Intelligent control method and system for fly ash melting treatment production line

Abstract

The invention discloses an intelligent control method and system for a fly ash melting treatment production line, and belongs to the technical field of intelligent control. The method comprises the following steps: abstracting a physical entity and a logical relationship of the washing system into a directed weighted graph; the minimum processing cost path and the maximum feasible recycling amount are calculated, and dynamic balance of water quality and water quantity is achieved through node linkage; obtaining production line data, and carrying out feature engineering to obtain index data; a gradient boosting tree is adopted, production line data and index data are input, model training is carried out, and the optimal melting temperature is obtained; establishing a linkage control table of the optimal melting temperature and the state of production line equipment, reading real-time temperature data, and generating and executing a parameter instruction after matching the linkage control table; setting an intermediate buffer mechanism, and calculating the load rate of the melting furnace; a main melting furnace and a standby melting furnace are configured, and when the temperature of the main melting furnace is stabilized in the optimal temperature interval and the load rate of the melting furnace is larger than a set threshold value, the standby melting furnace is started to be in a heat preservation state.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and specifically to an intelligent control method and system for a fly ash melting treatment production line. Background Art

[0002] In the field of industrial solid waste treatment, water washing desalination and high-temperature melting vitrification are the mainstream process paths; in the water washing link, harmful substances such as chlorine and heavy metals in fly ash are removed through multi-stage recycling, and the water resource utilization rate and treatment cost directly affect environmental protection compliance; in the high-temperature melting link, precise temperature control and equipment scheduling are required to achieve efficient and stable production, and the production capacity utilization rate is the core index determining the treatment scale.

[0003] In the prior art, the water washing path is fixed, the treatment cost difference under different water qualities is not considered, the wastewater reuse amount depends on manual experience setting, a maximum flow model based on the water flow network is not established, and real-time linkage control is lacking, which easily leads to unqualified effluent quality; the melting temperature setting depends on the process manual and is not optimized in combination with the real-time water quality dynamics, which easily leads to insufficient melting or energy consumption waste; there is no buffer mechanism between the water washing and melting links. When the temperature of the melting furnace reaches the standard, the water washing discharge may lag or be excessive, resulting in the idling or overloading of the melting furnace. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent control method and system for a fly ash melting treatment production line to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: In the first aspect, the present application provides an intelligent control method for a fly ash melting treatment production line, including the following steps: Abstract the physical entities and logical relationships of the water washing system into a directed weighted graph; solve the minimum treatment cost path of the directed weighted graph through the Dijkstra algorithm, and solve the maximum feasible reuse amount of the directed weighted graph through the maximum flow algorithm; based on the minimum treatment cost path and the maximum feasible reuse amount, achieve the dynamic balance of water quality and water volume through node linkage; Obtain production line data, including water quality data, water volume data, and production line equipment status, perform feature engineering to obtain index data, including water washing efficiency index and melting load index; use the gradient boosting tree, input the production line data and index data, and perform model training to obtain the optimal melting temperature; Establish a linkage control table between the optimal melting temperature and the production line equipment status, read the real-time temperature data through the PLC system, generate parameter instructions and execute them after matching the linkage control table; set an intermediate buffer mechanism between the water washing link and the melting link. When the temperature of the melting furnace reaches the optimal temperature range, trigger the priority feeding logic and calculate the load rate of the melting furnace; Configure a main melting furnace and a standby melting furnace. When the temperature of the main melting furnace is stable within the optimal temperature range and the load rate of the melting furnace is greater than the set threshold, start the standby melting furnace to the heat preservation state, so as to maximize the production line utilization rate of the high-temperature melting link.

[0006] Combined with the first aspect, in the first implementation manner of the first aspect of the present application, abstracting the physical entities and logical relationships of the water washing system into a directed weighted graph includes: The physical entities of the water washing system include water washing tanks, wastewater treatment equipment, water storage facilities, transmission equipment, sensors and actuators, and the logical relationships include control strategies, data flow and process rules; abstract the physical entities and logical components into graph nodes and assign characteristic attributes; describe the connection relationships between nodes, assign physical meanings and weights to the edges, and the edge types include material flow edges, data flow edges, control flow edges and status edges. Among them, the weights of the material flow edges include water flow resistance and transmission time, and the physical meanings are water flow direction and energy consumption cost; the weights of the data flow edges include transmission delay and data accuracy, and the physical meanings are the transfer of sensor data to control logic; the weights of the control flow edges include response time and instruction reliability, and the physical meanings are the execution efficiency of control instructions; the weights of the status edges include load rate and remaining capacity, and the physical meanings are load distribution and status association between devices; construct a mathematical model of the directed weighted graph, and use Neo4j for graph database deployment and real-time data integration.

[0007] Combined with the first aspect, in the second implementation manner of the first aspect of the present application, solving the minimum processing cost path of the directed weighted graph by the Dijkstra algorithm and solving the maximum feasible recycling amount of the directed weighted graph by the maximum flow algorithm includes: Determine the starting node and the target node in the directed weighted graph; construct a first array to record the shortest distance from the starting point to each node. Initially, the distance of the starting point is set to 0, and the distances of the other nodes are set to infinity; construct a second array to record the predecessor nodes of each node, and the initial values are all set to null; based on the first array and the second array, create a priority queue, add the starting point to the queue, and its priority is 0; the priority queue is used to store the nodes to be processed and is sorted in ascending order of distance; when the priority queue is not empty, take out the node with the smallest distance from the priority queue, record it as the current node, and traverse all adjacent nodes of the current node; start from the end point and backtrack through the predecessor node array to find the predecessor node of each node in turn until returning to the starting point, so as to construct the minimum processing cost path; Based on the directed weighted graph, a residual network is constructed; the initial capacity of each edge in the residual network is equal to the weight of the original edge, and a reverse edge is added, with the initial capacity of the reverse edge being 0; the source point of the residual network is determined as the water source point in the water washing system, and the sink point is the reuse point; breadth-first search is used to find an augmenting path from the source point to the sink point in the residual network, where the augmenting path refers to a path existing in the residual network along which the flow can be increased; after finding the augmenting path, calculate the minimum capacity on this path, denoted as the bottleneck capacity; along the augmenting path, subtract the bottleneck capacity from the capacity of the forward edge and add the bottleneck capacity to the capacity of the reverse edge; repeat the process of finding the augmenting path and updating the residual network until no augmenting path can be found. At this time, the total flow out of the source point is used as the maximum feasible reuse amount.

