Polycrystalline silicon reduction furnace control method and equipment and readable storage medium

Abstract

The invention provides a polycrystalline silicon reduction furnace control method and device and a readable storage medium. The polycrystalline silicon reduction furnace control method comprises a control stage of carrying out hydrogen flow PID control based on the total power of a polycrystalline silicon reduction furnace, wherein PID control is circularly carried out by taking a first time period as a control period of a PID control algorithm in the control stage; and determining a proportional error term and a differential error term of the total power of the polycrystalline silicon reduction furnace based on the target power curve in a control period, and determining the adjustment amount of the hydrogen flow in PID control according to the proportional error term and the differential error term. According to the method, the reaction process of the polycrystalline silicon reduction furnace is finely adjusted with the target power curve as the reference, the control algorithm can calculate the target power change value in real time according to the furnace condition and give the most reasonable hydrogen adjustment amount in combination with the current operation condition and the expected change, and it is ensured that the reduction furnace stably operates according to the optimal curve. The control strategy not only optimizes the energy use and reduces the energy consumption, but also improves the production quality of polycrystalline silicon through accurate control.

Description

Technical Field

[0001] The present invention relates to the technical field of polysilicon reduction furnace control, and in particular to a polysilicon reduction furnace control method, equipment and a readable storage medium. Background Art

[0002] Polysilicon is an important raw material for the photovoltaic industry. With the continuous development of renewable energy development and utilization, the supply and demand of polysilicon are constantly increasing. It is optimistically estimated that the global demand for polysilicon is expected to reach 1.13 million tons by 2030. The current mainstream method for producing polysilicon is the modified Siemens method, which uses the principle of chemical vapor deposition to reduce trichlorosilane with hydrogen at a high temperature of about 1100 degrees Celsius in a reduction furnace to obtain polysilicon deposited on the silicon core.

[0003] In traditional industrial manufacturing processes, especially in operations involving key equipment such as reduction furnaces, polysilicon reduction furnace control often relies on manual experience and regular monitoring. Traditional manual control methods are mainly based on the operator's intuitive judgment of process equipment and intermittent manual adjustments. For example, when a reduction reaction occurs in a polysilicon reduction furnace, the operator needs to rely on observing the situation in the furnace through a window mirror (such as the clarity and color depth of the reduction furnace) to judge the internal conditions of the reduction furnace and make targeted adjustments. This production method relies heavily on experience and has obvious limitations. First, manual control relies on the skills and experience of individual operators, making it difficult to ensure the consistency of the production process, and it is difficult to effectively guarantee the quality and yield of polysilicon; secondly, novice operators may not be able to accurately judge the status of the equipment and make targeted adjustments, increasing production risks and uncertainties; finally, manual adjustment responses often have delays, and operators may not be able to respond quickly to sudden changes in the production process, especially in continuous production processes, where the slightest carelessness may lead to production accidents or quality problems. In addition, manual observation is difficult to achieve the accuracy that machines can achieve, especially in the face of chemical reactions and heat treatment processes that require fine control. Summary of the invention

[0004] In order to achieve precise control of the polysilicon reduction process, the present invention provides a polysilicon reduction furnace control method, device and readable storage medium.

[0005] The technical solution of the present invention provides a polysilicon reduction furnace control method, including a control stage of performing hydrogen flow PID control based on the total power of the polysilicon reduction furnace, in which the PID control is performed in a cycle with a first time period T1 as the control period of the PID control algorithm; within a control period, a proportional error term E of the total power of the polysilicon reduction furnace is determined based on a target power curve. p and the differential error term E d ; Based on this, the adjustment amount ΔH of the hydrogen flow rate in PID control is determined.

[0006] Preferably, the control formula for determining the adjustment amount ΔH of the hydrogen flow rate in PID control is: , where k p , k d are the preset control parameters.

[0007] Preferably, the control formula for determining the adjustment amount ΔH of the hydrogen flow rate in PID control is: , where k p , k d , k w is the preset control parameter, and the correction term E w Determined based on the state parameters of the polysilicon reduction furnace.

