Presumably the muscovite used has been treated thermally by the producer.

Table 8: Comparison of artificial muscovite containing samples evaluated by hand counting and XRD.

sample hand counted

XRD difference sample hand counted

XRD difference

number-% weight-% (n-w) number-% weight-% (n-w)

G1 10 3.2 6.8 M1 10 -6.0 16.0

G1 10 -1.2 11.2 M1 10 -6.5 16.5

G2 20 2.0 18.0 M2 20 -5.8 25.8

G2 20 1.2 18.8 M2 20 -5.2 25.2

G3 30 6.7 23.3 M3 30 -2.2 32.2

G3 30 8.2 21.8 M3 30 -2.9 32.9

G4 40 4.1 35.9 M4 40 -3.7 43.7

G4 40 7.8 32.2 M4 40 -2.4 42.4

G5 50 8.7 41.3

G5 50 8.2 41.8

y = 0,6851x - 0,0681R² = 0,984

0,0

10,0

20,0

30,0

40,0

50,0

60,0

0 20 40 60 80 100

biotite-quartz porphyry XRD and hand count

hand count number-%

XR

D-w

eigh

t-%



The difference is given as hand count minus XRD results. The comparison did not show any useful correlation due to the defect structure of the muscovite used. Dr. Urs Eggenberger and his team from the University of Bern, Switzerland are kindly acknowledged for XRD analyses and discussion.

3.5.4 Automated microscopic analysis

Several different samples from the 0.25 to 0.5mm fraction were photographed at TransGeo AG, Switzerland using a binocular microscope and polarization light microscope with plane polarization, crossed polarization as well as crossed polarization with gypsum plate inserted.

Instrument specifications:

Binocular microscope: Olympus trinocular zoom stereo microscope SZ4045 Polarization light microscope: Olympus BX50Pol microscope Camera: Olympus, Camedia C-5060 Wide Zoom, digital compact camera

When analyzing rock samples usually thin sections with a thickness of 0.03mm are prepared for polarization microscopy. Each mineral has specific characteristics that help to distinguish it in a thin section from the other minerals. The samples from the 0.25 to 0.5mm fraction are very variable in thickness. Therefore, they will not display the same distinguishing characteristics when interacting with the light passing through them. Nevertheless, the 0.25 to 0.5mm fraction was put loosely on a plane glass plate that is usually used to make thin sections and the interaction with the light was observed.

3.5.5 Observations with the polarization light microscope

3.5.5.1 Characteristics of quartz In plane polarized light quartz has very often irregular contours. A single grain is usually dark grey to black – depending on the grain thickness - with a few small brighter patches (Figure 3-56, top). A thick quartz grain will appear totally black and will be hard to distinguish from a thin biotite flake stack that blocks the light from passing through it. Due to its roundish shape, some parts of the quartz will always appear out of focus.In crossed polarized light a single quartz grain will appear mostly multicolored (displaying patches of yellow, green, blue, violet and others) (Figure 3-56, middle). When the grain is rotated a colored patch will turn grey every 90° of rotation.In crossed polarized light with the gypsum plate inserted the quartz grain will still appear multicolored with colors such as yellow, blue and others. Overall the grains appear much darker (Figure 3-56, bottom). The colored patches will turn violet (instead of grey) every 90° of rotation.

3.5.5.2 Characteristics of muscovite In plane polarized light muscovite appears light grey to grey (Figure 3-56, top). It is light grey when it is very thin. Its color can be very close to the color of the glass plate on which it placed. Some black lines can be observed. They may be representing broken areas in the flaky stack. In crossed polarized light muscovite is white, sometimes slightly yellowish, and depending on the magnification it can be very bright (Figure 3-56, middle). When rotated the grains turn grey every 90°. At high magnification thin colored lines can be observed within the grains.



In crossed polarized light with the gypsum plate inserted muscovite changes between blue and orange every 90° of rotation (Figure 3-56, bottom). In between muscovite is violet.

3.5.5.3 Characteristics of biotite In plane polarized light biotite is brown if the grain is made of only a few flakes (Figure 3-56, top). It is dark brown or black with slightly brownish edges if the grain is made of many flakes stacked up. Usually at least one smooth grain boundary can be observed. In crossed polarized light biotite is dark brown if a grain is composed of only a thin stack of flakes to almost black if many flakes are stacked (Figure 3-56, middle). The edges of a grain are sometimes brighter if they are thinner than the center of the grain. The edges sometimes can be multicolored if they are intergrown with quartz. In crossed polarized light with the gypsum plate inserted biotite is brown to black (Figure 3-56, bottom). The edges of the grains are sometimes brighter or almost pink if they are thin. They can also be slightly multicolored if intergrown with quartz. On the occasion thin, short and multicolored inclusions can be observed. Samples (top, middle and bottom) in plane polarized light, crossed polarized light without and with gypsum plate, respectively; left at high magnification (4x) and right at lower magnification (2x).

Figure 3-56: Observations with the polarization light microscope Abbreviations: Bt=biotite, Bt+=biotite intergrown with another mineral, M=muscovite, Qz=quartz.

The unlabeled grains on the right are considered quartz and others.



3.5.5.4 Observations with the binocular microscope Many grains will be out of focus as grain thickness is very variable (Figure 3-57). This can make it difficult to distinguish between quartz and muscovite grains. Thick biotite grains that were hard to distinguish from thick quartz in plane polarized light (both appear black) are clearly distinguishable in the binocular microscope.

Figure 3-57: A mixed sample photographed through a binocular microscope.

