Direct-current submerged arc furnace and control system thereof

Abstract

The invention relates to the technical field of submerged arc furnace power supply systems, provides a direct-current submerged arc furnace and a control system thereof, and aims to solve the problem that in the prior art, dynamic adjustment of a direct-current submerged arc furnace is not matched with a power supply system, so that the operation efficiency is reduced. By constructing a control device comprising a data processing unit, a data analysis unit, a data comparison unit and a data optimization unit, accurate regulation and control of direct-current submerged arc furnace equipment and a power supply device are realized. A dynamic regulation and control model is constructed by using a random forest model, and dynamic regulation parameters are generated according to actual operation conditions. The adjustment state is detected through the production simulation model, the adjustment parameters are optimized through the genetic algorithm, and the operation efficiency is improved. The system also has the functions of model retraining, dynamic threshold adjustment, multi-objective optimization and the like, and improves the adaptability and accuracy of power supply control.

Description

Technical Field

[0001] The present invention relates to the technical field of submerged arc furnace power supply systems, and particularly to a DC submerged arc furnace and its control system. Background Art

[0002] A DC submerged arc furnace is an industrial electric furnace that uses direct current to generate high temperatures for ore smelting or chemical production. Its core principle is to introduce direct current into the conductive furnace charge (such as coke or metal particles) in the furnace hearth through electrodes, and use the arc heat and resistance heat (up to over 2000°C) generated when the current passes through the furnace charge to melt and reduce ores (such as chromite, manganese ore) or raw materials (such as calcium carbonate), producing ferroalloys, calcium carbide and other products; compared with traditional AC submerged arc furnaces, DC furnaces adopt a single-electrode structure, with stable magnetic fields and low electrode consumption, having advantages such as high power utilization efficiency, concentrated heat efficiency, precise process control, and low environmental pollution. It is particularly suitable for large-scale production of high-melting-point and high-resistivity materials and is a key equipment for efficient and low-carbon smelting in the modern metallurgy and chemical industries.

[0003] In the power supply control of a DC submerged arc furnace, the real-time changes in material composition, furnace charge resistivity, and arc state during the smelting process are highly non-linear and time-varying, and real-time regulation depends on a high-precision dynamic model. During the process of regulating the DC submerged arc furnace, not only the parameter adjustment of the DC submerged arc furnace needs to be considered, but also the power supply system of the DC submerged arc furnace needs to be adjusted. The prior art cannot adaptively adjust the power supply system according to the dynamic adjustment of the DC submerged arc furnace, resulting in a mismatch between the power supply power of the power supply system and the power required for the operation of the DC submerged arc furnace after parameter adjustment, leading to a reduction in the operating efficiency of the DC submerged arc furnace. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a DC submerged arc furnace and its control system to solve the problem that the prior art cannot adaptively adjust the power supply system according to the dynamic adjustment of the DC submerged arc furnace, resulting in a mismatch between the power supply power of the power supply system and the power required for the operation of the DC submerged arc furnace after parameter adjustment, leading to a reduction in the operating efficiency of the DC submerged arc furnace.

[0005] To solve the above technical problems, the specific technical solutions of the present invention are as follows: In a first aspect, a DC submerged arc furnace provided by the present invention includes: a DC submerged arc furnace main body, a DC submerged arc furnace control device, DC submerged arc furnace equipment, and a DC submerged arc furnace power supply device. The DC submerged arc furnace control device is used to control and adjust the operation of the DC submerged arc furnace equipment and the DC submerged arc furnace power supply device; The DC submerged arc furnace control device includes: A data processing unit acquires the operating condition data of a DC submerged arc furnace. The operating condition data of the DC submerged arc furnace includes the production efficiency data of the DC submerged arc furnace, the control parameter data of the DC submerged arc furnace, and the power supply data of the DC submerged arc furnace. The operating condition data of the DC submerged arc furnace is preprocessed to obtain the preprocessed operating condition data of the DC submerged arc furnace. A data analysis unit constructs a characteristic data set of the DC submerged arc furnace based on the preprocessed operating condition data of the DC submerged arc furnace, and uses the characteristic data set of the DC submerged arc furnace to train a random forest model to obtain a dynamic regulation model of the DC submerged arc furnace. A data comparison unit, in response to a control instruction for the DC submerged arc furnace, generates dynamic adjustment parameters for the DC submerged arc furnace through the dynamic regulation model of the DC submerged arc furnace. The dynamic adjustment parameters for the DC submerged arc furnace include power supply adjustment parameters for the power supply equipment and equipment adjustment parameters for the DC submerged arc furnace. A data optimization unit generates a simulated operating efficiency of the DC submerged arc furnace through a preset production simulation model of the DC submerged arc furnace, detects the dynamic adjustment state of the DC submerged arc furnace, optimizes the dynamic adjustment parameters for the DC submerged arc furnace according to the dynamic adjustment state of the DC submerged arc furnace, and uses the optimized dynamic adjustment parameters for the DC submerged arc furnace as execution parameters.

[0006] Furthermore, for the DC submerged arc furnace of the present invention, the data analysis unit includes: A data classification subunit is used to classify the preprocessed operating condition data of the DC submerged arc furnace according to a preset dimension. The preset dimension includes time dimension classification, equipment type classification, and process stage classification. A model training subunit constructs a characteristic data set of the DC submerged arc furnace based on the classified operating condition data of the DC submerged arc furnace. The characteristic data set of the DC submerged arc furnace contains the mapping relationship between equipment condition parameters and production efficiency parameters, and iteratively trains the random forest model through the mapping relationship. A model verification subunit uses the cross-validation method to verify the accuracy of the trained random forest model, and triggers a model retraining mechanism when the verification accuracy rate is lower than a preset threshold. The model retraining mechanism includes increasing the training sample size, adjusting the feature weight distribution, and optimizing the model hyperparameters.

[0007] Furthermore, for the DC submerged arc furnace of the present invention, the data comparison unit includes: An instruction parsing unit is used to parse the instruction type of the control instruction for the DC submerged arc furnace. The instruction type includes a real-time adjustment instruction and a timing adjustment instruction. A dynamic parameter generation unit calls the dynamic regulation model of the DC submerged arc furnace according to the instruction type to generate dynamic adjustment parameters for the DC submerged arc furnace that match the real-time working conditions. A parameter comparison unit compares the generated dynamic adjustment parameters for the DC submerged arc furnace with a historical adjustment parameter database and calculates a parameter deviation value. An adjustment strategy generation unit generates a parameter correction strategy when the parameter deviation value exceeds a preset safety range. The parameter correction strategy includes: When the instruction type is a real-time adjustment instruction, a progressive parameter adjustment method is adopted; When the instruction type is a timing adjustment instruction, a batch parameter optimization method is adopted.

[0008] Furthermore, for the DC submerged arc furnace of the present invention, the data optimization unit includes: A data generation unit, which collects the operation efficiency of the DC submerged arc furnace after executing the dynamic adjustment parameters of the DC submerged arc furnace, substitutes the DC submerged arc furnace control instruction into a preset DC submerged arc furnace production simulation model, and obtains the simulated operation efficiency of the DC submerged arc furnace; A state detection unit, which compares the simulated operation efficiency of the DC submerged arc furnace with the operation efficiency of the DC submerged arc furnace to obtain the operation efficiency error of the DC submerged arc furnace; A parameter optimization unit, which optimizes the dynamic adjustment parameters of the DC submerged arc furnace using a genetic algorithm based on the operation efficiency error of the DC submerged arc furnace, and obtains the optimized dynamic adjustment parameters of the DC submerged arc furnace. The optimized dynamic adjustment parameters of the DC submerged arc furnace include the optimized power supply adjustment parameters of the power supply equipment and the equipment adjustment parameters of the DC submerged arc furnace. A data execution unit, which sends the equipment adjustment parameters of the DC submerged arc furnace to the DC submerged arc furnace equipment for adjusting the operation parameters of the DC submerged arc furnace equipment, and sends the power supply adjustment parameters of the power supply equipment to the power supply device of the DC submerged arc furnace for adjusting the power supply parameters of the power supply device of the DC submerged arc furnace.

