Full-automatic feeding system for calcium carbide furnace

Abstract

The application provides a full-automatic feeding system of an electric furnace, and relates to the technical field of full-automatic feeding of the electric furnace.The full-automatic feeding system of the electric furnace comprises a data acquisition module, a feeding speed calculation module, an automatic feeding decision module and an automatic feeding execution module.The application improves the automation and intelligent level of the electric furnace feeding system from eight dimensions of accurate calculation of feeding speed, automatic feeding decision, closed-loop execution, data tracing, dynamic self-adaptation, safety warning, multi-furnace cooperation and reliable sensing, significantly reduces the labor cost and safety risk, and has outstanding substantial characteristics and significant progress.

Description

Technical Field

[0001] This invention relates to the field of fully automatic feeding technology for calcium carbide furnaces, specifically to a fully automatic feeding system for calcium carbide furnaces. Background Technology

[0002] Calcium carbide, as an important basic chemical raw material, plays a crucial role in industrial production. A single calcium carbide furnace is equipped with 15 hoppers. During production, due to differences in load, current, and furnace depth, the feeding speed of these 15 hoppers will vary. Traditional calcium carbide furnace feeding methods mostly rely on manual operation or semi-automated equipment, resulting in many problems such as low feeding accuracy, high labor intensity, low production efficiency, and safety hazards. Current automatic feeding projects simply control the scraper switch based on the material level, and cannot calculate the actual feeding speed of each individual hopper based on data such as the ratio and current, nor can they determine when to feed which hopper. In reality, personnel still need to constantly monitor the feeding situation, and if the material level gauge is inaccurate, it can lead to flash explosions due to insufficient material in the hoppers.

[0003] Therefore, a fully automatic feeding system for calcium carbide furnaces is provided. Summary of the Invention

[0004] To address the problems mentioned in the background art, the present invention provides the following technical solution: a fully automatic feeding system for a calcium carbide furnace, comprising: The data acquisition module is used to acquire the operating parameters of the calcium carbide furnace from the DCS system in real time via OPC or communication protocol. The operating parameters include at least: three-phase electrode current, furnace depth, load, proportion, and level gauge data from multiple furnace top silos. The feeding rate calculation module is used to calculate the feeding rate of each furnace top hopper based on the three-phase electrode current, furnace depth, load, and proportion data collected by the data acquisition module and a preset automatic feeding mathematical model. The automatic feeding decision module is used to calculate the time point and weight of feeding required for each furnace top hopper based on the feeding rate of each furnace top hopper, determine the priority of the feeding hopper, and generate automatic feeding control signals. The automatic feeding execution module is used to receive automatic feeding control signals and automatically control the weighing action of the batching scale, the operation of the large-angle belt, the operation of the transfer belt, and the feeding action of the rotating distributor to complete the feeding operation of each furnace top hopper.

[0005] Furthermore, it also includes: The automatic report generation module includes a data acquisition unit, a real-time database, a report recording service unit, and a report query client. The data acquisition unit collects real-time data from the DCS system via OPC or communication protocols; the real-time database receives and stores the collected real-time data; the report recording service unit performs unloading judgment logic calculations based on the unloading machine's operating signals, scraper switch signals, three-way valve switch signals, and feeding time to determine the target furnace top hopper into which the material enters, and records the feeding time, actual feeding weight, and feeding frequency; the report query client is used to query and print the generated batching reports.

[0006] Furthermore, the unloading judgment logic of the report recording service unit includes: When any raw material unloading machine is started, the unloading timer begins. It waits for other unloading machines to start within the set time lock. If the lock timer has not ended, the unloading logic will not be restarted again. Once the unloading timer reaches the set time, the furnace number and unloading weight are transmitted to the bin number calculation unit. The bin number calculation unit determines the cumulative time that each scraper is in the extended state based on the scraper switch signal. The time period when the cumulative time is greater than the material head time of the corresponding scraper but not greater than the material tail time of the corresponding scraper is included in the cumulative feeding time. When the cumulative feeding time of any furnace top hopper exceeds the set threshold or the total calculation time reaches the upper limit, the furnace number, hopper number, and weight are sent to the report recording service unit to trigger the recording.

[0007] Furthermore, the input parameters of the automatic feeding mathematical model include: three-phase electrode current, furnace depth, load, proportion, and material level gauge judgment data; the output parameter is the feeding rate of each furnace top hopper.

[0008] Furthermore, the feeding rate calculation module dynamically adjusts the calculation results of the feeding rate of each furnace top hopper based on the magnitude of the three-phase electrode current and the changing trend of the furnace entry depth.

[0009] Furthermore, the data acquisition module also includes a radar level testing system, which is used to acquire real-time level gauge judgment data of each furnace top silo. The automatic feeding decision module makes feeding decisions based on a strategy that prioritizes level gauge judgment data and uses electric furnace electrode current parameters as a secondary factor.

[0010] Furthermore, there are 15 furnace top hoppers, numbered 1# to 15#, and the feeding rate calculation module calculates the independent feeding rate of each furnace top hopper.