Abbreviations: Bt=biotite, Chl=chlorite, concr=concrete, M=muscovite, Qz=quartz and other minerals.

Based on the observation described above the best way to distinguish between muscovite, biotite and quartz by automated microscopy is a combination of two different microscopic settings, which are then combined by a picture processing software. The combination of crossed polarization light with binocular microscope appears to give the best results.

Several of the pictures were sent to Carl Zeiss AG, Switzerland. A picture shot in plane polarized light (Figure 3-58) was used for a short test with a basic picture processing software. Color differences were used to calculate the surface area of the minerals (personal communication Julien Toquant, Zeiss AG). The pictures were segmented with grey levels (blue, green and red channel). If colors of different minerals are similar it is difficult to distinguish between them. If the same minerals occur in different thicknesses then they will have different colors and may not be determined as the same type of mineral by the software. On the other hand, mineral grains could be determined based on shape. However, as the grain shape is variable this is not a feasible solution.

Distinguishing the 14 minerals present in the picture was challenging. The software result deviates between 2 to 11% from the hand count result (Table 9).



Figure 3-58: The picture used by Carl Zeiss AG for a short test with a basic picture processing software.

Table 9: Comparison of hand count results and results from surface area by a picture processing software.

% % %

muscovite biotite quartz remarks

Carl Zeiss AG 52 25 23 surface area

B+ G AG hand count

43 36 21 number percent

At this magnification only a few grains can be photographed. For statistical reasons 200 grains have to be evaluated, thus, a lower magnification must be used to fit more grains in one picture. Information on the characteristics of the mineral grains will consequently drop with lower magnification. Several photos of the same sample in different positions on the sample holder have to be acquired in order to get the required 200 grains determined.

If the number percent of the grains shall be calculated then the grains will have to be well apart from each other. If grains are in contact, the program will regard them as one grain which is not a problem if results are given in surface area. On the other hand, if grains have internal boundaries or striations in black the program will count them as two or more grains if the results are delivered in number percent. Again, if results are given in surface area this is not a problem. It will be very challenging to process samples that carry dust and dirt. Samples with many different minerals will be also difficult to process. In order to find out if the determination of mica content in a mixed mineral sample is possible by automated



microscopy a feasibility study costing approximately 6’000 Euro would have to be carried out. An automated microscope would cost around 66’000 Euro.

Additional unknown costs for software development would arise. Based on all the difficulties mentioned above determination of mica content by automated microscopy does not appear to be the best solution. Dagmar Riesen and the staff of TransGeo AG, Gümligen, Switzerland is kindly acknowledged for kind support with the microscopes and the camera. Tom De Bruyne and Julien Toquant from Carl Zeiss AG, Feldbach, Switzerland, are kindly acknowledged for their great help and support.

3.5.6 Determination by optical measurements

Hyperspectral imaging in visible light (400-800nm) and the classical spectrometry in near-infrared NIR (1000-2000nm) were kindly tested for the applicability to artificial biotite-muscovite-quartz porphyry samples (Table 4 and

Table 5) by Boris Larchevêque and Adrien Cougnon from Indatech (France). Pure samples of biotite (Bt10,

Table 5), muscovite (M10) and quartz porphyry (Q10) were analyzed as well as aliquots of mixtures (G1 to G5,

Table 5) containing all three minerals with a total mica content of 10, 20, 30, 40 and 50 number percent. Hyperspectal imaging experienced a lot of interferences due to light polarization induced by the minerals. Therefore a polarizer was installed in front of the hyperspectral camera. Halogen lightning was used and multipoint measurements of samples in 2D were carried out. Results from visible light (400-800nm) measurements show that the pure quartz porphyry and pure muscovite spectra both show the same absorption peaks at 680 and 700nm which makes it difficult to differentiate between the two minerals (Figure 3-59). Biotite has many different absorption peaks (Figure 3-59) which makes it difficult discern. Thus, the mixed samples were not measured with this method.

wavelength



Figure 3-59: Hyperspectral imaging in visible light (400-800nm)

For classical spectroscopy a horiba spectrometer was utilized in the NIR (1000-2000nm). Halogen lightning and single point measurements (but making several measurements on several points) were employed. Each pure mineral has been measured on 5 different points. Absorption peaks for quartz porphyry are at 1250, 1400, 1750 and 1950nm (Figure 3-60). Muscovite has one absorption peak at 1400nm which coincides with quartz porphyry. Biotite shows an absorption band around 1000-1200nm (Figure 3-60). These three pure samples have been classified using a main component analysis. This allows distinguishing between the three different pure minerals (Figure 3-61).

The mixed samples G1 to G5 correspond to a mica number content of 10 to 50% (

Table 5). Regarding the mixed samples the main component analysis has not been successful (Figure 3-61). This is very likely due to the nature of the thin and flakey mica. Consequently, the 50 number percent of the mixed sample (G5) equals a total of 3.3 weight percent of mica compared to a total of 96.7 weight percent of quartz porphyry. In the mixed sample with 10 number percent of mica there are 0.83 weight percent of mica compared to 99.17 weight percent of quartz porphyry.

integration tim

e

integration tim

e

integration

wavelength



Figure 3-60: Results from classical spectroscopy with a horiba spectrometer NIR (1000-2000nm) for pure quartz porphyry, muscovite, biotite (left) and mixed samples G1 to G5 (right).

Figure 3-61: Results from classical spectroscopy with a horiba spectrometer NIR (1000-2000nm) showing that the pure quartz porphyry, muscovite and biotite samples can be distinguished well. The mixed

samples G1 to G5, on the other hand, cannot be distinguished.

