A process parameter optimization method for pure titanium plate alkali manganese production

Abstract

The present application belongs to the technical field of alkali-manganese production, and provides a process parameter optimization method for pure titanium plate alkali-manganese production, comprising the following steps: using a pure titanium plate as an anode plate, and performing shaping and sand blasting treatment on the pure titanium plate; before electrolysis starts, the acidity of the electrolysis system is adjusted to 50-55 g / L, the current is controlled in a low current range of 6500-7000 A, and the electrolyte temperature is kept not lower than 97 DEG C; since the pure titanium plate process is sensitive to tank temperature, lower than 97 DEG C is easy to lead to tank voltage out of control, and a tank temperature stability mechanism based on time sequence prediction is established. The decreasing type of suspending agent addition strategy plays a role in fine regulation of the deposition process in the reaction environment, improves the product performance, and maximizes the relief of the inherent bottom deposition defects. Due to the effective relief of the suspending agent bottom deposition and the improvement of the deposition uniformity, the performance consistency of the same batch or even different batches of products is improved, and the requirement for product quality stability is met.

Description

Technical Field

[0001] This invention belongs to the field of alkaline manganese production technology, specifically a method for optimizing process parameters in the production of alkaline manganese from pure titanium plates. Background Technology

[0002] Electrolytic manganese dioxide (EMD) for batteries is typically produced using an acidic electrolyte, specific anode plate materials, and corresponding current densities. Currently, common anode materials include titanium manganese plates and pure titanium plates. Among them, pure titanium plates have gradually become an important development direction for the production of high-performance alkaline manganese dioxide in recent years due to their excellent conductivity uniformity, corrosion resistance, and mechanical stability.

[0003] However, the alkaline manganese production process is prone to producing a large amount of slag-like, bursting products under high acid and steam fluctuation conditions. At the same time, the product thickness is uneven, making it difficult to stably produce high-performance alkaline manganese products. Secondly, in the existing process, low acid electrolysis is difficult to obtain high-potential products. Although high acidity can significantly improve electrochemical performance, it is often accompanied by problems such as high cell pressure, difficulty in control, and increased bursting. It is also sensitive to the stability of steam pressure, which leads to frequent fluctuations in cell temperature during electrolysis. This, in turn, causes a chain of problems such as sudden increase in cell pressure, increased bursting rate, local excessively thick or thin deposits, large amount of suspending agent deposition at the bottom of the cell, and imbalance in the distribution of electrochemical performance between the upper and lower parts of the product. As a result, it is difficult to maintain stable electrochemical quality of alkaline manganese dioxide.

[0004] Therefore, the present invention provides a method for optimizing process parameters in the production of alkaline manganese from pure titanium plates. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: a method for optimizing process parameters in the production of alkaline manganese from pure titanium plates, comprising the following steps:

[0007] Pure titanium plates are used as anode plates, and the pure titanium plates are shaped and sandblasted.

[0008] Before electrolysis begins, adjust the acidity of the electrolysis system to 50-55 g / L and control the current within a low current range of 6500-7000 A, while keeping the electrolyte temperature not lower than 97℃.

[0009] Since the pure titanium plate process is sensitive to the cell temperature, it is easy to cause cell voltage runaway below 97℃. Therefore, a cell temperature stabilization mechanism based on time-series prediction is established.

[0010] During the electrolysis process, the suspending agent is added in a decreasing manner, and the addition cycle is divided into three stages: the early stage of electrolysis, the middle stage of electrolysis, and the late stage of electrolysis.

[0011] The cell pressure is continuously monitored during the electrolysis cycle. When the cell pressure shows an abnormal upward trend, it is adjusted by reducing acid, reducing current, or partially washing the cell.

[0012] Micro-suspension electrolysis is carried out under the process parameter combination consisting of the aforementioned current, acidity, temperature, and staged addition of suspending agent.

[0013] As a further aspect of the present invention: the flotation agent concentrations in the three stages of electrolysis—early, middle, and late stages—are as follows:

[0014] The concentration of the suspending agent in the early stage of electrolysis is 1.5–4 ppm;

[0015] The concentration of the suspending agent during the mid-term of electrolysis is 1.0–2.0 ppm;

[0016] The concentration of the suspending agent in the later stage of electrolysis is 0.3 to 1.0 ppm.

