A carbon steel lined plastic storage tank apparatus and method for storing a trichlorfon formulation

Abstract

This invention relates to the field of agricultural chemical storage stability control technology, and particularly to a carbon steel lined plastic storage tank device and method for storing valerate preparations. The method includes: S1, obtaining initial parameters for the batch to be stored; S2, loading the valerate preparation into the carbon steel lined plastic storage tank and sealing the tank; S3, obtaining detection data sequences of pH value, dissolved oxygen mass concentration, and storage temperature changing over time; S4, establishing a valerate decomposition kinetic model corresponding to the current batch; S5, predicting the predicted mass fraction of the active ingredient in valerate at the end of the planned storage period, and performing a storage condition adjustment step when the predicted mass fraction is lower than a preset lower limit; S6, correcting the parameters of the valerate decomposition kinetic model using the updated detection data sequence. This invention improves storage stability by online monitoring of valerate pH, dissolved oxygen, and temperature, establishing a kinetic model to predict the content at the end of the period, and regulating storage conditions.

Description

Technical Field

[0001] This invention relates to the field of agricultural chemical storage stability control technology, and in particular to a carbon steel lined plastic storage tank device and method for storing vesicochemical preparations. Background Technology

[0002] Metam-sodium, as a soil treatment agent, has active ingredients that are easily decomposed in aqueous solutions due to factors such as temperature, pH value, and contact with oxygen in the air. Hydrolysis is accelerated under slightly acidic conditions, and the decrease in the content of active ingredients during long-term storage will directly affect the efficacy and product quality stability.

[0003] In existing engineering practices, to improve the safety and chemical stability of easily oxidizable liquids in storage tanks, inert gas protection (nitrogen sealing, gas hooding) is commonly used. This involves introducing inert gas into the tank and replacing the air in the top space to reduce the oxygen content. For example, patent publication number US3946534A (hereinafter referred to as prior art 1) discloses an inert gas covering process. By repeatedly introducing inert gas into the bottom of the container, pressurizing, and depressurizing, dissolved oxygen and gaseous oxygen in the liquid and container are significantly removed. Inert gas is continuously introduced during the filling process to control the oxygen volume fraction in the top space of the tank to below 1% before the container is sealed, thus preventing risks such as liquid oxidation or explosion.

[0004] However, Comparative Document 1 primarily addresses the safe storage of easily oxidized liquids, focusing solely on inert gas replacement and pressure control to reduce oxygen content within the tank. It does not address pesticide formulations like valerate, which are highly sensitive to pH, dissolved oxygen concentration, and storage temperature. Furthermore, it fails to disclose online monitoring of pH, dissolved oxygen concentration, and storage temperature during storage, and does not establish a decomposition kinetic model for specific batches of valerate to predict the active ingredient content at the end of the planned storage period, nor does it provide a basis for synergistic optimization control of temperature, inert gas injection intensity, and pH adjustment. For scenarios using carbon steel-lined plastic tanks to store valerate, existing technologies typically rely solely on empirically defined storage temperatures and times, or simply employ inert gas shielding. This fails to ensure safety while providing precise prediction and proactive intervention regarding long-term content changes in valerate. This can easily lead to two problems: firstly, the active ingredient content prematurely drops to the lower limit of specifications, posing a quality risk; secondly, to mitigate this risk, the storage period may be shortened or residual formulations may be discarded, resulting in resource waste.

[0005] Therefore, the technical problem to be solved by this invention is: under the condition of long-term storage of valerate preparations in carbon steel lined plastic storage tanks, and given that inert gas protection technology alone is insufficient to balance safety and the stability of active ingredients, how to comprehensively consider key influencing factors such as pH value, dissolved oxygen mass concentration, and storage temperature, and predict the content of active ingredients of valerate based on batch decomposition kinetics model, and dynamically regulate storage conditions accordingly, thereby improving the storage stability and reliability of valerate preparations in carbon steel lined plastic storage tanks. Summary of the Invention

[0006] To overcome the aforementioned technical deficiencies, the present invention aims to provide a method for storing valerate preparations in carbon steel lined with plastic. This invention achieves proactive control over the long-term storage stability of valerate preparations by online monitoring of pH, dissolved oxygen concentration, and storage temperature within the carbon steel lined with plastic tank, establishing a decomposition kinetic model for that batch of valerate, predicting the content of effective components at the end of the planned storage period, and then dynamically adjusting temperature, inert gas introduction, and pH based on the prediction results.

[0007] This invention discloses a method for storing valerate preparations in a carbon steel lined with plastic, comprising the following steps: S1. Before loading the valerate preparation into a carbon steel-lined plastic storage tank, obtain the initial parameters of the batch to be stored. The initial parameters shall include at least the mass fraction of the active ingredient of valerate, pH value, dissolved oxygen mass concentration, impurity content, planned storage period and planned storage temperature. S2. The preparation of Weibaim is loaded into a carbon steel lined plastic storage tank and the carbon steel lined plastic storage tank is sealed. By introducing inert gas into the carbon steel lined plastic storage tank and discharging the original gas, the oxygen volume fraction in the gas phase space inside the carbon steel lined plastic storage tank is reduced to below the first oxygen volume fraction threshold. S3. During the planned storage period, the preparation of Weibaimu is tested at preset detection intervals to obtain the detection data sequence of pH value, dissolved oxygen mass concentration and storage temperature changing over time. S4. Based on the initial parameters and the detection data sequence, establish a kinetic model for the current batch of valerate. The kinetic model is used to characterize the decomposition rate of the active ingredient of valerate under different pH values, dissolved oxygen concentrations and storage temperatures. S5. Based on the decomposition kinetics model of methylphenidate and the remaining storage time, predict the predicted mass fraction of the active ingredient in methylphenidate at the end of the planned storage period. When the predicted mass fraction is lower than the preset lower limit of mass fraction, execute the storage condition adjustment steps. The storage condition adjustment steps shall include at least two of the following measures: a) Adjust the external temperature control conditions of the carbon steel plastic-lined storage tank to bring the storage temperature to the target temperature range; b) By continuing to introduce inert gas into the carbon steel-lined plastic storage tank and controlling the exhaust, the dissolved oxygen mass concentration is reduced to the target dissolved oxygen range; c) Intermittently add alkaline buffer to the Epclusa formulation to adjust the pH value to the target pH range; S6. After performing the storage condition adjustment step, continue to acquire the detection data sequence and use the updated detection data sequence to correct the parameters of the Weibaimu decomposition kinetic model.

[0008] Preferably, step S1 further includes conducting an accelerated aging test on the valerate preparation before canning, storing the valerate preparation at a test temperature higher than the planned storage temperature, and detecting data on the change in the mass fraction of the active ingredient of valerate over time, using the data to determine the initial parameters of the valerate decomposition kinetic model.

[0009] Preferably, in step S3, the preset detection interval includes a first detection interval and a second detection interval. The first detection interval, which is smaller than the second detection interval, is used in the first part of the planned storage period, and the second detection interval is used in the second part of the planned storage period. The time step of the Weibaimu decomposition kinetic model is adjusted when switching detection intervals.

[0010] Preferably, during the first part of the planned storage period, the values ​​of the first and second detection intervals are determined based on the sensitivity of the vemigra decomposition kinetic model to the changes in predicted mass fraction at different detection intervals, and the first and second detection intervals are recalculated after the vemigra decomposition kinetic model has undergone at least one parameter correction.

[0011] Preferably, in step S1, different batches of the valerate preparation are batch-graded according to the initial parameters, and the batches are divided into at least two decomposition risk levels, with different decomposition risk levels corresponding to different planned storage periods.

[0012] Preferably, in step S5, the batches classified as high decomposition risk level are subject to a target temperature range upper limit lower than that of the batches classified as low decomposition risk level and a target dissolved oxygen range upper limit lower than that of the batches classified as low decomposition risk level.

[0013] Preferably, in step S5, a decomposition risk index is calculated for the detection data sequence corresponding to each detection cycle. The decomposition risk index is determined based on the current storage temperature, dissolved oxygen mass concentration, pH value and remaining storage time. The storage condition adjustment step selects a combination of implementation measures a), b) and c) according to the magnitude of the decomposition risk index.

[0014] Preferably, the decomposition risk index includes a temperature component, a dissolved oxygen component, and a pH component. The temperature component, dissolved oxygen component, and pH component are updated in each detection cycle, and a storage condition adjustment step is performed when any component exceeds the corresponding component threshold.

[0015] Preferably, in step S3, in addition to obtaining the pH value, dissolved oxygen mass concentration and storage temperature, the gas phase concentration of volatile components in the decomposition products of the valerate preparation is also obtained, and in step S4, the gas phase concentration is used as an additional input parameter to update the valerate decomposition kinetic model.

