CN-122020946-A - Intelligent control method and system for flash furnace based on data analysis

CN122020946ACN 122020946 ACN122020946 ACN 122020946ACN-122020946-A

Abstract

The invention discloses an intelligent control method and system of a flash furnace based on data analysis, and relates to the technical field of flash furnace control; the method comprises the steps of carrying out continuous periodic anomaly judgment on each point temperature data of a flash furnace space simulation model, constructing a layered temperature line in the flash furnace space simulation model after local data processing to divide different temperature areas of the flash furnace space simulation model, carrying out operation temperature data distribution evaluation on each temperature area, carrying out temperature difference analysis by combining operation temperature data distribution evaluation results of adjacent temperature areas, carrying out overall operation space temperature data safety evaluation on the flash furnace space simulation model based on temperature difference analysis data of continuous adjacent temperature areas, and capturing the change trend of the temperature in the furnace, so that timely early warning and accurate analysis of temperature anomalies are realized, and the stable and safe operation requirements of the flash furnace are ensured.

Inventors

WU JUN
MA SHIBING
LV XICONG
DING YANGDONG
CHEN ZHUO
WANG GUOZHEN
LIU FEI
SONG YANPO
WAN XINGBANG
JIANG CHANGBO
LI WENBIN

Assignees

江西铜业股份有限公司
中南大学
江西铜业集团(贵溪)冶化新技术有限公司

Dates

Publication Date: 20260512
Application Date: 20251201

Claims (10)