In order to pursue optical measurement methods a calibration study has to be carried out on many different samples. This calibration study would cost between 10’000 to 12’000 Euros.

wavelength wavelengthwavelength

absorbance

absorbance



Based on the results above Indatech is quite confident that mica and quartz detection is possible online. Indatech recommends hyperspectral imaging in the NIR with a polarizer, using a conveyor belt not exceeding 5cm in width in order to have a high pixel resolution as the measurement error depends on it. The more pixels per mineral grain the better the result. The error also depends on ‘noise’ such as plastics, concrete, glass, wood etc. A clean sample delivers reliable results. Water could be taken into account up to about 10 weight percent. The hyperspectral imaging online system costs between estimated 70’000 to 90’000 Euros. A system with a failure back-up would cost around 130’000 to 150’000 Euros.

Boris Larchevêque, Adrien Cougnon from Indatec, Prades le Lez, France, are kindly acknowledge for their wonderful help with analyzing the samples and for fruitful discussions.

3.5.7 Mica separation by ‘slot’ bar sieves

Mica in the 0.25 to 0.5mm fraction is very often in the shape of thin flakes. Several such flakes were investigated under a binocular. The flakes were put on a 0.125mm sieve and with the help of a moist brush the flake thickness of mica (muscovite and biotite) and of other laminated mineral grains was compared with the 0.125 mesh. The mica flakes are much thinner than 0.125mm. However, some stacks are close to the 0.125mm.

A few flakes are bent. It is assumed that almost all of the examined mica flakes and stacks would pass through a slot of 0.125mm. It is estimated that the occasionally occurring laminated mineral grains other than mica would not fit or only very rarely fit through a 0.125mm slot. Based on these observations a new bar sieve prototype could be produced. Such a bar sieve could either be a round or square sieve pan with very sharp edges between the side wall and the ground (Figure 3-62). Level with the ground frequent slots with a width of 0.125mm would be cut into the sides walls. Thus, flaky micas could slip out of the sieve pan by the sides whereas non-mica minerals would stay in the sieve pan. According to the contacted locksmith (J. Blumer AG, Mitlödi, Switzerland) it is not possible to cut such a narrow slot into the side wall of the sieve pan. However, they suggested that the pan ground and the side wall with the precut slots on the edge that will be in contact with the ground could be produced separately and fit together. For stability reasons it is estimated, that the minimal thickness of the side wall has to be 1mm. This thickness combined with electrostatic forces, however, may hinder the small mica particles of 0.25 to 0.5mm in length to pass through the slots. This problem might be solved by closing the top of the sieve pan and applying negative pressure underneath the pan along the slots. Several techniques are looked into in order to find techniques that are able to cut the 0.125mm wide slots into the sieve pan ground directly.



Figure 3-62: Hypothetical sieve pan with 0.125mm narrow slots at the base of the side walls level with the

ground.

Quartz and other roundish mineral grains (in green) would remain in the pan whereas platy mica (in brown) would pass through the slots and be collected separately from the quartz fraction. J. Blumer AG, Mitlödi Switzerland is kindly acknowledged for helpful discussions.

3.5.8 Mica separation by air classification I

Mica separation was tested on a Bahco counterflow centrifugal classifier Delta NEU SSP (tolerating particle sizes <0.5mm, 3000rpm, Figure 3-63) at Umtec, Switzerland (with the kind help of Ivan Züst) with a natural sample that contains 30% of mica (B-R-30). Tests were conducted at maximum, intermediate and low airflow rate. Due to the thin, flaky size of the mica it was assumed that mica would be carried into the fine particle chamber whereas quartz and feldspar were expected to be collected by the coarse particle chamber (Figure 3-63, right).

Figure 3-63: Left: Bahco classifier at Umtec. Right: Counterflow centrifugal classifier modified from W.F. Hess [41] and K.A. Gustavson [40].



Approximately 99.5% of the sample material was collected in the chamber meant to collect the coarser quartz and feldspar particles. However, mica (muscovite and biotite) along with quartz and feldspar could also be found in this chamber. Only 0.5%, or less, of the particles were carried by the air flow into the fine particle chamber meant to collect mica. These particles comprised mica (muscovite and biotite) along with quartz or feldspars. As almost all particles were collected in the chamber for coarser particles and almost none were carried further to the fine particle chamber it is to conclude that a more powerful airflow would be needed to separate thin, flaky mica from quartz and feldspar. We are greatly indepted to Mr. Ivan Züst, Umtec and Professor Dr. Rainer Bunge, Institut für Umwelt- und Verfahrenstechnik, Rapperswil, Switzerland, for sample analyzes and interesting discussions.

3.5.9 Mica separation by air classification II

Mitchell and Evans [45] applied air classification with a zig-zag column for granitic pegmatite vein material to winnow out platy mica from quartz and feldspar (Figure 3-64, left). The air classifier they used was a Hosokawa Micron Multi-plex laboratory classifier type 1-40 MZM that was capable of classifying material ranging from 100µm to 6mm. They describe that the coarse, granular and higher density fraction falls down the column and the platy, finer-grained, less-dense fraction carried upward by the air stream (Figure 3-64, right). Mitchell and Evans [45] had to apply higher airflow rates for coarser fractions than for finer grains. They report that the feldspar-quartz product contained less than 1 weight percent of mica. The 50 number percent of mica of the artificially prepared sample of this project corresponds to 3.3 weight percent. Consequently it is not sure if zig-zag air classification will work well enough for automated mica separation/detection. Up to date it has not been possible to get information from Hosokawa about testing sample on one of their machines. The Zigzag Classifier - MZM and MZF Multi-Plex processes material ranging from 0.1 to 10mm. This machine would be suitable to test the 0.25 to 0.5mm sample material.