[0011] Furthermore, the feeding rate calculation module also includes a material shortage early warning unit. The material shortage early warning unit is used to generate a material shortage early warning signal when the feeding rate of any furnace top hopper exceeds the preset early warning threshold or the material level gauge judges the data to be lower than the preset safe material level. The material shortage early warning signal is used to trigger the backup feeding channel or interlock with the calcium carbide furnace control system to prevent material shortage flash explosion accidents in the hopper.

[0012] Furthermore, the feeding speed calculation module also includes an adaptive learning unit. The adaptive learning unit is used to collect historical feeding data and corresponding actual feeding speed values, and dynamically adjust the parameter weights of the automatic feeding mathematical model through machine learning algorithms to improve the accuracy of feeding speed calculation.

[0013] Furthermore, it also includes a multi-furnace collaborative scheduling module, which is used to collect the load, three-phase electrode current, and material level data of each calcium carbide furnace when there are two or more calcium carbide furnaces. Based on the operating status and raw material demand priority of each calcium carbide furnace, it collaboratively schedules the allocation of raw materials and the order of feeding to achieve optimized allocation of raw material resources for multiple furnaces.

[0014] Beneficial effects The present invention has the following beneficial effects: (1) The present invention solves the problem of the lag of traditional manual parameter recording by real-time acquisition of three-phase electrode current, furnace depth, load, proportion and material level gauge judgment data through the data acquisition module, and ensures the real-time and synchronous acquisition of operating parameters.

[0015] (2) This invention uses a built-in automatic feeding mathematical model to calculate the independent feeding speed of each hopper in real time, taking the average value of the three-phase electrode current, the average value of the electrode entry depth, the load change rate, and the proportion deviation as inputs. Compared with the traditional control method that only relies on the material level switch, the automatic feeding mathematical model of this invention can quantitatively reflect the comprehensive influence of electrode current, entry depth, load fluctuation and proportion deviation on the feeding speed, and distinguish the spatial position differences of different hoppers through local weighted allocation, which significantly improves the prediction accuracy of the feeding speed and provides a reliable basis for accurate feeding decisions.

[0016] (3) This invention uses an automatic feeding decision module to read the current material level data of each furnace top silo collected by the data acquisition module based on the instantaneous feeding rate value output by the feeding rate calculation module, and predicts the remaining time required for the material level of each silo to drop to the low material level threshold. Silos with a remaining time less than 15 minutes of the preset time threshold are marked as silos to be fed, and the feeding priority is determined according to the remaining time from smallest to largest. The feeding weight is calculated based on the current material level of the silo to be fed, the feeding rate value, and the target high material level threshold. This ensures that the silo with the most material shortage is replenished first, effectively preventing the calcium carbide furnace from tripping due to a material shortage in a single silo. At the same time, an automatic feeding control signal containing the target silo number, feeding weight, and feeding start time is automatically generated, completely replacing manual judgment and operation.

[0017] (4) After receiving the automatic feeding control signal, the automatic feeding execution module of this invention sequentially sends a first start command to the batching scale to complete the weighing, a second start command to the inclined belt, a third start command to the transfer belt to complete the material conveying, and a fourth start command containing the target hopper number to the rotary distributor, causing the rotary distributor to rotate to the corresponding position and start the distribution gate. After the feeding is completed, a distribution completion signal is returned, and the automatic feeding execution module stops each device sequentially and feeds back the feeding completion status to the automatic feeding decision module to update the material level data. The entire process does not require manual intervention, solving the problems of easy errors and high labor intensity of manual distribution, and realizing unmanned operation of the feeding process.

[0018] (5) In this invention, the report recording service unit in the automatic report generation module performs unloading judgment logic operations. When any raw material unloading machine starts, the unloading timer begins and waits for other unloading machines to start within the set time lock. The bin number calculation unit judges the cumulative time of each scraper in the extended state according to the scraper switch signal. The time period when the cumulative time is greater than the corresponding scraper head time but not greater than the material tail time is included in the cumulative feeding time. When the cumulative feeding time is greater than the set threshold or the total calculation time reaches the upper limit, the recording is triggered. This solves the technical problem of difficulty in accurately judging which bin the material enters when there are multiple unloading points and multiple scrapers operating at the same time. It automatically records the feeding time, the actual weight of the feeding, and the number of feedings, and supports querying and printing. Compared with manual paper records, it can save about 2 hours of recording workload per shift, while ensuring the accuracy and traceability of the data.

[0019] (6) This invention integrates a material shortage early warning unit into the feeding rate calculation module, receiving the feeding rate calculation value of each furnace top hopper in real time, and simultaneously reading the level gauge judgment data transmitted by the data acquisition module. When the feeding rate of any furnace top hopper exceeds the preset early warning threshold of 10 tons per hour or the continuous level radar measurement value is lower than 15% of the full scale, the material shortage early warning unit generates a material shortage early warning signal, which is output to the calcium carbide furnace control system via hard wiring. The calcium carbide furnace control system automatically executes interlocking operations: triggering the start of the backup feeding channel, automatically reducing the calcium carbide furnace load by 10%, and popping up a red alarm window on the operation station. This upgrades the accident response from passive handling to active prevention, effectively preventing material shortage flash explosion accidents in the hoppers.

