An intelligent control method and system for a fly ash melting treatment production line

Abstract

The present invention discloses an intelligent control method and system for a fly ash melting processing production line, which belongs to the field of intelligent control technology. The present invention abstracts the physical entities and logical relationships of the water washing system into a directed weighted graph; calculates the minimum processing cost path and the maximum feasible reuse amount, and realizes the dynamic balance of water quality and water quantity through node linkage; obtains production line data, performs feature engineering, and obtains index data; adopts a gradient boosting tree, inputs production line data and index data, performs model training, and obtains the optimal melting temperature; establishes a linkage control table of the optimal melting temperature and the production line equipment status, reads real-time temperature data, matches the linkage control table, generates parameter instructions, and executes them; sets an intermediate cache buffering mechanism, calculates the melting furnace load rate; configures the main melting furnace and the standby melting furnace, and when the temperature of the main melting furnace is stable 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.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent control method and system for a fly ash melting processing 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; the water washing process removes harmful substances such as chlorine and heavy metals in fly ash through multi-stage circulation, and its water resource utilization rate and treatment cost directly affect environmental compliance; the high-temperature melting process requires precise temperature control and equipment scheduling to achieve efficient and stable production, and capacity utilization is the core indicator that determines the scale of treatment.

[0003] In the existing technology, the water washing path is fixed, and the difference in treatment costs under different water qualities is not taken into account. The wastewater reuse amount is set based on manual experience, and a maximum flow model based on the water flow network is not established. There is a lack of real-time linkage control, which can easily lead to substandard effluent water quality; the melting temperature setting depends on the process manual and is not combined with real-time dynamic optimization of water quality, which can easily lead to insufficient melting or energy waste; the water washing and melting links lack a buffer mechanism. When the melting furnace temperature meets the standard, the water washing discharge may lag or exceed the amount, causing the melting furnace to idle or overload. Summary of the Invention

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

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, the present application provides an intelligent control method for a fly ash melting processing production line, comprising the following steps:

[0007] The physical entities and logical relationships of the water washing system are abstracted into a directed weighted graph. The minimum treatment cost path of the directed weighted graph is solved using the Dijkstra algorithm, and the maximum feasible reuse volume of the directed weighted graph is solved using the maximum flow algorithm. Based on the minimum treatment cost path and the maximum feasible reuse volume, a dynamic balance of water quality and water quantity is achieved through node linkage.

[0008] Obtain production line data, including water quality data, water quantity data, and production line equipment status, perform feature engineering, and obtain index data, including the water washing efficiency index and the melt load index. Use a gradient boosting tree to input the production line data and index data for model training to obtain the optimal melt temperature.

[0009] Establish a linkage control table between the optimal melting temperature and the status of production line equipment. Read real-time temperature data through the PLC system, match it with the linkage control table, generate parameter instructions, and execute them. Set up an intermediate cache buffer mechanism between the washing and melting stages. When the melting furnace temperature reaches the optimal temperature range, trigger the priority feeding logic and calculate the melting furnace load rate.

[0010] Configure a main melting furnace and a backup melting furnace. When the temperature of the main melting furnace is stable 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.

[0011] In combination with the first aspect, in a first implementation of the first aspect of the present application, the abstracting of the physical entities and logical relationships of the water washing system into a directed weighted graph includes:

[0012] 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; the physical entities and logical components are abstracted as graph nodes and assigned characteristic attributes; the connection relationship between nodes is described, and the physical meaning and weight of the edges are assigned. The edge types include material flow edges, data flow edges, control flow edges and state edges. Among them, the weight of the material flow edge includes water flow resistance and transmission time, and the physical meaning is 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.

[0013] In combination with the first aspect, in a second implementation 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 reuse amount of the directed weighted graph by the maximum flow algorithm include:

[0014] Determine the starting node and 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 remaining nodes are set to infinity; construct a second array to record the predecessor node of each node, and the initial value is set to null; based on the first and second arrays, create a priority queue and add the starting point to the queue with a priority of 0; the priority queue is used to store the nodes to be processed, sorted by distance from small to large; when the priority queue is not empty, take 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; starting from the end point, backtrack through the predecessor node array to find the predecessor node of each node in turn until returning to the starting point, thereby constructing the minimum processing cost path;

[0015] Based on 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 rate 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 bottleneck capacity is subtracted from the capacity of the forward edge, and the bottleneck capacity is added to the capacity of the reverse edge; the process of finding the augmenting path and updating the residual network is repeated until no augmenting path is found, at which point the total flow rate outflowing from the source point is taken as the maximum feasible reuse amount.

