CLIAMS:WE CLAIM:
1. A system for designing a flame reactor for nanoparticle synthesis, the system comprising:
a processor; and
a memory coupled to the processor, wherein the processor is capable of executing a plurality of modules stored in the memory, and wherein the plurality of modules comprising:
a receiving module receives,
a) data associated with the nanoparticle synthesis, reactants and gases used in the nanoparticle synthesis,
b) process variables associated with the nanoparticles synthesis,
c) design variables associated with the flame reactor, and
d) a reference set of performance variables;
a simulator simulates the nanoparticles synthesis in the flame reactor by using the data, the process variables, and the design variables in order to predict a set of performance variables;
an analyzing module,
compares the set of performance variables predicted by the simulator with the reference set of performance variables to provide a comparison outcome;
determines one or more differential variables by applying a set of predefined rules on the comparison outcome, wherein the one or more differential variables affect a performance of the flame reactor and the nanoparticle synthesis; and
modifies the one or more differential variables using a numerical optimization technique to align the set of performance variables with the reference set of performance variables for designing the flame reactor.

2. The system of claim 1, wherein the flame reactor is an aerosol flame reactor, and wherein the aerosol flame reactor is one of a burner or a furnace.

3. The system of claim 1, wherein the data comprises reaction conditions and reaction mechanisms involved in the nanoparticle synthesis, and wherein the data further comprises physical and thermodynamic properties of the reactants, and the gases used in the nanoparticle synthesis, wherein the reactants and the gases comprise a precursor, a fuel, an oxidant and a carrier gas, and wherein the reference set of performance variables comprise of a rate of production of a product, a particle size distribution of the product, a specific surface area of the product, and a mean diameter of the product, wherein the product is a nanoparticle powder generated in the nanoparticle synthesis.

4. The system of claim 3, wherein the process variables comprise:
a) a type, a concentration,a pressure, a temperature, and a flow rate of the precursor,
b) a type, a flow rate, a pressure, and a temperature of the fuel used in the flame reactor,
c) a type, a flow rate, a pressure, and a temperature of the oxidant used in the flame reactor,
d) a type, a flow rate, a pressure, and a temperature of the carrier gas used in the flame reactor,
e) a flow rate, a pressure, and a temperature of a coolant used in a furnace, and
f) a temperature and a pressure of a furnace.

5. The system of claim 1, wherein the flame reactor is a burner, and wherein the design variables comprise a type of the burner, a number of tubes in the burner, an inner diameter of a tube of the burner, a thickness of each tube of the burner, a length of each tube of the burner, a spacing between different tubes of the burner, and a material of construction of the burner.

6. The system of claim 1, wherein the flame reactor is a furnace, and wherein the design variables comprise a diameter of a nozzle of the furnace, a length of an inlet pipe of the furnace, an inner diameter of the furnace, a thickness of a refractory lining of the furnace, a type of the refractory of the furnace, a length of the furnace, a length of a heating zone of the furnace, a length of premixing zone, a location of an entry of a coolant to the furnace and a length of a cooling zone.

7. The system of claim 5, wherein the burner comprises three or more concentric tubes.

8. The system of claim 1, wherein the set of performance variables and the reference set of performance variables comprise flame characteristics, particle characteristics, a particle production rate, a flammability limit, a flame stability, velocity profiles in the tubes of the flame reactor, and an internal strength of the tubes of the flame reactor.

9. The system of claim 8, wherein the flame characteristics comprises a flammability limit, a combustion volume, a flame shape, a flame height, a flame temperature distribution, and a flame stability, and wherein the particle characteristics comprise a particle shape, a mean particle diameter, a particle size distribution, a specific surface area, and a phase composition.

10. The system of claim 1, wherein the simulator is a coupled Computational Fluid Dynamics-Population Balance Model simulator.

11. The system of claim 1, comprises a performance analysis module for evaluating a performance of the flame reactor by using one or more performance variables of the set of performance variables.

12. The system of claim 1, wherein the differential variables comprise at least one of one or more process variables and one or more design variables.

13. A method for designing a flame reactor for nanoparticle synthesis, the method comprising:
receiving,
a) data associated with the nanoparticle synthesis, reactants and gases used in the nanoparticle synthesis,
b) process variables associated with the nanoparticles synthesis,
c) design variables associated with the flame reactor, and
d) a reference set of performance variables;
simulating the nanoparticles synthesis in the flame reactor by using the data, the process variables, and the design variables in order to predict a set of performance variables;
comparing the set of performance variables predicted by the simulation with the reference set of performance variables to provide a comparison outcome;
determining one or more differential variables by applying a set of predefined rules on the comparison outcome, wherein the one or more differential variables affect a performance of the flame reactor and the nanoparticle synthesis; and
modifying the one or more differential variables using a numerical optimization technique to align the set of performance variables with the reference set of performance variables for designing the flame reactor, wherein at least one of the receiving, the simulating, the comparing, the determining, and the modifying are performed by a processor using programmed instructions stored in a memory.

14. The method of claim 13, wherein the data comprises reaction conditions and reaction mechanisms involved in the nanoparticle synthesis, and wherein the data further comprises physical and thermodynamic properties of the reactants, and the gasesused in the nanoparticle synthesis, wherein the reactants and the gases comprise a precursor, a fuel, an oxidant and a carrier gas, and wherein the reference set of performance variables comprise of a rate of production of a product, a particle size distribution of the product, a specific surface area of the product, and a mean diameter of the product, wherein the product is a nanoparticle powder generated in the nanoparticle synthesis.

15. The method of claim 14, wherein the process variables comprise:
a) a type, a concentration,a pressure, a temperature, and a flow rate of the precursor used in the flame reactor,
b) a flow rate, a pressure, and a temperature of the fuel used in the flame reactor,
c) a flow rate, a pressure, and a temperature of the oxidant used in the flame reactor,
d) a flow rate, a pressure, and a temperature of the carrier gas used in the flame reactor,
e) a flow rate, a pressure, and a temperature of a coolant used in a furnace, and
f) a temperature and a pressure of a furnace.

16. The method of claim 13, wherein the flame reactor is an aerosol flame reactor and the flame reactor is a burner, and wherein the design variables comprise a type of the burner, a number of tubes in the burner, an inner diameter of a tube of the burner, a thickness of each tube of the burner, a length of each tube of the burner, a spacing between different tubes, and a material of construction of the burner.

17. The method of claim 13, wherein the flame reactor is an aerosol flame reactor and the flame reactor is a furnace, and wherein the design variables comprise a diameter of a nozzle of the furnace, a length of an inlet pipe of the furnace, an inner diameter of the furnace, a thickness of a refractory lining of the furnace, a type of the refractory of the furnace, a length of the furnace, a length of a heating zone of the furnace, a length of premixing zone, a location of an entry of a coolant to the furnace and a length of a cooling zone.

18. The method of claim 13, wherein the set of performance variables and the reference set of performance variables comprise flame characteristics, particle characteristics, a particle production rate, a flammability limit, velocity profiles in the tubes of the flame reactor, and an internal strength of the tubes of the flame reactor and the method further comprises evaluating the performance of the flame reactor by using one or more performance variables of the set of performance variables.

19. The method of claim 13, wherein the simulation is performed by a coupled Computational Fluid Dynamics-Population Balance Model.

20. The method of claim 13, wherein the differential variables comprise at least one of one or more process variables and one or more design variables.

21. A non-transitory computer readable medium embodying a program executable in a computing device for designing a flame reactor for nanoparticle synthesis, the program comprising:
a program code for receiving,
a) data associated with the nanoparticle synthesis, reactants and gases used in the nanoparticle synthesis,
b) process variables associated with the nanoparticles synthesis,
c) design variables associated with the flame reactor, and
d) a reference set of performance variables;
a program code for simulating the nanoparticles synthesis in the flame reactor by using the data, the process variables, and the design variables in order to predict a set of performance variables and wherein the simulation is performed by a coupled Computational Fluid Dynamics-Population Balance Model;
a program code for comparing the set of performance variables predicted by the simulation with the reference set of performance variables to provide a comparison outcome;
a program code for determining one or more differential variables by applying a set of predefined rules on the comparison outcome, wherein the one or more differential variables affect a performance of the flame reactor and the nanoparticle synthesis; and
a program code for modifying the one or more differential variables using a numerical optimization technique to align the set of performance variables with the reference set of performance variables for designing the flame reactor. ,TagSPECI:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
DESIGNING A FLAME REACTOR