[0009] Furthermore, for the DC submerged arc furnace of the present invention, the data optimization unit further includes: A dynamic threshold adjustment module, which dynamically adjusts the convergence threshold of the optimization algorithm according to the deviation rate between the simulated operation efficiency and the measured operation efficiency of the DC submerged arc furnace. The convergence threshold adjustment rule includes: When the deviation rate > 10%, a first-level strict convergence criterion is adopted; When 5% < deviation rate ≤ 10%, a second-level conventional convergence criterion is adopted; When the deviation rate ≤ 5%, a third-level loose convergence criterion is adopted; A multi-objective optimization module, which performs multi-objective joint optimization on the dynamic adjustment parameters of the DC submerged arc furnace by constructing a comprehensive evaluation function including equipment energy consumption, production efficiency, and equipment loss rate; a parameter constraint module, which imposes at least one of the following constraint conditions during the optimization process: The adjustment range of the power supply adjustment parameters of the power supply equipment does not exceed ±15% of the rated value; The adjustment frequency of the equipment adjustment parameters of the DC submerged arc furnace is limited to ≤ 3 times per minute; The time interval between two adjacent parameter adjustments ≥ 10 seconds.

[0010] Second aspect, the present invention provides a DC submerged arc furnace control system, which is applied to the DC submerged arc furnace, including a DC submerged arc furnace main body, a DC submerged arc furnace control device, DC submerged arc furnace equipment, and a DC submerged arc furnace power supply device. The DC submerged arc furnace equipment is installed inside the DC submerged arc furnace main body, and the DC submerged arc furnace equipment is respectively communicatively connected to the DC submerged arc furnace power supply device and the DC submerged arc furnace control device; The DC submerged arc furnace control device includes: A data processing unit, which acquires the operating condition data of the DC submerged arc furnace. The operating condition data of the DC submerged arc furnace includes the production efficiency data of the DC submerged arc furnace, the control parameter data of the DC submerged arc furnace, and the power supply data of the DC submerged arc furnace. The operating condition data of the DC submerged arc furnace is preprocessed to obtain the preprocessed operating condition data of the DC submerged arc furnace; A data analysis unit, which constructs a characteristic data set of the DC submerged arc furnace based on the preprocessed operating condition data of the DC submerged arc furnace, and uses the characteristic data set of the DC submerged arc furnace to train a random forest model to obtain a dynamic regulation model of the DC submerged arc furnace; A data comparison unit, which, in response to a DC submerged arc furnace control instruction, generates dynamic adjustment parameters of the DC submerged arc furnace through the dynamic regulation model of the DC submerged arc furnace. The dynamic adjustment parameters of the DC submerged arc furnace include power supply adjustment parameters of the power supply equipment and adjustment parameters of the DC submerged arc furnace equipment; A data optimization unit, which generates a simulated operating efficiency of the DC submerged arc furnace through a preset production simulation model of the DC submerged arc furnace, detects the dynamic adjustment state of the DC submerged arc furnace, optimizes the dynamic adjustment parameters of the DC submerged arc furnace according to the dynamic adjustment state of the DC submerged arc furnace, and uses the optimized dynamic adjustment parameters of the DC submerged arc furnace as execution parameters.

[0011] Furthermore, in the DC submerged arc furnace control system of the present invention, the data analysis unit includes: A data classification sub-unit, which is used to classify the preprocessed operating condition data of the DC submerged arc furnace according to a preset dimension. The preset dimension includes time dimension classification, equipment type classification, and process stage classification; a model training sub-unit, which constructs a characteristic data set of the DC submerged arc furnace based on the classified operating condition data of the DC submerged arc furnace. The characteristic data set of the DC submerged arc furnace contains the mapping relationship between equipment condition parameters and production efficiency parameters, and iteratively trains the random forest model through the mapping relationship; a model verification sub-unit, which uses the cross-validation method to verify the accuracy of the trained random forest model, and triggers a model retraining mechanism when the verification accuracy rate is lower than a preset threshold. The model retraining mechanism includes increasing the training sample size, adjusting the feature weight distribution, and optimizing the model hyperparameters.

[0012] Furthermore, in the DC submerged arc furnace control system of the present invention, the data comparison unit includes: An instruction parsing unit for parsing the instruction types of DC submerged arc furnace control instructions, where the instruction types include real-time adjustment instructions and timed adjustment instructions; a dynamic parameter generation unit that calls a DC submerged arc furnace dynamic regulation model according to the instruction type to generate DC submerged arc furnace dynamic adjustment parameters matching the real-time working conditions; a parameter comparison unit that compares the generated DC submerged arc furnace dynamic adjustment parameters with a historical adjustment parameter database to calculate a parameter deviation value; an adjustment strategy generation unit that generates a parameter correction strategy when the parameter deviation value exceeds a preset safety range, and the parameter correction strategy includes: In the case where the instruction type is a real-time adjustment instruction, a progressive parameter adjustment method is adopted; In the case where the instruction type is a timed adjustment instruction, a batch parameter optimization method is adopted.

[0013] Further, in the DC submerged arc furnace control system of the present invention, the data optimization unit includes: A data generation unit that collects the operation efficiency of the DC submerged arc furnace after executing the DC submerged arc furnace dynamic adjustment parameters, and substitutes the DC submerged arc furnace control instruction into a preset DC submerged arc furnace production simulation model to obtain the simulated operation efficiency of the DC submerged arc furnace; A state detection unit that compares the simulated operation efficiency of the DC submerged arc furnace with the operation efficiency of the DC submerged arc furnace to obtain the operation efficiency error of the DC submerged arc furnace; A parameter optimization unit that optimizes the DC submerged arc furnace dynamic adjustment parameters using a genetic algorithm based on the operation efficiency error of the DC submerged arc furnace to obtain the optimized DC submerged arc furnace dynamic adjustment parameters. The optimized DC submerged arc furnace dynamic adjustment parameters include the optimized power supply adjustment parameters of the power supply equipment and the equipment adjustment parameters of the DC submerged arc furnace. A data execution unit that sends the equipment adjustment parameters of the DC submerged arc furnace to the DC submerged arc furnace equipment for adjusting the operation parameters of the DC submerged arc furnace equipment, and sends the power supply adjustment parameters of the power supply equipment to the power supply device of the DC submerged arc furnace for adjusting the power supply parameters of the power supply device of the DC submerged arc furnace.

[0014] Further, in the DC submerged arc furnace control system of the present invention, the data optimization unit further includes: A dynamic threshold adjustment module that dynamically adjusts the convergence threshold of the optimization algorithm according to the deviation rate between the simulated operation efficiency and the measured operation efficiency of the DC submerged arc furnace. The convergence threshold adjustment rules include: When the deviation rate > 10%, a first-level strict convergence criterion is adopted; When 5% < deviation rate ≤ 10%, a second-level conventional convergence criterion is adopted; When the deviation rate ≤ 5%, a third-level loose convergence criterion is adopted; The multi-objective optimization module performs multi-objective joint optimization on the dynamic adjustment parameters of the DC submerged arc furnace by constructing a comprehensive evaluation function that includes equipment energy consumption, production efficiency, and equipment loss rate; the parameter constraint module applies at least one of the following constraint conditions during the optimization process: The adjustment range of the power supply adjustment parameters of the power supply equipment does not exceed ±15% of the rated value; The adjustment frequency of the equipment adjustment parameters of the DC submerged arc furnace is limited to ≤3 times per minute; The time interval between two adjacent parameter adjustments ≥10 seconds.

