A method for detecting concentration of ammonium sulfate based on sodium sulfate

Abstract

The application discloses a kind of based on sodium sulfate preparation ammonium sulfate concentration detection method, comprising the following steps: including sodium sulfate dissolution, ammonium bicarbonate metathesis reaction, filtration, washing, drying baking soda, also including mother liquor cold separation, purification and recovery sodium sulfate reuse, vacuum evaporation, vacuum cooling by-product ammonium sulfate.The application can be monitored in real time by dividing reaction time into detection period and control time, and the data of this period is accurate data, based on the data of detection period, analysis is carried out to construct ammonium sulfate concentration prediction model, to predict the ammonium sulfate concentration at the end of reaction, based on the prediction result, potential concentration deviation can be found in time, to provide basis for adjustment and optimization in production process, control period is reserved to adjust its influence parameter and optimize reaction process, to reduce waste rate and rework rate caused by concentration deviation, to improve production efficiency and product quality.

Description

Technical Field

[0001] This invention relates to the field of ammonium sulfate preparation technology, and specifically to a method for detecting the concentration of ammonium sulfate prepared from sodium sulfate. Background Technology

[0002] Ammonium sulfate, as an important nitrogen fertilizer, plays a vital role in agricultural production. The quality of its preparation process directly affects the product quality and production cost, and thus has a profound impact on agricultural production and environmental protection. Traditional ammonium sulfate production methods mostly rely on multi-effect evaporation separation technology.

[0003] However, with increasing environmental awareness and higher energy efficiency requirements, new methods are constantly emerging. Among them, the method of producing ammonium sulfate by cold precipitation of sodium sulfate from sodium bicarbonate mother liquor is particularly prominent. However, the concentration detection of ammonium sulfate during the production process is often carried out at the end of the reaction, which makes it difficult to detect and warn of concentration deviations during the reaction process in a timely manner. This leads to delayed adjustment measures, which in turn affects production efficiency and product quality. When adjusting, the identification of factors affecting the concentration of ammonium sulfate often relies on experience or simple statistical analysis, resulting in inaccurate identification of influencing factors and blind adjustment measures, which in turn affects the adjustment effect and production efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a method for detecting the concentration of ammonium sulfate prepared from sodium sulfate, so as to solve at least one of the above-mentioned problems in the prior art.

[0005] This invention provides a method for detecting the concentration of ammonium sulfate prepared from sodium sulfate, comprising the following steps: dissolving sodium sulfate, undergoing ammonium bicarbonate metathesis reaction, filtering, washing, drying sodium bicarbonate, and further including cold precipitation of mother liquor, purification and recovery of sodium sulfate for reuse, vacuum evaporation, and vacuum cooling to produce ammonium sulfate as a byproduct.

[0006] The concentration of ammonium sulfate solution was detected and analyzed during the metathesis reaction. The specific steps were as follows:

[0007] Step 1: Based on the metathesis reaction time, the total reaction time is divided proportionally to obtain the detection period and the control period;

[0008] Step 2: Based on the detection period, obtain sodium sulfate concentration data for the detection period, analyze and judge it, and obtain an ammonium sulfate concentration prediction model;

[0009] Step 3: Based on the ammonium sulfate concentration prediction model, determine whether the ammonium sulfate concentration can be kept within the standard concentration range by the end of the reaction time;

[0010] Step 4: Based on the abnormal signals, identify the relevant factors in the metathesis reaction process and determine the influencing factors to be optimized.

[0011] Step 5: Based on the identified influencing factors to be optimized, adjust and control the influencing factors during the control period.

[0012] The beneficial effects of this invention are:

[0013] 1. This invention divides the reaction time into a detection period and a control period, thereby enabling real-time monitoring of the reaction during the detection period. The data during this period is real-time data. Based on the data from the detection period, an ammonium sulfate concentration prediction model is constructed to predict the ammonium sulfate concentration at the end of the reaction. Based on the prediction results, potential concentration deviations can be identified in a timely manner, providing a basis for adjustments and optimizations in the production process. A control period is reserved to adjust the influencing parameters to optimize the reaction process, reducing the scrap rate and rework rate caused by concentration deviations, thereby improving production efficiency and product quality.

