DESC:TECHNICAL FIELD
[0001] The present disclosure relates to a field of Silicon Carbide production. More particularly, the present disclosure relates to a system and method for facilitating optimum production of Silicon Carbide.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Silicon Carbide (SiC) has been noted as structural material used under high temperature because of their excellent heat resistance, low heat expansion coefficient and chemical stability and, in addition, sintered SiC products of high density have particularly high strength under high temperature.
[0004] Industrial applications of electricity are greatly entwined with the commercial manufacture of Silicon Carbide by the Acheson process and that of Aluminium by the Hall-Héroult process. Both these technologies are energy guzzlers and, therefore, it is not surprising that many attempts have been/are being made to improve the overall energy efficiencies of the above process technologies. The technical importance of SiC has been widely recognized since the end of the nineteenth century. Silicon carbide was discovered accidentally by Acheson in 1891. Later on, the produced SiC by carbothermal synthesis in a resistor furnace. The process is known as Acheson process.
[0005] Commercially, Silicon Carbide is produced, almost exclusively, by the Acheson Process. Acheson process uses a resistor furnace for the production of SiC. A stamped thermally insulating refractory lining protects the sidewalls of this rectangular tank furnace (resistor furnace). Carbon electrodes equipped with electric connections protrude into the furnace space through this lining. A characteristic part of SiC furnace is the resistor core (made of graphite) that connects the current-conducting electrodes and serves for the conversion of electric power into heat energy. This heat energy passes to the furnace charge (mainly quartz and petroleum coke) which surrounds the resistor core. During firing, the core temperature reaches up to 2600oC. This causes the chemical reaction, which forms SiC, to take place in the surrounding mixture. Firing is done for about 40hrs and after cooling, the sidewalls are removed to get the product.
[0006] Formation of SiC is a very complex process involving many physical and chemical phenomena such as vaporization, condensation, decomposition, and recrystallisation of various species. The CO produced during the process is burnt at the top of the charge which is open to the atmosphere. When removed from the furnace the SiC is a mass of interlocking iridescent crystals. The crystals vary in colour from pale green to black, depending on the amount of included impurities. Usually, two forms of SiC can be found in the final product which are the common high temperature hexagonal (a SiC) and the low temperature cubic (ß SiC) forms.
[0007] However, the above-mentioned process is inefficient in terms of the production of silicon carbide as only 12- 15% charge gets converted into silicon carbide. Some experimental efforts have been made to understand and optimize the process but not much success has been achieved in terms of increased production. There have been many attempts to produce silicon carbide in bulk via other routes such as by using solid phase reactions, liquid phase reactions or gaseous phase reactions.
[0008] The research over the past few decades have been focused primarily on the silicon carbide synthesis and material characterization, while modelling of the manufacturing route of such an important product received almost no attention, despite its widespread importance and application in science and industry. Gupta et. al. (G.S. Gupta, P. Vasant Kumar, V.R. Rudolph, M. Gupta, Metall. Trans. A 32, 2001, 1301), (P.V Kumar and G.S Gupta: Steel Research, 73(2), 2002, p31-38), and (P. Vasanth Kumar and G.S. Gupta, Modelling and simulation of the Acheson process, Conference CD-ROM, CHEMECA™99, Newcastle, Australia, 1999, p1034-39) have developed a simplified one-dimensional model to describe the Acheson process. Only conduction mode of heat transfer has been considered in this model. Another model is given by Derevyanko (I.V. Derevyanko and A.V. Zhadanos, Metallurgical and Mining Industry, 2010, vol 2 (5), 330-335), which does not give much description of it and no expression is given for reaction kinetics and gas effect in the model. This model also lacks in its validation. Obviously, a sound theoretical model is non-existent for such a process. Therefore, no scientific design criteria are available for this process, which, as mentioned earlier, has remained unchanged for more than one and quarter century.
[0009] There is, therefore, a need to provide an optimized method for production of high-quality Silicon Carbide in bulk. Further, there is, a need in the art to provide a model that facilitates variation of process variables in SiC production method to provide the optimum range of process variables for the production of SiC.

OBJECTS OF THE PRESENT DISCLOSURE
[0010] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0011] An object of the present disclosure to provide for a method that facilitates optimization of process variables which are used in the production of silicon carbide (SiC) in bulk.
[0012] An object of the present disclosure is to provide for a method that facilitates optimization of the processing temperature to get the optimum yield of SiC.
[0013] An object of the present disclosure is to provide for a method to determine the optimum processing time in order to maximize the SiC yield.
[0014] An object of the present disclosure is to provide for an approach for facilitating optimizing raw materials composition in order to get the optimum yield of the product.
[0015] An object of the present disclosure is to provide for a method that facilitates optimization of furnace returns in order to get the optimum yield.
[0016] An object of the present disclosure is to provide for a system and method for developing one-dimensional model of the process considering conduction, convection and radiation heat transfer.
[0017] An object of the present disclosure is to provide a system and method for developing two-dimensional model of the process considering all the three modes of heat transfer.
[0018] An object of the present disclosure is to provide for a method that facilitates generation of both the models through a display unit associated with a computing device.

SUMMARY
[0019] The present disclosure relates to a system and method for facilitating optimum production of Silicon Carbide.
[0020] In an aspect, the present disclosure provides for a method for facilitating optimum production of the product (Silicon Carbide or SiC). The method may include mixing a predefined amount of atleast a first raw material and atleast a second raw material in a ball mill to provide a mixture. Atleast the first raw material may be silica selected atleast in the range of 0 to 20% excess of stoichiometry quantity and atleast the second raw material maybe graphite selected in atleast in the range of 0 to 20% excess of stoichiometry quantity. Further, atleast a third raw material may include Silicon Carbon atleast in the range of 0 to 20 configured as furnace return; positioning atleast one electrode, and one or more temperature measurement devices in the furnace, Atleast the one electrode may be operatively coupled with electrical connections connected to a power supply unit, and the one or more temperature measurement devices may be configured to sense temperature inside the furnace; charging the mixture, the mixture may cover the one or more temperature measurement devices; increasing power supply from the power supply unit gradually, in steps, till a desired maximum value of a process temperature is reached and maintaining the desired maximum temperature inside the furnace. The process temperature may be selected atleast in the range 1700oC to 2100oC. As the heating continues, increasing the temperature inside the furnace to facilitate reaction between atleast the first raw material and atleast the second raw material. The reaction between atleast the first raw material and atleast the second raw material may be evident from a bluish flame coming out indicating formation of gaseous CO; poking of the raw materials during the reaction to release gas pockets formed inside the furnace and adding fresh material when required, and turning off the power supply after a predefined time and allowing the material inside the furnace to cool for atleast two days before removing the charge from the furnace, where the predefined process time is selected atleast in the range of 3 to 10 hours.
[0021] In an embodiment, prior to switching on the power supply to provide power supply to the furnace, the method may provide for turning on a plurality of exhaust fans operatively coupled to the furnace, a cooling system fluidically coupled to the electrode holder assembly and a carbon monoxide (CO) monitoring device.
[0022] In another embodiment, upon switching on the power supply unit for starting the method, the power supply unit may provide no power initially by keeping a transformer variac coupled to the power supply unit at zero position.