[0017] As a further aspect of the present invention: the time-series prediction-based temperature stabilization mechanism for alkaline-manganese electrolyzers includes the following steps:

[0018] Real-time acquisition of multi-dimensional time-series data from the electrolysis system;

[0019] The real-time collected data is used as the input to the dynamic prediction model of the electrolyte temperature, and the predicted value of the electrolyte temperature for future time periods is output.

[0020] The predicted electrolyte temperature is compared with the target temperature control value to determine if there is a risk of temperature instability. If there is a risk of temperature instability, a predictive control command is executed.

[0021] As a further aspect of the present invention: multi-dimensional time-series data includes:

[0022] Electrolyte temperature sequence: obtained by temperature sensors distributed inside the electrolytic cell;

[0023] Steam supply data sequence: including steam main pressure sequence and regulating valve opening sequence;

[0024] Environmental data sequence: including the ambient temperature sequence of the electrolysis workshop.

[0025] As a further aspect of the present invention: the dynamic prediction model for the tank temperature is a temporal neural network model based on an attention mechanism;

[0026] The temporal neural network model adopts an encoder-decoder architecture and integrates an attention mechanism;

[0027] A fixed-length time series data window with N time steps is used as the input to the model. The time series data window includes the standardized electrolyte temperature, steam main pressure, regulating valve opening, and ambient temperature.

[0028] The predicted electrolyte temperature sequence corresponding to the next M time steps is used as the output of the model.

[0029] Training sample data consisting of input sequence-output sequence pairs is extracted and constructed from historical stable operation and production data;

[0030] The mean squared error loss function is used to minimize the difference between the model-predicted future temperature series and the actual future temperature series;

[0031] The model parameters were iteratively optimized using the backpropagation algorithm and the Adam optimizer to obtain a dynamic prediction model for tank temperature.

[0032] As a further aspect of the present invention: the process of determining whether there is a risk of temperature instability:

[0033] Obtain the output sequence of predicted electrolyte temperatures;

[0034] The predicted electrolyte temperature sequence is compared with the target temperature control value. If the predicted electrolyte temperature is lower than the target temperature control value, there is a risk of instability. If the predicted electrolyte temperature is not lower than the target temperature control value, there is no risk of instability.

[0035] As a further aspect of the present invention: the predictive control command is: based on the predicted electrolysis temperature value, the ambient temperature value, and the time to temperature instability, the opening increment of the steam regulating valve is obtained through combined analysis.

[0036] As a further aspect of the present invention: the calculation process for the opening increment is as follows:

[0037] The total heat load demand is calculated based on the predicted electrolysis temperature and ambient temperature, and the static opening value is also calculated.

[0038] The ratio of the minimum effective response time to the time until temperature instability is used as the time urgency coefficient;

[0039] If the time from temperature instability is greater than the minimum effective response time of the system, the static opening value is multiplied by the time urgency coefficient to obtain the opening increment;

[0040] If the time from temperature instability is less than or equal to the minimum effective response time of the system, the maximum safe opening degree of the steam regulation is the opening degree increment.

[0041] As a further aspect of the present invention: the calculation process for the total heat load demand is as follows:

[0042] The time point at which the predicted electrolysis temperature first falls below the temperature control target value is taken as the temperature instability time.

[0043] Calculate the difference between the time of temperature instability and the current time, and use it as the time remaining until temperature instability.

[0044] Extract the minimum value from the electrolyte temperature prediction value sequence, calculate the difference between the temperature control target value and the minimum value from the electrolyte temperature prediction value sequence, and obtain the total temperature compensation value.

[0045] The ambient temperature deviation is calculated by subtracting the current ambient temperature from the ambient temperature reference value.

[0046] The environmental compensation load is obtained by multiplying the ambient temperature deviation by the ambient heat loss coefficient.

[0047] The total heat load demand is obtained by summing the ambient temperature deviation and the total temperature compensation value.

[0048] As a further aspect of the present invention: the calculation process for the static opening value is as follows:

[0049] When the electrolyte temperature is stably maintained at 97℃, record the opening degree of the steam regulating valve at the current ambient temperature as the baseline maintenance opening degree;

[0050] The formula for calculating the static opening value is: Static opening value = Basic maintenance opening + (Total heat load demand / Unit opening temperature rise capacity);

[0051] The total heat load demand is the sum of the total temperature compensation value and the environmental compensation load.