[0020] (7) This invention uses a multi-furnace collaborative scheduling module to collect data on the load, three-phase electrode current, and material level of each calcium carbide furnace via a data acquisition module. The priority of raw material demand is determined based on the lowest material level in each furnace's hopper; the lower the lowest material level, the higher the priority of the calcium carbide furnace. Under the limitation of the total conveying capacity of the raw material conveying system, when multiple calcium carbide furnaces simultaneously issue a feeding request, raw materials are allocated sequentially from highest to lowest priority, and a feeding permission signal is sent to the automatic feeding decision module of the calcium carbide furnace with the highest priority. The priority order of all calcium carbide furnaces is recalculated every 30 seconds. This avoids resource conflicts and system deadlocks when multiple furnaces feed simultaneously, improving the raw material utilization efficiency of the entire plant area.

[0021] (8) This invention includes a radar level testing system in the data acquisition module. A frequency-modulated continuous wave radar level gauge is installed on the top of each furnace top silo to acquire the continuous level radar measurement value of each furnace top silo. When the automatic feeding decision module executes the feeding decision, it adopts a strategy of using the level gauge judgment data as the main reference and the electric furnace electrode current parameter as the auxiliary reference: First, it reads the continuous level radar measurement value of each furnace top silo. If it is lower than the low level threshold, it is marked as an emergency feeding silo; if it is normal, it further reads the feeding rate value. The electrode current parameter is only used to verify the rationality of the feeding rate calculation result. When there is a contradiction between the level gauge judgment data and the electrode current parameter, the level gauge judgment data is adopted first and an instrument verification alarm is issued. This solves the safety hazards of inaccurate and falsely reported material shortages by traditional switch level gauges and improves the system's self-diagnostic capability.

[0022] (9) This invention comprehensively improves the automation and intelligence level of the calcium carbide furnace feeding system from eight dimensions: accurate calculation of feeding speed, automatic feeding decision, closed-loop execution, data traceability, dynamic adaptation, safety early warning, multi-furnace collaboration and reliable perception. It significantly reduces labor costs and safety risks, and has outstanding substantive features and significant progress.

[0023] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0024] Figure 1 This is a flowchart of the automatic feeding execution module of the present invention.

[0025] Figure 2 This is a flowchart of the automatic report generation module of the present invention.

[0026] Figure 3 This is a system flowchart of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Please see Figures 1 to 3 This invention provides a technical solution: a fully automatic feeding system for a calcium carbide furnace, comprising: The data acquisition module is used to acquire the operating parameters of the calcium carbide furnace from the DCS system in real time via OPC or communication protocol. The operating parameters include at least: three-phase electrode current, furnace depth, load, proportion, and level gauge data from multiple furnace top silos. The feeding rate calculation module is used to calculate the feeding rate of each furnace top hopper based on the three-phase electrode current, furnace depth, load, and proportion data collected by the data acquisition module and a preset automatic feeding mathematical model. The automatic feeding decision module is used to calculate the time point and weight of feeding required for each furnace top hopper based on the feeding rate of each furnace top hopper, determine the priority of the feeding hopper, and generate automatic feeding control signals. The automatic feeding execution module is used to receive automatic feeding control signals and automatically control the weighing action of the batching scale, the operation of the large-angle belt, the operation of the transfer belt, and the feeding action of the rotating distributor to complete the feeding operation of each furnace top hopper.

[0029] In practical implementation, the data acquisition module establishes a communication connection with the DCS system through the OPC server. The data acquisition module reads the operating parameters from the DCS system according to a preset scanning cycle of 200 milliseconds. This solves the problem of lag in traditional manual parameter recording, ensuring the real-time and synchronous acquisition of operating parameters. Operating parameters include the A-phase current, B-phase current, and C-phase current of the three-phase electrodes, the furnace insertion depth of each phase electrode, the total load of the calcium carbide furnace, the lime-to-semi-coke ratio, and level gauge data from multiple furnace top silos. Level gauge data includes high-level switch signals, low-level switch signals, and continuous level radar measurements for each furnace top silo.

[0030] The feeding rate calculation module receives real-time operating parameters from the data acquisition module. It incorporates an automatic feeding mathematical model. The core function of this model is to quantify the calcium carbide furnace's operating status into the feeding rate of each hopper, providing a basis for precise subsequent feeding. The automatic feeding mathematical model employs a multivariate regression algorithm. The input variables for this algorithm are the average three-phase electrode current, the average electrode depth into the furnace, the load change rate, and the proportioning deviation. Based on these input variables, the feeding rate calculation module calculates the feeding amount per unit time for each furnace top hopper. The module outputs the instantaneous feeding rate value for each furnace top hopper every minute. This one-minute interval balances calculation accuracy with system load, avoiding resource waste caused by excessive data density.

[0031] The automatic feeding mathematical model is built into the feeding rate calculation module. It employs a multivariate linear regression structure. This model solves the problem of quantitatively calculating feeding rate under multi-factor coupling, achieving a mapping from process parameters to feeding rate. Input variables for the automatic feeding mathematical model include the average value of the three-phase electrode current. Average electrode insertion depth Load change rate Mixing ratio deviation value The automatic feeding mathematical model outputs the single-bin baseline feeding rate as its variable. , in tons per hour.

[0032] The mathematical model for automatic feeding is: in: , unit 1,000 Ang.