[0023] In an aspect, the present disclosure provides for a system for facilitating production of optimum product of Silicon Carbide (SiC). The system may include an apparatus that may include a furnace configured with heating resistance having a longitudinal direction and atleast an electrode holder assembly, and in the electrode holder assembly, atleast one electrode maybe coupled that may extend along the length in the centre of the furnace. A plurality of refractory bricks may form an inner side of the furnace and may surround an inner heating zone. There may also be an insulating layer surrounding the inner heating zone. An exhaust system coupled to the top portion of the furnace may be configured to drive away gaseous by product formed during a reaction. The system may also include a cooling unit fluidically coupled to atleast the electrode holder assembly configured to avoid excess heating of the electrode holder assembly and one or more temperature measurement devices operatively coupled to the furnace. The one or more temperature measurement devices may include a plurality of sensors configured to sense temperature inside the furnace. Further, the system may include a data acquisition unit (DAS) operatively coupled to the one or more temperature measurement devices and communicatively coupled to one or more computing devices through a network. The DAS may be configured to record and process the temperature sensed by the one or more temperature measurement devices. Furthermore, a control unit may be coupled to the power supply unit configured to control and monitor power supply to the electrode holder assembly and temperature sensed by the one or more temperature measurement devices.
[0024] In an embodiment, atleast one wall of the furnace may open to slide to facilitate any or a combination of easy removal of heavy chunks of a product after completion of a reaction and collecting samples of the product.
[0025] In an embodiment, the control unit may include a plurality of temperature indicators and fuses, but not limited to the like. The temperature indicators may be configured as temperature switching unit to select a desired maximum temperature at the start of the run. The temperature switching unit may be configured to perform any or a combination of power supply cut off once a predefined threshold temperature is reached and power supply restore if the temperature falls below the predefined threshold temperature.
[0026] In an embodiment, an electrode holder assembly may be operatively coupled with electrical connections connected to the power supply unit for successful heating of product inside the furnace, and in the electrode holder assembly there can be atleast two electrode holders and the cooling unit fluidically coupled to atleast two electrode holders. The cooling unit may include atleast a water pump, atleast a water return pipe, and water tanks.
[0027] In an embodiment, a carbon monoxide (CO) monitoring unit coupled to the furnace may be configured to monitor formation of CO gas during the reaction.
[0028] In an embodiment, the plurality of sensors in the temperature measurement devices may include any or a combination of thermocouple, pyrometer, and electrode (core) temperature measurement assembly, but not limited to the like.
[0029] In an embodiment, the system may include a plurality of safety accessories configured to protect working personnel. The safety accessories may include any or a combination of CO gas monitor equipment, high temperature protecting clothes, shoes, high temperature viewing helmet, goggles, gas and dust masks, and the like with room ventilation having a good exhaust system that may include a plurality of exhaust fans and fire safety equipment.
[0030] In an aspect, the present disclosure provides for a device coupled to the furnace system for facilitating accurate measurement of electrode temperature. The device may include an electrode temperature measurement assembly that may include atleast two tubes inserted in a graphite block. Atleast a first tube may be configured to receive radiation from the electrode and Ultra-Pure Nitrogen (UHP) may be purged through the atleast first tube and may make the path clear from dust and gases. The electrode temperature measurement assembly may include atleast a second tube as an exhaust tube, and purged nitrogen gas may be discharged through atleast the second tube.
[0031] In an embodiment, a pyrometer may be coupled to the electrode to measure the received radiation from the electrode.
[0032] In an embodiment, the material for the electrode, atleast the first tube and electrode holder may be but not limited to a high-density graphite material.
[0033] In an aspect the present disclosure provides for a modelling system associated with the DAS and operatively coupled to the furnace system for facilitating modelling of variation and setting of operation variables to determine the optimum product output. The system may include one or more processors operatively coupled to a memory, the memory storing instructions executable by the one or more processors to: receive a first set of parameters pertaining to the operation variables associated with the process for production of Silicon Carbide, receive a second set of parameters pertaining to chemical and physical parameters associated with the Silicon Carbide production process and the furnace; and generate a first and a second set of details based on the received first and second set of parameters. The first and the second set of details may pertain to details about the product produced.
[0034] In an embodiment, the operation variables pertain to any or a combination of process time, process temperature, power, raw material composition, use of furnace return, but not limited to the like.
[0035] In an embodiment, the second set of parameters may pertain to parameters associated with vaporization, condensation, decomposition and recrystallization during the formation of silicon carbide.
[0036] In an embodiment, the first and the second set of details may be configured for a plurality of modes of heat transfer pertaining to conduction, convection and radiation.
[0037] In an aspect, the present disclosure provides for a method for facilitating modelling of variation and setting of operation variables to determine the optimum product output. The method may include the steps of receiving at one or more processors, a first set of parameters pertaining to the operation variables associated with the process for production of Silicon Carbide, receiving at the one or more processors, a second set of parameters pertaining to chemical and physical parameters associated with the Silicon Carbide production process and the furnace and generating at the one or more processors, a first and a second set of details based on the received first and second set of parameters. The first and the second set of details may pertain to details about the product produced.

BRIEF DESCRIPTION OF DRAWINGS
[0038] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The diagrams are for illustration only, which thus is not a limitation of the present disclosure.
[0039] In the figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
[0040] FIG. 1A illustrates an exemplary experimental set of the proposed system for production of Silicon Carbide, in accordance with an embodiment of the present invention.
[0041] FIG. 1B illustrates an internal view of a furnace of the proposed system showing provisions for temperature measurement locations. in accordance with an embodiment of the present invention.
[0042] FIG. 2 illustrates front view and side view of an electrode temperature measurement assembly of the proposed system, in accordance with an embodiment of the present invention.
[0043] FIG. 3 illustrates a process flow diagram of the proposed method for production of Silicon Carbide in the proposed system, in accordance with an embodiment of the present invention.
[0044] FIG. 4A illustrates an exemplary architecture of a processor associated with the modelling system in accordance with an embodiment of the present disclosure.
[0045] FIG. 4B illustrates a method illustrating the modelling of variation and setting of operation variables to determine the optimum product output in accordance with an exemplary embodiment of the present disclosure.
[0046] FIG. 5A illustrates an exemplary flow diagram of a 1-D model for optimization of the proposed method, in accordance with an embodiment of the present invention.
[0047] FIG. 5B illustrates an exemplary flow diagram of a 2-D model for optimization of the proposed method, in accordance with an embodiment of the present invention.
[0048] FIG. 6A and 6B illustrate exemplary modelling domains for the 1-D and 2-D model respectively, in accordance with an embodiment of the present invention.
[0049] FIG. 7 illustrates percentage of Silicon Carbide produced away from the electrode surface in radial distance in four directions in an exemplary experiment using the proposed method.
[0050] FIG. 8 illustrates temperature profile around the electrode in the exemplary experiment using the proposed method.
[0051] FIG. 9A illustrates an XRD of a sample collected at 1cm away from the electrode in the exemplary experiment using the proposed method.
[0052] FIG. 9B illustrates a SEM image of the sample collected at 1cm away from the electrode in the exemplary experiment using the proposed method.
[0053] FIG. 10 illustrates variation of average SiC with a distance at various electrode temperature using the proposed method.
[0054] FIG. 11 illustrates variation of average SiC with a distance at three electrode temperatures using the proposed method.
