Intelligent dioxin regulation and control method and system in plasma melting furnace starting process

Abstract

The invention discloses an intelligent dioxin regulation and control method and system in a plasma melting furnace starting process, and relates to the field of industrial waste gas treatment. An intelligent dioxin regulation and control method in the plasma melting furnace starting process comprises the steps that S1, key operation parameters influencing dioxin generation are collected in the plasma melting furnace starting process, the dioxin concentration in tail smoke is detected on line, and a data set is generated; s2, correlation between the key operation parameters and dioxin is analyzed, and influence degree sequencing is conducted on the multiple key parameters in the furnace starting process; s3, establishing an intelligent regulation and control model; s4, based on the received various data, obtaining a dioxin concentration prediction value; s5, performing comparative analysis according to the actual dioxin concentration in the tail smoke and a dioxin concentration predicted value, and correcting the intelligent regulation and control model; and S6, giving key parameter adjustment suggestions and adjustment result prediction according to the given scoring basis. The whole-process precise control and green up-to-standard emission of the dioxin are realized.

Description

Technical Field

[0001] The present invention relates to the field of industrial waste gas treatment, and in particular to a method and system for intelligently controlling dioxins during a plasma melting furnace startup process. Background Art

[0002] In order to meet the needs of harmless treatment, reduction and resource utilization of hazardous waste, it is urgent to explore practical and efficient hazardous waste disposal technologies. As one of the current advanced and efficient hazardous waste disposal technologies, plasma melting technology has become an important choice technology in the treatment fields of incineration waste, medical waste, low-level radioactive waste, etc., with its wide treatment range, high environmental protection degree, and product resource utilization. Plasma melting technology refers to the high-temperature cracking of organic matter in hazardous waste materials under high-temperature oxygen-deficient conditions by means of the unique properties of plasma such as high temperature, high energy, and conductivity, to produce combustible gases such as CO and H2. The remaining inorganic substances are melted at high temperature and converted into a completely inert, extremely low-permeability glass-like material, completing the solidification and disposal of heavy metals, fly ash, and radionuclides in hazardous waste materials. The generated combustible gas is then burned to complete the resource conversion, and the solidified glass body can be collected and used as cement ingredients, building materials, insulation materials, and other uses, thereby achieving the overall harmlessness and resource utilization of hazardous waste materials. As one of the processes of industrial combustion and heat treatment, the plasma high-temperature melting treatment process of hazardous waste will inevitably produce secondary pollution, among which the emission of persistent trace organic pollutants represented by dioxins is particularly concerning.

[0003] As the main source of dioxin emissions, the dioxin emissions during the start-up process of the plasma high-temperature melting device are generally in the form of waves, with high peak values, large discharge volume, long duration, and strong volatility, which has become the key and difficult process of pollutant control. Compared with other flue gas pollutants such as SO2, NOx, HCl and particulate matter, trace organic pollutants such as dioxins are the only substances that have not been detected online, and their emission reduction control has always been a key problem that needs to be solved in the disposal of hazardous waste materials by plasma high-temperature melting. At present, there is a lack of direct and effective emission reduction control measures for flue gas dioxin emissions. Generally, a relatively single combustion process temperature and flue gas residence time control is adopted, combined with flue gas purification measures such as inhibitor placement or activated carbon adsorption during or after combustion. The control measures are relatively rough and the emission reduction effect is difficult to guarantee. The detection of dioxins is still mainly based on the offline detection method of high-resolution gas chromatography-high-resolution mass spectrometry (HJ 77.2), which is difficult to meet the needs of real-time monitoring and feedback of changes in dioxin emissions from incineration sources. Dioxin emissions and control lack a pair of "eyes". The complex composition and structure (210 types, 17 of which are toxic), extremely low concentration (ng level), and numerous influencing factors (generated throughout the whole process) determine that the extensive control method without online detection means cannot achieve precise control and cannot achieve the goal of economical, green, and standard dioxin emissions.