[0014] 2. By comprehensively considering the synchronicity and dispersion of changes in ammonium sulfate concentration and related factors, this invention can more accurately identify key factors that significantly affect ammonium sulfate concentration, thereby improving the accuracy and reliability of detection. After identifying the influencing factors to be optimized, targeted adjustments and optimizations can be made. Furthermore, when identifying influencing factors, the relationship between changes in ammonium sulfate concentration and changes in related factors can be used to determine the proportion of subsequent adjustments, adding data support for adjustment control, making the adjustment process more refined, thereby improving production efficiency, reducing production costs, and also helping to improve product quality and stability.

[0015] 3. This invention calculates the control values ​​of influencing factors and adjusts the influencing factors to be optimized according to these control values ​​during the control period, thereby optimizing the reaction process. The mother liquor is controlled with a pH value of 5-5.5 to eliminate HCO3 and increase the possibility of the ammonium sulfate concentration reaching the standard concentration range. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for detecting the concentration of ammonium sulfate prepared from sodium sulfate according to the present invention;

[0018] Figure 2 This is a process flow diagram of the preparation of ammonium sulfate based on sodium sulfate according to the present invention;

[0019] Figure 3This is a schematic diagram of the structure of an apparatus for detecting the concentration of ammonium sulfate prepared from sodium sulfate according to the present invention.

[0020] In the diagram: 3. Computer equipment; 301. Processor; 302. Memory; 303. Computer program; Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] Example 1

[0023] Figure 1 This is a flowchart illustrating a method for detecting the concentration of ammonium sulfate produced from sodium sulfate according to Embodiment 1 of the present invention. This embodiment is applicable to detecting the concentration of the generated ammonium sulfate solution during a metathesis reaction. This method can be executed by a sodium sulfate-based ammonium sulfate concentration detection system, which can be implemented by software and / or hardware and can be configured in a sodium sulfate-based ammonium sulfate concentration detection device. Optionally, the sodium sulfate-based ammonium sulfate concentration detection device can be an electronic device, such as a laptop, desktop computer, or smart tablet, etc. This embodiment of the invention does not impose any limitations on this.

[0024] like Figure 2 As shown in the figure, the present invention provides a method for detecting the concentration of ammonium sulfate prepared from sodium sulfate, which specifically includes the following steps:

[0025] The process includes dissolving sodium sulfate, undergoing ammonium bicarbonate metathesis reaction, filtration, washing, drying sodium bicarbonate, as well as cold precipitation of mother liquor, purification and recovery of sodium sulfate for reuse, vacuum evaporation, and vacuum cooling to produce ammonium sulfate as a byproduct.

[0026] In this embodiment, specifically, the metathesis reaction is carried out under the conditions of Na2SO4 concentration controlled at 350-450 g / L, temperature 40-45℃, NH4HCO3 concentration of 350-450 g / L, and reaction time 90 min;

[0027] Reaction formula: Na₂SO₄ + 2NH₄HCO₃ = 2NaHCO₃ + (NH₄)₂SO₄;

[0028] Filtration separates the sodium bicarbonate produced by the reaction from the mother liquor into solid and liquid components. The washing process involves spraying with NaHCO3 semi-saturated water at the end of the vacuum conveyor, calculated at a ratio of 1:1.2 (350+400 / 2*1.2=450mL), mainly to remove NH4+ and SO4+ ions.

[0029] Drying involves further drying the surface water of the centrifuged and dehydrated wet baking soda at 45-50℃ for 3-5 hours, so that the baking soda meets national standards.

[0030] The process of producing ammonium sulfate from mother liquor by cold precipitation of sodium sulfate involves purifying the mother liquor from which sodium bicarbonate has been filtered out.

[0031] The first step is to adjust the pH to 5-5.5 with sulfuric acid to eliminate HCO3 ions as follows:

[0032] XNaHCO3.XNH4HCO3+XH2SO4=X(NH4)2SO4.NaSO4+XCO2↑+H2O;

[0033] The second step is to freeze the solution after removing excess HCO3- to 0°C, taking advantage of the low solubility of Na2SO4·10H2O (3-4 g / 100 mL water) and (NH4+). 4)2 The difference in the solubility of SO4 in 75g / 100mL of water resulted in the precipitation of Na2SO4·10H2O at low temperature. After crystallization for 4-6 hours, the mother liquor ((NH4)2SO4) was removed by centrifugation, and the solidified Glauber's salt was returned to the reaction process for reuse in the production of sodium bicarbonate.