[0055] FIG. 12 illustrates effect of time on yield of SiC at various time at two different temperatures using the proposed method.
[0056] FIG. 13 illustrates effect of excess carbon in the charge on SiC formation at 1900oC using the proposed method.
[0057] FIG. 14 illustrates effect of excess silica in the charge on SiC formation at 1900oC using the proposed method.
[0058] FIG. 15 illustrates effect of excess furnace returns (SiC) in the charge on SiC formation at 1900oC using the proposed method.
[0059] FIG. 16 illustrates effect of initial content of SiC in the charge on core temperature using model.
[0060] FIG. 17 illustrates effect of initial content of SiC in the charge on the product formation using model.
[0061] FIG. 18 illustrates effect of various power supply on core temperature using model.
[0062] FIG. 19 illustrates effect of various power supply on the product formation using model.
[0063] FIG. 20 illustrates effect of different final porosity on core temperature using model.
[0064] FIG. 21A to 21G illustrate exemplary views of a Graphical User Interface (GUI) of the proposed system.

DETAILED DESCRIPTION
[0065] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[0066] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure).
[0067] The present disclosure relates to a field of silicon carbide production. More particularly, the present disclosure relates to an optimized system and method for production of silicon carbide.
[0068] FIG. 1A illustrates an exemplary experimental set of the proposed system for production of Silicon Carbide, in accordance with an embodiment of the present invention.
[0069] FIG. 1B illustrates an internal view of a furnace of the proposed system showing provisions for temperature measurement locations. in accordance with an embodiment of the present invention.
[0070] According to an aspect, the present disclosure elaborates upon a system 100 for production of Silicon Carbide. Referring to FIG. 1A, the system can include a resistance heating furnace 130 (referred to as furnace 130 hereinafter), an electrode holder assembly 108, a cooling unit 124, a power supply unit 126 (interchangeably referred to as power unit 126), a data acquisition unit 128 (also referred to as DAS 128 herein), one or more temperature measurement devices 118 (collectively referred to as more temperature measurement devices 118 and individually referred to as more temperature measurement device 118), safety accessories, and raw materials, but not limited to the like.
[0071] The power unit 126 can be the mains supply 102 coupled to a phase II transformer 106 but not limited to it. A control panel 104 (interchangeably referred to as control unit 104) can be coupled to the mains 102 and the phase II transformer 106 to control and monitor power supply to the furnace 130.
[0072] Referring to FIG. 1B, in an embodiment, the resistance heating furnace 130 (also referred to as furnace 130, herein) can be in cylindrical shape, but not limited to the like, as the product (SiC) obtained from the furnace 130 is in cylindrical shape. The body 110 of the furnace 130 can be made from a stainless-steel sheet, but not limited to the like. High temperature ceramic wool can be used as the insulating material 136 for the body 110 of the furnace 130. The inner side 112 of the furnace 130 can be lined using high temperature magnesite refractory bricks but not limited to the like.
[0073] In an embodiment, atleast one of the side walls of the furnace 130 (the electrode side walls) can be a sliding door type, made of high temperature refractory bricks 112. This door can facilitate easy removal of heavy chunks of the product after completion of the reaction and can also facilitate to collect samples of the product for chemical analysis from precise angular and radial directions. The top portion 132 of the furnace 130 can be open to outer atmosphere to burn gaseous by product formed during the reaction. The flue gases can be driven away through an exhaust system fitted on the top of the furnace 130.
[0074] In an embodiment, the electrode holder assembly 108 must be ensured with proper electrical connections for successful heating of charge inside the furnace 130. The electrode holder assembly 108 can have a graphite electrode 116 at the centre of the furnace 130 running along its length. Further, atleast two graphite blocks acting as electrode holders 108 and a cooling unit 124 for the electrode holder assembly 108 can be provided. The cooling unit 124 can be a water-cooling system but not limited to the like and can serve both as graphite block cooler and power cable connector as shown in FIG. 1A. The cooling unit 124 can include atleast a water pump 120, water tanks 122, atleast a water return pipe 114 and the like. Water cooling of the electrode is necessary to avoid the excess heating of the electrode holders.
[0075] Referring to FIG. 1A, in an embodiment, the system 100 can include a control panel 104 with a power control switch, a thermocouple temperature indicator and High Rupturing Capacity (HRC) fuses, but not limited to the like. The temperature indicators can serve as temperature selection switch. Using this temperature selection switch, one could select the desired maximum temperature at the start of the run and once the set-point temperature is reached it will cut-off the power supply and restore the power supply if the temperature falls below the set-point value.
[0076] In an embodiment, a DAS 128 can be used. The DAS can include NI-9214 high accuracy thermocouple module to record the process temperature during the process, which can receive data from atleast 16 different thermocouples having a sample rate of atleast 1088 S/s (sample/second) but not limited to it. The DAS can further include multiple cold junction correction (CJC) sensors for cold junction compensation and measurement accuracy up to 0.450C but not limited to it. The DAS 128 can have provision for connecting the system 100 to one or more computing devices using a network interfacing through LabView to facilitate online post-processing of the acquired data. In an exemplary embodiment, the DAS 128 can be connected to the one or more computing devices using any or a combination of wired and wireless network, but not limited to the like.
[0077] In an embodiment, the temperature measurement device 118 can include any or combination of thermocouple, pyrometer, and electrode (core) temperature measurement assembly, but not limited to the like. In an exemplary embodiment, the thermocouples can be atleast a Tungsten-5% Rhenium (W-5%Re) v/s W-26%Re (C-type) ungrounded thermocouple of atleast 0.47mm wire diameter but not limited to it to measure the temperature atleast in the range 1800 to 2450K. This thermocouple can be used inside a graphite tube, in which UHP nitrogen gas can be continuously purged. In another exemplary embodiment, the thermocouple can be Pt-30%Rh v/s Pt-6%Rh (B-type) ungrounded thermocouple of atleast 0.47mm diameter but not limited to it, with non-limiting recrystallized alumina sheath of atleast 12mm inner diameter but not limited to it and non-limiting recrystallized alumina insulation, to measure the temperatures atleast in the range 900 to 1973K. In yet another exemplary embodiment, the thermocouple can be non-limiting 90%Ni-10%Cr v/s 95%Ni-2%Al-2%Mn-1%Si (K-type) ungrounded thermocouple of atleast 0.5mm wire diameter but not limited to it, with non-limiting recrystallized alumina sheath to measure temperatures atleast in the range 300 to 1200K.
[0078] In an embodiment, atleast M90R-2 two colour pyrometers can be used to measure the core temperature (atleast in the range 1200 to 3300K). The electrode temperature measurement assembly can facilitate measurement of the electrode (core) temperature.
[0079] In an embodiment, the safety accessories can be included in the system to protect working personnel. The safety accessories can include any or a combination of CO gas monitor equipment, high temperature protecting clothes, shoes, high temperature viewing helmet, goggles, gas and dust masks, etc. Further, proper room ventilation and good exhaust system with fire safety equipment can be provided with the proposed system.
[0080] In an embodiment, the raw materials can include materials such as silica and coke (graphite) fines, of good purity and quality. The raw materials can be procured from suitable sources, which can be weighed in stoichiometric ratio and can be mixed in a ball mill to prepare feeding charge for the furnace 130.