[0008] Preferably, the correction term E w Based on the tail gas temperature change rate ΔT of the polysilicon reduction furnace, the correction term E w The formula is: , Among them, T h is the preset threshold value of the exhaust gas temperature change rate.

[0009] Preferably, the proportional error term E of the total power of the polysilicon reduction furnace is determined based on the target power curve within one control cycle. p and the differential error term E d The steps are: Calculate the first-order difference ΔP of the target power curve at time point t with calculation period T2 goal,t : , Among them, P goal,t is the power value at time point t under the target power curve, P goal,(t-T2) The historical power value at the corresponding historical time point after the forward calculation period T2 of the time point t under the target power curve; Based on the power history curve collected in the current process, the first-order difference ΔP of the electric power of the polysilicon reduction furnace at time point t is determined real,t : , Among them, P real,t is the actual power value collected at time point t, P real,(t-T2) The actual power value collected at the historical time point corresponding to the calculation period T2 before the time point t; Taking the current time point t1 as time point t, calculate the difference P between the actual power change and the ideal power change at the current time point t1diff,t1 : , Take an interval period T3, push the current time point t1 forward by the interval period T3 to obtain a historical time point t2; Taking the current time point t2 as time point t, calculate the difference P between the actual power change and the ideal power change at the historical time point t2 diff,t2 : , but, Proportional error term E p and the differential error term E d for: , Among them, E i is the integral error term of PID control.

[0010] Preferably, target power consumption and target density are set, and the target power curve is obtained by averaging the corresponding moments of historical production cycles that meet the conditions that the power consumption does not exceed the target power consumption and the density of the obtained polysilicon is not less than the target density.

[0011] Preferably, it also includes a second control stage of performing PID control of the hydrogen flow rate based on the voltage drop of the polysilicon reduction furnace, and the second control stage is subsequent to the control stage of performing PID control of the hydrogen flow rate based on the total power of the polysilicon reduction furnace.

[0012] Preferably, the second control stage comprises the following steps: Determining a voltage representative value V of the polysilicon reduction furnace, which is a function of voltages of a plurality of polysilicon groups of the polysilicon reduction furnace; Based on the voltage representative value V, it is calculated as follows: From the time point t, push forward the time period T4, and use the time interval T5 as the calculation period to obtain the target voltage drop V goal : , From time point t, push forward the time period T6, and use the time interval T5 as the calculation period to obtain the voltage drop at time T6 as the historical target voltage drop V past,goal : , c is a preset correction term, and c can take the value of zero; Taking the current time t as the base point and presetting a time span k, the voltage drop difference at time t is: , Taking the current time t as the base point, presetting a time span k, and pushing forward a preset time length L, the difference in the historical voltage drop at time t is: , Determine the proportional error term E of PID control p and the differential error term E d : , Among them, E i is the integral error of PID control, PID adjustment is performed according to the PID control formula, which is: , where k p , k d are the preset control parameters.

[0013] The technical solution of the present invention also provides a polysilicon reduction furnace control device, comprising a memory storing a computer program; A processor is used to execute the computer program, and when the computer program is executed, the polysilicon reduction furnace control method described in any one of the above items is implemented.

[0014] The technical solution of the present invention further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the processor executes the steps of any one of the above-mentioned polysilicon reduction furnace control methods.

[0015] The present invention uses the target power curve as a benchmark to fine-tune the reaction process of the polysilicon reduction furnace. The control algorithm will combine the current operating conditions and expected changes, calculate the power target change value in real time according to the furnace conditions, and give the most reasonable hydrogen adjustment amount to ensure that the reduction furnace operates stably according to the optimal curve. Overall, this control strategy not only optimizes energy use and reduces energy consumption, but also improves the production quality of polysilicon through precise control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 An example of the resistance variation curve of a reduction furnace in historical production. DETAILED DESCRIPTION

[0017] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0018] The polysilicon reduction furnace control method of the present invention realizes relatively stable and reliable operation control by applying PID control based on power target in the complete or partial production cycle of the polysilicon reduction furnace. The following is an embodiment of the control scheme.