[0016] Preferably, in step S5, when the gas phase concentration of volatile components exceeds a preset gas phase concentration threshold, the inert gas introduction strategy is preferentially adjusted to change the target dissolved oxygen range, and the target pH range or target temperature range is adjusted after the gas phase concentration of volatile components drops below the gas phase concentration threshold.

[0017] Preferably, in step S6, after each correction of the parameters of the Weibaim decomposition kinetic model, the corrected parameters, the initial parameters of the corresponding batch, and the detection data sequence are stored in the historical dataset. When performing step S4 on subsequent batches, a historical batch with similar initial parameters to the current batch is selected as a reference batch based on the historical dataset, and the decomposition kinetic parameters of the reference batch are used as the initial parameters of the current batch.

[0018] Preferably, when selecting a reference batch, a similarity evaluation method using the mass fraction of active ingredient, pH value, and impurity content as inputs is used to sort the batches included in the historical dataset, and at least one batch whose similarity ranking is within a preset range is used as a reference batch.

[0019] Preferably, in step S3, abnormal data identification is performed on the detection data sequence. When a certain detection value deviates from the trend range related to the historical detection values ​​of the same batch by more than a preset deviation threshold, the detection value is marked as abnormal data and the abnormal data is excluded when updating the Weibaimu decomposition kinetic model.

[0020] Preferably, after each storage condition adjustment step, an additional detection cycle is started, and an additional detection data sequence is acquired at an additional detection interval that is less than the preset detection interval within the additional detection cycle. After the additional detection data sequence meets the preset stability criterion, the preset detection interval is restored.

[0021] Preferably, in step S5, for batches classified as high decomposition risk and whose predicted quality score is continuously lower than the preset lower limit of quality score within a preset number of tests, an early termination of storage step is performed, including stopping the storage condition adjustment step for the batch and transferring the batch of Vibrio formulation from the carbon steel-lined plastic storage tank.

[0022] In view of this, the present invention also provides a carbon steel lined plastic storage tank device for storing Viagra preparations, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described above.

[0023] Compared with existing technologies, the above technical solution has the following advantages: 1. Significantly improves the long-term storage stability of valerate preparations and substantially extends the qualified storage period. This invention establishes a valerate decomposition kinetic model and combines it with the synergistic control of storage temperature, dissolved oxygen concentration, pH value, and volatile component gas phase concentration. It predicts the mass fraction of the active ingredient in valerate in real time within the planned storage period and dynamically adjusts storage conditions based on the prediction results. This results in a significantly higher long-term storage retention rate of valerate preparations at different ambient temperatures such as 22℃ and 30℃ compared to existing simple inert gas protection or simple online adjustment schemes. Especially under high-temperature conditions, it can maintain a high mass fraction of the active ingredient over a longer storage period, thereby significantly improving the long-term storage stability of valerate preparations and reducing the risk of spoilage due to decomposition.

[0024] 2. Achieve quantitative assessment and graded control of decomposition risk, reducing batch scrap rate and quality fluctuations. This invention constructs a decomposition risk index composed of temperature, dissolved oxygen, and pH components. Through weighting coefficients and multi-level risk thresholds, it quantitatively assesses and grades the decomposition risk of valerate formulations. Under different risk ranges, strategies such as mild adjustment, intensive adjustment, or early termination of storage are adopted to intervene in high-risk batches before the decomposition risk accumulates excessively, significantly reducing batch non-conformity rates, minimizing extreme decomposition events and quality fluctuations, and ensuring more stable and controllable product quality upon leaving the tank.

[0025] 3. Implement differentiated and stringent control strategies for batches with high decomposition risk to enhance the overall operational safety of the storage tank. This invention, based on the initial effective ingredient mass fraction, initial pH value, and impurity content, introduces parameters such as conductivity to classify batches into high-decomposition-risk and low-decomposition-risk batches. Differentiated control is implemented in aspects such as planned storage period, target temperature range, target dissolved oxygen range, target pH range, and detection frequency. Stricter control conditions and more intensive detection schemes are applied to high-decomposition-risk batches, and an early termination of storage is triggered when the predicted mass fraction does not reach the lower limit. This improves the overall safety margin and risk control capabilities of the storage tank operation at the system level.

[0026] 4. By linking the two-level detection interval with the model time step, a balance is struck between monitoring accuracy and operating cost. This invention employs a two-level detection strategy with a first detection interval in the early stage and a second detection interval in the later stage. The time step of the Weibaimu decomposition kinetic model is adjusted synchronously when switching detection intervals, significantly reducing the number of detections in the middle and later stages while ensuring prediction accuracy. Simultaneously, through detection interval sensitivity analysis, a reasonable combination of detection intervals is automatically recommended based on stored prediction errors and detection costs, thereby achieving a better balance between monitoring accuracy and operating cost and avoiding unnecessary resource consumption caused by high-frequency detection throughout the entire cycle.

[0027] 5. Anomaly identification and additional detection cycles are introduced to enhance model reliability and the convergence speed of the adjustment closed loop. This invention uses sliding window trend prediction and deviation threshold judgment to identify anomalies in pH value, dissolved oxygen mass concentration, and storage temperature detection data. Anomaly data is removed from the data set for model parameter correction, and supplementary detection is arranged when anomalies occur, avoiding single-point failures or interference from causing incorrect correction of the vesicant decomposition kinetics model. Simultaneously, an additional detection cycle is set after storage condition adjustments are performed. Shorter additional detection intervals quickly verify the adjustment effect. Once the decomposition risk index and related parameters meet the stability criteria, the regular detection interval is restored, thereby accelerating the convergence speed of the adjustment closed loop and improving the accuracy and stability of dynamic control.

[0028] 6. Utilizing historical datasets and parameter transfer from similar batches accelerates model convergence for new batches and improves early prediction accuracy. This invention continuously refines historical datasets during storage, recording initial parameters, kinetic parameters of thiamethoxam decomposition, and effective detection data sequences for each batch. At the start of storage for a new batch, similarity is evaluated using indicators such as the initial thiamethoxam effective component mass fraction, initial pH value, impurity content, selectable conductivity, and initial dissolved oxygen mass concentration. Similar batches are selected as reference batches, and their kinetic parameters are used as initial values ​​for the new batch model. This significantly shortens the time required for model parameter calibration, improves prediction accuracy in the early stages of storage, and facilitates timely identification of potentially high-risk batches in the early stages of storage.

[0029] 7. Optimization of device structure and inert gas replacement process improves the consistency and safety of initial storage conditions. This invention achieves uniform control of temperature and dissolved oxygen in different areas of the storage tank through the multi-monitoring zone arrangement, multi-zone temperature regulating jacket, and multi-point inert gas inlet and outlet design of a horizontal carbon steel lined storage tank, reducing the risk of local decomposition caused by local overheating or high dissolved oxygen levels; the use of a binary inert gas mixture for zoned pulse pre-replacement and final replacement, combined with nitrogen sealing pressure control, stably controls the oxygen volume fraction in the gas phase space below a preset threshold, providing more consistent and safer initial storage conditions for the virgin formulation from the source, reducing the pressure of subsequent control.

[0030] 8. Comparative experiments have verified that this method possesses good engineering applicability and promotional value. Through long-term storage tests comparing this invention with a scheme that only uses inert gas protection and does not introduce a decomposition risk index control, the method demonstrates significant advantages in key indicators such as the retention rate of the active ingredient mass fraction, batch non-compliance rate, and the number of times the gas phase concentration of volatile components exceeds the limit. This proves that the method has good feasibility and stability under engineering conditions. After being validated in the storage scenario of methamidophos formulations, this method can be promoted and applied in the storage of similar pesticide formulations or other liquid chemicals sensitive to storage stability, showing good application prospects and economic value. Attached Figure Description

[0031] Figure 1 A schematic diagram showing the change in the mass fraction of the active ingredient in Weiwei per 100 acres as a function of storage time; Figure 2 A schematic diagram illustrating the change of decomposition risk index with storage time under different storage methods; Figure 3 This is a schematic diagram illustrating the steps of a carbon steel lined plastic storage tank device and method for storing vebuvir / methods according to the present invention. Detailed Implementation

[0032] The advantages of the present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments.

[0033] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0034] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0035] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0036] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0037] In the description of this invention, unless otherwise specified and limited, it should be noted that the terms "installation", "connection" and "linking" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two components. They can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0038] In the following description, suffixes such as "module," "part," or "unit" used to denote elements are used only for the convenience of the description of the invention and have no specific meaning in themselves. Therefore, "module" and "part" can be used interchangeably.