[0033] , in millimeters.

[0034] , This represents the current total load of the calcium carbide furnace. This represents the total load of the previous cycle. The sampling interval is 1 minute.

[0035] , This is the actual ratio of lime to semi-coke. Set the proportions for the process.

[0036] The coefficients k1, k2, k3, k4, and the intercept b were obtained through offline training. Offline training ensured the scientific validity of the coefficients in the automatic feeding mathematical model, avoiding biases caused by manual assignment based on experience. Training data was collected from 72 consecutive hours of historical data during the stable operation of the calcium carbide furnace, with a sampling interval of 1 minute. Least squares fitting was used, and the objective function was to minimize the predicted value. The root mean square error between the actual weighing and feeding speed and the actual feeding speed.

[0037] For example: k1 = 0.12 (unit: tons per hour per kiloampere) k2 = 0.03 (unit: tons per hour per millimeter) k3 = 0.25 (unit: tons per hour per megawatt per minute) k4 = -1.50 (Unit: tons per hour per proportion deviation) b = 2.80 (unit: tons per hour) Physical meaning explanation: A positive k1 indicates that for every 1 kA increase in the average three-phase electrode current, the baseline feeding rate increases by 0.12 tons per hour.

[0038] A positive k2 indicates that for every 1 mm increase in the average electrode depth into the furnace, the baseline feeding rate increases by 0.03 tons per hour.

[0039] A positive k3 indicates that for every 1 megawatt per minute increase in the load change rate, the baseline feed rate increases by 0.25 tons per hour.

[0040] A negative k4 indicates that for every 1 increase in the deviation between the actual and set proportions, the baseline feeding rate decreases by 1.50 tons per hour; the larger the deviation, the slower the feeding.

[0041] b is a constant term of 2.80 tons per hour, representing the basic feeding rate when all input variables are zero.

[0042] The units of the above coefficients are determined by the dimensions of the training data. In practical applications, they can be scaled and calibrated according to the specifications of the calcium carbide furnace.

[0043] For example: , , , ,but In practical applications, calibration is required based on the specific calcium carbide furnace model, raw material characteristics, electrode parameters, etc.

[0044] The feeding speed calculation module substitutes the current real-time operating parameters into the above expression every minute to calculate the feeding speed. The feeding rate calculation module calculates the feeding rate based on the spatial relationship between each furnace top hopper and the three-phase electrodes. Local weighted allocation is performed. This weighted allocation strategy addresses the issue of inconsistent feeding rates in different hoppers due to their proximity to different electrodes, thus improving calculation accuracy. Allocation coefficients. , , The hoppers corresponding to the A-phase electrode, B-phase electrode, and C-phase electrode respectively meet the requirements. The final feeding rate of each furnace top hopper The feeding speed calculation module will... Output to the automatic feeding decision module.

[0045] The automatic feeding decision module receives the instantaneous feeding rate value output by the feeding rate calculation module. It also reads the current material level data of each furnace top silo collected by the data acquisition module. This solves the problems of inaccurate timing and arbitrary weight adjustments in manual feeding decisions, achieving quantification and automation of feeding decisions. The automatic feeding decision module predicts the remaining time required for the material level in each furnace top silo to drop to the low-level threshold based on the instantaneous feeding rate value. It marks furnace top silos with remaining time less than a preset time threshold of 15 minutes as silos to be fed. The module prioritizes silos according to their remaining time, from smallest to largest. This priority ranking ensures that the most deficient silo is replenished first, preventing the calcium carbide furnace from tripping due to a single silo's lack of material. The module calculates the feeding weight based on the current material level of the silo to be fed, the feeding rate value, and the target high-level threshold. Finally, the module generates an automatic feeding control signal. This signal includes the target silo number, the feeding weight, and the feeding start time.

[0046] The automatic feeding execution module receives the automatic feeding control signal.

[0047] The automatic feeding execution module sends the first start command to the batching scale.

[0048] The decision signals are converted into equipment actions, replacing the tedious process of manually operating the batching scale, belt, and material distributor, thus reducing labor intensity.

[0049] The ingredient weigher completes the weighing action according to the weight of the added ingredients.

[0050] After the batching scale completes the weighing, it returns a weighing completion signal to the automatic feeding execution module.

[0051] After receiving the weighing completion signal, the automatic feeding execution module sends a second start command to the steep-angle belt.

[0052] The steeply inclined belt conveys the weighed mixture to the inlet of the transfer belt.

[0053] The automatic feeding module sends a third start command to the transfer belt.

[0054] The conveyor belt transports the material to the inlet of the rotary distributor.

[0055] The automatic feeding execution module sends a fourth start command to the rotary material distributor.

[0056] The fourth start command includes the target silo number.

[0057] The rotary material distributor rotates to the corresponding position according to the target hopper number and starts the material distribution gate.

[0058] The precise rotation of the rotary material feeder prevents misoperation by adding material to the wrong hopper, solving the problem of errors that easily occur when manually feeding materials.

[0059] After the rotary fabric feeder completes feeding, it sends a feeding completion signal back to the automatic feeding execution module.

[0060] The automatic feeding module stops the steep-angle belt, the transfer belt, and the rotating material distributor in sequence.