[0015] Advantages of the present invention: The advantages of the present invention are reflected in that by constructing a data-driven closed-loop control system, the problem of inaccurate matching between the dynamic adjustment of the DC submerged arc furnace and the power of the power supply system is systematically solved. The dynamic regulation algorithm based on the random forest model captures the complex non-linear relationship between the arc heat distribution, material resistivity, and power supply parameters, improving the generation accuracy of the adjustment parameters under real-time working conditions; the multi-objective optimization module balances the competitive relationship between equipment energy consumption, production efficiency, and loss rate within the hard constraint boundary through the genetic algorithm, avoiding system imbalance caused by single-parameter optimization; the dynamic threshold adjustment mechanism adaptively converges the conditions according to the deviation rate between the simulated and measured efficiencies, enhancing the response speed and optimization efficiency of the algorithm to process fluctuations; the parameter constraint module combines range limitation, time series verification, and penalty functions to ensure that the adjustment range of the power supply voltage, the adjustment frequency of the equipment parameters, and the interval time comply with the engineering safety specifications. Through the closed-loop link of data collection, model prediction, deviation correction, and execution feedback, the dynamic synchronous matching between the power supply power and the equipment demand is realized, effectively reducing the production efficiency loss and equipment abnormal loss caused by model lag and rough adjustment in the traditional technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can also be obtained according to the drawings without creative efforts.

[0017] Figure 1 It is a schematic diagram of a virtual device of a DC submerged arc furnace provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and the corresponding drawings. 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 fall within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention will be described in detail below with reference to the drawings.

[0019] To better understand the objectives of the present invention, the present invention will be further described in detail below.

[0020] In a first aspect, please refer to Figure 1 , a DC submerged arc furnace provided by the present invention includes: a DC submerged arc furnace main body, a DC submerged arc furnace control device, DC submerged arc furnace equipment, and a DC submerged arc furnace power supply device. The DC submerged arc furnace control device is used to control and adjust the operation of the DC submerged arc furnace equipment and the DC submerged arc furnace power supply device; The DC submerged arc furnace control device includes: A data processing unit that acquires the operating condition data of the DC submerged arc furnace. The operating condition data of the DC submerged arc furnace includes the production efficiency data of the DC submerged arc furnace, the control parameter data of the DC submerged arc furnace, and the power supply data of the DC submerged arc furnace. The operating condition data of the DC submerged arc furnace is preprocessed to obtain the preprocessed operating condition data of the DC submerged arc furnace; A data analysis unit that constructs a characteristic data set of the DC submerged arc furnace based on the preprocessed operating condition data of the DC submerged arc furnace, and uses the characteristic data set of the DC submerged arc furnace to train a random forest model to obtain a dynamic regulation model of the DC submerged arc furnace; A data comparison unit that, in response to a DC submerged arc furnace control instruction, generates dynamic adjustment parameters of the DC submerged arc furnace through the dynamic regulation model of the DC submerged arc furnace. The dynamic adjustment parameters of the DC submerged arc furnace include power supply adjustment parameters of the power supply equipment and adjustment parameters of the DC submerged arc furnace equipment; A data optimization unit that generates a simulated operating efficiency of the DC submerged arc furnace through a preset production simulation model of the DC submerged arc furnace, detects the dynamic adjustment state of the DC submerged arc furnace, optimizes the dynamic adjustment parameters of the DC submerged arc furnace according to the dynamic adjustment state of the DC submerged arc furnace, and uses the optimized dynamic adjustment parameters of the DC submerged arc furnace as execution parameters.

[0021] A DC submerged arc furnace provided by the present invention has a control device that achieves dynamic regulation and optimization through the collaborative action of multiple technical modules. The control device first collects the operating condition data of the DC submerged arc furnace through a data processing unit, including production efficiency, control parameters, and power supply data, and performs data cleaning, normalization, and denoising processing on the data to eliminate the interference of outliers and dimensional differences on subsequent analysis, generating a standardized operating condition data set. This preprocessing process provides high-quality input for subsequent model training and parameter optimization.

[0022] In the data analysis unit, the standardized data is classified according to the time dimension, equipment type, and process stage, constructing a feature data set containing the mapping relationship between equipment condition parameters and production efficiency parameters. The feature data is iteratively trained through a random forest model, and the decision tree integration method is used to capture non-linear relationships, generating a dynamic regulation model. During the model training process, the cross-validation method is introduced to evaluate the model accuracy. When the validation accuracy is lower than the preset threshold, a retraining mechanism is triggered, and the generalization ability of the model is improved by increasing the training sample size, adjusting the feature weights, and optimizing the hyperparameters, ensuring the adaptability of the dynamic regulation model to the real-time working conditions.

[0023] After parsing the control instruction type, the data comparison unit calls the dynamic regulation model to generate dynamic adjustment parameters matching the real-time working conditions. The generated parameters are compared with the historical adjustment parameter database, calculating the deviation value and generating a correction strategy based on the preset safety range. Under the real-time adjustment instruction, a progressive parameter adjustment method is adopted to avoid system fluctuations by making small adjustments multiple times; under the timed adjustment instruction, a batch optimization method is adopted to perform global optimization in combination with the historical optimal parameter set, balancing the adjustment efficiency and stability.

[0024] The data optimization unit predicts the operating efficiency after executing the parameters through a production simulation model, and generates an efficiency error by comparing it with the measured efficiency. Based on the error value, a genetic algorithm is used to perform multi-objective optimization on the adjustment parameters, iteratively searching for the optimal solution through selection, crossover, and mutation operations, and at the same time imposing constraint conditions on the adjustment range of power supply parameters, equipment adjustment frequency, and time interval to avoid over-adjustment or equipment loss. During the optimization process, the convergence threshold of the genetic algorithm is dynamically adjusted, and strict, conventional, or loose convergence criteria are set according to the deviation rate between the simulated efficiency and the measured efficiency, improving the optimization efficiency. The optimized parameters are synchronously sent to the power supply device and equipment for execution, forming a closed-loop control to achieve the dynamic matching of the power supply power and the equipment operating state.

[0025] The multi-objective optimization module further constructs a comprehensive evaluation function that includes equipment energy consumption, production efficiency, and equipment loss rate, and balances multi-objective conflicts through weighted summation or Pareto front methods to ensure that the control parameters reduce the comprehensive cost while meeting the process requirements. The parameter constraint module restricts the physical boundaries and operating frequencies of the adjustment parameters to ensure the safe operation of the system. Each technical module forms a collaborative mechanism through data flow and logical association, and finally realizes the synchronous improvement of the operation efficiency of the DC submerged arc furnace and the adaptability of the power supply system.

[0026] Specifically, for the DC submerged arc furnace described in the present invention, the data analysis unit includes: A data classification sub-unit for classifying the pre-processed operating condition data of the DC submerged arc furnace according to preset dimensions, where the preset dimensions include time dimension classification, equipment type classification, and process stage classification; a model training sub-unit for constructing a characteristic data set of the DC submerged arc furnace based on the classified operating condition data of the DC submerged arc furnace. The characteristic data set of the DC submerged arc furnace contains the mapping relationship between equipment condition parameters and production efficiency parameters, and iteratively trains the random forest model through the mapping relationship; a model verification sub-unit for verifying the accuracy of the trained random forest model using the cross-validation method, and triggering a model re-training mechanism when the verification accuracy rate is lower than a preset threshold. The model re-training mechanism includes increasing the training sample size, adjusting the feature weight distribution, and optimizing the model hyperparameters.

[0027] The data analysis unit realizes the construction and optimization of the dynamic regulation model through staged processing. The data classification sub-unit divides the pre-processed operating condition data into condition segments within a continuous production cycle according to the time dimension, distinguishes the operating parameters of the electrode system, cooling device, and power supply module according to the equipment type, and separates the data sets of the charging, melting, and discharging links according to the process stage. This classification method structurally reorganizes multi-dimensional heterogeneous data by establishing a data label system, providing a context association basis for subsequent feature extraction.

[0028] The model training sub-unit extracts the mapping relationship between equipment voltage, current, temperature condition parameters and unit time output, energy consumption efficiency based on the classified data set, and constructs a multi-dimensional feature matrix that includes time series features, equipment state features, and process stage features. Using the parallel decision tree structure of the random forest model, the non-linear associations in the feature matrix are learned distributively, and the optimal decision path between equipment conditions and production efficiency is gradually fitted through multiple rounds of iteration. During the training process, the out-of-bag error estimation method is used to dynamically adjust the decision tree depth and node splitting criteria to enhance the adaptability of the model to dynamic conditions.