[0004] In recent years, with the gradual development and application of online detection of dioxins by laser ionization time-of-flight mass spectrometry, the most direct real-time feedback and evaluation system for dioxin emissions has been provided, and it has also brought a possible opportunity for plasma high-temperature melting treatment to avoid improper regulation, improve the effectiveness and efficiency of operating condition regulation, reduce operating costs, and achieve standard emission of dioxins. Therefore, how to design an intelligent control method and system for dioxins suitable for the start-up process of plasma high-temperature melting device process to ensure the precise control of the whole process of dioxins in the flue gas of hazardous waste plasma high-temperature melting treatment and green standard emission is one of the problems that need to be solved urgently. Summary of the invention

[0005] The technical problem to be solved by the present invention is the lack of a dioxin emission control method that can realize real-time feedback and evaluation in the field of industrial waste gas treatment, which restricts the precise control of the whole process and green emission of dioxins in the flue gas of hazardous waste material plasma high-temperature melting treatment. The purpose is to provide a dioxin intelligent control method and system during the plasma melting furnace startup process, which solves the problem of not being able to monitor and adjust dioxin emissions in real time.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for intelligently controlling dioxins during a plasma melting furnace startup process comprises the following steps:

[0008] S1. Start the plasma melting device, collect key operating parameters that affect dioxin generation during the plasma melting furnace startup process, and simultaneously detect the dioxin concentration in the tail flue gas online to generate a data set;

[0009] S2. Analyze the correlation between key operating parameters and dioxins, select parameters with strong correlation with dioxin emissions, and rank the influence of multiple key parameters of the plasma high-temperature melting device during the start-up process;

[0010] S3. Establish an intelligent control model based on multiple linear regression and machine learning methods, use the selected key parameters as input variables and the measured dioxin concentration as output variables, train the intelligent control model, and obtain a revised intelligent control model;

[0011] S4. Continuously collect key operating parameter data that affect dioxin generation during the plasma melting furnace startup process, input the data into the revised intelligent control model, and obtain prediction results;

[0012] S5. Based on the intelligent control model, an overall quantitative evaluation is conducted on the operating status of the plasma melting device during the start-up process. The start-up process status is determined based on the given evaluation results, and suggestions for key parameter adjustments and predictions of adjustment results are given.

[0013] As a possible design, starting the plasma melting device in the above step S1 specifically controls the plasma characteristics of the plasma melting device and the overall temperature rise of the device system to complete the startup of the system device.

[0014] As a possible design, the above plasma characteristics include plasma density, electron temperature, electron density, plasma torch voltage, plasma torch current and working gas flow rate.

[0015] As a possible design, the key operating parameters affecting dioxin generation in the above step S1 include plasma torch temperature, combustion chamber temperature, heating rate, air flow rate, pressure difference before and after the bag filter, plasma torch power, feed type, plasma melting device operating conditions and / or flue gas treatment process parameters.

[0016] As a possible design, the above data set contains no less than 1,000 groups of data.

[0017] As a possible design, in the above step S2, combined with the online detection value change of tail gas dioxins of the plasma melting device, the influence of key operating parameters on actual dioxin changes is obtained, the influence degree of key operating parameters on dioxins is ranked, and the qualitative relationship between key parameters and dioxins is given.

[0018] As a possible design, in the above step S3, based on the dioxin concentration and the corresponding key operating parameters of plasma waste disposal, based on multiple linear regression and machine learning methods, with the key parameters of on-site operation as independent variables, the relationship between the key parameters and dioxin emissions is established, and the actual dioxin concentration is used for evaluation and optimization to establish an intelligent control model.

[0019] As a possible design, after the prediction, the above step S4 also performs parameter comparison on the prediction result according to the actual dioxin concentration in the tail flue gas, so as to calibrate the intelligent control model.

[0020] As a possible design, the quantitative evaluation in the above step S5 includes scoring the maximum value, stability, continuity, emission compliance time and other characteristics of dioxin online emissions during the furnace startup process.

[0021] A dioxin intelligent control system in the process of plasma melting furnace startup, comprising

[0022] Host equipment, used to implement the above-mentioned dioxin intelligent control method during the plasma melting furnace start-up process;

[0023] A model deployment device, in which a pre-trained language model is deployed, is used to receive key parameter detection text and key parameter adjustment suggestion text sent by the host device, is used to input the key parameter detection text into the pre-trained language model, and sends the key parameter detection information output by the pre-trained language model to the host device.

[0024] The present invention establishes closed-loop prediction logic and models to control the process parameters of the plasma high-temperature melting device in real time during the furnace startup process, thereby realizing the automatic closed-loop monitoring and full-process control of dioxins. There are 210 dioxin substances, 17 of which need to be monitored, and they are generated throughout the entire process, with a relatively complex generation mechanism. However, there is a certain correlation between each control parameter and the final generation of dioxins. Based on big data processing and scientific analysis methods, a correlation model between the full-process parameters and dioxins can be established, thereby achieving the purpose of closed-loop control of dioxins.