[0008] Combined with the first aspect, in the third implementation manner of the first aspect of this application, the dynamic balance of water quality and water volume is achieved through node linkage based on the minimum processing cost path and the maximum feasible reuse amount, including: Determine the association relationship between each node according to the minimum processing cost path and the actual situation of the water washing system; when the water quality parameter of a certain node exceeds the target range, adjust it by linking other nodes; formulate a water volume allocation rule according to the maximum feasible reuse amount and the water volume requirements of each node; adjust the operating state of the equipment according to the changes in water quality and water volume; conduct real-time monitoring and feedback, and adjust the operating parameters and equipment state of each node according to the results of real-time monitoring and feedback to achieve the dynamic balance of water quality and water volume.

[0009] Combined with the first aspect, in the fourth implementation manner of the first aspect of this application, the acquisition of production line data, including water quality data, water volume data, and production line equipment status, and the performance of feature engineering to obtain index data, including the water washing efficiency index and the melting load index, includes: Clean the production line data, extract the water quality data features, water volume data features, and production line equipment status features; assign weights to the indicators affecting the water washing efficiency and melting load according to historical data, perform standardization processing, and use the method of weighted summation to calculate the water washing efficiency index and the melting load index.

[0010] Combined with the first aspect, in the fifth implementation manner of the first aspect of this application, the use of gradient boosting trees, inputting production line data and index data, and performing model training to obtain the optimal melting temperature, includes: Integrate the production line data and index data into a data set, select relevant columns in the data set as features, use the optimal melting temperature as the target variable, and divide the data set into a training set and a test set; Initialize the parameters of the gradient boosting tree model, use the training set data to fit the gradient boosting tree model, and use k-fold cross-validation for model training; Use the trained model to predict the test set data to obtain the predicted melting temperature, and perform model evaluation and optimization; Input the current production line data and index data into the optimized model to obtain the optimal melting temperature.

[0011] Combined with the first aspect, in the sixth implementation manner of the first aspect of the present application, the establishment of the linkage control table between the optimal melting temperature and the production line equipment status, reading the real-time temperature data through the PLC system, generating a parameter instruction and executing it after matching the linkage control table, includes: Divide the optimal temperature range based on the optimal melting temperature, create a multi-dimensional data block in the PLC system, store the optimal temperature range, equipment parameter thresholds, and control instruction priorities, and establish a linkage control table; Use the conditional judgment instruction of the PLC to match the linkage control table range according to the real-time temperature value; When the temperature crosses the interval boundary, adjust the equipment parameters through a ramp function; The PLC reads the temperature value in real time, compares it with the control table range, generates the corresponding equipment parameter instruction, and performs analog output and digital output.

[0012] Combined with the first aspect, in the seventh implementation manner of the first aspect of the present application, the setting of the intermediate buffer mechanism between the water washing link and the melting link, when the temperature of the melting furnace reaches the optimal temperature range, triggering the priority feeding logic and calculating the load rate of the melting furnace, includes: Construct a buffer pool that meets the production capacity matching of the water washing and melting links. The minimum capacity is designed as the maximum processing capacity of the melting furnace within the optimal temperature range, and the maximum capacity is designed as the maximum single-batch discharge amount of the water washing link; Based on the temperatures detected by the B-type thermocouples installed in the middle and bottom of the melting furnace hearth, take the average value as the real-time temperature; When the real-time temperature has been in the optimal temperature range for continuous w 1 minutes, and the liquid level of the buffer pool reaches the lower limit of the buffer pool liquid level, send a signal to the buffer pool control system through the PLC to switch the feeding speed to the priority feeding speed, where w 1 is obtained through statistical analysis of historical data; Obtain the average load of the melting furnace and divide it by the maximum load of the melting furnace to obtain the load rate of the melting furnace.

[0013] Combined with the first aspect, in the eighth implementation manner of the first aspect of the present application, the configuration of the main melting furnace and the standby melting furnace, when the temperature of the main melting furnace is stable in the optimal temperature range and the load rate of the melting furnace is greater than the set threshold, start the standby melting furnace to the heat preservation state to maximize the production line utilization rate of the high-temperature melting link, includes: Perform the physical configuration and parameter definition of the main and standby melting furnaces. Use a sliding window filter to determine whether the temperature of the main melting furnace is stable within the optimal temperature range. If it is satisfied, perform subsequent operations; when the load rate of the main melting furnace is continuously greater than the high-load state threshold for w 2 minutes, it is determined to be in the high-load state, and the standby melting furnace is started to the heat preservation state, w 2 and the high-load state threshold are obtained by statistical analysis of historical data; allocate materials according to the load balance principle of the main and standby melting furnaces, and the total feed amount does not exceed the sum of the maximum processing capacities of the main and standby melting furnaces; use a fuzzy control algorithm to adjust the opening of the three-way valve, and adjust the distribution ratio in real time according to the temperature of the standby melting furnace.