Figure 3-64: Left: Hosokawa Zigzag Classifier - MZM and MZF Multi-Plex [42]; Right: An explanation of the processes occuring in the zig-zag column from G.U.N.T Gerätebau GmbH, Germany, with: A) the upward moving blue fine material like mica, B) the downward falling coarse material like quartz and feldspar C)

the upward moving air current D) sample inlet E) vortex cylinder [39]



3.5.10 Mica separation in a water stream

Due to its platy nature mica tends to float for a while on water before sinking to the ground (personal communication Prof. Igor Villa, [47]). A small experiment was set up at B+ G AG in order to test mica separation with water. A plastic bowl was filled with water and a 0.063mm sieve was immersed and loaded with 4 metal parts to keep it immersed in the water (Figure 3-66). In one corner of the plastic bowl, an artificial water current was produced with tap water. The sample was placed on a tin foil and dispersed into the water close to the water current (yellow arrow). Quartz grains were expected to sink directly to the ground of the bowl (orange arrow). Mica was expected to float and move over the sieve and sink to the ground of the sieve (white dashed arrow).

After about 30 minutes the sieve that was expected to contain mica was removed from the ground of the bowl (sample 1). Some mineral grains could be discerned to still float on the water. The water from the bowl was therefore gently poured into a 0.063mm sieve (sample 2). The mineral grains on the ground of the bowl remained in the bowl. They were flushed out with water into another 0.063mm sieve (sample 3). Sample 1, 2 and 3 all contained quartz and mica (muscovite and biotite). This experiment will have to be repeated with lower water current, a larger sieve on the ground of the bowl, a larger distance between the water current source (sample inflow) and the sieve and a longer pause before collecting the samples.

Figure 3-65: Experimental setup for mica separation in a water stream.

3.5.11 Mica separation with Bromoform and Na-tungstate (density separation)

Bromoform may work for density separation of mica and quartz [47]. However, Bromoform is toxic and dangerous for the environment. Therefore, density separation with Bromoform is not considered any further.



Na-tungstate may work but it is a rather viscous liquid which may not allow density separation of mica [47]. As the densities of mica (2.76-3.2) and quartz (2.65) are quite close a centrifuge may have to be used additionally. Furthermore, the density of the Na-tungstate would have to be kept very constant and no water input from the samples would be allowed. So far, this method has not been further investigated. Professor Igor Villa, University of Bern, Switzerland, is kindly acknowledged for sharing valuable information about mica separation.

3.5.12 Laboratory tests for the determination of the mica content with the shape separation table (MUL-variant)

The Montanuniversität of Leoben with the shape separation table is working on a new automated system for the determination of the phyllosilicate content within rock aggregates. The basis of this system is a vibrating table as it is already used in the processing industry in density sorting for many years.

The main advantage of this testing procedure is the large population of about 50000 “tested” particles in 2,5 grams which reduces the sensitivity for statistic outliers and inhomogeneities to a minimum.

3.5.12.1 Working principle The shape separation table works on basis of a vibrating table and is designed for the separation of flat (phyllosilicates: muscovite, biotite, chlorite, …) and cubic mineral particles (quartz, feldspar, …). The 10°inclination of the table plane to the horizontal and the vibration of the table lead to distinct movements of particles that differ according to shape. Flat particles stay on the table longer and move a longer way to the right side, whereas cubic and round particles start rolling and fall from the bottom edge of the table quite quickly. The working principle is shown in Figure 3-66.

Figure 3-66: Analysing process on the shape separation table



3.5.12.2 Testing The prepared sample for the analysis on the shape separation table consists of particles within a range of either 0,125mm to 0,250mm or from 0,250 to 0,500mm depending on the intergrowth of the minerals. For each test an amount of about 2,5g is needed.

The first step is to homogenise the material to avoid decomposition and to weigh the sample with an accuracy of a thousandth of a gram. The difference between the weighed sample and the retained material after testing has to be smaller than 2%.

The sample is then put into the vibrating feeder and the table is switched on. The vibration of the table has to be within a certain range to avoid particles jumping over the edge without being caught in one of the four boxes with ascending phyllosilicate content from box 1 to 4 (Figure 3-66). The feed of particles onto the table is also a sensitive topic to avoid agglomeration and interference of single minerals on their way to one of the containers and a distortion of the results.

The grains are separated according to their shape type:

Bowl 1 round/cubic Bowl 2 round/cubic and mixed Bowl 3 mixed and flat Bowl 4 flat

To improve the results it is sometimes necessary to feed bowl 3, which represents minerals that have not been distinguished clearly yet, back through the system. This procedure is necessary when there are more than 20 mass percent of the whole sample inside bowl 3 and if the phyllosilicates content is below 85 piece percent. Bowls 1, 2 and 4 usually contain either flat or cubic particles with just a small error that would not make sense to improve by separating again. Tests have shown that feeding bowls 1, 2 or 4 again does not influence or improve the results and the effort would be exponentially higher.

3.5.12.3 Evaluation After separation on the vibrating table each of the four bowls is weighed and approximately 200 grains are taken from each of the bowls and counted under the microscope to get the mica content in piece percent per bowl. This means, that if 10 out of 200 particles counted and evaluated under the microscope are mica, the mica content for this bowl is 5 piece percent. The mass and piece percentages are then processed within an MS-Excel sheet (Table 10). The result, the phyllosilicate content in mass percent is highlighted in green. Highlighted in red is the phyllosilicate content without application of a correction factor.

