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WO2017214738A1 - Procédé de traitement d'un matériau solide au moyen de décharges à haute tension - Google Patents

Procédé de traitement d'un matériau solide au moyen de décharges à haute tension Download PDF

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Publication number
WO2017214738A1
WO2017214738A1 PCT/CH2016/000090 CH2016000090W WO2017214738A1 WO 2017214738 A1 WO2017214738 A1 WO 2017214738A1 CH 2016000090 W CH2016000090 W CH 2016000090W WO 2017214738 A1 WO2017214738 A1 WO 2017214738A1
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WO
WIPO (PCT)
Prior art keywords
high voltage
determined
signal
discharge
process zone
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CH2016/000090
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English (en)
Inventor
Sébastien VAUCHER
Georgios Violakis
Bastian MEYLAN
Sergey Shevchik
Kilian WASMER
Seyed Abbas MOSADDEGHI
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Selfrag AG
Original Assignee
Selfrag AG
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Publication date
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Priority to PCT/CH2016/000090 priority Critical patent/WO2017214738A1/fr
Priority to AU2016411989A priority patent/AU2016411989B2/en
Priority to TW106113563A priority patent/TW201801126A/zh
Publication of WO2017214738A1 publication Critical patent/WO2017214738A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
    • B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
    • B02C19/00—Other disintegrating devices or methods
    • B02C19/18—Use of auxiliary physical effects, e.g. ultrasonics, irradiation, for disintegrating
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
    • B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
    • B02C25/00—Control arrangements specially adapted for crushing or disintegrating
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
    • B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
    • B02C19/00—Other disintegrating devices or methods
    • B02C19/18—Use of auxiliary physical effects, e.g. ultrasonics, irradiation, for disintegrating
    • B02C2019/183—Crushing by discharge of high electrical energy

Definitions

  • the invention concerns a method of treating, in particular fragmenting and/or weakening, a solid ma- terial, in particular rock or ore, by means of high vol- tage discharges as well as an arrangement for conducting the method according to the preambles of the independent claims .
  • a first aspect of the invention concerns a method of treating, preferably fragmenting and/or weakening, solid material, like e.g. rock material or ore, by means of high-voltage discharges.
  • a process zone is provided between at least two electrodes, which are arranged at a distance relative to each other.
  • This process zone is flooded with a process liquid and contains, arranged bet- ween the electrodes, the material that is to be treated, in particular is to be fragmented or weakened.
  • a high voltage dis- charge is generated by charging the electrodes with a high-voltage pulse provided by a tunable high voltage generator, which is set to predetermined pulse parame- ters.
  • Such pulse parameters can be, for example, the capacitance of the applied voltage (typically in the range between 1 nF and 500 nF) , the voltage of the pulse (typically in the range between 10 kV and 500 kV) , the voltage pulse front rise time (typically in the range between 1 nsec and 500 nsec) , the shape of the pulse (e.g. square, triangle or specific pattern) etc.
  • a signal representing at least one parameter of this high voltage discharge and/or representing an effect caused in the process zone by this high voltage discharge is determined and is compared, in its deter- mined form and/or in a processed form, with at least one reference.
  • Parameters of the high-voltage discharge are for example the discharge voltage, the discharge current, the discharge resistance and the ignition delay.
  • the term "reference” is to be understood here in its broadest possible interpretation.
  • the at least one reference could for example be some earlier determined signal (s) in its/their determined form and/or processed form, or a threshold value or a range of values, which distinguishes between signals which represent an intended operational state/effect and signals which represent a not-intended operational state/effect.
  • the at least one reference could for exam- pie be a representation, in particular in terms of para- meters of machine learning framework (support vectors and kernels for Support Vector Machines (SVM) , weights for neural networks, node weights for random forest algorithm etc.), into which the determined signal (s) in a processed form are integrated in order to decide if the high vol- tage discharge to which the determined signal (s) belong took place under intended operation conditions and/or generated an intended effect or not.
  • SVM Support Vector Machines
  • the pulse parameters of the high voltage generator are maintained or are changed.
  • a further high voltage dischar- ge is generated with the high voltage generator being set to the same (unchanged) or changed pulse parameters, the signal representing the at least one parameter of this high voltage discharge and/or of an effect caused in the process zone by this high voltage discharge is again de- termined and is again compared, in its determined form and/or in a processed form, with the at least one refer- ence and, depending on the result of the comparison of the signal with the reference, the pulse parameters of the high voltage generator are maintained or are changed.
  • the de- termined signal in its determined form and/or in a pro- Stepd form is compared with the reference, and if a de- viation is determined or a deviation which exceeds a pre- determined tolerated deviation is determined, one or se- veral of the voltage pulse parameters of the high voltage generator are changed in such a manner that, when subse- quently the steps of generating a further high-voltage discharge, determining the signal and comparing the sig- nal with the reference are repeated, no deviation between the determined signal and the reference is detected or the detected deviation is smaller than before.
  • the determined signal in its determined form and/or in a processed form is compared with the referen- ce, and if no deviation is determined or a deviation which is smaller than a predetermined minimum deviation is determined, one or several of the voltage pulse para- meters of the high voltage generator are changed in such a manner that, when subsequently the steps of generating a further high-voltage discharge, determining the signal and comparing the signal with the reference are repeated, there is a deviation between the determined signal and the reference or the detected deviation is bigger than before.
  • the determined signal in its determined form and/or in a processed form is compared with at least two references representing different high voltage discharge situations, one of which is the inten- ded high voltage discharge situation, and wherein in case the comparison reveals that the signal differs least from the reference representing the intended high voltage discharge situation, the voltage pulse parameters of the high voltage generator are kept unchanged, and in case the comparison reveals that the signal differs more from the reference representing the intended high voltage discharge situation than from another of the references, one or several of the voltage pulse parameters of the high voltage generator are changed.