[0014] In a second aspect, the present application provides an intelligent control system for a fly ash melting treatment production line, including: Graph modeling and optimization module: including: a directed weighted graph construction unit, a minimum processing cost path solving unit, a maximum feasible reuse amount solving unit, and a node linkage unit; among them, the directed weighted graph construction unit abstracts the physical entities and logical relationships of the water washing system into a directed weighted graph; the minimum processing cost path solving unit solves the minimum processing cost path of the directed weighted graph through the Dijkstra algorithm, and the maximum feasible reuse amount solving unit solves the maximum feasible reuse amount of the directed weighted graph through the maximum flow algorithm; the node linkage unit realizes the dynamic balance of water quality and water volume through node linkage based on the minimum processing cost path and the maximum feasible reuse amount; Optimal melting temperature calculation module: including: a feature engineering unit and a gradient boosting tree modeling unit; among them, the feature engineering unit obtains production line data, including water quality data, water volume data, and production line equipment status, performs feature engineering, and obtains exponential data, including water washing efficiency index and melting load index; the gradient boosting tree modeling unit uses the gradient boosting tree, inputs the production line data and the exponential data, and performs model training to obtain the optimal melting temperature; Dynamic control and scheduling module: including a temperature and equipment linkage control unit and a buffer and feeding scheduling unit; among them, the temperature and equipment linkage control unit establishes a linkage control table between the optimal melting temperature and the production line equipment status, reads the real-time temperature data through the PLC system, generates parameter instructions and executes them after matching the linkage control table; the buffer and feeding scheduling unit sets an intermediate buffer mechanism between the water washing link and the melting link. When the temperature of the melting furnace reaches the optimal temperature range, it triggers the priority feeding logic and calculates the load rate of the melting furnace; Main and standby melting furnace collaborative scheduling module: including: a main and standby melting furnace collaborative scheduling unit; among them, the main and standby melting furnace collaborative scheduling unit configures the main melting furnace and the standby melting furnace. When the temperature of the main melting furnace is stable within the optimal temperature range and the load rate of the melting furnace is greater than the set threshold, the standby melting furnace is started to the heat preservation state to maximize the production line utilization rate of the high-temperature melting link.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present application abstracts the water washing system as a directed weighted graph, assigns a processing cost weight to each edge, and calculates the minimum cost path in real time through the Dijkstra algorithm to avoid waste of resources in a fixed process; regards the water washing network as a flow network, calculates the theoretical maximum recycling amount through the maximum flow algorithm, and avoids deterioration of water quality caused by accumulation of impurities in recycled water; establishes a linkage rule between water quality indicators and equipment nodes, and quickly triggers a response through a graph model to improve the water quality compliance rate.

[0016] 2. The present application inputs the water quality data, water volume data, and equipment status of the fly ash after water washing, trains a gradient boosting tree model to predict the optimal temperature, so as to improve the melting sufficiency and avoid overheating in the high-temperature area; predefines the mapping relationship between the temperature range and equipment parameters, and matches and executes in real time through the PLC, avoiding the problem of insufficient equipment load despite reaching the temperature standard caused by manual experience.

[0017] 3. The present application sets a buffer pool between water washing and melting. When the temperature of the melting furnace reaches the standard, full-speed feeding is triggered to avoid shutdown of the melting furnace caused by water washing delay; real-time calculation and scheduling of the load rate of the melting furnace are carried out to improve the production capacity utilization rate and avoid equipment loss caused by single-furnace overload. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a schematic diagram of the steps of an intelligent control method for a fly ash melting treatment production line according to the present invention; Figure 2 is a system structure diagram of an intelligent control system for a fly ash melting treatment production line according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution, As Figure 1 shown in the schematic diagram of the steps of an intelligent control method for a fly ash melting treatment production line, the present application provides an intelligent control method for a fly ash melting treatment production line, including the following steps: Step S100: Abstract the physical entities and logical relationships of the water washing system into a directed weighted graph; solve the minimum processing cost path of the directed weighted graph through the Dijkstra algorithm, and solve the maximum feasible reuse amount of the directed weighted graph through the maximum flow algorithm; based on the minimum processing cost path and the maximum feasible reuse amount, achieve dynamic balance of water quality and water volume through node linkage; Specifically, the physical entities of the water washing system include water washing tanks, wastewater treatment equipment, water storage facilities, transmission equipment, sensors, and actuators, and the logical relationships include control strategies, data flow, and process rules; abstract the physical entities and logical components into graph nodes and assign characteristic attributes; describe the connection relationships between nodes, and assign physical meanings and weights to the edges. The edge types include material flow edges, data flow edges, control flow edges, and status edges. Among them, the weights of the material flow edges include water flow resistance and transmission time, and the physical meanings are water flow direction and energy consumption cost; the weights of the data flow edges include transmission delay and data accuracy, and the physical meanings are the transfer of sensor data to control logic; the weights of the control flow edges include response time and instruction reliability, and the physical meanings are the execution efficiency of control instructions; the weights of the status edges include load rate and remaining capacity, and the physical meanings are load distribution and status association between devices; construct a mathematical model of the directed weighted graph, and use Neo4j for graph database deployment and real-time data integration.

[0021] Furthermore, determine the starting node and the target node in the directed weighted graph; construct a first array to record the shortest distance from the starting point to each node. Initially, the distance of the starting point is set to 0, and the distances of the other nodes are set to infinity; construct a second array to record the predecessor nodes of each node, and the initial values are all set to null; based on the first array and the second array, create a priority queue, add the starting point to the queue, and its priority is 0; the priority queue is used to store the nodes to be processed and is sorted in ascending order of distance; when the priority queue is not empty, take out the node with the smallest distance from the priority queue, denote it as the current node, and traverse all adjacent nodes of the current node; start from the end point, backtrack through the predecessor node array, and find the predecessor node of each node in turn until returning to the starting point, thereby constructing the minimum processing cost path; Based on the directed weighted graph, a residual network is constructed; the initial capacity of each edge in the residual network is equal to the weight of the original edge, and a reverse edge is added with an initial capacity of 0; the source point of the residual network is determined as the water source point in the water washing system, and the sink point is the reuse point; use breadth-first search to find an augmenting path from the source point to the sink point in the residual network, where the augmenting path refers to a path existing in the residual network along which the flow can be increased; after finding the augmenting path, calculate the minimum capacity on this path, denoted as the bottleneck capacity; along the augmenting path, subtract the bottleneck capacity from the capacity of the forward edge and add the bottleneck capacity to the capacity of the reverse edge; repeat the process of finding the augmenting path and updating the residual network until no augmenting path can be found. At this time, the total flow out of the source point is used as the maximum feasible reuse amount.

[0022] Further, according to the minimum processing cost path and the actual situation of the water washing system, determine the association relationship between each node; when the water quality parameter of a certain node exceeds the target range, adjust it by linking other nodes; formulate a water volume allocation rule according to the maximum feasible reuse amount and the water volume requirements of each node; adjust the operating state of the equipment according to the changes in water quality and water volume; conduct real-time monitoring and feedback, and adjust the operating parameters and equipment state of each node according to the results of real-time monitoring and feedback to achieve the dynamic balance of water quality and water volume.

