CN117308102A - Control system and device for efficient, clean and intelligent operation of whole process of incinerator - Google Patents
Control system and device for efficient, clean and intelligent operation of whole process of incinerator Download PDFInfo
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- 238000002485 combustion reaction Methods 0.000 claims abstract description 38
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims abstract description 24
- 239000003546 flue gas Substances 0.000 claims abstract description 24
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 22
- 239000010813 municipal solid waste Substances 0.000 claims abstract description 22
- 238000000746 purification Methods 0.000 claims abstract description 21
- HGUFODBRKLSHSI-UHFFFAOYSA-N 2,3,7,8-tetrachloro-dibenzo-p-dioxin Chemical compound O1C2=CC(Cl)=C(Cl)C=C2OC2=C1C=C(Cl)C(Cl)=C2 HGUFODBRKLSHSI-UHFFFAOYSA-N 0.000 claims abstract description 16
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- HEMHJVSKTPXQMS-UHFFFAOYSA-M Sodium hydroxide Chemical compound [OH-].[Na+] HEMHJVSKTPXQMS-UHFFFAOYSA-M 0.000 claims description 18
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 17
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- 238000005200 wet scrubbing Methods 0.000 claims description 3
- CDBYLPFSWZWCQE-UHFFFAOYSA-L Sodium Carbonate Chemical compound [Na+].[Na+].[O-]C([O-])=O CDBYLPFSWZWCQE-UHFFFAOYSA-L 0.000 claims description 2
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23G—CREMATION FURNACES; CONSUMING WASTE PRODUCTS BY COMBUSTION
- F23G5/00—Incineration of waste; Incinerator constructions; Details, accessories or control therefor
- F23G5/50—Control or safety arrangements
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Incineration Of Waste (AREA)
Abstract
The invention discloses a control system and a device for efficient, clean and intelligent operation of an incinerator in the whole process, which belong to the technical field of incineration operation control of municipal solid waste, and comprise the following steps: the combustion process optimization control module is used for realizing boiler feeding and combustion control optimization through the first intelligent prediction model and generating a first optimization result; the flue gas purification process optimization module is used for realizing the control optimization of the flue gas purification of the boiler through a second intelligent prediction model and generating a second optimization result; the intelligent incineration control module is used for realizing the material layer thickness prediction, the live wire and combustion state detection, the real-time calculation of the garbage heat value, the NOx prediction, the conventional pollutant prediction and the dioxin prediction through an image recognition algorithm and a machine learning algorithm, and controlling and optimizing the whole process of the incinerator according to the first optimization result and the second optimization result; the invention realizes the real-time diagnosis and analysis of the stable combustion of the garbage incinerator, reduces the pollutant emission and the efficient clean utilization of the combustion.
Description
Technical Field
The invention relates to the technical field of incineration operation control of municipal solid waste, in particular to a control system and a device for efficient, clean and intelligent operation of an incinerator in the whole process.
Background
At present, urban household garbage treatment technology mainly comprises four types of sanitary landfill, garbage compost, garbage incineration and comprehensive utilization, wherein the garbage incineration treatment technology has the unique advantages of volume reduction, harmlessness and recycling.
The household garbage incineration is difficult to control stably in actual operation due to complex garbage composition, unstable heat value and the like. At present, the method for controlling the garbage incineration mainly comprises the following steps: based on manual prediction control, PID control and the like of a DCS system, most garbage incineration plants need to operate related operations such as primary air quantity, secondary air quantity, primary air proportion, pushing speed, fire grate operation speed and the like according to experience, and the small part of garbage incineration plants can automatically control local equipment such as primary air, secondary air, a pusher and the like, but the automatic control of the whole process is difficult to achieve. Overall, most garbage incinerator degree of automation is lower, lacks the automatic control system of overall process, needs to burn the manual operation of plant part module, needs a large amount of manual operations, and combustion stability is relatively poor, leads to unstable, pollutant such as dioxin, nitrogen oxide to exceed standard phenomenon easily, is difficult to realize high-efficient clean operation. Therefore, a need exists for an intelligent operation system that is efficient and clean throughout the entire process of incinerating garbage incinerator to solve the above problems.
