Full-automatic casting control system and method for ferrosilicon smelting

Abstract

The invention relates to the technical field of ferrosilicon smelting, and discloses a full-automatic casting control system and method for ferrosilicon smelting, which comprises the following modules: a molten iron transfer module consisting of a ladle, a 360-degree rotating track turntable and an electrified track car, and a casting execution module comprising a ladle overturning machine, a mold fixing car and a chute, all the devices in the casting process are controlled to operate cooperatively, and a cooling treatment module, a demolding and transferring module and a central control module are included. A flow prediction control algorithm is combined with a Kalman filter, a hydrodynamic model is established based on parameters such as the molten iron temperature and the ladle overturning machine dip angle, a viscosity function is dynamically corrected, molten iron flow velocity prediction is optimized by fusing a sensor actual measurement value, then the ladle overturning machine dip angle is adjusted through PID control, molten iron flow accurate control is achieved, manual intervention is reduced, and the efficiency is improved. The casting quality stability is improved, and the rejection rate caused by flow fluctuation is reduced.

Description

Technical Field

[0001] This invention relates to the field of ferrosilicon smelting technology, specifically to a fully automated casting control system and method for ferrosilicon smelting. Background Technology

[0002] Ferrosilicon smelting and casting is a key step in the ferrosilicon production process. It mainly involves introducing the high-temperature molten ferrosilicon produced from the smelting furnace into a fixed mold tank, and then cooling and demolding it to form the finished ferrosilicon product. This step directly affects the molding quality, production efficiency, and energy consumption of ferrosilicon.

[0003] In existing technologies, ferrosilicon smelting and casting mostly adopts a semi-automated operation mode: after the molten iron ladle is transferred to the casting station by a railcar, the tilt angle is adjusted by manual operation of the ladle tilting machine, so that the molten iron is poured into the fixed mold slot of the fixed mold car through the chute; the fixed mold car moves along a preset trajectory to complete continuous casting, and is equipped with a cooling device to cool the formed ferrosilicon. The demolding process is completed by manual assistance mechanical devices. Some production lines are equipped with flow sensors and temperature sensors to monitor the molten iron parameters in real time, but the linkage between parameter feedback and equipment control requires manual intervention for adjustment.

[0004] However, in the existing technology, the control accuracy of molten iron flow in current production equipment is low. Due to the lack of a dynamic prediction model based on multiple parameters, it is impossible to adjust the ladle turning machine action in combination with real-time parameters such as molten iron temperature and viscosity, resulting in large fluctuations in molten iron flow rate. This leads to uneven filling of molten iron in the fixed mold groove, affecting the stability of ferrosilicon forming quality. In view of this, we propose a fully automatic casting control system and method for ferrosilicon smelting. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a fully automated casting control system and method for ferrosilicon smelting. This solves the problem in existing technologies where the lack of a multi-parameter dynamic prediction model prevents the adjustment of the ladle turning machine's actions based on real-time parameters such as molten iron temperature and viscosity, resulting in large fluctuations in molten iron flow rate.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a fully automated casting control system and method for ferrosilicon smelting, comprising the following modules:

[0007] The molten iron transfer module consists of a ladle, a 360-degree rotating track turntable, and an electrified railcar. The track turntable is equipped with a first positioning interface for communication with the central control module, and the electrified railcar is equipped with a first mechanical interface for docking with the casting execution module.

[0008] The casting execution module includes a ladle turning machine, a fixed mold car, and a chute. The ladle turning machine is equipped with a suspension mechanism and an inclination sensor that cooperate with the molten iron ladle. The fixed mold car is equipped with a second positioning interface that connects with the cooling treatment module. The chute inlet is equipped with a flow sensor and a temperature sensor.

[0009] The cooling module consists of a shuttle car, a cooling station, and an atomizing cooling device. The shuttle car is equipped with a second mechanical interface for docking with the fixed mold car and a position synchronization device. The atomizing cooling device is equipped with a flow regulating valve and a pressure sensor.

[0010] The demolding and transfer module includes an automatic knocking device, an automatic mold flipping device, a buffer hopper, and a transfer hopper. The automatic mold flipping device is equipped with a third mechanical interface and a temperature triggering device that are compatible with the fixed mold carriage.