  • a signal representing the acoustic emissions caused in the process zone by the high voltage discharge is determined and, in its determined form and/or in a processed form, is compared with the at least one reference. It has been found that by using these acoustic emissions, is becomes possible to very well distinguish between different operational modes and effects, respectively, e.g. between a) no discharge through the material;
  • the signal is determined by one or several acoustic sensors placed in the process zone and/or outside the process zone, in the latter case e.g. at the outer surface of a process vessel containing the process zone.
  • the acoustic sensors are placed in or near to the process zone, it is preferred that they are placed near to the at least two electrodes, preferably with a distance of less than 50 mm from a line defined by a shortest distance between those two electrodes between which the high voltage discharges are generated. By doing so, the signal disturbances through reflections and noise created through movement of the material in the process zone can be kept low.
  • one or several optical acoustic- sensitive fiber sensors are used, preferably sensors with fibers with Bragg grating or with Fabry Perot fibers and/or fiber interferometers.
  • Such kind of optical sen ⁇ sors have the advantage that they are neutral to the strong electromagnetic fields generated by the high-vol ⁇ tage discharges and can be placed close to the discharge area.
  • the fiber of the sensor is pre-strained. By doing so, it is possible to tune the sensitivity of the sensor in the frequency domain.
  • the fiber axis together with a line defined by a shortest distance between those two electrodes, between which the high-vol- tage discharges are generated forms an angle in the ran- ge between 15° and 75°.
  • the adjustment of the angle may be done manually or automatically to a predetermined va- lue.
  • a signal representing the acoustic emis- sions caused in the process zone by the high voltage dis- charge is determined
  • several acoustic sensors are placed at different pre-defined positions, preferably in or near to the process zone, and from the signals determined by these sensors and their known positions the location of the high voltage discharge is determined. Suitable algo- rithms how to calculate the location of the high-voltage discharge from these data are known to the person skilled in the art and therefore are not further described here.
  • this determined location of the high-vol- tage discharge which is a processed form of the determi- ned signals of the sensors, is compared with the at least one reference, which is preferred, the possibilities to distinguish between several different operational modes and/or effect of the discharge on the material are sub- stantially improved.
  • a signal representing one or several electri- cal parameters of the high voltage discharge is determi- ned and, in its determined form and/or in a processed form, is compared with the at least one reference.
  • a signal representing the elec- trical current and/or the voltage of the high-voltage discharge is determined and, in its determined form and/- or in a processed form, is compared with the at least one reference.
  • Determining such signals has the advantage that they are relative safe and easy to determine and that typically they show little disturbance only.
  • a signal representing the electromagnetic fields caused in the process zone by the high-voltage discharge is determined and, in its determined form and/or in a processed form, is compared with the at least one reference.
  • this signal is determined by one or several Pockels-Kerr cells placed in or near to the process zone and/or outside the process zone.
  • a signal representing the light emissions caused in the process zone by the high voltage discharge is de- termined and is compared with the at least one reference.
  • signals representing the electromagnetic fields and/or the light emissions caused in the process zone by the high voltage discharge are used in combination with other signals, e.g. signals representing the acoustic emissions caused in the process zone by the high voltage discharge and/or signals repre ⁇ senting one or several electrical parameters of the high voltage discharge, the possibilities to distinguish bet- ween several different operational modes and/or effects of the discharge on the material are substantially im- proved.
  • the determined signal is processed by decom- position before it is compared with the at least one re- 5 ference, preferably by wavelet decomposition, polynomial decomposition/ functional decomposition or Fourier decora- position.
  • decomposition preferably by wavelet decomposition, polynomial decomposition/ functional decomposition or Fourier decora- position.
  • a support vector machine SVM
  • a random forest algorithm and/or an artificial neural network is used for comparing the determined signal in its deter- mined form and/or in a processed form with the at least one reference.
  • online machine learning is applied in com- paring the determined signal in its determined form and/- or in a processed form with the at least one reference.
  • Different methods of machine learning are known to the skilled person and therefore are not described here more into detail.
  • a pre-determined reference is used.
  • sample material is treated with high voltage discharges between the electrodes with different pulse parameter settings of the high-voltage generator, and the physical effect of each high-voltage pulse or high-voltage discharge, respectively, on the sample material is investigated, for example by visual inspection of the material, and is correlated to the de- termined signal.
  • the physical effect of the high- voltage pulse or the high-voltage discharge, respective- ly, on the sample material is expressed in a particle size distribution, in a bond index and/or in an axb-value and is correlated to the determined signal.
  • a determined signal or a number of determined signals which are representing high-voltage discharges that caused a desired effect on the sample material, as reference, in the determined form and/or in a processed form.
  • the reference can better be tai- lored to represent a specific effect on the material that is treated.
  • references are predetermined for at least two of the following effects:
  • the material that is to be treated is fed through the process zone, preferably by means of a conveyor carrying said material. This allows the treatment of a continuous stream of material accor- ding to the method.
  • process li- quid is fed to the process zone and process liquid is discharged from the process zone.
  • At least the comparing of the signal with the reference and the maintaining or changing of the pulse parameters of the high-voltage generator is performed automatically by an electronic control system.
  • the determining of the signal is performed automatically by the electronic control system.
  • a second aspect of the invention concerns an arrangement for conducting the method according to the first aspect of the invention.
  • This arrangement comprises a process zone formed between at least two electrodes which are arranged at a distance relative to each other, which process zone in the intended operation is flooded with a process li- quid, for example with water.
  • the arrangement further comprises a tunable high-voltage generator for generating, at predetermined pulse parameters of the generator, high-voltage dischar- ges between the at least two electrodes in the intended operation.
  • Such pulse parameters can be, for example, the capacitance of the applied voltage (typically in the range between 1 nF and 500 nF) , the voltage of the pulse (typically in the range between 10 kV and 500 kV) , the voltage pulse front rise time (typically in the range between 1 nsec and 500 nsec) , the shape of the pulse (e.g. square, triangle or specific pattern) etc.
