TECHNICAL FIELD

The present subject matter is related, in general to a fragment size estimation unit implemented in a blasting environment, and more particularly, but not exclusively, to a method and a system for tuning the fragment size estimation unit based on rock properties for stratum blasting.

BACKGROUND

Coal is a critical component in steelmaking value chain, and it is utilized at various stages of the process. Coal is mined in a variety of open cast and underground mines. Coal reserves are located in sandwiched strata in open cast mines. Blasting and excavation are used to remove the layer of rocks (commonly known as overburden). High-strength explosive is used in blasting to remove overburden from the coal stratum. The ability to the blast the rocks to fragment size has an impact on the number of tonnes of coal which may be retrieved from each blast and consequently the mine’s productivity. Based on the properties of the material being blasted and the material area being blasted, various variables may be changed to increase the overall efficiency of the blast. The main problem is determining how to test the model's efficiency. Because of the large volume of material and hence the associated cost, sieve examination of the muck pile is extremely challenging.

The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

SUMMARY

The present disclosure is directed to overcome one or more limitations stated above or any other limitation associated with the conventional arts.

In an embodiment of the present disclosure, a method for tuning blasting parameters for performing blasting in a strata is disclosed. The method comprises receiving, by a tuning system, one or more images of fragments of rocks obtained by blasting in a strata. After receiving one or more images, the method comprises determining, fragment size distribution for the blasting based on the one or more images. Thereafter the method comprises comparing, the fragment size distribution with a desired fragment size distribution to output a deviation in fragment size of the blasting. Further the method comprises tuning a rock property in the one or more blasting parameters to minimize the deviation for one or more subsequent blasting in the strata.

In an embodiment of the present disclosure, a tuning system for tuning a fragment size estimation unit is disclosed. The system comprises a processor and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to receive one or more images of fragments of rocks obtained by blasting in strata. Thereafter, the processor determines fragment size distribution for the blasting based on the one or more images. Once the fragment size distribution is determined, the processor compares the fragment size distribution with a desired fragment size distribution to output a deviation in fragment size of the blasting. Further, the processor tunes a rock property in the one or more blasting parameters to minimize the deviation for one or more subsequent blasting in the strata.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and regarding the accompanying figures, in which:

Fig. 1 shows an exemplary environment for tuning blasting parameters for performing blasting in a stratum, in accordance with some embodiments of the present disclosure;

Fig. 2 shows a detailed block diagram of a tuning system for tuning blasting parameters for performing blasting in a stratum, in accordance with some embodiments of the present disclosure;

Fig. 3 illustrates a flow diagram showing an exemplary method for tuning blasting parameters for performing blasting in a stratum, in accordance with some embodiments of the present disclosure;

Fig. 4a and Fig. 4b illustrates a perspective view of a blasting area, showing various blasting parameters, in accordance with some embodiments of the present disclosure;

Fig. 4c shows a schematic representation of an overburden and a coal layer, in accordance with some embodiments of the present disclosure;

Fig. 5a and Fig. 5b shows examples of a sample image of a mukepile captured at the blast site, in accordance with some embodiments of the present disclosure;

Fig. 5c shows an example of sample segmentation model output image, in accordance with some embodiments of the present disclosure;

Fig. 5d illustrates an example of a blast sample particle size distribution, in accordance with some embodiments of the present disclosure;

Fig. 5e illustrates an example of a sample estimated fragment size distribution for a blast, in accordance with some embodiments of the present disclosure;

Fig. 6 illustrates an example of a human-machine interface for entering blasting parameters, in accordance with some embodiments of the present disclosure;

Fig. 7a illustrates an example of a predicted fragment size distribution for a blast, in accordance with some embodiments of the present disclosure;

Fig. 7b illustrates an example of an estimated fragment size distribution for a blast, in accordance with some embodiments of the present disclosure; and

Fig. 8 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether such computer or processor is explicitly shown.

DETAILED DESCRIPTION

In the present document, the word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment or implementation of the present subject matter described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the specific forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.

The terms “comprise”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.

The terms “include”, “including”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that includes a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “includes… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.