Disclosure of Invention
In order to solve the problems, the invention provides a control system for the efficient, clean and intelligent operation of the whole process of an incinerator, which comprises the following components:
the combustion process optimization control module is used for realizing boiler feeding and combustion control optimization through the first intelligent prediction model and generating a first optimization result;
the flue gas purification process optimization module is used for realizing the control optimization of the flue gas purification of the boiler through a second intelligent prediction model and generating a second optimization result;
the intelligent incineration control module is used for realizing the material layer thickness prediction, the live wire and combustion state detection, the real-time calculation of the garbage heat value, the NOx prediction, the conventional pollutant prediction and the dioxin prediction through an image recognition algorithm and a machine learning algorithm, and controlling and optimizing the whole process of the incinerator according to a first optimizing result and a second optimizing result.
Preferably, the combustion process optimization control module is further used for taking data affecting a feeding grate, a turning grate, a sliding grate, a secondary air, an induced draft fan and steam water of the incinerator as first characteristic data according to collected working condition data of the incinerator, marking the first characteristic data, and generating a first control variable for optimizing boiler feeding and combustion control; meanwhile, based on the labels of the first characteristic data, a first variable relation between each first control variable is obtained, the first variable relation is endowed to the first characteristic data, a first data set is generated, the deep learning model is trained, and a first intelligent prediction model is built.
Preferably, the combustion process optimization control module is further configured to optimize boiler feed and combustion control based on a first control variable derived from a first intelligent predictive model, wherein the first control variable comprises:
the control variables of the feed grate include: setting the speed of the pusher and the speed coefficient of the fire grate of each unit;
the control variables of the turning fire grate include: the number of times each unit is turned over;
the control variables for the sliding grate include: the sliding times of each unit and the distribution coefficient of each unit;
the control variables of the primary air quantity include: the speed of the primary air blower of each unit;
the control variables of the secondary air quantity include: secondary fan frequency, left and right side air gate position;
the control variables of the induced draft fan include: the frequency of the induced draft fan and the position of an inlet air door of the induced draft fan;
the control variables of the soda water include: opening of the electric valve of the primary desuperheater and opening of the electric valve of the secondary desuperheater.
Preferably, the flue gas purification process optimization module is further used for taking data affecting semi-dry deacidification, SNCR denitration, active carbon, wet washing and bag dust removal of the incinerator as second characteristic data according to the collected working condition data of the incinerator, and performing standard injection to generate a second control variable for optimizing the flue gas purification control of the boiler; meanwhile, based on the labels of the second characteristic data, a second variable relation between every two second control variables is obtained, the second variable relation is endowed to the second characteristic data, a second data set is generated, the deep learning model is trained, and a second intelligent prediction model is built.
Preferably, the flue gas purification process optimization module is further configured to control and optimize the flue gas purification of the boiler according to a second control variable obtained by a second intelligent prediction model, where the second control variable includes:
the control variables of semi-dry deacidification include: the opening of the lime slurry valve and the opening of the tap water valve of the atomizer;
the control variables for SNCR denitration include: reducing agent valve opening and soft water regulating valve opening;
the control variables for activated carbon include: activated carbon feeder frequency;
the control variables for wet scrubbing include: opening of a regulating valve of the sodium hydroxide absorption liquid pump and opening of a regulating valve of the sodium hydroxide dehumidifying liquid pump;
the control variables of bag dust removal include: air cannon time interval, pulse valve time interval, ash removal time interval.
Preferably, the intelligent incineration control module is further configured to obtain a third variable relationship between the first control variable and the second control variable according to the relationship between the first characteristic data and the second characteristic data;
and based on the first variable relation, the second variable relation and the third variable relation, adjusting the first optimization result and the second optimization result through the first intelligent prediction model and the second intelligent prediction model, and controlling and optimizing the whole process of the incinerator.