[0039] See Figure 3As shown, this invention provides a method for storing valerate preparations in a carbon steel lined plastic tank, comprising the following steps: S1, before loading the valerate preparation into the carbon steel lined plastic tank, obtaining initial parameters for the batch to be stored, including at least the mass fraction of valerate active ingredient, pH value, dissolved oxygen mass concentration, impurity content, planned storage period, and planned storage temperature; S2, loading the valerate preparation into the carbon steel lined plastic tank and sealing the tank, and reducing the oxygen volume fraction in the gas phase space inside the tank to below a first oxygen volume fraction threshold by introducing inert gas into the tank and discharging the existing gas; S3, within the planned storage period, detecting the valerate preparation at preset detection intervals to obtain a sequence of detection data for pH value, dissolved oxygen mass concentration, and storage temperature changing over time; S4, based on the initial parameters and the detection data sequence, establishing a valerate decomposition kinetic model corresponding to the current batch, and the valerate decomposition kinetic model... The model is used to characterize the decomposition rate of the active ingredient in valerate under different pH values, dissolved oxygen concentrations, and storage temperatures; S5, based on the valerate decomposition kinetic model and the remaining storage time, predict the predicted mass fraction of the active ingredient in valerate at the end of the planned storage period. When the predicted mass fraction is lower than the preset lower limit of mass fraction, execute the storage condition adjustment step. The storage condition adjustment step includes at least two of the following measures: a) adjust the temperature control conditions outside the carbon steel-lined plastic storage tank to adjust the storage temperature to the target temperature range; b) reduce the dissolved oxygen concentration to the target dissolved oxygen range by continuing to introduce inert gas into the carbon steel-lined plastic storage tank and controlling the exhaust; c) intermittently add alkaline buffer to the valerate formulation to adjust the pH value to the target pH range; S6, after executing the storage condition adjustment step, continue to acquire detection data sequences and use the updated detection data sequences to correct the parameters of the valerate decomposition kinetic model.

[0040] The following will describe in detail a method for storing a carbon steel-lined plastic storage tank for vebuxoline preparations through Examples 1 and 2.

[0041] Example 1

[0042] This embodiment provides a control method for the long-term storage of valerate preparations in carbon steel lined plastic storage tanks. This method is used in conjunction with the carbon steel lined plastic storage tank body, an inert gas supply and emission system, an online detection system, and a control unit. By establishing a valerate decomposition kinetic model and combining it with online detection data for prediction and dynamic adjustment of storage conditions, proactive control of the long-term storage stability of valerate preparations is achieved.

[0043] Regarding system composition and basic configuration

[0044] A building with a nominal volume of 50 The vertical carbon steel lined storage tank is used to store valerate preparations. The inner wall of the tank is entirely lined with a polyethylene plastic layer. A nitrogen inlet and an exhaust port are located at the top, and a material inlet and outlet are located at the bottom. The outer wall of the tank is covered with an insulation layer and equipped with a temperature regulating jacket. Circulating cooling water or low-temperature heat transfer oil is circulated through the temperature regulating jacket to regulate the storage temperature of the valerate preparations.

[0045] The online monitoring system includes: a pH meter, a dissolved oxygen analyzer, and a temperature sensor installed in the liquid phase area of ​​the storage tank, which output real-time values ​​of pH, dissolved oxygen concentration, and storage temperature of the valerate preparation, respectively; and a volatile component gas sensor installed in the gas phase space of the storage tank, used to detect the concentration of volatile components in the gas phase of the valerate preparation decomposition products. The control unit uses an industrial control computer to collect the above online monitoring signals and control the temperature regulation actuator, nitrogen flow control valve, and alkaline buffer metering pump to dynamically adjust the storage temperature, dissolved oxygen concentration, and pH of the valerate preparation, and performs decomposition kinetic modeling, detection interval sensitivity analysis, decomposition risk assessment, and storage strategy optimization.

[0046] Step S1: Initial parameter acquisition, accelerated aging test and batch grading

[0047] Before the valerate preparation is loaded into carbon steel lined plastic storage tanks, the batch of valerate preparation to be stored is subjected to laboratory analysis. The initial mass fraction of the active ingredient in valerate is determined and recorded as follows: The initial pH value was measured and recorded as follows: The initial dissolved oxygen mass concentration was measured and recorded as follows: The impurity content was measured and recorded as follows: The planned storage period is determined based on the production and shipping schedule, and is denoted as... (For example, 180d), the planned storage temperature range is set based on process and environmental conditions, and denoted as... (e.g., 20–25°C). Initial mass fraction of active ingredient in Vibrio. Initial pH value Initial dissolved oxygen mass concentration Impurity content Planned storage period and planned storage temperature range These are input into the control unit as initial parameters.

[0048] To determine the decomposition patterns of this batch of valerate formulation under different temperature conditions, an accelerated aging test was conducted before canning. Samples were taken from this batch and placed in three sets of sealed test containers, stored at constant temperatures of 30℃, 40℃, and 50℃, and analyzed at multiple time points. Sampling was performed, and the storage time was determined. The effective ingredient mass fraction of 100 mu at that time is denoted as Assume that the decomposition process of the active ingredient in methylphenidate can be approximated by first-order kinetics, satisfying the following expression:

[0049] in, Storage time is The effective ingredient mass fraction at that time. The initial effective ingredient mass fraction of Viagra. This is the effective decomposition rate constant. For each experimental temperature, by adjusting... Nonlinear fitting of data points yields the effective decomposition rate constants at different temperatures. Then, the Arrhenius form was used to... With temperature The relationship is fitted to obtain an expression for the effect of temperature on the effective decomposition rate constant. The control unit records the initial value of the fitted effective decomposition rate constant as... and will The decomposition kinetics model of this batch was written into the model as the initial parameters for subsequent storage process prediction and correction.

[0050] Based on the completion of accelerated aging tests, the control unit determines the initial mass fraction of the active ingredient in Vibrio. Initial pH value and impurity content Batch grading was performed on different batches of the valerate formulation. Specifically, when Below the preset quality score threshold ,or Below the preset pH threshold or impurity content Higher than the preset impurity content threshold If a batch is classified as high-risk, it is classified as low-risk; otherwise, it is classified as low-risk. For high-risk batches, the control unit compresses the planned storage period to, for example, 120 days; for low-risk batches, the planned storage period remains at 180 days. The batch classification results, along with the initial parameters and accelerated aging test data, are written into the historical dataset to provide a basis for subsequent selection of similar batches and parameter migration.

[0051] Step S2: Filling and inert gas replacement

[0052] After completing the initial parameter acquisition and accelerated aging test, the batch of Vibrio formulation to be stored was slowly pumped into the carbon steel-lined plastic storage tank through the bottom material inlet pipeline. Before filling, the gas phase space of the carbon steel-lined plastic storage tank was pre-purged by opening the nitrogen inlet valve to introduce nitrogen into the gas phase space and opening the exhaust port to discharge the original air until the oxygen volume fraction in the gas phase space was significantly lower than the oxygen volume fraction in the ambient air.

[0053] Nitrogen gas is continuously introduced during the filling process to form a nitrogen hood. After filling is completed, the material inlet valve is closed, and the carbon steel-lined plastic storage tank undergoes final purging: nitrogen gas continues to be introduced into the gas phase space, and the gas is discharged through the exhaust port. The oxygen volume fraction in the gas phase space is monitored in real time, and the detected value is compared with the first oxygen volume fraction threshold. (e.g., 1.0%) is compared. When the detected gas phase space oxygen volume fraction is below the first oxygen volume fraction threshold... When the inert gas is introduced, the control unit closes the exhaust port and maintains a slightly positive pressure in the gas phase space through the nitrogen sealing pressure control valve to reduce the possibility of outside air re-entering the carbon steel-lined plastic storage tank. The inert gas introduction time, introduction flow rate, and nitrogen sealing pressure change curve are all recorded by the control unit and can be used as an auxiliary basis for assessing the change in dissolved oxygen mass concentration.

[0054] Step S3: Online detection, detection interval setting, and abnormal data identification

[0055] Throughout the planned storage period, this embodiment employs a two-level detection interval strategy. In the initial detection phase, the control unit records the first detection interval as... (e.g., 1 day), within 30 days prior to the planned storage period, according to the first detection interval. Perform high-frequency detection; afterwards, the control unit records the second detection interval as... (e.g., 3d), during the remaining storage time, according to the second detection interval Perform routine testing.

[0056] At each detection moment, the control unit activates the online detection system to monitor the current pH value, dissolved oxygen concentration, and storage temperature of the Eppendorf formulation inside the carbon steel-lined plastic storage tank, and records these values ​​as follows: , and ,in For the first The storage time corresponding to each detection. Simultaneously, the gas phase concentration values ​​of volatile components in the gas phase space are recorded as follows: The control unit stores the above detection values ​​in chronological order into the corresponding detection data sequence, forming a pH value detection data sequence. Dissolved oxygen mass concentration detection data sequence Storage temperature detection data sequence and the gas phase concentration detection data sequence of volatile components .