1. The intelligent control method of the flash furnace based on the data analysis is characterized by comprising the following steps of: collecting real-time operation temperature data in the flash furnace through a flash furnace operation temperature sensor; constructing a flash furnace space simulation model through space simulation based on the operation temperature acquisition data in the flash furnace, and determining space point position temperature data in the flash furnace; The method comprises the steps of carrying out a local data processing on a flash furnace space simulation model, carrying out different temperature region division on the flash furnace space simulation model by constructing a layered temperature line, carrying out operation temperature data distribution evaluation on each temperature region respectively, carrying out temperature difference analysis by combining operation temperature data distribution evaluation results of adjacent temperature regions, and carrying out overall operation space temperature data safety evaluation on the flash furnace space simulation model based on temperature difference analysis data of continuous adjacent temperature regions.
2. The intelligent control method for the flash furnace based on data analysis according to claim 1, wherein the intelligent control method comprises the following steps: Acquiring the data access authority of a flash furnace operation temperature sensor by accessing a flash furnace operation monitoring platform, and performing data retrieval on the flash furnace operation temperature sensor; the flash furnace operation temperature sensor acquires real-time operation temperature data in the flash furnace, acquires positioning data of the acquired temperature data, constructs a positioning temperature data set and transmits the positioning temperature data set to the flash furnace operation monitoring platform.
3. The intelligent control method for the flash furnace based on data analysis according to claim 2, wherein the intelligent control method is characterized by comprising the following steps of: Acquiring real-time operation temperature data of each space point in the space of the flash furnace by calling a positioning temperature data set acquired by each operation temperature sensor in the flash furnace; the method comprises the steps of (1) importing real-time operation temperature data of each space point in a flash furnace space into a three-dimensional simulation model by constructing the three-dimensional simulation model, and obtaining a current real-time space simulation model in the flash furnace; The method comprises the steps of carrying out real-time data evolution on operation temperature data of each space point in a space simulation model based on the space simulation model of a flash furnace, dividing continuous judging periods, carrying out continuous data abnormality judgment on the operation temperature data of each space point, carrying out corresponding simultaneous temperature data retrieval on any m adjacent space points in the space simulation model, and constructing a dataset, respectively calculating the mean value and standard deviation value of the temperature data corresponding to each temperature data in the dataset, and constructing a temperature data limiting interval [ A m -k*B m ,A m +k*B m ] corresponding to a current dataset based on a calculation result, wherein A m is the mean value of the dataset corresponding to the current m adjacent space points, B m is the standard deviation value of the dataset corresponding to the current m adjacent space points, k is a limiting coefficient, and m is a constant; The method comprises the steps of comparing each data in a data set of current m adjacent space points with a temperature data limiting section, judging the data to be abnormal data if the data does not belong to the temperature data limiting section, otherwise judging the data to be normal data, continuously judging the space points with abnormal data in the data set of the m adjacent space points according to a continuous judging period, judging the corresponding space points to be abnormal points if the space points with abnormal data are all abnormal data in the continuous judging period, otherwise judging the corresponding space points to be normal points, carrying out abnormal warning on the space points judged to be abnormal points, and carrying out data local compensation processing on the abnormal point data in a flash furnace space simulation model.
4. The intelligent control method for the flash furnace based on data analysis according to claim 3, wherein the intelligent control method comprises the following steps: determining the space point positions of the partition temperature data corresponding to different operation areas in the current flash furnace space simulation model according to the partition temperature data of the different operation areas respectively, and carrying out the same-temperature data retrieval on adjacent point positions in the current flash furnace space simulation model, connecting the retrieved space point positions according to the retrieval result to construct a layered temperature line, and partitioning the different areas in the current flash furnace space simulation model to obtain n different operation areas, wherein n is a constant; The method comprises the steps of carrying out temperature data distribution evaluation on each operation area in a current flash furnace space simulation model, determining operation temperature data of each space point in a corresponding operation area by carrying out space point overall planning on each operation area, carrying out distribution evaluation on the temperature data of the corresponding operation area based on the operation temperature data of each space point in the corresponding operation area, and obtaining the overall deviation of the temperature data of the space points in the corresponding operation area; Performing differential analysis on the distribution of the space point temperature data in the adjacent operation areas based on the overall deviation of the space point temperature data in each operation area, respectively calculating the absolute value of the overall deviation difference value of the space point temperature data in the adjacent operation areas, marking the distribution of the space point temperature data between the adjacent operation areas as a differential value of the space point temperature data distribution in the adjacent operation areas, performing abnormal analysis on the distribution of the space point temperature data between the adjacent operation areas by setting a differential threshold value, judging that the distribution of the space point temperature data between the adjacent operation areas is in a normal continuous state if the differential value of the space point temperature data distribution in the adjacent operation areas is smaller than the threshold value, and otherwise, judging that the distribution of the space point temperature data between the adjacent operation areas is in an abnormal fault state; Based on the current space model of the flash furnace, carrying out overall operation space temperature data safety evaluation on the space simulation model of the flash furnace by combining space point temperature data distribution bias data in each operation area and difference analysis data of continuous adjacent temperature areas; And introducing a judgment threshold value to compare and judge according to the safety evaluation data of the whole operation space temperature data of the current flash furnace space simulation model, judging that the unbalance risk exists in the operation temperature distribution of the current flash furnace if the safety evaluation data of the whole operation space temperature data of the current flash furnace space simulation model is larger than or equal to the judgment threshold value, and otherwise, judging that the operation temperature distribution of the current flash furnace is in a controllable state.
5. The intelligent control method for the flash furnace based on data analysis according to claim 4, wherein the intelligent control method comprises the following steps: mapping and outputting the space simulation model of the flash furnace, and feeding back the operation temperature distribution state of the flash furnace in real time; and continuously monitoring the operation temperature data of the space simulation model of the flash furnace under the condition that the operation temperature distribution of the flash furnace is in a controllable state.