[0011] The central control module, with a programmable logic controller as its core, includes a molten iron transfer control unit, a casting execution control unit, a cooling treatment control unit, and a demolding and transfer control unit. The casting execution control unit contains a flow prediction control algorithm. This algorithm establishes a fluid dynamics model based on the molten iron temperature, ladle tilting machine tilt angle, and chute geometric parameters, predicts the molten iron flow rate through a Kalman filter, and dynamically adjusts the ladle tilting machine tilt angle and the fixed mold car travel speed according to the prediction results.

[0012] Preferably, the first positioning interface of the track turntable of the molten iron transfer module includes an RFID-based position identification unit and a wireless transmission unit for communicating with the energized railcar. The wireless transmission unit exchanges position data with the PLC of the central control module via the Modbus protocol. The first mechanical interface of the energized railcar adopts a quick-locking structure, which includes an electromagnetic adsorption device and a mechanical buckle.

[0013] Preferably, the suspension mechanism of the casting execution module includes adjustable grippers and a torque balancing device. The torque balancing device is connected to the PLC of the central control module through a hydraulic system. The grippers are equipped with pressure sensors and the clamping force data is fed back through the Profibus bus. The flow sensor of the chute is an electromagnetic flow meter, and the temperature sensor is a platinum resistance thermometer. The output signals of both are transmitted to the PLC through a 4-20mA current loop and the Modbus RTU protocol, respectively.

[0014] Preferably, in the fluid dynamics model of the flow prediction control algorithm of the casting execution module, the predicted flow rate is associated with the flow coefficient, the cross-sectional area of ​​the chute outlet, the gravitational acceleration, the liquid level height in the ladle, the molten iron density, and the temperature-related viscosity function. The temperature-related viscosity function is dynamically corrected by the molten iron temperature collected in real time by the temperature sensor.

[0015] The state vector of the Kalman filter contains parameters such as the molten iron flow rate and the rate of change of the tilt angle of the ladle turning machine, while the observation vector contains the measured values ​​of the flow sensor and the tilt angle sensor. The molten iron flow rate is controlled through the state transition matrix, the control input matrix, the observation matrix, and the Kalman gain.

[0016] Preferably, the second mechanical interface of the cooling module adopts a conical positioning pin and guide groove structure, and the position synchronization device includes a laser rangefinder and an encoder. The data of both are transmitted to the central control module via Ethernet / IP protocol. The flow regulating valve of the atomizing cooling device adopts an electric regulating valve, and the pressure sensor output signal is connected to the PLC via HART protocol. The adjustment of the regulating valve opening is combined with the deviation between the target flow and the predicted flow, the cumulative value of the deviation, and the rate of change of the deviation.

[0017] Preferably, the third mechanical interface of the automatic mold flipping device of the demolding and transfer module includes a slot structure corresponding to the fixed mold carriage and an electromagnetic locking device. The control circuit of the electromagnetic locking device is connected in series with the temperature triggering device of the cooling treatment module. The temperature triggering device adopts a bimetallic strip triggering switch. The inner wall of the buffer hopper is provided with an elastic buffer layer composed of alternating rubber pads and metal springs. The elastic modulus of the metal springs matches the demolding impact force of the silicon iron.

[0018] Preferably, the molten iron transfer control unit dynamically adjusts the travel speed of the energized railcar based on the flow prediction result of the casting execution control unit, and the adjustment of the travel speed is related to the deviation between the predicted flow and the target flow; the drive system of the energized railcar includes a servo motor and a reducer, the encoder of the servo motor is connected to the PLC of the central control module through an EtherCAT bus, and the output shaft of the reducer is equipped with a torque sensor.

[0019] Preferably, the casting execution control unit transmits the predicted flow rate data to the cooling treatment control unit via an industrial bus, and the cooling treatment control unit dynamically adjusts the opening of the flow regulating valve of the atomizing cooling device based on the data; the mold unit of the mold car includes a modularly connected tank structure and a powder guiding channel that docks with the automatic powder dispensing equipment, and the inner wall of the guiding channel is provided with spiral guiding blades, the blade pitch of which matches the discharge speed of the powder dispensing equipment.

[0020] A fully automated casting control method for ferrosilicon smelting includes the following steps:

[0021] S1 molten iron transfer scheduling steps: Obtain the location information of the molten iron ladle, control the track turntable to rotate to the preset angle, drive the electrified railcar to run along the track, move the molten iron ladle from the side of the smelting furnace to the casting execution area, and at the same time send the molten iron ladle arrival signal to S2.