[0052] The beneficial effects of this invention are as follows:

[0053] The combination of the above process parameters for micro-suspension electrolysis provides a structural basis for achieving uniform deposition and suppressing plate bursting in the entire process;

[0054] The combination of macroscopic parameters—low current (6500-7000A), medium-high acidity (50-55 g / L), and high temperature (≥97℃)—provides a reaction environment that ensures both high electrochemical performance of the product and stable process fundamentals.

[0055] The incremental suspension addition strategy plays a role in finely controlling the deposition process in the reaction environment. While improving product performance, it alleviates the inherent sedimentation defects to the greatest extent. As the sedimentation of the suspension is effectively alleviated and the deposition uniformity is improved, the performance consistency of the same batch and even different batches of products is improved, thus meeting the requirements for product quality stability.

[0056] The dynamic monitoring and control of cell pressure based on multi-level thresholds serves as a dynamic stability guarantee mechanism for the entire process system, ensuring that the core reaction environment does not get out of control due to the passivation tendency of pure titanium plates during the electrolysis cycle.

[0057] By collecting multi-dimensional time-series data at high frequency, a time-series neural network model based on the attention mechanism is constructed and deployed to accurately predict future changes in the tank temperature. The predicted temperature, environmental factors and system response time are combined to calculate the steam valve opening increment that meets the heat compensation requirements while taking into account the system inertia, so that heating can be supplied on demand and compensation can be made in advance.

[0058] By predictive control, the temperature of the tank is reduced from dropping below 97°C due to fluctuations in steam pressure or changes in ambient temperature. This fundamentally avoids problems such as tank pressure spikes, increased plate bursts, and uneven deposition caused by temperature runaway. The stable tank temperature provides a constant thermal environment for the electrolytic reaction, ensuring the uniformity of the deposition process and thus improving the consistency of the electrochemical performance of the upper and lower parts of the product, meeting the quality requirements of high-performance alkaline manganese products. Attached Figure Description

[0059] The invention will now be further described with reference to the accompanying drawings.

[0060] Figure 1 This is a flowchart of the steps in Embodiment 1 of the present invention;

[0061] Figure 2 This is a flowchart of the steps in Embodiment 2 of the present invention. Detailed Implementation

[0062] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0063] Example 1

[0064] Please see Figure 1 As shown in the embodiment of the present invention, a method for optimizing process parameters in the production of alkaline manganese from pure titanium plates includes the following steps:

[0065] Step S1: Use a pure titanium plate as the anode plate, and shape and sandblast the pure titanium plate to form a uniformly distributed microporous structure on the surface, reduce the actual current density and improve the deposition uniformity.

[0066] Pure titanium plates have superior mechanical properties and electrical conductivity uniformity. Using pure titanium plates as anode plates can provide a foundation at the material level, fundamentally reducing the extreme thickness variation and slag-like plate bursting caused by uneven anode materials.

[0067] The purpose of sandblasting pure titanium plates is that the microporous structure formed by sandblasting can, on the one hand, increase the specific surface area, reduce the actual current density on the anode surface and slow down the intensity of the reaction, and on the other hand, enhance the adhesion of the deposits and provide more nucleation sites for uniform deposition.

[0068] Step S2: Before electrolysis begins, adjust the acidity of the electrolysis system to 50-55 g / L and control the current within a low current range of 6500-7000 A, while keeping the electrolyte temperature not lower than 97℃.

[0069] Because the pure titanium plate process is extremely sensitive to the cell temperature, a temperature below 97°C can easily lead to cell pressure runaway. It is necessary to ensure a stable steam supply. Therefore, it is necessary to ensure a continuous and stable steam supply during the production process and to establish a time-series prediction-based alkaline manganese electrolysis cell temperature stabilization mechanism to prevent cell temperature drop due to large fluctuations or interruptions in steam pressure, thereby avoiding cell pressure runaway and product quality problems caused by this.

[0070] Adjusting the acidity of the electrolysis system to 50-55 g / L can improve the isoelectric properties of pH-V potential and alkaline potential.

[0071] Controlling the current between 6500-7000 Ω helps slow down the deposition rate, improve crystallization quality, and prevent [deterioration / damage].