[0081] FIG. 2 illustrates front view and side view of an electrode temperature measurement assembly of the proposed system, in accordance with an embodiment of the present invention.
[0082] As illustrated, in an embodiment, the system 100 can include a special device 200 to measure the core (electrode) temperature accurately. Essentially, the electrode temperature measurement assembly 200 (also referred to as core temperature measurement assembly 200) can include a graphite block in which atleast two tubes can be inserted. Atleast a first tube 202 (also referred to as sighting tube 202 hereinafter) can act as a receiver for receiving the radiation from the electrode 116, which can be measured by pyrometer 212 but not limited to the like. In an implementation, non-limiting Ultra-Pure Nitrogen (UHP) 212 can be purged through the sighting tube 202 to make the path clear from dust and gases. Atleast a second tube 208 can act as an exhaust tube 208. Purged exhaust gas 210 can be discharged through the exhaust tube 208.
[0083] In another embodiment, the material for the electrode 116 and core temperature sighting tube 202 as well as electrode holder assembly 108 can be a high-density graphite but not limited to the like. Drilling, and further machining can be done to give them proper shape and size. In yet another embodiment, the insulating materials like high temperature resistance glass wool and magnesite bricks can be used in the system to protect the furnace 130 and to facilitate the easy removal of the product.
[0084] FIG. 3 illustrates a process flow diagram of the proposed method for production of Silicon Carbide in the system 100, in accordance with an embodiment of the present invention.
[0085] As illustrated, according to an aspect, the present disclosure elaborates upon a method for production of Silicon Carbide. The proposed method can include a step 302 of mixing a predefined amount of atleast a first raw material and atleast a second raw in a ball mill to provide a mixture, where atleast the first raw material can be silica selected atleast in the range of 0 to 20% excess of stoichiometry quantity, atleast the second raw material can be graphite selected in atleast in the range of 0 to 20% excess of stoichiometry quantity, and atleast a third raw material can include Silicon Carbon atleast in the range of 0 to 20%, where atleast the third raw material can be configured as furnace return.
[0086] In an embodiment, the method can further include a step 304 of positioning atleast one electrode 116 coupled to an electrode holder assembly 108, and one or more temperature measurement devices 118 in a furnace, where atleast the one electrode 116 can be operatively coupled with electrical connections connected to a power supply unit 126, and where the one or more temperature measurement devices 118 can be configured to sense temperature inside the furnace 130. The step 304 can further include charging a sufficient amount of the mixture obtained in the step 302 so that the mixture can cover the one or more temperature measurement devices.
[0087] In an embodiment, the method can include a step 306 of increasing power supply gradually, in steps, till a desired maximum value of process temperature can be reached (usually over a period of atleast 45 to 55 minutes but not limited to it), and maintaining the desired maximum temperature inside the furnace 130. The process temperature can be selected atleast in the range 1700oC to 2100oC.
[0088] In an exemplary implementation, before switching on the power to the furnace 130, the exhaust fans and the cooling unit 124 can be switched on. The UHP N2 gas purging can be started in the sighting tube 202 of the core temperature measurement device 200. The Carbon monoxide (CO) monitoring device can be switched on. And, then the power supply can be switched on, keeping the transformer variac at zero position.
[0089] It can be noted that after about one hour from the start of the method, a bluish flame can also start coming out indicating the formation of gaseous CO. As the heating continues in the step 306, the temperature inside the furnace 130 can start increasing and reaction between atleast the first raw material (silica) and atleast the second raw material (carbon) also can start rapidly, which can be evident from more evolution of CO gas at the top of the charge.
[0090] In an exemplary embodiment, the method can include a step 308 of poking of a charge during the reaction to release gas pockets formed inside the furnace 130. Further, more fresh material can be added from the top, when required.
[0091] After a predefined time, the temperature at the centre of the furnace 130 can become constant. As the reaction proceeds further, the current drawn from the electrode can start dropping and can become constant at a particular value. At this stage, though the current drawn from the electrode can be still significant, the temperature at the centre can be kept constant by adjusting the transformer variac. Gases coming out from the top of the charge can be stopped, indicating the end of the reaction. CO monitoring device can also be unable to detect any CO which can again be a good indication of the end of the reaction.
[0092] In an embodiment, the method can include a step 310 of turning off the power supply after a predefined process time and allowing the charge inside the furnace 130 to cool for atleast 2 days before removing the charge from the furnace 130 where the predefined process time is selected atleast in the range of 3 to 10 hours.
[0093] In an embodiment, a modelling system can include a Graphical User Interface that can facilitate varying and setting the operation variables for the model to determine the optimum product (SiC) output and corresponding operation variables.
[0094] FIG. 4A illustrates an exemplary architecture of a processor associated with the modelling system in accordance with an embodiment of the present disclosure.
[0095] As illustrated, the modelling system 402 can include one or more processor(s) 404. The one or more processor(s) 404 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) 404 are configured to fetch and execute computer-readable instructions stored in a memory 406 of the modelling system. The modelling system 402 can be further associated with DAS 128 of the furnace system 100. The memory 406 can store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 406 can include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0096] The modelling system 402 can also include an interface(s) 408. The interface(s) 408 may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, graphical interfaces, and the like. The interface(s) 408 can facilitate communication of the modelling system 402 with various devices coupled to the it. The interface(s) 408 can also provide a communication pathway for one or more components of the modelling system 402. Examples of such components include, but are not limited to, processing units 412 and database 410. Said interfaces can allow non-technical users to access and manage data stored in database 410.
[0097] The processing units 412 can be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing units 412. The database 410 can include data that is either stored or generated because of functionalities implemented by any of the components of the processing units 412.
[0098] In an example, the processing units 412 can include a parameter extraction unit 414, a model generation unit 416, a display unit 418 and other unit(s) 420. The other unit(s) 420 can implement functionalities that supplement applications or functions performed by the modelling system 402 or the processing units 412.
[0099] In an exemplary embodiment, modelling system 402 can include the parameter extraction unit 414 to receive and extract a first set of parameters related to operation variables acquired by the DAS 128 such as process time, process temperature, power, raw material composition, use of furnace return, but not limited to the like. The parameter extraction unit 414 can also receive and extract a second set of parameters related to chemical and physical parameters associated with the Silicon Carbide production process and the furnace 130. The second set of parameters can include parameters corresponding to vaporization, condensation, decomposition and recrystallization of various species occur during the formation of silicon carbide. These phenomena are coupled and are highly interdependent on one another. Heat can get consumed in vaporization and decomposition, while it may be recovered during condensation and recrystallization. So, total effect of these phenomena on heat transfer would not be significant and may be neglected. Moreover, these phenomena occur where the temperature is very high i.e., around the electrode surface. Far from the electrode surface these phenomena would be absent.
[00100] In another exemplary embodiment, the model generation unit 416 can generate a first set of details (also referred to as 1D model hereinafter) and a second set of details (also referred to as 2D model hereinafter) based on the received first and second set of parameters. The first and the second set of details can pertain to details about the product produced. In The modelling system, the first and the second set of details can be configured for a plurality of modes of heat transfer. The plurality of modes of heat transfer pertain to conduction, convection and radiation. The 1D and 2D models can be displayed through a display unit 418 coupled to a graphical user interface but not limited to it.