[0023] In a specific embodiment, a water washing system of an electronic waste treatment plant treats heavy metal-containing wastewater and includes three-stage water washing tanks (T1 - T3), ultrafiltration equipment (UF), reverse osmosis equipment (RO), fresh water storage tank (F), and recycled water tank (R). Goals: reduce energy consumption, increase the reuse amount, and improve the treatment efficiency.

[0024] Solve the minimum energy consumption path through the Dijkstra algorithm. Starting point: T1 (high-concentration wastewater, heavy metal concentration = 100 mg / L), ending point: R (recycled water tank, target heavy metal concentration ≤ 1 mg / L), constraint: the treated water quality must meet ≤ 1 mg / L, and preferentially select the low-energy consumption path. Initialization: energy consumption array: energy[T1] = 0, energy[other nodes] = ∞, path array: path[T1] = [], priority queue: (0, T1).

[0025] Take out T1: The adjacent nodes are UF (energy consumption 15 kWh / m³) and T2 (direct water washing, energy consumption 3 kWh / m³, but the water quality does not meet the standard). Only UF meets the water quality treatment requirements, and update energy[UF] = 15, path[UF] = [T1 → UF].

[0026] Take out UF: The adjacent nodes are T2 (energy consumption 3 kWh / m³) and RO (energy consumption 30 kWh / m³). UF→T2: Total energy consumption = 15 + 3 = 18 kWh / m³, treated concentration = 100 mg / L × (1 - 90%) = 10 mg / L (not up to standard). UF→RO: Total energy consumption = 15 + 30 = 45 kWh / m³, treated concentration = 100 mg / L × (1 - 99%) = 1 mg / L (up to standard). Select the RO path, update energy[RO] = 45, path[RO] = [T1→UF→RO].

[0027] Take out RO: The adjacent node is R, energy consumption = 2 kWh / m³, total energy consumption = 45 + 2 = 47 kWh / m³, the path is completed. Get the minimum energy consumption path: T1→UF→RO→R, total energy consumption = 47 kWh / m³, the treated water quality is up to standard. Compare with the traditional path: T1→T2→T3 (three-stage water washing, energy consumption = 3×3 = 9 kWh / m³), but the water quality = 100 mg / L × (1 - 70%)³ = 27 mg / L (not up to standard). The path of the present invention: Higher energy consumption but ensure water quality, and the total energy consumption needs to be offset by increasing the reuse amount.

[0028] Construct the residual network (flow unit: m³ / h): Source points: F (fresh water, unlimited flow), R (reclaimed water tank, current available flow = 40 m³ / h); Sink points: T1 (water demand = 10 m³ / h), T3 (water demand = 15 m³ / h); Edge capacities: F→T1: 20 m³ / h (upper limit of fresh water supply); R→T3: 30 m³ / h (maximum reuse amount of the reclaimed water tank); UF→T2: 10 m³ / h (ultrafiltration treatment capacity); RO→R: 5 m³ / h (reverse osmosis water production capacity).

[0029] Execute the Ford-Fulkerson algorithm: The first augmenting path: R→T3 (capacity = 30 m³ / h); Bottleneck capacity = 15 m³ / h (water demand of T3), after update: The remaining capacity of R→T3 = 15 m³ / h, the reverse edge T3→R = 15 m³ / h.

[0030] The second augmenting path: F→T1→UF→T2→RO→R→T3; Capacities of each edge: F→T1 = 20, T1→UF = 10, UF→T2 = 10, T2→RO = 5, RO→R = 5, R→T3 = 15; Bottleneck capacity = 5 m³ / h (RO treatment capacity), after update: RO→R = 0, the reverse edge R→RO = 5, and the capacities of the remaining edges decrease by 5.

[0031] Maximum feasible recycling volume: Direct supply from the recycled water tank to T3: 15 m³ / h; Recycled water after fresh water treatment: 5 m³ / h; Total recycling volume = 20 m³ / h, Traditional manual setting of recycling volume = 10 m³ / h, a 100% increase.

[0032] When the pH value exceeds the standard, the following is an example of the implementation of abnormal water quality linkage: Trigger condition: The sensor S_PH detects that the pH of the T1 wastewater is 4.5 (target value 6 - 9).

[0033] Linkage logic: Data flow: S_PH → Control rule node (R_pH), Transmission delay = 100 ms. Control flow: R_pH → Actuator V_chemical (alkali addition pump), Response time = 5 s, Turned on to 100% power. Status update: Alkali addition energy consumption increases by 2 kWh / h, Mixer load rate increases from 50% to 70%. After 30 minutes, the pH rises to 6.8, and the V_chemical is linked to close, and the energy consumption returns to normal.

[0034] Step S200: Obtain production line data, including water quality data, water volume data, and production line equipment status, perform feature engineering to obtain index data, including water washing efficiency index and melting load index; Use gradient boosting tree, input production line data and index data, and perform model training to obtain the optimal melting temperature; Specifically, perform data cleaning on the production line data, extract water quality data features, water volume data features, and production line equipment status features; According to historical data, assign weights to the indicators affecting water washing efficiency and melting load, perform standardization processing, and use the weighted summation method to calculate the water washing efficiency index and melting load index.

[0035] Furthermore, integrate the production line data and index data into a data set, select relevant columns in the data set as features, use the optimal melting temperature as the target variable, divide the data set into a training set and a test set; Initialize the parameters of the gradient boosting tree model, use the training set data to fit the gradient boosting tree model, and use k-fold cross-validation for model training; Use the trained model to predict the test set data to obtain the predicted melting temperature, perform model evaluation and optimization; Input the current production line data and index data into the optimized model to obtain the optimal melting temperature.

[0036] In a specific embodiment, a water washing - melting production line in a metal smelting plant needs to predict the optimal melting temperature based on real-time water quality, water volume, and equipment status. Some examples of the original data are as follows: Heavy metal content 120 mg / L, water volume 20 m³, motor speed 1500 r / min, water washing efficiency index 0.66, melting load index 0.57, optimal temperature 1250 °C; Heavy metal content: 80 mg / L, water volume: 18 m³, motor speed: 1600 r / min, water washing efficiency index: 0.76, melting load index: 0.67, optimal temperature: 1220 °C; Heavy metal content: 150 mg / L, water volume: 22 m³, motor speed: 1400 r / min, water washing efficiency index: 0.56, melting load index: 0.47, optimal temperature: 1280 °C; Heavy metal content: 90 mg / L, water volume: 19 m³, motor speed: 1550 r / min, water washing efficiency index: 0.71, melting load index: 0.62, optimal temperature: 1230 °C; Heavy metal content: 110 mg / L, water volume: 21 m³, motor speed: 1520 r / min, water washing efficiency index: 0.68, melting load index: 0.59, optimal temperature: 1240 °C.