[0072] Step S3: During the electrolysis process, the suspending agent is added in a decreasing manner, with the addition cycle divided into three stages: the early stage of electrolysis, the middle stage of electrolysis, and the late stage of electrolysis. The concentration of the suspending agent added must meet the following requirements:

[0073] The concentration of the suspending agent in the early stage of electrolysis is 1.5–4 ppm;

[0074] The concentration of the suspending agent during the mid-term of electrolysis is 1.0–2.0 ppm;

[0075] The concentration of the suspending agent in the later stage of electrolysis is 0.3–1.0 ppm;

[0076] And the overall concentration of the suspending agent gradually decreases with the electrolysis cycle;

[0077] Because suspension products have the drawbacks of easy settling of the suspending agent and serious low bottom performance, the suspending agent can play its main role in the early stage by adding it in a decreasing manner, while reducing the risk of settling in the later stage. This can alleviate the inherent defect of the suspension process of suspending agent settling, improve the problem of uneven performance distribution between the upper and lower parts of the product, and improve the overall consistency and peeling performance of the product while retaining the high electrical performance advantage of the suspension method.

[0078] Step S4: Continuously monitor the cell pressure during the electrolysis cycle. When the cell pressure shows an abnormal upward trend, adjust it by reducing acid, reducing current, or partially washing the cell to ensure that the cell pressure is within a controllable range.

[0079] Specifically, the cell pressure data of all electrolytic cells is collected in real time, and the cell pressure change rate per unit time is calculated based on the collected instantaneous cell pressure values.

[0080] The unit of time can be set to 1 hour;

[0081] There are three preset tank pressure thresholds and one preset rate of change threshold as control trigger conditions;

[0082] It should be noted that the first-level cell voltage threshold is a warning threshold, set at 2.5V, which is near the upper limit of the normal operating range (approximately 2.30V-2.45V). When the cell voltage is consistently higher than 2.50V, it indicates that the surface state of the anodic pure titanium plate has begun to undergo slight changes, possibly in the initial stage of passivation. Although it has not yet become uncontrollable, the upward trend indicates an instability risk. Setting the first-level cell voltage threshold as a warning line aims to initiate early and gentle intervention (such as proportionally reducing the current) to avoid significant impact on production and products.

[0083] The secondary tank voltage threshold is an intervention threshold, set at 2.65V. This threshold is based on the determination of the passivation critical point of the pure titanium plate. When the tank voltage exceeds 2.60V, the passivation process of the pure titanium plate often accelerates, the tank voltage will surge and be difficult to recover on its own. Therefore, setting the intervention threshold to 2.65V means that passivation has officially occurred and has begun to pose a substantial threat to the process. At this time, it is necessary to initiate mandatory and stronger comprehensive intervention (such as combined current reduction and acid reduction) to prevent the situation from deteriorating further.

[0084] The third-level cell voltage threshold is the emergency threshold, set at 2.8V, which corresponds to a severely passivated state. When the cell voltage spikes to 2.80V or even 2.90V, the effects of conventional process parameter adjustments (reducing current and acid) are very limited. A dense and stable passivation film may have formed on the anode surface. If no fundamental measures are taken at this point, the anode is very likely to be scrapped due to reaction stagnation. Therefore, the emergency treatment triggered by the emergency threshold (partial cell washing) is a physical and chemical restart of the electrode plates and the last guarantee to prevent the entire cell from being lost.

[0085] The rate of change threshold is set to 0.08V / h to capture the accelerating upward trend of the tank pressure. It is an important supplement to the absolute value threshold. Even if the absolute value of the tank pressure does not reach 2.50V, but it rises by more than 0.08V in 1 hour, this rate of change is far beyond the normal fluctuation range. It is a strong signal that passivation is about to occur. It can intervene in advance before the absolute value exceeds the limit, thereby improving the anticipation and reliability of the control from static threshold alarm to dynamic trend warning.

[0086] The process of identifying an abnormally rising trend and triggering corresponding control strategies is as follows:

[0087] When the instantaneous value of the tank pressure is greater than the first-level tank pressure threshold and the rate of change of the tank pressure is greater than the rate of change threshold, an early warning intervention is triggered.

[0088] The early warning intervention strategy is as follows: calculate and execute a proportional current reduction operation according to the following formula:

[0089] ;

[0090] in, The original current setting is (6500-7000A). The adjusted current command, and the current adjustment range does not exceed 20% to ensure stability. The instantaneous value of the tank voltage is 3000A / V, which means that for every 0.01V increase in voltage, a current reduction of 30A is required for compensation and intervention.

[0091] When the instantaneous value of the cell pressure exceeds the secondary cell pressure threshold, mandatory intervention is triggered.