[0057] To reduce the impact of measurement noise and transient interference on the model, the control unit incorporates abnormal data identification logic in the detection data processing flow. Specifically, for each new detection moment... The control unit uses data points from several previous detection times within the same batch to predict the expected pH value, dissolved oxygen concentration, and storage temperature at that detection time by fitting a trend function, and calculates the deviation between the measured values ​​and the expected values ​​for each. When the absolute value of the deviation of any of the pH value, dissolved oxygen concentration, or storage temperature exceeds a preset deviation threshold, the control unit marks the detection value as abnormal data, excludes it from the valid detection data sequence used for model parameter updates, and can automatically trigger repeated detection to confirm whether there is sensor error or field disturbance. Detection values ​​marked as abnormal data are only used for fault diagnosis and do not participate in the parameter correction of the vesinolab decomposition kinetic model.

[0058] In terms of numerical calculation, the control unit sets the model time step in the Weibaimu decomposition kinetic model, denoted as . Model time step This is used to discretize and store the time axis during numerical integration. To ensure model prediction accuracy and numerical stability, the control unit uses a first detection interval. At that time, the model time step can be... Set as the first detection interval A certain fraction, for example ,in It is a positive integer; in the detection interval from the first detection interval Switch to the second detection interval At the same time, the control unit synchronously adjusts the model time step, setting the model time step to... ,in The value is a positive integer, which ensures that the model time step matches the current detection interval, thereby guaranteeing that the numerical integration and the detection data update are consistent on the time axis.

[0059] Step S4: Establishment of the decomposition kinetic model of Vibrio, adjustment of time step and sensitivity analysis of detection interval.

[0060] As storage time progresses, after acquiring valid data at several detection points, the control unit establishes a decomposition kinetic model for the current batch of methylparaben using initial parameters, accelerated aging test results, and valid detection data sequences. This embodiment employs a model with the mass fraction of methylparaben's active ingredient as the state variable, assuming the decomposition rate is related to storage temperature, dissolved oxygen concentration, pH value, and the gaseous concentration of volatile components, satisfying the following equation:

[0061] in, Storage time is The effective ingredient mass fraction at that time. For storage temperature, This refers to the dissolved oxygen mass concentration. pH value This refers to the gaseous concentration of volatile components. Let be the effective decomposition rate function under the above conditions.

[0062] The control unit uses the initial value of the effective decomposition rate constant obtained from accelerated aging tests. Starting with the measured mass fractions of the active ingredient in Weibaimu obtained from offline testing at multiple time points during the storage process, the data were analyzed. By comparing the predicted values ​​with those from the model, and using numerical optimization methods such as least squares, the rate function is effectively decomposed. The parameters in the model were iteratively corrected to better fit the actual decomposition process under current storage conditions. (Data sequence of volatile component gas phase concentration detection) It is used as an additional input variable in the parameter fitting process to participate in the modeling, and is used to reflect the influence of the accumulation and dispersion of volatile components on the decomposition rate.

[0063] During the model numerical integration process, the control unit adopts the model time step. The above differential equation is discretized. To ensure that the model's numerical integration result remains consistent with the detection data time point when the detection interval changes, the control unit adjusts the model's time step before and after the detection interval switch: when the stored procedure is in the first detection interval... During the phase, the model time step Take as When the storage process enters the second detection interval During the phase, the model time step is changed to By adjusting the time step of the model as described above, the Weibaimu decomposition kinetic model can maintain both accuracy and synchronization with detection data at different detection intervals during numerical integration.

[0064] To reasonably determine the first detection interval Second detection interval The control unit performs sensitivity analysis on the prediction results of different detection interval combinations after the model is established. Specifically, the control unit sets several candidate detection interval combinations without changing other storage conditions. For each candidate combination, the model is numerically integrated over the entire planned storage period to obtain the predicted mass fraction of the active ingredient at the end of the planned storage period, denoted as . Using a certain baseline detection interval combination as a reference, the control unit calculates the deviation of the prediction result under the candidate combination from the baseline prediction result, and defines a sensitivity index for the first detection interval, for example:

[0065] in, For the sensitivity index of the first detection interval. To detect tiny increments in the interval, To adjust to the first detection interval The predicted mass fraction of the effective ingredient in Weibaimu. The first detection interval is The predicted value at that time. Similarly, a sensitivity index can be defined for the second detection interval. Based on the sensitivity index and detection cost constraints, the control unit selects a set of detection intervals whose prediction error does not exceed a preset allowable deviation and whose number of detections does not exceed a preset upper limit as the actual first detection interval. Second detection interval .

[0066] After the Weibaum decomposition kinetic model has completed at least one parameter correction, the control unit can perform the above sensitivity analysis steps again to recalculate the first detection interval. Second detection interval The recommended value is calculated; when the calculated recommended value differs significantly from the currently used detection interval, the control unit can prompt the operator to review and adjust the detection interval as needed, so as to improve the prediction accuracy and risk identification capability without increasing the detection cost too much.

[0067] During model building and sensitivity analysis, the control unit utilizes historical datasets to select similar batches. The control unit then sets the initial active ingredient mass fraction of the current batch. Initial pH value and impurity content The similarity evaluation input is compared with historical batches. Similarity indices are obtained by calculating normalized distance, etc. Historical batches are then sorted, and one or more batches with similarity rankings within a preset range are selected as reference batches. The Vieram decomposition kinetic parameters of the reference batches are used as the initial values ​​for the model parameters of the current batch. After each round of model parameter calibration, the control unit writes the updated Vieram decomposition kinetic parameters, the initial parameters of the current batch, and the valid detection data sequence into the historical dataset for use in subsequent batch modeling and similar batch selection.

[0068] Step S5: Predict, decompose, calculate and store risk indices, and dynamically adjust storage conditions.

[0069] At the end of each testing cycle, the control unit uses the current batch's decomposition kinetics model to determine the current storage temperature. Current dissolved oxygen mass concentration Current pH value Current gas phase concentration of volatile components and remaining storage time Substituting into the model, the predicted mass fraction of the active ingredient in the basil at the end of the planned storage period is calculated through numerical integration, denoted as . The control unit will predict the value. Compared with the preset lower limit of quality score By comparison, when the predicted value is lower than the preset lower limit of quality score, it is determined that there is a high risk of decomposition under the current storage conditions, and the storage conditions adjustment steps need to be performed.

[0070] To comprehensively reflect the impact of various parameters on decomposition risk, this embodiment defines a decomposition risk index in the control unit. It is broken down into temperature components. Dissolved oxygen content and pH components Temperature components According to storage temperature Relative to the target temperature range Determining the degree of deviation; dissolved oxygen content Based on dissolved oxygen mass concentration Relative to the target dissolved oxygen range Determination of the degree of deviation; pH component According to pH value The degree of deviation from the target pH range is determined. The control unit updates the temperature, dissolved oxygen, and pH components in each detection cycle and calculates the decomposition risk index. When the decomposition risk index exceeds the preset decomposition risk threshold, the storage condition adjustment step is triggered.

[0071] In the storage condition adjustment process, for batches with a high decomposition risk level, the control unit sets a lower upper limit for the target temperature range and a lower upper limit for the target dissolved oxygen range; for batches with a low decomposition risk level, the target range can be relatively more lenient. This applies when the decomposition risk primarily originates from the temperature component. At that time, the control unit adjusts the temperature or flow rate of the medium in the temperature regulating jacket to control the storage temperature. Adjust to the target temperature range; when the risk of decomposition mainly comes from dissolved oxygen. At that time, the control unit adjusts the nitrogen injection rate and exhaust strategy to control the dissolved oxygen mass concentration. Reduce to within the target dissolved oxygen range; when the risk of decomposition mainly comes from the pH component. At that time, the control unit drives the alkaline buffer metering pump to intermittently add alkaline buffer, thereby adjusting the pH value. Adjust to the target pH range. When the decomposition risk index is high and all three components are simultaneously elevated, the control unit can combine the above adjustment measures to reduce the decomposition risk more quickly.

[0072] Considering that the volatile components produced by the decomposition of methylphenidate may affect safety, this embodiment sets a threshold for the gas phase concentration of volatile components. When the concentration of volatile components in the gas phase is detected... Exceeding the threshold During this process, the control unit prioritizes adjusting the nitrogen injection strategy, increasing the inert gas injection rate while ensuring safe venting, to quickly reduce the concentration of volatile components in the gas phase below the threshold; simultaneously, it suppresses subsequent decomposition processes by reducing the dissolved oxygen concentration. After the concentration of volatile components in the gas phase recovers to below the threshold, the control unit further refines the target pH range and target temperature range to balance safety and stability.

[0073] After each storage condition adjustment step, the control unit initiates an additional detection cycle, setting the additional detection interval to [value missing]. (For example, 6 hours), high-frequency detection is performed at additional detection intervals within the additional detection cycle to obtain additional detection data sequences. Based on the additional detection data sequences, the change in the mass fraction of active ingredient in valerate within a short period is re-predicted. When the rate of change of the predicted mass fraction of active ingredient in valerate is lower than the preset stability criterion within several consecutive additional detection intervals, and the storage temperature, dissolved oxygen concentration, and pH value are all maintained within the target range, the control unit ends the additional detection cycle and restores the original first or second detection interval.