[0019] In this embodiment, the total power of the polysilicon reduction furnace is used as the control target, and the adjustment of the hydrogen flow rate is used as the control means to control the state of the polysilicon reduction furnace. This control process can be implemented based on the PID control algorithm.

[0020] Specifically, in the control process of the first stage, the PID control is performed with the first time period T1 as the control period of the PID control algorithm. The proportional error term E of the power of the polysilicon reduction furnace is determined within a control period. p and the differential error term E d , based on which the adjustment amount of hydrogen flow rate ΔH is determined:

[0021] where k p , k d These are control parameters determined based on experience or preliminary trials.

[0022] Generally speaking, in the above control process, the target power curve is preset by other methods, and the proportional error term of the PID control can be determined based on the absolute difference between the current power reading and the power value at the same time point on the target power curve, thereby realizing PID regulation in a general sense. However, in the control process of the polysilicon reduction furnace, relying solely on the error value at a fixed sampling time point cannot avoid the local disturbance of the system, resulting in reduced control efficiency, and causing the actual power curve to have certain ups and down fluctuations. Even after trying multiple sets of different control parameters, the fluctuation problem cannot be well solved. For this reason, this embodiment provides a PID control method based on the first-order difference of the target power curve to improve the accuracy and stability of power control.

[0023] In order to obtain the error term based on the first-order difference, the present embodiment first determines the calculation period T2. The first-order difference ΔP of the target power curve at time point t is calculated with the calculation period T2. goal : .

[0024] Among them, P goal,t is the power value at time point t under the target power curve, P goal,(t-T2) The historical power value at the corresponding historical time point after the calculation period T2 is pushed forward from the time point t under the target power curve.

[0025] At the same time, based on the power history curve collected in the current process, the first-order difference ΔP of the electric power of the polysilicon reduction furnace at time point t is determined real : .

[0026] Among them, P real,tis the actual power value collected at time point t, P real,(t-T2) It is the actual power value collected at the historical time point corresponding to the calculation period T2 forward from the time point t.

[0027] Based on this, we can get the difference P between the actual power change and the ideal power change. diff : .

[0028] Based on the above first-order differential logic, if the current time point t1 is defined as the time point t in the formula, the difference P between the actual power change and the ideal power change at the current time point t1 is obtained: diff,t1 : .

[0029] Take an interval period T3, which is usually equal to the first control period T1, and push the current time point t1 forward by the interval period T3 to obtain a historical time point t2. Based on the above first-order differential logic, if the historical time point t2 is defined as the time point t in the formula, the difference P between the actual power change and the ideal power change at the historical time point t2 is obtained. diff,t2 : .

[0030] The difference P between the actual power change and the ideal power change at the current time point t1 is diff,t1 As the proportional error term E of the PID control process p , the difference P between the actual power change and the ideal power change at the above historical time point t2 diff,t2 As the integral error term E of the control process i ,Right now: .

[0031] And thus determine the differential error of the PID control process: .

[0032] The resulting proportional error E p And the differential error E d By inputting it into the control equation, the corresponding hydrogen flow adjustment ΔH can be obtained.

[0033] In a specific control case, the first time period T1, the calculation period T and the interval period T3 can be selected according to the specific conditions of the polysilicon reactor. For example, the first time period T1=T3=20min and the calculation period T2=60min are selected.

[0034] Back to the PID control scheme, k p , k dAs a system control parameter in the PID control process, it is necessary to determine a specific value based on the specific conditions of the polysilicon reduction furnace. Generally, it is determined through trial operation. In this case, k is selected. p =0.5,k d =0.3.