The typical mass and mica content distribution within the 4 bowls is shown in Table 10 and Figure 3-67. The mixture of flat and cubic particles in bowl 3 is characteristic for the results obtained with the shape separation table and leads to the conclusion that the dividing cut lies in this certain area of the table. In standards for suitability of rock aggregates for the production of concrete and mortar the mica content is usually given in mass percent.



The difficulty is to find a way to determine the mass percentage out of the piece percent as the correlation between those two values is not linear. (The Swiss limit for free phyllosilicates in concrete and mortar is 2 mass percent according to Swiss standard SN 670 115.)

Table 10: Evaluation and summary sheet

Figure 3-67: Mass distribution and phyllosilicate content of separated fractions

For the conversion between piece- and mass percentages a formula, using a correction factor, has been developed by MUL:

% 100 ∗ %

100 100 100 ∗ % 100 ∗ 1 %

%∗

∗ ∗=

∗

~4

0,00

10,00

20,00

30,00

40,00

50,00

60,00

70,00

Bowls 1 ‐4

Mass fraction [%]

Bowl 1

Bowl 2

Bowl 3

Bowl 4

0,00

20,00

40,00

60,00

80,00

100,00

Bowls 1‐4

Phyllosilicate content [m

‐%]

Bowl 1

Bowl 2

Bowl 3

Bowl 4



% =∗

∗ %

∗ % ∗ % ∗ = %

% % ∗

% Piece percent phyllosilicates Number of phyllosilicate particles

% Mass percent phyllosilicates Mass of one phyllosilicate particle

Mass of phyllosilicates in the sample Mass of cubic particles in the sample

Mass of one cubic particle Number of cubic particles

C Correction factor

The correction factor is based on empirical determination of the mass ratio between phyllosilicate and cubic particles and has been determined to be between 3 and 4,5 for the investigated rocks within this project. This is a value that pretty much coincides with values found in the literature (e.g. Gaynor & Meininger, 1983; Danielsen & Rueslatten, 1984), which range from 1,1 to a maximum of 10 with the highest density of results around 3 to 5. A wrong determination or estimation of the correction factor leads to variances in the results for the mica content. To give numbers for the error a parameter study has been carried out. The effect of introducing a correction factor can be seen in Figure 3-68 where different factors are contrasted with each other. A “C” of 1 is equal to a calculation without a factor. It is obvious that with an increasing C the divergence between mass and piece percentages is increasing. The error is increasing to a maximum at 50 mass percent and decreasing from that point on. The related maximum error for piece percent depends on the correction factor and is indicated by the intersection points of the coloured lines with the black line. For a divergence of 1 the maximum error can be up to 7%. This illustrates the necessity for an accurate determination of the correction factor, which is different from rock type to rock type and from project to project. The shape separation table is a useful device for determining the content of phyllosilicates within rock aggregates, but there is still a lot of effort required to adapt the vibrating table into a fully automated analysing system.



Figure 3-68: Percentual alteration of the phyllosilicate content [m-%] in dependence on the correction factor

Figure 3-69 shows the error of calculation with correction factors deviating 0,5 , 1, and 1,5 from each other.

Figure 3-69: ∆ m-% for varying correction factors

3.6 Consistency of excavated material (soft ground)

3.6.1 Approach

On soft-ground tunnelling EPB shield machines the excavated material is directly used for the heading face support.

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Phyllosilicate content [m

‐%]

Phyllosilicate content [piece‐%]

C=1C=2C=3C=4C=5

0

1

2

3

4

5

6

7

8

9

10

11

0 10 20 30 40 50 60 70 80 90 100

∆ m

ass‐% betw

een different C [m‐%

]

Pyhllosilicate content [piece‐%]

delta m% bet.C=3 and C=4

delta m% bet.C=3 and C=3,5



Therefor the required material properties are often not existent, so the excavated material needs to be additionally conditioned in order to realize a sufficiently compressibility as well as low water permeability. Furthermore the plasticity in terms of flowability or consistency must also be observed for a proper operating excavation process. The major innovative potential concerning resource-efficient material processing of soft ground on EPB machines is the optimisation of the soil conditioning system. In WP4 the technological strategy for the automated processing approaches are worked out. They are primarily based on the minimisation of the consumption of soil conditioning agents. Today the soil conditioning system on EPB machines is operated by recommended preset parameters in the manual, semi-automated or automated mode. In this context the setting parameters are only indirectly controlled by the machine driver based on the subjective evaluation of the monitored TBM-data.

Due to frequently changing of the geotechnical situation in tunneling the soil conditioning process needs to be directly controlled by the relevant input parameters. Therefor an additional automated measuring method which directly correlates with the rheological properties of the conditioned excavated material needs to be developed. According to that there are already different standard methods in testing the rheological properties in the field of concrete technology available. This simplified practicable in-situ tests enable the estimation of the flowability and viscosity.

So the main idea is to use these existing devices for the automated testing of the conditioned soil. First of all the following technical requirements for the practical application are essentially investigated:

• Capability of the in-situ tests for the directly evaluation of plasticity respectively consistency by the determination of the corresponding flow characteristics

• Automation of the existing standard in-situ tests

3.6.1.1 Basically investigations for the determination of the rheological properties

The investigations for the determination of the rheological characteristics of conditioned soil by standard in-situ tests are carried out under the following mentioned terms: [35]

Soil preparation:

• Water content: 12 % (by weight)

• Foam generator lance: TLB

• Foam volume rate: 60l/min

• Tenside concentration (cf): 3 % (by weight)

• Foam expansion rate (FER-nominal)): 15

• Start of test procedure: 5 min after the foam-production and soil conditioning

The basics of the foam conditioning are mentioned in Deliverable 4.1 / 4.2.