  • It also comprises means for determining a signal representing at least one parameter of the high- voltage discharge and/or an effect caused in the process zone by the high voltage discharge, as well as a control system.
  • Parameters of the high-voltage discharge are for example the discharge voltage, the discharge current, the discharge resistance and the ignition delay.
  • the control system is adapted for comparing the determined signal, in its determined form and/or in a processed form, with at least one reference and for kee- ping the pulse parameters of the high voltage generator unchanged or changing one or several of the pulse para- meters of the high voltage generator, depending on the result of the comparison.
  • the term "reference" has alrea- dy been explained earlier under the first aspect of the invention.
  • the means for determining a signal are adapted for deter- mining a signal representing the acoustic emissions cau ⁇ sed in the process zone by the high-voltage discharge.
  • the control system is adapted for compa- ring this signal, in its determined form and/or in a pro- Stepd form, with the at least one reference.
  • the means for deter- mining signals representing the acoustic emissions com- prise one or several optical acoustic sensitive fiber sensors, by advantage sensors with fibers with Bragg gra- ting or with Fabry Perot fibers and/or fiber interfero- meters, wherein preferably the fibers of the sensors are in each case arranged pre-strained in a fiber holder.
  • Such kind of optical sensors have the advan- tage that they are neutral to electromagnetic fields and can be placed close to the discharge area. Furthermore, they can be tuned by the pre-straining with regard to the sensitivity of the fiber in the frequency domain.
  • the fiber axis of the sensor or the sensors can be aligned with respect to a line defined by a shortest distance between two electrodes, between which in the intended operation the high voltage dischar- ges are generated, such that it forms, together with said line, an angle in the range between 15° and 75°.
  • the arrangement comprises several acoustic sensors which are arranged at different pre-de- fined positions.
  • the control system prefer- ably is adapted to determine the location of the high- voltage discharge from the signals determined by these acoustic sensors and their known positions and to compare the determined location of the high voltage discharge with the at least one reference.
  • the means for determining the signal are adap ⁇ ted for determining a signal representing one or several electrical parameters of the high voltage discharge, like e.g. the discharge voltage, the discharge current, the discharge resistance and/or the ignition delay time, and the control system is adapted for comparing this signal, in its determined form and/or in a processed form, with the at least one reference.
  • Such signals can relatively safe and easy be determined and typically show little disturbance.
  • the means for determining a signal are adapted for determining a signal representing the elec- tromagnetic fields caused in the process zone by the high-voltage discharge.
  • the control system is adapted for comparing this signal with the at least one reference.
  • the means for determining a signal comprise one or several Pockels-Kerr cells for doing so.
  • the means for determining the signal are adapted for determining a signal representing the light emissions caused in the process zone by the high-voltage discharge and the control system is adapted for comparing this signal with the at least one reference.
  • control system of the arrangement is adapted to process the determined signal by decomposition before it is com- pared with the at least one reference, in particular by wavelet decomposition, polynomial decomposition, func- tional decomposition or Fourier decomposition.
  • control system of the arrangement is adapted to use a support vector machine (SVM) , a random forest algorithm and/or an artificial neural network for comparing the determined signal in its determined form and/or in a processed form with the at least one reference.
  • SVM support vector machine
  • the control system of the arrangement is adapted to apply online machine learning in comparing the determined sig- nal in its determined form and/or in a processed form with the at least one reference. Different methods of machine learning are known to the skilled person and therefore are not described here more into detail.
  • Fig. 1 is a schematic representation of a first arrangement according to the invention
  • Fig. 2 is a schematic representation of a second arrangement according to the invention.
  • Fig. 3 is a schematic representation of a third arrangement according to the invention.
  • Fig. 4 is a schematic representation of the process zone of the arrangement of Fig. 1;
  • Fig. 5 is a schematic representation of a holder for pre-strained optical fiber sensors
  • Fig. 6 is a schematic representation of a preferred variant of the process zone of the arrangement of Fig. 1;
  • Fig. 7a shows the ⁇ / ⁇ -signal representing the acoustic emissions generated in the process zone by a high-voltage discharge as determined with the fiber axis of the sensor oriented parallel with a line defined by a shortest distance between the two electrodes;
  • Fig. 7b shows the uV/ps-signal representing the acoustic emissions generated in the process zone by a high-voltage discharge as determined with the fiber axis of the sensor oriented perpendicular to a line defined by a shortest distance between the two electrodes;
  • Fig. 8 shows examples of transparent artifi- cial material samples before the treatment with electric discharges
  • Fig. 9 shows examples of transparent artifi- cial material samples after the treatment with electric discharges together with the corresponding data of the acoustic emissions caused by the discharges;
  • Fig. 10 shows two different discrete wavelet trans-formation (DWT) schemes
  • Fig. 11 is a table (Table I) with classifica- tion categories and training and test acoustic emission datasets;
  • Fig. 12 shows examples of transparent artifi- cial material samples after the treatment with electric discharges resulting in different effects together with the corresponding data of the acoustic emissions caused by the discharges;
  • Fig. 13 shows the characteristics of a 4-band data adaptive wavelet
  • Fig. 14 shows principal component analysis (PCA) weight maps for frequency bands extracted with a 4- band discrete wavelet transformation (DWT); and
  • Fig. 15 shows a table (Table II) with classi- fication test accuracy results.
  • Fig. 1 shows a schematic representation of a first arrangement for pre-weakening or fragmenting of rock material 4 by means of high-voltage discharges 5 according to the invention.
  • the arrangement comprises a tunable high-voltage generator 1, a process chamber 2 with a process zone formed between two electrodes 20, 20' arranged at a distance relative to each other, which pro ⁇ cess zone is flooded with water 21 as process liquid.
  • the rock material 4 that is to be weakened or fragmented is arranged between the two electrodes 20, 20' immersed in the water 21.
  • the arrangement further comprises a control system 3 (see dotted line) comprising an acoustic emis- sion sensor 31, which is immersed in the process chamber 2 in the water 21 close to the electrodes 20, 20' and is connected via a data-line B with a signal processing unit 32.