APPLICANT:
Tata Consultancy Services Limited
A company incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.
CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application does not claim priority from any patent application.
TECHNICAL FIELD
[002] The present subject matter described herein, in general, relates to nanoparticle synthesis using an aerosol flame technology, and more particularly to optimizing a design of a flame reactor for nanoparticles synthesis.
BACKGROUND
[003] Flame aerosol synthesis of nanoparticles in a burner is a complex process which involves chemical reactions, transport phenomena, and aerosol dynamics. Typically the chemical reactions take place in a flame region where high temperatures prevail, wherein the flame region may be near a mouth of the burner. The chemical reactions result in molecular clusters which grow further and result in primary particles. The primary particles grow further through coagulation and coalescence or sintering to form aggregates. The formation and growth of the particles occurs in milliseconds and the particle residence time in the flame region is very short. Therefore, collection of representative samples for particle characterization is a challenging task. Hence, process design and scale up, process control and optimization of the nanoparticle synthesis by aerosol flame technology becomes difficult.
[004] Although the aerosol flame technology has a great potential for synthesis of spectrum of nanoparticles, the aerosol flame technology is commercially successful for the production of fine particles of a few materials such as carbon black, titanium dioxide, silicon dioxide and to some extent zinc oxide. Further, the nanoparticles produced have a broad particle size distribution. The major challenges faced are scaling up of the process and the design of the aerosol flame reactor to produce nanoparticles on the industrial scale with consistently narrow size distribution. The existing knowledge about the nanoparticle synthesis by the aerosol flame technology has evolved mainly through trial and error rather than through aerosol reaction engineering principles. At present, the design and the scale up of an aerosol flame reactor is merely an art which comes through experience rather than scientific principles.
[005] Few scientific communities have attempted to propose procedures and correlations for the design and the scale up of the aerosol flame reactor at a lab scale and pilot scale. A rational design theory for synthesis of Silicon Carbide (SiC) particles was disclosed in the prior art. However, the rational design theory speaks more about a conceptual design of the aerosol flame reactor and particularly lacks in a detailed design aspect. Detailed and generic design procedures are missing in the prior art. Hence, the design and the scale up procedures for the aerosol flame reactor for the nanoparticle synthesis are not completely understood.
[006] Further, the design and the scale up procedures disclosed in the prior art have proposed scaling laws for the aerosol flame reactor at the lab scale, however applicability of the scaling laws for the aerosol flame reactor at a pilot scale and at an industrial scale needs to be verified. Further, the applicability of the scaling laws needs to be verified by a designer in a real time execution. The design and the scale up procedures disclosed in the prior art particularly focus on an effect of process parameters on particle characteristics. Further, the particle characteristics are studied at different production rates for the aerosol flame reactor at the lab scale. Although, the effect of the process parameters on the particle characteristics is understood, very little emphasis is presented on a design method of the flame reactor. Hence the design and the scale up procedures for the nanoparticle synthesis by the aerosol flame technology are not sufficiently guided.
SUMMARY
[007] This summary is provided to introduce aspects related to systems and methods for optimizing design of a flame reactor and the aspects are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[008] In one implementation, a system for designing a flame reactor for nanoparticle synthesis is disclosed. The system comprises a processor and a memory coupled to the processor. The processor is capable of executing a plurality of modules stored in the memory. The plurality of modules comprising a receiving module, a simulator, an analyzing module and a performance analysis module. The flame reactor is an aerosol flame reactor, and wherein the aerosol flame reactor is one of a burner or a furnace. The receiving module receives, a) data associated with the nanoparticle synthesis, reactants and gases used in the nanoparticle synthesis, b) process variables associated with the nanoparticle synthesis, c) design variables associated with the flame reactor, and d) a reference set of performance variables.
[009] The simulator simulates the nanoparticles synthesis in the flame reactor by using the data, the process variables, and the design variables in order to predict a set of performance variables. The simulator is a coupled Computational Fluid Dynamics-Population Balance Model (CFD-PBM) simulator. The analyzing module compares the set of performance variables predicted by the simulator with the reference set of performance variables to provide a comparison outcome. The analyzing module determines one or more differential variables by applying a set of predefined rules on the comparison outcome. The one or more differential variables affect a performance of the flame reactor and the nanoparticle synthesis. The analyzing module further modifies the one or more differential variables using a numerical optimization technique. The analyzing module modifies the one or more differential variables using a numerical optimization technique to align the set of performance variables with the reference set of performance variables for designing the flame reactor. The performance analysis module evaluates the performance of the flame reactor by using one or more performance variables of the set of performance variables.
[010] In one implementation, a method for designing a flame reactor for nanoparticle synthesis is disclosed. The method comprises receiving, a) data associated with the nanoparticle synthesis, reactants and gases used in the nanoparticle synthesis, b) process variables associated with the nanoparticles synthesis, c) design variables associated with the flame reactor, and d) a reference set of performance variables. The method further comprises simulating the nanoparticles synthesis in the flame reactor by using the data, the process variables, and the design variables in order to predict a set of performance variables. The method further comprises comparing the set of performance variables predicted by the simulation with the reference set of performance variables to provide a comparison outcome. The method further comprises determining one or more differential variables by applying a set of predefined rules on the comparison outcome, wherein the one or more differential variables affect a performance of the flame reactor and the nanoparticle synthesis. The method further comprises modifying the one or more differential variables using a numerical optimization technique to align the set of performance variables with the reference set of performance variables for designing the flame reactor. The method further comprises evaluating a performance of the flame reactor by using one or more performance variables of the set of performance variables. The receiving, the simulating, the comparing, the determining, the modifying and the evaluating are performed by a processor.
[011] In one implementation, a non-transitory computer readable medium embodying a program executable in a computing device for designing a flame reactor for nanoparticle synthesis is disclosed. The program comprises a program code for receiving, a) data associated with the nanoparticle synthesis, reactants and gases used in the nanoparticle synthesis, b) process variables associated with the nanoparticles synthesis, c) design variables associated with the flame reactor, and d) a reference set of performance variables. The program further comprises a program code for simulating the nanoparticles synthesis in the flame reactor by using the data, the process variables, and the design variables in order to predict a set of performance variables. The simulation is performed by a coupled Computational Fluid Dynamics-Population Balance Model. The program further comprises a program code for comparing the set of performance variables predicted by the simulation with the reference set of performance variables to provide a comparison outcome. The program further comprises a program code for determining one or more differential variables by applying a set of predefined rules on the comparison outcome. The one or more differential variables affect a performance of the flame reactor and the nanoparticle synthesis. The program further comprises a program code for modifying the one or more differential variables using a numerical optimization technique to align the set of performance variables with the reference set of performance variables for designing the flame reactor.
BRIEF DESCRIPTION OF THE DRAWINGS
[012] The detailed description is described with reference to the accompanying figures. 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 drawings to refer like features and components.
[013] Figure 1 illustrates a network implementation of a system(s) for designing a flame reactor, in accordance with an embodiment of the present subject matter.
[014] Figure 2 illustrates the system for designing a flame reactor, in accordance with an embodiment of the present subject matter.
[015] Figure (3)-a illustrates a schematic of an Aerosol Flame Reactor (AFR) set up with a burner , in accordance with an embodiment of the present subject matter.
[016] Figure (3)-b illustrates the schematic of the Aerosol Flame Reactor (AFR) set up with a furnace, in accordance with an embodiment of the present subject matter.
[017] Figure 4 illustrates a design methodology for the Aerosol Flame Reactor (AFR) set up, in accordance with an embodiment of the present subject matter.
[018] Figure 5 illustrates a typical concentric tube diffusion flame burner (a) side view and (b) top view for the Aerosol Flame Reactor (AFR) set up, in accordance with an embodiment of the present subject matter.
[019] Figure 6 illustrates a design strategy for designing the burner by evaluation of performance variables, in accordance with an embodiment of the present subject matter.
[020] Figure 7 illustrates technical implementation/working of an analyzing module 216, in accordance with an embodiment of the present subject matter.
[021] Figure 8 illustrates, flame temperature contours at various oxidant flow rates for three burners-Burner-1, Burner-2 and Burner-3 for synthesis of Titania nanoparticles, in accordance with an exemplary embodiment of the present subject matter.
[022] Figure 9 illustrates flame lengths at different oxidant flow rates in the Burner-1, the Burner-2 and the Burner-3 for synthesis of titanium dioxide nanoparticles, in accordance with an exemplary embodiment of the present subject matter.
[023] Figure 10 illustrates concentration profiles of Titanium isopropoxide (TTIP) in the Burner-1, the Burner-2 and the Burner-3 for synthesis of titanium dioxide nanoparticles, in accordance with an exemplary embodiment of the present subject matter.
[024] Figure 11 illustrates axial flame temperature profiles for synthesis of titanium dioxide nanoparticles at oxidant flow rate of 14332cc/min in the Burner-1 and the Burner-2, in accordance with an exemplary embodiment of the present subject matter.
[025] Figure 12 illustrates average particle size at various oxidant velocities for the Burner-1 and the Burner-2 for synthesis of titanium dioxide nanoparticles, in accordance with an exemplary embodiment of the present subject matter.
[026] Figure 13 illustrates predicted velocity profiles of a precursor and gases at reactor operating conditions as mentioned in Table 1 through the Burner-1, the Burner-2 and the Burner-3 at various oxidant velocities, in accordance with an exemplary embodiment of the present subject matter.
[027] Figure 14 illustrates a method for designing a flame reactor, in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION
[028] The present disclosure provides systems and methods designing a flame reactor for nanoparticle synthesis and further optimizing therein. The flame reactor may either be a burner or a furnace. The systems and methods may also guide a designer for setting up a flame reactor setup for nanoparticle synthesis through aerosol flame technology. Designing the flame reactor and further optimizing the design of the flame reactor is an iterative process. Initially data associated with the nanoparticle synthesis, reactants, and gases may be received. Subsequently, process variables, design variables, and reference or targeted performance variables may be received. Subsequently, the nanoparticle synthesis inside the flame reactor may be simulated by using the data, the design variables, and the process variables. The simulation of the nanoparticle synthesis for the flame reactor may predict performance variables, such as flame characteristics and particle characteristics. Further, the performance variables may be compared with the reference performance variables or targeted performance variables to find out differential variables affecting the nanoparticle synthesis. The differential variables may be further modified to align with the reference (targeted) performance variables.