[0016] In combination with the first aspect, in a third embodiment of the first aspect of the present application, 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:

[0017] 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.

[0018] In combination with the first aspect, in a fourth embodiment of the first aspect of the present application, the production line data is obtained, including water quality data, water quantity data, and production line equipment status, and feature engineering is performed to obtain index data, including a water washing efficiency index and a melt load index, including:

[0019] The production line data is cleaned to extract water quality data characteristics, water quantity data characteristics and production line equipment status characteristics; based on historical data, weights are assigned to various indicators affecting 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.

[0020] In combination with the first aspect, in a fifth embodiment of the first aspect of the present application, the method of using a gradient boosting tree, inputting production line data and index data, performing model training, and obtaining the optimal melting temperature includes:

[0021] The production line data and index data are integrated into a dataset. The relevant columns in the dataset are selected as features. The optimal melting temperature is used as the target variable, and the dataset is divided into a training set and a test set. The parameters of the gradient boosting tree model are initialized, and the gradient boosting tree model is fitted using the training set data. The model is trained using k-fold cross-validation. The trained model is used to predict the test set data 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.

[0022] In combination with the first aspect, in a sixth embodiment of the first aspect of the present application, establishing a linkage control table between the optimal melting temperature and the production line equipment status, reading real-time temperature data through a PLC system, matching the linkage control table, generating and executing parameter instructions, includes:

[0023] 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 range according to the real-time temperature value. When the temperature crosses the range 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 range, generates the corresponding device parameter instructions, and performs analog and digital output.

[0024] In combination with the first aspect, in a seventh embodiment of the first aspect of the present application, the intermediate cache buffer mechanism between the water washing step and the melting step is set, and when the melting furnace temperature reaches the optimal temperature range, the priority feeding logic is triggered and the melting furnace load rate is calculated, including:

[0025] A buffer pool is constructed to match 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 capacity 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 averaged as the real-time temperature. When the real-time temperature remains within the optimal temperature range for w1 consecutive minutes and the buffer pool liquid level reaches the lower limit of the buffer pool, a signal is sent to the buffer pool control system via the PLC to switch the feeding speed to the priority feeding speed, where w1 is obtained from statistical analysis of historical data.

[0026] 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.

[0027] In combination with the first aspect, in an eighth embodiment 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 stabilized in the optimal temperature range and the melting furnace load rate is greater than a 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, includes:

[0028] 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 distributed 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 distribution ratio is adjusted in real time according to the temperature of the standby melting furnace.

[0029] In a second aspect, the present application provides an intelligent control system for a fly ash melting processing production line, comprising:

[0030] Graph modeling and optimization module: includes: 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 uses the Dijkstra algorithm to solve the minimum treatment cost path of the directed weighted graph, and the maximum feasible reuse amount solving unit uses the maximum flow algorithm to solve the maximum feasible reuse amount of the directed weighted graph; the node linkage unit achieves a dynamic balance between water quality and water quantity through node linkage based on the minimum treatment cost path and maximum feasible reuse amount;

[0031] The optimal melting temperature calculation module includes a feature engineering unit and a gradient boosting tree modeling unit. The feature engineering unit obtains production line data, including water quality data, water quantity data, and production line equipment status, performs feature engineering, and obtains index data, including the water washing efficiency index and the melt load index. The gradient boosting tree modeling unit uses a gradient boosting tree to input the production line data and index data, performs model training, and obtains the optimal melting temperature.

[0032] Dynamic Control and Scheduling Module: This module includes a temperature and equipment linkage control unit and a buffer and feed scheduling unit. The temperature and equipment linkage control unit establishes a linkage control table between the optimal melting temperature and the production line equipment status. The PLC system reads real-time temperature data, matches the linkage control table, generates parameter instructions, and executes them. The buffer and feed scheduling unit establishes an intermediate buffer mechanism between the washing and melting stages. When the melting furnace temperature reaches the optimal temperature range, it triggers the priority feeding logic and calculates the melting furnace load rate.