[0022] S2 casting execution adjustment steps: After receiving the ladle arrival signal, control the ladle flipping machine to suspend the ladle, start the flow prediction control algorithm, predict the molten iron flow rate based on the molten iron temperature, ladle flipping machine tilt angle, and chute parameters, adjust the ladle flipping machine tilt angle and fixed mold car travel speed according to the prediction results, so that the molten iron is injected into the fixed mold car fixed mold slot through the chute, and after completion, send the casting completion signal to S3.

[0023] S3 Cooling Processing Steps: After receiving the casting completion signal, control the shuttle car to move the mold car to the cooling station, start the atomizing cooling device to cool the ferrosilicon mold, monitor the ferrosilicon temperature in real time, and send the cooling completion signal to S4 when the temperature reaches the set threshold.

[0024] S4 Demolding and Transfer Steps: After receiving the cooling completion signal, control the automatic striking device to strike the ferrosilicon mold, start the automatic mold flipping device to demold the ferrosilicon into the buffer hopper, and control the transfer hopper to receive the ferrosilicon and transfer it to the storage area.

[0025] S5 parameter feedback optimization steps: Collect parameters such as molten iron flow rate, temperature change, and cooling time during this casting process, compare and analyze them with historical data, and dynamically adjust the rotation angle of the track turntable in S1, the flow prediction control algorithm parameters in S2, the opening curve of the flow regulating valve of the atomizing cooling device in S3, and the striking frequency and force in S4.

[0026] Preferably, in S3, the opening of the flow regulating valve of the atomizing cooling device is adjusted in stages according to the predicted iron flow rate in S2. In the initial stage, the opening is 60% to 70% of the maximum opening, and when the temperature of the ferrosilicon drops below the melting point, it is adjusted to 30% to 40%. In S4, the automatic mold flipping device is triggered by the temperature triggering device in S3 and the position signal of the fixed mold car. The mold flipping action is only performed when the fixed mold car is completely stopped and the temperature reaches the threshold. In S5, the calculation formula parameters of the temperature threshold in S3 are dynamically corrected according to the deviation between the cooling time and the target value.

[0027] This invention provides a fully automated casting control system and method for ferrosilicon smelting. It has the following beneficial effects:

[0028] 1. This invention combines a flow prediction control algorithm with a Kalman filter to establish a fluid dynamics model based on parameters such as molten iron temperature and ladle tilting angle. It dynamically corrects the viscosity function, integrates sensor measurements to optimize molten iron flow rate prediction, and then adjusts the ladle tilting angle through PID control to achieve precise control of molten iron flow rate, reduce manual intervention, improve casting quality stability, and reduce scrap rate caused by flow fluctuations.

[0029] 2. This invention designs a linkage algorithm between the molten iron transfer control unit and the casting execution module. Based on the flow deviation, change trend and prediction confidence, the speed of the railcar is dynamically adjusted to match the molten iron supply with the casting demand, avoid molten iron accumulation or insufficient supply, shorten the production cycle and improve equipment utilization and production efficiency.

[0030] 3. This invention coordinates various modules through a central control module, adjusts cooling parameters based on flow prediction through a cooling treatment module, and optimizes demolding timing by combining cooling status with demolding and transfer modules, forming a closed-loop control of the entire process. This reduces coordination errors in each link, improves the level of system automation, reduces manual operation in high-temperature environments, and ensures production safety. Attached Figure Description

[0031] Figure 1 This is a module diagram of the fully automated casting control system for ferrosilicon smelting;

[0032] Figure 2 This is a flowchart of the fully automated casting control method for ferrosilicon smelting. Detailed Implementation

[0033] The technical solutions in 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.

[0034] Example:

[0035] Please see the appendix Figure 1 This invention provides a fully automated casting control system for ferrosilicon smelting, comprising the following modules:

[0036] In the molten iron transfer module, the RFID-based position identification unit of the 360-degree rotating track turntable is used to accurately locate the transfer position of the molten iron ladle, ensuring precise docking between the railcar and the casting execution module. The wireless transmission unit exchanges position data with the central control module PLC via the Modbus protocol, enabling real-time monitoring and scheduling of the molten iron ladle transfer trajectory. In the fast-locking structure of the electrified railcar, electromagnetic adsorption devices and mechanical buckles are used to firmly fix the molten iron ladle on the railcar, preventing molten iron spillage due to vibration during transfer and ensuring transfer safety.

