Toxic gas treatment method using plasma flame

Abstract

The present application relates to the field of electrochemistry, especially a toxic gas treatment method using plasma flame, comprising the following steps: S1: a plasma torch generates a plasma flame; S2: toxic gas is collected by an air inlet cavity and transmitted to a reaction cavity; S3: the reaction cavity decomposes and reacts the toxic gas by the plasma flame generated by the plasma torch; S4: the transition of the product is carried out through an intermediate flange; S5: soluble products and dust are absorbed and treated through a water tank and a spray tower, and finally the product is released from an exhaust port. The present application uses a gas transmission path containing a plasma flame to treat harmful gas, judges the remaining gas in the plasma torch through a remaining capacity model, and safely locks the torch according to the remaining capacity threshold, ensuring the safe operation of the equipment; and the treatment process is automatically operated throughout, simple to operate, the equipment is corrosion-resistant, has a long service life, and has excellent harmful gas treatment efficiency.

Description

Technical Field

[0001] This invention relates to the field of electrochemistry, and in particular to a method for treating toxic gases using plasma flame. Background Technology

[0002] Toxic gas processors are widely used in the manufacturing processes of the semiconductor industry (semiconductors, panels, solar energy, LEDs), including CVD, Diffusion, Etching, and Ion implantation, and can safely and effectively handle fluoride, chloride, hydride gases, and general harmful gases.

[0003] Existing technologies are cumbersome and consume significant amounts of electricity. Methods for treating toxic gases using plasma rely on electrocoupling to process different toxic gases entering through a single inlet. Ionization breaks down toxic gas molecules, forming tiny particles such as positive and negative ions, electrons, and neutral particles. These particles are then adsorbed or undergo chemical reactions to purify the toxic gases. For example, the gas treatment system and method disclosed in application CN115646148A and the gas treatment method disclosed in application CN110822973A offer advantages. The former enables online, real-time cooling and heating, improving absorption and desorption efficiency; the all-plastic membrane is corrosion-resistant and easy to maintain, reducing overall system maintenance costs; it can utilize new energy sources such as solar energy and low-grade waste heat for operation, and can be coupled with a heat pump to provide the heat released during absorption to the desorption process, further reducing system operating costs; after membrane wetting or fouling, it can be thoroughly cleaned, restored, and dried online, significantly reducing the cost of replacing membrane modules. Furthermore, it boasts high gas absorption and desorption efficiency, achieving high gas absorption and desorption yields high efficiency. The latter method is low-cost, simple, and easy to operate. It recovers heat from the gas through a treatment system and purifies the impurities in the gas before introducing it into the industrial system, avoiding air pollution and energy waste, thus achieving "zero emissions" and improving the ecological environment. However, the technical means used in both comparative documents increase the residence time to ensure that the gas is discharged after meeting the emission standards. This results in low treatment efficiency, long processing time, increased energy consumption, and reduced service life. Furthermore, toxic gases entering through a single inlet are prone to mutual reactions, which is detrimental to subsequent treatment processes. In addition, the treatment of the aforementioned toxic gases can also lead to problems such as thermal damage, corrosion, and dust accumulation. Summary of the Invention

[0004] The purpose of this invention is to address the deficiencies mentioned in the background art by proposing a method for treating toxic gases using plasma flame.

[0005] The technical solution adopted in this invention is as follows:

[0006] A method for treating toxic gases using plasma flame is provided, comprising the following steps:

[0007] S1: The plasma torch generates a plasma flame;

[0008] S2: Toxic gas is collected from the air intake chamber and transferred to the reaction chamber;

[0009] S3: The reaction chamber decomposes and reacts with toxic gases through the plasma flame generated by the plasma torch;

[0010] S4: Transition of the product is achieved through an intermediate flange;

[0011] S5: Soluble byproducts and dust are absorbed and treated by water tanks and spray towers, and the byproducts are finally released through exhaust ports.