[029] Based on the difference between the performance variables and the reference performance variables, the design variables or the process variables may be modified in order to align the performance variables with the reference performance variables. Further, the flame characteristics and the particle characteristics may be predicted with modified design variables or/and modified process variables. The iterative process of designing and optimizing the design of the flame reactor may continue until required flame characteristics and required particle characteristics are achieved for a predetermined production rate in the nanoparticle synthesis. The iterative process of designing and optimizing the design may be carried out using the modified design variables and/or the modified process variables. Subsequently, based upon the modified design variables or/and the modified process variables, the flame reactor setup comprising a gas and precursor delivery unit and a powder collection apparatus may be designed.
[030] While aspects of the system and method for designing the flame reactor and optimizing thereafter may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following system.
[031] Referring now to Figure 1, a network implementation 100 of a system 102 for designing a flame reactor and optimizing therein is illustrated, in accordance with an embodiment of the present subject matter. In one embodiment, the system 102 receives data associated with nanoparticle synthesis, reactants, and gases used therein. The system may further receive process variables associated with the nanoparticle synthesis, design variables of the flame reactor, and a reference set of performance variables. The system 102 may simulate the nanoparticle synthesis in the flame reactor by using the data, the design variables and the process variables and predicts a set of performance variables. The system 102 may compare the set of performance variables with the reference set of performance variables to provide a comparison outcome. Further, the system may determine one or more differential variables by applying a set of predefined rules on the comparison outcome. The one or more differential variables affect the performance of the flame reactor and the nanoparticle synthesis. The system 102 further may modify the one or more differential variables using a numerical optimization technique to align the set of performance variables with the reference set of performance variables, thereby designing the flame reactor and further may be optimizing the design of the flame reactor.
[032] Although the present subject matter is explained considering that the system 102 is implemented on a server, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, and the like. The system 102 may also be implemented as a cloud system. It will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2…104-N, collectively referred to as user 104 hereinafter, or applications residing on the user devices 104. Examples of the user devices 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices 104 are communicatively coupled to the system 102 through a network 106.
[033] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, remote direct memory access (RDMA),and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[034] Referring now to Figure 2, the system 102 is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 206.
[035] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with a user directly or through the client devices 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[036] The memory 206 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
[037] The memory 206 may include modules 208 and system information 210. The modules 208 include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules 208 may include a receiving module 212, a simulator 214, an analyzing module 216, a performance analysis module 218 and other modules 220. The other modules 220 may include programs or coded instructions that supplement applications and functions of the system 102.
[038] The system information 210, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules 208. The system information 210 may also include a system database 222, and other system information 224. The other system information 224 may include data generated as a result of the execution of one or more modules in the other modules 220.
[039] In one implementation, at first, a user may use the client device 104 to access the system 102 via the I/O interface 204. The user may register using the I/O interface 204 in order to use the system 102. The user may be a human being or a computer implemented system. The working of the system 102 may be explained in detail referring to Figures 4 to 13 as provided below. The system 102 may be used for optimizing the design of the flame reactor.
[040] According to an embodiment of the present disclosure, referring to figure 2 and figure 4, detail working of the system 102 is explained. Figure 4 illustrates a design methodology for the Aerosol Flame Reactor (AFR) set up, in accordance with an embodiment of the present subject matter. The receiving module 212 of the system 102, in step 402, may receive data associated with the nanoparticle synthesis, the reactants and the gases used therein. The data may be received from a user, the system database 222, from one or more sensors, or from any other storage known to a person skilled in the art. The data may comprise operating conditions, reaction conditions and reaction mechanisms involved in the nanoparticle synthesis. The data may further comprise physical and thermodynamic properties of the reactants, and the gases used in the nanoparticle synthesis. The reactants and gases may comprise a precursor, a fuel, an oxidant and a carrier gas.
[041] The receiving module, in step 402, may further receive process variables associated with the nanoparticle synthesis. The process variable may include a type of the precursor, a concentration of the precursor, a pressure of the precursor, a temperature of the precursor, and a flow rate of the precursor, a type of the fuel, a concentration of the fuel, a pressure of the fuel, a temperature of the fuel, and a flow rate of the fuel, a type of the oxidant, a concentration of the oxidant, a pressure of the oxidant, a temperature of the oxidant, and a flow rate of the oxidant, and a type of the carrier gas, a concentration of the carrier gas, a pressure of the carrier gas, a temperature of the carrier gas, and a flow rate of the carrier gas, a flow rate of a coolant, a pressure of the coolant, and a temperature of the coolant, a pressure and a temperature of the furnace.
[042] Further, operating conditions of the flame reactor may comprise one or more combination of the process variables. The process variables comprising but not limited to, the flow rate of the reactants(s), the flow rate of the carrier gas(es), the flow rate of oxidant, and the flow rate of the fuel(s); the pressure of the reactants(s), the pressure of the carrier gas(es), the pressure of the oxidant, and the pressure of the fuel(s); the temperature of reactant(s), the temperature of the carrier gas(es), and the temperature of the fuel(s), the temperature of the oxidant; chemical compositions of the reactant(s), chemical compositions of the carrier gas(es), chemical compositions of the oxidant, and chemical compositions of the fuel(s); temperature of air and pressure of the air; operating pressure of the vacuum pump, and the like.
[043] The receiving module, in step 402, may further receive the design variables associated with the flame reactor. The flame reactor may be an aerosol flame reactor (AFR), and wherein the aerosol flame reactor (AFR) may be one of a burner or a furnace. In case the flame reactor is a burner, the design variables associated with the burner may comprise a type of the burner, a number of tubes in the burner, an inner diameter of a tube of the burner, a thickness of the tube of the burner, a length of each tube of the burner, a spacing between different tubes of the burner, and a material of construction of the burner.
[044] However, in case the flame reactor is a furnace, the design variables associated with the furnace may comprise a diameter of a nozzle of the furnace, a length of an inlet pipe of the furnace, an inner diameter of the furnace, a thickness of a refractory lining of the furnace, a type of the refractory of the furnace, a length of the furnace, a length of a heating zone of the furnace, a length of premixing zone, a location of an entry of a coolant to the furnace and a length of a cooling zone.
[045] After receiving the design variables, the receiving module, in step 402, may receive a reference set of performance variables. The reference set of performance variables may comprise flame characteristics, particle characteristics, a particle production rate, a flammability limit, velocity profiles in tubes of the flame reactor, and an internal strength of tubes of the flame reactor at the nanoparticle synthesis. The product is a nanoparticle powder generated in the nanoparticle synthesis. The nanoparticle powder may be of type selected from the group consisting of organic nanoparticles and inorganic nanoparticles. The organic nanoparticles may comprise carbon, liposome, liquid crystals and the like. Inorganic nanoparticle may include metal nanoparticles and non-metal nanoparticles. Metal nanoparticles may comprise gold, aluminum, copper, Ag, Zn, Al, Titanium and the like and their compounds. Non-metal nanoparticles may comprise compounds of Silicon, Phosphorus. Herein the nanoparticles are metal oxide nanoparticles comprising titanium dioxide, silicon dioxide, zinc oxide, aluminum oxide etc.
[046] It must be understood that the process variables, the design variables and the reference set of performance variables may be received from any one of or a combination of a user, the system database 222, one or more sensors and any other storage known to a person skilled in the art.
[047] Post receiving the data, the process variables, and the design variables, and the reference set of performance variable, the system 102 may simulate the nanoparticle synthesis in the flame reactor. Specifically, the system 102 may employ the simulator 214 to simulate the nanoparticle synthesis in the flame reactor using the data which may include the process variables, and the design variables. The simulator 214 may predict a set of performance variables post simulation. In one example, the simulator 214 may be a coupled Computational Fluid Dynamics (CFD)–Population Balance Model (PBM) simulator. The set of performance variables may comprise flame characteristics, particle characteristics, a particle production rate, a flammability limit, velocity profiles in tubes of the flame reactor, and an internal strength of tubes of the flame reactor against the working pressure at the nanoparticle synthesis. The set of performance variables comprise performance variables as shown in Table 1. The coupled CFD-PBM may have two models, namely, a flame dynamics model and a population balance model. The flame characteristics, the velocity profile in the tubes of the burner may be predicted by the flame dynamics model and the particle characteristics may be predicted by the population balance model. The flame characteristics may comprise a flammability limit, a combustion volume, a flame shape, a flame height, a flame temperature distribution and flame stability. The particle characteristics may comprise a particle shape, a mean particle diameter, a particle size distribution, a specific surface area, and a phase composition. The particle production rate may be calculated based on results from the CFD model and the PBM. The internal strength of the tubes may be calculated based on CFD calculations of the gas flow profile inside the burner tubes, similar to the results shown in Figure 12. The flammability limits for the fuel are provided as input to the system but the results of the CFD model for concentrations of the fuel and the oxidant are checked against the input values in order to check whether predicted results are within the flammability limits. If not, it will be understood that the flame may get extinguished which will be evident from the flame temperature profiles.
[048] According to an embodiment of the present disclosure, an aspect of designing the flame reactor and optimizing the design of the flame reactor is use of the coupled CFD-PBM simulator. The coupled CFD-PBM simulator may be used for simulation of a continuous process of the nanoparticle synthesis. In the present disclosure, a mathematical model may be used for the nanoparticle synthesis in the flame reactor. The mathematical model may consist of two models. The two models may be a flame dynamics model and a Population Balance Model (PBM). In the flame dynamics model, the flame characteristics may be determined by solving flame dynamics equations along with chemical reaction kinetics of the flame dynamics. The flame dynamics model may predict the flame characteristics which may include flame temperature, gas velocity and species concentration fields in the flame reactor. The flame temperature, the velocity and the species concentration fields may be used by the PBM to predict the particle characteristics. In one example, the PBM may be a monodisperse PBM which is robust and computationally less expensive as compared to solving the complete Population Balance Equation (PBE). The PBM could also be a detailed one- dimensional PBM or two-dimensional PBM predicting the complete particle size distribution as well as the particle surface area distribution.