[0037] In the casting execution module, the adjustable grippers of the ladle-turning machine are used to adapt to ladles of different sizes. A pressure sensor provides feedback on the clamping force to ensure the ladle is securely suspended. A torque balancing device, connected to the PLC via a hydraulic system, balances the torque during the ladle-turning process, preventing violent shaking caused by a shift in the center of gravity when the ladle is tilted. An electromagnetic flowmeter at the chute inlet monitors the molten iron flow rate in real time, and a platinum resistance thermometer collects the molten iron temperature. Both transmit data to the PLC via a 4-20mA current loop and Modbus RTU protocol, providing real-time parameters for the flow prediction and control algorithm. The temperature data is used to dynamically correct the viscosity function. Combined with a fluid dynamics model, this enables accurate prediction of the molten iron flow rate, providing a basis for adjusting the ladle-turning machine's tilt angle and the fixed mold carriage's travel speed, ensuring uniform injection of molten iron into the fixed mold slot. The relevant algorithms of the established fluid dynamics model are as follows:

[0038] Temperature-dependent viscosity function calculation

[0039] The viscosity of molten iron changes significantly with temperature, requiring calculation and the construction of a fluid dynamics model. The specific formula is as follows:

[0040]

[0041] Parameter details:

[0042] η(T): Dynamic viscosity of molten iron at the current temperature (unit: Pa·s). The higher the viscosity, the worse the fluidity. η0: Reference viscosity value (4.7 mPa·s), corresponding to the viscosity of molten iron at 1800 K.

[0043] E a Viscosity activation energy (40000 J / mol) reflects the effect of intermolecular forces on viscosity;

[0044] R: gas constant (8.314 J / (mol·K)), fundamental thermodynamic constant;

[0045] T0: Reference temperature (1800K), close to the typical temperature during molten iron casting;

[0046] T: Real-time measured temperature of molten iron (unit: K), obtained by an infrared temperature sensor;

[0047] Fluid dynamics model flow prediction

[0048] Flow prediction formula derived from Bernoulli's equation:

[0049]

[0050] Parameter details:

[0051] Q: Predicted molten iron flow rate (unit: m) 3 / s), which is the amount of molten iron passing through the chute per unit time;

[0052] α: Flow coefficient (0.85), considering corrections for chute inner wall friction and outlet local resistance;

[0053] A: Cross-sectional area of ​​the chute outlet (unit: m²) 2 The dimensions are determined by the design of the chute.

[0054] g: acceleration due to gravity (9.81 m / s²) 2 ), the main power source driving the flow of molten iron;

[0055] h: Liquid level height inside the ladle (unit: m). The higher the liquid level, the greater the pressure and the faster the flow rate.

[0056] ρ: Density of molten iron (7000 kg / m³) 3 The approximate density value of liquid iron;

[0057] η(T): Temperature-dependent viscosity value calculated using the temperature-dependent viscosity function;

[0058] The state vector of the Kalman filter includes parameters such as molten iron flow velocity and the rate of change of the ladle tilting angle, while the observation vector includes the measured values ​​from the flow sensor and tilting angle sensor. The molten iron flow velocity is controlled through the state transition matrix, control input matrix, observation matrix, and Kalman gain, and includes the following algorithms:

[0059] State vector definition:

[0060]

[0061] v: molten iron flow velocity (unit: m / s), calculated by dividing the flow rate Q by the cross-sectional area A of the chute;

[0062] The rate of change of tilt angle of the bag-turning machine (unit: rad / s) reflects the rotational speed of the bag-turning machine;

[0063] State prediction equation:

[0064] Prediction of the current moment based on the state of the previous moment;

[0065] F k State transition matrix (e.g.) );

[0066] 0.95 indicates that the flow velocity has a certain inertia and will not change abruptly;

[0067] 0.1 represents the coefficient of influence of the inclination angle on the flow velocity;

[0068] B k: Control input matrix (e.g. ) indicates the degree of influence of tilt angle adjustment on flow velocity; u k : Control input (tilt angle adjustment of the bag-turning machine, unit: rad)

[0069] Observation equation:

[0070] z k =H k ·x k +v k

[0071] z k : Vector of actual measured values H k Observation matrix (e.g.) )

[0072] This represents the rate of change of directly observed flow velocity and inclination angle, v. k The measurement noise follows a Gaussian distribution with a variance matrix of R. k

[0073] State update equation:

[0074] The residual between the measured and predicted values;

[0075] K k Multiplying by the residual indicates the degree of correction to the predicted value.

[0076] Step 4: PID control of the bag-turning machine's tilt angle. Based on the flow velocity estimated by Kalman filtering, calculate the tilt angle adjustment of the bag-turning machine.