[0012] As a preferred embodiment of the present invention: the plasma torch in S1 ionizes the nitrogen gas between the cathode and the anode to generate a plasma arc, thereby generating a plasma flame.

[0013] As a preferred embodiment of the present invention: the plasma torch collects temperature and pressure data inside the torch as input to the model, establishes a remaining capacity model, and performs a safety self-locking of the torch based on the current internal remaining capacity threshold of the plasma torch. The remaining capacity model is as follows:

[0014]

[0015] in, This is the deviation amount. For kernel function, For the input sample, For the features of the input sample, The number of input data, This is the initial input value. For a matrix containing penalty parameters, For kernel parameters, For Lagrange multipliers;

[0016] As a preferred embodiment of the present invention: the remaining capacity model optimizes the penalty parameter and kernel function based on the beetle whisker-particle swarm optimization algorithm.

[0017] Let the search space be 3D space, number of particles The number of iterations is At that time, the first Velocity information of individual particles and location information as follows:

[0018]

[0019]

[0020] in, Indicates the number of iterations. Time Dimensional space The velocity components of each particle Indicates the number of iterations. Time Dimensional space Position components of each particle;

[0021] Optimal solution in the particle's neighborhood and the current population optimal solution as follows:

[0022]

[0023]

[0024] in, Indicates the number of iterations. Time Dimensional space The optimal position components of each particle. Indicates the number of iterations. Time Dimensional space The global optimal component of each particle;

[0025]

[0026]

[0027] in, Indicates the number of iterations. Time The speed of each particle Indicates the number of iterations. Time The position of each particle. and Represents the learning factor. express Random numbers between Indicates the velocity coefficient. Indicates the number of iterations. Time The function of incremental position movement of each particle Represents weight, weight satisfy:

[0028]

[0029] This represents the maximum weight. This represents the minimum weight. Indicates the maximum number of iterations;

[0030] The iteration satisfies:

[0031]

[0032]

[0033] in, Indicates the number of iterations. Time The function of incremental position movement of each particle Indicates the number of iterations. The size of the step factor of the time particle. Indicates the number of iterations. The target function vector of the left tentacle of the particle. Indicates the number of iterations. The target function vector of the right tentacle of the particle. Indicates the position of the particle's left tendril. Indicates the position of the particle's right tentacle.

[0034]

[0035] in, Indicates the distance between the two tentacles of a particle;

[0036] Perform crossover operations on all particles, select 10% of the crossover offspring for mutation operations, and determine whether the iteration termination condition is met. If it is met, the algorithm ends; otherwise, continue the search and finally output the optimal solution.

[0037] As a preferred technical solution of the present invention: the air intake chamber in S2 is equipped with an independent air intake port to collect different gases, and nitrogen purging is performed during the collection process.

[0038] As a preferred embodiment of the present invention, aluminum oxide is used as the casting material inside the reaction chamber in S3.

[0039] As a preferred technical solution of the present invention: the intermediate flange in S4 is provided with a nitrogen air curtain and a cyclone water curtain for the transition from the dry area to the wet area.

[0040] As a preferred technical solution of the present invention: the water tank in S5 absorbs and treats water-soluble gases and dust by water spraying.

[0041] As a preferred embodiment of the present invention, both the sidewall of the cyclone water curtain and the water tank of the water tank are coated with Teflon coating.

[0042] As a preferred technical solution of the present invention: the spray tower in S5 treats water-soluble gaseous byproducts and dust through a long gas flow path, while controlling the exhaust temperature.