[0025] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0026] The present invention can display the key dioxin process parameters of the current plasma high-temperature melting device process and the dioxin emission concentration of the tail flue gas in real time during the furnace startup process; give the recommended range of each operating condition, predict in real time the change value of the dioxin emission concentration caused by the regulation and control changes of each operating condition parameter, and give corresponding warnings according to the predicted dioxin emission concentration. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0028] Figure 1 A logic control diagram of a dioxin intelligent control system in a plasma melting furnace startup process provided by an embodiment of the present invention;

[0029] Figure 2 A diagram showing key parameters of a method for intelligently controlling dioxins during a plasma melting furnace startup process provided by an embodiment of the present invention;

[0030] Figure 3 A flow chart of a method for intelligently controlling dioxins during a plasma melting furnace startup process provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with examples and drawings. The illustrative embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention. If specific conditions are not specified in the examples, they are carried out according to conventional conditions or conditions recommended by the manufacturer. If the manufacturer is not specified for the reagents or instruments used, they are all conventional products that can be purchased commercially.

[0032] The present invention is applicable to any plasma melting device on the market.

[0033] A method for intelligently controlling dioxins during the plasma melting furnace startup process, referring to Figures 1 to 3 , including the following steps:

[0034] S1. Start the plasma melting device, collect key operating parameters that affect dioxin generation during the plasma melting furnace startup process, and simultaneously detect the dioxin concentration in the tail flue gas online to generate a data set.

[0035] In some embodiments of the present invention, starting the plasma melting device in the above step S1 specifically controls the plasma characteristics of the plasma melting device and the overall temperature rise of the device system to complete the startup of the system device.

[0036] In some embodiments of the present invention, the above-mentioned plasma characteristics include plasma density, electron temperature, electron density, plasma torch voltage, plasma torch current and working gas flow rate.

[0037] Specifically, the plasma torch temperature, heating rate, and air flow rate of the first and second combustion chambers are key operating parameters. After starting the furnace, the plasma torch temperature of the first combustion chamber should be greater than 1400°C, and the plasma torch temperature of the second combustion chamber should be greater than 1200°C to ensure the treatment effect of hazardous waste. During the heating process of the plasma torch, dioxins will continue to be emitted in large quantities. At this time, the back-end flue gas purification device process should be kept open in advance to ensure dioxin emission reduction control.

[0038] In some embodiments of the present invention, the key operating parameters affecting the generation of dioxins in the above step S1 include plasma torch temperature, combustion chamber temperature, heating rate, air flow rate, pressure difference before and after the bag filter, plasma torch power, feed type, plasma melting device operating conditions and / or flue gas treatment process parameters.

[0039] In some embodiments of the present invention, the data set includes no less than 1000 sets of data.

[0040] Preferably, the above-mentioned key operating parameters are at least 5.

[0041] S2. Analyze the correlation between key operating parameters and dioxins, select parameters with strong correlation with dioxin emissions, and rank the influence degree of multiple key parameters of the plasma high-temperature melting device during the furnace startup process.

[0042] In some embodiments of the present invention, in the above step S2, combined with the changes in the online detection value of tail gas dioxins of the plasma melting device, the impact of key operating factors on actual dioxin changes is obtained, the degree of impact of key operating parameters on dioxins is ranked, and the qualitative relationship between key parameters and dioxins is given.

[0043] S3. An intelligent control model is established based on multivariate linear regression and machine learning methods. The selected key parameters are used as input variables and the measured dioxin concentrations are used as output variables. The intelligent control model is trained to obtain a revised intelligent control model.

[0044] In some embodiments of the present invention, in the above step S3, based on the dioxin concentration and the corresponding key operating parameters of cold plasma waste disposal, based on multiple linear regression and machine learning methods, the key parameters of on-site operation are used as independent variables, and the relationship between key parameters and dioxin emissions is established. The actual dioxin concentration is used for evaluation and optimization, and an intelligent control model is established.

[0045] In some embodiments of the present invention, the above-mentioned machine learning method includes a support vector regression method, a least squares method and / or a multivariate linear regression method.

[0046] Preferably, the above-mentioned multivariate linear regression includes machine learning methods such as multivariate linear regression model based on least squares method, support vector machine regression, etc.; deep learning methods mainly based on deep neural network, convolutional deep neural network, deep belief network; neural network algorithms mainly based on convolutional neural network, recurrent neural network, feedforward neural network, etc.

[0047] Preferably, the above-mentioned key parameters for on-site operation include feed type, plasma torch power and air supply volume in the furnace.