3.6.1.2 Slump-tests with foam conditioned soil The slump test is a standard method (EN 12350-2 /-8) and normally used as a practicable in-situ test for the evaluation of the concrete consistency.[35]

In Figure 3-70 the principle of the slump test is illustrated from the left to the right. At first the metal cone is put on the table (bigger opening downwards). Then the testing material is filled and compressed in the metal cone. After that the metal cone is lifted up in vertical direction. Due to the impact of gravimetry the resulting shear forces within the matrix of the testing material induce the material flowing until they are lower than structural forces. The dimension of the structural forces is mainly influenced by the yield point. Finally the vertical slump value and if necessary the additional horizontal slump flow value (rectangular horizontal spread values) are measured.[35]

Figure 3-70: Slump test [35]

The slump tests with the before mentioned foam conditioned materials are carried out according the previously defined procedure. In order to obtain accurate results a proper vertical lifting of the metal cone is enabled by using an additional manually operated lifting device. The assembly of the lifting device is shown in Figure 3-71. [35]

Furthermore always 5 samples of each material are tested to check the reproducibility. In the following figures the detailed results of the slump tests are listed.

In Table 11 the slump test values for fine sand – w=12 % - FIR 10 = 50 % related to the theoretical slump value of 10 cm are illustrated. The variation of the measured slump values and also the slump flow values seems to be in a good range. So the reproducibility by the previously defined procedure of the slump test is verified. [35]

conditioned soil FIR 10 in % FIR 20 in %

fine sand 50 75

sand 12 35



Figure 3-71: Slump test lifting device [35]

Table 11: Slump test values for fine sand – w=12 % - FIR 10 = 50 % [35]

cond

ition

ed s

oil

wat

er c

onte

nt

FIR

10

slum

p

slum

p flo

w

SF1 SF2

[-] [%] [%] [cm] [cm] [cm]

fine sand 12 50

13,0 27,0 26,0

11,0 28,0 26,0

10,5 27,5 25,0

13,0 28,0 26,0

11,0 26,0 25,0

In Table 12 slump test values for sand – w=12 % - FIR 10 = 12 % related to the theoretical slump value of 10 cm are illustrated. The comparison of the two different materials shows no significant difference in the variation of the slump values and also the slump flow values.

Table 13 and Table 14 show the slump test values for fine sand – w=12 % - FIR 20 = 75 % and for sand – w=12 % - FIR 20 = 35 % related to the theoretical slump value of 20 cm. The variations of the slump values and also of the slump flow values are marginal. In this context a high degree of reproducibility is verified. [35]



Table 12: slump test values for sand – w=12 % - FIR 10 = 12 % [35]

cond

ition

ed

soil

wat

er c

onte

nt

FIR

10

slum

p

slum

p flo

w

SF1 SF2

[-] [%] [%] [cm] [cm] [cm]

fine sand 12 50

13,0 27,0 26,0

11,0 28,0 26,0

10,5 27,5 25,0

13,0 28,0 26,0

11,0 26,0 25,0

Table 13: Slump test values for fine sand – w=12 % - FIR 20 = 75 % [35]

cond

ition

ed s

oil

wat

er c

onte

nt

FIR

10

slum

p

slum

p flo

w

SF1 SF2

[-] [%] [%] [cm] [cm] [cm]

fine sand 12 50

13,0 27,0 26,0

11,0 28,0 26,0

10,5 27,5 25,0

13,0 28,0 26,0

11,0 26,0 25,0



Table 14: slump test values for sand – w=12 % - FIR 20 = 35 % [35]

cond

ition

ed s

oil

wat

er c

onte

nt

FIR

20

slum

p

slum

p flo

w

SF1 SF2

[-] [%] [%] [cm] [cm] [cm]

sand 12 35

19,0 36,0 33,0

20,0 38,0 36,0

21,0 38,0 37,0

20,0 37,0 34,0

20,0 36,0 35,0

20,0 38,0 36,0

Apart from that the profiles of the slump test samples are additionally measured by the registration of the two-dimensional coordinates with a gauge plate and also by taking pictures.

Figure 3-72: Gauge plate [35]



In the following Figure 3-73 the two-dimensional photo-optical created profiles of the slump test samples by overlapping of the respective pictures are illustrated. Furthermore the directly measured values for the profile of the different slump test samples are shown. Additionally the profile of the metal cone is shown, in order to imagine the flowing process of the different tested material. [35]

Figure 3-73: Comparison of the photo-optical created profiles with the directly measured coordinates for the slump test samples [35]

The illustrations show that apart from the top sections the measured coordinates of the different slump test samples exactly suit to the photo-optical generated profiles. The deviations within the top sections are resulted due to the time offset of 30 seconds for measuring the coordinates after taking the pictures.

On the basis of the before mentioned findings the material specific profiles can be systematically combined to generate universal profiles of the sample shapes for a certain slump value. Additionally the different universal profiles are summarised as an overall profile, which correlates with the acceptable range of consistency. In Figure 3-74 the universalised profiles for foam conditioned fine sand and sand are summarised to obtain the overall profile for the consistency range with slump values 10 to 20 cm. [35]



Figure 3-74: Summarisation of universal profiles for defined slump values [35]

In the practical application the measured contour of the slump test sample is just overlapped with the previously defined overall profile. If the dimensions of the slump test sample fit to the overall profile with the maximum outer dimensions, than the material consistency is evaluated as acceptable. In this context the photo-optical evaluation is much more technically feasible.