[00101] FIG. 4B illustrates a method illustrating the modelling of variation and setting of operation variables to determine the optimum product output in accordance with an exemplary embodiment of the present disclosure.
[00102] In an aspect, the proposed method as elaborated hereunder can be described in general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method can also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[00103] The order in which the method as 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 or alternate methods. Additionally, individual blocks may be deleted from the method 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. However, for ease of explanation, in the embodiments described below, the method may be considered to be implemented in the above described system.
[00104] In an aspect, a method for facilitating modelling of variation and setting of operation variables to determine the optimum product output, can include at block 422, receiving at one or more processors, a first set of parameters pertaining to the operation variables associated with the process for production of Silicon Carbide. Further, at block 424, the method can include a step of receiving at the one or more processors, a second set of parameters pertaining to chemical and physical parameters associated with the Silicon Carbide production process and the furnace; and furthermore at block 426, the method can include a step of generating at the one or more processors, a first and a second set of details based on the received first and second set of parameters, wherein the first and the second set of details pertain to details about the product produced.
[00105] FIG. 5A illustrates an exemplary flow diagram of a 1-D model for optimization of the proposed method, in accordance with an embodiment of the present invention.
[00106] Referring to FIG. 5A exemplary flow diagrams of a 1-D and 2-D model for optimization of the proposed method are shown. According to another aspect, the present disclosure elaborates upon a modelling system for 1-D and 2-D modelling of the proposed system and method.
[00107] As illustrated in FIG. 5A, the flow diagram can include at block 502 an instruction to read fumace configuration, number of grid point, total time of simulation, time step, charge composition and define error limit for convergence and then perform another step at block 504 to initialize grid and define initial properties at grid point and at cell interface. then at block 506, time is incremented by t = t + ?t. after which at block 508 calculate properties like specific heat, thermal conductivity, porosity etc which are function of temperature. then at block 510 instruction is provided to calculate TDMA coefficient and solve the matrix to get the temperature. if at block 512, a decision of is solution converged? provides a no, then steps starting from block 508 to calculate properties like specific heat, thermal conductivity, porosity etc which are function of temperature can be repeated. If at block 512, a decision of is solution converged? provides a yes, then at block 514 another decision is T< TFINAL is encountered. if it provides a yes, then again process can return to block 506, where time is incremented by t = t + ?t and the following steps can be repeated. if at block 514 the decision is T< TFINAL provides a no, then at block 516, instruction to save the result to file can be provided.
[00108] FIG. 5B illustrates an exemplary flow diagram of a 2-D model for optimization of the proposed method, in accordance with an embodiment of the present invention.
[00109] Referring to 5B, exemplary flow diagrams of a 1-D and 2-D model for optimization of the proposed method are shown. According to another aspect, the present disclosure elaborates upon a modelling system for 1-D and 2-D modelling of the proposed system and method.
[00110] As illustrated, in FIG. 5B, the flow diagram can include at block 522 an instruction to read furnace configuration, total time, number of grid points in both r and ? direction, material composition, time step, convergence criterion, initial temperature etc. and at block 524 an instruction to initialize grid and define initial properties at grid point and at cell interfaces. At block 526 t = t + ?t. At block 528, an instruction to calculate properties like specific heat, thermal conductivity, porosity etc which are function of temperature and at block 530 an instruction to calculate r – direction on TDMA coefficients for heat balance and solve the TDMA matrix to get temperature in r – direction. If at block 532, a decision of is solution converged? provides a yes, then another decision at block 534 IS ? < ?max ? is encountered. It it provides a yes, then at block 536 increment in T can be done and then go back to block 528 to calculate properties like specific heat, thermal conductivity, porosity etc which are function of temperature and the following steps can be repeated. If at block 532, a decision of is solution converged? provides a no, then again go to block 528 to calculate properties like specific heat, thermal conductivity, porosity etc which are function of temperature and the following steps are repeated. If at block 534 IS ? < ?max ? provides a no, then at block 538, calculate properties like specific heat, thermal conductivity, porosity etc which are function of temperature. at block 540 calculate r – direction on TDMA coefficients for heat balance and solve the TDMA matrix to get temperature in r – direction. If at block 542, a decision of is solution converged? provides a yes, then another decision at block 544 IS r < rmax ? is encountered. It it provides a yes, then at block 546 increment in T can be done and then go back to block 538 calculate properties like specific heat, thermal conductivity, porosity etc which are function of temperature and the following steps can be repeated. if at block 542, a decision of is solution converged? provides a no, then again go to block 538 to calculate properties like specific heat, thermal conductivity, porosity etc which are function of temperature and the following steps are repeated. If at block 544 IS r < rmax ? provides a no, then an instruction at block 548 to solve the TDMA matrix similarly for mass balance can be provided after which a decision at block 550 IS T < TFINAL ? is encountered. If it provides a yes, then go to block 526 t = t + ?t and the following steps can be repeated. If block 550 provides a yes, then perform the instruction at block 552 to save the result into a file.
[00111] In an embodiment, the model can consider various modes of heat transfer that can take place during the proposed method. The modes of heat transfer can include conduction, convection and radiation. In conduction, the heat generated on the surface of the graphite resistor due to flow of electric current is transferred to the reactive mixture chiefly through conduction. The reactive mixture, which is a mixture of silica, carbon and silicon carbide, has a very low thermal conductivity. As such, the specific heat and thermal conductivity of silicon carbide is very high. Thus, one can expect there can be a steep gradient of temperature, at any time in the furnace 130 between reactant and charge. The temperature in the furnace 130 decreases radially. In an embodiment, the conduction heat transfer can be modelled using the standard Fourier heat equation.
[00112] In convection, the heat transferred by the by-product gases, which diffuses through the reactive materials. Due to many chemical reactions, which take place at high temperature, many gaseous products/by-products are released. Mainly, CO is produced as a by-product gas along with other gaseous compounds such as silicon mono-oxide. These gases diffuse out through the reactive mixture and burn at the top of the charge. These gases, while diffusing, contribute to heat transfer in the furnace. At any point of time, the volume of gases generated is not very high and these gases are generated over a period of time as the temperature of the charge increases gradually. Hence, the convective heat transfer due to these gases has been modelled in a simplified way, by considering the diffusive heat flux of the CO gas i.e., if N is the molar flux of CO diffusing through the mixture then, the heat carried by the gas can be calculated using Equation 1 below.
N Cpg Tg, , (Equation 1)
where Cpg and Tg are the specific heat and temperature of the CO gas respectively.
[00113] In radiation, the reactive mixture in the furnace near the core is at very high temperature, and one may expect a major contribution of radiations in heat transfer. However, the charge is packed nicely around the core and the void fraction is also low, therefore one may not expect a significant contribution of radiation in heat transfer process except at the outer surface of the charge which is open to the atmosphere. Nevertheless, radiation effect has been considered in the present case using Rossenland approximation. The radiant heat flux for the case of optically thick media near the thermodynamic equilibrium can be approximated by Equation 2 below:
(Equation 2)
Where n is the refractive index, ? is the Stefan-Boltzmann constant, k is the Rosseland mean extension coefficient and T is the temperature in K.
[00114] Based on the above discussion, the following heat transfer equations have been proposed considering axis-symmetric problem in cylindrical co-ordinates (see FIG. 6A and 6B) in order to study the heat transfer for resistance furnace process. FIG. 6A and 6B illustrate exemplary modelling domains for the 1-D and 2-D model, in accordance with an embodiment of the present invention.