[0092] The mandatory intervention strategy is as follows: on the basis of the aforementioned proportional reduction of current, a quantitative acid reduction operation is calculated and executed according to the following formula.

[0093] ;

[0094] in, The current acidity (50-55 g / L) For the target acidity, the unit of coefficient 10 is (g / L) / V [(g / L) / V]. The physical meaning is: for every 0.1 volts that the cell voltage exceeds the intervention threshold (2.65V), the acidity of the electrolyte will be reduced by 1 g / L.

[0095] When the instantaneous value of the tank pressure exceeds the level 3 tank pressure threshold, emergency handling is triggered.

[0096] The emergency handling control strategy is as follows: trigger the highest level audible and visual alarm, generate a local tank cleaning work order containing the specific tank location on the control interface, and perform standardized operations according to the work order instructions: drain the electrolyte from the tank, rinse the anode plate surface with 60-70℃ hot water, and soak and clean it with a 5-10% dilute sulfuric acid solution for 30 minutes to thoroughly remove the dense passivation film. After completion, re-inject the electrolyte and initialize the process parameters.

[0097] Step S5: Micro-suspension electrolysis is carried out under the process parameter combination consisting of the current, acidity, temperature and the amount of suspending agent added in stages, so that the upper, middle and lower parts of the obtained alkaline manganese dioxide product are uniformly deposited, the plate bursting is significantly reduced and the electrical performance is improved.

[0098] The combination of the above process parameters for micro-suspension electrolysis provides a structural basis for achieving uniform deposition and suppressing plate bursting in the entire process;

[0099] In step S2, the combination of macroscopic parameters of low current (6500-7000A), medium to high acidity (50-55 g / L) and high temperature (≥97℃) provides a reaction environment that can both ensure the high electrochemical performance of the product and maintain the stability of the process basis.

[0100] In step S3, the decreasing suspension addition strategy plays a role in finely controlling the deposition process in the above reaction environment. While improving product performance, it alleviates the inherent sedimentation defects to the greatest extent. As the sedimentation of the suspension is effectively alleviated and the deposition uniformity is improved, the performance consistency of the same batch and even different batches of products is improved, which meets the requirements for product quality stability.

[0101] In step S4, the dynamic monitoring and control of cell pressure based on multi-level thresholds serves as a dynamic stability guarantee mechanism for the entire process system, ensuring that the core reaction environment does not get out of control due to the passivation tendency of pure titanium plates during the electrolysis cycle.

[0102] Example 2

[0103] Based on the foregoing embodiments, please refer to Figure 2 As shown in the embodiment of the present invention, a method for optimizing process parameters in the production of alkaline manganese from pure titanium plates, based on a time-series prediction-based temperature stabilization mechanism for the alkaline manganese electrolytic cell, includes the following steps:

[0104] Step S201: Real-time acquisition of multi-dimensional time-series data of the electrolysis system, including: electrolyte temperature data, steam supply data, and ambient temperature data;

[0105] Specifically, the following time-series data sequences will be synchronously acquired at a frequency of not less than 0.2 Hz;

[0106] Electrolyte temperature sequence: obtained by temperature sensors distributed inside the electrolytic cell;

[0107] Steam supply data sequence: including steam main pressure sequence and regulating valve opening sequence;

[0108] Environmental data series: including the ambient temperature series of the electrolysis workshop;

[0109] Step S202: Use the real-time collected data as input to the electrolyte temperature dynamic prediction model and output the predicted value of electrolyte temperature for future time periods. The electrolyte temperature dynamic prediction model is a time-series neural network model based on the attention mechanism.

[0110] Specifically, the construction process of the bath temperature dynamic prediction model is as follows:

[0111] The temporal neural network model adopts an encoder-decoder architecture and integrates an attention mechanism. The model architecture includes:

[0112] Encoder: Composed of multiple layers of gated loop units stacked together, used to encode the input multidimensional time-series data, extract its inherent time-series features and hidden states, and the final set of hidden states of the encoder;

[0113] Attention mechanism layer: It receives all hidden states of the encoder and the hidden state of the decoder at the current time step. Its core function is to dynamically calculate and assign different weights to the hidden state of the encoder at each time step, so that the model can focus on the historical information most relevant to the current prediction (e.g., a specific pattern of steam pressure decline or a continuous downward trend in ambient temperature) when making predictions.

[0114] Decoder: Also composed of multiple layers of GRU. In each decoding step, it uses the context vector that has been weighted and summarized by attention weights, the hidden state of the previous time step, and the output (or true value) of the previous time step as input to gradually output the future prediction sequence.