Preferably, the intelligent incineration control module is further used for realizing detection of the fire wire and the combustion state through a U-Net full convolution neural network.
Preferably, the intelligent incineration control module is further used for predicting the dioxin by fitting a nonlinear relation between the incineration working condition and the dioxin through an autoregressive moving average model and a support vector machine model.
The invention discloses a control device for efficient, clean and intelligent operation of an incinerator in the whole process, which comprises the following components:
the sensor cluster is used for collecting physical control parameters of each position of the incinerator in real time and providing real-time working condition data of the incinerator;
the industrial personal computer is used for carrying a control system, controlling and optimizing the whole process of the incinerator and realizing the efficient and clean operation of the auxiliary incinerator;
and the data line is used as a data transmission medium for connecting the sensor cluster and the industrial personal computer.
Preferably, the data storage device is used for storing real-time working condition data and data generated by the industrial personal computer;
the display device is used for visually displaying the whole process of the incinerator and displaying real-time working condition data, a first optimizing result and a second optimizing result which are predicted by the control system.
The invention discloses the following technical effects:
the invention realizes the real-time diagnosis and analysis of the stable combustion of the garbage incinerator, reduces the pollutant emission and the efficient clean utilization of the combustion.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of an overall process high-efficiency cleaning intelligent operating system according to the present invention;
FIG. 2 is a schematic diagram of the control principle of primary air model predictive control according to the present invention;
FIG. 3 is a graph showing the mean change of 5 minutes of furnace temperature before and after the actual use of the system according to the invention;
FIG. 4 shows the tail NO of the incinerator before and after the actual use system of the invention x Change versus graph.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
As shown in fig. 1-4, the present invention provides an intelligent operation system for efficient cleaning of the whole process of an incinerator, comprising: the intelligent combustion control system comprises a combustion process optimization control module, a flue gas purification process optimization module and an intelligent combustion control module.
The combustion process optimization control module is used for realizing optimization of boiler feeding and combustion control and comprises a feeding grate, a turning grate, a sliding grate, a secondary air, an induced draft fan and steam and water;
the flue gas purification process optimization module is used for realizing the control optimization of the flue gas purification of the boiler, and comprises semi-dry deacidification, SNCR denitration, activated carbon, wet washing and bag-type dust removal;
the intelligent incineration control module integrates a combustion process and a flue gas purification process, so that the whole process control optimization of the incinerator is realized; meanwhile, the method realizes the material layer thickness prediction of the fire grate, the detection of the fire wire and the combustion state, the real-time calculation of the garbage heat value, the NOx prediction, the conventional pollutant prediction and the dioxin prediction through an image recognition algorithm and a machine learning algorithm, realizes the visualization and the quantification of the combustion state, and assists in realizing the efficient clean operation of the incinerator.
In the combustion process optimization control module, the model based on model predictive control comprises a feeding grate control model, a turning grate control model, a sliding grate control model, a secondary air quantity control model, a draught fan control model and a steam-water control model. Model predictive control is a time sequence control mode, in each moment, according to the state of a controlled object and a predictive model, predicting the state of the target in a future period, solving an optimal group of control sequences according to a certain performance index (cost function), transmitting the first control action of the group of control sequences as output to an executing mechanism, and simultaneously continuously executing an optimization algorithm at the next moment, and continuously updating the control sequences. The control variables of the feed grate control model include: setting the speed of the pusher and the speed coefficient of the fire grate of each unit; the control variables of the turning fire grate control model comprise: the number of times each unit is turned over; the control variables of the sliding grate control model include: the sliding times of each unit and the distribution coefficient of each unit; the control variables of the primary air volume control model include: the speed of the primary air blower of each unit; the control variables of the secondary air volume control model include: secondary fan frequency, left and right side air gate position; the control variables of the induced draft fan control model comprise: the frequency of the induced draft fan and the position of an inlet air door of the induced draft fan; the control variables of the steam-water control model include: opening of the electric valve of the primary desuperheater and opening of the electric valve of the secondary desuperheater.