6. The intelligent control system of the flash furnace based on the data analysis is characterized by comprising a data supervision module, an abnormality judgment module, a temperature distribution evaluation module and a decision output module; The data supervision module collects real-time operation temperature data in the flash furnace through the flash furnace operation temperature sensor, the abnormality judgment module constructs a flash furnace space simulation model based on the operation temperature collection data in the flash furnace through space simulation and determines space point position temperature data in the flash furnace, continuous periodic abnormality judgment is carried out on each point position temperature data of the flash furnace space simulation model and local data processing is carried out, the temperature distribution evaluation module divides different temperature areas of the flash furnace space simulation model based on the flash furnace space simulation model after the local data processing through constructing a layered temperature line, operation temperature data distribution evaluation is carried out on each temperature area respectively, temperature difference analysis is carried out by combining operation temperature data distribution evaluation results of adjacent temperature areas, overall operation space temperature data safety evaluation is carried out on the flash furnace space simulation model based on temperature difference analysis data of continuous adjacent temperature areas, and the decision output module feeds back the flash furnace simulation model temperature distribution state data in real time and carries out corresponding decisions according to corresponding temperature distribution states.
7. The intelligent control system of the flash furnace based on data analysis according to claim 6, wherein the data supervision module comprises a permission access acquisition unit and a temperature data acquisition unit; The permission access acquisition unit acquires the data access permission of the flash furnace operation temperature sensor by accessing the flash furnace operation monitoring platform, and performs data retrieval on the flash furnace operation temperature sensor; the temperature data acquisition unit acquires real-time operation temperature data in the flash furnace through the flash furnace operation temperature sensor, acquires positioning data of the acquired temperature data, constructs a positioning temperature data set and transmits the positioning temperature data set to the flash furnace operation monitoring platform.
8. The intelligent control system of the flash furnace based on data analysis of claim 7, wherein the abnormality judgment module comprises a space simulation model construction unit and an abnormality data judgment unit; the space simulation model construction unit acquires real-time operation temperature data of each space point in the space of the flash furnace by calling a positioning temperature data set acquired by each operation temperature sensor in the flash furnace; The abnormal data judging unit is used for carrying out real-time data evolution through operation temperature data of each space point in the space simulation model based on the space simulation model of the flash furnace, dividing continuous judging periods, carrying out continuous data abnormal judgment on the operation temperature data of each space point, carrying out corresponding simultaneous temperature data retrieval through arbitrary m adjacent space points in the space simulation model, and constructing a data set, respectively calculating the mean value and standard deviation value of each temperature data in the data set, and constructing a temperature data limiting interval [ A m -k*B m ,A m +k*B m ] corresponding to the current data set based on the calculation result, wherein A m is the mean value of the data set corresponding to the current m adjacent space points, B m is the standard deviation value of the data set corresponding to the current m adjacent space points, k is a limiting coefficient, and m is a constant; The method comprises the steps of comparing each data in a data set of current m adjacent space points with a temperature data limiting section, judging the data to be abnormal data if the data does not belong to the temperature data limiting section, otherwise judging the data to be normal data, continuously judging the space points with abnormal data in the data set of the m adjacent space points according to a continuous judging period, judging the corresponding space points to be abnormal points if the space points with abnormal data are all abnormal data in the continuous judging period, otherwise judging the corresponding space points to be normal points, carrying out abnormal warning on the space points judged to be abnormal points, and carrying out data local compensation processing on the abnormal point data in a flash furnace space simulation model.
9. The intelligent control system of the flash furnace based on data analysis of claim 8, wherein the temperature distribution evaluation module comprises an operation area dividing unit, an operation area temperature distribution evaluation unit, an adjacent area difference analysis unit and a flash furnace temperature state judgment unit; The operation area dividing unit is used for determining space point positions corresponding to the dividing temperature data of different operation areas in the current flash furnace space simulation model according to the dividing temperature data of the different operation areas respectively and searching the same temperature data of adjacent point positions in the current flash furnace space simulation model by calling the dividing temperature data of the different operation areas in the flash furnace corresponding to the current operation history data after the local data processing; the operation region temperature distribution evaluation unit is used for respectively carrying out temperature data distribution evaluation on each operation region in the current flash furnace space simulation model, determining operation temperature data of each space point in the corresponding operation region by carrying out space point overall planning on each operation region; The adjacent region difference analysis unit performs difference analysis on the operation temperature data distribution in the adjacent operation region based on the whole deviation of the space point position temperature data of each operation region, calculates absolute values of the whole deviation difference values of the space point position temperature data of the adjacent operation region respectively, and records the absolute values as deviation difference values of the space point position temperature data distribution in the adjacent operation region; the method comprises the steps of setting a difference threshold value to perform abnormal analysis on the distribution condition of the space point position temperature data between adjacent operation areas, judging that the distribution condition of the space point position temperature data between the adjacent operation areas is in a normal continuous state if the difference value of the distribution deviation of the space point position temperature data between the adjacent operation areas is smaller than the threshold value, otherwise, judging that the distribution condition of the space point position temperature data between the adjacent operation areas is in an abnormal fault state, and marking the corresponding adjacent operation areas as temperature data abnormal distribution areas if the distribution condition of the space point position temperature data between the adjacent operation areas is in an abnormal fault state; The flash furnace temperature state judging unit is used for carrying out overall operation space temperature data safety evaluation on the flash furnace space simulation model by combining space point temperature data distribution deviation data in each operation area and difference analysis data of continuous adjacent temperature areas in the current flash furnace space model, introducing a judging threshold value according to the overall operation space temperature data safety evaluation data of the current flash furnace space simulation model to carry out comparison judgment, judging that unbalance risk exists in the current flash furnace operation temperature distribution if the overall operation space temperature data safety evaluation data of the current flash furnace space simulation model is larger than or equal to the judging threshold value, and otherwise judging that the current flash furnace operation temperature distribution is in a controllable state.
10. The intelligent control system of the flash furnace based on data analysis according to claim 9, wherein the decision output module comprises a data output unit and a decision implementation unit; the data output unit performs mapping output on the space simulation model of the flash furnace and feeds back the operation temperature distribution state of the flash furnace in real time; The decision implementation unit alerts the condition that the operation temperature distribution of the flash furnace has unbalance risk, and keeps the space simulation model of the flash furnace to continuously monitor the operation temperature data under the condition that the operation temperature distribution of the flash furnace is in a controllable state.