[0115] A fixed-length time series data window with N time steps is used as the input to the model. The time series data window includes the standardized electrolyte temperature, steam main pressure, regulating valve opening, and ambient temperature.

[0116] The predicted electrolyte temperature sequence corresponding to the next M time steps is used as the output of the model.

[0117] Training sample data consisting of input sequence-output sequence pairs is extracted and constructed from historical stable operation and production data;

[0118] The mean squared error loss function is used to minimize the difference between the model-predicted future temperature series and the actual future temperature series;

[0119] The model parameters were iteratively optimized using the backpropagation algorithm and the Adam optimizer until the prediction accuracy of the model on an independent validation dataset tended to be stable and the risk of overfitting was minimized, thus obtaining the dynamic prediction model for tank temperature.

[0120] The trained and validated dynamic temperature prediction model is deployed in the central controller of the production environment.

[0121] In real-time control, the latest N time-series data windows are captured at a set frequency (e.g., every minute) and input into the cell temperature dynamic prediction model to obtain the predicted electrolyte temperature for the next M time steps.

[0122] The cell temperature dynamic prediction model can capture the complex relationship between the thermal dynamic characteristics of the electrolytic cell system and external disturbances, enabling forward-looking prediction of core process parameters and cell temperature, and reducing the possibility of cell temperature falling below 97℃.

[0123] Step S203: Compare the output electrolyte temperature prediction value with the temperature control target value to determine whether there is a risk of temperature instability. If there is a risk of temperature instability, execute the predictive control command.

[0124] The predictive control command is: based on the predicted electrolysis temperature, ambient temperature, and time to temperature instability, the opening increment of the steam regulating valve is obtained through combined analysis.

[0125] Specifically, obtain the sequence of predicted electrolyte temperatures for the next M time steps;

[0126] The electrolyte temperature prediction sequence is compared with the temperature control target value (97℃). If the electrolyte temperature prediction value is lower than the temperature control target value, there is a risk of instability. If the electrolyte temperature prediction value is not lower than the temperature control target value, there is no risk of instability.

[0127] Given the existence of instability risk;

[0128] The time point at which the predicted electrolysis temperature first falls below the temperature control target value is taken as the temperature instability time.

[0129] Calculate the difference between the time of temperature instability and the current time, and use it as the time remaining until temperature instability.

[0130] Extract the minimum value from the electrolyte temperature prediction value sequence, calculate the difference between the temperature control target value and the minimum value from the electrolyte temperature prediction value sequence, and obtain the total temperature compensation value.

[0131] The preset ambient temperature reference value is generally selected as the average temperature of the electrolysis workshop under conditions without active cooling, such as 25 degrees Celsius.

[0132] The ambient temperature deviation is calculated by subtracting the current ambient temperature from the ambient temperature reference value.

[0133] When the current ambient temperature is lower than the ambient temperature reference value, the ambient temperature deviation is positive, indicating that the environment absorbs heat from the electrolytic cell and additional heat loss needs to be compensated.

[0134] The environmental compensation load is obtained by multiplying the ambient temperature deviation by the ambient heat loss coefficient.

[0135] The environmental heat loss coefficient represents the additional heat input required for each degree Celsius decrease in ambient temperature. The unit can be the compensation amount per degree Celsius decrease in ambient temperature. The specific value can be obtained by conducting thermal balance tests and calculations on the system, reflecting the thermal insulation performance and heat dissipation characteristics of the electrolytic cell system.

[0136] The total heat load demand is obtained by summing the ambient temperature deviation and the total temperature compensation value.

[0137] The significance of calculating the total heat load demand is that it is necessary not only to make up for the heat shortage caused by changes in internal operating conditions in the future (the electrolyte temperature is predicted by the dynamic prediction model of the tank temperature), but also to offset the heat loss caused by changes in the external environment in real time.

[0138] By calibrating the steady-state change in electrolyte temperature caused by a unit change in the opening degree of the steam valve, i.e., the static opening degree value;

[0139] It should be noted that the calculation process for the static opening value is as follows:

[0140] When the electrolyte temperature is stably maintained at 97℃, record the opening degree of the steam regulating valve at the current ambient temperature as the baseline maintenance opening degree;

[0141] Based on the steady state, the steam valve opening is increased in a stepwise manner. After the temperature stabilizes again, the temperature rise is recorded, and the temperature rise capacity per unit opening is calculated (i.e., the temperature increase value that can be achieved by increasing the opening by 1%).