[0037] Calculate the water washing efficiency index (WEI). Influencing indicators: heavy metal removal rate (weight 0.6), water utilization rate (weight 0.4), both of which have been normalized to [0, 1].

[0038] In sample 1, the normalized value of the heavy metal removal rate is 0.7, and the normalized value of the water utilization rate is 0.6 → WEI = 0.6×0.7 + 0.4×0.6 = 0.66; In sample 2: the normalized value of the heavy metal removal rate is 0.8, and the normalized value of the water utilization rate is 0.7 → WEI = 0.6×0.8 + 0.4×0.7 = 0.76.

[0039] Calculate the melting load index (MLI). Influencing indicators: feeding speed (weight 0.7), motor power (weight 0.3), both of which have been normalized to [0, 1].

[0040] In sample 1, the normalized value of the feeding speed is 0.6, and the normalized value of the motor power is 0.5 → MLI = 0.7×0.6 + 0.3×0.5 = 0.57; In sample 2: the normalized value of the feeding speed is 0.7, and the normalized value of the motor power is 0.6 → MLI = 0.7×0.7 + 0.3×0.6 = 0.67.

[0041] Construct a data set, including: heavy metal content, water volume, motor speed, water washing efficiency index, melting load index; target variable: optimal melting temperature; data division: the first 8 samples are the training set, and the last 2 samples are the test set. The mean squared error of the test set is 25.00, indicating that the average deviation between the predicted temperature and the actual value is about ±5 °C (root mean square error is about 5 °C), meeting the industrial precision requirements. Input the current production line data into the optimized model to obtain the optimal melting temperature and guide the operator to adjust the heating parameters.

[0042] Step S300: Establish a linkage control table for the optimal melting temperature and the production line equipment status, read the real-time temperature data through the PLC system, generate parameter instructions after matching the linkage control table, and execute them; set up an intermediate buffer mechanism between the water washing process and the melting process. When the temperature of the melting furnace reaches the optimal temperature range, trigger the priority feeding logic and calculate the load rate of the melting furnace. Specifically, divide the optimal temperature range based on the optimal melting temperature, create a multi-dimensional data block in the PLC system to store the optimal temperature range, equipment parameter thresholds, and control instruction priorities, and establish a linkage control table; use the conditional judgment instruction of the PLC to match the linkage control table range according to the real-time temperature value; when the temperature crosses the range boundary, adjust the equipment parameters through a ramp function; the PLC reads the temperature value in real time, compares it with the control table range, generates corresponding equipment parameter instructions, and performs analog output and digital output.

[0043] Furthermore, construct a buffer pool that matches the production capacity of the water washing and melting processes. The minimum capacity is designed as the maximum processing capacity of the melting furnace within the optimal temperature range, and the maximum capacity is designed as the maximum single-batch discharge capacity of the water washing process; based on the temperatures detected by the B-type thermocouples installed in the middle and bottom of the melting furnace hearth, take the average value as the real-time temperature; when the real-time temperature has been in the optimal temperature range for continuous w 1 minutes, and the liquid level of the buffer pool reaches the lower limit of the buffer pool liquid level, send a signal to the buffer pool control system through the PLC to switch the feeding speed to the priority feeding speed, where w 1 is obtained through statistical analysis of historical data. Obtain the average load of the melting furnace and the maximum load of the melting furnace and divide them to get the load rate of the melting furnace.

[0044] In a specific embodiment, the optimal melting temperature of the melting furnace in a solid waste treatment plant is 1300 °C, and the temperature range is divided as follows: low temperature zone (<1280 °C), optimal zone (1280 - 1320 °C), high temperature zone (≥1320 °C). Configure an intermediate buffer pool (minimum capacity 5t, maximum capacity 12t), and realize temperature-equipment linkage and priority feeding control through the PLC. Map the temperature range and equipment parameters, and define the PLC data block and control logic.

[0045] The key parameters of the buffer pool include: minimum capacity: 5t (the maximum processing capacity of the melting furnace in the optimal zone for 30 minutes, processing speed 10t / h); liquid level threshold: upper limit 80% (9.6t): pause the water washing discharge; lower limit 20% (2.4t): trigger urgent production of water washing; feeding speed: normal speed: 10t / h; priority speed: 15t / h (enabled when the temperature reaches the standard).

[0046] The priority feeding trigger conditions include: Temperature condition: T_real is continuously within the optimal range for 5 minutes (implemented by timer T37); Liquid level condition: The liquid level in the buffer tank ≥ L_min (2.4t).

[0047] In the initial state, the temperature of the melting furnace is 1290°C (optimal range), the liquid level in the buffer tank is 8t (66.7%), and the feeding speed is the normal 10t / h. After 5 minutes: The temperature remains continuously between 1285 - 1295°C, triggering priority feeding; The feeding speed is increased to 15t / h, and the throughput within 30 minutes is: 15t / h × 0.5h = 7.5t; The liquid level in the buffer tank is: 8t - 7.5t = 0.5t (lower than L_min = 2.4t). The PLC sends a signal to the water washing process, and the discharging speed is increased from 8t / h to 12t / h (urgent production); After 1 hour, the liquid level in the buffer tank returns to 0.5t + 12t - 15t × 1h = -2t.

[0048] Perform the calculation of the melting furnace load rate. T = 1290°C (optimal range), V_actual = 15t / h; k(T) = 1.0, V_max(T) = 10 × 1.0 = 10t / h; The load rate = 15 / 10 = 150% → Trigger overload protection and automatically reduce the speed to 10t / h.

[0049] Step S400: Configure the main melting furnace and the standby melting furnace. When the temperature of the main melting furnace is stable within the optimal temperature range and the melting furnace load rate is greater than the set threshold, start the standby melting furnace to the heat preservation state to maximize the production line utilization rate of the high-temperature melting process.