[0077]

[0078] Parameter details:

[0079] Δθ: The tilt angle of the bag-turning machine that needs adjustment (unit: rad)

[0080] e(t) = v target -v estimated Deviation between target flow velocity and estimated flow velocity

[0081] K p The proportionality factor (10.0) provides adjustment proportional to the error; for example, if the deviation is 0.1 m / s, an adjustment of 10 × 0.1 = 1.0 rad will be generated immediately.

[0082] K i The integral coefficient (0.5) accumulates historical errors to eliminate steady-state deviations. If the error persists, the integral term will continue to increase until the error is eliminated.

[0083] K dThe differential coefficient (2.0) provides predictive adjustments based on the rate of change of error. If the error decreases rapidly, the differential term will suppress the adjustment to prevent overshoot.

[0084] Control logic:

[0085] When the actual flow rate is lower than the target value, increasing the inclination angle increases the flow rate of molten iron.

[0086] When the actual flow rate is higher than the target value, reduce the tilt angle to decrease the flow rate of molten iron.

[0087] The integral term ensures that the system eventually stabilizes at the target flow rate, while the derivative term improves the response speed.

[0088] The cooling module consists of a shuttle car, a cooling station, and an atomizing cooling device. The shuttle car has a second mechanical interface for docking with the mold-fixing car and a position synchronization device. The atomizing cooling device has a flow regulating valve and a pressure sensor. The second mechanical interface of the shuttle car in the cooling module adopts a conical positioning pin and guide groove structure. The position synchronization device includes a laser rangefinder and an encoder, and the data from both are transmitted to the central control module via Ethernet / IP protocol. The flow regulating valve of the atomizing cooling device is an electric regulating valve, and the pressure sensor output signal is connected to the PLC via HART protocol. The adjustment of the valve opening is combined with the deviation between the target flow and the predicted flow, the cumulative deviation value, and the deviation change rate.

[0089] The demolding and transfer module includes an automatic impact device, an automatic mold-turning device, a buffer hopper, and a transfer hopper. The automatic mold-turning device is equipped with a third mechanical interface adapted to the fixed mold carriage and a temperature triggering device. The third mechanical interface of the automatic mold-turning device of the demolding and transfer module includes a slot structure corresponding to the fixed mold carriage and an electromagnetic locking device. The control circuit of the electromagnetic locking device is connected in series with the temperature triggering device of the cooling module. The temperature triggering device adopts a bimetallic strip trigger switch. The inner wall of the buffer hopper is equipped with an elastic buffer layer composed of alternating rubber pads and metal springs. The elastic modulus of the metal springs matches the demolding impact force of the silicon iron.

[0090] The central control module, with a programmable logic controller (PLC) at its core, includes a molten iron transfer control unit, a casting execution control unit, a cooling treatment control unit, and a demolding and transfer control unit. The casting execution control unit incorporates a flow prediction control algorithm. This algorithm establishes a fluid dynamics model based on molten iron temperature, ladle tilting machine angle, and chute geometry parameters. It controls the molten iron flow rate through a Kalman filter and dynamically adjusts the ladle tilting machine angle and the fixed mold car's travel speed based on the prediction results. The molten iron transfer control unit dynamically adjusts the travel speed of the electrified railcar based on the flow control results from the casting execution control unit. The adjustment of the travel speed is related to the deviation between the predicted flow rate and the target flow rate, and includes the following algorithms:

[0091] Track vehicle speed adjustment based on flow deviation

[0092] Formula relating railcar speed to flow rate deviation:

[0093]

[0094] Parameter explanation:

[0095] v cart Real-time speed of the trolley vehicle (unit: m / s);

[0096] v base Base speed (default: 1.2m / s): The minimum speed required to ensure smooth operation of the railcar;

[0097] K v Speed ​​adjustment coefficient (0.8) controls the sensitivity of speed response to flow deviation;

[0098] Q pred : Molten iron flow rate predicted by the casting execution module (unit: m) 3 / s);

[0099] Q target Target traffic (default: 0.05m) 3 / s), the ideal casting flow rate required by the process;

[0100] When the predicted flow is greater than the target flow (Q) pred Q target The railcar accelerates, reducing the dwell time of the molten iron ladle;

[0101] When the predicted flow is less than the target flow (Q) pred target The railcar slows down, extending the time for supplying molten iron ladles;

[0102] Speed ​​feedforward control considering flow rate variation trends

[0103] Introducing feedforward control of flow rate change to improve system response speed:

[0104]

[0105] New parameter:

[0106] K a Acceleration coefficient (0.3), controls the response strength to changes in flow rate.