[0061] The automatic feeding execution module feeds back the feeding completion status to the automatic feeding decision module.

[0062] The automatic feeding decision module updates the material level data of the corresponding furnace top hopper.

[0063] The automatic feeding decision module is waiting for the next feeding decision cycle.

[0064] Furthermore, it also includes: The automatic report generation module includes a data acquisition unit, a real-time database, a report recording service unit, and a report query client. The data acquisition unit collects real-time data from the DCS system via OPC or communication protocols; the real-time database receives and stores the collected real-time data; the report recording service unit performs unloading judgment logic calculations based on the unloading machine's operating signals, scraper switch signals, three-way valve switch signals, and feeding time to determine the target furnace top hopper into which the material enters, and records the feeding time, actual feeding weight, and feeding frequency; the report query client is used to query and print the generated batching reports.

[0065] In practical implementation, the automatic report generation module includes a data acquisition unit, a real-time database, a report recording service unit, and a report query client. The data acquisition unit collects real-time data from the DCS system via an OPC server. This solves the problems of heavy workload, error-proneness, and difficulty in traceability associated with manual paper recording, enabling automatic archiving and querying of feeding data. The real-time database receives real-time data from the data acquisition unit and performs storage operations. The report recording service unit reads the unloading machine's operating signals, scraper switch signals, three-way valve switch signals, and feeding time. The report recording service unit executes unloading judgment logic operations, determining the target furnace top hopper number into which the material enters. The report recording service unit records the feeding time, actual feeding weight, and number of feedings. The report recording service unit writes the recorded data into the report data table of the real-time database. The report query client provides a human-machine interface. After receiving user query conditions, the report query client reads the corresponding report data from the real-time database. The report query client calls the accompanying automatic printing system to complete report printing. The automatic printing function reduces manual transcription time, saving approximately 2 hours of recording workload per shift.

[0066] Furthermore, the unloading judgment logic of the report recording service unit includes: When any raw material unloading machine is started, the unloading timer begins. It waits for other unloading machines to start within the set time lock. If the lock timer has not ended, the unloading logic will not be restarted again. It solves the technical problem of accurately determining which hopper the material enters when there are multiple unloading points and multiple scrapers operating simultaneously, thus ensuring the accuracy of report data.

[0067] Once the unloading timer reaches the set time, the furnace number and unloading weight are transmitted to the bin number calculation unit. The bin number calculation unit determines the cumulative time that each scraper is in the extended state based on the scraper switch signal. The time period when the cumulative time is greater than the material head time of the corresponding scraper but not greater than the material tail time of the corresponding scraper is included in the cumulative feeding time. When the cumulative feeding time of any furnace top hopper exceeds the set threshold or the total calculation time reaches the upper limit, the furnace number, hopper number, and weight are sent to the report recording service unit to trigger the recording.

[0068] In practice, the unloading judgment logic of the report recording service unit is executed according to the following steps. The unloading judgment logic solves the technical problem of accurately determining which silo the material enters when there are multiple unloading points and multiple scrapers operating simultaneously, thus ensuring the accuracy of the report data.

[0069] Step 1: Detect the start signal of any raw material unloading machine.

[0070] The unloading timer starts when a start signal is detected. It waits for other unloaders to start within a set time lockout period. The unloading logic will not restart until the lockout time has expired. This locking mechanism prevents repeated triggering of records within a short period, thus preventing data redundancy in reports.

[0071] Step 2: After the unloading timer reaches the set time, read the current furnace number and unloading weight. Transfer the furnace number and unloading weight to the bin number calculation unit.

[0072] Step 3: The bin number calculation unit reads the switch signals of each scraper. The bin number calculation unit determines the cumulative time that each scraper is in the extended state. The time period when the cumulative time is greater than the material head time of the corresponding scraper but not greater than the material tail time of the corresponding scraper is included in the cumulative feeding time. The material head and tail time window judgment method is adopted to eliminate the interference of scraper movement jitter on signal judgment.

[0073] The head time refers to the time from when the material starts to when it reaches the first scraper, while the tail time refers to the time from when the material stops to when the last scraper finishes feeding.

[0074] Step 4: A recording action is triggered when the cumulative feeding time of any furnace top hopper exceeds a set threshold or the total calculation time reaches its upper limit. The hopper number calculation unit sends the furnace number, hopper number, and unloading weight to the report recording service unit. Upon receiving the data, the report recording service unit generates a batching record.

[0075] Furthermore, the input parameters of the automatic feeding mathematical model include: three-phase electrode current, furnace depth, load, proportion, and material level gauge judgment data; the output parameter is the feeding rate of each furnace top hopper.

[0076] In practical implementation, the input parameters of the automatic feeding mathematical model include three-phase electrode current, furnace depth, load, mix ratio, and material level gauge judgment data. The combination of input parameters covers the main factors affecting the feeding rate of the calcium carbide furnace, ensuring the completeness and generalization ability of the automatic feeding mathematical model.

[0077] The three-phase electrode current includes phase A current, phase B current, and phase C current.

[0078] The furnace entry depth includes the furnace entry depth of phase A electrode, phase B electrode, and phase C electrode.

[0079] The load is the total load of the calcium carbide furnace.