[0074] For batches with a high risk of decomposition, if the mass fraction of the active ingredient in Vibrio is predicted to be lower than the preset lower limit of mass fraction at the end of the planned storage period within a preset number of tests, the control unit will execute an early termination of storage procedure, stop further adjustment of storage conditions for the batch, and issue an instruction to transfer the batch of Vibrio formulation from the carbon steel-lined plastic storage tank to the downstream use unit or safe disposal unit, thereby avoiding continued decomposition in the storage tank that could lead to product scrapping or safety risks.

[0075] Step S6: Model parameter calibration and historical dataset update

[0076] During storage, the control unit continuously calibrates the parameters of the methylparaben decomposition kinetic model using newly acquired offline and online test data, according to a preset calibration cycle or after significant adjustments to storage conditions. The control unit compares the measured values ​​of the effective component mass fraction of methylparaben obtained from offline testing over a period of time with the model predictions, and adjusts the effective decomposition rate function using numerical optimization methods such as the least squares method. The parameters in the model are adjusted to better fit the actual decomposition process under the new storage conditions. After parameter correction, the control unit recalculates the prediction results for the remaining storage time using the updated Weibaimu decomposition kinetics model, and updates the decomposition risk index and storage condition adjustment strategy accordingly.

[0077] After each round of model parameter calibration, the control unit writes the updated venom decomposition kinetic parameters, the initial parameters for the current batch, and all valid detection data sequences into the historical dataset. Each record in the historical dataset includes the initial venom effective component mass fraction, initial pH value, initial dissolved oxygen mass concentration, impurity content, planned storage period, planned storage temperature range, final venom decomposition kinetic parameters, and corresponding valid detection data sequences for that batch. These valid detection data sequences include storage temperature detection data sequences, dissolved oxygen mass concentration detection data sequences, pH value detection data sequences, and volatile component gas phase concentration detection data sequences. When modeling subsequent new batches, the control unit can retrieve historical batches with similar initial parameters to the current batch from the historical dataset, select a reference batch using a similarity evaluation method, and use the venom decomposition kinetic parameters of the reference batch as the initial values ​​for the model parameters of the current batch, thereby improving the prediction accuracy of the model in the early stages of storage.

[0078] After the model parameters are updated, the control unit can perform the detection interval sensitivity analysis again to re-evaluate the settings of the first and second detection intervals. When the detection interval optimization results indicate that it is necessary to adjust the detection interval to improve risk identification capabilities or reduce detection costs, the control unit can issue suggestions to the operator through the human-machine interface. After confirmation by the operator, the detection interval configuration is updated, and the control unit synchronously adjusts the model time step to ensure that the model calculation is consistent with the new detection strategy.

[0079] Comparative test

[0080] To verify the effectiveness of the method in this embodiment compared to existing technologies that only use inert gas hoods, a comparative experiment was designed. In the comparative experiment, the same batch of valerate was selected and divided into two groups: the example group was stored using the method in this embodiment, with online monitoring and dynamic adjustment of the pH value, dissolved oxygen concentration, and storage temperature of the valerate preparation, and a valerate decomposition kinetic model was established for prediction; the control group used existing inert gas hood methods, only reducing the oxygen volume fraction in the gas phase space to below 1% through nitrogen replacement during filling, without subsequent online monitoring and dynamic adjustment, and without establishing a decomposition kinetic model.

[0081] Storage tests were conducted for 180 days at two different ambient temperatures: 25℃ and 35℃. During this period, detection and adjustment were performed according to their respective strategies. Offline analysis was performed on the two groups of valerate preparations at 90 days and 180 days of the test to determine the retention rate of the active ingredient mass fraction of valerate. Some test results are shown in Table 1.

[0082] Table 1 Comparison of the retention rate of active ingredient mass fraction under different storage methods and temperatures of Weibaimu

[0083] Meanwhile, to reflect the differences in risk decomposition and operational strategies, this embodiment statistically analyzed indicators such as batch non-conformity rate, average number of storage condition adjustments, and average proportion of batches prematurely terminated under different storage methods at 35°C. The results are shown in Table 2.

[0084] Table 2 compares the operational performance of the method in this embodiment with that of the inert gas shielding method under 35°C conditions.

[0085] As can be seen from Tables 1 and 2, under the same storage time and temperature conditions, the retention rate of the active ingredient mass fraction of valerate using the method of this embodiment is significantly higher than that of the comparative example which only uses inert gas protection, especially at 35°C. At the same time, although the method of this embodiment made several adjustments to the storage conditions during the storage process and terminated the storage of some batches with high decomposition risk levels in advance, the overall batch failure rate was significantly lower than that of the comparative example. This indicates that by establishing a decomposition kinetic model and implementing dynamic adjustment of storage conditions based on the prediction results, the storage stability of valerate formulation can be improved while ensuring safety.

[0086] Figure 1 This is a schematic graph illustrating the change in the mass fraction of the active ingredient in methylphenidate as a function of storage time. The horizontal axis represents storage time, and the vertical axis represents the retention rate of the mass fraction of the active ingredient in methylphenidate. Curve 1 shows the change in the mass fraction of the active ingredient in methylphenidate as a function of storage time when using the method of this embodiment, and curve 2 shows the change in the mass fraction of the active ingredient in methylphenidate as a function of storage time when using the comparative method with inert gas protection. From Figure 1 It can be clearly seen that, within the same storage period, the downward slope of curve 1 is significantly smaller than that of curve 2, and it remains at a high level at the end of the planned storage period.

[0087] As can be seen from the above embodiments, this method provides detailed explanations of each step, including initial parameter acquisition, accelerated aging test and batch grading, canning and inert gas replacement, online detection and abnormal data identification, decomposition kinetic model establishment and time step adjustment, detection interval sensitivity analysis and optimization settings, decomposition risk index calculation and dynamic adjustment of storage conditions, and model parameter correction and historical dataset update. It can provide a clear and complete technical solution for those skilled in the art to implement.

[0088] Example 2

[0089] System composition and basic configuration

[0090] In this embodiment, a building with a nominal volume of 80 is used. A horizontal carbon steel plastic-lined storage tank is used to store the preparation of methylphenidate. The carbon steel plastic-lined storage tank has a horizontal cylindrical structure, with the inner surface of both end caps entirely lined with a polypropylene plastic layer. The inner cavity of the tank is divided into three monitoring zones along its length: a front zone, a middle zone, and a rear zone. Each monitoring zone is equipped with a liquid phase temperature sensor and a dissolved oxygen analyzer. Three inert gas inlets and two exhaust ports are spaced apart along the length of the tank top for multi-point inert gas replacement and nitrogen sealing pressure regulation.

[0091] The outer wall of the tank is covered with a composite insulation layer and is equipped with a multi-zone temperature regulating jacket. The multi-zone temperature regulating jacket is divided into three temperature control zones along the length of the tank: front zone, middle zone and rear zone. Each temperature control zone is independently connected to the circulating cooling water pipeline and is equipped with regulating valves and flow meters to differentiate the storage temperature of different monitoring areas.

[0092] The online detection system includes: pH meters and dissolved oxygen analyzers arranged in three monitoring areas to measure the pH value and dissolved oxygen mass concentration of the valerate preparation in each monitoring area; a conductivity sensor arranged in the central monitoring area to help determine the changes in dissolved impurities; and two volatile component gas sensors installed in the gas phase space to detect the gas phase concentration of volatile components of the valerate preparation decomposition products in the gas phase space.

[0093] The control unit uses a redundant industrial control computer to acquire the above-mentioned online detection signals through the data acquisition module, and controls the medium temperature and flow rate of the multi-zone temperature regulating jacket, the output flow rate and component ratio of the inert gas mixing device, and the dosage of the alkaline buffer metering pump. This enables dynamic adjustment of the storage temperature, dissolved oxygen mass concentration, and pH value of the valerate preparation. At the same time, it performs functions such as valerate decomposition kinetic modeling, detection interval sensitivity analysis, decomposition risk assessment, and storage strategy optimization.

[0094] Step S1: Initial parameter acquisition, accelerated aging test and batch grading

[0095] In this embodiment, the planned storage period is set to... This is an extension of 180 days compared to another implementation method, to accommodate cross-seasonal storage requirements. Initial parameters of the batch of valerate to be stored were obtained through laboratory analysis, including: the initial mass fraction of the active ingredient in valerate. Initial pH value Initial dissolved oxygen mass concentration Impurity content and initial conductivity Based on the environmental conditions and energy costs of the storage tank, the planned storage temperature range is set as follows: The initial parameters and planned storage temperature range are input into the control unit.