[0033] The main and standby melting furnace collaborative scheduling module includes: a main and standby melting furnace collaborative scheduling unit; wherein, the main and standby melting furnace collaborative scheduling unit is configured with a main melting furnace and a 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, 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.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. This application abstracts the water washing system into 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 resource waste in fixed processes; the water washing network is regarded as a flow network, and the theoretical maximum reuse amount is calculated through the maximum flow algorithm to avoid water quality deterioration caused by the accumulation of impurities in the recycled water; establishes linkage rules between water quality indicators and equipment nodes, and quickly triggers responses through graph models to improve the water quality compliance rate.

[0036] 2. This application inputs the water quality data, water quantity data and equipment status of the fly ash after water washing, and trains the gradient boosting tree model to predict the optimal temperature, so as to improve the sufficiency of melting and avoid overheating in the high-temperature area; the mapping relationship between the predefined temperature range and the equipment parameters is matched and executed in real time through the PLC to avoid the problem of insufficient equipment load despite the temperature meeting the standard due to manual experience.

[0037] 3. This application sets up 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 due to water washing delay; real-time calculation and scheduling of the melting furnace load rate are performed to improve production capacity utilization and avoid equipment loss caused by overload of a single furnace. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a schematic diagram of the steps of an intelligent control method for a fly ash melting processing production line according to the present invention;

[0039] Figure 2 This is a system structure diagram of an intelligent control system for a fly ash melting processing production line according to the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution.

[0042] like Figure 1 As shown in a schematic diagram of the steps of an intelligent control method for a fly ash melting processing production line, the present application provides an intelligent control method for a fly ash melting processing production line, comprising the following steps:

[0043] Step S100: 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 using the Dijkstra algorithm, and the maximum feasible reuse amount of the directed weighted graph is solved using the maximum flow algorithm; based on the minimum processing cost path and the maximum feasible reuse amount, a dynamic balance of water quality and water quantity is achieved through node linkage;

[0044] 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; 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. Among them, the weight of the material flow edge includes water flow resistance and transmission time, and the physical meaning is 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.

[0045] Furthermore, the starting node and target node in the directed weighted graph are determined; a first array is constructed 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; a second array is constructed to record the predecessor node of each node, and the initial values are all set to null; based on the first and second arrays, a priority queue is created, and the starting point is added to the queue with a priority of 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, the node with the smallest distance is taken from the priority queue, recorded as the current node, and all adjacent nodes of the current node are traversed; starting from the end point, backtracking through the predecessor node array, finding the predecessor node of each node in turn, until returning to the starting point, thereby constructing the minimum processing cost path;

[0046] Based on 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 rate 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 bottleneck capacity is subtracted from the capacity of the forward edge, and the bottleneck capacity is added to the capacity of the reverse edge; the process of finding the augmenting path and updating the residual network is repeated until no augmenting path is found, at which point the total flow rate outflowing from the source point is taken as the maximum feasible reuse amount.

[0047] Furthermore, the correlation between each node is determined 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, adjustments are made by linking other nodes; water allocation rules are formulated based on the maximum feasible reuse volume and the water demand of each node; the operating status of the equipment is adjusted according to changes in water quality and water quantity; real-time monitoring and feedback are carried out, and based on the results of real-time monitoring and feedback, the operating parameters and equipment status of each node are adjusted to achieve a dynamic balance between water quality and water quantity.

[0048] In one specific example, the water washing system at an electronic waste treatment plant treats wastewater containing heavy metals. It includes three water washing tanks (T1-T3), ultrafiltration (UF), reverse osmosis (RO), a fresh water storage tank (F), and a reuse water tank (R). The goal: to reduce energy consumption, increase reuse volume, and improve treatment efficiency.