In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

The present disclosure relates to a method and a system for tuning blasting parameters for performing blasting in a strata. At first, the proposed method is configured to receive one or more images from image capturing unit of fragments of rocks obtained by blasting in a stratum. The size of the fragments produced by blasting provides a clear indicator of the blast’s quality. Thereafter the method is configured to determine fragment size distribution for the blasting based on the one or more images and also historic images of fragments of rocks. Once the fragment size is determined, the method is configured to compare the fragment size distribution with a desired fragment size distribution to output a deviation in the fragment size of the blasting. Further, the method is configured to tune a rock property in the one or more blasting parameters to minimize the deviation for one or more subsequent blasting in the strata.

In this manner, the present disclosure discloses a method and a system for tuning blasting parameters for performing blasting in a stratum which helps to reduce the number of blast events as well as the overall cost of materials and blasting time.

Fig. 1 shows an exemplary environment for tuning blasting parameters for performing blasting in a stratum, in accordance with some embodiments of the present disclosure.

In an embodiment, an exemplary environment 100 may include, without limiting to, an image capturing unit 101, a desired fragment size distribution providing unit 103, a tuning system 105 and a blasting unit 107. The tuning system 105 comprises a processor 109 interfacing the memory 113 for tuning a rock property in one or more blasting. The tuning system 105 may also include an Input/Output (I/O) interface 111.

In an embodiment, the tuning system 105 receives input from the image capturing unit 101 and the desired fragment size distribution providing unit 103 for tuning blasting parameters. As an example, previously, overburden 411 or coal strata were blasted individually in order to remove waste or burden in open-cut coal strip mining and blasting distinct layers of material. The overburden 411 is removed from the coal layer 413 by blasting with a high-powered explosive. The size of fragments generated from this overburden 411 is typically a reliable indicator of blasting quality. A successful blast produces an optimal fragment size distribution, making it easier to handle the fragment for subsequent processing such as transportation and crushing. One or more images of rock fragments obtained by blasting are captured by the image capturing unit 101 and the one or more images are transmitted to the tuning system 105 for further analysis.

In an embodiment, the image capturing unit 101 may capture a high-definition image/ hyperspectral images (for example 300nm-1000nm wavelengths) in color say RGB image during the first blast event and transmit the captured images to the tuning system 105. In an embodiment, the image capturing unit 101 may be a custom purpose-built device or a commercial system. The image capturing unit 101 may be installed on either a ground-based platform or an aerial platform such as a drone. The image capturing unit 101 may comprise multiple camera systems which capture a blasting event occurring from various angles. This multi-camera recording may be used to reconstruct an image of the scene. The image capturing unit 101 can be communicatively coupled with a tuning system 105.

In an embodiment, the desired fragment size distribution providing unit 103 provides estimate size distribution of blast fragments based on one or more blasting parameters. The one or more blasting parameters comprises a rock burden 401 between rows related to blast holes drilled in rock bed in the strata, a spacing 403 between the blast holes and a bench height 405. The bench height 405 indicates the distance between crest and toe of the rock bed. The one or more blasting parameters also comprises a hole diameter 407 indicating the diameter of the blast holes, a hole length 409 indicating the length of the blast holes, a particle vibration velocity, and the rock property. The rock property may be one of soft, medium, and hard. In an embodiment, the one or more blasting parameters also comprises rock factor, a mass of explosive per hole, powder factor, relative weight strength of the explosive, and calibration factor. The tuning system 105 may communicate with an image capturing unit 101 and desired fragment size distribution providing unit 103 via a communication link. A wired communication interface (for example, USB, Ethernet, fibre optic) or a wireless communication interface (for example, Bluetooth, ZigBee, Wi-Fi) may be used as a communication link. The communication link may be utilized to communicate directly or indirectly with the tuning system 105, for example, via a network. The information captured by the image capturing unit 101 and desired fragment size distribution providing unit 103 is further processed by the tuning system 105 and estimate the particle size distribution by utilizing imagining.

In an embodiment, the tuning system 105 may include a processor 109, I/O interface 111, and a memory 113. In some embodiments, the memory 113 may be communicatively coupled to the processor 109. The memory 113 stores instructions, executable by the processor 109, which, on execution, may cause the tuning system 105 for tuning blasting parameters for performing blasting in a strata, as disclosed in the present disclosure. In an embodiment, the memory 113 may include data 115 and one or more modules 117. The one or more modules 117 may be configured to perform the steps of the present disclosure using the data 115, for tuning a fragment size estimation unit. In an embodiment, each of the one or more modules 117 may be a hardware unit which may be present outside the memory 113 and coupled with the tuning system 105. The tuning system 105 may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a Personal Computer (PC), a notebook, a smartphone, a tablet, e-book readers, a server, a network server, a cloud-based server, and the like. In an embodiment, the tuning system 105 may be a cloud-based server. In an embodiment, the tuning system 105 may be associated with the blasting area during the blasting event.