[0107] Predicted rate of change in flow (unit: m) 3 / s 2 ), calculated using difference: ​

[0108]

[0109] Where Δt is the sampling period (default value: 0.5 seconds).

[0110] When traffic increases rapidly Accelerate the development of railcars in advance to avoid supply shortages.

[0111] When the flow rate decreases rapidly Reduce the speed of the railcar in advance to prevent molten iron from spilling.

[0112] Adaptive control based on flow prediction confidence

[0113] By combining the estimation error of the Kalman filter, the control sensitivity is dynamically adjusted:

[0114]

[0115] New parameter:

[0116] σ meas Standard deviation of flow measurement (default value: 0.003m) 3 / s), characterizing sensor reliability

[0117] σ pred Standard deviation of flow forecast (calculated using the covariance matrix obtained through Kalman filtering) Where P 11 The first diagonal element of the state estimation covariance matrix P

[0118] Physical meaning:

[0119] When the prediction uncertainty is high (σ pred When the speed adjustment range is large (σ), reduce the speed adjustment range to avoid over-response. When the measurement reliability is low (σ), meas When the speed adjustment range is increased, it relies more on model prediction for multi-ladle coordinated scheduling optimization.

[0120] When multiple molten iron ladles are operating simultaneously, a priority scheduling algorithm is introduced:

[0121]

[0122] Among them, the weighting factors are:

[0123]

[0124] Parameter explanation:

[0125] v i : The speed at which the molten iron ladle is distributed on the railcar;

[0126] wi The scheduling priority weight of the i-th ladle of molten iron;

[0127] Q i : Predicted flow rate at the casting position corresponding to the i-th ladle;

[0128] d i : The remaining distance from the i-th ladle to the target casting position;

[0129] L: Total length of the track;

[0130] α, β: weight coefficient (α=2.0, β=1.5);

[0131] The drive system of the electrified railcar includes a servo motor and a reducer. The encoder of the servo motor is connected to the PLC of the central control module via an EtherCAT bus. The output shaft of the reducer is equipped with a torque sensor. The casting execution control unit transmits the predicted flow rate data to the cooling treatment control unit via an industrial bus. The cooling treatment control unit dynamically adjusts the opening of the flow regulating valve of the atomizing cooling device based on the data. The mold-fixing unit of the mold-fixing car includes a modularly connected tank structure and a powder guiding channel that docks with the automatic powder distribution equipment. The inner wall of the guiding channel is equipped with spiral guide blades, and the blade pitch is matched with the discharge speed of the powder distribution equipment.

[0132] Please see the appendix Figure 2 The fully automated casting control method for ferrosilicon smelting includes the following steps:

[0133] S1 molten iron transfer scheduling steps: Obtain the location information of the molten iron ladle, control the track turntable to rotate to the preset angle, drive the electrified railcar to run along the track, move the molten iron ladle from the side of the smelting furnace to the casting execution area, and at the same time send the molten iron ladle arrival signal to S2.

[0134] S2 casting execution adjustment steps: After receiving the ladle arrival signal, control the ladle flipping machine to suspend the ladle, start the flow prediction control algorithm, predict the molten iron flow rate based on the molten iron temperature, ladle flipping machine tilt angle, and chute parameters, adjust the ladle flipping machine tilt angle and fixed mold car travel speed according to the prediction results, so that the molten iron is injected into the fixed mold car fixed mold slot through the chute, and after completion, send the casting completion signal to S3.

[0135] S3 Cooling Processing Steps: After receiving the casting completion signal, control the transfer car to move the fixed mold car to the cooling station, start the atomizing cooling device to cool the ferrosilicon mold, monitor the ferrosilicon temperature in real time, and send a cooling completion signal to S4 when the temperature reaches the set threshold. The opening of the flow regulating valve of the atomizing cooling device in S3 is adjusted in stages according to the iron flow rate prediction result in S2. The initial stage opening is 60% to 70% of the maximum opening, and when the ferrosilicon temperature drops below the melting point, it is adjusted to 30% to 40%. The timing of the automatic mold flipping device in S4 is triggered by the temperature triggering device in S3 and the fixed mold car position signal. The mold flipping action is only performed when the fixed mold car is completely stopped and the temperature reaches the threshold. S5 Dynamically corrects the calculation formula parameters of the temperature threshold in S3 according to the deviation between the cooling time and the target value.