[0049] Use the Dijkstra algorithm to find the minimum energy path. The starting point is T1 (high-concentration wastewater, heavy metal concentration = 100 mg / L) and the end point is R (recycling tank, target heavy metal concentration ≤ 1 mg / L). Constraints: treated water quality must be ≤ 1 mg / L. Low-energy paths are preferred. Initialization: Energy array: energy[T1] = 0, energy[other nodes] = ∞, path array: path[T1] = [], priority queue: (0, T1).

[0050] Remove T1: Its adjacent nodes are UF (energy consumption 15 kWh / m³) and T2 (direct water washing, energy consumption 3 kWh / m³, but substandard water quality). Only UF meets the water quality requirements. Update energy[UF] to 15 and path[UF] to [T1→UF].

[0051] Remove UF: 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, concentration after treatment = 100 mg / L × (1-90%) = 10 mg / L (not meeting the standard). UF → RO: Total energy consumption = 15 + 30 = 45 kWh / m³, concentration after treatment = 100 mg / L × (1-99%) = 1 mg / L (meets the standard). Select the RO path, update energy[RO] = 45, and path[RO] = [T1 → UF → RO].

[0052] Remove RO: The adjacent node is R, energy consumption = 2 kWh / m³, total energy consumption = 45 + 2 = 47 kWh / m³, and the path is complete. The minimum energy consumption path is obtained: T1 → UF → RO → R, with a total energy consumption of 47 kWh / m³. The treated water quality meets the standard. Compared 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 meeting the standard). The path of the present invention: While energy consumption is higher, water quality is guaranteed, and the total energy consumption needs to be offset by increasing the reuse volume.

[0053] Construct a residual network (flow unit: m³ / h): Source points: F (fresh water, unlimited flow), R (reuse pool, current available flow = 40 m³ / h); Sink points: T1 (water demand = 10 m³ / h), T3 (water demand = 15 m³ / h); Edge capacity: F→T1: 20 m³ / h (upper limit of fresh water supply); R→T3: 30 m³ / h (maximum reuse volume of reuse pool); UF→T2: 10 m³ / h (ultrafiltration treatment capacity); RO→R: 5 m³ / h (reverse osmosis water production capacity).

[0054] Implement the Ford-Fulkerson algorithm:

[0055] First augmented path: R→T3 (capacity = 30 m³ / h); bottleneck capacity = 15 m³ / h (water demand of T3), after update: R→T3 remaining capacity = 15 m³ / h, reverse edge T3→R = 15 m³ / h.

[0056] Secondary augmenting path: F→T1→UF→T2→RO→R→T3; edge capacity: F→T1=20, T1→UF=10, UF→T2=10, T2→RO=5, RO→R=5, R→T3=15; bottleneck capacity = 5m³ / h (RO processing capacity). After update: RO→R=0, reverse edge R→RO=5, and the capacities of the remaining edges decrease by 5.

[0057] Maximum feasible reuse volume: Direct supply from the reuse water pool to T3: 15m³ / h; reuse of fresh water after treatment: 5m³ / h; total reuse volume = 20m³ / h, traditional manually set reuse volume = 10m³ / h, an increase of 100%.

[0058] When the pH value exceeds the standard, an example of water quality abnormal linkage is as follows: Trigger condition: Sensor S_PH detects that the pH of T1 wastewater is 4.5 (target value 6-9).

[0059] Linkage Logic: Data flow: S_PH → Control Rule Node (R_pH), transmission delay = 100ms. Control flow: R_pH → Actuator V_chemical (alkali pump), response time = 5s, powered up to 100% power. Status update: Energy consumption for alkali addition increases by 2kWh / h, and the agitator load factor increases from 50% to 70%. After 30 minutes, the pH rises to 6.8, and V_chemical is shut down, returning to normal energy consumption.

[0060] Step S200: Acquire production line data, including water quality data, water quantity data, and production line equipment status, perform feature engineering, and obtain index data, including a water washing efficiency index and a melt load index. A gradient boosting tree is used to input the production line data and index data for model training to obtain the optimal melt temperature.

[0061] Specifically, the production line data is cleaned to extract water quality data characteristics, water quantity data characteristics and production line equipment status characteristics; based on historical data, weights are assigned to various indicators affecting 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.

[0062] Furthermore, the production line data and index data are integrated into a data set, 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 trained model is used to predict the test set data 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.