[0034] The process of producing ammonium sulfate by vacuum evaporation and vacuum cooling is as follows:

[0035] The first step involves removing the mother liquor using a centrifuge and then performing vacuum evaporation to further increase (NH4+). 4)2 SO4 concentration and precipitation of sodium sulfate (NaSO4) at 108-110℃ 4) After removing sodium sulfate using a centrifuge, the separated mother liquor is evaporated under vacuum to increase (NH4+) concentration. 4)2 SO4 concentration, then the high-temperature liquid is subjected to vacuum flash evaporation and cooled to 45-50℃ to precipitate (NH4+). 4)2 SO4 crystals were used to extract ammonium sulfate by centrifuging to remove water.

[0036] The second step involves combining the dehydrated mother liquor with NaSO4·Na2SO4·10H2O and heating the mixture to 45-50℃ to dissolve it. This solution is then sent to the reaction process to react with NH4HCO3 to produce sodium bicarbonate.

[0037] The technical solution of this embodiment is as follows: By using sodium sulfate to produce sodium bicarbonate mother liquor and then cold-precipitating Glauber's salt to produce ammonium sulfate, compared with the existing method of separating sodium sulfate from the mother liquor by multi-effect evaporation and then flash-evaporating to obtain ammonium sulfate, this method has the advantages of energy saving, environmental protection, and stable and guaranteed quality of ammonium sulfate.

[0038] Example 2

[0039] Based on the above embodiments, such as Figure 1 As shown in the embodiment of the present invention, a method for detecting the concentration of ammonium sulfate in the preparation of ammonium sulfate based on sodium sulfate is provided. During the metathesis reaction, in order to ensure that the reaction conditions meet the process requirements, it is necessary to detect the concentration of the generated ammonium sulfate solution. Monitoring the concentration of the ammonium sulfate solution is a crucial step in ensuring product quality and improving production efficiency. The method specifically includes the following steps:

[0040] Step 1: Based on the metathesis reaction time, the total reaction time is divided proportionally to obtain the detection period and the control period;

[0041] The division ratios include, but are not limited to: 7:3 and 8:2.

[0042] In this embodiment, the reaction time for metathesis is 90 min. If the time is divided according to a ratio of 7:3, the detection period is 0-63 min and the control period is 63-90 min. If the time is divided according to a ratio of 8:2, the detection period is 0-72 min and the control period is 72-90 min.

[0043] By dividing the reaction time into a detection period and a control period, the reaction can be monitored in real time during the detection period. The data during this period is real-time data. Based on the data of the detection period, analysis and prediction are performed. Based on the prediction results, a control period is reserved to adjust the influencing parameters to optimize the reaction process and reduce the scrap rate and rework rate caused by concentration deviation.

[0044] Step 2: Based on the detection period, obtain sodium sulfate concentration data for the detection period, analyze and judge it, and obtain an ammonium sulfate concentration prediction model;

[0045] In some embodiments, the detection period is divided into several detection time points based on the detection period, wherein the time difference between any two adjacent time points is the same;

[0046] The ammonium sulfate concentration at each detection time point is obtained, and the detection methods for ammonium sulfate concentration include, but are not limited to: titration, conductivity method, and online monitoring instruments;

[0047] In this embodiment, practitioners in the art can select a dedicated ammonium sulfate concentration sensor in an online monitoring instrument (such as the DERACE online ammonium sulfate concentration meter), which has a high degree of automation and can provide faster data feedback;

[0048] The ammonium sulfate concentrations during the detection period were integrated into an ammonium sulfate concentration dataset in chronological order.

[0049] Assuming that the concentration of ammonium sulfate changes linearly with time, a linear regression fitting model is obtained using the least squares method;

[0050] Based on the linear regression fitting model, the mean squared error and standard deviation are calculated to determine whether the ammonium sulfate concentration dataset conforms to a linear change. The specific process is as follows:

[0051] The formula for calculating the mean squared error (MSE) is: Where n is the number of detection time points, y i It is the actual value of ammonium sulfate concentration at the i-th detection time point (i.e., the value in the ammonium sulfate concentration dataset). It is the predicted value of ammonium sulfate concentration corresponding to the i-th detection time point;

[0052] The calculation process for the predicted ammonium sulfate concentration is as follows: the detection time point is output to the independent variable of the linear regression fitting model, and the predicted value of ammonium sulfate concentration is output.