[0035] In the above control process, since the error term of the PID control process is obtained based on the first-order difference, the calculation period selected by the difference determines that the PID control process has better stability and reduces the target fluctuation in the power following process. However, there are also disadvantages, that is, the response speed of the PID control process may not be timely, and the processing of sudden instantaneous changes cannot be handled well. For this reason, a further technical solution can introduce a correction term E in the PID control process. w It is used to make up for the deficiency of the original solution in the transient response of the system. That is, the control equation for determining the adjustment amount ΔH of the hydrogen flow is:

[0036] Among them, the correction term E w It is determined by the specific state change of the polysilicon reduction furnace, such as the tail gas temperature, the power-on current, the power-on voltage or the cooling water temperature. In the present embodiment, the tail gas temperature is preferably used because the change sensitivity of the power-on current and the power-on voltage is too high, which is easily interfered by irrelevant factors and is difficult to form a stable corresponding relationship with the polysilicon growth rate in the polysilicon reduction furnace. If the power-on current or the power-on voltage is used, it is easy to cause control fluctuations, or seriously, it may cause the system to run away. The reason why the use of cooling water temperature, flow rate and other states is not considered is from the perspective of the reproducibility of the control method. For any different polysilicon reduction furnace, the cooling water system is not only affected by the reaction conditions in the polysilicon reduction furnace, but also related to the water circulation system. This means that for each polysilicon reduction furnace, the phenomenon of the change of the state in the furnace reflected on the cooling water system is different, resulting in different polysilicon reduction furnace systems. The relationship between the cooling water change and the state in the furnace is not consistent, so when the control system is migrated to a different polysilicon reduction furnace for production, it is faced with the problem of needing to recalibrate the PID control parameters, which makes the control scheme of the present invention unable to be replicated on a large scale at low cost. The solution of using tail gas temperature monitoring to make corrections can solve these problems relatively well. The tail gas temperature is directly output from the furnace environment of the polysilicon reduction furnace, so it can accurately reflect the internal reaction of the polysilicon reduction furnace. In addition, the tail gas of the polysilicon reduction furnace is directly collected from the polysilicon reduction furnace through the tail gas emission pipeline. The tail gas temperature can not only respond to the changes in the reaction conditions in the reduction furnace in time, but also has good consistency in the production process of different reduction furnaces. It is a relatively ideal state parameter for error correction. Among them, according to the test data of the trial operation stage, kw The value is about 0.5.

[0037] The correction term E can be determined by the exhaust gas temperature as follows w In the corresponding PID control loop, the exhaust temperature change rate ΔT is calculated by the collected exhaust temperature values. When the exhaust temperature changes too fast, it is necessary to add hydrogen to return the internal reaction process to normal levels. If the exhaust temperature changes normally, there is no need to impose additional influence on the aforementioned control process. Therefore, this control logic is realized by presetting the threshold value Th of the exhaust temperature change rate per unit time. w Determined as follows:

[0038] This means that the correction term E w It is only triggered when the exhaust gas temperature change rate exceeds a certain level, thus preventing the system from changing too quickly in a short period of time.

[0039] As mentioned above, in the first stage, the total power is used as the control target to control the hydrogen flow rate to achieve PID control. The basic logic is that the electric power reflects the silicon deposition rate in the polysilicon reduction furnace from the side, and the input amount of raw materials can affect the chemical vapor deposition process of trichlorosilane in the reduction furnace and affect the silicon deposition rate. We can certainly consider controlling the electric power of the reaction by controlling the current, but the essence of adjusting the current is to act on the circuit where the silicon core is located, rather than on the raw materials for chemical vapor deposition. Therefore, by adjusting the current, the electric power may be significantly adjusted, but it is only a change in the circuit itself, and cannot directly act on the material input and control system. In addition, in the modified Siemens method, in order to ensure the conversion rate of trichlorosilane and improve the conversion rate of silicon, in the actual feeding process, hydrogen is excessive, so the internal polysilicon deposition rate is generally controlled by controlling the amount of hydrogen input.