The approach of the before mentioned investigations to classify the consistency respectively plasticity of different foam conditioned soils by the correlated photo-optical determination and evaluation of the slump test values has been verified for foam conditioned fine sand and sand. In this connection the profile for the recommended consistency range is previously defined, in order to realise a simplified automated evaluation. The development of further material-specific profiles will allow the general use for the automated online qualitative classification of conditioned soil. Furthermore the additional registration of the coordinates of the slump test samples may enable the creation of mathematical functions for the simulation of the specific profiles.

In Figure 3-75 the design concept of the automated slump test with photo-optical evaluation for the simplified practical application is illustrated. It enables the systematical evaluation of the relevant plasticity / consistency based on a previously specified profile range. The combination with a vibrating conveyor enables the continuous material flow regarding an automated online analysis and enforces the gravimetric impact for an enhanced determination of the flow characteristic. The relation to the respective geological situation is verified by periodical manual calibration.



Figure 3-75: Design concept of the automated slump test

This automated measuring system is going to be used to determine the rheological characteristics of conditioned excavated material primarily in permeable geological situations. In line with the prototype assembly in WP 5 this system is installed and first tests are carried out.



4 References

1 [1] 2 G. Schwedt, Taschenatlas der Analytik, 2., überarbeitete und erweiterte Auflage ed., Stuttgart: Georg Thieme Verlag, 1996.

3 [2] 4 W. Gottwald und K. H. Heinrich, UV/VIS-Spektroskopie für Anwender, Weinheim: Wiley-VCH, 1998.

5 [3] 6 T. Lehmann, “UV/VIS-Spektroskopie,” Freie Universität Berlin, Berlin, 2007.

7 [4] 8 G. Amthauer and M. K. Pavicevic, Physikalisch-chemische Untersuchungsmethoden in den Geowissenschaften, vol. 2, Stuttgart: E. Schweizerbart´sche Verlagsbuchhandlung, 2001.

9 [5] 10 M. Dehler, “Color versus NIR/XRT,” AT Mineral Processing, vol. Volume 52, no. 07-08-2011, pp. 68-75, 2011.

11 [6] 12 Binder+CO, 23 1 2014. [Online]. Available: http://www.binder-co.com/de/produkte/sortieren/Glas/Clarity/clarity_animation.php. [Accessed 23 1 2014].

13 [7] 14 Polytec, “Prozesskontrolle mit NIR Spektroskopie,” Polytec GmbH, Berlin, 2007.

15 [8] 16 H. Günzler, Infrarotspektroskopie: Highlights aus dem Analytiker-Taschenbuch, Berlin Heidelberg: Springer, 1996.

17 [9] 18 ABB, “Online Analyser SpectraFlow,” ABB Schweiz, Baden-Dättwil, Schweiz, 2013.

19 [10] 20 Steinert Elektromagnetbau GmbH, “http://www.steinertglobal.com/home/applications/,” [Online]. [Accessed 23 01 2014].

21 [11] 22 G. Vinzelberg, Laserinduced Fluorescence as Sensor, Aachen: Rheinisch-Westfälischen Technischen Hochschule Aachen, 2008.

23 [12] 24 Siebtechnik GmbH, “LIF Online Analyser,” Siebtechnik GmbH, Mülheim an der Ruhr, 2014.

25 [13] 26 D. L. Andrews and A. A. Demidov, An Introduction to Laser Spectroskopy, Norwich: Plenum Press, 1995.

27 [14] 28 C. Fricke-Begemann, P. Jander, H. Wotruba and M. Gaastra, “Laser-based online analysis of minerals,” ZKG international, no. 10, 2012.



29 [15] 30 E. Matthies, Grundlagenuntersuchung der Möglichkeiten einer Online Analyse mit Hilfe der Laser induzierten Plasmaspektroskopie (LIPS) am Beispiel von Kalkstein, Clausthal: Technischen Universität Clausthal, 2008.

31 [16] 32 M. K. Pavicevic and G. Amthauer, Physikalisch-chemische Untersuchungsmethoden in den Geowissenschaften, vol. 1, Stuttgart: E. Schweizerbart´sche Verlagsbuchhamdlung, 2000.

33 [17] 34 S. K. Dhanjal, L. Young and P. Storer, “Automatic Control of Cement Quality using On-Line XRD,” in IEEE, Montreal, 2006.

35 [18] 36 EAG, “TN 109 EAGLABS X-ray Diffraction (XRD) Services Technique Note,” Evans Analytical Group, LLC, Sunnyvale, 2009.

37 [19] 38 FLSmidth, “Laboratory AUtomation,” FLSmidth A/S, Copenhagen, 2010.

39 [20] 40 E. Zeiger, “Sortierung verschiedener Abfallströme mit Mogensen-,” Aufbereitungs Technik, pp. 1-5, 2006.

41 [21] 42 M. Dehler, “FARBE versus NIR/XRT,” AT Mineral Processing, pp. 68-75, 07 08 2011.

43 [22] 44 S. Foster and J. Bond, “The history and future of nuclear elemental analysers for product optimization in the cement industry,” in Cement Industry Technical Conference, IEEE, 2006.