[0053] The formula for calculating the standard deviation (SD) is: in, Let i be the residual at the i-th detection time point. This represents the average of all residuals, where n is the number of detection time points;

[0054] Substituting the mean squared error (MSE) and standard deviation (SD) into the formula PD = s1 * MSE + s2 * SD 2 The judgment value PD is calculated, where s1 and s2 are preset proportional coefficients, with s1 taking the value of 0.524 and s2 taking the value of 0.476.

[0055] Set a judgment threshold, compare the judgment value with the judgment threshold. If the judgment value is less than the judgment threshold, the ammonium sulfate concentration dataset conforms to the linear regression model and a conformity signal is generated. If the judgment value is greater than or equal to the judgment threshold, the ammonium sulfate concentration dataset does not conform to the linear regression model and a non-conformity signal is generated.

[0056] The specific value of the judgment threshold is set by the implementer according to the specific implementation situation, and no restrictions are imposed here;

[0057] Based on the generated coincident signal, the linear regression model is the ammonium sulfate concentration prediction model;

[0058] Based on the generation of non-compliant signals, other models are used to train the ammonium sulfate concentration dataset. These other models include, but are not limited to, decision trees, random forests, support vector machines (SVMs), neural networks (including recurrent neural networks such as LSTM or GRU), or gradient boosting tree (GBDT) models.

[0059] In this embodiment, the LSTM (Long Short-Term Memory) model is selected to train the ammonium sulfate concentration dataset. The reason for selecting the LSTM model is that it has better capabilities when processing data with time series characteristics.

[0060] The specific process is as follows:

[0061] The ammonium sulfate concentration dataset is normalized to eliminate the influence of different units on model training. The normalization formula can be min-max normalization to scale the data to the [0,1] interval. At the same time, the time data is converted into a format suitable for model input, such as time step sequence.

[0062] The preprocessed ammonium sulfate concentration dataset was divided according to a certain ratio (e.g., 80% training set, 20% test set);

[0063] Set the input dimension based on the time step and the number of features (in this example, it is a single feature of ammonium sulfate concentration, but the time step will be determined based on the time interval and the total duration);

[0064] By selecting an appropriate number of LSTM units (such as 50, 100, etc.) and setting the activation functions of the forget gate, input gate, and output gate (usually sigmoid), as well as the activation function of the cell state (usually tanh), multiple layers of LSTM can be stacked to enhance the learning ability of the model.

[0065] After the LSTM layer, add one or more fully connected layers to extract features and output predicted values. The number of output units of the last layer should match the prediction target (1 in this example, i.e., ammonium sulfate concentration).

[0066] The predicted ammonium sulfate concentration value is output using a linear activation function;

[0067] Set the number of training epochs and the batch size in each epoch, then run the model for training. During training, monitor the loss value and performance metrics on the validation set, and adjust the learning rate or use early stopping as needed to prevent overfitting.

[0068] The trained LSTM model is evaluated on the test set by calculating and comparing the difference between the predicted values ​​and the true values ​​to assess the model's generalization ability.

[0069] Based on the evaluation results, it may be necessary to adjust the model structure (such as increasing the number of LSTM layers or changing the number of units), optimizer parameters, or data preprocessing methods, and then retrain the model.

[0070] The trained LSTM (Long Short-Term Memory) network model is the ammonium sulfate concentration prediction model.

[0071] Step 3: Based on the ammonium sulfate concentration prediction model, determine whether the ammonium sulfate concentration can be kept within the standard concentration range by the end of the reaction time;

[0072] The standard concentration range is set by practitioners in this field based on historical experimental data and production process standards.

[0073] In some embodiments, based on the acquired ammonium sulfate concentration prediction model, the end time of the metathesis reaction is used as the input value, and the ammonium sulfate concentration prediction model outputs the corresponding ammonium sulfate concentration prediction value.

[0074] Compare the predicted ammonium sulfate concentration with the standard concentration range.

[0075] If the predicted concentration of ammonium sulfate is within the standard concentration range, it indicates that at the end of the reaction, the concentration of ammonium sulfate meets the production process standards and generates a normal signal.

[0076] If the predicted concentration of ammonium sulfate is not within the standard concentration range, it indicates that the concentration of ammonium sulfate does not meet the production process standard at the end of the reaction, generating an abnormal signal.