[0040] In the first stage, the above technical solution selected power as the control target instead of the resistance of the polysilicon core, mainly considering the problem of control accuracy. Figure 1The figure shows the resistance change curve of the polysilicon reduction furnace in a production cycle. Basically, in the complete production cycle, the resistance value of the silicon core varies between 0 and 10 ohms, especially after a period of deposition, the resistance of the silicon core quickly drops below 1 ohm. In most of the subsequent production time, the resistance of the silicon core only slowly decreases between 0 and 1 ohm. It can be seen that although the resistance of the silicon core does change with the continuous development of the deposition process, the absolute value of the resistance change is very small, only changing between 0 and 10 ohms, and even most of the time the resistance change is limited to 0-1 ohm. Due to this problem, if the silicon core resistance is used as the control target, on the one hand, it is difficult to achieve effective control at this accuracy due to the small change difference of the control target and the limitation of the PID adjustment algorithm itself. Therefore, it is impossible to effectively implement resistance as the control target in practice. On the other hand, even if resistance control with this accuracy can be achieved, another problem that must be faced is that since the resistance value is too small, any disturbance in the production process will cause a significant change in the resistance value. Even errors in the data acquisition process will cause significant changes in the test value. Therefore, if the resistance value is used as the control target, since the resistance value is too small, the noise signal has a significant impact on the measured value. Even if effective PID control can be achieved, it is difficult to achieve the desired stable control goal.

[0041] In fact, in the production process of polysilicon reduction furnace, the system current is generally regulated, and the voltage is passively changed, that is, when necessary, the reaction process can be controlled by comprehensively controlling the hydrogen flow, trichlorosilane flow and current. At this time, the voltage, tail gas temperature, cooling water temperature and other parameters are passively changed dependent variables. Based on this, any scheme that uses current or voltage as a control variable or control target alone has certain shortcomings, because the two are related, not independent. Based on the above discussion, using the electric power of the reduction furnace as the control target is necessary in this scheme, which is of great significance for stable and reliable process control.

[0042] In the above scheme, the target power curve is given in advance and is an integral part of the production process. In order to achieve high-quality and low-energy polysilicon reduction production. The target power curve can be determined as follows. Collect monitoring data of the same polysilicon reduction furnace in multiple complete production cycles (a production cycle refers to the complete production process of the reduction furnace from starting to stopping to prepare a batch of polysilicon rods). The above multiple complete production cycles are screened, and the polysilicon rods with a density not less than the first threshold value ρ are retained. h And the power consumption is not higher than the second threshold W hSeveral production cycles of the selected production cycle are taken as the selected production cycle. In order to obtain the target power curve, the following operations are performed. The power at the same time in the selected production cycle is taken as the target power value at the same time, and the target power curve is obtained by fitting the target power values ​​at multiple times in a production cycle. In the process of taking the average value as the target power value at the same time, it is inevitable that there is a problem of missing local data in the production cycle. At this time, the production cycle with missing data is not counted, and the remaining valid data can be averaged.

[0043] That is, the preset target power curve can be determined based on several complete production cycles of the same reduction furnace under the same material list and other conditions. That is, extract the excellent cycles that meet certain conditions in several complete production cycles in history, and take their average as the target power curve. For example, the target unit electricity consumption and target density rate can be set. When the unit electricity consumption of certain production cycles does not exceed the target unit electricity consumption and the density rate of the obtained polysilicon is not less than the target density rate, we regard these production cycles as excellent cycles, and then average the corresponding moments of the recorded power curve to obtain the required target power curve. In the above example, the target unit electricity consumption can be 42kWh / kg, and the target density rate can be 65%.

[0044] It should be noted that for the production process control of the polysilicon reduction furnace, the material input, including the trichlorosilane flow rate and the hydrogen flow rate, can obviously be actively regulated. Generally, the current of the reduction furnace can also be regulated by the controller. It can be understood that the resistance of the silicon core is only related to the reaction situation inside the reduction furnace. When the current is adjustable, the voltage cannot be actively regulated. Generally, it has the ability to monitor and collect voltage. The corresponding other system outputs, such as cooling water temperature, tail gas temperature, etc., are also passively changed. In this case, the same polysilicon reduction furnace referred to above should have the same production process strategy in multiple complete production cycles, that is, only regulating the hydrogen flow rate as a means of regulation, and the same control rules are usually used for trichlorosilane flow and current. For example, the trichlorosilane flow rate at each moment in the production cycle is determined by a formulated trichlorosilane feed table, and the current is given by a preset current change curve.