  • a control system 3 comprising an acoustic emis- sion sensor 31, which is immersed in the process chamber 2 in the water 21 close to the electrodes 20, 20' and is connected via a data-line B with a signal processing unit 32.
  • the pulse parame- ters of the tunable high-voltage generator 1 can be set via a control line 33.
  • the high-voltage generator 1 is set to certain pulse parame- ters by the control system 3, and a high-voltage dischar- ge 5 is generated between the two electrodes 20, 20' .
  • the line defined by a shortest distance between the two elec- trodes 20, 20' between which the high voltage discharge 5 is generated, is denominated with 22.
  • the acoustic emission sensor 31 determines a signal representing the acoustic emissions 30 in the pro- cess zone caused by this high voltage discharge 5 and sends it via the data-line B to the signal processing unit 32, which processes it and compares it with a refer- ence that has been determined before in trial runs with sample material and which represents the effect "dischar- ge through the material causing disintegration of the material (fragmentation)".
  • the pulse parameters of the high-voltage generator 1 are changed by the control system 3 in such a manner that the likelihood of a frag- mentation of the material 4 is increased, e.g. by increa- sing the voltage of the next pulse.
  • the high-voltage generator 1 is set to certain pulse parame- ters by the control system 3, and a high-voltage dischar- ge 5 is generated between the two electrodes 20, 20' .
  • the acoustic emission sensor 31 determines a signal represen- ting the acoustic emissions 30 in the process zone caused by this high voltage discharge 5 and sends it via the data-line B to the signal processing unit 32, which pro- Completes it and compares it with a reference that has been determined before in trial runs with sample material and which represents the effect "discharge through the mate- rial causing mainly internal damages, in particular cracks (pre-weakening)".
  • Fig. 2 shows a schematic representation of a second arrangement for pre-weakening or fragmenting of rocks 4 by means of high-voltage discharges according to the invention.
  • this arrangement as well com- prises a tunable high-voltage generator 1, a process chamber 2 with a process zone formed between two electro- des 20, 20' arranged at a distance relative to each other, which process zone is flooded with water 21 as process liquid. Also here, the rock material 4 that is to be pre-weakened or fragmented is arranged between the two electrodes 20, 20' immersed in water 21.
  • the control system 3 of this arrangement comprises a signal processing unit 32, by means of which also here the pulse parameters of the tunable high-vol- tage generator 1 can be set via a control line 33.
  • This signal processing unit 32 is furthermore connected with the high-voltage generator 1 via a data-line A. Via this data-line A, signals representing parameters of the high- voltage discharges caused by the high-voltage pulse of the generator 1 can be determined and send to the signal processing unit 32.
  • signals are, for example, the discharge voltage, the discharge current, the discharge resistance and the ignition delay.
  • the high-voltage generator 1 is set to certain pulse parame- ters by the control system 3 and a high-voltage discharge 5 is generated between the two electrodes 20, 20' .
  • the line defined by a shortest distance between the two elec- trodes 20, 20' between which the high voltage discharge 5 is generated, is denominated with 22.
  • signals representing a parameter of the high-voltage discharge are determined and send to the signal processing unit 32, for example the discharge voltage and the ignition delay, which processes them and compares them with a reference range or with a reference that has been determined before in trial runs with sample material, and which represents the effect ⁇ discharge through the material causing disin- tegration of the material (fragmentation)".
  • the pulse parameters of the high-voltage generator 1 are changed by the control system 3 in such a manner that the likelihood of a fragmentation of the material is increased, e.g. by increasing the voltage of the next pulse.
  • the high-voltage generator 1 is set to certain pulse parame- ters by the control system 3 and a high-voltage discharge 5 is generated between the two electrodes 20, 20' .
  • signals representing a parameter of the high-voltage discharge 5 are determined and are send to the signal processing unit 32, for example the discharge voltage and the ignition delay, which processes them and compares them with a re- ference range or with a reference that has been determi- ned before in trial runs with sample material and which represents the effect "discharge through the material causing mainly internal damages, in particular cracks (pre-weakening) ".
  • Fig. 3 shows a schematic representation of a third arrangement for pre-weakening or fragmenting of rock material 4 by means of high-voltage discharges 5 according to the invention.
  • This arrangement is a combi- nation of the two before described arrangements.
  • Its con- trol system 3 processes the signals representing the acoustic emissions 30 as well as the signals representing the parameters of the high voltage discharge and compares them with a combined reference or with separate referen- ces.
  • Fig. 4 shows a schematic representation of the process zone of the arrangement of Fig. 1, with the acoustic sensor 31, which is an acoustic-sensitive fiber sensor, alternatively positioned with the axis L of the fiber parallel to the line 22 defined by a shortest dis ⁇ tance between the two electrodes 20, 20' between which the high voltage discharge 5 is generated (designated with reference numeral 311) or at an angle ⁇ of 90° thereto (designated with reference numeral 312) .
  • the dis- tance between the line 22 that is defined by a shortest distance between the two electrodes 20, 20' and the acoustic fiber sensor 311 or 312, respectively, is desig- nated with d.
  • Fig. 5 shows a schematic representation of a holder 6 for keeping optical fiber of the sensor 31 tightened.
  • the strength of the strain of the fiber of the optical sensor 31 affects its sensitivity.
  • the strain of a fused silica fiber can for example be in the range bet- ween 10 - 5000 um/m. In case of a polymer optical fiber, it can largely be extended up to 10000 um/m.
  • the holder 6 comprises a frame 60 and clamps 61 for tightening the fiber from both sides of the sensor area C with a pre-strain onto the holder 6.
  • a light source 62 is shown transmitting light in direction of the fiber axis L through the fiber.
  • the pre- strain of the fiber can be changed manually or automati- cally during operation in order to tune the sensitivity of the sensor 31.