[0063] In one specific embodiment, a metal smelting plant's water washing and melting production line needs to predict the optimal melting temperature based on real-time water quality, water quantity, and equipment status. An example of some raw data is as follows:

[0064] Heavy metal content 120mg / L, water volume 20m³, motor speed 1500r / min, washing efficiency index 0.66, melt load index 0.57, optimal temperature 1250℃;

[0065] Heavy metal content 80mg / L, water volume 18m³, motor speed 1600r / min, washing efficiency index 0.76, melt load index 0.67, optimal temperature 1220℃;

[0066] Heavy metal content 150mg / L, water volume 22m³, motor speed 1400r / min, washing efficiency index 0.56, melt load index 0.47, optimal temperature 1280℃;

[0067] Heavy metal content 90mg / L, water volume 19m³, motor speed 1550r / min, washing efficiency index 0.71, melt load index 0.62, optimal temperature 1230℃;

[0068] Heavy metal content: 110mg / L, water volume: 21m³, motor speed: 1520r / min, washing efficiency index: 0.68, melt load index: 0.59, optimum temperature: 1240℃.

[0069] The water washing efficiency index (WEI) was calculated, and the influencing indicators: heavy metal removal rate (weight 0.6) and water utilization rate (weight 0.4) were standardized to [0,1].

[0070] In sample 1, the normalized value of heavy metal removal efficiency is 0.7, and the normalized value of water utilization efficiency is 0.6 → WEI = 0.6 × 0.7 + 0.4 × 0.6 = 0.66;

[0071] In sample 2: the standardized value of heavy metal removal rate is 0.8, the standardized value of water utilization rate is 0.7 → WEI = 0.6 × 0.8 + 0.4 × 0.7 = 0.76.

[0072] The melt load index (MLI) was calculated, and the influencing indicators: feed rate (weight 0.7) and motor power (weight 0.3) were normalized to [0,1].

[0073] In sample 1, the feed rate is normalized to 0.6, and the motor power is normalized to 0.5 → MLI = 0.7 × 0.6 + 0.3 × 0.5 = 0.57;

[0074] In sample 2: feed rate normalized value 0.7, motor power normalized value 0.6 → MLI = 0.7 × 0.7 + 0.3 × 0.6 = 0.67.

[0075] A data set was constructed, including heavy metal content, water volume, motor speed, washing efficiency index, and melt load index. The target variable was the optimal melt temperature. The data was divided into a training set of the first eight samples and a test set of the last two. The mean square error (RMS) of the test set was 25.00, indicating that the average deviation between the predicted and actual temperatures was approximately ±5°C (RMS error of approximately 5°C), meeting industrial accuracy requirements. Current production line data was input into the optimized model to determine the optimal melt temperature, guiding operators in adjusting heating parameters.

[0076] Step S300: Establish a linkage control table between the optimal melting temperature and the production line equipment status. The PLC system reads real-time temperature data, matches the linkage control table, generates parameter instructions, and executes them. A buffer mechanism is set up between the washing and melting stages. When the melting furnace temperature reaches the optimal temperature range, the priority feeding logic is triggered and the melting furnace load rate is calculated.

[0077] Specifically, 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 instruction, and performs analog output and digital output.

[0078] Furthermore, a buffer pool is constructed to match 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 capacity 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 averaged as the real-time temperature. When the real-time temperature remains within the optimal temperature range for w1 consecutive minutes and the buffer pool liquid level reaches the lower limit of the buffer pool liquid level, a signal is sent to the buffer pool control system via the PLC to switch the feeding speed to the priority feeding speed, where w1 is obtained from statistical analysis of historical data.

[0079] 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.

[0080] In one specific example, the optimal melting temperature of a solid waste treatment plant's melting furnace is 1300°C. The temperature ranges are divided into: a low-temperature zone (<1280°C), an optimal zone (1280-1320°C), and a high-temperature zone (≥1320°C). An intermediate buffer pool (minimum capacity 5 tons, maximum capacity 12 tons) is configured, and temperature-device linkage and priority feeding control are implemented through a PLC. The temperature ranges are mapped to device parameters, and the PLC data blocks and control logic are defined.