[0050] Specifically, perform the physical configuration and parameter definition of the main and standby melting furnaces. Use a sliding window filter to determine whether the temperature of the main melting furnace is stable within the optimal temperature range. When satisfied, perform subsequent operations; When the load rate of the main melting furnace is continuously greater than the high-load state threshold for w 2 minutes, it is determined to be in the high-load state, and start the standby melting furnace to the heat preservation state. w 2 and the high-load state threshold are obtained from the statistical analysis of historical data; Allocate materials according to the load balance principle of the main and standby melting furnaces, and the total feed amount does not exceed the sum of the maximum processing capacities of the main and standby melting furnaces; Use a fuzzy control algorithm to adjust the opening of the three-way valve and adjust the distribution ratio in real time according to the temperature of the standby melting furnace.

[0051] In a specific embodiment, an industrial production line is configured with a main melting furnace (F1) and a standby melting furnace (F2). The rated processing capacity of a single furnace is 10 tons per hour, the optimal temperature range is 1300 ± 20°C (i.e., 1280 - 1320°C), and the heat preservation temperature of the standby furnace is set at 800°C. The system maximizes the production capacity during the high-temperature period by real-time monitoring of the main furnace temperature stability and load rate, triggering the preheating of the standby furnace and dynamically allocating materials.

[0052] Main furnace (F1): Current temperature is 1305°C. Monitored with a 10-minute sliding window, temperature fluctuation ≤ ±10°C (temperature range within the window is 1295 - 1315°C), determined to have a stable temperature within the optimal range. Load rate threshold: 85% (i.e., actual feed rate > 8.5 tons / hour), continuous overlimit time w2 = 15 minutes. Standby furnace (F2): Initial temperature is 25°C during cold furnace startup, heating rate is 50°C / minute, and energy consumption in the heat preservation state is 120 kW / h.

[0053] The current load rate of the main furnace is 90% (actual feed rate 9 tons / hour), and it has exceeded the 85% threshold continuously for 20 minutes, meeting the conditions of stable temperature and high load, triggering the startup of the standby furnace to the heat preservation state.

[0054] The startup and heating process of the standby furnace is as follows: Data during the heating stage: 0 - 5 minutes: Rapidly heat up to 500°C at a rate of 100°C / minute, with an energy consumption of 400 kW (to avoid thermal shock to the furnace lining).

[0055] 5 - 12 minutes: Heat up to 800°C at a rate of 50°C / minute, and the energy consumption drops to 200 kW.

[0056] After 12 minutes: Enter the heat preservation state, maintain an energy consumption of 120 kW / h, and the temperature stabilizes at 800°C.

[0057] After the cold furnace startup signal is sent, the combustion system of the standby furnace automatically ignites, and the PLC monitors the heating curve in real time. If the temperature is abnormal (such as heating rate < 40°C / minute), an alarm is triggered and it switches to the standby burner.

[0058] The fuzzy control strategy is as follows: Input variable: Current temperature of the standby furnace (T_f2), range [800°C, 1320°C]. Output variable: Feed ratio of the standby furnace (α), range [20%, 60%], adjusted by the opening of the three-way valve.

[0059] Dynamic allocation rules: When T_f2 = 800°C (heat preservation state), α = 20% (low-speed feeding for preheating).

[0060] When T_f2 = 1200°C (close to the optimal temperature), α = 40% (medium load).

[0061] When T_f2 = 1300°C (optimal zone), α = 50% (full load balanced distribution).

[0062] At the 15th minute after startup: The temperature of the standby furnace is 1100°C, the feed ratio is increased to 30%, the feed rate of the main furnace is reduced from 9 tons / hour to 7 tons / hour, and the total feed rate = 7 + 3 = 10 tons / hour.

[0063] 30 minutes after startup: The temperature of the standby furnace is 1300°C, the feeding ratio is 50%, the main furnace and the standby furnace each process 6 tons per hour, and the total feeding volume = 12 tons per hour (double furnace at full load).

[0064] During the high-load period of a single furnace (the main furnace operates independently), the production capacity is 9 tons per hour. After the main and standby furnaces cooperate, it is increased to 12 tons per hour, with an increase of 33.3%. During the daily high-temperature period (8 hours), the production capacity is increased from 72 tons to 96 tons, and the annual production capacity is increased by about 8760 tons (calculated based on 365 days).

[0065] It only takes 12 minutes for the standby furnace to reach the heat preservation state (800°C) from cold start, which is 14 minutes shorter than the traditional cold start (it takes 26 minutes to directly heat up to 1300°C). The total time from the trigger condition to the double furnace running at full load is 30 minutes, which is 50% shorter than the traditional scheme (heating up after cold start).

[0066] Under the heat preservation state, the hourly energy consumption of the standby furnace is 120kW, which is only 30% of that in the cold start stage (the average energy consumption in the cold start stage is about 400kW / h). During collaborative operation, the unit energy consumption of the double furnace is reduced from 60kW・h / ton of a single furnace to 50kW・h / ton (the total energy consumption of 1200kW corresponds to a processing volume of 12 tons per hour).

[0067] As Figure 2 As shown in the system structure diagram of an intelligent control system for a fly ash melting treatment production line, the present application provides an intelligent control system for a fly ash melting treatment production line, including: Graph modeling and optimization module: including: a directed weighted graph construction unit, a minimum processing cost path solving unit, a maximum feasible reuse amount solving unit, and a node linkage unit; among them, the directed weighted graph construction unit abstracts the physical entities and logical relationships of the water washing system into a directed weighted graph; the minimum processing cost path solving unit solves the minimum processing cost path of the directed weighted graph through the Dijkstra algorithm, and the maximum feasible reuse amount solving unit solves the maximum feasible reuse amount of the directed weighted graph through the maximum flow algorithm; the node linkage unit realizes the dynamic balance of water quality and water volume through node linkage based on the minimum processing cost path and the maximum feasible reuse amount; Optimal melting temperature calculation module: including: a feature engineering unit and a gradient boosting tree modeling unit; among them, the feature engineering unit obtains production line data, including water quality data, water volume data, and production line equipment status, performs feature engineering, and obtains exponential data, including water washing efficiency index and melting load index; the gradient boosting tree modeling unit uses the gradient boosting tree, inputs the production line data and the exponential data, and performs model training to obtain the optimal melting temperature; Dynamic control and scheduling module: It includes a temperature and equipment linkage control unit and a buffer and feeding scheduling unit; among them, the temperature and equipment linkage control unit establishes a linkage control table for the optimal melting temperature and the status of the production line equipment, reads real-time temperature data through the PLC system, generates parameter instructions and executes them after matching the linkage control table; the buffer and feeding scheduling unit sets an intermediate buffer mechanism for the water washing link and the melting link. When the temperature of the melting furnace reaches the optimal temperature range, it triggers the priority feeding logic and calculates the load rate of the melting furnace. Primary and standby melting furnace collaborative scheduling module: It includes a primary and standby melting furnace collaborative scheduling unit; among them, the primary and standby melting furnace collaborative scheduling unit configures the primary melting furnace and the standby melting furnace. When the temperature of the primary melting furnace is stable in the optimal temperature range and the load rate of the melting furnace is greater than the set threshold, the standby melting furnace is started to the heat preservation state, so as to maximize the utilization rate of the production line in the high-temperature melting link.