[049] According to an exemplary embodiment of the present disclosure, the coupled CFD-PBM model along with precursor reaction kinetics equations may be implemented in commercial CFD software CFX 11.0 Execution of the coupled CFD-PBM simulator may comprise two steps. In a first step, the flame dynamics equations may be implemented in the CFX11.0 to predict the flame characteristics. The flame characteristics may be obtained from the flame dynamics model. The flame dynamics model may generate a plurality of output variables, such as the flame temperature, the velocity, and the species concentration. These output variables may be used as input variables for the PBM to obtain a spatial distribution of the particle characteristics. The particle characteristics such as a particle number concentration (N), an aggregate surface area (a) and an aggregate volume (v) of the nanoparticle directly depend on the flame characteristics.
[050] Specifically, in a second step, the PBM equations may be solved by using the input variables (provided by the flame dynamics model) in the PBM equations. In one example, in order to compute the particle characteristics using the PBM, the source terms in PBE may be written as subroutines. The subroutines may be written in a FORTRAN language. Further the subroutines may be called by the CFX11.0 solver through a source code interface during execution.
[051] According to an embodiment of the present disclosure the coupled CFD-PBM model is described. By way of an example, the flame dynamics model may be used to design, evaluate, and optimize the design of the burner or the furnace. The coupled CFD-PBM may also be used for troubleshooting the operation of the burner. The flame dynamics model may be used to understand and accurately predict heat released during the operation of the burner and transfer of the heat to the precursor. The coupled CFD-PBM may also be used to understand the effect of the flame characteristics on the growth of the particles and the properties of the particles. The particles may be nanoparticles. The particles may be micro-particles.
[052] The flame dynamics model consists of a continuity equation, a Navier-Stokes equations for momentum, a k-ε turbulence equations, a species transport equations, an enthalpy transport equation and a radiation transport equation. The momentum equations are shown below.
…… (Equation 1)
where is a gas density (kg m-3), is a velocity (m s-1), is a turbulent momentum flux tensor (kg s-2 m-1), is a stress tensor (kg s-2 m-1) and is a gravitational acceleration (m s-2).
[053] The Turbulence equations (Launder and Spalding 1974) are provided below. The transport equations for a turbulent kinetic energy, k (m-2 s-2) and a turbulent kinetic energy dissipation rate, ε (m-2 s-3) are given below:
……….(Equation 2)
….(Equation 3)
[054] where Cε1, Cε2, σk and σε are numerical constants and following numerical values are assigned to them (Launder and Spalding 1974): Cε1= 1.44, Cε2 = 1.92, Cμ = 0.09, σε = 1.3, σk = 1.0. Pk is turbulence production due to viscous and buoyancy forces (kg s-3). Pk can be computed using the following equation:
…..(Equation 4)
[055] The species transport equations (ANSYS CFX) are provided below. For a reacting component I ,the continuity equation can be written as follows:
…………..(Equation 5)
where is a net rate of production of species i by a chemical reaction, is a rate of creation by addition from a dispersed phase, is a mass fraction of the component i and is a diffusion flux of species i wherein the diffusion flux arises due to concentration gradients.
[056] The energy equations (ANSYS CFX) are provided below. The energy equation in terms of a specific enthalpy h (J kg-1) is given by:
……..(Equation 6)
Where is thermal conductivity of a gas present in the flame reactor (J m-1 s-1 K-1) and is a source term representing a rate of heat liberated due to chemical reaction and heat absorption due to radiation. The product viscous dissipation, , is always negative. A P-1 radiation model (Siegel and Howell 1992) may be incorporated in enthalpy balance to account for irradiation and radiant energy absorption.
[057] An eddy-dissipation model (Magnussen and Hjertager 1976) may be used to compute a rate of fuel combustion. The eddy-dissipation model assumes that the chemical reaction is faster as compared to the transport processes. The eddy-dissipation model consists of two rate expressions, as given below (ANSYS CFX). Equation 7 represents a reaction rate R1 (kmol m-3 s-1) accounting for mixing of the reactants in turbulent eddies.
……….(Equation 7)
[058] where (kmol m-3) is a concentration of a reactant I, is a stoichiometric coefficient of reactant (component) I and AEDM is a numerical constant. Equation 8 represents a reaction rate R2 (kmol m-3 s-1) accounting for mixing of hot product gases with cold reactant gases when a heat transport to unreacted gases is a limiting factor.
……….(Equation 8)
[059] Wherein P loops over all product components in the reaction which means that both numerator and denominator in the bracketed quantity is summation for all the product components present in the reaction. In Equation 8, (kg kmol-1) is a molar mass of the component I and BEDM is a numerical constant. The rate of combustion is determined by smaller of the two rates R1 and R2.
[060] According to an embodiment of the present disclosure, the monodisperse population balance model is described. By way of an example, the monodisperse population balance model (PBM) for a continuous process (Buddhiraju and Runkana, 2012) is used in the present work to predict the particle characteristics. It may be assumed that the particle population can be represented in terms of mean properties of the particles (Johannessen et al., 2000). The monodisperse population balance model is coupled with the flame dynamics model. The monodisperse PBM may consists of equations for a number concentration of particles ‘N’, a mean particle surface area ‘a’, and the solid volume ‘v’ of a particle. The monodisperse model proposed by Kruis et al. (1993) for batch processes is modified by Buddhiraju and Runkana (2012) for the continuous process. In the monodisperse PBM for the continuous process, three quantities mentioned above undergo diffusion and convection in addition to being generated and depleted. The rate of change of particle number concentration (N) is given by the following Equation 9.
……….(Equation 9)
[061] where η (m2 s-1) is a diffusion coefficient,  (m3 s-1) is a coagulation rate coefficient and I (m-3 s-1) is a rate of nucleation, given by (Ji et al.2007):
……….(Equation 10)
Where (m-3 s-1) is a nucleation rate constant, S is a degree of super-saturation and an exponent is taken as 1. Last term on RHS of Equation 9 represents a reduction of particle number concentration N by coagulation.
[062] The coagulation rate coefficient β is calculated using a Fuchs equation (Fuchs, 1964; Seinfeld, 1986).
……….(Equation 11)
Where D (m2 s-1) is a particle diffusion coefficient, c (m s-1) is a particle velocity, (m) is particle collision radius and is transition parameter (m) (Kruis et al., 1993; Seinfeld, 1986).
[063] The particle diffusion coefficient D is calculated as follows (Johannessen, 1999)
……….(Equation 12)
Where Kn is a Knudsen number (λ / rc), λ (m) is a mean free path of the gas mixture present in the flame reactor and μ (Pa s) is a viscosity of the gas mixture.
[064] Total surface area density of particles A (m2 m-3) decreases by coalescence but remains unaffected by coagulation. A rate of change of total surface area of the particles (A) is given by the following equation:
……….(Equation 13)
Where ap is a monomer surface area (m2), is a characteristic coalescence time (s) and Amin is a minimal total surface area density, given by:
……….(Equation 14)
Where V is a total particle volume density (m3 m-3)
[065] The characteristic coalescence time depends on primary particle size, flame temperature, material being synthesized, and a sintering mechanism (Kruis et al. 1993). Growth of the Titania particles is predominantly by a coagulation driven process (Johannessen, 1999). Growth process takes place by two phenomena, namely, surface growth and coalescence-coagulation. In particle systems where a particle growth is primarily due to a coalescence of the particles and a coagulation of the particles, characteristic time for sintering ( ) and the coagulation of the particles determine a particle morphology (Kruis et al., 1993).
[066] Several mechanisms are available to control the coalescence of the particles, namely, a surface diffusion, a grain boundary diffusion, a solid state diffusion, and a viscous flow. There could be more than one mechanism prevalent or dominant during the growth of a particular material. In sintering of Titania, the solid state diffusion is a dominant mechanism (Xiong et al., 1993). Lehtinen et al. (1998) proposed empirical expressions for calculation of coalescence rates for different coalescence mechanisms. But, determining control of the coalescence rate is tedious process as determination of the control of the coalescence rate requires estimation of several physical parameters such as a surface tension, various activation energies and grain boundary width (Johannessen, 1999).
[067] Due to uncertainties involved in estimation of physical parameters mentioned above, most commonly followed approach for determining coalescence parameters is to use experimental data with a model and adjust parameters to match data (Johannessen 1999). Hence, in the present disclosure, expression for coalescence time of two separate, contacting spherical particles is considered and values of the parameters are modified as per a coalescence mechanism (Johannessen et al., 2000):
……….(Equation 15)
where EA is an activation energy (J mol-1), dp is an initial diameter of two primary contacting spherical particles in an aggregate (m), k0 is pre-exponential term (m-4 s), T0 is temperature (C) and R is universal gas constant (J K-1 kmol-1). The exponent of dp, ‘m’ varies from 1 to 4 depending upon the coalescence mechanism. The value of m is taken as 4 for Titania (solid state diffusion).
[068] The total particle volume density V (m3 m-3) increases by nucleation and chemical reaction.
……….(Equation 16)
Where vp is a monomer volume (m3) and is a rate of increase of total particle mass per unit volume of the particle system (kg m-3s-1) given by the following equation:
……….(Equation 17)
Where G is a total growth rate of a particle (m s-1)
[069] The total growth rate of the particle G under combined control of chemical reaction and mass transfer is calculated as:
……….(Equation 18)
Where is a growth rate of a particle under control of mass transfer (m s-1) and GC is growth rate of a particle under control of chemical reaction kinetics (m s-1).
[070] Linear growth rate of the particle under the control of mass transfer (m s-1) is given by (Ji et al. 2007):
(S-1) ……….(Equation 19)
where Vmol is a molar volume of the titania molecule produced (m3 mol-1), DA–O2 is a diffusion coefficient of A (titania) through O2 (m2s-1), S is a degree of super-saturation, dn is a number-averaged particle size (m), is a mole fraction of A at saturation temperature T.
[071] The growth rate of the particle under the control of chemical reaction kinetics, GC is given by the following expression (Ji et al. 2007):
……….(Equation 20)
Where kgc is a particle growth rate constant (m s-1) The exponent Ng is assumed to be 1. When , GC is set equal to zero (Ji et al. 2007). The degree of super-saturation S in Equation 20 is calculated using partial pressure and vapor pressure data of Titania.
[072] The vapor pressure of Titania at various temperatures is calculated using following expression.
……….(Equation 21)
Where is a vapor pressure of Titania at temperature T (C). Equation 21 is obtained by fitting the vapor pressure data of Titania at various temperatures available in literature. The diffusion coefficients of Titania through oxygen are fitted in the standard form to match with experimental data.
………. (Equation 22)
Where E is an activation energy, and A is a pre-exponential factor
[073] Still referring to figure 2 and figure 4, after predicting the set of performance variables, the system 102, in step 408, may employ the analyzing module 216 to compare the set of performance variables with the reference set of performance variables to provide a comparison outcome. As may be understood, the reference set of performance variables are desired values of the performance variables that may be apt to obtain desired characteristics of the nanoparticles. The analyzing module, while comparing the set of performance variables with the reference set of performance variables, may determine a difference/comparison outcome between these performance variables. In other words, the comparison outcome may provide an indication of an extent of deviation of the performance of the flame reactor from a desired performance of the flame reactor. The deviation of the performance of the flame reactor may indicate deviation/lacunae in a design of the flame reactor.