[0077] The technical solution of this embodiment is as follows: by dividing the reaction time to obtain the detection period and the control period, a prediction model is constructed using the ammonium sulfate concentration data within the detection period. Based on the linear regression fitting of the dataset, a suitable model (such as LSTM) is selected for training to obtain the ammonium sulfate concentration prediction model. The model is used to predict the ammonium sulfate concentration at the end of the reaction and compare it with the standard concentration range to determine whether the product quality meets the production process standards.

[0078] By constructing an ammonium sulfate concentration prediction model, the ammonium sulfate concentration at the end of the reaction can be predicted more accurately, providing strong support for product quality control. At the same time, the model can be flexibly selected according to the linear regression fit of the dataset, such as LSTM, to adapt to the needs of different data characteristics and improve the generalization ability of the model.

[0079] By predicting the ammonium sulfate concentration at the end of the reaction, potential concentration deviations can be detected in a timely manner, providing a basis for adjustments and optimizations in the production process. This can improve production efficiency and product quality, reduce scrap and rework rates caused by concentration deviations, thereby lowering production costs and improving the economic benefits of enterprises.

[0080] Example 3

[0081] Based on the above embodiments, such as Figure 1 As shown in the figure, the present invention provides a method for detecting the concentration of ammonium sulfate prepared from sodium sulfate, which specifically includes the following steps:

[0082] Step 4: Based on the abnormal signals, identify the relevant factors in the metathesis reaction process and determine the influencing factors to be optimized.

[0083] Among them, the relevant factors in the metathesis reaction process include, but are not limited to: reaction temperature, reaction pressure, and stirring rate;

[0084] In some embodiments, the relevant factor parameter values ​​corresponding to the detection period are obtained based on the ammonium sulfate concentration data during the detection period;

[0085] During the detection period, the time interval between two adjacent detection time points is marked as the analysis period;

[0086] In each analysis period, the difference between the ammonium sulfate concentration at the end of the analysis period and the ammonium sulfate concentration at the beginning of the analysis period is calculated to obtain the change in ammonium sulfate concentration. Similarly, the difference between the relevant factor parameter values ​​at the end of the analysis period and the relevant factor parameter values ​​at the beginning of the analysis period is calculated to obtain the change in relevant factors.

[0087] The changes in ammonium sulfate concentration and the changes in related factors within the same analysis period are grouped together as data.

[0088] In the same data set, changes in ammonium sulfate concentration that have the same sign as changes in related factors are marked as changes in the same direction; changes in ammonium sulfate concentration that have different signs from changes in related factors are marked as changes in opposite directions.

[0089] It should be explained that when the change value of ammonium sulfate concentration and the change value of related factors have the same sign, it means that both the change value of ammonium sulfate concentration and the related change value are positive or both are negative.

[0090] The quantities of changes in the same direction and the quantities of changes in opposite directions are statistically marked and summed to obtain the total quantity. The ratio of the quantities of changes in the same direction to the total quantity is calculated to obtain the ratio of quantities in the same direction, which is marked as TS.

[0091] Extract the data sets corresponding to the same direction of change, and calculate the ratio of the change value of ammonium sulfate concentration in each data set to the change value of related factors to obtain the change ratio;

[0092] Arrange the change ratios in chronological order and calculate the discrete representation value BZ of the change ratio data. The calculation formula is as follows: Where n is the number of changes, S k This represents the k-th change ratio. JC represents the average of all adjacent change ratios of the k-th change ratio, JZ represents the difference between the maximum and minimum values ​​in the change ratio data, and JZ represents the average of all change ratios.

[0093] The discrete representation values ​​are normalized to eliminate the influence of dimensions. Normalization is a well-known processing method in this field, such as the normalization of linear functions.

[0094] Substituting the same-direction quantity ratio TS and the discrete representation value BZ into the formula XG=p1*ln(TS+1.003)+p2*BZ -2 The relevant values ​​are obtained, where p1 and p2 are preset proportional coefficients, with p1 taking the value of 1.524 and p2 taking the value of 1.476.

[0095] It should be noted that the same-direction quantity ratio is directly proportional to the correlation value, that is, the larger the same-direction quantity ratio, the larger the correlation value, and the greater the correlation between ammonium sulfate concentration and the corresponding related factors. The discrete characterization value is inversely proportional to the correlation value, that is, the smaller the discrete characterization value, the larger the correlation value, and the greater the correlation between ammonium sulfate concentration and the corresponding related factors.

