CN-121971969-A - Intelligent control method and device for adding amount of industrial desulfurizing agent

CN121971969ACN 121971969 ACN121971969 ACN 121971969ACN-121971969-A

Abstract

The application provides an intelligent control method and device for the addition amount of an industrial desulfurizing agent, which are applied to the technical field of data processing. The application collects the flue gas The method comprises the steps of preprocessing multisource real-time data such as concentration and equipment operation parameters and key parameters such as desulfurization efficiency standard, generating a standardized data set, analyzing the data by a control platform, sequencing the data according to priority, determining a control strategy by combining scenes, throughput and the like with an industrial mechanism model, generating a control instruction packet containing target addition amount, synchronously checking working condition matching degree after the instruction is transmitted to an execution unit, monitoring and feeding back in real time in the addition process, dynamically adjusting instruction parameters through a real-time optimizing algorithm if emission or medicament consumption is abnormal, circularly optimizing until indexes reach standards, and realizing accurate control, energy saving and consumption reduction.

Inventors

CHEN JING
LI CHENGYU
XIE XUEJING
YAO YE

Assignees

南京交通职业技术学院
上海宇垒自动控制技术有限公司
上海交通大学

Dates

Publication Date: 20260505
Application Date: 20260126

Claims (8)

1. An intelligent control method for the addition amount of an industrial desulfurizing agent is characterized by comprising the following steps: Obtaining and containing flue gas The method comprises the steps of completing data cleaning and normalization through a data preprocessing module to generate a standardized control data set, wherein the key parameters comprise concentration, desulfurizing agent consumption, equipment operation parameters, multisource real-time data of environmental temperature and humidity, desulfurizing efficiency standards and medicament cost thresholds; The control platform reads real-time indexes, parameter association degree and data fluctuation range of the data set, analyzes the standardized control data set and sorts the standardized control data set according to the parameter influence priority; according to the type of an industrial scene, the treatment capacity of a desulfurization system and the real-time emission requirement, a control strategy is negotiated and determined by a control platform and an industrial mechanism model, and according to the fluctuation range of the concentration of the smoke pollutants and the response speed of equipment, the feeding adjustment step length and the response period of the desulfurizing agent are set, so that a control instruction packet containing real-time working condition data and target feeding amount is formed; The control platform transmits the control instruction packet to the desulfurizing agent adding and executing unit according to the response period, synchronously guides in real-time data of the same type of working conditions through the parallel acquisition machine, and checks the working condition matching degree of instruction execution; The feeding execution unit accurately feeds according to the target feeding amount in the control instruction packet, monitors the desulfurization efficiency, the medicament consumption rate and the equipment running state in real time in the execution process, and feeds back to the control platform; And the control platform compares the feedback data with a preset standard, if the emission or the medicament consumption is abnormal, the dynamic adjustment is triggered, the mapping relation between the working condition and the addition amount is optimized through a real-time optimizing algorithm, the parameters of the instruction packet are adjusted according to the smoke purification, equipment coordination and cost control weighting, the weight is dynamically adapted, and the circulation optimization is carried out until the indexes reach the standards.
2. The method of claim 1, wherein the control platform reads real-time metrics, parameter correlations, data fluctuation ranges of the data sets, parses the standardized control data sets, ranks the control data sets by parameter impact priority, comprising: The control platform reads real-time indexes, parameter association degrees and data fluctuation ranges of the standardized control data set, and analyzes the dimensional integrity and working condition adaptation effectiveness of the data; The method comprises the steps of determining the influence weight of desulfurization efficiency, the cost control association coefficient and the core evaluation index of the equipment cooperative adaptation threshold, and finishing quantitative labeling according to the influence degree; comprehensively scoring the sensitivity of the desulfurization effect, the cost association degree and the fluctuation interference degree of each parameter, and establishing a parameter influence priority evaluation system; Calculating the comprehensive score of each parameter through a weight calibration algorithm to form a priority ordering rule; according to the quantized scoring result, according to the smoke And (3) sorting the priorities of the concentration, the equipment operation parameter, the desulfurizing agent consumption and the environmental temperature and humidity, and generating a parameter sorting table with weight labels.
3. The method of claim 1, wherein the control strategy is determined by negotiating a control platform and an industrial mechanism model according to the industrial scene type, the desulfurization system throughput and the real-time emission requirement, and the desulfurizing agent addition adjustment step length and the response period are set according to the fluctuation range of the concentration of the flue gas pollutants and the response speed of equipment to form a control instruction packet containing real-time working condition data and target addition amount, and the method comprises the following steps: receiving the type of an industrial scene, the treatment capacity of a desulfurization system and the real-time emission requirement associated with a standardized control data set, and analyzing to obtain a core control target and a constraint boundary for up-to-standard emission and cost optimization; The control platform calls a parameter priority ranking table, is matched with the industrial mechanism model in a linkage way, and negotiates to determine a basic adding control frame guided by core parameters; Based on the key control dimension of frame disassembly, accurately calculating the feeding adjustment step length of the desulfurizing agent according to the fluctuation range of the concentration of the flue gas pollutants, and defining a corresponding control response period by combining the response speed of equipment; embedding real-time working condition data and the calculated target addition amount, and integrating the adjustment step length and the response period to form a closed-loop control logic; And packaging the core information according to the industrial control instruction specification to generate a control instruction packet which can be directly issued to the execution unit.