[0081] The key parameters of the buffer pool include: minimum capacity: 5t (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): suspend water washing discharge; lower limit 20% (2.4t): trigger water washing expedited production; feeding speed: normal speed: 10t / h; priority speed: 15t / h (enabled when the temperature meets the standard).

[0082] Priority feeding trigger conditions include: temperature condition: T_real is in the optimal zone for 5 consecutive minutes (achieved by timer T37); liquid level condition: buffer tank liquid level ≥ L_min (2.4t).

[0083] Initially, the melting furnace temperature was 1290°C (optimal range), the buffer tank level was 8 tons (66.7%), and the feed rate was a standard 10 tons / hour. Five minutes later, the temperature remained between 1285°C and 1295°C, triggering priority feeding. The feed rate increased to 15 tons / hour, and the processing capacity within 30 minutes was 15 tons / hour × 0.5 hours = 7.5 tons. The buffer tank level was 8 tons - 7.5 tons = 0.5 tons (lower than L_min = 2.4 tons). The PLC sent a signal to the washing process, increasing the discharge rate from 8 tons / hour to 12 tons / hour (expedited production). One hour later, the buffer tank level returned to -2 tons (0.5 tons + 12 tons - 15 tons × 1 hour).

[0084] Calculate the melting furnace load rate: T = 1290°C (optimal zone), V_actual = 15 t / h; k(T) = 1.0, V_max(T) = 10 × 1.0 = 10 t / h; load rate = 15 / 10 = 150% → overload protection is triggered, and the speed is automatically reduced to 10 t / h.

[0085] Step S400: Configure the main melting furnace and the backup melting furnace. When the temperature of the main melting furnace is stable 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.

[0086] Specifically, the physical configuration and parameter definition of the main and standby melting furnaces are carried out, and a sliding window filter is used to determine whether the temperature of the main melting furnace is stable in the optimal temperature range. If the temperature is satisfied, subsequent operations are performed. 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 distributed 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 distribution ratio is adjusted in real time according to the temperature of the standby melting furnace.

[0087] In one specific example, an industrial production line is equipped with a main melting furnace (F1) and a backup melting furnace (F2). Each furnace has a rated processing capacity of 10 tons / hour, an optimal temperature range of 1300±20°C (i.e., 1280-1320°C), and a backup furnace holding temperature set at 800°C. The system monitors the main furnace's temperature stability and load rate in real time, triggering preheating of the backup furnace and dynamically allocating material to maximize production during high-temperature periods.

[0088] Main furnace (F1): Current temperature 1305°C. Using a 10-minute sliding window, temperature fluctuations of ≤±10°C (within the window temperature range of 1295-1315°C) indicate the temperature is stable within the optimal range. Load rate threshold: 85% (i.e., actual feed rate > 8.5 tons / hour), with a continuous overrun time w2 = 15 minutes. Standby furnace (F2): Initial temperature 25°C upon cold start, heating rate 50°C / minute, and energy consumption in the hold state 120 kW / h.

[0089] The current load rate of the main furnace is 90% (actual feed rate is 9 tons / hour), and it has exceeded the 85% threshold for 20 consecutive minutes, meeting the conditions of temperature stability and high load, triggering the backup furnace to start and enter the insulation state.

[0090] The standby furnace startup and heating process is as follows:

[0091] Data of warming stage:

[0092] 0-5 minutes: Rapidly heat up to 500°C at 100°C / minute, with an energy consumption of 400kW (to avoid thermal shock to the furnace lining).

[0093] 5-12 minutes: Raise the temperature to 800°C at 50°C / min, and reduce energy consumption to 200kW.

[0094] After 12 minutes: Enter the insulation state, maintain energy consumption at 120kW / h, and stabilize the temperature at 800℃.

[0095] After the cold furnace start-up signal is issued, the standby furnace combustion system automatically ignites, and the PLC monitors the heating curve in real time. If the temperature is abnormal (such as the heating rate is less than 40℃ / minute), an alarm is triggered and the standby burner is switched to.

[0096] The fuzzy control strategy is as follows:

[0097] 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.

[0098] Dynamic allocation rules:

[0099] When T_f2=800℃ (keeping state), α=20% (low speed feeding preheating).