[0142] The static opening value is calculated by the following formula: Static opening value = Basic maintenance opening + (Total heat load demand / Unit opening temperature rise capacity);

[0143] The total heat load demand is the sum of the total temperature compensation value and the environmental compensation load.

[0144] Based on the inertia of heat transfer, the above static opening value is dynamically corrected according to the time of temperature instability.

[0145] The dynamic correction principle is: the shorter the remaining response time, the greater the instantaneous steam input power required, based on a preset minimum effective system response time.

[0146] The ratio of the system's minimum effective response time to the time until temperature instability is used as the time urgency coefficient;

[0147] If the time from temperature instability is greater than the minimum effective response time of the system, the static opening value is multiplied by the time urgency coefficient to obtain the opening increment;

[0148] If the time from temperature instability is less than or equal to the minimum effective response time of the system, the maximum safe opening degree of the steam regulation is the opening degree increment.

[0149] It should be noted that when the time since temperature instability is long (greater than the minimum effective response time of the system), the time urgency coefficient is less than 1, which means there is enough time for gradual adjustment. Therefore, the static opening value is multiplied by this coefficient to obtain a small opening increment, so as to achieve smooth control and avoid frequent large-scale operation of the steam valve.

[0150] When the time to temperature instability is short (less than or equal to the minimum effective response time of the system), the time urgency coefficient is greater than or equal to 1, indicating that the system is in an emergency. Gradual adjustment cannot prevent the temperature from dropping in time. Therefore, the maximum safe opening of the steam regulator is directly used as the increment to maximize the steam input and quickly increase the tank temperature.

[0151] By collecting multi-dimensional time-series data (tank temperature, steam pressure, valve position, ambient temperature) at high frequency, a time-series neural network model based on attention mechanism is constructed and deployed to accurately predict future changes in tank temperature. The predicted temperature, environmental factors and system response time are combined to calculate the steam valve opening increment that meets both heat compensation requirements and system inertia, so as to provide heat on demand and compensate in advance.

[0152] By predictive control, the tank temperature is prevented from dropping below 97°C due to fluctuations in steam pressure or changes in ambient temperature. This fundamentally eliminates problems such as tank pressure spikes, increased plate bursts, and uneven deposition caused by temperature runaway. The stable tank temperature provides a constant thermal environment for the electrolytic reaction, ensuring the uniformity of the deposition process and thus improving the consistency of the electrochemical performance of the upper and lower parts of the product, meeting the quality requirements of high-performance alkaline manganese products.

[0153] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing process parameters in the production of alkaline manganese from pure titanium plates, characterized in that: Includes the following steps: Pure titanium plates are used as anode plates, and the pure titanium plates are shaped and sandblasted. Before electrolysis begins, adjust the acidity of the electrolysis system to 50-55 g / L and control the current within a low current range of 6500-7000 A, while keeping the electrolyte temperature not lower than 97℃. Since the pure titanium plate process is sensitive to the cell temperature, it is easy to cause cell voltage runaway below 97℃. Therefore, a cell temperature stabilization mechanism based on time-series prediction is established. During the electrolysis process, the suspending agent is added in a decreasing manner, and the addition cycle is divided into three stages: the early stage of electrolysis, the middle stage of electrolysis, and the late stage of electrolysis. The cell pressure is continuously monitored during the electrolysis cycle. When the cell pressure shows an abnormal upward trend, it is adjusted by reducing acid, reducing current, or partially washing the cell. Micro-suspension electrolysis is carried out under the process parameter combination consisting of the aforementioned current, acidity, temperature, and staged addition of suspending agent.

2. The method for optimizing process parameters in the production of alkaline manganese from pure titanium plates according to claim 1, characterized in that: The concentration of flotation agent in the three stages of electrolysis: early stage, middle stage, and late stage: The concentration of the suspending agent in the early stage of electrolysis is 1.5–4 ppm; The concentration of the suspending agent during the mid-term of electrolysis is 1.0–2.0 ppm; The concentration of the suspending agent in the later stage of electrolysis is 0.3 to 1.0 ppm.