[0136] S4 Demolding and Transfer Steps: After receiving the cooling completion signal, control the automatic striking device to strike the ferrosilicon mold, start the automatic mold flipping device to demold the ferrosilicon into the buffer hopper, and control the transfer hopper to receive the ferrosilicon and transfer it to the storage area.

[0137] S5 parameter feedback optimization steps: Collect parameters such as molten iron flow rate, temperature change, and cooling time during this casting process, compare and analyze them with historical data, and dynamically adjust the rotation angle of the track turntable in S1, the flow prediction control algorithm parameters in S2, the opening curve of the flow regulating valve of the atomizing cooling device in S3, and the striking frequency and force in S4.

[0138] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A fully automatic casting control system for ferrosilicon smelting, characterized in that, Includes the following modules: The molten iron transfer module consists of a ladle, a 360-degree rotating track turntable, and an electrified railcar. The track turntable is equipped with a first positioning interface for communication with the central control module, and the electrified railcar is equipped with a first mechanical interface for docking with the casting execution module. The casting execution module includes a ladle turning machine, a fixed mold car, and a chute. The ladle turning machine is equipped with a suspension mechanism and an inclination sensor that cooperate with the molten iron ladle. The fixed mold car is equipped with a second positioning interface that connects with the cooling treatment module. The chute inlet is equipped with a flow sensor and a temperature sensor. The cooling module consists of a shuttle car, a cooling station, and an atomizing cooling device. The shuttle car is equipped with a second mechanical interface for docking with the fixed mold car and a position synchronization device. The atomizing cooling device is equipped with a flow regulating valve and a pressure sensor. The demolding and transfer module includes an automatic knocking device, an automatic mold flipping device, a buffer hopper, and a transfer hopper. The automatic mold flipping device is equipped with a third mechanical interface and a temperature triggering device that are compatible with the fixed mold carriage. The central control module, with a programmable logic controller as its core, includes a molten iron transfer control unit, a casting execution control unit, a cooling treatment control unit, and a demolding and transfer control unit. The casting execution control unit contains a flow prediction control algorithm. This algorithm establishes a fluid dynamics model based on the molten iron temperature, ladle tilting machine tilt angle, and chute geometric parameters, predicts the molten iron flow rate through a Kalman filter, and dynamically adjusts the ladle tilting machine tilt angle and the fixed mold car travel speed according to the prediction results.

2. The fully automatic casting control system for ferrosilicon smelting according to claim 1, characterized in that, The first positioning interface of the track turntable of the molten iron transfer module includes an RFID-based position identification unit and a wireless transmission unit that communicates with the energized railcar. The wireless transmission unit exchanges position data with the PLC of the central control module via the Modbus protocol. The first mechanical interface of the energized railcar adopts a quick-locking structure, which includes an electromagnetic adsorption device and a mechanical buckle.

3. The fully automatic casting control system for ferrosilicon smelting according to claim 1, characterized in that, The casting execution module's overturning machine suspension mechanism includes adjustable grippers and a torque balancing device. The torque balancing device is connected to the PLC of the central control module via a hydraulic system. The grippers are equipped with pressure sensors and feedback clamping force data via a Profibus bus. The chute's flow sensor is an electromagnetic flow meter, and the temperature sensor is a platinum resistance thermometer. The output signals of both are transmitted to the PLC via a 4-20mA current loop and the Modbus RTU protocol, respectively.

4. The fully automatic casting control system for ferrosilicon smelting according to claim 1, characterized in that, In the fluid dynamics model of the flow prediction control algorithm of the casting execution module, the predicted flow rate is related to the flow coefficient, the cross-sectional area of ​​the chute outlet, the gravitational acceleration, the liquid level height in the ladle, the molten iron density, and the temperature-related viscosity function. The temperature-related viscosity function is dynamically corrected by the molten iron temperature collected in real time by the temperature sensor. The state vector of the Kalman filter contains parameters such as the molten iron flow rate and the rate of change of the tilt angle of the ladle turning machine, while the observation vector contains the measured values ​​of the flow sensor and the tilt angle sensor. The molten iron flow rate is controlled through the state transition matrix, the control input matrix, the observation matrix, and the Kalman gain.