4. The method of claim 1, wherein the control platform transmits the control instruction packet to the desulfurizing agent adding and executing unit according to the response period, synchronously guides in real-time data of the same type of working conditions through the parallel acquisition machine, and checks the working condition matching degree of the instruction execution, and the method comprises the following steps: The control platform transmits a control instruction packet to the desulfurizing agent adding and executing unit according to a preset response period, synchronously starts a parallel acquisition mechanism and imports real-time data of the same type as the current working condition; Extracting target working condition parameters, an addition threshold value and core characteristics of real-time acquisition data in an instruction packet, and establishing a matching dimension index of the instruction parameters and the working condition data; calculating deviation values of the instruction preset parameters and real-time working condition data, checking the matching degree of the concentration of the smoke pollutants and the running state of the equipment, and generating a working condition matching degree quantization score; Setting a matching degree qualification threshold, if the score meets the standard, confirming that the instruction is executable, and if the score does not meet the standard, marking a deviation dimension and feeding back to the control platform; and integrating the matching degree scoring result, the deviation analysis data and the working condition real-time characteristics to generate a working condition verification report containing the execution feasibility conclusion and the deviation details.
5. The method of claim 4, wherein the control platform compares the feedback data with a preset standard, triggers dynamic adjustment if the emission or the medicament consumption is abnormal, optimizes the mapping relation between the working condition and the addition amount through a real-time optimizing algorithm, adjusts parameters of the instruction packet according to smoke purification, equipment coordination and cost control weighting, dynamically adapts weights, and circularly optimizes the instruction packet until the index reaches the standard, and comprises the following steps: the control platform receives the desulfurization efficiency, the medicament consumption rate and the equipment running state data fed back by the adding execution unit, and performs deviation comparison with a preset desulfurization efficiency standard and a medicament cost threshold value to generate a data standard reaching judgment result and an abnormal dimension label; Triggering a dynamic adjustment mechanism under the condition of exceeding emission standard or abnormal medicament consumption, and carrying out iterative optimization on the mapping relation between the working condition parameters and the dosage of the desulfurizing agent through a real-time optimizing algorithm to generate an optimized dosage adaptation model; Based on the smoke purification priority, the equipment cooperative efficiency coefficient and the cost control weight duty ratio, carrying out weighted adjustment on the addition amount, the adjustment step length and the response period in the control instruction packet; dynamically adapting each weight ratio by combining real-time pollutant concentration fluctuation, equipment operation load change and energy-saving targets to generate a parameter adjustment scheme and a weight update coefficient; and repackaging the optimized parameters into a new control instruction packet, and sending the new control instruction packet to an execution unit, and circularly executing the data feedback, deviation comparison and parameter adjustment processes until the desulfurization efficiency and the medicament consumption index reach the standard.
6. An intelligent control device for the addition amount of an industrial desulfurizing agent is characterized by comprising: The multi-source data acquisition and preprocessing module is used for acquiring multi-source real-time data comprising the concentration of flue gas SO 2 , the consumption of a desulfurizing agent, the running parameters of equipment and the environmental temperature and humidity, and key parameters of desulfurization efficiency standards and medicament cost thresholds, and generating a standardized control dataset through data cleaning and normalization processing; the data analysis and priority ordering module is used for reading real-time indexes, parameter association degrees and data fluctuation ranges of the standardized control data set, completing data set analysis and ordering according to the parameter influence priority; The control strategy negotiation and instruction generation module is used for negotiating and determining a control strategy through the control platform and the industrial mechanism model according to the type of an industrial scene, the treatment capacity of a desulfurization system and the real-time emission requirement, and setting the feeding adjustment step length and the response period of the desulfurizing agent according to the fluctuation range of the concentration of the smoke pollutants and the response speed of equipment to form a control instruction packet containing real-time working condition data and target feeding amount; The command transmission and working condition verification module is used for transmitting a control command packet to the desulfurizing agent adding and executing unit according to a response period, synchronously introducing the same type of working condition real-time data through the parallel acquisition machine, and verifying the working condition matching degree of command execution; The accurate dosing and state feedback module is used for enabling the dosing execution unit to finish accurate dosing according to the target dosing amount in the control instruction packet, monitoring desulfurization efficiency, medicament consumption rate and equipment running state in real time in the execution process, and feeding back monitoring data to the control platform; The dynamic optimization and closed-loop regulation module is used for comparing feedback data with preset standards through the control platform, triggering dynamic adjustment if emission or medicament consumption is abnormal, optimizing the mapping relation between working conditions and addition amount through a real-time optimizing algorithm, weighting and adjusting instruction packet parameters according to smoke purification, equipment coordination and cost control, and dynamically adapting weights to form a circulation optimization mechanism until each index reaches the standard.
7. An electronic device, comprising: and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the intelligent control method for the feeding amount of the industrial desulfurization agent according to any one of claims 1 to 5 by executing the executable instructions.
8. A computer-readable storage medium having a computer program stored thereon, wherein the computer program when executed by a second processor implements the intelligent control method for an industrial desulfurization agent dosage according to any one of claims 1 to 5.