[074] In one embodiment, after providing the comparison outcome, the system may employ the analyzing module 216, in step 410, to determine one or more differential variables based upon the comparison outcome. The differential variables may be indicative of deviation/lacunae in the design of the flame reactor. The one or more differential variables may affect the performance of the flame reactor and the nanoparticle synthesis as the differential variables directly affect the design of the flame reactor. The one or more differential variables may comprise one or more process variables and/or one or more design variables. The one or more process variables may be selected from the process variables. The one or more design variables may be selected from the design variables.
[075] According to an embodiment of the present disclosure, the analyzing module 216 in step 410, may determine the differential variables, by applying a set of predefined rules on the comparison outcome. The set of pre-defined rules may determine or select a variable or a set of variables in a hierarchical fashion. The variable or the set of variables may be determined (or may be selected) based on a deviation between a predicted value and a target value. By way of an example, in the present disclosure, the set of pre-defined rules may determine (or select) the one or more differential variable from the process variables and/or the design variables in a hierarchical fashion. The process variables and/or the design variables may be determined (or may be selected) based on the comparison outcome. The comparison outcome may be a deviation or difference between the set of performance variables and the reference set of performance variables. By way of an example, the system may first select and modify the process variable and then further if required the system may select and modify the design variable. By way of an example, the process variable may be a concentration of the precursor or a flow rate of the fuel. By way of an example the design variable may be the diameter of the burner tubes.
[076] Post determining the differential variables, the system 102 may employ the analyzing module 216, in step 410, to modify the one or more differential variables to align the set of performance variables with the reference set of performance variables. The analyzing module may use a numerical optimization technique to align the set of performance variables with the reference set of performance variables. The analyzing module may use the numerical optimization technique (algorithm) to make differential changes in the one or more differential variables to optimize the design and the performance of the flame reactor. The numerical optimization technique (algorithm) may be used to make differential changes in the one or more differential variables in order to optimize the nanoparticle synthesis. The analyzing module may modify the one or more differential variables to align the set of performance variables with the reference set of performance variables. The analyzing module may further modify the one or more differential variables by using a numerical optimization technique to align the set of performance variables with the reference set of performance variables. Aligning the set of performance variables with the reference set of performance variables means minimizing or nullifying the deviation/lacunae in the performance of the flame reactor from a desired performance of the flame reactor. In other words, aligning the set of performance variables with the reference set of performance variables means minimizing or nullifying the deviation / lacunae in the design of the flame reactor. Thereby, the analyzing module may optimize the design of the flame reactor.
[077] The numerical optimization algorithm may be designed to modify the variable or the set of variables. The numerical optimization algorithm may be executed either through repeated simulations or using a gradient search algorithm such as a sequential quadratic programming. The numerical optimization algorithm may modify one or more differential variables by using repeated simulations or using the gradient search algorithm, such as the sequential quadratic programming. The numerical optimization algorithm may modify the one or more process variables or the one or more design variables to align the set of performance variables with the reference set of performance variables. The numerical optimization algorithm may modify the one or more process variables or the one or more design variables iteratively to optimize the design of the flame reactor to achieve the targeted or required performance of the flame reactor.
[078] Referring to figure 7 technical implementation of the analyzing module 216 is explained according to an exemplary embodiment. The reference set of performance variables may be targeted flame characteristics and particle characteristics. The targeted flame characteristics and particle characteristics may be defined by the user. The reference set of performance variables may, for example, include maximum flame temperature, flame length, mean particle size and production rate. The reference set of performance variables and the predicted set of performance variables are given as inputs to a comparator. The comparator compares the reference set of performance variables and the predicted set of performance variables and provides the comparison outcome (CO) as an output. The comparison outcome CO is defined as follows:
CO= (predicted performance variable)-(reference (targeted) performance variable)…Equation 23
[079] The comparison outcome (CO) as calculated by the equation 23 is sent to a corrector 1 wherein the corrector 1 corrects the one or more differential variables such as the process variables using a set of predefined rules in order to make the CO under a tolerable limit Ɛ1. The first order optimization algorithm i.e. gradient search method is used to optimize the process variables by minimizing the CO. The first order optimization algorithm i.e. gradient search method is used to modify the process variables by minimizing the CO. By way of an example, if the CO for the performance variable such as the maximum flame temperature is greater than zero (COTmax>0) then the process variables such as the flow rate of oxidant or flow rate of the fuel is reduced. Similarly, if the CO for the performance variable such as mean particle size is greater than zero (CODmean>0), may be due to the higher particle residence time at high flame temperatures, then the process variable such as the maximum flame temperature is reduced (modified) by reducing the oxidant flow rate or the fuel flow rate. Similarly, the production rate can be modified by varying the precursor flow rate. Further, the modified process variables is given as an input to the simulator 214 where the simulator predicts a new set of flame characteristics and particle characteristics. The new set of flame characteristics and particle characteristics are further provided as an input to the comparator. The modification of the process variables to achieve the target or reference performance variable is an iterative process until the CO reaches below Ɛ1.
[080] The corrector 1 is used for fine tuning the process variables as long as CO <Ɛ2. If CO >Ɛ2, the CO is corrected using corrector 2 wherein the design variables are modified in order to make the CO under tolerable limit (Ɛ1). The first order optimization algorithm i.e. gradient search method is used to optimize the design variables by minimizing CO. For example, if the CO for the performance variable the maximum flame temperature is greater than zero (CO Tmax>0) then the design variable such as the diameter of the tubes are slightly increased (modified) in order to make the mixing less efficient so as to delay the complete combustion resulting in reduction in the maximum flame temperature and increase in the flame length. Similarly, if the CO for the performance variable such as the mean particle size is greater than zero (CODmean>0) which could be due to the higher particle residence time at high flame temperatures, then the maximum flame temperature is (modified) reduced by increasing the design variable such as the diameter of the burner tubes. The diameter of the burner tubes triggers the slow combustion of the reactants due to poor mixing. The modification of the design variables to achieve the target or reference performance variable is an iterative process and continues until CO approaches Ɛ1.
[081] According to another embodiment of the present disclosure, post optimization the design of the flame reactor, the system may employ the performance analysis module 218 to evaluate the performance of the flame reactor. The performance of the flame reactor may be evaluated by using one or more performance variables selected from the set of performance variables.
[082] Referring to figure 3(a), a schematic of the aerosol flame reactor (AFR) setup for the nanoparticle synthesis is shown. The aerosol flame reactor (AFR) set up primarily consists of three components such as (i) Gas and precursor delivery unit, (ii) the flame reactor i.e. a burner and (iii) Powder collection unit. The burner may be used to produce a flame and formation and growth of the nanoparticles take place in the flame region of the burner.
[083] Referring to figure 3(b), a schematic of the aerosol flame reactor (AFR) setup for the nanoparticle synthesis is shown. The aerosol flame reactor (AFR) set up primarily consists of three components such as (i) Gas and precursor delivery unit, (ii) the flame reactor i.e., a furnace and (iii) Powder collection unit. The furnace may be used to produce a flame and formation and growth of the nanoparticles take place in the flame region of the furnace.
[084] According to an exemplary embodiment of the present disclosure, a forced draft diffusion type gas burner is used to explain implementation of the system 102 for designing and optimization of the burner for the nanoparticle synthesis. By way of an example, the burner is a three concentric tubes burner. Referring to figure 5, a side and a top view of the burner having three concentric tubes and a diffusion flame is shown.
[085] The flame characteristics may depend on flame reactor geometry, flow rate of reactants and flow rate of gases. The flame may be of type laminar or turbulent. Generally, a laboratory scale burner may produce the laminar flame whereas a pilot scale burner and an industrial burner or the furnace may produce the turbulent flame.
[086] According to an embodiment, the designing of the burner is explained. The objective of the burner may be to produce an optimum heat flux by combustion of a hydrocarbon fuel. The burner may be designed to provide the optimum heat flux to the precursor. The optimum heat flux may be provided to the precursor for oxidation/dehydrogenation/cracking of the precursor. The oxidation/dehydrogenation/cracking of the precursor may result in formation of a product monomer. The product monomer grows to form nanoparticles by undergoing various processes such as a surface growth, coagulation and coalescence and the like in the flame. Formation and growth of the nanoparticles may be controlled by a flame temperature distribution in a combustion zone of the burner and a particle residence time in the flame. The flame temperature distribution is proportional to a heat flux produced by the burner. The heat flux required for the oxidation/dehydrogenation/cracking of the precursor and the growth of the nanoparticles comprising the surface growth, the coagulation, the coalescence and the like may vary based on the process variables, such as the reaction kinetics of the precursor, the flow rate of the precursor, the flow rate of the fuel, the flow rate of the oxidant, the flow rate of the gases and the concentration of the precursor and the like. Hence, the burner may be designed by studying the reaction kinetics of the precursor, a flame dynamics of the burner and a particle growth mechanism to supply the optimum heat flux to the precursor. The burner may be designed for manufacturing the nanoparticles of a spectrum of materials.
[087] According to an exemplary embodiment of the present disclosure, the design variables, the process variables and performance variables of the burner are listed in Table 1.
Table 1: The design variables, the process variables and the performance variables of the burner
Design variables Performance variables
1 Burner tube inner diameters 1 Flammability limit
2 Burner tube thickness 2 Combustion volume
3 Burner tube length 3 Flame shape
4 Material of construction of the burner tubes 4 Flame height
Process variables 5 Flame temperature distribution
1 Gas flow rates 6 Flame stability
2 Precursor concentration 7 Product particles shape
3 Type of a precursor, a fuel, and an oxidant 8 Product particle size
4 Burner configuration 9 Product particle size distribution
10 Particle production rate
11 Velocity Profile in the burner tubes
12 Internal strength/design pressure of the burner tubes