[0029] The model validation subunit uses the K-fold cross-validation method to divide the training set into multiple mutually exclusive subsets, and sequentially uses different subsets as validation data to evaluate the model prediction accuracy. When the validation accuracy is lower than the preset threshold, the model retraining mechanism is triggered: expand the training samples to cover extreme working condition data such as high load and low resistivity, adjust the feature weights to enhance the sensitivity to power supply parameter fluctuations, and optimize hyperparameters such as the number of decision trees, maximum depth, and minimum number of samples in the leaf nodes through grid search. The retrained model is reconnected to the validation process until the accuracy requirement is met, forming an adaptive model update closed-loop.

[0030] Each subunit is connected in series through data hierarchical processing and feedback mechanism. Data classification provides structured working condition information for model training, and model validation improves the generalization ability of the model through dynamic evaluation and parameter iteration. The definition of the classification dimension and feature mapping relationship directly affects the quality of the model input space, while the cross-validation and retraining mechanisms ensure the dynamic matching of the model output and real-time working conditions, jointly supporting the accurate generation of control parameters.

[0031] Specifically, for the DC submerged arc furnace described in the present invention, the data comparison unit includes: An instruction parsing unit for parsing the instruction type of the DC submerged arc furnace control instruction, where the instruction type includes a real-time adjustment instruction and a timing adjustment instruction; a dynamic parameter generation unit that calls the DC submerged arc furnace dynamic control model according to the instruction type to generate DC submerged arc furnace dynamic adjustment parameters matching the real-time working conditions; a parameter comparison unit that compares the generated DC submerged arc furnace dynamic adjustment parameters with the historical adjustment parameter database to calculate the parameter deviation value; an adjustment strategy generation unit that generates a parameter correction strategy when the parameter deviation value exceeds the preset safety range, and the parameter correction strategy includes: In the case where the instruction type is a real-time adjustment instruction, a progressive parameter adjustment method is adopted; In the case where the instruction type is a timing adjustment instruction, a batch parameter optimization method is adopted.

[0032] The data comparison unit realizes the dynamic adaptation and correction of the control parameters through hierarchical logic processing. The instruction parsing unit distinguishes between real-time adjustment instructions and timing adjustment instructions based on the metadata identifier of the control instruction: real-time instructions carry an instant timestamp and a priority mark, triggering a millisecond-level response mechanism; timing instructions contain preset cycle parameters, activating a periodic task scheduling module. The process stage code and target parameter threshold in the instruction are extracted during the parsing process to provide context constraint conditions for subsequent parameter generation.

[0033] The dynamic parameter generation unit selects the invocation mode of the dynamic regulation model according to the instruction type: under real-time instructions, it activates the streaming computing interface of the model and generates power supply voltage and power adjustment parameters based on the real-time data of the current electrode current, furnace temperature, and material resistivity; under timed instructions, it invokes the batch prediction interface of the model and generates multiple sets of equipment operation parameter combinations by combining historical concurrent operating conditions data and production plan targets. The generated dynamic adjustment parameters are attached with timestamps and version identifiers, forming an associated mapping with the optimal parameter records in the historical database.

[0034] The parameter comparison unit matches the current operating condition characteristics with the historical records through database indexing, and calculates the power supply voltage deviation rate, power adjustment amplitude difference value, and equipment parameter offset. The deviation calculation uses a weighted comprehensive evaluation method, where the voltage deviation weight reflects the stability requirements of the power supply system, and the equipment parameter offset weight is associated with the equipment loss risk. The comparison result generates a structured report containing deviation types, deviation degrees, and impact levels, providing a quantitative basis for policy generation.

[0035] The adjustment strategy generation unit selects the correction mode according to the safety level division in the deviation report: under real-time adjustment instructions, it adopts progressive parameter adjustment, gradually approaching the target value with a preset gradient, restricting the voltage fluctuation within 2% of the reference value in each adjustment cycle, and controlling the single adjustment amount of equipment parameters within 30% of the historical maximum variation range; under timed adjustment instructions, it starts batch parameter optimization, screens the effective parameter set under similar operating conditions from the historical database, generates a new parameter group that meets multi-objective constraints through genetic algorithm crossover and mutation, and batch-updates the equipment control sequence after simulation verification. Progressive adjustment gives priority to ensuring the real-time stability of the system, while batch optimization focuses on improving the overall efficiency. The two modes achieve a balance between control accuracy and resource consumption through instruction type diversion.

[0036] Each sub-unit forms a closed-loop processing link driven by the instruction flow: instruction parsing determines the control mode boundary, parameter generation establishes a dynamic response baseline, deviation comparison quantifies the adjustment requirements, and strategy generation outputs the execution rules. The historical database serves as an empirical knowledge base, providing both a reference benchmark for parameter comparison and accumulating case data for strategy optimization, realizing the continuous self-optimization ability of the control system.

[0037] Specifically, for the DC submerged arc furnace described in the present invention, the data optimization unit includes: The data generation unit collects the operation efficiency of the DC submerged arc furnace after executing the dynamic adjustment parameters of the DC submerged arc furnace, and substitutes the DC submerged arc furnace control instructions into the preset DC submerged arc furnace production simulation model to obtain the simulated operation efficiency of the DC submerged arc furnace. The state detection unit compares the simulated operation efficiency of the DC submerged arc furnace with the operation efficiency of the DC submerged arc furnace to obtain the operation efficiency error of the DC submerged arc furnace. The parameter optimization unit optimizes the dynamic adjustment parameters of the DC submerged arc furnace using a genetic algorithm based on the operation efficiency error of the DC submerged arc furnace, and obtains the optimized dynamic adjustment parameters of the DC submerged arc furnace. The optimized dynamic adjustment parameters of the DC submerged arc furnace include the power supply adjustment parameters of the optimized power supply equipment and the equipment adjustment parameters of the DC submerged arc furnace. The data execution unit sends the equipment adjustment parameters of the DC submerged arc furnace to the DC submerged arc furnace equipment to adjust the operation parameters of the DC submerged arc furnace equipment, and sends the power supply adjustment parameters of the power supply equipment to the power supply device of the DC submerged arc furnace to adjust the power supply parameters of the power supply device of the DC submerged arc furnace.

[0038] The data optimization unit realizes the adaptive optimization and execution of the control parameters through multi-level linkage technical means. The data generation unit collects the actual operation efficiency data of the DC submerged arc furnace after the dynamic adjustment parameters are executed, including the output per unit time, energy consumption index and equipment status parameters, and inputs the current control instruction into the preset production simulation model. This model is constructed based on historical operation data and process constraints, and simulates the coupling relationship of the arc heat distribution, material melting rate and power supply power in the furnace through physical equations to generate the simulated operation efficiency under the corresponding instruction. The simulation results are attached with operating condition labels to form a spatio-temporal alignment comparison benchmark with the actual operation data.

[0039] The state detection unit conducts multi-dimensional comparison between the simulated efficiency and the actual efficiency, calculates the output deviation rate, energy consumption difference value and equipment status offset. During the comparison process, a sliding window mechanism is introduced to extract the efficiency fluctuation characteristics within a continuous time series, and the comprehensive error value is calculated by weighted combination according to the process stage division. The error analysis results are marked as normal fluctuation, local deviation or system mismatch levels, providing classified input for subsequent optimization.

[0040] The parameter optimization unit starts the genetic algorithm optimization process based on the error level: the initial population consists of the historical optimal parameter set and the current dynamic adjustment parameters, and the individual parameters' correction ability to the comprehensive error is evaluated through the fitness function. The selection operation retains the individuals with high fitness, the crossover operation conducts segment exchange between the power supply parameters and the equipment parameters to explore the solution space, and the mutation operation makes small random perturbations to the parameter values to enhance the diversity. During the optimization process, dynamic constraint conditions are embedded to limit the power supply voltage adjustment range not exceeding ±15% of the rated value, and the equipment parameter adjustment frequency is controlled within 3 times per minute. After iterative convergence, the non-dominated solution set on the Pareto front is output, and the optimal parameter combination that takes into account both efficiency improvement and equipment loss is selected by combining multi-objective weights.