As shown in fig. 2, taking a primary air volume control model as an example, a control target of the primary air volume control model is a target load of a boiler, a control variable is a primary air fan speed of each unit, and a specific embodiment of control includes:
(1) Primary air volume prediction model: the prediction model is the basis of model prediction control, and the primary air quantity prediction model obtains current boiler load state information through a DCS, and then adds input variables (primary air fan speeds of all units) for future boiler load control to predict the state of future boiler load. Optionally, the form of the prediction model includes: state equation or transfer function, step response or impulse response;
(2) And (3) rolling optimization: the model prediction control adopts a rolling type finite time domain optimization strategy, and at each sampling moment, the optimal speed setting of a finite time period from the moment is solved according to the optimization performance index of the primary air quantity at the moment. The calculated primary fan speed control action sequence is actually executed at the current moment, and the optimal speed setting is obtained again according to the method at the next sampling moment, so that the optimal speed setting is continuously optimized.
(3) Feedback correction: and comparing the actually output boiler load with the estimated boiler load of the model to obtain a prediction error of the model, and correcting a predicted value of the model for the primary fan speed by using the model prediction error to further obtain a more accurate predicted value of the boiler load output.
In the flue gas purification process optimization module, the model predictive control model comprises a semi-dry deacidification control model and an SNCR denitration control model. The control variables of the semi-dry deacidification control model comprise: the opening of the lime slurry valve and the opening of the tap water valve of the atomizer; the control variables of the SNCR denitration control model include: reducing agent valve opening and soft water regulating valve opening; the control variables of the activated carbon control model include: activated carbon feeder frequency; the control variables of the wet scrubbing control model include: opening of a regulating valve of the sodium hydroxide absorption liquid pump and opening of a regulating valve of the sodium hydroxide dehumidifying liquid pump; the control variables of the bag-type dust removal control model comprise: air cannon time interval, pulse valve time interval, ash removal time interval. Taking an SNCR denitration control model as an example, the specific implementation mode comprises the following steps:
(1) Prediction model: the SNCR denitration prediction model predicts the concentration of the flue gas NOx by the flue gas NOx concentration information at the current moment, the opening of a reducing agent valve and the opening of a soft water regulating valve. Optionally, the form of the prediction model includes: state equation or transfer function, step response or impulse response;
(2) And (3) rolling optimization: and at each sampling moment, according to the optimized performance index of the reducing agent at the moment, solving the optimal control values of the opening of the reducing agent valve and the opening of the soft water regulating valve in a limited time period from the moment. The calculated control action sequence is actually executed at the current moment, and the optimal control value is calculated again according to the method at the next sampling moment, so that the optimal control value is continuously optimized;
(3) Feedback correction: comparing the NOx value actually output with the NOx value estimated by the model to obtain a prediction error of the model, and correcting the predicted value of the model by using the model prediction error to further obtain a more accurate NOx output predicted value.
The intelligent incineration control module integrates the combustion process and the flue gas purification process, displays the real-time control states and effects of boiler feeding control, combustion control and flue gas purification control, and can perform operation switching of input/stop;
in the intelligent incineration control module, the image recognition algorithm comprises a discharge layer thickness recognition algorithm and a fire wire and combustion state detection recognition algorithm;
taking a live wire recognition algorithm based on image recognition as an example, the specific implementation steps are as follows:
(1) The method comprises the steps of collecting images combusted in a furnace through an industrial camera and manually labeling to form a data set, and performing model training through a U-Net full convolution neural network cycle to obtain an identification model capable of accurately identifying a fire wire;
(2) The model is deployed in an industrial personal computer, so that the functions of data acquisition, processing and identification of the live wire identification model are realized, and the result is stored;
(3) Based on the historical data, when the model prediction result deviates too much from the actual result, the model is automatically optimized and redeployed.
In the intelligent incineration control module, the machine learning algorithm comprises a garbage heat value real-time calculation algorithm, a NOx prediction algorithm, a conventional pollutant prediction algorithm and a dioxin prediction algorithm.