[0045] In actual polysilicon production, the above embodiment still has certain problems. The main problem is that after the polysilicon reduction furnace system is started for a period of time, the resistance of the silicon core decreases and the change rate is not obvious, which will cause the system's electric power to change slowly. Since the above PID adjustment strictly relies on the power difference component for adjustment, when the electric power slows down, the difference component will gradually degenerate to zero value, and the PID adjustment process based on the power curve will not be applicable. In order to solve this problem, the technical solution of the present invention provides a second embodiment with staged control.

[0046] The second embodiment divides the production process of the polysilicon reduction furnace into two consecutive stages. The first stage is a target power control stage, and the second stage is a voltage difference control stage.

[0047] Based on production practice and actual production data, it is found that the voltage drop curve is relatively flat in the later stage and has relatively good linearity. In addition, different reduction furnaces have different working conditions and different voltage drop speeds, making it difficult to achieve a unified target curve for the same power. By using the characteristics of linear voltage drop to achieve post-stage control, control accuracy and robustness can be guaranteed, while also taking into account the application of control methods between different reduction furnaces.

[0048] Voltage difference control can also be considered as voltage drop control, which controls the control process of the latter section as a smooth linear decline process as much as possible, so that the polysilicon deposition at the back end is smooth and effective. The specific method of voltage difference control at this stage first needs to determine a standard voltage calculation method, which is called the voltage representative value V. Its function is to hide the voltage difference from different sources and facilitate the accurate and stable calculation of voltage differences. The voltage representative value is a quantity related to the voltage of multiple groups of silicon rod ends in the reduction furnace, such as its average value, etc. The differential PID control process based on the voltage representative value is generally as follows.

[0049] Since there is no historical curve as the control target for voltage difference control, only the smoothness of voltage drop is controlled, so the voltage representative value V only needs to be determined from the historical data of the current process. Taking the current time point t as the time base point, pushing forward the time period T4, and taking a time interval T5 as the calculation period, the voltage drop at time T4 can be obtained as the target voltage drop V goal .

[0050]

[0051] Similarly, by moving forward to the time period T6 and taking the time interval T5 as the calculation period, the voltage drop at time T6 can be obtained as the historical target voltage drop V past,goal .

[0052]

[0053] In the above formula, c is a preset correction term, and c can be zero, that is, no correction.

[0054] The measured value of the voltage representative value used as a reference is calculated by the difference based on the current time t as the base point. A time span k is preset, and the difference in voltage drop at time t is: .

[0055] At the same time, the measured value of the historical voltage representative value used as a reference is calculated by the difference based on the current time t. A time span k is preset, and the difference of the historical voltage drop at time t after a preset time length L is obtained as follows: .

[0056] Therefore, the PID tuning parameters at this stage can be determined as follows: .

[0057] Back to the PID control scheme, k p , k d is the preset PID tuning parameter, so the hydrogen flow can be controlled based on the PID control formula to achieve smooth linear control of the voltage drop. In fact, in order to optimize the response of the system, the PID control parameters are segmented. If |E p |<2, use a smaller gain value, such as k p = 0.1, k d =0.03. If |E p |≥2, use a larger gain value, such as K p =0.2, K d =0.01.