  • Fig. 6 shows a schematic representation of a preferred variant of the process zone of the arrangement of Fig. 1 having eight optical fiber sensors 31 being equally arranged at an equal distance d from the line 22 defined by a shortest distance between the two electrodes 20, 20' around the process zone.
  • All optical fiber sen- sors 31 or the fiber axis L thereof, respectively, are tilted at the same angle ⁇ with respect to the line 22. With this arrangement, from the signals determined by these sensors 31 and their known positions, the location of the high-voltage discharge 5 can be determined.
  • Fig. 8 shows some of the transparent artifi- cial samples (TAS) before electric discharge:
  • Fig. 8 (a) shows a side view of poly methyl methacrylate (PMMA) sample without inclusions
  • Fig. 8 (b) shows top and side views of pressed PMMA samples with mineral inclusions, its diameter and height are 50 mm and 20 mm respectively
  • Fig. 8 (c) shows top and side views of pressed epoxy sample with cylindrical glass inclusions and size 50x20 mm
  • Fig. 8 (d) shows a schematic view of the sample location in the discharge chamber, filled with water.
  • TAS Two types of TAS were manufactured from two polymers based materials; poly methyl methacrylate (PMMA) and epoxy resin.
  • the dielectric constants of the selected materials is around 3 for PMMA and around 4 for epoxy which fit into the range of dielectric constants of most natural solid materials (natural rocks) that are between 3 and 20.
  • the dielectric properties of the TAS can be compared to the ones of quartz, which is a component of a broad range of natural solid materials.
  • the dielectric strength of the PMMA and epoxy varies in the range of 15-20 MV/m as compared to the one of natural solid materials of 1.9 - 7 MV/m.
  • a metallic pin was introduced inside the sample to provide an electric field enhancement and thus provoke the electric discharge through the TAS medium.
  • the pin is shown in Fig. 8a, and its position with respect to electrode is schematically demonstrated in Fig. 8d.
  • TAS with and without inclusions were produced for collecting AE data.
  • the TAS with inclusions were used to simulate the disordered grain structure of real solid materials in a simplified two-component model.
  • the vari- ations in TAS inner structure provided the stochastic de- velopment of discharges inducing pre-weakening, in accor- dance with the mechanisms described above.
  • inclusions were incorporated into the TAS at various concentrations and different po- sitions.
  • glass pieces of various shape (ball, cylinder, cube) and sizes (from 5 to 40 mm) as well as table salt or mineral particles (from 2 to 8 mm) from magnetite (Fe304) and hematite (or-Fe203) were em- ployed and some examples are given in Fig. 8b and c.
  • the TAS made of PMMA without inclusions pos- sess a homogeneous medium and were manufactured by cut- ting of 50 mm square rod of pure PMMA into slices of 20 mm thickness as shown in Fig.8a.
  • the TAS made of PMMA (or PMMA TAS) with inclusions were produced by hot pressing under vacuum of acrylic hot mounting re- sin powder (Clarofast from Struers) at 170 °C and 25MPa for 15 min and then cooled down. The inclusions were in- corporated inside the powder before hot pressing.
  • the TAS from epoxy were produced by chemical reaction of the two components, of a clear epoxy resin, inside a 50 mm square mold.
  • the epoxy samples were composed of two layers (as shown in Fig.8c). After the solidification of the first layer, the inclusions were placed inside the second layer at defined positions.
  • the discharge events inside the TAS were ini- tiated using a big scale voltage generator from Selfrag AG (Kerzers, Switzerland) . It allowed tuning of the ope- rating voltage and storage capacitance in the range of 90 - 200 kV, negative polarity, and 2.5 - 75 nF, respective- ly.
  • the voltage exposure of the TAS was carried out in a chamber filled with water.
  • the setup is a standard indus- trial environment to provoke discharge preferentially in- side the solid materials in a given voltage range and it is schematically represented in Fig. 8d.
  • the gap between the electrodes was 50 mm with the cathode (as the polari- ty is negative) touching the pin electrode of the sample.
  • the detection of the acoustic signals was made directly inside the water filled chamber using an acoustic hydrophone sensor R30UC (Physical acoustics cor- poration, USA) . It was grounded and placed at a distance of 20 cm from the electrode gap (see Fig. 8d) .
  • the AE signals were recorded with a 10 MHz sampling rate and an electrical signal amplification of 20 dB.
  • the recording time was 16 ms and it was initiated via a record trigger synchronized with the discharge initiation.
  • Fig. 9 (al) and (bl) are top views of TAS 1 and TAS 2, respectively, after an electric dis- charge, where with the white markers point the glass inclusions.
  • the inclusions are glass balls placed inside the sample and the insert shows the
  • Fig. 9 (a2) the broken glass inclusions can be observed around the center of TAS 2 and a detail is visible in the insert.
  • Fig. 9 (a2) and (b2) are AE signals of the electric discharge in TAS 1 and TAS 2, respectively.
  • the top right insert is a zoom of the crack acoustic echo showing the abrupt changes in frequency content.
  • Fig. 9 (a3) and (b3) are sonograms of AE from both TAS. Both samples were exposed to the same electrical pulse with voltage and capacitance of 120 kV and 2.5 nF, respectively. The metal pin electrode is placed at the center of the TAS.
  • Both presented TAS are epoxy made with glass inclusions of diameter of 5 mm (Fig. 9 (al) ) and 6-8 mm (Fig. 9 (bl)).
  • the inclusions were placed at the periphe- ry in the first TAS, whereas in the second one, they were located in the center.
  • Both TAS were exposed to identical electrical pulses with a voltage and capacitance of 120 kV and 5 nF, respectively. The process resulted in the occurrence of multiple cracks without samples disintegra- tion (pre-weakening) . Under such circumstances, the stress wave propagation can be described as a cylindri- cally symmetrical process that starts from the discharge area in the middle of the TAS and propagates to its periphery.
  • the frequency energy distribution for both signals is presented in Fig. 9 (a3) and Fig. 9 (b3) .