Taking a dioxin prediction algorithm based on machine learning as an example, the specific implementation steps are as follows:
(1) Acquiring time sequence data such as incineration working conditions and dioxin through a DCS system, preprocessing the time sequence data to form a data set, and fitting a nonlinear relation between the incineration working conditions and the dioxin through an autoregressive moving average model (ARIMA) and a Support Vector Machine (SVM) model to obtain a prediction model capable of accurately fitting the dioxin;
(2) The model is deployed in an industrial personal computer, so that the real-time data acquisition, processing and recognition of each function of the dioxin prediction model are realized, and the result is stored;
(3) Based on the historical data, when the model prediction result deviates too much from the actual result, the model is automatically optimized and redeployed.
In the invention, the real-time input data of the model and the algorithm described in any one of the above items is used for extracting DCS parameters by each point physical sensor, thereby achieving the purposes of real-time control and efficient clean operation.
The invention also provides an intelligent operation device for the whole process of the incinerator, which comprises the following components:
each point physical sensor is used for acquiring each DCS control parameter and providing real-time data calculated by each module;
the industrial personal computer is internally provided with a whole process control module, is provided with an intelligent operation system, realizes intelligent identification and prediction of the whole process of the incinerator, controls the incinerator and realizes the efficient and clean operation of the auxiliary incinerator;
and the data line is used for connecting the physical sensors at all the points and the industrial personal computer.
As illustrated in fig. 3 and 4, the operating system is effective in improving the stability of the operation of the incinerator while reducing pollutant emissions.
The invention integrates the combustion automatic control and the flue gas treatment control of the garbage incinerator, integrates a feeding grate control model, a turning grate control model, a sliding grate control model, a secondary air volume control model, a draught fan control model, a steam-water control model, a semi-dry deacidification control model, an SNCR denitration control model, an active carbon control model, a wet washing control model and a cloth bag dust removal control model, and realizes the whole process automatic control of the incineration plant. The image recognition algorithm and the machine learning algorithm are applied to realize the prediction of the thickness of the material layer of the incinerator, the detection of the fire wire and the combustion state, the real-time calculation of the heat value of garbage, the prediction of NOx and other conventional pollutants, and the prediction of dioxin, so that operators can know the running state and the smoke emission level of the incinerator in real time, and the operators can adjust the load strategy according to the running state, thereby realizing the clean and efficient running of the whole process of the incinerator.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In the description of the present invention, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. A control system for the efficient, clean and intelligent operation of the whole process of an incinerator is characterized by comprising the following components:
the combustion process optimization control module is used for realizing boiler feeding and combustion control optimization through the first intelligent prediction model and generating a first optimization result;
the flue gas purification process optimization module is used for realizing the control optimization of the flue gas purification of the boiler through a second intelligent prediction model and generating a second optimization result;
the intelligent incineration control module is used for realizing the material layer thickness prediction, the live wire and combustion state detection, the real-time calculation of the garbage heat value, the NOx prediction, the conventional pollutant prediction and the dioxin prediction through an image recognition algorithm and a machine learning algorithm, and controlling and optimizing the whole process of the incinerator according to the first optimization result and the second optimization result.
2. The control system for efficient, clean and intelligent operation of the whole process of the incinerator according to claim 1, wherein:
the combustion process optimization control module is further used for taking data affecting a feeding grate, a turning grate, a sliding grate, a secondary air, an induced draft fan and steam water of the incinerator as first characteristic data according to collected working condition data of the incinerator, marking the data, and generating a first control variable for boiler feeding and combustion control optimization; meanwhile, based on the labels of the first characteristic data, a first variable relation between each first control variable is obtained, the first variable relation is endowed to the first characteristic data, a first data set is generated, a deep learning model is trained, and the first intelligent prediction model is constructed.