In an embodiment, a blasting unit 107 may be a user dashboard that connects to a machine, system, or a device in the tuning system 105. The device may include, but is not limited to, smartphones, tablets, computer monitor, touch screen devices, and so on. In an embodiment, the blasting unit 107 may be used for displaying the deviation based on the comparison results, where the comparison results are calculated from the fragment size distribution with a desired fragment size distribution to output a deviation in fragment size of the blasting. Further, tunes a rock property in the one or more blasting parameters to minimize the deviation for one or more subsequent blasting in the strata. Rock property may be one of soft or medium or hard.

Fig. 2 shows a detailed block diagram of a tuning system for tuning blasting parameters for performing blasting in a stratum, in accordance with some embodiments of the present disclosure.

In some implementations, the tuning system 105 receives data 115 through the I/O interface 111. As an example, the received data 115 is stored within memory 113. In an embodiment, the data 115 stored in the memory 113 may include one or more images data 201, fragment size distribution data 202, one or more blasting parameters 203, desired fragment size distribution data 204, tuning data 205, deviation data 206, and other data 207 associated with the tuning system 105. In the illustrated Fig. 2, one or more modules 117 stored in memory 113 are described herein in detail.

In one embodiment, the data 115 may be stored in memory 113 in the form of various data structures. Additionally, the aforementioned data 115 can be organized using data models, such as relational or hierarchical data models. The other data 207 may store data, including various temporary data and temporary files, generated by modules 117 for performing the various functions of the tuning system 105. As an example, the other data 207 may include, without limitation, temporarily stored previous input data or stored data collected from the previous blasting event.

In an embodiment, the one or more images data 201 comprises one or more images of fragments of rocks obtained from the initial blasting event and also subsequent blasting event images. The captured images are high-definition images RGB color images or hyperspectral images (for example 100 images). The one or more images data 201 may also contain historic images of fragments of rocks (for example testing 20 images and for validation 20 images) obtained from a various blasting event.

In an embodiment, the fragment size distribution data 202 may be the raw data entered by a user prior to the first blast event. The fragment size distribution date 202 includes historic images of fragments of rocks obtained from a various blasting event. The desired fragment size distribution data 204 is obtained from conventional trained model based on one or more blasting parameters in a strata.

In an embodiment, the one or more blasting parameters data 203 comprises a rock burden 401 (indicating between rows related to blast holes drilled in rock bed in the strata), a spacing 403 (indicating between the blast holes), a bench height 405 (indicating a distance between crest and toe of the rock bed), a hole diameter 407 (indicating the diameter of the blast holes), a hole length 409 (indicating the length of the blast holes), a particle vibration velocity, and the rock property. The rock property is one of are soft, medium, and hard. The one or more blasting parameters data 203 also comprises rock factor, a mass of explosive per hole, powder factor, relative weight strength of the explosive, and calibration factor.

In an embodiment, the tuning data 205 includes rock property in the one or more blasting parameters, wherein blasting parameters comprise a rock burden 401, a spacing 403, a bench height 405, a hole diameter 407, a hole length 409, a particle vibration velocity. In an embodiment, the deviation data 206 is the comparative output generated by comparing fragment size distribution and desired fragment size distribution.

In an embodiment, the data 115 stored in memory 113 may be processed by one or more modules 117 of the tuning system 105. The modules 117 may be stored within the memory 113 as shown in Fig. 2. In an embodiment, the one or more modules 117 may be implemented as dedicated units and when implemented in such a manner, said modules 117 may be configured with the functionality defined in the present disclosure to result in a novel hardware. As used herein, the term module may refer to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a Field-Programmable Gate Arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and/or other suitable components that provide the described functionality. In an example, the modules 117, communicatively coupled to the processor 109, may also be present outside the memory 113.

In one implementation, the modules 117 may include, for example, a receiving module 209, a determining module 210, a comparing module 211, a tuning module 212, and other modules 213. The other modules 213 may be used to perform various miscellaneous functionalities of the tuning system 105. It will be appreciated that such aforementioned modules 117 may be represented as a single module or a combination of different modules 117.