For one-dimensional model
(Equation 3)
For two-dimensional model
(Equation 4)
Where,
– CCO is the concentration of carbon mono-oxide (CO), kgmol/m3
– CPe is effective specific heat of gas-solid mixture, J/kgmol - K
– CPg is effective specific heat of CO gas, J/kgmol - K
– DCO-air,e is the effective mass diffusivity of CO in air, m2/s
– ?Hr is the rate of heat consumption during reaction, W/m3
– ke is the effective thermal conductivity of raw material, W/m- K
– L is the length of the furnace, m
– e is the porosity of raw material
– ?e is the effective density of solid mixture, kgmol/m3
Where, heat of reaction is given by, ?Hr = ?H × rate of reaction ( ), and where , is the rate of reaction for the reaction SiO2 + 3C = SiC + 2CO, and is discussed below.
[00115] In an embodiment, kinetics equation for Silicon Carbide formation can be considered in the model. Accepting as a base case that SiC formation is diffusion controlled (the gas-solid reaction), the fraction reacted is obtained from the rate equation of chemical kinetics below.
= K DSiO-CO (Equation 5)
where , ,
Where, A is the area and ?S is the thickness through which the diffusion is taking place. M and R are molecular weight and universal gas constant respectively. The diffusion coefficient is given by
,
where Pt is total gas pressure. is partial pressure of silicon monoxide and is dependent on the partial pressure of CO and other parameters.
[00116] In an embodiment, the following boundary condition can be considered in the model.
• At time t = 0, for all r and ?, T = Ti
• At time t > 0, for r = ri (at electrode surface)
, where Power = I2 Re; Or actual power supply. I is the furnace current supplied to the electrode and Re is the resistance of the electrode.
• At ? = 0 and ? = p (applicable for all r) for time t > 0

• At r = ro (at the inner periphery of furnace) for time t > 0

Where,
;
Here,
– TSS is the temperature of outer steel shell, K
– Tref is the reference temperature, K
– h is the convective heat transfer coefficient for heat loss between the surface of steel shell and atmospheric air, W/m2-K
– r3 and r4 are defined in Figure 8, m
The initial conditions for reaction rate Equation 5 are:
at time t = 0, T = Ti, and, xSiO2 = 0
[00117] In an embodiment, the model can consider mass transfer that may occur during Silicon Carbide production. The mass transfer processes occurring in this process are, the diffusion of CO gas through the reactive material matrix and the mass transfer due to chemical reactions28. In order to solve for mass flux of CO gas, standard Fick’s law of diffusion can be solved in transient form coupled with the energy equation. To solve for the chemical reactions and to know the concentration of the species after every time step, appropriate reaction kinetics, described in reaction kinetics section in chapter 1, can be used for this system. Considering only diffusive flux of CO through the reacting mixture and adopting the similar approach for mass balance as adopted for heat balance above, one can formulate the mass balance equation for CO as given below.
(Equation 6)
Here, is the rate of CO generation.
Using stoichiometric ratio of reaction SiO2 + 3C = SiC + 2CO, it can be shown easily that rate of formation/generation of CO=(2/1) x Rate of depletion of silica. Where, the Rate of depletion of silica is given by Equation 5.
[00118] In an embodiment, the following boundary condition can be considered by the model for mass balance.
• At time t = 0, for all r and ? (inside the furnace)

CCO = 0

• At time t > 0, for r = ri at all ?, (at the surface of electrode)

• At time t > 0, for r = ro (at the inner periphery of furnace)

[00119] In an embodiment, the Equations (3) (or (4)), (5) and (6) along with appropriate boundary conditions can be discretized using Finite Difference Method, and appropriate numerical techniques can be used by the one or more processors of the computing unit to solve them. Fully explicit and fully implicit formulation schemes can be solved for 1-D model, whereas for 2-D only fully implicit scheme can be solved by applying line-by-line TDMA.
[00120] In line-by-line TDMA, for a particular time step, firstly the converged solution in one direction is determined; the one direction say in r-direction at a particular ?, assuming the quantities to be constant in the neighbourhood of ’r’ in ?-direction. Thus, TDMA in r-direction and sweep in ?-direction can be applied. By doing so, the full domain of consideration can be covered. Same procedure can be adopted while applying TDMA in ?-direction and sweeping in r-direction.
[00121] In an embodiment, the computing unit can be configured to receive the operation variables associated with the process for production of Silicon Carbide and process the operation variables by solving the equations for 1-D and 2-D model based on the flowchart for the solution of discretized equations in 1-D and 2-D model respectively, as shown in FIG. 4 and 5 to provide details about the product produced. The operation variables can be varied and corresponding product yield can be monitored to determine the values of optimum value of operation variables. Further experiments can be conducted to verify the optimum values of operation variables for optimized production of Silicon Carbide.
[00122] FIG. 7 illustrates percentage of Silicon Carbide produced away from the electrode surface in radial distance in four directions in an exemplary experiment using the proposed method.
[00123] FIG. 8 illustrates temperature profile around the electrode in the exemplary experiment using the proposed method.
[00124] In an exemplary embodiment, following experimental input parameters can be used during this experiment, as listed in the table below.
Experiment 1
Input parameter of experiment 1
Charge composition Electrode Temp. Time(min) Total Power Total charge Product weight
Stoichiometry 1800°C 180 53.72kWh 11.96 kg 9.72 kg

[00125] Referring to FIG. 7, it is clear that percentage of SiC produced keeps on decreasing as one moves away from the electrode surface, which is more due to decrease in temperature as can be seen from FIG. 8. The temperature away from the electrode does not reach the desired reaction temperature even after 3 hours contributing in lowering the percentage of SiC formation.
[00126] FIG. 9A illustrates an XRD of a sample collected at 1cm away from the electrode in the exemplary experiment using the proposed method.
[00127] FIG. 9B illustrates a SEM image of the sample collected at 1cm away from the electrode in the exemplary experiment using the proposed method.
[00128] Referring to FIG. 9A, a typical XRD of a sample collected from 1cm (wall side) away from the electrode is shown. This indicates that at this distance almost all raw material has converted into the product due to favourable condition of temperature. Referring to FIG. 9B, the SEM image of the sample collected from 1cm (front side) away of the electrode. The sample shows good crystal growth of SiC and more in crystalline form.
[00129] FIG. 10 illustrates variation of average SiC with a distance at various electrode temperature using the proposed method.
[00130] FIG. 11 illustrates variation of average SiC with a distance at three electrode temperatures using the proposed method.
[00131] Referring to FIG. 10, the percentage of average SiC formed with distance from the electrode surface at various temperatures is shown. The average SiC percentage can be taken by averaging the percentage of SiC content formed in the four angular directions i.e. BS, TS, FS and WS. From FIG. 10, it is obvious that 1800 and 1900 oC electrode temperatures give good yield (%SiC) than any other temperatures. It is also observed that most of the places 1900oC is better but at some places 1800oC is better in terms of giving the yield. Therefore, to resolve this problem further three more experiments were done at 1800, 1850 and 1900oC. The results are shown in FIG. 11. From FIG. 11, it is clear that 1900oC gives better yield of SiC even at far away distances from the electrode surface, though 1850oC is also close to 1900oC. Therefore, the temperature value 1900oC is the optimum temperature for the proposed method and system.
