“SYSTEM AND METHOD FOR MEASURING BURDEN PROFILE IN A BLAST FURNACE” TECHNICAL FIELD Present disclosure relates in general to a field of metallurgy and furnace. Particularly, but not exclusively, the present disclosure relates to method and system for measuring burden profile inside a blast furnace. BACKGROUND OF THE DISCLOSURE Iron making process using blast furnace may be considered to be a leading process for providing steel making raw materials. Operation performed inside the blast furnaces is not entirely known. This is because implementation of any direct measurement technique inside the blast furnace is hindered by harsh conditions inside the blast furnace. The blast furnace being mother plant for an integrated steel plant, any disturbance in the blast furnace may drastically and adversely affect overall steel plant’s production. Among all factors that influence operations of the blast furnace, profile distribution of burden surface inside the blast furnace is the most important factor that is to be measured. Such burden profile distribution helps in modulating burden charging sequences to increase productive efficiency. Also, knowledge of changing burden profile distribution of burden material in the blast furnace is a valuable aid in improving the stability and control of furnace operation. The burden profile distribution is directly influenced by gas permeability, which is result of the charging angle juxtaposition. With uniform gas permeability, iron-making productivity and furnace campaign, life are incremented in a high heat utilization furnace. It is required to achieve an accurate measurement of the burden profile distribution without gas leakage risks and heavy maintaining load. However, with high temperatures and pressure and hostile atmosphere, both performance and life cycle of installed mechanisms of measurements may be affected negatively. It may be particularly difficult to understand the distribution of burden materials because of the complex behavior of particular materials. Obtaining a burden profile distribution for the blast furnace at an elevated accuracy, resolution and high data throughput is a demanding task in research field of metrology. Many techniques for performing measurement of the burden profile distribution include installing multiple units with mechanical movement. However, engineering costs of such techniques are prohibitive. Some convention techniques depend on mathematical models and approximations to operate the blast furnace. Such modelling methods to measure the burden profile distribution may be implemented using physical experiment method or mechanism-based method or data-driven method. Application of measuring technologies using hardware components placed inside the furnace have been hindered by the harsh conditions in the blast furnaces. Also, building compact size prototypes for measuring the burden profile distribution have lacked the accuracy because of situations such as charging of burden in real-time, high temperature environment and so on. Some non-contact methods including vision-based methods, interferometry, as well as time- of-flight technique may be implemented to measure the burden profile distribution. Few other techniques like Radar’s use radio waves for the measurement. However, as the radio waves have longer wavelength, measurement resolution is poor. Also, the conventional radars are prone to false echoes from various surrounding metallic structures which are usually present at industrial site Use of laser based scanning and imaging system has also made possible to measure 3D top burden profile for blast furnace. High intensity laser scanner using class-3 lasers and imaging system to measure top burden profile may be one of the solutions to achieve high resolution 3D top burden profile, but they demand rugged mechanical scanners with mirror and a high-speed imaging system. Also, these visible light-based scanners are out of operation in case the flame of central chimney is bright. Hence, there is a demand for standalone scanners, which operate in non-visible wavelength of electromagnetic spectrum, as well as have small enough wavelength to perform high resolution scanning. Light detecting and ranging (LiDAR) system operating in infrared wavelength is best suited for this application. LiDAR’s with Flash mode of operation are suitable for this proposed invention, as LiDAR’s with pulse scanning mode of operations will have lag in the capture of multiple points in 3D space. 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 OF THE DISCLOSURE One or more shortcomings of the prior art are overcome by a system and a method as disclosed and additional advantages are provided through the system and the method as described in the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure. In an embodiment, the present disclosure discloses a system for measuring burden profile in a blast furnace. The system comprises a Light Detection and Ranging (LiDAR) unit configured to scan a top burden surface in the blast furnace and capture 3D point cloud data of the top burden surface; and a processing unit which is configured to filter noise from the 3D point cloud data of the top burden surface; and generate a 3D burden profile using the 3D point cloud data that is filtered of noise. In an embodiment the present disclosure discloses a method of measuring burden profile distribution in a blast furnace. The method comprises scanning a top burden surface in the blast furnace and capturing 3D point cloud data of the top burden surface using a Light Detection and Ranging (LiDAR) unit; filtering, by a processing unit, noise from the 3D point cloud data of the top burden surface; and generating, by the processing unit, a 3D burden profile using the 3D point cloud data that is filtered of noise. In an embodiment, the LiDAR unit is positioned inside the blast furnace to capture the top burden surface. In an embodiment, the LiDAR unit