3. The method for optimizing process parameters in the production of alkaline manganese from pure titanium plates according to claim 1, characterized in that: The time-series prediction-based temperature stabilization mechanism of alkaline manganese electrolyzers includes the following steps: Real-time acquisition of multi-dimensional time-series data from the electrolysis system; The real-time collected data is used as the input to the dynamic prediction model of the electrolyte temperature, and the predicted value of the electrolyte temperature for future time periods is output. The predicted electrolyte temperature is compared with the target temperature control value to determine if there is a risk of temperature instability. If there is a risk of temperature instability, a predictive control command is executed.

4. The method for optimizing process parameters in the production of alkaline manganese from pure titanium plates according to claim 3, characterized in that: Multi-dimensional time series data includes: Electrolyte temperature sequence: obtained by temperature sensors distributed inside the electrolytic cell; Steam supply data sequence: including steam main pressure sequence and regulating valve opening sequence; Environmental data sequence: including the ambient temperature sequence of the electrolysis workshop.

5. The method for optimizing process parameters in the production of alkaline manganese from pure titanium plates according to claim 3, characterized in that: The temperature prediction model for the bath is a time-series neural network model based on an attention mechanism; The temporal neural network model adopts an encoder-decoder architecture and integrates an attention mechanism; A fixed-length time series data window with N time steps is used as the input to the model. The time series data window includes the standardized electrolyte temperature, steam main pressure, regulating valve opening, and ambient temperature. The predicted electrolyte temperature sequence corresponding to the next M time steps is used as the output of the model. Training sample data consisting of input sequence-output sequence pairs is extracted and constructed from historical stable operation and production data; The mean squared error loss function is used to minimize the difference between the model-predicted future temperature series and the actual future temperature series; The model parameters were iteratively optimized using the backpropagation algorithm and the Adam optimizer to obtain a dynamic prediction model for tank temperature.

6. The method for optimizing process parameters in the production of alkaline manganese from pure titanium plates according to claim 3, characterized in that: The process of determining whether there is a risk of temperature instability: Obtain the output sequence of predicted electrolyte temperatures; The predicted electrolyte temperature sequence is compared with the target temperature control value. If the predicted electrolyte temperature is lower than the target temperature control value, there is a risk of instability. If the predicted electrolyte temperature is not lower than the target temperature control value, there is no risk of instability.

7. The method for optimizing process parameters in the production of alkaline manganese from pure titanium plates according to claim 3, characterized in that: The predictive control command is: based on the predicted electrolysis temperature, the ambient temperature, and the time since the temperature instability, the opening increment of the steam regulating valve is obtained through combined analysis.

8. The method for optimizing process parameters in the production of alkali manganese from pure titanium plates according to claim 7, characterized in that: The calculation process for the opening increment is as follows: The total heat load demand is calculated based on the predicted electrolysis temperature and ambient temperature, and the static opening value is also calculated. The ratio of the minimum effective response time to the time until temperature instability is used as the time urgency coefficient; If the time from temperature instability is greater than the minimum effective response time of the system, the static opening value is multiplied by the time urgency coefficient to obtain the opening increment; If the time from temperature instability is less than or equal to the minimum effective response time of the system, the maximum safe opening degree of the steam regulation is the opening degree increment.

9. The method for optimizing process parameters in the production of alkaline manganese from pure titanium plates according to claim 8, characterized in that: The calculation process for total heat load demand is as follows: The time point at which the predicted electrolysis temperature first falls below the temperature control target value is taken as the temperature instability time. Calculate the difference between the time of temperature instability and the current time, and use it as the time remaining until temperature instability. Extract the minimum value from the electrolyte temperature prediction value sequence, calculate the difference between the temperature control target value and the minimum value from the electrolyte temperature prediction value sequence, and obtain the total temperature compensation value. The ambient temperature deviation is calculated by subtracting the current ambient temperature from the ambient temperature reference value. The environmental compensation load is obtained by multiplying the ambient temperature deviation by the ambient heat loss coefficient. The total heat load demand is obtained by summing the ambient temperature deviation and the total temperature compensation value.

10. The method for optimizing process parameters in the production of alkaline manganese from pure titanium plates according to claim 8, characterized in that: The calculation process for the static opening value is as follows: When the electrolyte temperature is stably maintained at 97℃, record the opening degree of the steam regulating valve at the current ambient temperature as the baseline maintenance opening degree; The formula for calculating the static opening value is: Static opening value = Basic maintenance opening + (Total heat load demand / Unit opening temperature rise capacity); The total heat load demand is the sum of the total temperature compensation value and the environmental compensation load.

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