Description

Intelligent control method and device for adding amount of industrial desulfurizing agent Technical Field The invention relates to the technical field of data processing, in particular to an intelligent control method and device for the addition amount of an industrial desulfurizing agent. Background The current industrial desulfurization system mostly adopts a fixed addition control or manual experience adjustment mode, and has the following technical defects: The conventional method does not fully consider the flue gas Dynamic fluctuation of multisource parameters such as concentration, equipment operation parameters, environmental temperature and humidity and the like is judged and adjusted only according to fixed standards or manual judgment, so that excessive addition of the desulfurizing agent easily causes cost waste, or insufficient addition causes excessive discharge, and the balance between environment protection standard and economic cost is difficult to realize. The operation effect of the desulfurization system is influenced by multi-factor coupling such as smoke components, equipment loads, environmental conditions and the like, the existing control mode does not establish a correlation analysis mechanism among all parameters, the addition strategy cannot be dynamically optimized according to parameter fluctuation, the desulfurization efficiency is unstable, the cooperative operation requirements of equipment such as a circulating pump, an oxidation fan and the like are ignored, and the energy consumption is high. The existing system does not form closed loop control of data acquisition, analysis, decision-making, execution and feedback, the monitoring feedback of key indexes such as desulfurization efficiency, medicament consumption rate and the like is lagged, when working conditions change or abnormality occurs, an adjusting mechanism cannot be triggered quickly, manual intervention and adjustment are needed, the response speed is low, and unplanned shutdown or environmental protection risks can be caused by manual operation errors. The desulfurization system processing capacity and emission requirements of different industrial scenes (such as power plants and ceramic plants) are different, the conventional control method lacks generalized parameter adaptation and strategy adjustment logic, is difficult to adapt to diversified working conditions, and limits the popularization and application of the technology. It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art. Disclosure of Invention Other features and advantages of the application will be apparent from the following detailed description, or may be learned by the practice of the application. According to one aspect of the application, an intelligent control method for the adding amount of an industrial desulfurizing agent is provided, which comprises the steps of obtaining a flue gas containing the flue gasThe method comprises the steps of completing data cleaning and normalization through a data preprocessing module to generate a standardized control data set, reading real-time indexes, parameter association degree and data fluctuation range of the data set by a control platform, analyzing the standardized control data set, sequencing according to parameter influence priorities, determining a control strategy according to industrial scene types, desulfurization system treatment capacity and real-time emission requirements through negotiation of the control platform and an industrial mechanism model, setting a desulfurization agent addition adjustment step length and a response period according to the fluctuation range of the flue gas pollutant concentration and the response speed of the equipment to form a control instruction packet containing real-time working condition data and target addition amount, transmitting the control instruction packet to a desulfurization agent addition execution unit according to the response period, guiding the real-time data through a parallel acquisition machine synchronously, checking the working condition matching degree of instruction execution, accurately adding the target addition amount in the control instruction packet, monitoring efficiency, the consumption rate and the equipment operation state in real time in the execution process according to the parameter influence priority, feeding back to the control platform, triggering the control instruction packet according to the preset feedback data and the abnormal emission control instruction, optimizing the dynamic control instruction packet, and optimizing the relation according to the abnormal emission control parameter, and the dynamic adjustment parameter of the abnormal operation standard, and optimizing the control instructio