3. The control system for efficient, clean and intelligent operation of the whole process of the incinerator according to claim 2, wherein:
the combustion process optimization control module is further used for optimizing boiler feeding and combustion control according to a first control variable obtained by the first intelligent prediction model, wherein the first control variable comprises:
the control variables of the feed grate include: setting the speed of the pusher and the speed coefficient of the fire grate of each unit;
the control variables of the turning fire grate include: the number of times each unit is turned over;
the control variables for the sliding grate include: the sliding times of each unit and the distribution coefficient of each unit;
the control variables of the primary air quantity include: the speed of the primary air blower of each unit;
the control variables of the secondary air quantity include: secondary fan frequency, left and right side air gate position;
the control variables of the induced draft fan include: the frequency of the induced draft fan and the position of an inlet air door of the induced draft fan;
the control variables of the soda water include: opening of the electric valve of the primary desuperheater and opening of the electric valve of the secondary desuperheater.
4. A control system for efficient, clean and intelligent operation of an incinerator overall process according to claim 3, wherein:
the flue gas purification process optimization module is further used for taking data affecting semi-dry deacidification, SNCR denitration, active carbon, wet washing and cloth bag dust removal of the incinerator as second characteristic data according to the collected working condition data of the incinerator, and performing standard injection to generate a second control variable for boiler flue gas purification control optimization; and simultaneously, acquiring a second variable relation between each second control variable based on the label of the second characteristic data, giving the second variable relation to the second characteristic data, generating a second data set, training a deep learning model, and constructing the second intelligent prediction model.
5. The control system for efficient, clean and intelligent operation of the whole process of the incinerator according to claim 4, wherein:
the flue gas purification process optimization module is further configured to control and optimize boiler flue gas purification according to a second control variable obtained by the second intelligent prediction model, where the second control variable includes:
the control variables of semi-dry deacidification include: the opening of the lime slurry valve and the opening of the tap water valve of the atomizer;
the control variables for SNCR denitration include: reducing agent valve opening and soft water regulating valve opening;
the control variables for activated carbon include: activated carbon feeder frequency;
the control variables for wet scrubbing include: opening of a regulating valve of the sodium hydroxide absorption liquid pump and opening of a regulating valve of the sodium hydroxide dehumidifying liquid pump;
the control variables of bag dust removal include: air cannon time interval, pulse valve time interval, ash removal time interval.
6. The control system for efficient, clean and intelligent operation of the whole process of the incinerator according to claim 5, wherein:
the intelligent incineration control module is further used for acquiring a third variable relation between the first control variable and the second control variable according to the relation between the first characteristic data and the second characteristic data;
and based on the first variable relation, the second variable relation and the third variable relation, adjusting the first optimization result and the second optimization result through the first intelligent prediction model and the second intelligent prediction model, and then controlling and optimizing the whole process of the incinerator.
7. The control system for efficient, clean and intelligent operation of the whole process of the incinerator according to claim 6, wherein:
the intelligent incineration control module is also used for realizing detection of a fire wire and a combustion state through a U-Net full convolution neural network.
8. The control system for efficient, clean and intelligent operation of the whole process of the incinerator according to claim 7, wherein:
the intelligent incineration control module is further used for predicting dioxin by fitting a nonlinear relation between incineration working conditions and the dioxin through an autoregressive moving average model and a support vector machine model.
9. The utility model provides a control device of clean intelligent operation of incinerator overall process high efficiency which characterized in that includes:
the sensor cluster is used for collecting physical control parameters of each point of the incinerator in real time and providing real-time working condition data of the incinerator;
the industrial personal computer is used for carrying the control system of any one of claims 1-8, controlling and optimizing the whole process of the incinerator and realizing the efficient and clean operation of the auxiliary incinerator;
and the data line is used as a data transmission medium for connecting the sensor cluster with the industrial personal computer.
10. The control device for efficient, clean and intelligent operation of the whole process of the incinerator according to claim 9, wherein:
the data storage device is used for storing the real-time working condition data and the data generated by the industrial personal computer;
and the display device is used for visually displaying the whole process of the incinerator and displaying the real-time working condition data, the first optimizing result and the second optimizing result which are predicted by the control system.
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| CN202311378303.2A CN117308102A (en) | 2023-10-24 | 2023-10-24 | Control system and device for efficient, clean and intelligent operation of whole process of incinerator |
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