[00132] FIG. 12 illustrates effect of time on yield of SiC at various time at two different temperatures using the proposed method.
[00133] Referring to FIG. 12, the effect of time on the yield (%SiC) at two different temperatures is shown. It is clear that if operating time is 5hr then one gets the maximum yield. Therefore, the operating time value of 5 hr is the optimum operating time value for the proposed method and system.
[00134] FIG. 13 illustrates effect of excess carbon in the charge on SiC formation at 1900oC using the proposed method.
[00135] Referring to FIG. 13, as the percentage of excess carbon increases in the charge the yield is improved. However, the improvement in yield decreases with increasing the excess carbon content in the charge.
[00136] FIG. 14 illustrates effect of excess silica in the charge on SiC formation at 1900oC using the proposed method.
[00137] Referring to FIG. 14, the effect of excess silica than stoichiometry ratio in the charge material on the product formation at 1900oC is shown. As the percentage of excess silica in the charge increases, initially the yield increases a little and with further increase of SiO2 the yield is decreased.
[00138] FIG. 15 illustrates effect of excess furnace returns (Sic) in the charge on SiC formation at 1900oC using the proposed method.
[00139] Referring to FIG. 15, the effect of excess furnace returns (SiC) in the charge on SiC formation at 1900oC is shown. An increase in the percentage of furnace returns (%SiC) in the charge increases the yield slightly but does not have very significant effect.
[00140] From FIG. 10 to 15, it is found that electrode temperature is a key parameter to get the good product and to control the process. Therefore, this parameter can be used to control the overall process.
[00141] FIG. 16 illustrates effect of initial content of SiC in the charge on core temperature using model.
[00142] Referring to FIG. 16, the core temperature profiles at the centre of the furnace with time for three different initial percentages (weight) of silicon carbide in the charge is shown. Except the initial silicon carbide content, all other simulations parameters can be kept constant for all the three runs. It is clear from the fact that as the initial percentage of silicon carbide is increased from 0% to 20.0% the time for the first drop increases from 35 minutes to 60 minutes. This can be explained by the fact that silicon carbide being a refractory material has higher specific heat content when compared to the other two materials. The thermal conductivity of silicon carbide is also higher than the other two materials. Because of these properties, as one increases the silicon carbide content in the initial precursor material, the time needed for heating up the material to reach the reaction temperature is much higher (as heat is conducted away much faster than 0% SiC material). As the initial silicon carbide content is increased, the reacting material per control volume decreases and hence, the endothermic requirement per control volume also decreases correspondingly.
[00143] FIG. 17 illustrates effect of initial content of SiC in the charge on the product formation using model.
[00144] As the heat can be easily conducted to neighbouring grids with the presence of SiC in the initial charge, reaction temperature is able to penetrate deeper in the furnace from the core thus increasing the production formation which is clearly visible from FIG. 17. However, it should be noted that there is an optimum amount of SiC which should be present in the charge to get the higher product. Beyond that amount, it will not help to increase the product. This point can be understood from Fig. 17. When percentage of SiC is increased from 0 to 10%, product formation goes up from about 3.3cm to 6.1cm converted core radius. However, when it is increased from 10 to 20%, product formation goes up from 6.1cm to 6.7cm which quite low in comparison to first case.
[00145] FIG. 18 illustrates effect of various power supply on core temperature using model.
[00146] Referring to FIG. 18, the effect of various power inputs on the temperature with time is shown. Input power supply has reasonable influence on the temperature as it is obvious from this figure. The maximum temperature in the process varies from 2000 K to 2150 K as the constant input power varies from 85 to 44 kWh. These are expected results, because as the power is decreased, it is not able to meet the total requirement of the process and hence the temperature in the final stage is decreased. Also, the time to reach the first peak temperature is increased as input power decreases. Total time to reach the first peak value of the temperature varies from 35 to 55 minutes.
[00147] FIG. 19 illustrates effect of various power supply on the product formation model.
[00148] Referring to FIG. 19, as the power supply increases there is an increase in the conversion radius, indicating the distance between the centre of electrode and the last layer of product formed has increased.
[00149] FIG. 20 illustrates effect of different final porosity on core temperature using model.
[00150] The initial (charge) and final (product) porosities can be kept constant i.e. 0.39 and 0.62 respectively. Referring to FIG. 20, as the final porosity is decreased the core temperature is decreased substantially. This is expected as for the same control volume the total mass has increased due to decrease in porosity. However, the supply of heat to the control volume is constant; therefore, this can delay the rise in temperature
[00151] FIG. 21A to 21G illustrate exemplary views of a Graphical User Interface (GUI) of the proposed system.
[00152] Referring to FIG. 21A to 21G, various exemplary views of a GUI developed for the proposed system and method are shown. FIG. 21 A shows a schematic of the furnace of the proposed system. FIG. 21B to 21D show interfaces for feeding the inputs required for operation variables associated with the proposed method. The operation variables can be furnace dimension variables, raw material compositions, process variables, and variables required for the computing.
[00153] In an exemplary embodiment, the furnace dimension variables can be electrode radius, furnace radius, refractory thickness, steel shell thickness, and length of the furnace. The raw material compositions can include weight of petroleum coke, weight of silica, and initial Silicon Carbide (%). The process variables can include process time, power energy, steel shell temperature, and room temperature. The variables required for computing can include number of nodes, and number of time steps.
[00154] In an embodiment, the various inputs can be given consequently by selecting the various options from the ‘Pre-Processing’ Menu. After giving all the required inputs, the program is ready to run. If the user wants to analyze his/her own data obtained from the experiments then one has to enter those data by selecting ‘Experimental Inputs’ from the ‘Pre-Processing’ Menu (see FIG. 21E). For running the model, the option ‘Run’ from the ‘Solver’ Menu has to be clicked. FIG. 21F shows an exemplary cross-sectional view of the furnace on the GUI, Further, the GUI can provide various options to users to view the results as shown in FIG. 21G,
[00155] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE PRESENT DISCLOSURE
[00156] The present disclosure provides fora speech enabled query system that efficiently handles and manages data in a manner such that only relevant data or processed data is transmitted to devices that are operatively coupled with it.
[00157] The present disclosure provides for a method for optimization of process variables which are used in the production of silicon carbide (SiC) in bulk.
[00158] The present disclosure provides for a method that facilitates optimization of the processing temperature to get the optimum yield of SiC.
[00159] The present disclosure provides for a method to determine the optimum processing time in order to maximize the SiC yield.
[00160] The present disclosure provides for an approach for optimizing raw materials composition in order to get the optimum yield of the product.
[00161] The present disclosure provides for a method to optimize the furnace returns in order to get the optimum yield.
[00162] The present disclosure provides for a system and method for developing one-dimensional model of the process considering conduction, convection and radiation heat transfer.
[00163] The present disclosure provides for a system and method for developing two-dimensional model of the process considering all the three modes of heat transfer.
[00164] The present disclosure provides for a method that facilitates generation of both the models through a display unit associated with a computing device.