[0100] When T_f2=1200℃ (close to the optimal temperature), α=40% (medium load).

[0101] When T_f2=1300℃ (optimal zone), α=50% (full load balanced distribution).

[0102] 15 minutes after startup: the standby furnace temperature is 1100℃, the feeding ratio is increased to 30%, the main furnace feeding speed is reduced from 9 tons / hour to 7 tons / hour, and the total feeding amount = 7+3=10 tons / hour.

[0103] 30 minutes after startup: the standby furnace temperature is 1300℃, the feeding ratio is 50%, the main furnace and the standby furnace each process 6 tons / hour, and the total feeding amount = 12 tons / hour (double furnaces at full load).

[0104] During high-load periods (with the main furnace operating independently), the single-furnace capacity was 9 tons / hour. This increased to 12 tons / hour with coordinated operation of the main and standby furnaces, a 33.3% increase. During the daily high-temperature period (8 hours), capacity increased from 72 tons to 96 tons, resulting in an annual capacity increase of approximately 8,760 tons (based on a 365-day period).

[0105] The furnace preparation process from cold start to holding temperature (800°C) takes only 12 minutes, 14 minutes shorter than a conventional cold start (which directly heats up to 1300°C, taking 26 minutes). The total time from triggering conditions to full-load operation of the dual furnaces is 30 minutes, a 50% reduction compared to the conventional solution (cold start followed by heating).

[0106] In the insulation mode, the standby furnace consumes 120kW per hour, just 30% of the energy consumed during a cold start (average cold start energy consumption is approximately 400kW / h). When operating in tandem, the unit energy consumption of the two furnaces drops from 60kW / h per ton for a single furnace to 50kW / h per ton (total energy consumption of 1200kW corresponds to a processing capacity of 12 tons per hour).

[0107] like Figure 2 As shown in the system structure diagram of an intelligent control system for a fly ash melting processing production line, the present application provides an intelligent control system for a fly ash melting processing production line, comprising:

[0108] Graph modeling and optimization module: includes: 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 uses the Dijkstra algorithm to solve the minimum treatment cost path of the directed weighted graph, and the maximum feasible reuse amount solving unit uses the maximum flow algorithm to solve the maximum feasible reuse amount of the directed weighted graph; the node linkage unit achieves a dynamic balance between water quality and water quantity through node linkage based on the minimum treatment cost path and maximum feasible reuse amount;

[0109] The optimal melting temperature calculation module includes a feature engineering unit and a gradient boosting tree modeling unit. The feature engineering unit obtains production line data, including water quality data, water quantity data, and production line equipment status, performs feature engineering, and obtains index data, including the water washing efficiency index and the melt load index. The gradient boosting tree modeling unit uses a gradient boosting tree to input the production line data and index data, performs model training, and obtains the optimal melting temperature.

[0110] Dynamic Control and Scheduling Module: This module includes a temperature and equipment linkage control unit and a buffer and feed scheduling unit. The temperature and equipment linkage control unit establishes a linkage control table between the optimal melting temperature and the production line equipment status. The PLC system reads real-time temperature data, matches the linkage control table, generates parameter instructions, and executes them. The buffer and feed scheduling unit establishes an intermediate buffer mechanism between the washing and melting stages. When the melting furnace temperature reaches the optimal temperature range, it triggers the priority feeding logic and calculates the melting furnace load rate.

[0111] The main and standby melting furnace collaborative scheduling module includes: a main and standby melting furnace collaborative scheduling unit; wherein, the main and standby melting furnace collaborative scheduling unit is configured with a main melting furnace and a 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, 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.