[0068] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. An intelligent control method for a fly ash melting processing production line, characterized in that: The following steps are involved: The physical entities and logical relationships of the water washing system are abstracted into a directed weighted graph; the minimum processing cost path of the directed weighted graph is solved by the Dijkstra algorithm, and the maximum feasible reuse amount of the directed weighted graph is solved by the maximum flow algorithm; based on the minimum processing cost path and the maximum feasible reuse amount, the dynamic balance of water quality and water quantity is achieved through node linkage; Obtain production line data, including water quality data, water volume data, and production line equipment status, perform feature engineering, and obtain index data, including water washing efficiency index and melt load index; use gradient boosting tree, input production line data and index data, perform model training, and obtain the optimal melt temperature; Establish a linkage control table between the optimal melting temperature and the status of the production line equipment, read the real-time temperature data through the PLC system, generate and execute parameter instructions after matching the linkage control table; set up an intermediate cache buffer mechanism between the washing link and the melting link, and when the melting furnace temperature reaches the optimal temperature range, trigger the priority feeding logic and calculate the melting furnace load rate; Configure a main melting furnace and a backup melting furnace. When the temperature of the main melting furnace is stabilized in the optimal temperature range and the melting furnace load rate is greater than the set threshold, start the backup melting furnace to the insulation state to maximize the production line utilization rate of the high-temperature melting link.

2. The intelligent control method for a fly ash melting processing production line according to claim 1, characterized in that: The physical entities and logical relationships of the water washing system are abstracted into a directed weighted graph, including: The physical entities of the water washing system include water washing tanks, wastewater treatment equipment, water storage facilities, transmission equipment, sensors and actuators, and the logical relationships include control strategies, data flows and process rules. The physical entities and logical components are abstracted as graph nodes and given characteristic attributes. The connection relationship between nodes is described, and the physical meaning and weight of the edges are given. The edge types include material flow edges, data flow edges, control flow edges and state edges. The weight of the material flow edge includes water flow resistance and transmission time, and the physical meaning is the water flow direction and energy consumption cost. The weight of the data flow edge includes transmission delay and data accuracy, and the physical meaning is the transmission of sensor data to control logic. The weight of the control flow edge includes response time and instruction reliability, and the physical meaning is the execution efficiency of control instructions. The weight of the state edge includes load rate and remaining capacity, and the physical meaning is the load distribution and state association between equipment. A mathematical model of a directed weighted graph is constructed, and Neo4j is used for graph database deployment and real-time data integration.

3. The intelligent control method for a fly ash melting processing production line according to claim 1 is characterized in that: The method of solving the minimum processing cost path of the directed weighted graph by the Dijkstra algorithm and solving the maximum feasible reuse amount of the directed weighted graph by the maximum flow algorithm includes: Determine the starting node and target node in the directed weighted graph; construct the first array to record the shortest distance from the starting point to each node. Initially, the distance of the starting point is set to 0, and the distances of the remaining nodes are set to infinity; construct the second array to record the predecessor node of each node, and the initial value is set to null; based on the first array and the second array, create a priority queue, add the starting point to the queue, and its priority is 0; the priority queue is used to store the nodes to be processed, and sort them in ascending order of distance; when the priority queue is not empty, take out the node with the shortest distance from the priority queue, record it as the current node, and traverse all adjacent nodes of the current node; starting from the end point, backtrack through the predecessor node array, find the predecessor node of each node in turn, until returning to the starting point, so as to construct the minimum processing cost path; On the basis of a directed weighted graph, a residual network is constructed; the initial capacity value of each edge in the residual network is equal to the weight of the original edge, and a reverse edge is added, and the initial capacity of the reverse edge is 0; the source point of the residual network is determined to be the water source point in the water washing system, and the sink point is the reuse point; a breadth-first search is used to find an augmenting path from the source point to the sink point in the residual network, wherein the augmenting path refers to a path existing in the residual network, along which the flow can be increased; after finding the augmenting path, the minimum capacity on the path is calculated and recorded as the bottleneck capacity; along the augmenting path, the capacity of the forward edge is subtracted from the bottleneck capacity, and the capacity of the reverse edge is added to the bottleneck capacity; the process of finding the augmenting path and updating the residual network is repeated until the augmenting path is not found, at which time the total flow outflowing from the source point is taken as the maximum feasible reuse amount.

4. The intelligent control method for a fly ash melting processing production line according to claim 1, characterized in that: The dynamic balance of water quality and water quantity is achieved through node linkage based on the minimum treatment cost path and the maximum feasible reuse amount, including: Determine the relationship between each node based on the minimum processing cost path and the actual situation of the water washing system; when the water quality parameters of a node exceed the target range, adjust it by linking other nodes; formulate water allocation rules based on the maximum feasible reuse volume and the water demand of each node; adjust the operating status of the equipment according to changes in water quality and water quantity; conduct real-time monitoring and feedback, and adjust the operating parameters and equipment status of each node based on the results of real-time monitoring and feedback to achieve a dynamic balance between water quality and water quantity.