,CLAIMS:1. A method for facilitating optimum production of the product (Silicon Carbide or SiC) said method comprising:
mixing a predefined amount of atleast a first raw material and atleast a second raw in a ball mill to provide a mixture, wherein atleast the first raw material is silica selected atleast in the range of 0 to 20% excess of stoichiometry quantity, wherein atleast the second raw material is graphite selected in atleast in the range of 0 to 20% excess of stoichiometry quantity, and wherein atleast a third raw material comprises of Silicon Carbon atleast in the range of 0 to 20%, wherein atleast the third raw material is configured as furnace return;
positioning atleast one electrode coupled to an electrode holder assembly, and one or more temperature measurement devices in a furnace, wherein atleast the one electrode is operatively coupled with electrical connections connected to a power supply unit, and wherein the one or more temperature measurement devices are configured to sense temperature inside the furnace;
charging the mixture, wherein the mixture covers the one or more temperature measurement devices;
increasing power supply from the power supply unit to the furnace gradually, in steps, till a desired maximum value of process temperature is reached and maintaining the desired maximum process temperature inside the furnace, wherein the process temperature is selected atleast in the range 1700oC to 2100oC;
as the heating continues, increasing the process temperature inside the furnace to facilitate reaction between atleast the first raw material and atleast the second raw material, wherein the reaction between atleast the first raw material and atleast the second raw material is evident from a bluish flame coming out indicative of formation of gaseous CO;
poking of the raw materials during the reaction to release gas pockets formed inside the furnace and adding fresh material when required;
turning off the power supply after a predefined process time and allowing the material inside the furnace to cool for atleast two days before removing the charge from the furnace, wherein the predefined process time is selected atleast in the range of 3 to 10 hours.
2. The method as claimed in claim 1, wherein prior to switching on the power supply to provide power supply to the furnace, the method comprises of turning on a plurality of exhaust fans operatively coupled to the furnace, a cooling system fluidically coupled to the electrode holder assembly and a carbon monoxide (CO) monitoring device.
3. The method as claimed in claim 1, wherein upon switching on the power supply unit for starting the method, the power supply unit provides no power initially by keeping a transformer variac coupled to the power supply unit at zero position.
4. A system for facilitating production of optimum product of Silicon Carbide (SiC), said system comprising:
an apparatus comprising a furnace configured with heating resistance having a longitudinal direction and atleast an electrode holder assembly, wherein atleast one electrode is coupled to the electrode holder assembly, wherein atleast the one electrode extends along the length in the centre of the furnace, wherein a plurality of refractory bricks form an inner side of the furnace, wherein the plurality of refractory bricks surround an inner heating zone and an insulating layer surround the inner heating zone, wherein an exhaust system coupled to the top portion of the furnace is configured to drive away gaseous by product formed during a reaction;
a cooling unit fluidically coupled to atleast the electrode holder assembly configured to avoid excess heating of the electrode holder assembly;
one or more temperature measurement devices operatively coupled to the furnace, wherein the one or more temperature measurement devices comprises a plurality of sensors configured to sense temperature inside the furnace;
a data acquisition unit (DAS) operatively coupled to the one or more temperature measurement devices and communicatively coupled to one or more computing devices through a network, wherein the DAS is configured to record and process the temperature sensed by the temperature measurement assembly;
a control unit operatively coupled to a power supply unit, wherein the control unit is configured to control and monitor power supply to the electrode holder assembly and temperature sensed by the one or more temperature measurement devices.
5. The system as claimed in claim 4, wherein atleast one wall of the furnace opens to slide to facilitate any or a combination of easy removal of heavy chunks of a product after completion of a reaction and collecting samples of the product.
6. The system as claimed in claim 4, wherein the control unit comprises of a plurality of temperature indicators and fuses, but not limited to the like, wherein the temperature indicators are configured as temperature switching unit to select a desired maximum temperature at the start of the run, wherein the temperature switching unit is configured to perform any or a combination of power supply cut off once a predefined threshold temperature is reached and power supply restore if the temperature falls below the predefined threshold temperature.
7. The system as claimed in claim 4, wherein the electrode holder assembly is operatively coupled with electrical connections connected to the power supply unit for successful heating of product inside the furnace, wherein the electrode holder assembly comprises of atleast two electrode holders and the cooling unit fluidically coupled to atleast two electrode holders, and wherein the cooling unit comprises of a water pump, a water return pipe, and water tanks.
8. The system as claimed in claim 4, wherein a carbon monoxide (CO) monitoring unit coupled to the furnace is configured to monitor formation of CO gas during the reaction.
9. The system as claimed in claim 4, wherein the plurality of sensors in the temperature measurement devices comprise of any or a combination of thermocouple, pyrometer, and electrode (core) temperature measurement assembly, but not limited to the like.
10. The system as claimed in claim 4, the system comprises of a plurality of safety accessories configured to protect working personnel wherein the safety accessories comprises of any or a combination of CO gas monitor equipment, high temperature protecting clothes, shoes, high temperature viewing helmet, goggles, gas and dust masks, and the like with room ventilation having good exhaust system comprising of a plurality of exhaust fans and fire safety equipment.
11. A device coupled to the furnace system for facilitating accurate measurement of electrode (core) temperature, said device comprising:
an electrode (core) temperature measurement assembly comprising of atleast two tubes inserted in a graphite block,
wherein, atleast a first tube is configured to receive radiation from the electrode and Ultra-Pure Nitrogen (UHP) is purged through the atleast first tube, wherein the UHP makes the path clear from dust and gases, and
wherein, atleast a second tube is configured as an exhaust tube, wherein purged nitrogen gas is discharged through atleast the second tube.
12. The device as claimed in claim 11, wherein a pyrometer coupled to the electrode is configured to measure the received radiation from the electrode.
13. The device as claimed in claim 11, wherein the material for the electrode, atleast the first tube and electrode holder is but not limited to a high-density graphite material.
14. A modelling system associated with DAS operatively coupled to the furnace system for facilitating modelling of variation and setting of operation variables to determine the optimum product output, said system comprising:
one or more processors operatively coupled to a memory, the memory storing instructions executable by the one or more processors to:
receive a first set of parameters pertaining to the operation variables associated with the process for production of Silicon Carbide;
receive a second set of parameters pertaining to chemical and physical parameters associated with the Silicon Carbide production process and the furnace; and
generate a first and a second set of details based on the received first and second set of parameters, wherein the first and the second set of details pertain to details about the product produced.
15. The modelling system as claimed in claim 14, wherein the operation variables pertain to any or a combination of process time, process temperature, power, raw material composition, use of furnace return, but not limited to the like.
16. The modelling system as claimed in claim 14, wherein the second set of parameters pertain to parameters associated with vaporization, condensation, decomposition and recrystallization during the formation of silicon carbide.
17. The modelling system as claimed in claim 14, wherein the first and the second set of details are configured for a plurality of modes of heat transfer, and wherein the plurality of modes of heat transfer pertain to conduction, convection and radiation.
18. A method for facilitating modelling of variation and setting of operation variables to determine the optimum product output, said method comprising:
receiving at one or more processors, a first set of parameters pertaining to the operation variables associated with the process for production of Silicon Carbide;
receiving at the one or more processors, a second set of parameters pertaining to chemical and physical parameters associated with the Silicon Carbide production process and the furnace; and
generating at the one or more processors, a first and a second set of details based on the received first and second set of parameters, wherein the first and the second set of details pertain to details about the product produced.