[0041] The data execution unit classifies and distributes the optimized parameters according to the instruction type: the power supply adjustment parameters of the power supply equipment are transmitted to the rectifier control module through the real-time communication protocol to dynamically adjust the output current and voltage waveforms; the adjustment parameters of the DC submerged arc furnace equipment are written into the parameter queue of the equipment controller after verification, and the electrode lifting speed, cooling water flow and other actuator actions are updated according to the preset time stamp or trigger condition. During the distribution process, a redundancy check and rollback mechanism is adopted. If an abnormal alarm is triggered after the parameter execution, it will automatically switch to the previous valid parameter set to maintain the continuity of system operation.

[0042] Each sub-unit forms an iterative optimization link through a data closed-loop: data generation establishes the mapping relationship between simulation and reality, state detection quantifies the control effect, parameter optimization generates improvement solutions, and data execution realizes the response of the physical system. The production simulation model serves as a virtual test environment to support the pre-verification of parameter optimization; the genetic algorithm approaches the global optimal solution through multiple generations of evolution; the parameter feedback mechanism of the execution unit provides incremental data input for the next round of optimization, ultimately achieving the continuous improvement of the operation efficiency of the DC submerged arc furnace and the adaptability of the power supply system.

[0043] Specifically, for the DC submerged arc furnace described in the present invention, the data optimization unit further includes: A dynamic threshold adjustment module that dynamically adjusts the convergence threshold of the optimization algorithm according to the deviation rate between the simulated operation efficiency and the measured operation efficiency of the DC submerged arc furnace. The convergence threshold adjustment rules include: When the deviation rate > 10%, a first-level strict convergence standard is adopted; When 5% < deviation rate ≤ 10%, a second-level conventional convergence standard is adopted; When the deviation rate ≤ 5%, a third-level loose convergence standard is adopted; A multi-objective optimization module that jointly optimizes the dynamically adjusted parameters of the DC submerged arc furnace through constructing a comprehensive evaluation function including equipment energy consumption, production efficiency and equipment loss rate; a parameter constraint module that imposes at least one of the following constraint conditions during the optimization process: The adjustment range of the power supply adjustment parameters of the power supply equipment does not exceed ±15% of the rated value; The adjustment frequency of the adjustment parameters of the DC submerged arc furnace equipment is limited to ≤ 3 times per minute; The time interval between two adjacent parameter adjustments ≥ 10 seconds.

[0044] The data optimization unit enhances the adaptability and security of parameter optimization through a hierarchical control mechanism. The dynamic threshold adjustment module monitors the deviation rate between the simulated operation efficiency and the measured efficiency in real time, and dynamically sets the convergence threshold of the genetic algorithm based on the degree of deviation: when the deviation rate exceeds 10%, the first-level strict convergence criterion is activated, the convergence condition is set to that the fitness change rate of the optimal solution of the population is less than 0.5% for 20 consecutive generations, and the population size is forced to increase to 1.5 times the benchmark value to improve the global search ability; when the deviation rate is between 5% and 10%, the second-level conventional convergence criterion is enabled, requiring the fitness change rate to be less than 1% for 15 consecutive generations, and maintaining the standard population size; when the deviation rate is less than 5%, it switches to the third-level loose convergence criterion, allowing the fitness change rate to be within 2% for 10 consecutive generations, and at the same time reducing the number of iterations to reduce the computational load. The threshold grading mechanism realizes the dynamic balance between optimization accuracy and computational efficiency.

[0045] The comprehensive evaluation function constructed by the multi-objective optimization module integrates the equipment energy consumption weight coefficient, the production efficiency gain coefficient, and the equipment loss rate penalty coefficient, and transforms the multi-objective into a single-objective optimization problem through linear weighting. The weight coefficients are dynamically configured according to the process priority: the production efficiency weight is increased during the smelting stage, and the energy consumption optimization weight is strengthened during the steady-state operation stage. In the optimization process, the non-dominated sorting genetic algorithm (NSGA-II) is used to generate the Pareto optimal solution set, and the diversity of the solution set is maintained through crowding comparison. Finally, the operator or the preset strategy selects the parameter combination with the highest comprehensive score. This design takes into account the competitive relationship between multiple objectives and avoids system imbalance caused by single-index optimization.

[0046] The parameter constraint module embeds hard boundary conditions in the optimization iteration stage: the adjustment range constraint of the power supply equipment is realized through the value range limit during parameter coding, and the power supply voltage adjustment value is clamped within the range of ±15% of the rated value during algorithm initialization; the equipment adjustment frequency constraint is transformed into a time series optimization problem, and a penalty term for violating the frequency is introduced into the fitness function of the genetic algorithm, and the fitness of individuals exceeding 3 adjustments per minute is attenuated proportionally; the adjacent adjustment interval constraint is implemented through a timestamp verification mechanism, and candidate solutions with an interval less than 10 seconds are filtered out during the parameter mutation operation. The multi-level embedding method of the constraint conditions not only ensures the safe operation of the physical system but also maintains the search freedom of the optimization process.

[0047] Each module forms a closed-loop optimization system through collaborative actions: the dynamic threshold adjustment module regulates the convergence characteristics of the algorithm based on real-time deviations, the multi-objective optimization module explores the optimal parameter combination within the constraint framework, and the parameter constraint module ensures the engineering feasibility of the output solution. The linkage between the deviation rate grading and the convergence threshold enhances the response speed of the algorithm to operating condition fluctuations; the multi-objective weight configuration and the constraint penalty mechanism jointly guide the parameter optimization direction; the combination of the hard boundary and the soft penalty realizes the unity of safety and optimization effect. The historical optimal solution set serves as an important source of the initial population, and the continuously accumulated case data optimizes the weight coefficient and the constraint threshold setting through the feedback mechanism, promoting the progressive improvement of the overall control performance of the system.

[0048] In a second aspect, the present invention provides a DC submerged arc furnace control system, which is applied to the DC submerged arc furnace. The DC submerged arc furnace control system includes a DC submerged arc furnace main body, a DC submerged arc furnace control device, DC submerged arc furnace equipment, and a DC submerged arc furnace power supply device. The DC submerged arc furnace equipment is installed inside the DC submerged arc furnace main body, and the DC submerged arc furnace equipment is respectively communicatively connected to the DC submerged arc furnace power supply device and the DC submerged arc furnace control device; The DC submerged arc furnace control device includes: A data processing unit, which acquires the operating condition data of the DC submerged arc furnace. The operating condition data of the DC submerged arc furnace includes the production efficiency data of the DC submerged arc furnace, the control parameter data of the DC submerged arc furnace, and the power supply data of the DC submerged arc furnace. The data processing unit preprocesses the operating condition data of the DC submerged arc furnace to obtain the preprocessed operating condition data of the DC submerged arc furnace; A data analysis unit, which constructs a characteristic data set of the DC submerged arc furnace based on the preprocessed operating condition data of the DC submerged arc furnace, and uses the characteristic data set of the DC submerged arc furnace to train a random forest model to obtain a dynamic regulation model of the DC submerged arc furnace; A data comparison unit, which, in response to a DC submerged arc furnace control instruction, generates dynamic adjustment parameters of the DC submerged arc furnace through the dynamic regulation model of the DC submerged arc furnace. The dynamic adjustment parameters of the DC submerged arc furnace include power supply adjustment parameters of the power supply equipment and adjustment parameters of the DC submerged arc furnace equipment; A data optimization unit, which generates a simulated operating efficiency of the DC submerged arc furnace through a preset production simulation model of the DC submerged arc furnace, detects the dynamic adjustment state of the DC submerged arc furnace, optimizes the dynamic adjustment parameters of the DC submerged arc furnace according to the dynamic adjustment state of the DC submerged arc furnace, and uses the optimized dynamic adjustment parameters of the DC submerged arc furnace as execution parameters.

[0049] Specifically, for the DC submerged arc furnace control system of the present invention, the data analysis unit includes: A data classification subunit for classifying the preprocessed operation condition data of the DC submerged arc furnace according to preset dimensions, where the preset dimensions include time dimension classification, equipment type classification, and process stage classification; a model training subunit for constructing a characteristic data set of the DC submerged arc furnace based on the classified operation condition data of the DC submerged arc furnace, where the characteristic data set of the DC submerged arc furnace contains the mapping relationship between equipment condition parameters and production efficiency parameters, and iteratively trains a random forest model through the mapping relationship; a model verification subunit for verifying the accuracy of the trained random forest model using the cross-validation method, and triggering a model retraining mechanism when the verification accuracy rate is lower than a preset threshold, where the model retraining mechanism includes increasing the training sample size, adjusting the feature weight distribution, and optimizing the model hyperparameters.