[0048] Preferably, the above-mentioned smart model establishment includes the following steps:

[0049] S31. Use the multiple linear regression model as the baseline model, take the selected key parameters as independent variables, and dioxin concentration as the dependent variable to establish a regression equation;

[0050] S32. Select appropriate machine learning methods for modeling based on the performance of the baseline model;

[0051] S33, dividing the data set into a training set and a test set, using the training set to train the machine learning model, and using the test set to verify the model;

[0052] S34. Use the test set to evaluate the prediction performance of the model, and further optimize the model based on the evaluation results;

[0053] S35. The optimized machine learning model is used as an intelligent control model, and the model is further modified based on the actual dioxin concentration in the tail gas to improve the accuracy and reliability of the model.

[0054] Preferably, the above-mentioned intelligent control model will be deployed in the control system of the plasma melting device.

[0055] In some embodiments of the present invention, the single parameter correlation coefficient of the above key parameters is not less than 0.6.

[0056] S4. Continuously collect key operating parameter data that affect dioxin generation during the plasma melting furnace startup process, input the data into the revised intelligent control model to obtain prediction results, and calibrate the parameters of the prediction results according to the actual dioxin concentration in the tail flue gas.

[0057] In some embodiments of the present invention, the determination coefficient of the intelligent control model in step S4 is not less than 0.9.

[0058] Preferably, after the prediction, the above step S4 also compares the prediction results according to the actual dioxin concentration in the tail flue gas, so as to calibrate the intelligent control model. Specifically, under the operating condition, the dioxin in the tail flue gas is sampled offline, and the analysis results of the offline sampling are compared with the analysis prediction values ​​of the dioxin online measurement, so as to calibrate the dioxin online measurement model, and thus calibrate the intelligent control model.

[0059] Preferably, the above-mentioned correction intelligent control model realizes the domestication and calibration of the debugging model by comparing and analyzing the results, continuously updating, sorting, screening and inputting the key parameters and the dioxin online detection data.

[0060] S5. Based on the intelligent control model, an overall quantitative evaluation is conducted on the operating status of the plasma melting device during the start-up process. The start-up process status is determined based on the given evaluation results, and suggestions for key parameter adjustments and predictions of adjustment results are given.

[0061] In some embodiments of the present invention, the quantitative evaluation of step S5 includes scoring the maximum value, stability, continuity, and emission time of dioxins online during the furnace startup process. Scoring refers to an overall quantitative evaluation of the operating state of the plasma melting device based on the dioxin emission results. The specific score can be adjusted according to the degree of attention paid by the assessor to the process, such as paying more attention to the stability of the emission (such as the RSD of the dioxin emission concentration value for 6 consecutive hours is less than 20%, indicating stable emission, and the score is 10 points; less than 50%, the emission has a certain fluctuation, and the score is 5 points, etc.), and a detailed division is made, and this part of the indicators is assigned a proportion; the maximum value of the emission (such as the dioxin emission concentration value for 2 consecutive hours is less than 0.1, indicating low emission without exceeding the standard, and the score is 10 points; the concentration is less than 0.5, indicating compliance with the standard requirements of the furnace startup process, and the score is 5 points; the concentration is less than 1, indicating that the emission exceeds the standard, and the score is 2 points, etc.), and the proportion of this part of the indicators is assigned. Comprehensively, an overall score is given.

[0062] The above parameter adjustment suggestions refer to adjusting the operating parameters of the plasma melting device that cause dioxin to exceed the standard or increase, and providing adjustment suggestions (such as increase or decrease, etc.). The parameter adjustment suggestions and the predicted dioxin emissions that may be generated by the suggestions are obtained based on the above big data association model. The scoring is a method in the process of building the association model, and the original intention is to sort according to the correlation with the dioxin emission value to obtain a more accurate intelligent control model.

[0063] Preferably, the system is also subjected to fault inspection and operating condition optimization according to the evaluation result of S5.

[0064] Fault inspection refers to the detection of tail flue gas dioxin emission concentration starting or continuing to exceed the standard (> 0.1ng TEQ / m3 ), check the key operating parameters of the plasma melting device (such parameters have been realized in the Internet of Things and can be viewed in the central control room), and further accurately locate the actual fault location, so as to carry out maintenance or parameter adjustment steps.

[0065] Working condition optimization, based on the intelligent control model of the actual large amount of data obtained, repairs the faulty equipment according to the key working conditions with faults checked, and adjusts the abnormal working conditions in a coordinated manner according to the dioxin prediction results.