[0043] The toxic gas treatment method utilizing plasma flame provided by this invention has the following advantages compared with the prior art:

[0044] This invention utilizes a sophisticated gas transport path incorporating a plasma flame to treat harmful gases. It assesses the remaining gas volume within the plasma torch using a residual capacity model and implements a safety self-locking mechanism based on the residual capacity threshold, ensuring safe operation. The entire process is automated, simple to operate, corrosion-resistant, has a long service life, and exhibits excellent harmful gas treatment efficiency. By transferring the reaction products of harmful gases within the reaction chamber to the intermediate flange, a crucial transition zone between the dry and wet areas, the intermediate flange's special design effectively addresses corrosion and dust accumulation issues within the reaction chamber. A nitrogen curtain perfectly separates the dry and wet areas, preventing thermal damage, maintaining a dry reaction chamber, and preventing corrosion. Automatic cyclone water curtain flushing prevents dust accumulation, and the water curtain's sidewalls are coated with Teflon for corrosion protection. Water spraying from a water tank absorbs and treats water-soluble gases and dust. Furthermore, the plasma power supply used in this invention converts external power into the energy required to ignite the plasma flame, converting AC to DC to provide a stable power supply to the processor. A high-voltage protection circuit ensures the processor's safe operation. The plasma power supply also features an ignition mode and an energy-saving mode to ensure processor controllability. Attached Figure Description

[0045] Figure 1 This is a flowchart of a preferred embodiment of the present invention;

[0046] Figure 2 This is a gas transport path diagram in a preferred embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of the intermediate flange structure in a preferred embodiment of the present invention. Detailed Implementation

[0048] It should be noted that, unless otherwise specified, the embodiments and features described in this embodiment can be combined with each other. The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0049] Reference Figure 1 A preferred embodiment of the present invention provides a method for treating toxic gases using plasma flame, comprising the following steps:

[0050] S1: The plasma torch generates a plasma flame;

[0051] S2: Toxic gas is collected from the air intake chamber and transferred to the reaction chamber;

[0052] S3: The reaction chamber decomposes and reacts with toxic gases through the plasma flame generated by the plasma torch;

[0053] S4: Transition of the product is achieved through an intermediate flange;

[0054] S5: Soluble byproducts and dust are absorbed and treated by water tanks and spray towers, and the byproducts are finally released through exhaust ports.

[0055] The plasma torch in S1 ionizes the nitrogen gas between the cathode and anode to generate a plasma arc, thereby producing a plasma flame.

[0056] The plasma torch collects temperature and pressure data inside the torch as input to the model, establishes a remaining capacity model, and performs a safety self-locking mechanism for the torch based on the current internal remaining capacity threshold. The remaining capacity model is as follows:

[0057]

[0058] in, This is the deviation amount. For kernel function, For the input sample, For the features of the input sample, The number of input data, This is the initial input value. For a matrix containing penalty parameters, For kernel parameters, For Lagrange multipliers;

[0059] The remaining capacity model optimizes the penalty parameter and kernel function based on the beetle whisker-particle swarm optimization algorithm:

[0060] Let the search space be 3D space, number of particles The number of iterations is At that time, the first Velocity information of individual particles and location information as follows:

[0061]

[0062]

[0063] in, Indicates the number of iterations. Time Dimensional space The velocity components of each particle Indicates the number of iterations. Time Dimensional space Position components of each particle;

[0064] Optimal solution in the particle's neighborhood and the current population optimal solution as follows:

[0065]

[0066]

[0067] in, Indicates the number of iterations. Time Dimensional space The optimal position components of each particle. Indicates the number of iterations. Time Dimensional space The global optimal component of each particle;

[0068]

[0069]

[0070] in, Indicates the number of iterations. Time The speed of each particle Indicates the number of iterations. Time The position of each particle. and Represents the learning factor. express Random numbers between Indicates the velocity coefficient. Indicates the number of iterations. Time The function of incremental position movement of each particle Represents weight, weight satisfy:

[0071]

[0072] This represents the maximum weight. This represents the minimum weight. Indicates the maximum number of iterations;

[0073] The iteration satisfies:

[0074]

[0075]

[0076] in, Indicates the number of iterations. Time The function of incremental position movement of each particle Indicates the number of iterations. The size of the step factor of the time particle. Indicates the number of iterations. The target function vector of the left tentacle of the particle. Indicates the number of iterations. The target function vector of the right tentacle of the particle. Indicates the position of the particle's left tendril. Indicates the position of the particle's right tentacle.