[088] According to an embodiment, an impact of the performance of the burner on the nanoparticle synthesis is explained in detail. Properties of the nanoparticles and the production rate of the nanoparticles may be governed by the performance of the burner. The performance of the burner is discussed in detail and may be applied similarly to the furnace as well. The flame characteristics may be determined and controlled by the performance of the burner. A small improvement in the burner performance may have a significant effect on the flame characteristics and this effect may have subsequent effect on the particle characteristics and the production rate. The burner may be designed to transfer the optimum heat flux to the precursor.
[089] An effect of the design variables on the set of performance variables are shown in Figure 6. Figure 6 illustrates a design strategy for designing the burner by evaluation of performance variables, in accordance with an embodiment of the present subject matter. In one embodiment, referring to the figure 6, the set of performance variables such as the flammability limit, the combustion volume, the flame characteristics, the particle characteristics, the particle production rate and the flame stability may be mainly governed by the design variable such as the burner tubes inner diameter. Further, the internal strength of the burner tubes (design pressure) may be governed by the burner tubes thickness. The velocity profile of the gases in the burner tubes may be mainly governed by the burner tubes length.
[090] By way of an example, the burners of three different sizes are used for evaluating the performance of the burner. The burners may be represented as Burner-1, Burner-2 and Burner-3 in increasing order of size. Typically, the burner dimensions and a minimum flow rate and a maximum flow rate are shown in Table 2 (K. Wegner and S.E. Pratsinis, Powder Technology 150 (2005) 117– 122). By way of an example, referring to Table 2, the design variables, such as the burner dimensions and the process variables, such as the flow rates of the precursor plus carrier gas, the flow rates of the fuel, and the flow rates of the oxidant in Burner-1, Burner-2 and Burner-3 for the nanoparticle synthesis are provided. Each burner is subjected to various flow rates. The various flow rates mentioned in the Table 2 may cover entire range of operation (minimum to maximum) of the burner.
Table 2: Burner dimensions and flow rates of the precursor + carrier gas, the fuel, and the oxidant
Design Variables + Process variables Central tube
(Ar+TTIP/ HMDSO)
Carrier Gas + Precursor Second tube
(Methane)
Fuel Outer tube
(Oxygen)
Oxidant
Burner-1
Inner diameter (mm) 1.9 3.4 5.0
Outer diameter (mm) 2.8 4.1 5.6
Flow rate(cc/min) 500 300 1370 - 25000
Burner-2
Inner diameter (mm) 4.8 6.4 9.2
Outer diameter (mm) 5.7 7.6 10.0
Flow rate(cc/min) 500 300 1431-36077
Burner-3
Inner diameter (mm) 5.4 11.0 25.0
Outer diameter (mm) 6.0 12.0 27.0
Flow rate(cc/min) 500 300 14332-53986
[091] The performance of the burner may be measured by evaluating the flame characteristics. Evaluation of the flame characteristics using the flame dynamics model is explained in detail. The flame characteristics, such as the flame height, the flame shape, the flame temperature distribution and the flame stability depend on the performance of the burner. The growth of the particles directly depends on the flame temperature and the particle residence time in the flame. Hence, the design variables may be designed to be capable of producing the reference (required) flame characteristics. Figure 8 shows the flame temperature contours at varying values of the oxidant flow rate (process variable) for the three burners -Burner-1, Burner-2 and Burner-3. Figure 8 explains the properties of the flame such as the flame shape, the flame stability and the flame temperature distribution at various oxidant flow rates.
[092] According to another embodiment, evaluation of the flame height is explained. The flame height may be evaluated to characterize diffusion flames. A definition of the flame height is a distance between a burner tip and a location where the fuel and the oxidizer are in stoichiometric proportions along a central axis. Flame height indicates overall mixing of the fuel and the oxidant in the diffusion flames. Flame height measurements may be used to evaluate the flame structure so as to calculate the particle residence time. In the design of the flame reactor, the flame height information may be useful in determining the location of the particle collector. By way of an example, in experiments, the flame height is defined as an average position of a luminous flame tip wherein the luminous flame tip is visible to naked eye. Roper (1977) proposed a formula for theoretical flame height as shown below.
…….Equation (24)
Where is theoretical flame height, is fuel flow rate, is flame temperature, is the diffusion coefficient of O2 in N2.
[093] The flame height predicted by the simulator 214 is shown in figure 8. Further Figure 8 also illustrates that the flame height varies with the flow rate of the oxidant. The flame height decreases as the flow rate of the oxidant increases. The reason for decrease in the flame height is more oxygen is available near a tip of the burner at higher values of the oxidant flow rates. Hence, a complete combustion reaction takes place near a mouth of the burner resulting in short and hot flames. At low values of the oxidant flow rates, combustion reaction takes place along the flame axis. The combustion reaction takes place along the flame axis due to shortage of oxidant near a tip of the burner for complete combustion. The combustion reaction along the flame axis leads to long diffusion flames at low oxidant flow rates.
[094] The diffusion flame structure is not well defined unlike a premixed flame structure. The reason may be stated as, in the diffusion flame, chemical reaction and mixing of the fuel and the oxidant may occur simultaneously and the mixing of the fuel and the oxidant may not be confined to a well defined region. Further, in diffusion flame, most of the chemical reaction takes place in a narrow reaction zone. In the narrow reaction zone the fuel and the oxidant may be in a stoichiometric ratio, which may be approximated as a flame surface. The flame structure indicates that a rate of combustion may be controlled by a mixing of the fuel and the oxidant in a reaction zone by molecular diffusion. The flame structure predominantly depends on the flow rate of the fuel, the flow rate of the oxidant and the flow rate of the precursor. In diffusion flames, the flame structure primarily depends on a diameter of a tube for supply of the fuel and a mass flow rate of the fuel. Figure 9 shows qualitative nature of the flame structure presented at various values of the precursor (Titanium isopropoxide TTIP for titanium dioxide particle synthesis) concentration profiles. Figure 9 illustrates flame lengths at different oxidant flow rates in the Burner-1, the Burner-2 and the Burner-3 for synthesis of titanium dioxide nanoparticles, in accordance with an exemplary embodiment of the present subject matter.
[095] The precursor concentration profiles can be approximately treated as narrow reaction zones (flame structure) in the diffusion flames. Figure 10 illustrates concentration profiles of Titanium isopropoxide (TTIP) in the Burner-1, the Burner-2 and the Burner-3 for synthesis of titanium dioxide nanoparticles, in accordance with an exemplary embodiment of the present subject matter. Figure 11 is derived from the figure 8. Referring to Figure 11, axial flame temperatures for Burner 1 and Burner 2 at oxidant flow rate of 14332cc/min for synthesis of titanium dioxide are provided. Figure 11 also illustrates that the flame temperature increases steeply and reaches the maximum at slightly above the burner tip, and decreases gradually towards the downstream of the flame. Similar trend is observed in Burner 1 and Burner 2. However, temperature gradients are steeper in burner 1 over burner 2 as burner 2 operates at higher oxidant velocities.
[096] The flame stability may be characterized by the flame lift off height, the lift off velocity and the blow out velocity. At higher values of the gas flow rates (the flow rates of the fuel, the flow rates of the oxidant and the flow rates of the precursor, the flow rate of the carrier gas) the flame gets lifted above the burner mouth. The height between the burner tip and flame base is the flame lift off height and corresponding jet velocity is the flame lift off velocity. Turbulent mixing may exist in a region between the burner mouth and the flame base and appears as un-ignited jet. As gas velocities increase, the flame lift off height increases until the flame gets extinguished. The corresponding jet velocity is the blow out velocity. Gas velocities should be maintained much below the blow out velocity to make diffusion flames stable. Figure 7, shows that a flame front originates from the burner tip at selected operating conditions for the three burners. The flame is attached to the burner mouth at very low gas flow rates (i.e. laminar diffusion flames). Therefore flames may be stable over the operating range. In the present disclosure, by way of an example, the flame lift off height and the lift off velocity do not have any significance in describing the flame stability.
[097] Numerical values of model parameters used in simulations for synthesis of titanium dioxide are provided in Table 3.The model parameters are constant parameters used in the simulations related to physical and chemical phenomena occurring in the flame synthesis process.
Table 3: Numerical values of the model parameters used in the simulation
Parameter Value Units
Eddy dissipation parameter, AEDM 1.0 …
Eddy dissipation parameter, BEDM 0.5 …
Mass fractal dimension of the aggregates 1.7 ….
Nucleation rate constant in equation (10), kn 3.3 ×10-4 m3s-1
Pre-exponential factor in equation (15), ko 1.3× 10-12 m-4s
Activation energy in equation (15), EA 6.2× 105 Jmol-1
Particle growth rate constant in equation (21), kgc 1.6× 10-12 ms-1
[098] Properties of the nanoparticles particularly depend on the particle characteristics. The particle characteristics directly depend on the flame characteristics. Figure 12 shows the particle characteristics such as an average particle size of titanium dioxide at different oxidant flow rates for Burner-1 and Burner-2. The flame height, the maximum flame temperature and the flame temperature distribution vary with the flow rates of the fuel, flow rate of the oxidant, and flow rate of the carrier gas, the burner configuration and the burner tube dimensions. The particle residence time at higher flame temperature decreases at higher oxidant velocities due to short diffusion flames of the burner. Similarly, the particle residence time increases at low oxidant flow rates due to long diffusion flames of the burner. Hence the particles grow bigger in size at the lower oxidant flow rates than at the higher oxidant flow rates. Therefore, smaller particles are produced at the higher oxidant flow rates and bigger particles are produced at the lower oxidant flow rates as shown in Figure 12.
[099] According to another embodiment, the flammability limit may be considered as performance variable for designing the flame reactor and optimization therein. The flammability limit indicates that combustible gases or combustible vapors must be within defined limits in order to make the flame self-propagative from the ignition source. The burner should be capable of maintaining the fuel and the oxidant concentrations in the flammability limit over a wide range of operating conditions. When the fuel and the oxidant concentrations are outside the range of the flammability limit, the flame gets extinguished. The flammability limits of fuels are generally determined using standard apparatus and minimum and maximum flammability limits for various gaseous fuels are shown in Table 4. Empirical equations are also available for determining the flammability limits. When flammability limits are difficult to measure experimentally (Hristova and Tchaoushev, 2006), the flammability limits can be calculated by using Equations 25 and 26 provided below.
……..Equation (25)
……..Equation (26)
Wherein and are lower flammability and upper flammability respectively, is stoichiometric concentration of a flammable compound for complete combustion in the air. The fuel and the oxidant need to be selected based on the flammability limit. The flammability limit indirectly gives a range of the burner tubes inner diameters to supply the fuel and the oxidant to maintain the process variables in a predetermined range. The results from Figure 8 show that, for the entire range of operation, the flame is self-propagative i.e. sustainable for the entire range of operation. The results further show that the burners are designed to produce the flame to be within the flammability limit.
Table 4: Flammability limit of the gaseous fuels
Gas Flammability limit (% gas by volume at STP)
Lower Upper
Hydrogen 4 75
Methane 5 15
Ethane 3 12.4
Propane 2.1 9.5
Butane 1.8 8.4
Ethylene 2.7 36
Propylene 2.4 11
Butylene 1.7 9.7