[0096] To determine the decomposition characteristics of this batch of valerate formulation over a wider temperature range, this example selected three temperatures—35℃, 45℃, and 55℃—exceeding the planned storage temperature range for accelerated aging tests. Equal-volume samples were taken from this batch and placed in constant temperature incubators for 60 days of accelerated aging tests at each temperature condition. Samples were taken every 5 days, and the storage time was determined. The effective ingredient mass fraction of the product at that time. And the corresponding pH value and conductivity.

[0097] Assuming that the decomposition process of the active ingredient in methylphenidate can be approximated as a first-order kinetic process at a single temperature, satisfying:

[0098] in, Storage time is The effective ingredient mass fraction at that time. The initial effective ingredient mass fraction of Viagra. This represents the effective decomposition rate constant at that temperature. This is determined by applying values ​​at various temperatures... The data points are fitted to obtain , , Then, the Arrhenius form is used to... With temperature The relationship is fitted to obtain an initial function of the effective decomposition rate with temperature as the independent variable. The control unit denotes the fitted initial function of the effective decomposition rate as follows: and in subsequent modeling As an initial estimate.

[0099] Regarding batch grading, this embodiment uses the initial mass fraction of the active ingredient in Viagra. Initial pH value and impurity content Based on this, the initial conductivity is also taken into account. As auxiliary indicators, the mass fraction threshold, pH threshold, impurity content threshold, and conductivity threshold were set as follows: , , and .

[0100] when: ,or, ,or, ,or, If any of the following conditions are met, the batch is classified as a high-risk batch; otherwise, it is classified as a low-risk batch. For high-risk batches, the control unit limits the planned storage period to an upper limit of 210 days and sets stricter control intervals and higher detection frequencies thereafter; for low-risk batches, the planned storage period can reach 240 days. The batch classification results, accelerated aging test data, and initial parameters are all written into the historical dataset.

[0101] Step S2: Canning and Partitioned Inert Gas Replacement

[0102] This embodiment uses a binary inert gas mixing method for displacement. First, high-purity nitrogen and high-purity carbon dioxide are mixed in an inert gas mixing device at a volume ratio of 7:3 to obtain an inert gas mixture, which is used to reduce the oxygen volume fraction and also meet the requirements of antistatic and fire extinguishing.

[0103] Before filling, the control unit sequentially opens three inert gas inlets to introduce an inert gas mixture into the gas phase space of the storage tank in a pulsed manner. Each pulse lasts for 30 seconds, followed by a 60-second interval, while the existing gas is discharged through two exhaust ports until the oxygen volume fraction in the gas phase space drops from approximately 21% to below 3%. Afterward, the Vibrio formulation is injected into the storage tank through the bottom material feed line. During the filling process, a low-flow-rate inert gas mixture is continuously introduced through the central inert gas inlet to reduce air entrainment caused by filling disturbances.

[0104] After filling is completed, the control unit switches to final purging mode, continuously introducing an inert gas mixture into the three inert gas inlets and discharging the gas through the two exhaust ports. The oxygen volume fraction in the gas phase is monitored in real time, and the detected value is compared with the first oxygen volume fraction threshold. (In this embodiment, 0.5% is used for comparison.) When the oxygen volume fraction in the gas phase space is detected to be below 0.5%, the control unit closes one exhaust port, leaving only the exhaust channel for nitrogen sealing overflow open. A pressure control valve maintains the gas phase space at a nitrogen sealing pressure slightly above atmospheric pressure to ensure the storage tank remains under an inert atmosphere for an extended period. The inert gas introduction time, flow rate, and nitrogen sealing pressure changes are recorded by the control unit to evaluate the replacement effect.

[0105] Step S3: Online detection, adaptive detection interval, and abnormal data identification

[0106] This embodiment employs a two-level detection interval strategy and adaptively fine-tunes the second detection interval. In the initial stage, the first detection interval is set to... That is, a test is performed every 12 hours, with high-frequency testing used at the first testing interval during the first 20 days of storage; thereafter, a second testing interval is set. During the storage period from day 21 to day 180, routine testing is performed according to the second testing interval. In the later stages of storage (e.g., from day 181 to the end of the planned storage period), the control unit adaptively fine-tunes the second testing interval within a range of 2 to 4 days based on the sensitivity results predicted by the previous model, but always maintains a two-level structure of "the first testing interval is used in the previous stage and the second testing interval is used in the next stage".

[0107] At each detection time The control unit activates the online monitoring system to measure the pH value, dissolved oxygen concentration, and storage temperature of the Vibrio preparation in the three monitoring areas. For the central monitoring area, the pH value detection data sequence, dissolved oxygen concentration detection data sequence, and storage temperature detection data sequence are respectively denoted as... , and The gas phase concentration of volatile components in the gas phase space is recorded as follows: This forms a sequence of gas phase concentration detection data for volatile components. In this embodiment, the detection data of the central monitoring area is used as the input data for the decomposition kinetic model of the volatile organic compound (VOC). At the same time, the detection data of the front and rear areas are used to monitor whether there is significant unevenness in the temperature distribution and local dissolved oxygen anomalies in the storage tank.

[0108] For anomaly data identification, the control unit employs a trend prediction method based on a sliding window. For each detected quantity... (Including pH value, dissolved oxygen mass concentration, and storage temperature), select data points from the three most recent detection times before the current detection time, fit a trend function, and obtain the expected value of the detected quantity at the current detection time. And calculate the deviation. When the absolute value of the deviation When a detected value exceeds the dynamic deviation threshold estimated based on historical variance, the control unit marks it as abnormal data, excludes it from the valid detection data sequence used for model parameter updates, and records the corresponding detection quantity, monitoring area, and sensor number for subsequent equipment status diagnosis and maintenance. For detected values ​​marked as abnormal data, the control unit automatically schedules a supplementary detection within a short period (e.g., within 30 minutes) to determine whether the anomaly is an occasional disturbance. Detected values ​​marked as abnormal data are only used for fault diagnosis and do not participate in the parameter correction of the Viagra decomposition kinetic model.

[0109] The numerical integration of the model uses the model time step. When using the first detection interval At that time, the model time step is set to When switching to the second detection interval At that time, the model time step was adjusted to When the second detection interval is adaptively adjusted within the range of 2 to 4 days, the model time step is adjusted synchronously to maintain a fixed divisibility relationship between the numerical integration step and the detection interval, so that the model update is consistent with the detection time axis.

[0110] Step S4: Establishment of the decomposition kinetic model of Vibrio and sensitivity analysis of the detection interval

[0111] This embodiment employs a kinetic model of methylparaben decomposition with the mass fraction of its active ingredient as the state variable. It assumes that the decomposition rate is related to storage temperature, dissolved oxygen concentration, pH value, and the gaseous concentration of volatile components, satisfying the following:

[0112] in, Storage time is The effective ingredient mass fraction at that time. For storage temperature, This refers to the dissolved oxygen mass concentration. pH value This refers to the gaseous concentration of volatile components. This is an effective decomposition rate function.

[0113] In one specific implementation of this embodiment, the effective decomposition rate function may optionally be written as:

[0114] in, For frequency factors, As the apparent activation energy, This is the universal gas constant. , and These are dimensionless functions reflecting the effects of dissolved oxygen, pH, and the gas phase concentration of volatile components, respectively.

[0115] The control unit uses the initial function of the effective decomposition rate obtained from accelerated aging tests. Starting with the model, the measured values ​​of the effective component mass fraction of methylphenidate obtained from offline testing at multiple time points during the storage process are compared with the model predictions. Numerical optimization methods such as least squares are used to optimize the effective decomposition rate function. The parameters in the model were iteratively corrected to better fit the actual decomposition process under current storage conditions. (Data sequence of volatile component gas phase concentration detection) It is used as an additional input variable in the parameter fitting process to participate in the modeling, and is used to reflect the influence of the accumulation and dispersion of volatile components on the decomposition rate.

[0116] The numerical integration of the model uses the aforementioned model time step. During the detection interval, the first detection interval is used. Switch to the second detection interval At the same time, the control unit adjusts the model time step synchronously to ensure the correspondence between the numerical integration results and the time points of the detection data.

[0117] To reasonably determine the first detection interval Second detection interval The specific values ​​are also determined using detection interval sensitivity analysis in this embodiment. The control unit sets multiple candidate detection interval combinations without changing other storage conditions. For each candidate combination, the model is numerically integrated to obtain the predicted mass fraction of the active ingredient at the end of the planned storage period. Using a certain reference combination as a benchmark, the sensitivity index for the first detection interval is defined as follows:

[0118] in, For the sensitivity index of the first detection interval. To detect tiny increments in the interval, Set the first detection interval to The predicted value at that time The first detection interval is The predicted value at that time. Similarly, a sensitivity index can be defined for the second detection interval.

[0119] After considering factors such as sensitivity, detection cost, and ease of operation, the control unit selects a set of detection intervals whose prediction error does not exceed the allowable deviation and whose number of detections is controlled within the target range as the actual first detection interval. Second detection interval After at least one parameter correction is completed in the Weibaimu decomposition kinetic model, a sensitivity analysis is performed again. If the new analysis results show that the detection frequency can be reduced while ensuring stability, the detection interval optimization suggestion is issued to the operator through the human-machine interface.