  • the crack formation frequencies are in the range of 0-160
  • Each sig- nal includes a random number of fluxes that occurs at random time.
  • the challenge in classification of such sig- nals is in the extraction of a fixed combination of fre- quencies that uniquely characterize each pre-weakening state. The methodology for this will be described in section III.
  • Wavelet transform was introduced Daube- chies (Daubechies I., Ten Lectures on Wavelets; CBMS-NSF Lecture Notes nr. 61, SIAM (1992)) as an alternative to Fourier technique, expanding the analysis from the fre ⁇ quency to the time-frequency domain.
  • This possibility is given by employing short oscillating functions as a sig- nal decomposition basis. They are known as wavelets and are localized in both time and frequency domains.
  • the basis wavelets are prototyped from a single wavelet func ⁇ tion called mother wavelet and the WT of continuous sig ⁇ nal f(t) is defined as: where ⁇ * is the wavelet function scaled by j and trans- lated by k f and t is the time stamp.
  • the continuous wavelet transform is replaced by its discrete counterpart called discrete wavelet trans- form (DWT) , which operates with discrete sampled signals f [i] and has a limited number of computational steps.
  • DWT is defined as: where the wavelet function is defined as
  • the DWT can be also defined in ter- ms of filter banks (Daubechies I., Ten Lectures on Wave- lets; CBMS-NSF Lecture Notes nr. 61, SIAM (1992)), in which both the scaling and wavelet functions act as fil- ter channels, extracting the low and the high frequency content of the signal correspondently.
  • the definition of DWT is as follows:
  • ⁇ p(n) is the cor- responding scaling function at the decomposition level j
  • ⁇ () is the wavelet
  • ho, h «-i are the low pass and high pass filters, respectively.
  • the wavelet function is character- ized by vanishing moments N, satisfying the condition: where k-1 ,.,, ⁇ . The greater number of vanish- ing moments allows representing the complex signals with smaller number of wavelet coefficients.
  • Fig. 10 shows schemes of two different DWTs.
  • Fig. 10 (a) shows the scheme of a Standard DWT, where hO and hi are the low pass and high pass filters respecti- vely;
  • the full DWT for several scales is carried out using a pyramidal scheme presented in the Fig. 10 (a), which includes several stages and the sequential split of the low frequency content is carried out at each stage. This results the division of the signal frequency content into narrow frequency bands, which are localized in time.
  • the two-channel DWT can be extended to multiple channels providing more detailed partitioning of the time-frequency space. This is achieved by involving M-l wavelets instead of one that are associated with the scale function. Each wavelet is applied to the individual subspace of the signal thus providing with more precise decomposition (Gupta A., Joshi S.D., Prasad S., A new approach for estimation of statistically matched wavelet; IEEE transactions on signal processing, vol.
  • the energy of the individual frequency band is defined as:
  • d are the wavelet or scale function coefficients extracted from Eqs (3) and (4).
  • the relative energies are the normalized version of the sub band energies and defined as:
  • PCA principal component analysis
  • I.T. Jolliffe Principal Component Analy- sis, second edition (Springer), 2002.
  • the disposal of non-informative features decreases the noise in classi- fication and additionally reduces the computational com- plexity (I.T. Jolliffe, Principal Component Analysis, second edition (Springer), 2002).
  • the features selection with PCA is carried out by projecting the features from their original featu- re space Uo into the linear subspace Ui that has a reduced dimensionality and is a linear approximation of Uo .
  • the new features coordinates in the subspace Ui are defined through projection, which is defined as:
  • Xuo is the matrix of dimensions [n, p]
  • p is the number of features
  • n is the number of measurements
  • w is the principal components matrix that includes the weights are the new features coordinates in the re- mapped space.
  • the projection w is constructed to maximize the variance of Eq. (7) :
  • S is the covariance matrix of Xoo (I.T. Jolliffe, Principal Component Analysis, second edition (Springer) , 2002) .
  • the solution can be obtained by a diagonalization of S using singular value decomposition, selecting eigen- vectors with the highest eigenvalues.
  • the elements ob- tained from w are known as principal components and the selection of informative features is achieved by employ- ing only w with the greatest variance values.
  • Support vector machine is a statistical machine learning technique proposed by Cortes and Vapnik (Cortes, C, Vapnik, V., Support-vector networks, Machine Learning 20 (3): 273, (1995), doi:10.1007/BF00994018) .
  • the objective of the classifier is to separate high dimensional feature sets that belong to two categories into two distinct groups, which are labeled This is performed by constructing a decision hyperplane that separates the sets in the fea- ture space providing the maximum margin between them.
  • the construction of hyperplane is a quadric programming pro- blem which is resolved during an SVM training procedure using pre-labelled feature sets, called training sets.
  • the data points from the two features sets that are neighboring the decision hyperplane are known as support vectors and are the essential margin characteristic.
  • the constructed hyperplane allows defining the decision func- tion for classification of unlabeled data, defined as: where f(y) is the label for the current feature vector and is equal to either 1 or -1, xi are the values
  • s sV is the support vector
  • ⁇ i is the Lagrangian multiplier
  • Jb is a bias, computed du- ring the training procedure (Cortes, C, Vapnik, V., Support-vector networks, Machine Learning 20 (3): 273, (1995), doi:10.1007/BF00994018; Hofraann, Thomas; Schol- kopf, Bernhard; Smola, Alexander J. Kernel Methods in Machine Learning, (2008)).
  • the classification scheme described abo- ve, can be applied for cases with more than two catego- ries using a cascade from binary classifiers which was used in the present work.
  • the basic implementation of SVM was taken from work (C.-C. Chang and C.-J. Lin. LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1—27:27, 2011) . III. RESULTS AND DISCUSSION
  • the AE signals were divided into categories and subcategories.
  • the categories scheme with their corresponding number of included signals and TAS are presented in Fig. 11 (Table I) .