[0100] According to an embodiment, the velocity profile in the burner tubes may be the performance variable considered for designing of the flame reactor and optimizing therein. The length of the burner tubes may be designed to yield the velocity profile in a fully developed form for the entire range of operation of the gases. The burners may be implemented in the flame dynamics model for predicting the velocity profile. The performance variable such as the velocity profiles for the maximum operating conditions (process variables) for the three burners- Burner 1, Burner 2 and Burner 3 are shown in Figure 13. By way of an example, the velocity profiles are predicted by the simulator 214. By way of an example, the results show that the velocity profiles are fully developed for the entire range of the operation of the three burners. The results shown in the figure 13 proved that, there is no instability of the flame due to the gas flow rates in the burner.
[0101] According to an embodiment, the internal strength of the burner tubes may be the performance variable considered for designing of the flame reactor and optimizing therein. The working pressure may be a pressure which the tube thickness is able to withstand. The working pressure can be calculated by using the internal strength of the burner tubes. The following expression can be used for calculating the thickness of the tubes from design pressure (Beriasa et al., 2005).
…….. Equation (27)
Where the design pressure is based on yield strength, is safety factor, is thickness of the tube and is yield strength of the tube material. The working pressure must be much smaller than the design pressure obtained from the equation 26. The design pressure is first calculated based on the material data and tube thickness. The design pressure so calculated is then used for comparison with the actual working pressure calculated using the results from the CFD module.
The working pressure is calculated using the flow rate and optimized burner design data from the simulator.
[0102] According to an exemplary embodiment, referring to Table 5, using Equation (26) the design pressure may be calculated for given thicknesses of the burner tubes. Different tubes may have different diameters. Similarly, the spacing between the tubes may or may not be same. The working pressure of the gas in the burner may be calculated using the maximum operating conditions (i.e. maximum range of the process variables). The design pressure and maximum working pressure are shown in Table 5. The data from Table 5 shows that the maximum working pressure is less than the design pressure of the burner tubes. Therefore, the burners designed in the present working example can withstand the maximum working pressure.
Table 5: Burner dimensions, the design pressure and the maximum working pressure
Dimensions Design pressure ( ) Mpa
Maximum Working pressure (Mpa)
ID(mm) OD(mm) Thickness (mm)
BURNER 1
Inner tube 1.9 2.8 0.9 247.23 3.59648E-06
Central tube 3.4 4.1 0.7 71.22 0.018862685
Outer tube 5 5.6 0.6 37.46 0.018861344
BURNER 2
Inner tube 4.8 5.7 0.9 63.39 1.86334E-07
Central tube 6.4 7.6 1.2 63.39 0.003233866
Outer tube 9.2 10 0.8 26.16 0.003233735
BURNER 3
Inner tube 5.4 6 0.6 34.34 1.04441E-07
Central tube 11 12 1 27.47 7.06074E-05
Outer tube 25 27 2 23.89 7.05375E-05
[0103] As discussed above, the simulator 214 may be provided with the design variables. The design variables comprise dimensions of the three burners and the process variables such as the flow rate of Carrier gas + Precursor (Ar + TTIP+AMDSO), the flow rate of the fuel (Methane), and the flow rate of the oxidant (Oxygen) as shown in table 2. The simulator simulates nanoparticle synthesis to predict the performance variables such as the flame temperature contours. The simulator further predicts the properties of the flame such as the flame shape, the flame stability and the flame temperature distribution at the various oxidant flow rates as shown in Figure 8. Further, the simulator simulates the nanoparticle synthesis to predict the performance variables such as a qualitative nature of the flame structure as shown in Figure 9. The qualitative nature of the flame structure may be predicted at various values of the precursor concentration. By way of an example, the precursor is titanium isopropoxide (TTIP) for titanium dioxide synthesis. Referring to Figure 11, the performance variable, such as an axial flame temperature predicted by the simulator at different oxidant flow rate is shown. Further, Figure 12 shows the particle characteristic, such as the average particle size of titanium dioxide at various oxidant flow rates for the burner 1 and the burner 2. Figure 12 shows that smaller particles are produced at the higher oxidant flow rates and bigger particles are produced at lower oxidant flow rates.
[0104] According to an exemplary embodiment of the present disclosure, the design variables, such as the burner dimensions and the process variables, such as the oxidant flow rate and the precursor concentration and the precursor flow rates may be evaluated by the analyzing module. The design variables and the process variables may be evaluated in order to obtain the reference (required) flame characteristics and the particle characteristics. The three burners are tested for producing the reference (required) performance variable such as desired size of the nanoparticles by modifying the process variables and the design variables. By way of an example, modified process variables are the gas flow rates, the burner configurations, and modified design variables are the burner dimensions. The length of the burner tubes are designed to yield the velocity profiles in the fully developed form for the entire range of operation of the gases. The results showed in figure 13 proved that there is no instability of the flame due to the gas flow rates (the carrier gas, the precursor, the oxidant and the fuel gas) in the Burner-1, Burner-2 and Burner-3.
[0105] The working of the analyzer module 216 is explained using figures 7 to12. For example, assume the reference set of performance variables as the maximum flame temperature Tmax=2000K, the flame length=6mm and the mean particle diameter =10nm and predict the flame and particle characteristics using simulator 214 for Burner-1 with the flow rates of precursor (TTIP), carrier gas (argon), fuel (methane) and oxidant (oxygen) of 6.5 g/h, 300cc/min, 500cc/min and 1370 cc/min (velocity=3.55m/s) respectively. The resulting performance variables are maximum flame temperature Tmax=1800K, flame length=6.8 mm and mean diameter =57nm. The resulting comparison outcome are COTmax=-200K, COflame_length=0.8 mm and CODmean=47nm. Now, tolerance limits Ɛ1 and Ɛ2 can be assumed for above three performance variables the Tmax , the flame length and the mean particle diameter as ±50K, ±0.2mm and ±5nm, and ±100K, ±0.5mm and ±10nm respectively. It can observed here that |CO| >Ɛ2 for the three performance variables the Tmax , the flame length and the mean particle diameter which makes CO to be corrected by tuning the process variables in corrector 1. By using the predefined guidelines for each performance variable, the new operating conditions are corrected to 6.5 g/h, 300cc/min, 500cc/min and 3000 cc/min (velocity=7.77 m/s) for precursor (TTIP), carrier gas (argon), fuel (methane) and oxidant (oxygen) respectively. Using this new set of operating conditions, the predicted performance variables are Tmax=2020K, flame length=4.7 mm and mean diameter =52 nm. The resulting comparison outcome are COTmax=20K, COflame_length=-1.3mm and CODmean=42nm for the three performance variables. The differential variable |CO| is still less than Ɛ2. The iteration process using corrector 1 continues until either |CO|=Ɛ1 or |CO|<Ɛ2. If |CO|<Ɛ2, then the CO is corrected using the corrector 2 where design variables are modified.
[0106] According to an embodiment of the present disclosure, referring to figure 4, the gas and precursor delivery unit and the powder collection apparatus may be designed. The gas and precursor delivery unit and the powder collection apparatus may be designed, in step 414, based on the one or more modified process variables and the one or more modified design variables. The one or more modified process variables and the one or more modified design variables may be received from the iterative process of the designing of the burner. In step 414, the gas and precursor delivery unit may be designed to maintain the one or more modified process variables in the burner. Subsequently, the tubing of the burner may be designed using maximum values of the one or more modified process variables. Finally, in step 416, the powder collection apparatus comprising a bag house filter, a glass fiber filter, a vacuum pump and a scrubber may be designed using the one or more modified burner dimensions and the particle production rate. The detailed design aspects of components of the AFR set up are described in the present disclosure.
[0107] According to an exemplary embodiment, the AFR set up for the nanoparticle synthesis, comprises the gas and precursor delivery unit 302-a. After designing the burner 304-a, the fuel, the oxidant and the precursor may be selected. As shown in Table 2, the burner dimensions and type of the product may be used for selecting the fuel, the oxidant and the precursor. Subsequently, the fuel, the oxidant and the precursor may be used for designing the gas and precursor delivery unit 302-a. Since the AFR set up components are highly sensitive (to moisture and other impurities) and the product may be of high purity, gases and the precursors may need to be cleaned to remove the impurities before supplying them to the burner.
[0108] According to an exemplary embodiment, the AFR setup may further comprise the precursor delivery unit. The precursors used for the nanoparticle synthesis may be in liquid form. The precursor may be delivered to the burner by saturating an inert gas with the precursor. The precursor delivery apparatus may be called a bubbler or a saturator as shown in Figure 3(a) and Figure 3(b). The bubbler or the saturator may be a stainless steel flask. The stainless steel flask may be heated at the bottom. The inert gas may pass through the bubbler and get saturated with the precursor. Further, the precursor laden inert gas may be delivered to the burner. The bubbler is connected to a precursor reservoir to maintain a constant precursor level in the bubbler. A constant precursor flow rate needs to be maintained in the burner. A flask temperature of the bubbler may be calculated from a theoretical temperature of the precursor using vapor pressure data of the precursor. The amount of the precursor to be delivered to the burner may be calculated from the vapor pressure data of the precursor and Raoult’s law provided by the equation (5) below.
…………………Equation (28)
Where is partial pressure of the precursor component, is vapor pressure of the pure precursor component and is mole fraction of the precursor component i in the solution.
[0109] By way of an example, some of the precursors used for the synthesis of titanium dioxide, silicon dioxide and aluminum oxide and flask temperature for the precursors are shown in Table 6. Based on maximum flask temperature data for various precursors, heating element for the bubbler may be selected. By way of an example, hot plate which can heat up to 250oC may be selected. When bubbler temperature is higher than room temperature, delivery tubing of the precursor, from the bubbler to the burner and the burner tubes need to be heated. The delivery tubing and the burner tubes may be heated up to 25oC higher than the bubbler temperature. The delivery tubing and the burner tubes may be heated using heating tapes. The delivery tubing and the burner tubes may be heated to avoid condensation of the precursor vapor in the delivery tubes and the burner tubes. When the precursor is highly volatile (for example silicon tetrachloride), hot plate (or any heating element) in the bubbler may be replaced with an ice bath. The flask temperature should be kept much less than the decomposition temperature of the precursor. Many of the precursors are unstable even at low temperatures when moisture or oxygen is present (for example Al-tri-sec-butoxide (ATSB)) in the precursor delivery unit.
Table 6: Suggested flask temperatures for different precursors
SNo Precursor Flask temperature (oC)
1 Titanium Tetrachloride (TiCl4) 25
2 Titanium tetraisopropoxide (TTIP) 125
3 Silicon tetra chloride (SiCl4) 0-25
4 Hexamethyl-disiloxane (HMDSO) 25
5 Ooctamethylcyclotetrasiloxane (OMCTS) 75
6 Al-tri-sec-butoxide (ATSB) 120-160
[0110] According to an embodiment, the AFR setup may further comprise the particle collection unit 306-a. The nanoparticles formed in a combustion region of the burner may be collected using two different filters. The two different filters may comprise a glass fiber filter and a bag-house filter. The glass fiber filters may be used for low production rate of the nanoparticles whereas the bag-house filters may be used for higher production rate of nanoparticles. The design of the particle collection unit is described below. The glass fiber filter details described herein. One of the crucial parameter in selecting the glass fiber filter may be strength of the filter against effluent particle laden gas (flue gas) temperatures and flow rates of flue gas. The bag house filter details are provided herein. The bag house filter or a fabric filter may be used to remove ultrafine particles which can’t be recovered by cyclone separator. The bag house filter captures the product particles, ranging from submicron to several hundred microns with efficiencies 99% to 99.99%. The bag house filter may be designed by using following design variables (Gabites, 2007; Gabites et al., 2008; McKenna and Turner, 1993).