5. The fully automatic casting control system for ferrosilicon smelting according to claim 1, characterized in that, The second mechanical interface of the cooling module's shuttle car adopts a conical positioning pin and guide groove structure. The position synchronization device includes a laser rangefinder and an encoder, and the data from both are transmitted to the central control module via the Ethernet / IP protocol. The flow regulating valve of the atomizing cooling device is an electric regulating valve, and the pressure sensor output signal is connected to the PLC via the HART protocol. The adjustment of the regulating valve opening is combined with the deviation between the target flow and the predicted flow, the cumulative value of the deviation, and the rate of change of the deviation.

6. The fully automatic casting control system for ferrosilicon smelting according to claim 1, characterized in that, The third mechanical interface of the automatic mold flipping device of the demolding and transfer module includes a slot structure corresponding to the fixed mold car and an electromagnetic locking device. The control circuit of the electromagnetic locking device is connected in series with the temperature triggering device of the cooling treatment module. The temperature triggering device adopts a bimetallic strip triggering switch. The inner wall of the buffer hopper is provided with an elastic buffer layer composed of alternating rubber pads and metal springs. The elastic modulus of the metal springs matches the demolding impact force of the silicon iron.

7. The fully automatic casting control system for ferrosilicon smelting according to claim 1, characterized in that, The molten iron transfer control unit dynamically adjusts the travel speed of the energized railcar based on the flow prediction results of the casting execution control unit. The adjustment of the travel speed is related to the deviation between the predicted flow and the target flow. The drive system of the energized railcar includes a servo motor and a reducer. The encoder of the servo motor is connected to the PLC of the central control module via an EtherCAT bus, and the output shaft of the reducer is equipped with a torque sensor.

8. The fully automatic casting control system for ferrosilicon smelting according to claim 1, characterized in that, The casting execution control unit transmits the predicted flow rate data to the cooling treatment control unit via an industrial bus. The cooling treatment control unit dynamically adjusts the opening of the flow regulating valve of the atomizing cooling device based on the data. The mold unit of the mold car includes a modularly connected tank structure and a powder guiding channel that docks with the automatic powder distribution equipment. The inner wall of the guiding channel is provided with spiral guiding blades, and the blade pitch is matched with the discharge speed of the powder distribution equipment.

9. The fully automated casting control method for ferrosilicon smelting according to claim 1, characterized in that, Includes the following steps: S1 molten iron transfer scheduling steps: Obtain the location information of the molten iron ladle, control the track turntable to rotate to the preset angle, drive the electrified railcar to run along the track, move the molten iron ladle from the side of the smelting furnace to the casting execution area, and at the same time send the molten iron ladle arrival signal to S2. S2 casting execution adjustment steps: After receiving the ladle arrival signal, control the ladle flipping machine to suspend the ladle, start the flow prediction control algorithm, predict the molten iron flow rate based on the molten iron temperature, ladle flipping machine tilt angle, and chute parameters, adjust the ladle flipping machine tilt angle and fixed mold car travel speed according to the prediction results, so that the molten iron is injected into the fixed mold car fixed mold slot through the chute, and after completion, send the casting completion signal to S3. S3 Cooling Processing Steps: After receiving the casting completion signal, control the shuttle car to move the mold car to the cooling station, start the atomizing cooling device to cool the ferrosilicon mold, monitor the ferrosilicon temperature in real time, and send the cooling completion signal to S4 when the temperature reaches the set threshold. S4 Demolding and Transfer Steps: After receiving the cooling completion signal, control the automatic striking device to strike the ferrosilicon mold, start the automatic mold flipping device to demold the ferrosilicon into the buffer hopper, and control the transfer hopper to receive the ferrosilicon and transfer it to the storage area. S5 parameter feedback optimization steps: Collect parameters such as molten iron flow rate, temperature change, and cooling time during this casting process, compare and analyze them with historical data, and dynamically adjust the rotation angle of the track turntable in S1, the flow prediction control algorithm parameters in S2, the opening curve of the flow regulating valve of the atomizing cooling device in S3, and the striking frequency and force in S4.

10. The fully automated casting control method for ferrosilicon smelting according to claim 9, characterized in that, The opening of the flow regulating valve of the atomizing cooling device in S3 is adjusted in stages according to the predicted iron flow rate in S2. The initial opening is 60% to 70% of the maximum opening, and when the temperature of the ferrosilicon drops below the melting point, it is adjusted to 30% to 40%. The timing of the automatic mold flipping device in S4 is triggered by the temperature triggering device in S3 and the position signal of the fixed mold car. The mold flipping action is only performed when the fixed mold car is completely stopped and the temperature reaches the threshold. S5 dynamically corrects the calculation formula parameters of the temperature threshold in S3 according to the deviation between the cooling time and the target value.

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