[0077]

[0078] in, Indicates the distance between the two tentacles of a particle;

[0079] Perform crossover operations on all particles, select 10% of the crossover offspring for mutation operations, and determine whether the iteration termination condition is met. If it is met, the algorithm ends; otherwise, continue the search and finally output the optimal solution.

[0080] The air intake chamber in S2 is equipped with an independent air inlet to collect different gases, and nitrogen purging is performed during the collection process.

[0081] The reaction chamber in S3 uses aluminum oxide as the casting material.

[0082] The intermediate flange in S4 is equipped with a nitrogen air curtain and a cyclone water curtain for the transition from the dry area to the wet area.

[0083] The water tank in S5 absorbs and treats water-soluble gases and dust through water spraying.

[0084] Both the sidewalls of the cyclone water curtain and the water tank of the water tank are coated with Teflon.

[0085] The spray tower in S5 treats water-soluble gaseous byproducts and dust through a long gas flow path, while controlling the exhaust temperature.

[0086] In this embodiment, refer to Figure 2 The plasma torch consists of a cathode, an anode, nitrogen gas, and PCW. The N2 between the anode and cathode is ionized to generate a plasma arc at a temperature of over 3000°C. It features low energy loss, high efficiency, excellent PFC gas handling efficiency, and long-life electrodes.

[0087] When the gas volume inside the plasma torch changes, the temperature and pressure inside the torch also change. Therefore, temperature and pressure data inside the torch are collected as input to the model to establish a remaining capacity model. The remaining capacity model is based on the LSSVM algorithm. The LSSVM algorithm has a strong self-learning capability, allowing it to automatically extract data patterns from the training data. Then, based on the current remaining capacity threshold inside the plasma torch, a safety self-locking mechanism is implemented to ensure the safe operation of the plasma torch. The established remaining capacity model is as follows:

[0088]

[0089] in, This is the deviation amount. For kernel function, For the input sample, For the features of the input sample, The number of input data. This is the initial input value. For a matrix containing penalty parameters, For kernel parameters, For Lagrange multipliers;

[0090] The remaining capacity model optimizes the penalty parameter and kernel function based on the beetle whisker-particle swarm optimization algorithm. In the beetle whisker-particle swarm optimization algorithm, the dominance rule is used to determine the dominant and non-dominated solutions among all particles. All non-dominated particles are selected and stored in a archive during the iteration process. Information is transferred between individual particles, and their evolutionary direction is easily dominated by the swarm optimal solution and the individual optimal solution. This evolutionary strategy lacks the search for local information around the current particle, making it prone to getting trapped in local optima in multidimensional complex environments, and its convergence speed is slow. To enhance its global search capability and improve convergence speed, the individual's sensing mechanism of the surrounding environment in the beetle whisker search algorithm is integrated with the swarm concept, realizing the combination of global and local information of particles.

[0091] Assuming the search space is 8-dimensional, the number of particles is 100, and the number of iterations is 8, the th iteration... Velocity information of individual particles and location information as follows:

[0092]

[0093]

[0094] in, This indicates that when the number of iterations is 8, the 8th iteration... Dimensional space The velocity components of each particle This indicates that when the number of iterations is 8, the 8th iteration... Dimensional space Position components of each particle;

[0095] Optimal solution in the particle's neighborhood and the current population optimal solution as follows:

[0096]

[0097]

[0098] in, This indicates that when the number of iterations is 8, the 8th iteration... Dimensional space The optimal position components of each particle. This indicates that when the number of iterations is 8, the 8th iteration... Dimensional space The global optimal component of each particle;

[0099] By introducing the beetle antennae sensing mechanism into the beetle antennae search algorithm, particles can share information, enriching the information available for evolution.