[0058] Going back to the starting point of the above-mentioned voltage drop control scheme, the specific expression formula of the voltage representative value V is not unique, but in order to have a comprehensive characterization of the state of the system, it is generally necessary to be a function of the voltage of each group of silicon rods in the system. Here is an embodiment. For a large-scale polysilicon reduction furnace, it may have a group consisting of multiple inner and outer circles of polysilicon rods, and the number of polysilicon rods in each group of polysilicon rods is not unique. Under normal circumstances, the voltage difference at both ends of the polysilicon rods between groups will not be particularly obvious, but the voltage at both ends of the inner and outer circles of polysilicon rods is relatively obvious. If only the voltage at both ends of several polysilicon rods or the voltage at both ends of several groups are considered, it may not be possible to fully reflect the true state of the polysilicon furnace. For this reason, the voltage representative value V can usually be obtained by dividing the sum of the voltages of all groups of polysilicon rods by the number of polysilicon rods, that is, obtaining the average voltage value at both ends of a polysilicon rod. Taking polysilicon with only inner and outer circles of polysilicon rods as an example, the outer circle voltage V can usually be measured. out And the inner ring voltage V in , and the number of polysilicon rods N is known when feeding.

[0059] In this embodiment, it is preferred that the switching timing of the two stages can be judged more accurately to ensure that the control switching process of the polysilicon reduction furnace is smooth and in line with the actual situation of the internal reaction. The two-stage switching method determined based on long-term practice is based on the actual feeding table design and the power curve. The switching standard is that when the amount of trichlorosilane (TCS) begins to decrease over time and the power drop per hour is less than a certain value, it means that the power change is not obvious at this time, and it is difficult to effectively control based on the power curve. In practical applications, the threshold value of the power drop per hour is generally 20kW.

[0060] In the actual control process, the data collected by the system often fluctuates greatly due to various interferences, including noise in the process. In order to make the collected data better reflect the actual reaction process, it is also necessary to perform Gaussian filtering on the data used in the above control process. The filtering method is as follows. First, we determine two objective functions, namely the objective functions of one-dimensional space and two-dimensional space. In one-dimensional space, the objective function: ,in is the distance from the center point, is the standard deviation; in two-dimensional space, the objective function becomes: , here and are the horizontal and vertical distances from the midpoint to the center point in the two-dimensional space, respectively. The value of σ (standard deviation) has a decisive influence on the filtering effect. It controls the width of the window, that is, it affects the balance between smoothness and edge preservation. Choosing a suitable σ value is crucial to achieving the desired image processing or signal processing results. In the implementation example, the σ value selected can be 100. Different σ values ​​need to be selected according to specific filtering requirements in different situations.

[0061] Compared with the traditional manual control reaction and the traditional adaptive control system, the present invention has the following advantages: The first-order difference of the target power curve is used as a reference. Compared with using the absolute value of power, the difference can more sensitively capture the immediate changes in the system state. This method allows the control system to respond faster to sudden changes, thereby optimizing the adjustment speed and efficiency. And the differential data provides a direct perspective on how the system changes over time, which helps to adjust and optimize dynamic behavior.

[0062] The control strategy is applied to fine-tune the reaction process in the furnace. The control algorithm will combine the current operating conditions and expected changes, calculate the power target change value in real time according to the furnace conditions, and give the most reasonable hydrogen adjustment amount to ensure that the reduction furnace operates stably according to the optimal curve. Overall, this control strategy not only optimizes energy use and reduces energy consumption, but also improves the production quality of polysilicon through precise control.

[0063] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0064] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0067] The above content only describes the preferred implementation mode of the present invention, and does not limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solution of the present invention by ordinary technicians in this field should fall within the protection scope determined by the claims of the present invention.

Claims

1. A polysilicon reduction furnace control method, characterized in that: The invention comprises a control stage of performing PID control of hydrogen flow rate based on the total power of the polysilicon reduction furnace, in which the PID control is performed in a cycle with the first time period T1 as the control period of the PID control algorithm; and determining the proportional error term E of the total power of the polysilicon reduction furnace based on the target power curve within one control period. p and the differential error term E d ; Based on this, the adjustment amount ΔH of the hydrogen flow rate in PID control is determined.