comprises: a transmitter configured to transmit signals, and a receiver configured to receive signals from the top burden surface, where the LiDAR unit is oriented at a predefined angle on an inner surface of the blast furnace. In an embodiment, the processing unit comprises a noise reduction unit, a data separation unit, and a map generation unit. In an embodiment, the noise reduction unit comprises one or more filters to remove the noise from the 3D point cloud data. In an embodiment, the data separation unit is configured to separate the 3D point cloud data of the top burden surface from 3D point cloud data of furnace wall surface. In an embodiment, the map generation unit generates the 3D burden profile using the 3D point cloud data. It is to be understood that the aspects and embodiments of the disclosure described above may be used in any combination with each other. Several of the aspects and embodiments may be combined to form a further embodiment of the disclosure. 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 FIGURES 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: Figures 1a, 1b and 1c illustrate schematic representations of a system for measuring burden profile distribution insidea blast furnace, in accordance with some embodiments of present disclosure; Figure 2 shows a flowchart illustrating an exemplary method for measuring top burden surface inside a blast furnace, in accordance with some embodiments of present disclosure; and Figures 3illustrate exemplary embodiment associated with a system depicting a measured burden profile distribution inside a blast furnace, in accordance with some embodiments of 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 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 “comprises”, “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 “includes”, “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. Present disclosure discloses a system and method for measuring burden profile distribution in a blast furnace. The system includes Light Detection and Ranging (LiDAR) unit for measuring burden profile distribution inside a blast furnace. The LiDAR unit includes a processing unit placed exterior to the blast furnace. The proposed system is configured to perform scan inside the blast furnace and measure the burden profile distribution based on the scans. The proposed system is a compact scanning system which accurately measures the burden profile distribution without obstructing normal operation of the blast furnace. Figures 1a illustrates schematic representation of a system for measuring burden profile distribution in a blast furnace 104. The system may be implemented in an exemplary environment 100 comprising the blast furnace 104. The blast furnace 104 is a type of metallurgical furnace used for smelting to produce industrial metals. The industrials metals may include, but are not limited to, pig iron, lead, copper and so on. The blast furnace 104 may be a vertical shaft furnace that produces liquid metals by reaction of a flow of air introduced under pressure into bottom of the blast furnace 104 with a mixture of materials fed into top. The mixture may include, but is not limited to, at least one of metallic ore such as hematite, coke, and flux such as limestone. The mixture may be termed as “burden” or “charge”. Studies on operation of the blast furnace 104 include measurement or determination of burden level distribution shape (also termed as burden profile distribution) inside the blast furnace 104. Such measurement may be used to effectively control gas injection into the blast furnace 104 and smooth operation of the blast furnace 104. Therefore, measurement of the burden profile distribution is an important step of automated operation of the blast furnace 104. The measurement of the burden profile distribution includes to accurately obtain burden shape information in real-time. The proposed system is configured to accurately obtain the burden profile distribution in the blast furnace 104 without affecting the operations of the blast furnace _104. The system for measuring the burden profile distribution in the blast furnace 104 comprises a Light Detection and ranging (LiDAR) unit 101. LiDAR unit 101 is configured to scan top burden surface in the blast furnace 104. Further, LiDAR unit 101 is configured to measure the burden profile distribution inside the blast furnace 104 based on the scans. In an embodiment, LiDAR unit 101 may include an optical unit 102 and a processing unit 103. The scans may be performed using the optical unit 102. The measurement of the burden profile distribution may be performed by the processing unit 103. In an embodiment, the optical unit 102 in LiDAR unit 101 includes an array of laser source which are controlled to emit signals. The beams of signals are directed to point in different directions. In an embodiment, directing the signal may be programmed. Considering the position of the LiDAR unit 101, the direction of the signals may be pre-defined. The optical unit 102 may include rugged array of LiDAR assembly provided in a dust cleaning and cooling enclosure. The optical unit 102 may include an array of transmitters and an array of receivers configured to transmit and receive signals respectively. The transmitters are configured to transmits the signals to the top burden surface. The receivers are configured to capture the signals reflected by the top burden surface. The signals are then added together in the optic or electronic domain to form the 3D point cloud data. In an embodiment, as shown in Figure 1a and Figure 1b, the optical unit 102 may be placed on inner surface of top of the blast furnace 104. In such embodiment, as shown in Figure 1c, the optical unit 102 may be oriented at a predefined angle on the inner surface to perform scans. The orientation of the optical unit 102 is in such a way that entire region across a stock line 105 of the blast furnace 104 is scanned by