[0050] Specifically, for the DC submerged arc furnace control system of the present invention, the data comparison unit includes: An instruction parsing unit for parsing the instruction type of the DC submerged arc furnace control instruction, where the instruction type includes a real-time adjustment instruction and a timing adjustment instruction; a dynamic parameter generation unit for calling a DC submerged arc furnace dynamic regulation model according to the instruction type to generate DC submerged arc furnace dynamic adjustment parameters matching the real-time working conditions; a parameter comparison unit for comparing the generated DC submerged arc furnace dynamic adjustment parameters with a historical adjustment parameter database and calculating a parameter deviation value; an adjustment strategy generation unit for generating a parameter correction strategy when the parameter deviation value exceeds a preset safety range, and the parameter correction strategy includes: In the case where the instruction type is a real-time adjustment instruction, an incremental parameter adjustment method is adopted; In the case where the instruction type is a timing adjustment instruction, a batch parameter optimization method is adopted.

[0051] Specifically, for the DC submerged arc furnace control system of the present invention, the data optimization unit includes: A data generation unit for collecting the operation efficiency of the DC submerged arc furnace after executing the DC submerged arc furnace dynamic adjustment parameters, and substituting the DC submerged arc furnace control instruction into a preset DC submerged arc furnace production simulation model to obtain the simulated operation efficiency of the DC submerged arc furnace; A state detection unit for comparing the simulated operation efficiency of the DC submerged arc furnace with the operation efficiency of the DC submerged arc furnace to obtain the operation efficiency error of the DC submerged arc furnace; A parameter optimization unit for optimizing the DC submerged arc furnace dynamic adjustment parameters using a genetic algorithm based on the operation efficiency error of the DC submerged arc furnace to obtain optimized DC submerged arc furnace dynamic adjustment parameters, where the optimized DC submerged arc furnace dynamic adjustment parameters include optimized power supply adjustment parameters of the power supply equipment and DC submerged arc furnace equipment adjustment parameters. A data execution unit that sends the adjustment parameters of the DC submerged arc furnace equipment to the DC submerged arc furnace equipment for adjusting the operating parameters of the DC submerged arc furnace equipment, and sends the power supply adjustment parameters of the power supply equipment to the power supply device of the DC submerged arc furnace for adjusting the power supply parameters of the power supply device of the DC submerged arc furnace.

[0052] Specifically, for the DC submerged arc furnace control system described in the present invention, the data optimization unit further includes: A dynamic threshold adjustment module that dynamically adjusts the convergence threshold of the optimization algorithm according to the deviation rate between the simulated operating efficiency and the measured operating efficiency of the DC submerged arc furnace. The convergence threshold adjustment rules include: When the deviation rate > 10%, a first-level strict convergence criterion is adopted; When 5% < deviation rate ≤ 10%, a second-level conventional convergence criterion is adopted; When the deviation rate ≤ 5%, a third-level loose convergence criterion is adopted; A multi-objective optimization module that jointly optimizes the dynamic adjustment parameters of the DC submerged arc furnace through constructing a comprehensive evaluation function including equipment energy consumption, production efficiency, and equipment loss rate; a parameter constraint module that imposes at least one of the following constraint conditions during the optimization process: The adjustment range of the power supply adjustment parameters of the power supply equipment does not exceed ±15% of the rated value; The adjustment frequency of the adjustment parameters of the DC submerged arc furnace equipment is limited to ≤ 3 times per minute; The time interval between two adjacent parameter adjustments ≥ 10 seconds.

[0053] The explanations of the technical feature terms in the technical solution of the present invention are as follows: The main body of the DC submerged arc furnace: refers to the core equipment for ore smelting, which forms arc heat and resistance heat between the electrode and the conductive charge through direct current to achieve high-temperature smelting. Its single-electrode structure has the characteristics of high magnetic field stability and low electrode loss compared with the AC furnace, and is suitable for large-scale production of high-resistivity materials.

[0054] The control device of the DC submerged arc furnace: an intelligent control module integrating data processing, analysis, comparison, and optimization functions, which dynamically coordinates the power supply system and equipment operating parameters by collecting real-time working condition data such as voltage, current, and temperature to solve the power matching problem. The core lies in constructing a closed-loop control link to achieve dynamic adaptation of process parameters.

[0055] The data processing unit: a preprocessing module responsible for cleaning, normalizing, and time series alignment of the original working condition data (such as production efficiency, control parameters, power supply data). By eliminating noise interference and dimension differences, a standardized data set is generated to provide a high signal-to-noise ratio input for subsequent modeling.

[0056] Data analysis unit: A model training module based on the random forest algorithm that learns the mapping relationship between equipment operating parameters (such as arc length and material resistivity) and production efficiency through classification and regression tree ensemble learning. The cross-validation and retraining mechanism enables the model to adapt to time-varying operating conditions such as material composition fluctuations and furnace charge resistivity changes, improving the dynamic regulation accuracy.

[0057] Data comparison unit: A logic processing module that includes instruction parsing, parameter generation, and deviation analysis. It parses the control instruction type (real-time / timed) in real-time, calls the dynamic regulation model to generate adjustment parameters, and performs weighted deviation calculation with the optimal parameters in the historical database to identify the matching degree difference between the power supply and the equipment demand.

[0058] Data optimization unit: A multi-objective optimization module using the genetic algorithm, with equipment energy consumption, production efficiency, and loss rate as optimization objectives, and iteratively searches for the Pareto optimal solution within the constraint conditions (such as voltage adjustment range ±15% and adjustment frequency ≤ 3 times / minute). The dynamic threshold adjustment module grades and tightens or relaxes the convergence criteria according to the deviation rate between simulation and measured efficiency, balancing the optimization speed and accuracy.

[0059] Dynamic threshold adjustment module: An adaptive module that dynamically sets the genetic algorithm convergence conditions according to the deviation rate. When the deviation rate > 10%, a strict convergence criterion is adopted (such as the fitness change < 0.5% for 20 consecutive generations), and when the deviation rate ≤ 5%, a loose criterion is enabled (such as the change < 2% for 10 consecutive generations), realizing the matching of algorithm resource allocation and operating condition fluctuations.

[0060] Multi-objective optimization module: Constructs a comprehensive evaluation function, dynamically allocates the optimization weights of equipment energy consumption, efficiency, and loss through weighted coefficients. It focuses on production efficiency during the smelting stage and strengthens energy consumption optimization during the steady state stage. It uses non-dominated sorting (NSGA-II) to generate the solution set and selects the parameter combination that takes into account multiple objectives by combining crowding comparison.

[0061] Parameter constraint module: An optimization constraint module that embeds hard boundaries and soft penalties. The power supply parameter adjustment range is restricted by value range clamping, the equipment adjustment frequency is filtered by time series verification to eliminate illegal solutions, and the adjacent adjustment intervals are constrained by mutation operations to prevent equipment overload or arc instability.

[0062] Model retraining mechanism: A self-update process triggered when the model verification accuracy is lower than the threshold. By expanding extreme operating condition samples, adjusting feature weights (such as enhancing voltage fluctuation sensitivity), and optimizing hyperparameters (number and depth of decision trees), it improves the model's generalization ability for new operating conditions.

[0063] The specific implementation of the present invention is based on the requirement of dynamic matching between the power supply system and the operating state of equipment during the smelting of high-resistivity materials in a DC submerged arc furnace, and realizes precise regulation of process parameters through hierarchical control and closed-loop optimization. In the data acquisition stage, sensors deployed on the furnace electrodes, cooling system, and power supply module collect voltage, current, temperature, and material resistivity data in real time. After being processed by the data preprocessing unit for denoising, normalization, and time alignment, a multi-dimensional data set including the smelting stage, equipment state, and power supply parameters is generated. The preprocessed data is divided into a charging and preheating period, an arc smelting period, and a discharging and stabilizing period according to the process stage. Each stage corresponds to a different baseline power supply requirement, providing a structured input for subsequent analysis.