[0111] Air to cloth ratio (filtration velocity, ( , ft3/min)/ (ft2))in the bag house filter can be defined as a ratio of inlet flow rate of a gas to the bag house filter to an area of a filter media. The air to cloth ratio is the most important design variable and can be described as shown below.
Air to cloth ratio …… Equation (29)
Where is the inlet flow rate of the gas to the bag house filter, is the area of the filter media.
[0112] Differential pressure drop ( , in. H2O (cmH2O) is a pressure drop across the bag house filter. The differential pressure drop can be defined as a pressure difference between inlet and out points of the bag house filter or sum of the pressure drops across a clean filter and a filter cake.
…………. Equation (30)
…………. Equation (31)
Where is inlet pressure to the bag house filter, is outlet pressure from the bag house filter, is pressure drop across the clean filter ( , is Pressure drop across the filter cake ( , is a fabric resistance, is a cake resistance, is a dust particle concentration in a inlet gas (g/cm3), is filtration time (s).
[0113] Filter drag (S, in. H2O/ (ft/min)) is a filter resistance exhibited by the filter cake (dust layer). Filter drag is directly proportional to an amount of a dust accumulated on the filter (bag) media.
…………. Equation (32)
[0114] Elutriation velocity ((ft3/min)/(ft2) is a ratio of a gas flow rate to the bag house filter to difference of cross sectional areas of a bag house and cross sectional areas of total bags. Elutriation velocity can be expressed as
……….. Equation (33)
Where is the cross sectional area of a bag house, is cross sectional area of the total bags.
[0115] Collection efficiency is defined as a ratio of the particles collected using the bag house filter and amount of particles present in an inlet flow. Collection efficiency is one of the key parameters to evaluate the bag house filter performance.
………. Equation (34)
Where is a mass flow rate of a powder in a outlet air stream, is a mass flow rate of the powder in a feed stream.
[0116] According to an exemplary embodiment, the designing of the bag house filter is explained. The design variable, such as a gas to cloth ratio may be selected based on the type of material to be collected using the bag house filters. The air to cloth ratio for various particulate materials is available in standard design text books (McKenna and Turner, 1993). In the present application, the bag house filter may be used to collect carbon black, silicon dioxide, titanium dioxide, and aluminum oxide nanoparticles. Hence suitable air to cloth ratio for working with the materials selected herein may be taken as =1.5 (ft3/min)/ft2. Based on the flow rate data and the burner dimensions, the maximum gas flow rate from the burner outlet is calculated as = 1.764 ft3/min. By substituting the value in the air to cloth ratio, gross bag filter area can be calculated, which is = 1.417 ft2 (0.1317m2). Number of the bags and dimensions of the bags may be selected to match total filtration area 1.417 ft2 (0.1317m2). The bag dimensions selected in the present working example are 150mm diameter, 150mm length. Further, filtration area available for a single bag is calculated as 7.6 ft2 (0.7065 m2) ( ). Hence, in the present working example, the single bag is sufficient to provide the gross bag filter area. Further,the bag house may be designed for placing the bags and removing the particles from the bag house filters. By way of the present working example, dimensions of the filter house are 200mm x200mm x150mm.
[0117] According to an embodiment of the present disclosure, selection/design of other accessories is provided herein. There may be many accessories required to connect/ support/ complement major components discussed above in AFR set up such as tubing for components of the AFR setup, a burner conical section, heating tapes, an enclosure, temperature sensors, a vacuum pump and a scrubber etc.
[0118] The design of the tubing for the components of the AFR set up is provided herein. The tubing from the gas and precursor delivery unit to the burner and the tubing from the burner conical section to the particle collection unit may be designed based on the burner dimensions, the maximum operating flow rates of the precursor and the gases etc. By way of an example, central tube dimensions of the Burner-1 are inner diameter: 5.0 mm and thickness: 0.6 mm. Maximum operating flow rate of the oxygen through the tubingis 50000cc/min. By way of an example, oxygen gas passes through the outer tube. For designing the tube for delivering oxygen gas from the gas purification panel to the burner tube, the continuity equation is solved which gives the burner outer tube diameter of 6.35mm (1/4").
[0119] According to another embodiment, designing of the conical section of the burner is explained herein.The conical section of the burner (shown in figure 3-a) may be used to absorb particle laden gas stream which further may be sent to the filter for collection of the particles. The conical section may be placed in a location where the flame ends. As seen in the previous description, the burner, small in size, produces longer flames and the burners, big in size, produces shorter flames, at the same operating conditions (i.e. the process variables). The distance between the burner mouth and the conical section depends on a size of the burner and the flow rates of the reactants/precursor and gases. The flame height data (Figure 9) may give a location where the conical section of the burner can be placed.
[0120] According to another embodiment, design details for the heating tapes of the burner design are provided herein. The heating tapes may be used to maintain the precursor temperature constant along the tubing from the bubblerto the burner. The heating tapes may be selected using the bubbler temperature data (Table 5) for the precursors selected herein. By way of an example, heating tape’s set temperature is 25oC more than the bubbler temperature.
[0121] According to another embodiment, the enclosure design details are provided herein. For some experiments where control flame (free from ambient currents) is required, the burner may be covered with the enclosure. By way of an example, the transparent, plexiglass enclosure may be used so that the flame can be visible to the naked eye. Since the melting point of the plexiglass is very less (160oC) compared to the flame temperature, theenclosure has to be placed at a location wherein radiation of the flame doesn’t exceed melting point of the plexiglass. The flame temperature distribution data shown in Figure 11 may be helpful in finding the location to place the enclosure.
[0122] According to another embodiment, temperature sensors design details are provided herein. The temperature sensors are used to measure the flame temperature. By way of an example, the temperature sensors may be selected based on the maximum flame temperature data for the fuels and the precursors in the Burner-1, Burner-2 and Burner-3. The maximum flame temperatures data from Figure 8 at various operating conditions for the three burners can be used in a selection of the temperature sensors.
[0123] According to another embodiment, vacuum pump design details are provided herein. The vacuum pump is used to suck the particle laden gas stream from downstream of the burner for collecting the particles. The capacity of the pump may be determined based on the maximum flow rates of the gases and precursor and the maximum precursor concentration.
[0124] According to another embodiment, scrubber design details are provided herein. The scrubber may be used to remove Cl2, HCL and any uncollected particles from the flue gases. By way of an example, NaOH may be used as an absorbent in the scrubber. The size of the scrubber and a volume of the NaOH may be determined based on a capacity of the vacuum pump.
[0125] Referring now to Figure 14, a method 1400 for designing a flame reactor and optimizing therein is shown, in accordance with an embodiment of the present subject matter. The method 1400 may be described in a 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 1400 may 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.
[0126] The order in which the method 1400 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 1400 or alternate methods. Additionally, individual blocks may be deleted from the method 1400 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 1400 may be considered to be implemented in the above describedsystem 102.
[0127] Referring to figure 14, the method (1400) for designing a flame reactor is described. In step 1402, data may be received. The data may be associated with, a nanoparticle synthesis, reactants, and gases used therein. In step 1402, process variables may be received. The process variables may be associated with the nanoparticle synthesis. In step 1402, design variables may be received. The design variables may be associated with the flame reactor. Further, in step 1402, a reference set of performance variables may be received. In one implementation, the data, the process variables, the design variables, and the reference set of performance variables may be received by the receiving module 212.
[0128] In step 1404, the nanoparticle synthesis may be simulated in the flame reactor by using the data, the process variables and the design variables. In step 1404, the nanoparticle synthesis may be simulated in order to predict a set of performance variables. In one implementation, the nanoparticle synthesis may be simulated in the flame reactor by the simulator 214 by using the data, the process variables and the design variables. The nanoparticle synthesis in the flame reactor may be simulated by using the coupled CFD-PBM simulator. In step 1406, the set of performance variables predicted by the simulation may be compared with the reference set of performance variables to provide a comparison outcome. In one implementation, the set of performance variables predicted by the simulation may be compared with the reference set of performance variables by the analyzing module 216. In one implementation, the set of performance variables may be compared with the reference set of performance variables by the analyzing module 216 to provide the comparison outcome.
[0129] In step 1408, one or more differential variables may be determined, by applying a set of predefined rules on the comparison outcome. The one or more differential variables may affect the performance of the flame reactor and the nanoparticle synthesis. In one implementation, the one or more differential variables may be determined by the analyzing module 216, by applying the set of predefined rules on the comparison outcome.
[0130] In step 1410, the one or more differential variables may be modified using the numerical optimization technique to align the set of performance variables with the reference set of performance variables. In step 1410, the one or more differential variables may be modified to optimize the design of the flame reactor. In one implementation, the one or more differential variables may be modified by the analyzing module 216 using the numerical optimization technique to align the set of performance variables with the reference set of performance variables.
[0131] The method 1400 may further comprise evaluating a performance of the flame reactor by using one or more performance variables of the set of performance variables. In one implementation, the performance analysis module 218 may evaluate the performance of the flame reactor by using the one or more performance variables of the set of performance variables. The receiving, the simulating, the comparing, the determining, the modifying and the evaluating may be performed by a processor. In one implementation, the receiving, the simulating, the comparing, the determining, the modifying and the evaluating may be performed by the processor 202.
[0132] Although implementations for methods and systems for designing a flame reactor for nanoparticle synthesis have been described in a language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for designing a flame reactor for nanoparticle synthesis.
[0133] The embodiments were chosen and described in order to explain the principles of the designing and their practical application to enable others skilled in the art to utilize the design and various embodiments and with various modifications as are suited to the particular use contemplated. Alternative embodiments will become apparent to those skilled in the art to which the present disclosure pertains without departing from their spirit and scope. Accordingly, the scope of the present disclosure is defined by the appended claims rather than the foregoing description and the exemplary embodiments described therein.