[0112] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

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 treatment cost path of the directed weighted graph is solved using the Dijkstra algorithm, and the maximum feasible reuse volume of the directed weighted graph is solved using the maximum flow algorithm. Based on the minimum treatment cost path and the maximum feasible reuse volume, a dynamic balance of water quality and water quantity is achieved through node linkage. Obtain production line data, including water quality data, water quantity data, and production line equipment status, perform feature engineering, and obtain index data, including the water washing efficiency index and the melt load index. Use a gradient boosting tree to input the production line data and index data for model training to obtain the optimal melt temperature. Establish a linkage control table between the optimal melting temperature and the status of production line equipment. Read real-time temperature data through the PLC system, match it with the linkage control table, generate parameter instructions, and execute them. Set up an intermediate cache buffer mechanism between the washing and melting stages. 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 stable 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 flow and process rules; the physical entities and logical components are abstracted as graph nodes and assigned characteristic attributes; the connection relationship between nodes is described, and the physical meaning and weight of the edges are assigned. The edge types include material flow edges, data flow edges, control flow edges and state edges. Among them, the weight of the material flow edge includes water flow resistance and transmission time, and the physical meaning is 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, 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 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 remaining nodes are set to infinity; construct a second array to record the predecessor node of each node, and the initial value is set to null; based on the first and second arrays, create a priority queue and add the starting point to the queue with a priority of 0; the priority queue is used to store the nodes to be processed, sorted by distance from small to large; when the priority queue is not empty, take 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; starting from the end point, backtrack through the predecessor node array to find the predecessor node of each node in turn until returning to the starting point, thereby constructing the minimum processing cost path; Based on 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 rate 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 bottleneck capacity is subtracted from the capacity of the forward edge, and the bottleneck capacity is added to the capacity of the reverse edge; the process of finding the augmenting path and updating the residual network is repeated until no augmenting path is found, at which point the total flow rate 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 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 production line data is obtained, including water quality data, water quantity data and production line equipment status, and feature engineering is performed to obtain 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; based on historical data, weights are assigned to various indicators affecting 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 dataset. The relevant columns in the dataset are selected as features. The optimal melting temperature is used as the target variable, and the dataset is divided into a training set and a test set. The parameters of the gradient boosting tree model are initialized, and the gradient boosting tree model is fitted using the training set data. The model is trained using k-fold cross-validation. The trained model is used to predict the test set data 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 process of establishing a linkage control table between the optimal melting temperature and the production line equipment status, reading real-time temperature data through the PLC system, matching the linkage control table, generating and executing parameter instructions, includes: 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 range according to the real-time temperature value. When the temperature crosses the range 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 range, generates the corresponding device parameter instructions, and performs analog 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 washing stage and the melting stage is set up. When the melting furnace temperature reaches the optimal temperature range, the priority feeding logic is triggered and the melting furnace load rate is calculated, including: A buffer pool is constructed to match 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 capacity 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 averaged as the real-time temperature. When the real-time temperature remains within the optimal temperature range for w1 consecutive minutes and the buffer pool liquid level reaches the lower limit of the buffer pool, a signal is sent to the buffer pool control system via the PLC to switch the feeding speed to the priority feeding speed, where w1 is obtained from 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 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, starts the backup 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 distributed 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 distribution 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 the 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: includes: 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 uses the Dijkstra algorithm to solve the minimum treatment cost path of the directed weighted graph, and the maximum feasible reuse amount solving unit uses the maximum flow algorithm to solve the maximum feasible reuse amount of the directed weighted graph; the node linkage unit achieves a dynamic balance between water quality and water quantity through node linkage based on the minimum treatment cost path and maximum feasible reuse amount; The optimal melting temperature calculation module includes a feature engineering unit and a gradient boosting tree modeling unit. The feature engineering unit obtains production line data, including water quality data, water quantity data, and production line equipment status, performs feature engineering, and obtains index data, including the water washing efficiency index and the melt load index. The gradient boosting tree modeling unit uses a gradient boosting tree to input the production line data and index data, performs model training, and obtains the optimal melting temperature. Dynamic Control and Scheduling Module: This module includes a temperature and equipment linkage control unit and a buffer and feed scheduling unit. The temperature and equipment linkage control unit establishes a linkage control table between the optimal melting temperature and the production line equipment status. The PLC system reads real-time temperature data, matches the linkage control table, generates parameter instructions, and executes them. The buffer and feed scheduling unit establishes an intermediate buffer mechanism between the washing and melting stages. 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 collaborative scheduling module includes: a main and standby melting furnace collaborative scheduling unit; wherein, the main and standby melting furnace collaborative scheduling unit is configured with a main melting furnace and a 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, 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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