[0064] The construction of the dynamic regulation model takes the random forest algorithm as the core and is trained using the mapping relationship between equipment operating condition parameters and production efficiency in historical data. During the training process, the model selects the arc length, material bulk density, and cooling water flow rate as key input variables through feature importance analysis, and dynamically adjusts the depth and number of decision trees based on the cross-validation results. When the prediction error of the model exceeds 5% under the new operating conditions during the validation stage, the retraining process is triggered: the training set is expanded to include recent extreme operating condition data, and the feature weight distribution is optimized to enhance the sensitivity to voltage fluctuations. Finally, a dynamic regulation model adapted to the current smelting process is generated. The model responds to the type of control instruction, generates real-time power supply adjustment suggestions and electrode lifting rate parameters, and performs deviation analysis with the historical optimal solution set through the parameter comparison unit.

[0065] In the parameter optimization and execution stage, the genetic algorithm takes the power supply power deviation rate, equipment loss increment, and production efficiency improvement rate as the optimization objectives, and iteratively searches for the Pareto optimal solution within the constraint conditions. The adjustment range of the power supply voltage is limited to ±15% of the rated value to avoid arc instability caused by over-adjustment; the adjustment frequency and interval time of equipment parameters are constrained by the time series verification module to prevent the actuator from being overloaded. After the optimized parameters are verified by the simulation model, they are sent by the execution unit with different priorities: real-time instructions trigger the millisecond-level response of the power supply module, and timed instructions drive the batch parameter update of the equipment controller. The actual operation efficiency data is fed back to the simulation model to form a closed loop, and the dynamic threshold adjustment module tightens or relaxes the convergence conditions according to the deviation rate level, enabling the system to quickly converge to a stable state during process fluctuations. Through the above implementation, the output power of the power supply system is dynamically matched with the furnace demand, effectively solving the problem of operation efficiency loss caused by parameter lag and multi-objective conflicts in traditional technologies.

[0066] In view of the problem of insufficient adaptability between the dynamic adjustment of DC submerged arc furnaces and the power supply system in the prior art, a technical solution of hierarchical regulation and multi-objective collaborative optimization is proposed. By constructing a dynamic regulation model based on random forests, multi-dimensional feature extraction and non-linear relationship modeling are performed on the operating condition data of DC submerged arc furnaces, and adjustment instructions for power supply power and equipment parameters that match the real-time arc state and changes in material resistivity are generated. During the model training process, a cross-validation and re-training mechanism is introduced to enhance the adaptability to time-varying operating conditions, solve the defect that traditional static models cannot capture dynamic process characteristics, and avoid inaccurate power supply power prediction caused by model deviation.

[0067] The data comparison unit identifies the matching degree deviation between the output power of the power supply system and the actual demand of the furnace body through the difference analysis between the historical parameter database and the real-time generated parameters. When it is detected that the deviation value exceeds the preset safety threshold, a progressive or batch optimization strategy is selected according to the instruction type: under the real-time adjustment instruction, a small step size is used to iteratively approach the target value, and under the timed adjustment instruction, the historical optimal parameter set is integrated for global optimization. A sliding window mechanism is embedded in the parameter correction process, and the correction amplitude is dynamically adjusted in combination with the process stage characteristics, overcoming the limitation of the traditional single correction mode's lagging response to sudden operating conditions, and realizing the dynamic synchronization of the power supply power and the equipment operating state.

[0068] The multi-objective optimization module jointly optimizes the equipment energy consumption, production efficiency and loss rate through the genetic algorithm, and generates a Pareto optimal solution set within the hard boundaries of the power supply adjustment amplitude, frequency and interval set by the parameter constraint module. The dynamic threshold adjustment module hierarchically sets the algorithm convergence standard according to the deviation rate between the simulation and the measured efficiency. When the deviation rate is high, the global search ability is strengthened, and when the deviation rate is low, local fine optimization is emphasized. The power supply device and the equipment actuator are synchronously adjusted based on the optimized parameters to form a closed-loop control link, solving the problem of operation efficiency loss caused by the lag in the adjustment of the power supply system and multi-objective conflicts in traditional technologies.

Claims

1. A DC ore-fired furnace, characterized in that: include: DC ore-burning furnace main body, DC ore-burning furnace control device, DC ore-burning furnace equipment and DC ore-burning furnace power supply device. The DC ore-burning furnace control device is used to control and adjust the operation of the DC ore-burning furnace equipment and the DC ore-burning furnace power supply device; The DC ore-arc furnace control device includes: The data processing unit obtains the DC ore-fired furnace operating condition data, the DC ore-fired furnace operating condition data includes the DC ore-fired furnace production efficiency data, the DC ore-fired furnace control parameter data and the DC ore-fired furnace power supply data, and pre-processes the DC ore-fired furnace operating condition data to obtain the pre-processed DC ore-fired furnace operating condition data; The data analysis unit constructs a DC ore-fired furnace characteristic data set based on the preprocessed DC ore-fired furnace operating condition data, and uses the DC ore-fired furnace characteristic data set to train a random forest model to obtain a DC ore-fired furnace dynamic control model; The data comparison unit generates a dynamic adjustment parameter of the DC submerged arc furnace through a dynamic control model of the DC submerged arc furnace in response to a control instruction of the DC submerged arc furnace, wherein the dynamic adjustment parameter of the DC submerged arc furnace includes a power supply adjustment parameter of a power supply device and an equipment adjustment parameter of the DC submerged arc furnace; The data optimization unit generates the DC ore-fired furnace simulation operation efficiency through a preset DC ore-fired furnace production simulation model, detects the dynamic adjustment state of the DC ore-fired furnace, optimizes the dynamic adjustment parameters of the DC ore-fired furnace according to the dynamic adjustment state of the DC ore-fired furnace, and uses the optimized dynamic adjustment parameters of the DC ore-fired furnace as execution parameters.

2. The DC ore-fired furnace according to claim 1, characterized in that: The data analysis unit comprises: The data classification subunit is used to classify the pre-processed DC blast furnace operating condition data according to preset dimensions, and the preset dimensions include time dimension classification, equipment type classification and process stage classification; the model training subunit constructs a DC blast furnace feature data set based on the classified DC blast furnace operating condition data, and the DC blast furnace feature data set contains a mapping relationship between equipment operating condition parameters and production efficiency parameters, and iteratively trains the random forest model through the mapping relationship; the model verification subunit uses the cross-validation method to verify the accuracy of the trained random forest model, and triggers the model retraining mechanism when the verification accuracy is lower than the preset threshold, and the model retraining mechanism includes increasing the training sample size, adjusting the feature weight distribution and optimizing the model hyperparameters.

3. The DC ore-fired furnace according to claim 1, characterized in that: The data comparison unit comprises: The instruction parsing unit is used to parse the instruction type of the DC ore-fired furnace control instruction, and the instruction type includes real-time adjustment instructions and timing adjustment instructions; the dynamic parameter generating unit calls the DC ore-fired furnace dynamic control model according to the instruction type to generate the DC ore-fired furnace dynamic adjustment parameters matching the real-time working conditions; the parameter comparing unit compares the generated DC ore-fired furnace dynamic adjustment parameters with the historical adjustment parameter database to calculate the parameter deviation value; the adjustment strategy generating unit generates a parameter correction strategy when the parameter deviation value exceeds a preset safety range, and the parameter correction strategy includes: When the instruction type is a real-time adjustment instruction, a progressive parameter adjustment method is adopted; When the instruction type is a timing adjustment instruction, a batch parameter optimization method is adopted.