[0120] Regarding the selection of similar batches, this embodiment always considers the initial mass fraction of the active ingredient in the bacitracin when calculating the similarity index. Initial pH value and impurity content As the primary comparison parameter, initial conductivity can also be optionally introduced. or initial dissolved oxygen mass concentration As a supplementary dimension, without changing the , and Based on the core similarity evaluation, the precision of similarity evaluation should be improved.

[0121] Step S5: Predict, decompose, calculate and store risk indices, and dynamically adjust storage conditions.

[0122] In this embodiment, after each detection cycle, the control unit uses the current batch's decomposition kinetics model to determine the current storage temperature. Current dissolved oxygen mass concentration Current pH value Current gas phase concentration of volatile components and remaining storage time Substituting the values ​​into the model and performing numerical integration, we obtain the predicted mass fraction of the active ingredient in the methylphenidate at the end of the planned storage period. And compare the predicted value with the preset lower limit of quality score. Comparisons are made to identify whether there is a higher risk of decomposition.

[0123] To improve the sensitivity of risk assessment, this embodiment decomposes the risk index. Weighted and graded thresholds are introduced into the composition. Temperature component. Dissolved oxygen content and pH components Based on the current measured value relative to the target temperature range Target dissolved oxygen range The degree of deviation from the target pH range is determined. The decomposition risk index can optionally be defined as:

[0124] in, , , These are the weighting coefficients for the temperature, dissolved oxygen, and pH components. These weighting coefficients can be adjusted based on the sensitivity characteristics of different batches. For batches with a high decomposition risk level, the weights of the dissolved oxygen and pH components can be appropriately increased to enhance the response to oxidative decomposition and acid-base imbalances.

[0125] The example divides the risk index threshold into three levels: low risk threshold. Medium risk threshold and high risk threshold ,in, .when At that time, a mild adjustment strategy is implemented, that is, while keeping the storage temperature and pH value basically unchanged, the risk is reduced by slightly increasing the inert gas flow rate and moderately shortening the second detection interval; when At that time, an enhanced adjustment strategy was implemented, including lowering the storage temperature, significantly increasing the inert gas flow rate, and adding an alkaline buffer, to quickly bring all parameters back to the target range; when In such cases, storage may be terminated early, taking into account subsequent forecast results.

[0126] Regarding the control of volatile component gas phase concentration, this embodiment sets a threshold for volatile component gas phase concentration. When detected Exceed In this case, the control unit prioritizes adjusting the inert gas introduction strategy, increasing the inert gas mixture introduction rate while ensuring safe venting, so that the concentration of volatile components in the gas phase space drops below the threshold as quickly as possible, while simultaneously inhibiting the decomposition process by reducing the dissolved oxygen mass concentration. After the concentration of volatile components in the gas phase recovers to below the threshold, the control unit reassesses whether further adjustments to the storage temperature and pH value are needed based on the decomposition risk index.

[0127] After each storage condition adjustment step, the control unit initiates an additional detection cycle, setting the additional detection interval to [value missing]. During the additional detection cycle, at least three consecutive tests are conducted on the central monitoring area at additional detection intervals. The new detection data are input into the vedophos decomposition kinetic model to calculate the predicted change in the mass fraction of vedophos active ingredients in the short term. When the rate of change of the predicted values ​​from at least three consecutive additional tests is lower than the preset stability criterion, and the storage temperature, dissolved oxygen concentration, and pH value are all stable within the target range, the control unit ends the additional detection cycle and restores the original detection interval. For batches with high decomposition risk, a shorter second detection interval can also be maintained for a certain period of time to further reduce the risk.

[0128] For batches with a high decomposition risk level, when the mass fraction of the active ingredient in Vibrio is predicted to be lower than the preset lower limit of mass fraction at the end of the planned storage period within a preset number of tests, and the decomposition risk index remains at a high risk level for an extended period, the control unit triggers an early termination of storage procedure, stops further adjustments to the storage conditions for the batch, and notifies the operator to transfer the batch of Vibrio formulation from the carbon steel-lined plastic storage tank to the downstream use unit or safe disposal unit to avoid further decomposition during storage, which could lead to product scrapping or safety hazards.

[0129] Step S6: Model parameter calibration and historical dataset update

[0130] In this embodiment, the control unit initiates the model parameter calibration process according to a preset calibration cycle (e.g., every 30 days) or after a significant adjustment in storage conditions. The control unit compares the measured values ​​of the effective component mass fraction of valerate obtained from offline testing over a period of time with the predicted values ​​from the valerate decomposition kinetics model, and uses the least squares method or other numerical optimization algorithms to adjust the effective decomposition rate function. The parameters in the data are corrected, and the sensitivity of the prediction results under different combinations of detection intervals is re-evaluated after correction to determine whether the detection strategy needs to be optimized.

[0131] After parameter calibration, the control unit writes the updated vebuxokinase decomposition kinetic parameters, the initial parameters for this batch, and all valid detection data sequences into the historical dataset. These valid detection data sequences include storage temperature detection data sequences, dissolved oxygen mass concentration detection data sequences, pH value detection data sequences, and volatile component gas phase concentration detection data sequences. In subsequent batch modeling, the control unit retrieves the initial vebuxokinase active ingredient mass fraction from the historical dataset. Initial pH value and impurity content Historical batches similar to the current batch, with initial conductivity optionally considered. or initial dissolved oxygen mass concentration After ranking by similarity evaluation, several reference batches are selected, and the dynamic parameters of the Weibaimu decomposition of the reference batches are used as the initial values ​​of the model parameters of the current batch, so as to shorten the model convergence time and improve the initial prediction accuracy.

[0132] Based on model parameter updates and historical dataset expansion, the control unit can build an empirical control strategy library for batches with different risk levels. When a new high-decomposition-risk batch begins storage, based on the control effects of similar batches in the historical dataset, a more conservative target temperature range, target dissolved oxygen range, and target pH range, as well as a shorter second detection interval, are pre-set. This allows for stricter control of high-decomposition-risk batches from the initial storage stage, thereby improving overall storage stability.

[0133] Comparative test

[0134] To verify the effectiveness of the method in this embodiment compared to existing technologies that only use inert gas shielding and to improved schemes that do not perform zoned temperature control and weighted decomposition risk index control, the following comparative experiment was designed.

[0135] The same batch of cypermethrin formulation was selected and divided into three groups: Example group: The method of this example was used for storage, and the pH value, dissolved oxygen mass concentration and storage temperature of the valerate preparation were monitored and dynamically adjusted online. Multi-zone temperature control jacket, binary inert gas mixing and replacement, weighted decomposition risk index and early termination of storage strategy were adopted. Comparative Example 1: Using only the inert gas hood method, the oxygen volume fraction in the gas phase space is reduced to below 1% by inert gas replacement during filling. No online monitoring or dynamic adjustment is carried out afterward, and no decomposition kinetic model of Vembamethrin is established. Comparative Example 2: Online detection and single-zone temperature control are adopted, but a weighted decomposition risk index is not established, decomposition risk classification and early termination of storage strategy are not introduced, and the temperature and inert gas flow rate are simply adjusted when the limit is exceeded.

[0136] Storage tests were conducted for 240 days at two different ambient temperatures: 22℃ and 30℃. During this period, detection and adjustment were performed according to their respective strategies. Offline analysis was performed on the three groups of valerate preparations at 120 days and 240 days of the test to determine the retention rate of the active ingredient mass fraction of valerate. Some test results are shown in Table 3.

[0137] Table 3 Comparison of the retention rate of active ingredient mass fraction under different storage methods and temperatures of Weibaimu

[0138] Meanwhile, to reflect the differences in risk decomposition and operational strategies, this embodiment statistically analyzed indicators such as batch non-compliance rate, average number of storage condition adjustments, average proportion of batches prematurely terminated from storage, and number of times the gas phase concentration of volatile components in the gas phase space exceeded the limit under different storage methods at 30°C. The results are shown in Table 4.

[0139] Table 4 compares the operating performance of the method in this embodiment with that of the comparative example under 30°C conditions.

[0140] As shown in Tables 3 and 4, under the same storage time and ambient temperature conditions, the retention rate of the active ingredient mass fraction of valerate using the method of this embodiment is significantly higher than that of Comparative Example 1, which only uses inert gas protection, especially at 30°C. Compared with Comparative Example 2, which only performs simple online adjustments, this embodiment also has advantages in terms of retention rate and batch rejection rate at the end of long-term storage. At the same time, by introducing a weighted decomposition risk index, multi-level risk thresholds, and an early termination of storage strategy, this embodiment significantly reduces the batch rejection rate and the number of times the gas phase concentration of volatile components exceeds the limit, while sacrificing a small amount of storage time. This indicates that predictive control and risk classification adjustment based on the valerate decomposition kinetic model can effectively improve the storage stability and safety of valerate formulations in carbon steel lined plastic storage tanks.