[0080] The ratio is that of lime to semi-coke.

[0081] The data for level gauge judgment includes high level switch signals, low level switch signals, and continuous level radar measurement values ​​for each furnace top silo.

[0082] The output parameter of the automatic feeding mathematical model is the feeding rate of each furnace top hopper. The fine granularity of the output parameter reaches each hopper, providing a data foundation for subsequent differentiated feeding.

[0083] The output parameters are in tons per hour.

[0084] Furthermore, the feeding rate calculation module dynamically adjusts the calculation results of the feeding rate of each furnace top hopper based on the magnitude of the three-phase electrode current and the changing trend of the furnace entry depth.

[0085] In practical implementation, the feeding rate calculation module monitors the magnitude and trend of the three-phase electrode current in real time. Simultaneously, the module also monitors the changing trend of the electrode insertion depth into the furnace. This dynamic adjustment function solves the problem of inaccurate calculations in the static model when the calcium carbide furnace operating conditions fluctuate, improving the adaptability of the feeding rate prediction.

[0086] When the rate of increase of the three-phase electrode current exceeds the preset slope threshold, it is determined that the calcium carbide furnace has entered the high-load feeding stage.

[0087] The feeding rate calculation module multiplies the baseline feeding rate output by the automatic feeding mathematical model by an acceleration factor of 1.2. This acceleration factor causes the feeding system to respond to the increased load earlier, preventing a rapid drop in material level that could lead to a shortage of material.

[0088] When the rate of decrease of the three-phase electrode current exceeds the preset slope threshold, it is determined that the calcium carbide furnace has entered the low-load feeding stage.

[0089] The feeding rate calculation module multiplies the baseline feeding rate by a deceleration coefficient of 0.8. This deceleration coefficient prevents overfeeding at low loads, which could cause silo overflow.

[0090] An increase in electrode depth into the furnace indicates that the electrode is inserted deeper into the material layer.

[0091] The feeding rate calculation module should correspondingly add a feeding rate distribution coefficient for the top hopper near the electrode. .

[0092] When the electrode insertion depth into the furnace decreases, the corresponding distribution coefficient should be reduced. The distribution coefficient is adjusted in real time based on the electrode insertion depth, which solves the problem of uneven feeding speed in different areas due to changes in electrode position.

[0093] The dynamically adjusted feeding rate calculation results are output to the automatic feeding decision module.

[0094] Furthermore, the data acquisition module also includes a radar level testing system, which is used to acquire real-time level gauge judgment data of each furnace top silo. The automatic feeding decision module makes feeding decisions based on a strategy that prioritizes level gauge judgment data and uses electric furnace electrode current parameters as a secondary factor.

[0095] In practical implementation, the data acquisition module includes a radar level testing system. This system replaces traditional digital level gauges, eliminating the safety hazards of inaccurate level gauge readings, false alarms, and material shortages that could lead to flash explosions.

[0096] The radar level testing system installs one frequency-modulated continuous wave radar level gauge on the top of each furnace top silo.

[0097] The radar level testing system acquires real-time level gauge data from each furnace top silo. This real-time level gauge data includes continuous radar level measurements.

[0098] The automatic feeding decision module employs a strategy that prioritizes level gauge data and uses electric furnace electrode current parameters as a secondary indicator when making feeding decisions. This primary-secondary strategy clarifies the priority of the decision-making criteria and avoids system logic confusion when different data sources conflict.

[0099] Specifically, the automatic feeding decision module first reads the continuous material level radar measurement value of each furnace top hopper.

[0100] If the continuous level radar measurement value is lower than the low level threshold, the corresponding silo will be marked as an emergency feeding silo. The emergency feeding silo marking mechanism ensures a rapid response when the level is abnormal, preventing accidents caused by material shortages.

[0101] If the continuous material level radar measurement is normal, the feeding rate value output by the feeding rate calculation module is further read. The electric furnace electrode current parameter is only used to verify the rationality of the feeding rate calculation result.

[0102] When a discrepancy arises between the level gauge reading and the electrode current parameter, the automatic feeding decision module prioritizes the level gauge reading and issues an instrument calibration alarm. This discrepancy detection and alarm function helps maintenance personnel promptly identify instrument malfunctions, enhancing the system's self-diagnostic capabilities.

[0103] Furthermore, there are 15 furnace top hoppers, numbered 1# to 15#, and the feeding rate calculation module calculates the independent feeding rate of each furnace top hopper.

[0104] In practice, the calcium carbide furnace is equipped with 15 top silos. The 15 top silos are evenly arranged on the top of the calcium carbide furnace in a circumferential direction.

[0105] The silos are numbered 1#, 2#, 3#, 4#, 5#, 6#, 7#, 8#, 9#, 10#, 11#, 12#, 13#, 14#, and 15#.

[0106] Each top hopper corresponds to a feeding pipe area inside the calcium carbide furnace. The feeding rate calculation module calculates the independent feeding rate of each top hopper.

[0107] The formula for calculating the independent feeding speed is as follows: Where i ranges from 1 to 15. Allocation coefficient. Calibration is performed based on the spatial distance between the i-th hopper and the three-phase electrode. The hopper distribution coefficient closest to the A-phase electrode is also considered. Take 0.4. The hopper distribution coefficient near the B-phase electrode. Take 0.4. The hopper distribution coefficient near the C-phase electrode. Take 0.2.