45 [23] 46 Thermo Scientific, “CBX-M Online PGNAA Elemental Analyser,” Thermo Fisher Scientific Inc. Product Specification, 2007.

47 [24] 48 Sodern, “Neutron Elemental Analysis,” Sodern EADS, 2011. [Online]. Available: http://www.sodern.com/sites/en/ref/Neutron-elemental-analysis_33.html. [Accessed 04 03 2013].

49 [25] 50 J. Gondeau, “Real Time Analytical System: Sodern CNA Cross Belt Analyser,” BHM, vol. 157. Jg., no. Heft 6-7, pp. 288-289, 2012.

51 [26] 52 Dr. A. Klein, “An Improved Concept for Online Coal Analysis”, 2013.

53 [27] 54 Stiess M., 2009, Mechanische Verfahrenstechnik - Partikeltechnologie 1, Berlin: Springer Verlag

55 [28] 56 Siebtechnik GmbH, Probenahme und Probevorbereitung, Online-Dokument, verfügbar unter: www.siebtechnik-gmbh.de/_leadmin/user_upload/PDF/.../Wb240d.pdf

57 [29] 58 Haver & Boecker: Product- Flyer



59 [30] 60 Entacher M., et al.; 2012, Cutter force measurement on tunnel boring machines –System design, Tunneling and Underground Space Technology.

61 [31] 62 Entacher M, et al.; 2013, Cutter force measurement on tunnel boring machines – Implementation at Koralm tunnel, Tunneling and Underground Space Technology.

63 [32] 64 Thuro K.; The New Suggested Method No. 5 ofd the AK 3.3 – Point Load Tests on Rock Samples.

65 [33] 66 Hudson JA. Et al.; 1993, Rock properties, testing methods and site characterisation, Comprehensive rock engineering - principles, practice and projects, p. 46-51.

67 [34] 68 ISRM; 1985, Suggested Methods for determining point load strength, - ISRM Commission on Testing Methods, Working Group on Revision of the Point Load Test Method, Int. J. Rock Mech. Min. Sci. &Geomech. Abstr., 22, 51-60.

69 [35] 70 Gesing C., Korrelationsuntersuchung zum Fließverhalten von Partikel-Schaum-Gemischen unter Verwendung verschiedener Bewertungsverfahren, Bochum 2013

71 [36] 72 Siemens AG, Kundenschulung Milltronics Bandwaagen, 2008 http://www.siemens.de/waegetechnik

73 [37] 74 SICK, Bulkscan 210, Volume and mass flow measurement of bulk goods on conveyors

75 [38] 76 Fookes, P. and Revie, W. (1982), Mica in concrete – a case history from Eastern Nepal, Concrete March 82

77 [39] 78 G.U.N.T Gerätebau GmbH, Germany, http://www.gunt2e.de/s5159_1.php?p1=&p2=&pN=;;

79 [40] 80 Gustavson, K. E., Teknisk Tidskrift (1948), Vol. 78, No. 10, pp. 667-670.

81 [41] 82 Hess,W.F., (2006), Fachhochschule Flensburg, Ueberpruefung der Eichkurve eines Siral-Gegenstrom-Windsichter (BAHCO-Sichter), http://www.fh-flensburg.de/vt/docs/BahcoSichter2006b.pdf

83 [42] 84 Hosokawa Zigzag Classifier - MZM and MZF Multi-Plex (Picture from: https://www.hosokawa-alpine.com/powder-particle-processing/machines/classifiers-and-air-classifiers/mzm-and-mzf-zigzag-classifiers/)



85 [43] 86 Huber, H., (1971), Der Einfluss von gebrochenen Gneiszuschlagstoffen auf den Beton der Zemmkraftwerke. Unveröff. Diss. Uni Innsbruck.

87 [44] 88 Klein, C., (2002), The 22nd Edition of the Manual of Mineral Science, Editors: Wiley & Sons, Inc., pp. 641, ISBN: 0-471-25177-1

89 [45] 90 Mitchell, C.J.; Evans, E.J.. (1997), Mineral processing of mine and quarry waste. In: Innovation in physical separation technologies. The Institution of Mining and Metallurgy, 395-403, 406pp.

91 [46] 92 Thalmann, C., (1996). Beurteilung und Möglichkeiten der Wiederverwertung von Ausbruchmaterial aus dem maschinellen Tunnelvortrieb zu Betonzuschlagstoffen, Beiträge zur Geologie der Schweiz, Geotechnische Serie, Lieferung 91, 115pp

93 [47] 94 Igor Villa,I., (2014), personal communication, University of Bern, Switzerland

95 [48] 96 Yuga, AL., Dascalescu, L., Morar, R., Samuila, A., Cuglesan, I., (1995), Electrostatic shape separation of flake mica from pegmatites. Magnetic and Electrical Separation, Vol. 6, pp. 135-149.

97 [35] 98 Central European Ar-Laboratory; Mineral Separation for Geochronology, http://www.geol.sav.sk/ceal

99 [36] 100 Danielsen, S., and Rueslatten, H., (1984), Feldspar and mica – key minerals for fine aggregate quality. Bulletin of the International Association of Engineering Geology, 30.

101 [37] 102 Empa (Swiss Ferderal Laboratories for Materials Testing and Research, 1998). Die Auswirkungen von freien Schichtsilikaten im Zuschlag auf die Eigenschaften von Mörtel und Beton. Untersuchungsbericht Nr. 166’184.

dragon d3.2 online analysis technologiesfigure 1-3: steinert rtt nir sorter [10] 1.2.3 laser induced...

Documents