4. The DC ore-fired furnace according to claim 1, characterized in that: The data optimization unit comprises: The data generation unit collects the operation efficiency of the DC ore-fired furnace after executing the dynamic adjustment parameters of the DC ore-fired furnace, substitutes the DC ore-fired furnace control instructions into a preset DC ore-fired furnace production simulation model, and obtains the DC ore-fired furnace simulation operation efficiency; The state detection unit compares the DC ore-fired furnace simulation operation efficiency with the DC ore-fired furnace operation efficiency to obtain the DC ore-fired furnace operation efficiency error; The parameter optimization unit uses a genetic algorithm to optimize the dynamic adjustment parameters of the DC submerged arc furnace based on the operation efficiency error of the DC submerged arc furnace to obtain the optimized dynamic adjustment parameters of the DC submerged arc furnace. The optimized dynamic adjustment parameters of the DC submerged arc furnace include the optimized power supply adjustment parameters of the power supply equipment and the DC submerged arc furnace equipment adjustment parameters. The data execution unit sends the DC submerged arc furnace equipment adjustment parameters to the DC submerged arc furnace equipment for adjusting the operating parameters of the DC submerged arc furnace equipment, and sends the power supply adjustment parameters of the power supply equipment to the DC submerged arc furnace power supply device for adjusting the power supply parameters of the DC submerged arc furnace power supply device.

5. The DC ore-fired furnace according to claim 1, characterized in that: The data optimization unit further includes: The dynamic threshold adjustment module dynamically adjusts the convergence threshold of the optimization algorithm according to the deviation rate between the simulated operating efficiency and the measured operating efficiency of the DC ore-fired furnace. The convergence threshold adjustment rules include: When the deviation rate is >10%, the first level strict convergence standard is adopted; When 5%<deviation rate≤10%, the second level conventional convergence standard is used; When the deviation rate is ≤5%, the third level loose convergence standard is adopted; The multi-objective optimization module performs multi-objective joint optimization on the dynamic adjustment parameters of the DC submerged arc furnace by constructing a comprehensive evaluation function including equipment energy consumption, production efficiency and equipment loss rate. The parameter constraint module imposes at least one of the following constraints during the optimization process: The adjustment range of the power supply adjustment parameters of the power supply equipment shall not exceed ±15% of the rated value; The adjustment frequency of DC ore-fired furnace equipment adjustment parameters is limited to ≤3 times per minute; The time interval between two adjacent parameter adjustments is ≥ 10 seconds.

6. A DC ore-fired furnace control system, applied to the DC ore-fired furnace as claimed in any one of claims 1 to 5, characterized in that: include: A DC ore-burning furnace body, a DC ore-burning furnace control device, a DC ore-burning furnace equipment and a DC ore-burning furnace power supply device. The DC ore-burning furnace body is equipped with the DC ore-burning furnace equipment. The DC ore-burning furnace equipment establishes communication connections with the DC ore-burning furnace power supply device and the DC ore-burning furnace control device respectively. The DC ore-arc furnace control device includes: The data processing unit obtains the DC ore-fired furnace operating condition data, the DC ore-fired furnace operating condition data includes the DC ore-fired furnace production efficiency data, the DC ore-fired furnace control parameter data and the DC ore-fired furnace power supply data, and pre-processes the DC ore-fired furnace operating condition data to obtain the pre-processed DC ore-fired furnace operating condition data; The data analysis unit constructs a DC ore-fired furnace characteristic data set based on the preprocessed DC ore-fired furnace operating condition data, and uses the DC ore-fired furnace characteristic data set to train a random forest model to obtain a DC ore-fired furnace dynamic control model; The data comparison unit generates a dynamic adjustment parameter of the DC submerged arc furnace through a dynamic control model of the DC submerged arc furnace in response to a control instruction of the DC submerged arc furnace, wherein the dynamic adjustment parameter of the DC submerged arc furnace includes a power supply adjustment parameter of a power supply device and an equipment adjustment parameter of the DC submerged arc furnace; The data optimization unit generates the DC ore-fired furnace simulation operation efficiency through a preset DC ore-fired furnace production simulation model, detects the dynamic adjustment state of the DC ore-fired furnace, optimizes the dynamic adjustment parameters of the DC ore-fired furnace according to the dynamic adjustment state of the DC ore-fired furnace, and uses the optimized dynamic adjustment parameters of the DC ore-fired furnace as execution parameters.

7. The DC ore-fired furnace control system according to claim 6, characterized in that: The data analysis unit comprises: The data classification subunit is used to classify the pre-processed DC blast furnace operating condition data according to preset dimensions, and the preset dimensions include time dimension classification, equipment type classification and process stage classification; the model training subunit constructs a DC blast furnace feature data set based on the classified DC blast furnace operating condition data, and the DC blast furnace feature data set contains a mapping relationship between equipment operating condition parameters and production efficiency parameters, and iteratively trains the random forest model through the mapping relationship; the model verification subunit uses the cross-validation method to verify the accuracy of the trained random forest model, and triggers the model retraining mechanism when the verification accuracy is lower than the preset threshold, and the model retraining mechanism includes increasing the training sample size, adjusting the feature weight distribution and optimizing the model hyperparameters.

8. The DC ore-fired furnace control system according to claim 6, characterized in that: The data comparison unit comprises: The instruction parsing unit is used to parse the instruction type of the DC ore-fired furnace control instruction, and the instruction type includes real-time adjustment instructions and timing adjustment instructions; the dynamic parameter generating unit calls the DC ore-fired furnace dynamic control model according to the instruction type to generate the DC ore-fired furnace dynamic adjustment parameters matching the real-time working conditions; the parameter comparing unit compares the generated DC ore-fired furnace dynamic adjustment parameters with the historical adjustment parameter database to calculate the parameter deviation value; the adjustment strategy generating unit generates a parameter correction strategy when the parameter deviation value exceeds a preset safety range, and the parameter correction strategy includes: When the instruction type is a real-time adjustment instruction, a progressive parameter adjustment method is adopted; When the instruction type is a timing adjustment instruction, a batch parameter optimization method is adopted.

9. The DC ore-fired furnace control system according to claim 6, characterized in that: The data optimization unit comprises: The data generation unit collects the operation efficiency of the DC ore-fired furnace after executing the dynamic adjustment parameters of the DC ore-fired furnace, substitutes the DC ore-fired furnace control instructions into a preset DC ore-fired furnace production simulation model, and obtains the DC ore-fired furnace simulation operation efficiency; The state detection unit compares the DC ore-fired furnace simulation operation efficiency with the DC ore-fired furnace operation efficiency to obtain the DC ore-fired furnace operation efficiency error; The parameter optimization unit uses a genetic algorithm to optimize the dynamic adjustment parameters of the DC submerged arc furnace based on the operation efficiency error of the DC submerged arc furnace to obtain the optimized dynamic adjustment parameters of the DC submerged arc furnace. The optimized dynamic adjustment parameters of the DC submerged arc furnace include the optimized power supply adjustment parameters of the power supply equipment and the DC submerged arc furnace equipment adjustment parameters. The data execution unit sends the DC submerged arc furnace equipment adjustment parameters to the DC submerged arc furnace equipment for adjusting the operating parameters of the DC submerged arc furnace equipment, and sends the power supply adjustment parameters of the power supply equipment to the DC submerged arc furnace power supply device for adjusting the power supply parameters of the DC submerged arc furnace power supply device.

10. The DC ore-fired furnace control system according to claim 6, characterized in that: The data optimization unit further includes: The dynamic threshold adjustment module dynamically adjusts the convergence threshold of the optimization algorithm according to the deviation rate between the simulated operating efficiency and the measured operating efficiency of the DC ore-fired furnace. The convergence threshold adjustment rules include: When the deviation rate is >10%, the first level strict convergence standard is adopted; When 5%<deviation rate≤10%, the second level conventional convergence standard is used; When the deviation rate is ≤5%, the third level loose convergence standard is adopted; The multi-objective optimization module performs multi-objective joint optimization on the dynamic adjustment parameters of the DC submerged arc furnace by constructing a comprehensive evaluation function including equipment energy consumption, production efficiency and equipment loss rate. The parameter constraint module imposes at least one of the following constraints during the optimization process: The adjustment range of the power supply adjustment parameters of the power supply equipment shall not exceed ±15% of the rated value; The adjustment frequency of DC ore-fired furnace equipment adjustment parameters is limited to ≤3 times per minute; The time interval between two adjacent parameter adjustments is ≥ 10 seconds.

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