[0108] The specific values ​​were calibrated through on-site material flow tests.

[0109] Furthermore, the feeding rate calculation module also includes a material shortage early warning unit. The material shortage early warning unit is used to generate a material shortage early warning signal when the feeding rate of any furnace top hopper exceeds the preset early warning threshold or the material level gauge judges the data to be lower than the preset safe material level. The material shortage early warning signal is used to trigger the backup feeding channel or interlock with the calcium carbide furnace control system to prevent material shortage flash explosion accidents in the hopper.

[0110] In practical implementation, the material feeding rate calculation module integrates a material shortage early warning unit. This unit solves the major safety problem of flash explosions caused by delayed material level monitoring and operators' failure to detect material shortages in time.

[0111] The material shortage early warning unit receives the calculated feeding rate of each furnace top hopper in real time. Simultaneously, it reads the level gauge data transmitted from the data acquisition module. The unit has a built-in preset warning threshold, which is the upper limit of the feeding rate of 10 tons per hour. It also sets a safe level threshold, which is when the continuous level radar measurement value is below 15% of full scale. When the feeding rate of any furnace top hopper exceeds 10 tons per hour or the level gauge data is below 15% of full scale, the unit generates a material shortage early warning signal. This signal is output to the calcium carbide furnace control system via hardwiring. Upon receiving the warning signal, the control system automatically performs the following interlocking operations: triggering the backup charging channel, automatically reducing the calcium carbide furnace load by 10%, and displaying a red alarm window on the operating station. These interlocking operations enable automatic emergency response in fault conditions, minimizing accident risks. The unit also records the warning events and stores them in a historical database.

[0112] Furthermore, the feeding speed calculation module also includes an adaptive learning unit. The adaptive learning unit is used to collect historical feeding data and corresponding actual feeding speed values, and dynamically adjust the parameter weights of the automatic feeding mathematical model through machine learning algorithms to improve the accuracy of feeding speed calculation.

[0113] In practical implementation, the feeding speed calculation module integrates an adaptive learning unit. The adaptive learning unit solves the problem that the fixed mathematical model gradually becomes inaccurate due to equipment aging, changes in raw materials, etc., and realizes long-term self-optimization of the automatic feeding mathematical model.

[0114] The adaptive learning unit collects historical feeding data. This historical data includes the average three-phase electrode current, average electrode depth into the furnace, load change rate, and proportion deviation before each feeding. Simultaneously, the adaptive learning unit collects the actual weighing and feeding rate within the corresponding feeding cycle. The adaptive learning unit uses this data to construct a training sample set.

[0115] The adaptive learning unit uses a gradient descent algorithm to fine-tune the coefficients k1, k2, k3, k4, and intercept b of the automatic feeding mathematical model online. Fine-tuning is performed once every 24 hours. This daily fine-tuning frequency ensures that the automatic feeding mathematical model tracks changes in operating conditions in a timely manner while avoiding instability caused by overly frequent updates. The fine-tuning objective is to minimize the absolute percentage error between the predicted feeding rate and the actual weighing feeding rate. The adaptive learning unit sets limits on coefficient changes. After each fine-tuning, the change in each coefficient does not exceed 5% of the initial value. The adaptive learning unit writes the updated coefficients to non-volatile memory. The feeding rate calculation module loads the new coefficients for calculation in the next cycle.

[0116] Furthermore, it also includes a multi-furnace collaborative scheduling module, which is used to collect the load, three-phase electrode current, and material level data of each calcium carbide furnace when there are two or more calcium carbide furnaces. Based on the operating status and raw material demand priority of each calcium carbide furnace, it collaboratively schedules the allocation of raw materials and the order of feeding to achieve optimized allocation of raw material resources for multiple furnaces.

[0117] In practical implementation, the fully automatic feeding system for calcium carbide furnaces includes a multi-furnace collaborative scheduling module. This module solves the problems of insufficient capacity of long-distance conveying systems and feeding conflicts when multiple calcium carbide furnaces are running simultaneously, achieving optimal allocation of raw material resources.

[0118] The multi-furnace collaborative scheduling module is used to manage the raw material allocation for two or more calcium carbide furnaces.

[0119] The multi-furnace coordinated scheduling module collects the load, three-phase electrode current, and material level data of each calcium carbide furnace through the data acquisition module.

[0120] The multi-furnace collaborative scheduling module reads the raw material demand priority of each calcium carbide furnace. The raw material demand priority is sorted according to the lowest material level in each furnace's hopper. The lower the lowest material level, the higher the priority. This minimum material level priority strategy ensures that the furnace with the most urgent needs receives raw materials first, preventing any calcium carbide furnace from shutting down due to material shortage. The multi-furnace collaborative scheduling module also considers the total conveying capacity limit of the raw material conveying system. When multiple calcium carbide furnaces simultaneously issue charging requests, the multi-furnace collaborative scheduling module allocates raw materials sequentially according to priority from high to low. The multi-furnace collaborative scheduling module sends a charging permission signal to the automatic charging decision module of the highest priority calcium carbide furnace. This sequential processing mechanism avoids system deadlock or overload caused by multiple furnaces competing for resources. The multi-furnace collaborative scheduling module recalculates the priority order of all calcium carbide furnaces every 30 seconds. This 30-second refresh frequency strikes a balance between response speed and computational overhead.