  • the first classification level includes three catego- ries that describe electric discharge propagation, while the second classification level contains several subcate- gories that describe the electric discharge efficiency in terms of pre-weakening.
  • the classification of categories precedes the one of subcategories.
  • Fig. 12 shows optical microscope view of TAS subjected to different electric discharges resulting in different effects and the respective AE signals recorded.
  • Fig. 12 (a) is a top view of TAS made of epoxy from the category Discharge in TAS, subcategory Pre-weakening, The applied electrical pulse is 90 kV and 2.5 nF. The arrow indicates the exit of the discharge. Cracks propagated from the discharge path but are contained within the sample. Most of the glass inclusions are also cracked.
  • Fig. 12 (b) is a top view of TAS made of epoxy from the category Discharge in TAS, subcategory Break-down. The applied electrical pulse is 130 kV and 8 nF. The arrow indicates the material loss in the central area of the pin electrode.
  • Fig. 12 (b) is a top view of TAS made of epoxy from the category Discharge in TAS, subcategory Break-down. The applied electrical pulse is 130 kV and 8 nF. The arrow indicates the material loss in the central area of
  • FIG. 12 (c) is a side view of the pin electrode area of a TAS made of PMMA for the category Discharge fail, subcategory Discharge tree.
  • the applied electrical pulse is 120 kV and 2.5 nF.
  • Fig. 12 (d) is a side view of a TAS made of PMMA from the category Surface discharge, subcategory Without pre-weakening.
  • Fig. 12 (e) is a top view of a TAS made of epoxy from the category Surface discharge, sub category With pre-weakening.
  • the applied electrical pulse is 120 kV and 2.5 nF.
  • the glass inclusion (large square of 40mm x 40mm x 15mm) has many cracks induced by the surface discharge.
  • the epoxy matrix is intact around the inclusion.
  • Fig. 12 (f) is a side view of a TAS made of PMMA after a discharge exposure of 120 kV and a capacitance of 8 nF, where left are cracks around the pin electrode zone and right is
  • the category Dlechnrge in TAS includes the AE that describes the pre-weakening due to the electric dis- charge propagation inside the TAS medium.
  • the TAS from the corresponding subcategories pre-weakening and break- down are presented in Fig. 12 (a) and Fig. 12 (b) , res- pectively.
  • the discharge area is visible at the center and multiple cracks are propagating from the TAS center where the metallic pin is placed.
  • the shorter cracks are confined inside the TAS without propagation to its edges and thus it pre- vents the disintegration of the TAS in pieces (as shown in Fig. 12 (a).
  • the crack formation area around the elec- tric discharge propagation path for this subcategory is additionally depicted in Fig. 12 (f) and can be observed in all TAS after the discharge treatment.
  • Multiple cracks compactly surround the pin electrode area as shown in Fig. 12 ⁇ f) (left) .
  • the cross-section of the same area in Fig. 12 (f) (right) shows the presence of the discharge channel responsible for the surrounding cracks.
  • the TAS belongings to the subcategory break-down are easily reco- gnized as the cracks are propagating to the edges leading to the breakage of the TAS in several separate parts as seen in Fig. 12 (b) . In several cases the break-down pro- cess was accompanied by the loss of material.
  • the category Discharge fall includes par- tial or no discharge and includes two subcategories: no discharge and discharge tree (see Fig. 11 (Table I)).
  • the subcategory no discharge occurs when the voltage of the electrical pulse is lower than the dielectric strength of the TAS. Consequently, all TAS preserved their integrity with no visible damage.
  • the TAS were treated iteratively by increasing the voltage until the dielectric break-down level is exceeded so that the electric discharge happened.
  • the signals from this subcategory were used for estimating the intrinsic noise of the generator.
  • the corresponding frequency sub-bands were removed from further analysis according to descrip- tion given in Section II-A.
  • the subcategory discharge tree includes AE from events when the applied voltage was enough to provide the growth of the streamer towards the counter electrode but the energy of the pulse was still low so it was absorbed by the AS medium before reaching the counter electrode.
  • the branching chan- nels of the damping discharge propagation inside the TAS medium were observed and depicted in Fig. 12 (c) .
  • the nature of this structure is a surface interface inside the TAS medium created by plasma formation.
  • the AE sig- nals from both subcategories (no discharge and discharge tree) are characterized by low acoustic energy and short duration and evidence of this is seen from the corres- ponding AE signals in Fig. 12 (c) .
  • the category Surface discharge contains the
  • AE signals when the discharge occurred in the surrounding water environment or along the sample surface.
  • This hap- pens when break-down voltage of the interface TAS/water is lower than the one of TAS.
  • the surface discharge leads to two events that are described by the corresponding subcategories in Fig. 11 (Table I) .
  • the subcategory of surface discharge without pre-weakening is recognized by scratches on the TAS surface as shown in Fig. 12 (d) . Actually, in this case, no cracks inside the TAS medium are observed but only surface scratches indicating the discharge propagation path. Noteworthy, despite having a discharge occurring outside the TAS, crack formations are still possible due to propagation of pressure waves indu- ced by the outer electric discharge through the sample.
  • the two-level classification presented in Table I was carried out with four cascade SVM classifiers using "one against all" classification scheme.
  • the classifiers were trained and tested on sepa- rate datasets for which the total number of included AE signals is also given in Table I.
  • the first classification level (categories level) incorpo- rates all signals from the lower level (subcategories level) .
  • the wavelet basis was taken as a biorthogonal and was constructed using several signals taken from the different categories (see Fig. 11 (Table I)).
  • the appli- cation of the 4-band wavelet allowed to tile the time - frequency space into separate frequency bands of width in the range of approx. 9.7 kHz to 2.5 MHz.
  • the energies of those frequency bands were consi- dered as features and computed according to Eq. (6) .
  • the sequence of those is the input information for the SVM classifier.