the optical unit 102. In an embodiment, the processing unit 103 is electronically coupled with the optical unit 102. The signals received by the optical unit 102 may be provided to the processing unit 103 for processing and measuring the burden profile distribution inside the blast furnace 104. As shown in Figure 1a, the processing unit 103 may be placed exterior to the blast furnace 104. Thus, the processing unit 103 may not be impacted by higher temperatures of the blast furnace 104. In an embodiment, the processing unit 103 may include a processor, I/O interface, and a memory (not shown in the figure). In some embodiments, the memory may be communicatively coupled to the processor. The memory stores instructions, executable by the processor, which, on execution, may cause the processing unit 103 to measure the burden profile distribution, as disclosed in the present disclosure. In an embodiment, the memory may include one or more modules and data. The one or more modules may be configured to perform the steps of the present disclosure using the data, to measure the burden profile distribution. In an embodiment, each of the one or more modules may be a hardware unit which may be outside the memory and coupled with the processing unit 103. In an embodiment, the processing unit 103, for measuring the burden profile distribution, 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 processing unit 103 may be implemented in a cloud- based server or a dedicated server and may be in communication with the optical unit 102 via a communication network. The communication network may include, without limitation, a direct interconnection, Local Area Network (LAN), Wide Area Network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, and the like. In an embodiment, the processing unit 103 may be configured to receive and transmit data via the I/O interface. In an embodiment, the processing unit 103 comprises a noise reduction unit, a data separation unit, and a map generation unit. The noise reduction unit comprises one or more filters to remove the noise from the 3D point cloud data. The noise may include stray signals from received by receiver of sensor from other source or signals after multiple reflections inside the furnace The data separation unit is configured to separate the 3D point cloud data of the top burden surface from 3D point cloud data of furnace wall surface. The burden profile need not include the furnace wall as blast furnace operators need to evaluate the top burden profile of charges raw material. Also processing the top burden surface to generate layer profile from multiple charges is needed, unwanted signals from furnace wall may create incorrect layer profile subsequently miss-leading the operations. The map generation unit generates the 3D burden profile using the 3D point cloud data by converting the point-cloud data in blast furnace co-ordinates considering the actual dimension of blast furnace and designed stock level of top burden surface. The data in new co-ordinate system is rendered and visualized as 3D surface. Figure 2 shows a flowchart illustrating an exemplary method for measuring the burden profile distribution inside the blast furnace 104. Using the proposed system, the steps of method of measuring the burden profile may be performed in real-time, without altering regular operation of the blast furnace 104. Further, the method is performed using LiDAR unit 101 which includes the optical unit 102 and the processing unit 103. LiDAR unit 101 is configured to perform scans on regular basis or on continuous basis. At block 201, the optical unit 102 of LiDAR unit 101 may be configured to perform the scans in interior of the blast furnace 104 using the electronic beamforming to capture 3D point cloud data of top burden surface. In an embodiment, electronics associated with the optical unit 102 may be placed inside a dust cleaning and cooling enclosure to withstand harsh conditions inside the blast furnace 104. Further, a cooling unit may be additionally provided to cool the optics and electronics of LiDAR unit 101. In an embodiment, the scans may be performed by transmitting and receiving the signals in the interior of the blast furnace 104. The optical unit 102 may be placed on inner surface of top of the blast furnace 104. In an embodiment, electronics and optics configured to perform transmitting and receiving of the signal may be installed inside the blast furnace 104. In an embodiment, the optical unit 102 may be placed at the predefined angle on the inner surface to perform the scans. In an embodiment, the optical unit 102 may be placed at top of the blast furnace 104, along vertical axis of the blast furnace 104. The LiDAR unit 101 scans the entire region inside the blast furnace 104 in a single scan. In an embodiment, the 3D point cloud data is collected without lag using interfacing drivers. In an embodiment, the 3D point cloud data is stored along with a timestamp. Upon performing the scans, the processing unit 103 of LiDAR unit 101 may be configured to measure the burden profile distribution of the blast furnace 104. In an embodiment, the processing unit 103 may be electronically coupled with the optical unit 102 to receive the signals and measure the burden profile distribution based on the received signals. At block 202, the processing unit 103 is configured to filter the noise from the 3D point cloud data. LiDAR is subjected to noise data. However, LiDAR provide better penetration into dust particles. But the captured 3D point cloud data still includes noise caused from the dust and needs to be filtered to obtain clean data. One or more filters are used to reduce the noise from the captured 3D point cloud data. The noise is included due to for example, multiple reflections from wall surfaces of the Blast Furnace 104. Noise reduction may also comprise separating the 3D point cloud data of the top burden surface from 3D point cloud data of