[0096] Set a correlation threshold, compare the correlation value with the correlation threshold, and if the correlation value is greater than the correlation threshold, generate a correlation signal; if the correlation value is less than or equal to the correlation threshold, generate an uncorrelated signal.

[0097] Extract the relevant factors and values ​​corresponding to the generated relevant signals, arrange the relevant values ​​in descending order to obtain a relevant value sorting table, and extract the relevant factor corresponding to the first relevant value in the sorting table as the influencing factor to be optimized;

[0098] It should be noted that if there are factors tied for first place in the ranking table, the factors with a larger number of factors in the same direction will be extracted as the factors to be optimized. If the number of factors in the same direction is also the same, the implementer will select the factors to be optimized based on historical experience.

[0099] The technical solution of this embodiment is as follows: by monitoring the changes in ammonium sulfate concentration and related factors (such as reaction temperature, reaction pressure, and stirring rate) during the metathesis reaction process, the number and ratio of the same-direction and opposite-direction changes in ammonium sulfate concentration and the changes in related factors are calculated. The discrete characterization value of the change ratio is further calculated, and the correlation value is calculated by combining the same-direction number ratio and the discrete characterization value. Finally, the influencing factors to be optimized are determined by comparing the correlation value with the correlation threshold.

[0100] By comprehensively considering the synchronicity and dispersion of changes in ammonium sulfate concentration and related factors, key factors that significantly affect ammonium sulfate concentration can be identified more accurately, thereby improving the accuracy and reliability of detection. After identifying the influencing factors to be optimized, targeted adjustments and optimizations can be made. Furthermore, when identifying influencing factors, the proportion of subsequent adjustments can be determined by the relationship between changes in ammonium sulfate concentration and changes in related factors, which adds data support for adjustment control and makes the adjustment process more refined. This, in turn, improves production efficiency, reduces production costs, and also helps to improve product quality and stability.

[0101] Example 4

[0102] Based on the above embodiments, such as Figure 1 As shown in the figure, the present invention provides a method for detecting the concentration of ammonium sulfate prepared from sodium sulfate, which specifically includes the following steps:

[0103] Step 5: Based on the identified influencing factors to be optimized, adjust and control the influencing factors during the control period;

[0104] In some embodiments, based on the determined influencing factors to be optimized, the parameter values ​​of the influencing factors to be optimized during the detection period are obtained and marked as the current parameter values ​​of the influencing factors;

[0105] Obtain the average change ratio of the influencing factors to be optimized;

[0106] The difference between the predicted ammonium sulfate concentration and the standard concentration value is calculated to obtain the ammonium sulfate concentration deviation value, where the standard concentration value is the median of the standard concentration range;

[0107] The ratio of the ammonium sulfate concentration deviation value to the average change ratio of the corresponding influencing factors to be optimized is used to obtain the adjustment value of the influencing factor parameter.

[0108] It should be noted that the adjustment values ​​for the influencing factor parameters may be positive or negative.

[0109] The current influencing factor parameter value is summed with the adjustment value of the influencing factor parameter to obtain the influencing factor control value;

[0110] During the control period, the adjustment values ​​of the parameters of the influencing factors to be optimized and the control values ​​of the influencing factors can be adjusted, thereby optimizing the reaction process and increasing the possibility that the ammonium sulfate concentration will meet the standard concentration range.

[0111] The technical solution of this embodiment is as follows: by obtaining the parameter values ​​of the influencing factors to be optimized during the detection period as the current values, and calculating the corresponding average change ratio, by comparing the predicted value of ammonium sulfate concentration with the median of the standard concentration range, the concentration deviation value is obtained, and this deviation value is compared with the average change ratio to obtain the parameter adjustment value of the influencing factor. Whether the value is positive or negative, the adjustment value will be added to the current parameter value of the influencing factor to obtain the control value of the influencing factor. During the control period, the influencing factors to be optimized are adjusted according to this control value, thereby optimizing the reaction process. The mother liquor is controlled with a pH value of 5-5.5 to eliminate HCO3 and increase the possibility of the ammonium sulfate concentration reaching the standard concentration range.

[0112] Example 5

[0113] Based on the above embodiments, the present invention provides a system for detecting the concentration of ammonium sulfate produced from sodium sulfate, specifically comprising:

[0114] Time segmentation module: Based on the reaction time of the metathesis reaction, the total reaction time is divided proportionally to obtain the detection period and the control period;

[0115] The division ratios include, but are not limited to: 7:3 and 8:2.