In an embodiment, the tuning system 105 comprises a receiving module 209 that may be configured to receive one or more images of fragments of rocks obtained by blasting in a strata associated with a first blasting event.

In an embodiment, the tuning system 105 comprises a determining module 210 that may be configured to determine fragment size distribution for the blasting based on the one or more images. The fragment size distribution is determined by using an image assessment model, based on one or more post blasting events in a strata. The image assessment model may be artificial intelligence-based image processing training model. For example, after every blast event, mukepile images are captured through image capturing unit 101 were analyzed for fragment size distribution. Equation 1 below calculates the mean fragment size for the mukepile of a bench blasting.

x ¯=A_t [A Q^(1/6)/q^0.8 (115/E)^(19/30) ] C(A) …1

Where x ¯ is the mean fragment size distribution, A is the rock factor (for example, 7, 10, 13 depending on rock hardness during the blast event), Q is the mass of explosive been used in kg, q is the powder factor (specific charge) in kg/m3, E is the relative weight strength of the explosive with respect to Ammonium Nitrate /Fuel Oil (ANFO or AN/FO) percentage, C(A) is the calibration factor (for example, 0.5 to 2) and At is the delay timing factor. Equation 2 may be used to compute the delay timing factor (At) based on whether is higher or less than 1.

A_t= {¦(0.66t^3-0.13 t^2-0.58 t+2.1 (if t =1)@0.9+0.1 (t-1) (if t =1))¦ …2

Where:
t= ?T/T_max
?T = the normal in-row delay (ms) and
T_max= (15.6 B)/C_P
CP = the P-wave velocity (m/ms) and
B = the burden 401 (m)

The degree of uniformity in their fragment sizes is estimated by equation 3 below for the uniformity of the blast outcomes.

n= n_S v((2-30B/d) ) v(((1+ S/B))/2) (1-W/B) (L/H)^0.3 (A/6)^0.3 C(n) …3

Where
n = the uniformity index,
d = the hole diameter 407 (mm),
W = the standard deviation of drilling accuracy (m),
L = the total length of the drilled hole (m),
H = the bench height (m) 405 and
n_S and R_(S )are
n_S=0.206+ (1-R_S/4) 0.8 and R_(S )=6 S_T/?T
S_T is standard deviation of the in-row delay time ?T.

In an embodiment, the tuning system 105 comprises a comparing module 211 that may be configured to compare the fragment size distribution with a desired fragment size distribution. The desired fragment size distribution is computed based on one or more blasting parameters in a strata. The one or more blasting parameters comprises a rock burden 401 (between rows related to blast holes drilled in rock bed in the strata), a spacing 403 (between the blast holes), a bench height 405 (indicating the distance between crest and toe of the rock bed), a hole diameter 407 (indicating the diameter of the blast holes), a hole length 409 (indicating the length of the blast holes), a particle vibration velocity, and the rock property. The desired fragment size distribution data 204 comprises rock factor, mass of explosive per hole, powder factor, relative weight strength of the explosive and calibration factor. When the fragment size distribution analysis results were compared to those of desired fragment size distribution analysis, it was discovered that there were some discrepancies between the two distributions, and the analysis size distribution did not contain particles with sizes smaller than the average maximum fines fragment size. Comparison output fragment size distributions were averaged based on equation 4 to determine the ratio of passing percentage for fines fragment size.

% passing=100- (100* e^(-0.693* (meshsize/X_50 )^n ) ) …4

In an embodiment, the tuning system 105 comprises a tuning module 212 that may be configured to tune a rock property in the one or more blasting parameters to minimize the deviation for one or more subsequent blasting in the strata. After obtaining the deviation in fragment size distribution, disparity if any is removed by tuning the blasting parameters which are related to the geological property of the rock. The tuned parameters are used for any further blasting in the same geographical region.

Fig. 3 illustrates a flow diagram showing an exemplary method for tuning blasting parameters for performing blasting in a stratum, in accordance with some embodiments of the present disclosure.

As illustrated in Fig. 3, the method comprises one or more blocks for tuning blasting parameters operation in a tuning system 105. The method may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

The order in which the method is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

At block 301, the method comprises receiving, by a tuning system 105, one or more images of fragments of rocks obtained by blasting in a stratum. As an example, one or more images of fragments of rock are captured by image capturing unit 101 obtained at initial stage of blasting in a stratum.

