A steady-state detection method and system for the evaporation process of aluminum oxide production

Abstract

The present invention relates to the technical field of steady-state detection of the evaporation process of aluminum oxide production, and specifically to a steady-state detection method and system for the evaporation process of aluminum oxide production. The present invention comprises: first, a steady-state variable correlation analysis method is proposed, which obtains variable correlation features in an initial data matrix through discrete equations, and sorts the correlation features by significance to analyze the correlation coefficients of the steady-state variables; the method can accurately analyze the functional relationship between various variables in the evaporation process of aluminum oxide production; second, a steady-state detection method is proposed, which detects abnormal variable data by establishing a steady-state detection function, and accurately evaluates the changes in the evaporation variables at specific times through evaluation indicators at different time points; finally, an adaptive evaporation variable coordination method is proposed, which obtains the optimal evaporation variable through multiple iterative optimizations to meet the control target, thereby ensuring the quality of aluminum oxide production.

Description

Technical Field

[0001] The present invention relates to the technical field of steady-state detection of an evaporation process in aluminum oxide production, and in particular to a steady-state detection method and system for an evaporation process in aluminum oxide production. Background Art

[0002] Alumina is an important inorganic compound with a white crystalline structure. It possesses the following physical properties: high hardness, with a hardness of 9 on the Mohs scale, second only to diamond and silicon carbide; a high melting point of approximately 2072°C; and good corrosion resistance to most acids and bases, especially at high temperatures.

[0003] Due to its excellent physical and chemical properties, alumina is widely used in industry, science, and medicine. For example, in industry, alumina is the main component in the manufacture of high-temperature resistant materials such as refractory ceramics, refractory bricks, and refractory coatings. It is used for lining and protecting various industrial furnaces and metal smelting equipment. In science, alumina is an ideal carrier for many catalysts, used to support and disperse active components, improving their efficiency and stability. In medicine, alumina is used as a key component in artificial joints and dental fillings.

[0004] The alumina production process typically includes several key steps: extraction and pretreatment of alumina raw materials, preparation, refining, and processing of aluminum hydroxide. The refining process is crucial to its production. The goal of refining is to remove impurities and further purify the alumina to meet specific quality standards and application requirements. Evaporation and concentration are the primary tasks in the refining process. Evaporators are often used to heat and evaporate the alumina solution, removing water and concentrating the solution to the desired concentration.

[0005] The evaporation process of alumina solution is a very critical step, and its steady-state has an important impact on the quality of the product. The steady-state of the evaporation process in alumina production can not only maintain the solution concentration and ensure the quality of alumina, but also improve production efficiency and increase cost-benefit. Therefore, it is very necessary to conduct steady-state detection during the evaporation process of alumina production.

[0006] There are many excellent methods in the prior art for performing steady-state detection during the evaporation process of alumina production. For example, Chinese patent application publication number CN108664000A discloses a method and system for steady-state detection during the evaporation process of alumina production. The method comprises: selecting multiple steady-state detection variables from the process variables based on the correlation between the process variables and the concentration of the four-flash discharge during the evaporation process; performing outlier detection on a first sample data set corresponding to each steady-state detection variable using an improved K-means algorithm to obtain a second sample data set corresponding to each steady-state detection variable; obtaining the change rate and change acceleration of each steady-state detection variable at any moment within the time period to be detected based on the second sample data set corresponding to each steady-state detection variable; obtaining a multivariate steady-state evaluation index at any moment based on the change rate and change acceleration of each steady-state detection variable at any moment, and determining whether the evaporation process is in steady-state at any moment based on the multivariate steady-state evaluation index. However, this steady-state detection method relies solely on collected data to analyze the correlation of process variables, but cannot guarantee the accuracy of detection when the data is erroneous. In order to further improve the accuracy of steady-state detection during the evaporation process of alumina production and ensure product production quality, the present invention proposes a method for detecting the steady-state of alumina production and ...