[0116] Prediction model acquisition module: Based on the detection period, acquire sodium sulfate concentration data for the detection period, analyze and judge it, and obtain an ammonium sulfate concentration prediction model;

[0117] Concentration standard judgment module: Based on the ammonium sulfate concentration prediction model, it determines whether the ammonium sulfate concentration can be kept within the standard concentration range by the end of the reaction time;

[0118] The module for determining the impact factors to be optimized: Based on abnormal signals, the module identifies the relevant factors in the metathesis reaction process to determine the impact factors to be optimized.

[0119] Optimization control module: Based on the identified influencing factors to be optimized, the module adjusts and controls the influencing factors during the control period.

[0120] Example 6

[0121] like Figure 3 As shown, this embodiment of the invention also provides a computer device 3, including: a memory 302 and a processor 301, and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, it implements a method for detecting the concentration of ammonium sulfate prepared from sodium sulfate as described in any of the above methods.

[0122] The computer device 3 may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will understand that...

[0123] Figure 3 The computer device 3 is merely an example and does not constitute a limitation on the computer device 3. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0124] The processor 301 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0125] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may be an external storage device of the computer device 3, such as a plug-in hard disk, SmartMediaCard (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device 3. Furthermore, the memory 302 may include both internal and external storage units of the computer device 3. The memory 302 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0126] Example 7

[0127] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for detecting the concentration of ammonium sulfate prepared from sodium sulfate as described in any of the above methods.

[0128] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0129] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0130] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0131] In the embodiments disclosed in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0134] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for detecting the concentration of ammonium sulfate based on sodium sulfate, characterized by, It comprises the following steps: It comprises the dissolution of sodium sulfate, the ammonium bicarbonate double decomposition reaction, filtration, washing, drying baking soda, also including mother liquor cold separation, purification and recycling of sodium sulfate for reuse, vacuum evaporation, vacuum cooling by-product ammonium sulfate; In the double decomposition reaction process, the concentration of ammonium sulfate solution is detected and analyzed, and the specific steps are as follows: Step one: based on the double decomposition reaction time, the total reaction time is divided in proportion to obtain the detection period and the control period; Step two: based on the detection period, the ammonium sulfate concentration data of the detection period is obtained, analyzed and judged to obtain the ammonium sulfate concentration prediction model; The acquisition process of the ammonium sulfate concentration prediction model is: Based on the detection period, the detection period is divided into several detection time points; Get the ammonium sulfate concentration of each detection time point; The ammonium sulfate concentration of the detection period is integrated into the ammonium sulfate concentration data set in the order of time; Use the least square method to obtain the linear regression fitting model; Based on the linear regression fitting model, the mean square error MSE and the standard deviation SD are obtained; The mean square error MSE and the standard deviation SD are substituted into the formula to calculate a judgment value PD, wherein s1 and s2 are preset proportion coefficients. Set the judgment threshold, compare the judgment value with the judgment threshold, if the judgment value is less than the judgment threshold, the ammonium sulfate concentration data set conforms to the linear regression model, and a conforming signal is generated, if the judgment value is greater than or equal to the judgment threshold, the ammonium sulfate concentration data set does not conform to the linear regression model, and a non-conforming signal is generated; Based on the generation of the conforming signal, the linear regression model is the ammonium sulfate concentration prediction model; Based on the generation of the non-conforming signal, other models are used to train the ammonium sulfate concentration data set, and the trained model is the ammonium sulfate concentration prediction model; Step three: based on the obtained ammonium sulfate concentration prediction model, the end time of the double decomposition reaction is taken as the input value, and the ammonium sulfate concentration prediction model outputs the corresponding ammonium sulfate concentration prediction value; Compare the ammonium sulfate concentration prediction value with the standard concentration range If the ammonium sulfate concentration prediction value is not in the standard concentration range, an abnormal signal is generated; Step four: based on the abnormal signal, the related factors of the double decomposition reaction process are identified to determine the to-be-optimized influencing factors; Step five: based on the determined to-be-optimized influencing factors, the influencing factors are adjusted and controlled in the control period.