[0141] In this embodiment, Figure 2 This is a schematic graph showing the change of decomposition risk index as a function of storage time for the present embodiment group and Comparative Examples 1 and 2 at 30°C. The horizontal axis represents storage time, and the vertical axis represents the decomposition risk index. Curve 1 shows the change of decomposition risk index as a function of storage time when using the method of this embodiment; curve 2 shows the change of decomposition risk index as a function of storage time when using an inert gas hood for Comparative Example 1; and curve 3 shows the change of decomposition risk index as a function of storage time when using simple online adjustment for Comparative Example 2. Figure 2 It can be clearly seen that, within the same storage period, the peak value of curve 1 is significantly lower than that of curves 2 and 3. Furthermore, by adding a detection period and implementing an early termination storage strategy, this embodiment can take timely measures when the decomposition risk index approaches the high-risk threshold, thereby controlling the decomposition risk index within an acceptable range.

[0142] In summary, this embodiment describes in detail the specific implementation details, such as changing the tank structure and volume, the inert gas replacement method, the detection interval setting, the form of the vaprol decomposition kinetic model, and the composition of the decomposition risk index. It also describes in detail each aspect, including the acquisition of initial parameters and batch classification, the adjustment of the two-level detection interval and model time step, the prediction and risk assessment based on the vaprol decomposition kinetic model, the participation of volatile component gas phase concentration in modeling and safety control, the dynamic adjustment of storage conditions driven by the decomposition risk index, the additional detection cycle, and the strategy of early termination of storage.

[0143] It should be noted that this embodiment also provides a carbon steel lined plastic storage tank device for storing valerate preparations. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for storing valerate preparations using a carbon steel lined plastic storage tank provided in the above embodiment.

[0144] It should be noted that the embodiments of the present invention have better implementability and are not intended to limit the present invention in any way. Any person skilled in the art may use the above-disclosed technical content to change or modify it into equivalent effective embodiments. However, any modifications or equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for storing a carbon steel-lined plastic storage tank for valerate preparations, characterized in that, Includes the following steps: S1. Before loading the valerate preparation into a carbon steel-lined plastic storage tank, obtain the initial parameters of the batch to be stored. The initial parameters include at least the mass fraction of valerate active ingredient, pH value, dissolved oxygen mass concentration, impurity content, planned storage period, and planned storage temperature. S2. The preparation of Viagra is loaded into the carbon steel lined plastic storage tank and the carbon steel lined plastic storage tank is sealed. By introducing inert gas into the carbon steel lined plastic storage tank and discharging the original gas, the oxygen volume fraction in the gas phase space inside the carbon steel lined plastic storage tank is reduced to below the first oxygen volume fraction threshold. S3. During the planned storage period, the valerate preparation is tested at a preset detection interval to obtain a sequence of detection data on pH value, dissolved oxygen mass concentration and storage temperature that change over time. S4. Based on the initial parameters and the detection data sequence, establish a kinetic model for the current batch of valerate. The kinetic model is used to characterize the decomposition rate of the active ingredient of valerate under different pH values, dissolved oxygen concentrations and storage temperatures. S5. Based on the decomposition kinetics model of valerate and the remaining storage time, predict the predicted mass fraction of the active ingredient of valerate at the end of the planned storage period. When the predicted mass fraction is lower than a preset lower limit of mass fraction, execute the storage condition adjustment step, which includes at least two of the following measures: a) Adjust the external temperature control conditions of the carbon steel-lined plastic storage tank to adjust the storage temperature to the target temperature range; b) By continuing to introduce inert gas into the carbon steel-lined plastic storage tank and controlling the exhaust, the dissolved oxygen mass concentration is reduced to the target dissolved oxygen range; c) Intermittently add an alkaline buffer to the Vibram formulation to adjust the pH value to the target pH range; S6. After performing the storage condition adjustment step, continue to acquire the detection data sequence, and use the updated detection data sequence to correct the parameters of the Weibaimu decomposition kinetic model.

2. The method for storing valerate preparations using a carbon steel lined with plastic as described in claim 1, characterized in that, In step S1, the method further includes conducting an accelerated aging test on the valerate preparation before canning, storing the valerate preparation at a test temperature higher than the planned storage temperature, and detecting the data on the change of the mass fraction of the active ingredient of valerate over time, and using the data to determine the initial parameters of the valerate decomposition kinetic model. In step S6, after each correction of the parameters of the Weibaim decomposition kinetic model, the corrected parameters, the initial parameters of the corresponding batch, and the detection data sequence are stored in the historical dataset. When performing step S4 on subsequent batches, a historical batch with similar initial parameters to the current batch is selected as a reference batch based on the historical dataset, and the decomposition kinetic parameters of the reference batch are used as the initial parameters of the current batch.

3. The method for storing valerate preparations using a carbon steel lined with plastic as described in claim 2, characterized in that, When selecting the reference batch, a similarity evaluation method using the mass fraction of active ingredient, pH value and impurity content as input is used to sort the batches included in the historical dataset, and at least one batch with a similarity ranking within a preset range is used as the reference batch.

4. The method for storing valerate preparations using a carbon steel lined with plastic as described in claim 2, characterized in that, In step S3, abnormal data identification is performed on the detection data sequence. When a detection value deviates from the trend range related to the historical detection values ​​of the same batch by more than a preset deviation threshold, the detection value is marked as abnormal data and the abnormal data is excluded when updating the Weibaimu decomposition kinetic model.

5. The method for storing valerate preparations using a carbon steel lined with plastic as described in claim 2, characterized in that, After each execution of the storage condition adjustment step, an additional detection cycle is started. During the additional detection cycle, an additional detection data sequence is acquired at an additional detection interval that is less than the preset detection interval. After the additional detection data sequence satisfies the preset stability criterion, the preset detection interval is restored.

6. The method for storing valerate preparations using a carbon steel lined with plastic as described in claim 1, characterized in that, In step S3, the preset detection interval includes a first detection interval and a second detection interval. The first detection interval, which is smaller than the second detection interval, is used in the first part of the planned storage period, and the second detection interval is used in the second part of the planned storage period. The time step of the Weibaimu decomposition kinetic model is adjusted when switching detection intervals. During the first part of the planned storage period, the values ​​of the first detection interval and the second detection interval are determined based on the sensitivity of the vemigrain decomposition kinetic model to the change in predicted mass fraction at different detection intervals, and the first detection interval and the second detection interval are recalculated after the vemigrain decomposition kinetic model has undergone at least one parameter correction.

7. The method for storing valerate preparations using a carbon steel lined with plastic as described in claim 1, characterized in that, In step S1, different batches of the valerate preparation are batch-graded according to the initial parameters, and the batches are divided into at least two decomposition risk levels, with different decomposition risk levels corresponding to different planned storage periods. In step S5, the batches classified as high decomposition risk level are subject to a target temperature range upper limit lower than that of the batches classified as low decomposition risk level and a target dissolved oxygen range upper limit lower than that of the batches classified as low decomposition risk level. In step S5, for batches classified as high decomposition risk and whose predicted quality score is continuously lower than the preset quality score lower limit within a preset number of tests, an early termination of storage step is performed, including stopping the storage condition adjustment step for the batch and transferring the batch of Vibrio preparation from the carbon steel-lined plastic storage tank.

8. The method for storing valerate preparations using carbon steel lined with plastic according to claim 1, characterized in that, In step S5, a decomposition risk index is calculated for the detection data sequence corresponding to each detection cycle. The decomposition risk index is determined based on the current storage temperature, dissolved oxygen mass concentration, pH value and remaining storage time. The storage condition adjustment step selects a combination of measures a), b) and c) to be implemented according to the magnitude of the decomposition risk index. The decomposition risk index includes a temperature component, a dissolved oxygen component, and a pH component. The temperature component, dissolved oxygen component, and pH component are updated in each detection cycle, and the storage condition adjustment step is performed when any of the components exceeds the corresponding component threshold.

9. The method for storing valerate preparations using a carbon steel lined with plastic as described in claim 1, characterized in that, In step S3, in addition to obtaining the pH value, dissolved oxygen mass concentration and storage temperature, the gas phase concentration of volatile components in the decomposition products of the valerate preparation is also obtained, and in step S4, the gas phase concentration is used as an additional input parameter to update the valerate decomposition kinetic model. In step S5, when the gas phase concentration of the volatile component exceeds a preset gas phase concentration threshold, the inert gas introduction strategy is preferentially adjusted to change the target dissolved oxygen range. After the gas phase concentration of the volatile component drops below the gas phase concentration threshold, the target pH range or the target temperature range is then adjusted.

10. A carbon steel lined plastic storage tank device for storing methimazole preparations, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1 to 9.

Images