At block 303, the method comprises determining, by the tuning system 105, fragment size distribution for the blasting based on the one or more images. The fragments generated out of blasting gives a clear indication about the quality of blast.

At block 305, the method comprises comparing, by the tuning system 105, the fragment size distribution with a desired fragment size distribution to output a deviation in fragment size of the blasting. An image assessment model/training model is used to predict the outcome of the blast with given set of previous blasting images. Further, artificial intelligent based on image processing model computes the size distribution of the fragmented particles.

At block 307, the method comprises tuning, by the tuning system 105, a rock property in the one or more blasting parameters to minimize the deviation for one or more subsequent blasting in the strata. By tuning the blasting parameters that are linked to the geological characteristic of the rock, the size distribution is compared, and any difference (if any) is eliminated. This fine-tuned characteristic is now utilized for any future blaze in the same geographic area.

Fig. 4a and Fig. 4b illustrates a perspective view of a blasting area, showing various blasting parameters, in accordance with some embodiments of the present disclosure.

Fig. 4a depicts an illustrative image of the blasting region where the tuning system 105 will be employed, as well as various blasting parameters that may be used to determine fragment size distribution. For example, Fig. 4a indicates the characteristic of rock area such as a rock burden 401 between rows related to blast holes drilled in rock bed in the strata, a spacing 403 between the blast holes (shown in Fig. 4b), a bench height 405 indicating the distance between crest and toe of the rock bed, a hole diameter 407 indicating the diameter of the blast holes (shown in Fig. 4b), a hole length 409 indicating the length of the blast holes, a particle vibration velocity. As an example, coal is mined from various open cast or underground mines. The coal deposits in open cast mines are found in between overburden 411 layers as shown in Fig. 4c. The method may begin with the identification of an initial blast pattern and first blast pattern criteria and keeping calibration factor for rock property and uniformity as 1, wherein the initial blast pattern may include one or more blasting parameters are tabulated in Table 1 below.

Sl. No. Blasting Parameters Values
1. Burden (m) 401 5
2. Spacing (m) 403 6
3. Bench Height (m) 405 14
4. Hole Diameter (mm) 407 162
5. Hole Length (m) 409 14
6. P-Wave Velocity (m/ms) 6
7. In Row Delay (ms) 17
8. Rock Property Medium
9. Standard Deviation for In Row Delay (ms) 1
10. Standard Deviation for Drilling Accuracy (m) 1
11. Calibration Factor for Rock Properties 0.3
12. Calibration Factor for Uniformity 1
Table. 1

Based on the identification of the blast parameters, the image capturing unit 101 captures hyperspectral pictures including fragmentation of the blasted region, as shown in Fig. 5a. The tuning system 105 receives captured one or more images data 201 from the image capturing system in real-time via the communication link. The tuning system 105 may also receive captured one or images data 201 from other user computing devices or a network. The present one or more images data 201 is then used by the artificial intelligence image processing model to generate an output desired fragment size distribution data 204 (Fig. 5c). The training model may also accept input from the historic images of fragments of rock (for example say 100 images shown in Fig. 5b) and one or more parameters and parameters are tuned up in the prediction model. The one or more parameters comprises rock factor (for example, 0.3), a mass of explosive per hole (for example, 188 kg per hole), powder factor, relative weight strength of the explosive (for example, 95% with respect to ANFO) and calibration factor. The previously used blasting parameters may be obtained via a manual input (for example, human machine interface shown in Fig. 6), or automatically using past system outputs, among other things. A historical data set for a blasted region, a specific mine, or a geographic area and also data relating to blast sites may be stored in memory 113 by the tuning system 105. All previously utilized data, such as earlier blast patterns or previously extracted coal blasting may be included in the historical data collection. The training model receives the present captures one or more images data 201, and the historical data and predicts the accuracy after blast using imaging model. Further, imagining becomes a lever in imaging model to continuously assess and refine the model. In an embodiment, based on the results, the training model may give a more efficient blast pattern. For example, the training model may suggest a change of a blasting parameters, in response to determining that the region to be blasted includes soft or medium or hard of rock property that originally estimated. If the results match, then use the same calibration factor and other tuning one or more parameters for next blast until the conventional trained model predicts the desired particle size distribution. The calibration factor for rock property may vary from 0.5 to 2 and uniformity varies from 0.5 to 2.2.