[0100]

[0101]

[0102] in, This indicates that when the iteration number is 9, the 9th iteration... The speed of each particle This indicates that when the iteration number is 9, the 9th iteration... The position of each particle. and Represents the learning factor. express Random numbers between Indicates the velocity coefficient. This indicates that when the number of iterations is 8, the 8th iteration... The function of incremental position movement of each particle Represents weight, weight satisfy:

[0103]

[0104] This represents the maximum weight. This represents the minimum weight, and the maximum number of iterations is 500.

[0105] The iteration satisfies:

[0106]

[0107]

[0108] in, This indicates that when the iteration number is 9, the 9th iteration... The function of incremental position movement of each particle This represents the step size factor of the particle when the number of iterations is 8. This represents the target function vector of the particle's left tentacle when the number of iterations is 8. This represents the objective function vector of the particle's right tentacle when the number of iterations is 8. Indicates the position of the particle's left tendril. Indicates the position of the particle's right tentacle.

[0109]

[0110] in, Indicates the distance between the two tentacles of a particle;

[0111] Perform a crossover operation on all particles, select 10% of the crossover offspring (i.e., 10 crossover offspring) for mutation operation, and determine whether the iteration termination condition is met. If it is met, the algorithm ends; otherwise, continue the search and finally output the optimal solution.

[0112] In the beetle whisker-particle swarm optimization algorithm, the sensing mechanism from the beetle whisker algorithm is introduced. All particles are first crossbred, and then a subset of the crossbred offspring are mutated. The crossbred and mutated particles are non-dominated particles from the archive. This approach ensures that in the early iterations, a small number of non-dominated individuals are stored in the archive, and fewer crossbred operators are needed, thus maintaining the same optimization speed as the beetle whisker-particle swarm optimization algorithm. The mutation operator effectively prevents the algorithm from getting trapped in local optima in the early iterations. As the number of iterations increases, the crossbred particles in the archive generate more and more offspring, increasing the diversity of non-dominated particles and improving the algorithm's accuracy in searching for the global optimum near the current Pareto front.

[0113] Harmful gases, such as Si compounds, flammable gases, water-soluble gases, toxic gases, and PFCs, enter the processor through the intake chamber. To prevent mutual reactions between the intake gases, the intake chamber is equipped with independent intake ports. Multiple intake ports prevent reactions between gases before they enter the combustion chamber. Nitrogen purging prevents dust blockage at the intake ports, dilutes process gases, and provides backfire protection.

[0114] The 600 LPM reaction chamber uses a plasma flame to decompose and react harmful gases. The chamber's interior is filled with alumina, which offers excellent high-temperature and corrosion resistance, preventing corrosion and overheating. Sufficient reaction space is ensured.

[0115] The harmful gaseous reaction products within the reaction chamber are transferred to the intermediate flange. (Refer to...) Figure 3 The intermediate flange serves as a crucial transition zone between the dry and wet areas. Special design features of the intermediate flange effectively address corrosion and dust accumulation within the reaction chamber. A nitrogen gas curtain perfectly separates the dry and wet zones, preventing thermal damage, maintaining a dry reaction chamber, and protecting against corrosion. Automatic cyclone water curtain flushing further prevents dust accumulation, and the sidewalls of the water curtain are coated with Teflon for corrosion protection.

[0116] The waste products passing through the intermediate flange are sprayed with water from the water tank to absorb and treat water-soluble gases and dust. Water can be recycled, reducing water consumption. Water mist spraying can maximize water treatment efficiency. The water tank is also coated with Teflon coating for corrosion protection.

[0117] The remaining material then enters the spray tower, where it further treats water-soluble gas byproducts and dust through a long gas flow path, ensuring efficient emissions. This maximizes the treatment efficiency of water-soluble gases, cleans deposited dust, provides further cooling, and controls the exhaust temperature of the gas exiting through the exhaust port.

[0118] The plasma power supply converts external power into the energy needed to ignite the plasma flame, transforming alternating current into direct current. This provides a stable power supply to the processor, and its high-voltage protection circuitry ensures safe operation. The plasma power supply also features ignition and energy-saving modes to maintain processor controllability.