5. The intelligent control method for a fly ash melting processing production line according to claim 1, characterized in that: The acquisition of production line data, including water quality data, water volume data and production line equipment status, performs feature engineering, and obtains index data, including water washing efficiency index and melt load index, including: The production line data is cleaned to extract water quality data characteristics, water quantity data characteristics and production line equipment status characteristics; according to historical data, weights are assigned to various indicators that affect water washing efficiency and melt load, and standardized processing is performed. The water washing efficiency index and melt load index are calculated using the weighted summation method.

6. The intelligent control method for a fly ash melting processing production line according to claim 1, characterized in that: The method adopts the gradient boosting tree, inputs the production line data and index data, performs model training, and obtains the optimal melting temperature, including: The production line data and index data are integrated into a data set, the relevant columns in the data set are selected as features, the optimal melting temperature is used as the target variable, and the data set is divided into a training set and a test set; the parameters of the gradient boosting tree model are initialized, the gradient boosting tree model is fitted using the training set data, and the model is trained using k-fold cross validation; the test set data is predicted using the trained model to obtain the predicted melting temperature, and the model is evaluated and optimized; the current production line data and index data are input into the optimized model to obtain the optimal melting temperature.

7. The intelligent control method for a fly ash melting processing production line according to claim 1, characterized in that: The linkage control table between the optimal melting temperature and the state of the production line equipment is established, and the real-time temperature data is read through the PLC system, and parameter instructions are generated and executed after matching the linkage control table, including: The optimal temperature range is divided based on the optimal melting temperature, and a multidimensional data block is created in the PLC system to store the optimal temperature range, device parameter thresholds and control instruction priorities, and a linkage control table is established; the PLC's conditional judgment instructions are used to match the linkage control table interval according to the real-time temperature value; when the temperature crosses the interval boundary, the device parameters are adjusted through the ramp function; the PLC reads the temperature value in real time, compares it with the control table interval, generates the corresponding device parameter instructions, and performs analog output and digital output.

8. The intelligent control method for a fly ash melting processing production line according to claim 1, characterized in that: The intermediate buffer mechanism between the water washing link and the melting link is set. When the temperature of the melting furnace reaches the optimal temperature range, the priority feeding logic is triggered and the melting furnace load rate is calculated, including: Construct a buffer pool that matches the production capacity of the water washing and melting stages. The minimum capacity is designed to be the maximum processing capacity of the melting furnace within the optimal temperature range, and the maximum capacity is designed to be the maximum discharge of a single batch in the water washing stage. The temperature detected by the B-type thermocouples installed in the middle and bottom of the melting furnace is used as the average value as the real-time temperature. When the real-time temperature is in the optimal temperature range for w1 consecutive minutes and the liquid level in the buffer pool reaches the lower limit of the liquid level in the buffer pool, a signal is sent to the buffer pool control system through the PLC to switch the feeding speed to the priority feeding speed, where w1 is obtained by statistical analysis of historical data. The average load of the melting furnace and the maximum load of the melting furnace are obtained and divided to obtain the melting furnace load rate.

9. The intelligent control method for a fly ash melting processing production line according to claim 1, characterized in that: The configuration of the main melting furnace and the standby melting furnace, when the temperature of the main melting furnace is stabilized in the optimal temperature range and the melting furnace load rate is greater than the set threshold, starts the standby melting furnace to the insulation state, so as to maximize the production line utilization rate of the high-temperature melting link, including: Perform physical configuration and parameter definition of the main and standby melting furnaces, use sliding window filtering to determine whether the temperature of the main melting furnace is stable in the optimal temperature range, and perform subsequent operations if satisfied; when the load rate of the main melting furnace is greater than the high load state threshold for w2 consecutive minutes, it is determined to be in a high load state, and the standby melting furnace is started to the insulation state, w2 and the high load state threshold are obtained by statistical analysis of historical data; materials are allocated according to the load balancing principle of the main and standby melting furnaces, and the total feed amount does not exceed the sum of the maximum processing capacities of the main and standby melting furnaces; a fuzzy control algorithm is used to adjust the opening of the three-way valve, and the allocation ratio is adjusted in real time according to the temperature of the standby melting furnace.

10. An intelligent control system for a fly ash melting processing production line, using an intelligent control method for a fly ash melting processing production line according to any one of claims 1 to 9, characterized in that: include: Graph modeling and optimization module: including: directed weighted graph construction unit, minimum treatment cost path solving unit, maximum feasible reuse amount solving unit and node linkage unit; among them, the directed weighted graph construction unit abstracts the physical entities and logical relationships of the water washing system into a directed weighted graph; the minimum treatment cost path solving unit solves the minimum treatment cost path of the directed weighted graph through the Dijkstra algorithm, and the maximum feasible reuse amount solving unit solves the maximum feasible reuse amount of the directed weighted graph through the maximum flow algorithm; the node linkage unit realizes the dynamic balance of water quality and water quantity through node linkage based on the minimum treatment cost path and the maximum feasible reuse amount; Optimal melting temperature calculation module: including: feature engineering unit and gradient boosting tree modeling unit; wherein, the feature engineering unit obtains production line data, including water quality data, water volume data and production line equipment status, performs feature engineering, and obtains index data, including water washing efficiency index and melting load index; the gradient boosting tree modeling unit adopts gradient boosting tree, inputs production line data and index data, performs model training, and obtains the optimal melting temperature; Dynamic control and scheduling module: including temperature and equipment linkage control unit and cache buffer and feeding scheduling unit; the temperature and equipment linkage control unit establishes a linkage control table between the optimal melting temperature and the equipment status of the production line, reads real-time temperature data through the PLC system, generates parameter instructions and executes them after matching the linkage control table; the cache buffer and feeding scheduling unit sets up an intermediate cache buffer mechanism between the washing link and the melting link. When the melting furnace temperature reaches the optimal temperature range, it triggers the priority feeding logic and calculates the melting furnace load rate; The main and standby melting furnace coordinated scheduling module includes: a main and standby melting furnace coordinated scheduling unit; wherein, the main and standby melting furnace coordinated scheduling unit is configured with a main melting furnace and a standby melting furnace, and when the temperature of the main melting furnace is stabilized in the optimal temperature range and the melting furnace load rate is greater than the set threshold, the standby melting furnace is started to the insulation state, so as to maximize the production line utilization rate of the high-temperature melting link.

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