[0066] The present invention establishes a control model through existing data sets, and then controls the operating parameters of the plasma high-temperature melting device based on the output of the evaluation system model. The control result is compared with the actual detection value of the dioxin online detection system, and the comparison result becomes the input of the evaluation system model, thereby continuously optimizing and adjusting, and finally achieving ultra-low emission and automatic transformation control.

[0067] A dioxin intelligent control system in the process of plasma melting furnace startup, comprising

[0068] Host equipment, used to implement the above-mentioned dioxin intelligent control method during the plasma melting furnace start-up process;

[0069] A model deployment device, in which a pre-trained language model is deployed, is used to receive key parameter detection text and key parameter adjustment suggestion text sent by the host device, is used to input the key parameter detection text into the pre-trained language model, and sends the key parameter detection information output by the pre-trained language model to the host device.

[0070] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for intelligently controlling dioxins during the plasma melting furnace startup process, characterized in that: The steps include: S1. Start the plasma melting device, collect key operating parameters that affect dioxin generation during the plasma melting furnace startup process, and simultaneously detect the dioxin concentration in the tail flue gas online to generate a data set; S2. Analyze the correlation between key operating parameters and dioxins, select parameters with strong correlation with dioxin emissions, and rank the influence of multiple key parameters of the plasma high-temperature melting device during the start-up process; S3. Establish an intelligent control model based on multiple linear regression and machine learning methods, use the selected key parameters as input variables and the measured dioxin concentration as output variables, train the intelligent control model, and obtain a revised intelligent control model; S4. Continuously collect key operating parameter data that affect dioxin generation during the plasma melting furnace startup process, input the data into the revised intelligent control model, and obtain prediction results; S5. Based on the intelligent control model, an overall quantitative evaluation is conducted on the operating status of the plasma melting device during the start-up process. The start-up process status is determined based on the given evaluation results, and suggestions for key parameter adjustments and predictions of adjustment results are given.

2. The method for intelligently controlling dioxins during the plasma melting furnace startup process according to claim 1, characterized in that: The starting of the plasma melting device in step S1 specifically controls the plasma characteristics of the plasma melting device and the overall temperature rise of the device system to complete the starting of the system device.

3. The method for intelligently controlling dioxins during the plasma melting furnace startup process according to claim 2, characterized in that: The plasma characteristics include plasma density, electron temperature, electron density, plasma torch voltage, plasma torch current, and working gas flow rate.

4. The method for intelligently controlling dioxins during the plasma melting furnace startup process according to claim 1, characterized in that: The key operating parameters affecting the generation of dioxins in step S1 include plasma torch temperature, combustion chamber temperature, heating rate, air flow rate, pressure difference before and after the bag filter, plasma torch power, feed type, plasma melting device operating conditions and / or flue gas treatment process parameters.

5. The method for intelligently controlling dioxins during the plasma melting furnace startup process according to claim 1, characterized in that: The data set contains no less than 1000 sets of data.

6. The method for intelligently controlling dioxins during the plasma melting furnace startup process according to claim 1, characterized in that: In step S2, the influence of key operating parameters on actual dioxin changes is obtained by combining the changes in the online detection value of tail gas dioxins of the plasma melting device, the influence of key operating parameters on dioxins is ranked, and the qualitative relationship between key parameters and dioxins is given.

7. The method for intelligently controlling dioxins during the plasma melting furnace startup process according to claim 1, characterized in that: In step S3, based on multiple linear regression and machine learning methods, the key parameters of on-site operation are used as independent variables to establish the relationship between the key parameters and dioxin emissions, and the actual dioxin concentration is used for evaluation and optimization to establish an intelligent control model.

8. The method for intelligently controlling dioxins during the plasma melting furnace startup process according to claim 1, characterized in that: After the prediction, step S4 also compares the parameters of the prediction results according to the actual dioxin concentration in the tail flue gas, so as to calibrate the intelligent control model.

9. The method for intelligently controlling dioxins during the plasma melting furnace startup process according to claim 1, characterized in that: The quantitative evaluation in step S5 includes scoring the maximum value, stability, continuity, emission compliance time and other characteristics of dioxin online emissions during the furnace startup process.

10. A dioxin intelligent control system during the plasma melting furnace startup process, characterized in that: include A host device for implementing the dioxin intelligent control method in the plasma melting furnace start-up process as described in any one of claims 1 to 9; A model deployment device, in which a pre-trained language model is deployed, is used to receive key parameter detection text and key parameter adjustment suggestion text sent by the host device, is used to input the key parameter detection text into the pre-trained language model, and sends the key parameter detection information output by the pre-trained language model to the host device.

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