Claims

1. A fully automatic feeding system for a calcium carbide furnace, characterized in that: include: The data acquisition module is used to acquire the operating parameters of the calcium carbide furnace from the DCS system in real time via OPC or communication protocol. The operating parameters include at least: three-phase electrode current, furnace depth, load, proportion, and level gauge data of multiple furnace top silos. The feeding rate calculation module is used to calculate the feeding rate of each furnace top hopper based on the three-phase electrode current, furnace depth, load, and proportion data collected by the data acquisition module and a preset automatic feeding mathematical model. The automatic feeding decision module is used to calculate the time point and weight of feeding required for each furnace top hopper based on the feeding rate of each furnace top hopper, determine the priority of the feeding hopper, and generate automatic feeding control signals. The automatic feeding execution module is used to receive the automatic feeding control signal and automatically control the weighing action of the batching scale, the operation of the large-angle belt, the operation of the transfer belt, and the feeding action of the rotating material distributor to complete the feeding operation of each furnace top hopper.

2. The fully automatic feeding system for a calcium carbide furnace according to claim 1, characterized in that: Also includes: The automatic report generation module includes a data acquisition unit, a real-time database, a report recording service unit, and a report query client. The data acquisition unit collects real-time data from the DCS system via OPC or communication protocol; the real-time database receives and stores the collected real-time data; the report recording service unit performs unloading judgment logic calculations based on the unloading machine's operating signal, scraper switch signal, three-way valve switch signal, and feeding time to determine the target furnace top hopper into which the material enters, and records the feeding time, actual feeding weight, and feeding times. The report query client is used to query and print the generated ingredient reports.

3. The fully automatic feeding system for a calcium carbide furnace according to claim 2, characterized in that: The unloading judgment logic of the report recording service unit includes: When any raw material unloading machine is started, the unloading timer begins. It waits for other unloading machines to start within the set time lock. If the lock timer has not ended, the unloading logic will not be restarted again. Once the unloading timer reaches the set time, the furnace number and unloading weight are transmitted to the bin number calculation unit. The bin number calculation unit determines the cumulative time that each scraper is in the extended state based on the scraper switch signal. The time period when the cumulative time is greater than the material head time of the corresponding scraper but not greater than the material tail time of the corresponding scraper is included in the cumulative feeding time. When the cumulative feeding time of any furnace top hopper exceeds the set threshold or the total calculation time reaches the upper limit, the furnace number, hopper number, and weight are sent to the report recording service unit to trigger the recording.

4. The fully automatic feeding system for a calcium carbide furnace according to claim 1, characterized in that: The input parameters of the automatic feeding mathematical model include: three-phase electrode current, furnace depth, load, proportion, and material level gauge judgment data; the output parameter is the feeding speed of each furnace top hopper.

5. The fully automatic feeding system for a calcium carbide furnace according to claim 1, characterized in that: The feeding rate calculation module dynamically adjusts the calculation results of the feeding rate of each furnace top hopper based on the magnitude of the three-phase electrode current and the changing trend of the furnace entry depth.

6. The fully automatic feeding system for a calcium carbide furnace according to claim 1, characterized in that: The data acquisition module also includes a radar level testing system, which is used to acquire real-time level gauge judgment data of each furnace top silo. The automatic feeding decision module makes feeding decisions based on a strategy that prioritizes level gauge judgment data and uses electric furnace electrode current parameters as a secondary factor.

7. The fully automatic feeding system for a calcium carbide furnace according to claim 1, characterized in that: The number of furnace top hoppers is 15, namely hoppers 1# to hoppers 15#. The feeding speed calculation module calculates the independent feeding speed of each furnace top hopper.

8. The fully automatic feeding system for a calcium carbide furnace according to claim 1, characterized in that: The feeding speed calculation module also includes a material shortage early warning unit. The material shortage early warning unit is used to generate a material shortage early warning signal when the feeding speed of any furnace top hopper exceeds a preset early warning threshold or the material level gauge judges that the data is lower than a preset safe material level. The material shortage early warning signal is used to trigger the backup feeding channel or interlock with the calcium carbide furnace control system to prevent a flash explosion accident caused by material shortage in the hopper.

9. The fully automatic feeding system for a calcium carbide furnace according to claim 1, characterized in that: The feeding speed calculation module also includes an adaptive learning unit, which is used to collect historical feeding data and corresponding actual feeding speed values, and dynamically adjust the parameter weights of the automatic feeding mathematical model through machine learning algorithms to improve the accuracy of feeding speed calculation.

10. The fully automatic feeding system for a calcium carbide furnace according to claim 1, characterized in that: It also includes a multi-furnace collaborative scheduling module, which is used to collect the load, three-phase electrode current and material level data of each calcium carbide furnace when there are two or more calcium carbide furnaces. Based on the operating status and raw material demand priority of each calcium carbide furnace, it collaboratively schedules the allocation of raw materials and the order of feeding to achieve the optimal allocation of raw material resources of multiple furnaces.

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