Upon determining an updated blasting parameter, the tuning system 105 may provide the information for display on a user computing device. The tuning system 105 may also store the updated training model, and improved blast parameters in a memory 113 with an know identification (ID) (for example, “BLAST01HARD”), or transmit it to a cloud storage platform. For example, the training model may correlate the identified changes in the fragment size, with the environmental factors to assist in determining the training model of the region to be blasted. For example, the proper output may be specified for each input supplied to the training model during training. Instead of training on the full available collection of examples, a selection of instances is used before each modification. This may increase the model’s generalizability as well as the efficiency with which it is trained.

For example, if the determined fragment size of the individual blast data (Fig. 7b) does not match the predicted data (Fig. 7a) acquired from the training model, the system is tuned by tweaking a rock property for one or more future blasting in the strata. For example, invoke the saved parameters at any time associated with a blast (for example, same strata, same hardness and so on). The improved blasting parameters may additionally be used for subsequent blasting events, as a new initial blast pattern. For example, if the rock property defect is a hard, the training model may propose medium or soft, which allows for less explosive around the defect, more uniform breakage, and less wasted explosive.

Computer System

Fig. 8 illustrates a block diagram of an exemplary computer system 800 for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system 800 may be a tuning system 105 illustrated in Fig. 1 which may be used for performing blasting in a strata. The computer system 800 may include a central processing unit (“CPU” or “processor”) 802. The processor 802 may comprise at least one data processor for executing program components for executing user or system-generated business processes. The processor 802 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

The processor 802 may be disposed in communication with one or more input/output (I/O) devices (811 and 812) via I/O interface 801. The I/O interface 801 may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial bus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE) or the like), etc. Using the I/O interface 801, the computer system 800 may communicate with one or more I/O devices 811 and 812. The computer system 800 may receive data from image capturing unit 101 and desired fragment size providing unit 103.

In some embodiments, the processor 802 may be disposed in communication with a communication network 809 via a network interface 803. The network interface 803 may communicate with the communication network 809. The network interface 803 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc.

The communication network 809 can be implemented as one of the several types of networks, such as intranet or Local Area Network (LAN) and such within the organization. The communication network 809 may either be a dedicated network or a shared network, which represents an association of several types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network 809 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.

In some embodiments, the processor 802 may be disposed in communication with a memory 805 (e.g., RAM 813, ROM 814, etc. as shown in Fig. 8) via a storage interface 804. The storage interface 804 may connect to memory 805 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.

The memory 805 may store a collection of program or database components, including, without limitation, user /application 806, an operating system 807, a web browser 808, mail client 815, mail server 816, web server 817 and the like. In some embodiments, computer system 800 may store user /application data 806, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle® or Sybase®.

The operating system 807 may facilitate resource management and operation of the computer system 800. Examples of operating systems include, without limitation, APPLE MACINTOSH® OS X, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTIONTM (BSD), FREEBSDTM, NETBSDTM, OPENBSDTM, etc.), LINUX DISTRIBUTIONSTM (E.G., RED HATTM, UBUNTUTM, KUBUNTUTM, etc.), IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), APPLE® IOSTM, GOOGLE® ANDROIDTM, BLACKBERRY® OS, or the like. A user interface may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to the computer system 800, such as cursors, icons, check boxes, menus, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed, including, without limitation, APPLE MACINTOSH® operating systems, IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), Unix® X-Windows, web interface libraries (e.g., AJAXTM, DHTMLTM, ADOBE® FLASHTM, JAVASCRIPTTM, JAVATM, etc.), or the like.

Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, nonvolatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.

In an embodiment, the present disclosure provides a method and system for tuning blasting parameters for performing blasting in a strata.

In an embodiment, the present disclosure predicts the fragment size distribution in advance and improves blast pattern for subsequent blasts.

In an embodiment, the present disclosure helps to minimize the deviation in fragment size of the blasting.

In an embodiment, the present disclosure reduces the number of blast events as well as the environmental effect of noise, vibration, and dust.

In an embodiment, the present disclosure is extremely useful by reducing total duration of the blasting.

The terms "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean "one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise.

The terms "including", "comprising", “having” and variations thereof mean "including but not limited to", unless expressly specified otherwise.