Description

Intelligent control method and system for flash furnace based on data analysis Technical Field The invention relates to the field of flash furnace control, in particular to an intelligent control method and system for a flash furnace based on data analysis. Background In the modern nonferrous metal smelting field, a flash furnace is a key device by virtue of the characteristics of high efficiency and continuity, but the temperature monitoring is challenged by the high temperature, strong corrosion and complex chemical reaction environment in the furnace, the traditional temperature monitoring by utilizing a temperature sensor is single in monitoring means and limited by the factors of the device, the accuracy and continuity of the detection data are difficult to ensure in long-time monitoring, the temperature distribution in the flash furnace is complex, the traditional monitoring mode is difficult to comprehensively capture the variation trend of the temperature in the furnace, and therefore the traditional monitoring technology has defects in timely early warning and accurate analysis of temperature abnormality, and the stable and safe operation requirement of the flash furnace cannot be met. Disclosure of Invention The invention aims to provide a flash furnace intelligent control method and system based on data analysis, which are used for solving the problems in the prior art. In order to achieve the above purpose, the present invention provides the following technical solutions: a flash furnace intelligent control method based on data analysis comprises the following steps: collecting real-time operation temperature data in the flash furnace through a flash furnace operation temperature sensor; constructing a flash furnace space simulation model through space simulation based on the operation temperature acquisition data in the flash furnace, and determining space point position temperature data in the flash furnace; The method comprises the steps of carrying out a local data processing on a flash furnace space simulation model, carrying out different temperature region division on the flash furnace space simulation model by constructing a layered temperature line, carrying out operation temperature data distribution evaluation on each temperature region respectively, carrying out temperature difference analysis by combining operation temperature data distribution evaluation results of adjacent temperature regions, and carrying out overall operation space temperature data safety evaluation on the flash furnace space simulation model based on temperature difference analysis data of continuous adjacent temperature regions. Further, the data access authority of the flash furnace operation temperature sensor is obtained by accessing the flash furnace operation monitoring platform, and the data access authority of the flash furnace operation temperature sensor is used for carrying out data acquisition on the flash furnace operation temperature sensor, wherein the flash furnace operation monitoring platform is used for carrying out real-time monitoring acquisition on the data during the operation of the flash furnace; the flash furnace operation temperature sensor acquires real-time operation temperature data in the flash furnace, acquires positioning data of the acquired temperature data, constructs a positioning temperature data set and transmits the positioning temperature data set to the flash furnace operation monitoring platform. Further, acquiring real-time operation temperature data of each space point in the space of the flash furnace by calling a positioning temperature data set acquired by each operation temperature sensor in the flash furnace; the method comprises the steps of (1) importing real-time operation temperature data of each space point in a flash furnace space into a three-dimensional simulation model by constructing the three-dimensional simulation model, and obtaining a current real-time space simulation model in the flash furnace; The method comprises the steps of carrying out real-time data evolution on operation temperature data of each space point in a space simulation model based on the space simulation model of a flash furnace, dividing continuous judging periods, carrying out continuous data abnormality judgment on the operation temperature data of each space point, carrying out corresponding simultaneous temperature data retrieval on any m adjacent space points in the space simulation model, and constructing a dataset, respectively calculating the mean value and standard deviation value of the temperature data corresponding to each temperature data in the dataset, and constructing a temperature data limiting interval [ A m-k*Bm,Am+k*Bm ] corresponding to the current dataset based on the calculation result, wherein A m is the mean value of the dataset corresponding to the current m adjacent space points, B m is the standard deviation value of the dataset corresponding to the c