2. The polysilicon reduction furnace control method according to claim 1, characterized in that: The control formula for determining the adjustment amount ΔH of hydrogen flow in PID control is: ， where k p , k d are the preset control parameters.

3. The polysilicon reduction furnace control method according to claim 1, characterized in that: The control formula for determining the adjustment amount ΔH of hydrogen flow in PID control is: ， where k p , k d , k w is the preset control parameter, and the correction term E w Determined based on the state parameters of the polysilicon reduction furnace.

4. The polysilicon reduction furnace control method according to claim 3, characterized in that: Correction E w Based on the tail gas temperature change rate ΔT of the polysilicon reduction furnace, the correction term E w The formula is: ， Among them, T h is the preset threshold value of the exhaust gas temperature change rate.

5. The polysilicon reduction furnace control method according to claim 1, characterized in that: The proportional error term E of the total power of the polysilicon reduction furnace is determined based on the target power curve within a control cycle. p and the differential error term E d The steps are: Calculate the first-order difference ΔP of the target power curve at time point t with calculation period T2 goal,t : ， Among them, P goal,t is the power value at time point t under the target power curve, P goal,(t-T2) The historical power value at the corresponding historical time point after the forward calculation period T2 of the time point t under the target power curve; Based on the power history curve collected in the current process, the first-order difference ΔP of the electric power of the polysilicon reduction furnace at time point t is determined real,t : ， Among them, P real,t is the actual power value collected at time point t, P real,(t-T2) The actual power value collected at the historical time point corresponding to the calculation period T2 before the time point t; Taking the current time point t1 as time point t, calculate the difference P between the actual power change and the ideal power change at the current time point t1 diff,t1 : ， Take an interval period T3, push the current time point t1 forward by the interval period T3 to obtain a historical time point t2; Taking the current time point t2 as time point t, calculate the difference P between the actual power change and the ideal power change at the historical time point t2 diff,t2 : ， but, Proportional error term E p and the differential error term E d for: ， Among them, E i is the integral error term of PID control.

6. The polysilicon reduction furnace control method according to claim 1, characterized in that: The target unit power consumption and the target density ratio are set, and the target power curve is obtained by averaging the corresponding moments of the historical production cycle that meets the conditions that the unit power consumption does not exceed the target unit power consumption and the density ratio of the obtained polysilicon is not less than the target density ratio.

7. The polysilicon reduction furnace control method according to any one of claims 1 to 6, characterized in that: It also includes a second control stage of performing PID control of the hydrogen flow rate based on the voltage drop of the polysilicon reduction furnace, and the second control stage is subsequent to the control stage of performing PID control of the hydrogen flow rate based on the total power of the polysilicon reduction furnace.

8. The polysilicon reduction furnace control method according to claim 7, characterized in that: The second control stage comprises the following steps: Determining a voltage representative value V of the polysilicon reduction furnace, which is a function of voltages of a plurality of polysilicon groups of the polysilicon reduction furnace; Based on the voltage representative value V, it is calculated as follows: From the time point t, push forward the time period T4, and use the time interval T5 as the calculation period to obtain the target voltage drop V goal : ， From time point t, push forward the time period T6, and use the time interval T5 as the calculation period to obtain the voltage drop at time T6 as the historical target voltage drop V past,goal : ， c is a preset correction term, and c can take the value of zero; Taking the current time t as the base point and presetting a time span k, the voltage drop difference at time t is: ， Taking the current time t as the base point, presetting a time span k, and pushing forward a preset time length L, the difference in the historical voltage drop at time t is: ， Determine the proportional error term E of PID control p and the differential error term E d : ， Among them, E i is the integral error of PID control, PID adjustment is performed according to the PID control formula, which is: ， where k p , k d are the preset control parameters.

9. A polysilicon reduction furnace control device, characterized in that: include a memory storing a computer program; A processor is used to execute the computer program, and when the computer program is executed, the polysilicon reduction furnace control method as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the processor is caused to execute the steps of the polysilicon reduction furnace control method as described in any one of claims 1 to 8.

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