[0119] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0120] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for treating toxic gases using plasma flame, characterized in that: Includes the following steps: S1: The plasma torch generates a plasma flame; S2: Toxic gas is collected from the air intake chamber and transferred to the reaction chamber; S3: The reaction chamber decomposes and reacts with toxic gases through the plasma flame generated by the plasma torch; S4: Transition of the product is achieved through an intermediate flange; S5: Soluble byproducts and dust are absorbed and treated by water tanks and spray towers, and the byproducts are finally released through exhaust ports; The plasma torch collects temperature and pressure data within the torch, which are used as input to a model to establish a remaining capacity model. Based on the current remaining capacity threshold of the plasma torch, the torch is automatically locked in place for safety. The remaining capacity model... Specifically as follows: ； in, This is the deviation amount. For kernel function, For the input sample, For the features of the input sample, The number of input data. This is the initial input value. For a matrix containing penalty parameters, For kernel parameters, For Lagrange multipliers; The remaining capacity model optimizes the penalty parameter and kernel function based on the beetle whisker-particle swarm optimization algorithm: Let the search space be 3D space, number of particles The number of iterations is At that time, the first Velocity information of individual particles and location information as follows: ； ； in, Indicates the number of iterations. Time Dimensional space The velocity components of each particle Indicates the number of iterations. Time Dimensional space Position components of each particle; Optimal solution in the particle's neighborhood and the current population optimal solution as follows: ； ； in, Indicates the number of iterations. Time Dimensional space The optimal position components of each particle. Indicates the number of iterations. Time Dimensional space The global optimal component of each particle; ； ； in, Indicates the number of iterations. Time The speed of each particle Indicates the number of iterations. Time The position of each particle. and Represents the learning factor. express Random numbers between Indicates the velocity coefficient. Indicates the number of iterations. Time The function of incremental position movement of each particle Represents weight, weight satisfy: ； This represents the maximum weight. This represents the minimum weight. Indicates the maximum number of iterations; The iteration satisfies: ； ； in, Indicates the number of iterations. Time The function of incremental position movement of each particle Indicates the number of iterations. The size of the step factor of the time particle. Indicates the number of iterations. The target function vector of the left tentacle of the particle. Indicates the number of iterations. The target function vector of the right tentacle of the particle. Indicates the position of the particle's left tendril. Indicates the position of the particle's right tentacle. ； in, Indicates the distance between the two tentacles of a particle; Perform crossover operations on all particles, select 10% of the crossover offspring for mutation operations, and determine whether the iteration termination condition is met. If it is met, the algorithm ends; otherwise, continue the search and finally output the optimal solution.

2. The method for treating toxic gases using plasma flame according to claim 1, characterized in that: The plasma torch in S1 ionizes the nitrogen gas between the cathode and anode to generate a plasma arc, thereby producing a plasma flame.

3. The method for treating toxic gases using plasma flame according to claim 1, characterized in that: The air intake chamber in S2 is equipped with an independent air inlet to collect different gases, and nitrogen purging is performed during the collection process.

4. The method for treating toxic gases using plasma flame according to claim 1, characterized in that: The reaction chamber in S3 uses aluminum oxide as the casting material.

5. The method for treating toxic gases using plasma flame according to claim 1, characterized in that: The intermediate flange in S4 is equipped with a nitrogen air curtain and a cyclone water curtain for the transition from the dry area to the wet area.

6. The method for treating toxic gases using plasma flame according to claim 5, characterized in that: The water tank in S5 absorbs and treats water-soluble gases and dust through water spraying.

7. The method for treating toxic gases using plasma flame according to claim 6, characterized in that: Both the sidewalls of the cyclone water curtain and the water tank of the water tank are coated with Teflon.

8. The method for treating toxic gases using plasma flame according to claim 1, characterized in that: The spray tower in S5 treats water-soluble gaseous byproducts and dust through a long gas flow path, while controlling the exhaust temperature.

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