The enumerated listing of items does not imply that any or all the items are mutually exclusive, unless expressly specified otherwise.

The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise.

A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

When a single device or article is described herein, it will be clear that more than one device/article (whether they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether they cooperate), it will be clear that a single device/article may be used in place of the more than one device or article, or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.

Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Referral Numerals:
Reference Number Description
100 Environment
101 Image Capturing Unit
103 Desired Fragment Size Distribution Providing Unit
105 Tuning System
107 Blasting Unit
109 Processor
111 I/O Interface
113 Memory
115 Data
117 Modules
200 Tuning System Environment
201 One or More Images Data
202 Fragment Size Distribution Data
203 One or More Blasting Parameters Data
204 Desired Fragment Size Distribution Data
205 Tuning Data
206 Deviation Data
207 Other Data
209 Receiving Module
210 Determining Module
211 Comparing Module
212 Tuning Module
213 Other Modules
400 Exemplary Blasting strata
401 Burden
403 Spacing
405 Bench Height
407 Hole Diameter
409 Hole Length
411 Overburden
413 Coal Layer
501 Image Captured at Blast site
503 Segmentation Model Output
800 Exemplary Computer System
801 I/O Interface of the exemplary computer system
802 Processor of the exemplary computer system
803 Network Interface
804 Storage Interface
805 Memory of the exemplary computer system
806 User /Application
807 Operating System
808 Web Browser
809 Communication Network
811 Input Devices
812 Output Devices
813 RAM
814 ROM
815 Mail Client
816 Mail Server
817 Web Server

We Claim:

1. A method for tuning blasting parameters for performing blasting in a strata, the method comprising:
receiving, by a tuning system (105), one or more images of fragments of rocks obtained by blasting in a strata

determining, by the tuning system (105), fragment size distribution for the blasting based on the one or more images;

comparing, by the tuning system (105), the fragment size distribution with a desired fragment size distribution to output a deviation in fragment size of the blasting; and

tuning, by the tuning system (105), a rock property in the one or more blasting parameters to minimize the deviation for one or more subsequent blasting in the strata.

2. The method as claimed in claim 1, wherein the one or more blasting parameters comprise a rock burden (401) between rows related to blast holes drilled in rock bed in the strata, a spacing (403) between the blast holes, a bench height (405) indicating distance between crest and toe of the rock bed, a hole diameter (407) indicating diameter of the blast holes, a hole length (409) indicating length of the blast holes, a particle vibration velocity, the rock property, rock factor, mass of explosive per hole, powder factor, relative weight strength of the explosive and calibration factor.

3. The method as claimed in claim 1, wherein one or more images of fragments of rocks also contains historic images of fragments of rocks obtained from a various blasting event.

4. The method as claimed in claim 1, wherein the desired fragment size distribution is computed using a conventional trained model based on one or more blasting parameters in a strata.

5. The method as claimed in claim 1, wherein the rock property is one of are soft, medium, and hard.

6. A tuning system (105) for tuning a fragment size estimation unit, the system comprising:
a processor (109); and
a memory (113) communicatively coupled to the processor (109), wherein the memory (113) stores processor-executable instructions, which, on execution, causes the processor (109) to:
receive one or more images of fragments of rocks obtained by blasting in a strata;

determine fragment size distribution for the blasting based on the one or more images;

compare the fragment size distribution with a desired fragment size distribution to output a deviation in fragment size of the blasting; and

tune a rock property in the one or more blasting parameters to minimize the deviation for one or more subsequent blasting in the strata.

7. The system as claimed in claim 6, wherein one or more blasting parameters comprises a rock burden (401) between rows related to blast holes drilled in rock bed in the strata, a spacing (403) between the blast holes, a bench height (405) indicating distance between crest and toe of the rock bed, a hole diameter (407) indicating diameter of the blast holes, a hole length (409) indicating length of the blast holes, a particle vibration velocity, the rock property, rock factor, mass of explosive per hole, powder factor, relative weight strength of the explosive and calibration factor.

8. The system as claimed in claim 6, wherein one or more images of fragments of rocks also contains historic images of fragments of rocks obtained from a various blasting event.

9. The system as claimed in claim 6, wherein the desired fragment size distribution is computed using a conventional trained model based on one or more blasting parameters in a strata.

10. The system as claimed in claim 6, wherein the rock property is one of are soft, medium, and hard.