furnace wall surface. In an exemplary embodiment, a sigma filter may be used to remove the noise. Further, machine learning (ML) techniques or deep learning (DL) techniques may be used to remove the noise. For example, unsupervised clustering techniques can be used to detect outliers in the 3D point cloud data and separate the outliers. In another example, convolution neural network (CNN) can be used to remove noise from the 3D point cloud data. In yet another example, K-nearest neighbour (KNN) can be applied. In an exemplary embodiment, when a 6-sigma filter is used, outliers greater than +3^ and lower than -3^ are removed. The equation for the 6-sigma filter is given in equation 1. Were c = x, y, and z are co-ordinates of 3D point cloud data, ^ is the mean of ^ number of points. The 3D point cloud data is unstructured data. Further process is required for obtaining the co- ordinates for top burden surface and eliminate the points due to dust and blast furnace wall, it converted to structured data by data converting means. The data convertor means is configured with co-ordinate shifting logic for the structured points. The tip of optical unit 102 can be considered as a reference origin for co-ordinate and new origin is shifted along a center line of the furnace at an elevation of design stock level for the furnace. The one or more filters are configured to identify the points that lie on wall of furnace by comparing them with generated points for wall of furnace. The one or more filters are also configured with a multi-frame comparison method, where the 3D point cloud data from multiple frames captured by the optical means 102 in a predefined time frames (e.g., 5 seconds) are compared. Dynamic points due to dust particles in these frames are identified and removed while static points from top burden surface are retained. The resultant 3D point cloud data is only for top furnace surface. An exemplary representation of an image showing a 3D burden profile distribution 302 is illustrated in Figure 3d. In an embodiment, the scans and the measuring of the burden profile distribution may be performed during regular operation of the blast furnace 104. In an embodiment, the system may be configured to operate automatically at regular interval of time to perform the method 200. In an embodiment, the system may be configured to perform the step upon receiving trigger from a user associated with the blast furnace 104. In an embodiment, the burden profile distribution measured by the system may be used to control amount of gas/hot air injected inside the blast furnace 104. In an embodiment, a control unit may be fed with the burden profile distribution measured by the system, to automatically control injection of the gas/hot air by analyzing the burden profile distribution. At block 203 the processing unit 102 generates the 3D burden profile using the noise free 3D point cloud data. A 3D top surface extracting means may be coupled with the data converting means. The 3D top furnace surface extracting means may be configured to store the co- ordinates for 3D top burden surface measured at any instant. A layer profile visualizer means may be coupled with the 3D top furnace surface extractor means, which is configured to generate 3D layer profile using top burden surface of consecutive charges dumped in furnace. A 3D layer profile generator may use a volume balance mechanism, which descents the 3D layers based on volume of burden dumped in furnace for a charge to generate the resultant 3D layer profile in blast furnace. A burden descent rate calculator means may be coupled with the 3D top burden surface extracting means, and may be configured to compare the x, y and z co- ordinates of multiple 3D top burden surface measured at predefined frequency (example 1hz) in a predefined time duration (example 2 minutes) for a charge dumped in furnace. Further, the burden descent rate is calculated at each point along the surface of top furnace surface. The burden descent rate calculating means is configured with the visualization means to highlight areas of low and high descent rates. As illustrated in Figure 2, the method 200 may include one or more blocks for executing processes in the phased array-based LiDAR unit 101. The method 200 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 200 are described may 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 scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. LiDAR unit eliminates, need for movable parts for efficient scan may be eliminated. Further, mechanical errors caused due to such movable parts are also reduced. Also, the system can be operated any time without obstructing normal operation of the blast furnace. An embodiment of the present disclosure provisions to perform efficient scanning and measurement of the burden profile distribution. Higher resolution of the burden profile distribution may be achieved by performed the interpolation of data obtained from scanned regions. 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 of 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 readily apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent 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. The illustrated operations of Figure 2 shows certain events occurring in a certain order. In alternative embodiments, certain operations may be performed in a different order, modified, or removed. Moreover, steps may be added to the above described logic and still conform to the described embodiments. Further, operations described herein may occur sequentially or certain operations may be processed in parallel. Yet further, operations may be performed by a single processing unit or by distributed processing units. 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 disclosure of the embodiments of the invention is 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 being indicated by the following claims. Referral numerals: Reference