2. The method for detecting the concentration of ammonium sulfate prepared based on sodium sulfate according to claim 1, characterized in that, The acquisition process of the mean square error MSE and the standard deviation SD is: The calculation formula of mean square error MSE is: Wherein, n is the number of detection time points, is the actual value of ammonium sulfate concentration corresponding to the i th detection time point, that is, the value in the ammonium sulfate concentration data set, is the predicted value of ammonium sulfate concentration corresponding to the i th detection time point; The calculation process of the ammonium sulfate concentration prediction value is: the detection time point is output to the independent variable of the linear regression fitting model, and the prediction value of the ammonium sulfate concentration is output; The standard deviation SD is calculated according to the formula: wherein is the residual error at the i-th detection time point, denotes the average of all residual errors, and n is the number of detection time points.

3. The method for detecting the concentration of ammonium sulfate prepared based on sodium sulfate according to claim 1, characterized in that, The process of the to-be-optimized influencing factors is: Get the correlation value, set the correlation threshold, compare the correlation value with the correlation threshold, if the correlation value is greater than the correlation threshold, a correlation signal is generated, if the correlation value is less than or equal to the correlation threshold, a non-correlation signal is generated; Extract the related factors and related values corresponding to the generated correlation signal, arrange the related values in descending order to obtain a related value sorting table, and extract the related factors corresponding to the first related value in the sorting table as the to-be-optimized influencing factors.

4. The method for detecting the concentration of ammonium sulfate prepared based on sodium sulfate according to claim 3, characterized in that, The acquisition process of the related value is: Get the same direction quantity ratio TS and the discrete representation value BZ; Substitute the same direction quantity ratio TS and the discrete representation value BZ into the formula , to obtain a correlation value, wherein p1 and p2 are preset proportion coefficients.

5. The method for detecting the concentration of ammonium sulfate prepared based on sodium sulfate according to claim 4, characterized in that, The acquisition process of the same direction quantity ratio TS is: Based on the ammonium sulfate concentration data of the detection period, the parameter value of the related factors corresponding to the detection period is obtained; The time period between two adjacent detection time points is marked as an analysis period during the detection period; In each analysis period, the ammonium sulfate concentration value at the end time point of the analysis period is subtracted from the ammonium sulfate concentration value at the start time point of the analysis period to obtain an ammonium sulfate concentration change value, and the related factor parameter value at the end time point of the analysis period is subtracted from the related factor parameter value at the start time point of the analysis period to obtain a related factor change value; The ammonium sulfate concentration change value and the related factor change value of the same analysis period are marked as the same group of data; The ammonium sulfate concentration change value and the related factor change value in the same group of data are marked as the same direction change if they are of the same sign, and are marked as the opposite direction change if they are of different signs; The number of the same direction changes and the number of the opposite direction changes are counted and summed to obtain a total number, and the number of the same direction changes is divided by the total number to obtain a same direction number ratio, which is marked as TS.

6. The method for detecting the concentration of ammonium sulfate prepared based on sodium sulfate according to claim 4, characterized in that, The acquisition process of the discrete characteristic value BZ is as follows: The data group corresponding to the same direction change is extracted, and the ammonium sulfate concentration change value and the related factor change value in each data group are divided to obtain a change ratio; The change ratios are arranged in time sequence, and a discrete representation value BZ of the change ratio data is calculated, and the calculation formula is wherein n is the number of change ratios, represents the kth change ratio, represents the average of all adjacent change ratios of the kth change ratio, JC represents the difference between the maximum value and the minimum value in the change ratio data, and JZ represents the average of all change ratios.

7. The method for detecting the concentration of ammonium sulfate prepared based on sodium sulfate according to claim 6, characterized in that, The process of adjusting and controlling the influencing factors in the control period is as follows: Based on the determined to-be-optimized influencing factor, the parameter value of the to-be-optimized influencing factor in the detection period is obtained, which is marked as the current influencing factor parameter value; The average value of the change ratio corresponding to the to-be-optimized influencing factor is obtained; The ammonium sulfate concentration prediction value is subtracted from the standard concentration value to obtain an ammonium sulfate concentration deviation value, wherein the standard concentration value is the median value of the standard concentration range; The ammonium sulfate concentration deviation value is divided by the average value of the change ratio corresponding to the to-be-optimized influencing factor to obtain an influencing factor parameter adjustment value; The current influencing factor parameter value is summed with the influencing factor parameter adjustment value to obtain an influencing factor control value; In the control period, the parameter of the to-be-optimized influencing factor is adjusted to the influencing factor control value.

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