  • PCA principal component analysis
  • Fig. 14 shows PCA weight maps for frequency bands extracted with the 4-band DWT, wherein: Fig. 14 (a) shows the weight map for classification of the categories Discharge in TAS, Discharge fail and Surface discharge (categories 1 , 2, 3 in Fig. 11 (Table I)), Fig. 14 (b) shows the weight map for classification of the subcatego- ries from the category Discharge in TAS (categories 1.1 and 1.2 in Fig. 11 (Table I)), Fig. 14 (c) shows the weight map for classification of the subcategories from the category Discharge fail (categories 2 . 1 and 2.2 in Fig. 11 (Table I)) and Fig.
  • Fig. 14 the computed variances in the low dimensional feature space are presented for all four da- tasets. The different shades of grey encode the relative variance, which was computed as the variance normalized by the cumulative variance. As can be seen, most of the variance is provided by the frequency bands ranging from 9. 765 to 156. 25 kHz. They are concentrated mostly in the time span of 0-3 ms after the signal start (see Fig. 14 (a-c) ) . In contrast, for the surface discharge subcatego- ries (Fig. 14 (d) ) , the features with the maximum varian ⁇ ce are in the time span of 0-9 ms after signal start. As the AE signals have a longer duration in comparison to the ones from other categories (see signal duration in
  • the variance for surface discharge is distribu- ted among a greater number of features than the other cases. Consequently, the most informative features, taken for further analysis, were selected according to a fixed threshold, which is marked by a red horizontal marker on the scale bars in Fig. 14. This threshold was selected after an exhaustive search to determine the optimal com- promise between classification accuracy and the features number.
  • the category Discharge in TAS includes the5 pre-weakening events which is of upmost importance for practical applications, in particular for the mining in- dustry. 10 % of the misclassification are coming from the category Surface discharge (see 3. in Fig. 15 (Table II)). The signals from the latter have, in some cases, the same duration and intensity as the ones from the category Discharge in TAS which is the cause of errors. Few errors (3%) are coming from the categories Discharge rail (see 2. in Fig. 15 (Table II)). This can be explain- ed by the shorter duration of the AE signals as compared to the ones from the other categories and the lower in- tensity levels of generated AE (see Fig. 12 (c) ) .
  • the second level classification for the subcategories Pre- weaking (see 1.1 in Fig. 15 (Table II)) and Break-down (see 1.2. in Fig. 15 (Table II)) have an accuracy of 85% and 89%, respectively.
  • the main sources of misclassifi- cation for those subcategories are in their mutual over- lapping. This is certainly due to the closeness of the two categories. The only difference is that, in the sub- category Break-down, the cracks propagate until the sur- face of the sample thus breaking the latter in several pieces.
  • the category Discgarge full embraces the sub- categories Discharge tree (see 2.1 in Fig. 15 (Table II)) and No discharge (see 2.2 in Fig. 15 (Table II)).
  • the classification of this category and sub- categories showed the highest accuracy. This is attribu- ted to the short duration of the signals as compared to the other categories.
  • the category Surface discharge incorporates the subcategories Surface discharge with pre-weakening (see 3.1 in Fig. 15 (Table II)) and Surface discharge without pxe-weakening (see 3.2 in Fig. 15 (Table II)). With a classification rate of 84%, this category is the least accurate. It is seen that 15% of the misclassifica- tion is made with Discharge in TAS. Although, this mis- classification is not significant, this is most critical error since the overlapping of the categories Surface discharge with the Discharge in HAS decreases slightly the solids materials processing efficiency. Therefore, additional investigations are pursued to enhance this classification rate.
  • the subcategories Surface discharge with pre-ireakening and Surface discharge without pre-weakening remain the least accurate with 73% and 81%, respectively.
  • most of the errors come from cross-classification between the two subcategories. This can be explained by the high variability of signals fea- tures of these subcategories.
  • the AE from cracks forma- tions during the surface discharge is very weak and is hardly detectable compared to the strong AE created by the Shockwave in water. That brings to the mutual over- lapping of both subcategories as shown in Fig. 15 (Table II) .
  • Another source of errors is in the classification of Pre-weakening (3% in 1.1, Fig.

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Abstract

L'invention concerne un procédé de fragmentation et/ou d'affaiblissement d'un matériau solide (4) au moyen de décharges (5) à haute tension. Le procédé comprend les étapes suivantes consistant : a) à prévoir une zone de traitement entre au moins deux électrodes (20, 20') disposées à distance l'une de l'autre, laquelle zone de traitement est inondée d'un liquide de traitement (21) et contient, entre les deux électrodes (20, 20'), le matériau (4) à traiter ; • b) à générer une décharge (5) à haute tension entre les au moins deux électrodes (20, 20') par chargement des au moins deux électrodes avec une impulsion à haute tension au moyen d'un générateur de haute tension accordable (1) réglé à des paramètres d'impulsion prédéterminés ; • c) à déterminer un signal représentant au moins un paramètre de la décharge (5) à haute tension et/ou un effet provoqué dans la zone de traitement par la décharge (5) à haute tension ; • d) à comparer le signal déterminé sous sa forme déterminée et/ou sous une forme traitée avec au moins une référence ; et e) en fonction du résultat de la comparaison, à conserver les paramètres d'impulsion du générateur à haute tension (1) et à répéter les étapes b) à e) ou à modifier un ou plusieurs des paramètres d'impulsion du générateur à haute tension (1) et répéter les étapes b) à e).
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CN114217662A (zh) * 2021-11-23 2022-03-22 华中科技大学 一种高电压脉冲碎岩技术的匹配电压波头确定方法及系统
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WO2022094737A1 (fr) * 2020-11-09 2022-05-12 Ngen Power Spa Procédé de traitement d'un minerai
CN114217662A (zh) * 2021-11-23 2022-03-22 华中科技大学 一种高电压脉冲碎岩技术的匹配电压波头确定方法及系统
CN114113947A (zh) * 2021-11-30 2022-03-01 国网辽宁省电力有限公司铁岭供电公司 一种基于紫外成像法的开关柜及其放电状态感知方法

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