[0007] To this end, the present invention proposes a steady-state detection method and system for the evaporation process of aluminum oxide production. Summary of the Invention

[0008] The purpose of the present invention is to provide a steady-state detection method and system for the evaporation process of aluminum oxide production, which can realize the steady-state detection of evaporation variables and adaptive adjustment of evaporation variables in the evaporation process of aluminum oxide production; the method and system mainly include: first, collecting relevant historical data of aluminum oxide in multiple production evaporation processes according to timestamps, and using an evaporation variable analysis method to perform correlation analysis on the collected historical data; the analysis method obtains the variable correlation characteristics in the initial data matrix through discrete equations, and performs significance sorting according to the variable correlation characteristics to obtain the relevant differences between the variables, and selects steady-state variables in combination with the initial matrix; according to the selection results, a steady-state correlation coefficient matrix is obtained; the method helps to optimize the selection process of evaporation variables, ensuring that the selected variables are conducive to improving the subsequent stability The accuracy of steady-state detection is improved; secondly, the present invention proposes a steady-state detection method, which establishes a steady-state detection function. The function performs steady-state detection on evaporation variables at different time points through a steady-state anomaly detection algorithm, and establishes a multivariate steady-state evaluation index through historical data in the function, which can accurately evaluate the evaporation variables; this method can not only provide an accurate evaluation of the steady state of the evaporation variables, but also accurately understand the changes in the evaporation variables at a specific time; finally, the present invention proposes a method for adaptively coordinating evaporation variables in an evaporation process, which adaptively adjusts the evaporation variables; this method achieves the control target without losing stability by adjusting the evaporation variables, and obtains the optimal evaporation variables through multiple iterative optimizations to meet the set control target, thereby ensuring the quality of alumina production.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A steady-state detection method for an evaporation process in aluminum oxide production, comprising:

[0011] Obtaining relevant historical data of the alumina during multiple production evaporation processes according to the timestamp; wherein the relevant historical data includes: historical raw liquid parameters, historical evaporation variables, historical evaporation efficiency, and historical product concentration at the outlet of the final flash evaporator;

[0012] Furthermore, the relevant historical data is processed to obtain standardized relevant historical data; wherein, the data processing includes: processing abnormal values and missing values in the relevant historical data to obtain first relevant historical data; normalizing the first relevant historical data to obtain second relevant historical data; and standardizing the second relevant historical data to obtain the standardized relevant historical data.

[0013] Furthermore, the historical evaporation variables are marked; wherein the marking content includes: 1 represents a stable state; 0 represents an unstable state;

[0014] Furthermore, an evaporation variable analysis is performed on the standardized relevant historical data to obtain an evaporation variable correlation matrix; wherein the calculation process of the evaporation variable correlation matrix includes: establishing an initial matrix based on the standardized relevant historical data; the initial matrix is expressed as: in, Expressed as the standardized historical stock solution parameter at the mth time point; SV mn Expressed as the nth standardized historical evaporation variable at the mth time point; Expressed as the normalized historical product concentration at the mth time point;

[0015] Substitute each row of data in the initial matrix into the discrete equation; wherein the discrete equation formula is:

[0016]

[0017] in, Expressed as the normalized historical product concentration of row i in the initial matrix; It is represented by the normalized historical stock solution parameter of the i-th row in the initial matrix; SV i It is represented as the normalized historical evaporation variable vector of the i-th row in the initial matrix; f() is represented as a nonlinear analytical function; E() is represented as the environmental function of the aluminum oxide in the production evaporation process;

[0018] Solving the correlation coefficient vector of each data in the initial matrix according to the discrete equation, and establishing the evaporation variable correlation matrix;

[0019] Furthermore, a steady-state correlation coefficient matrix is calculated using the evaporation variable correlation matrix; wherein the calculation process of the steady-state correlation coefficient matrix includes: performing significance sorting on the evaporation variable correlation matrix to obtain a significance sorting result of the evaporation variables; performing steady-state variable selection based on the significance sorting result of the evaporation variables combined with the marking content of the initial matrix; and obtaining the steady-state correlation coefficient matrix using a correlation analysis method based on the result of the steady-state variable selection.

[0020] Furthermore, a steady-state detection is performed on the current alumina production evaporation process in combination with the steady-state correlation coefficient matrix to obtain a steady-state detection result; wherein the steady-state detection process includes: obtaining a multivariate steady-state evaluation index corresponding to the timestamp based on the marked standardized relevant historical data; wherein the multivariate steady-state evaluation index is expressed as:

[0021]

[0022] Where SI(t) represents the steady-state index at time t; n represents the total number of evaporation variables; X i(t) represents the parameter value of the i-th evaporation variable at time t; σ i (t) represents the mean value of the ith evaporation variable at time t; μ i (t) represents the variance of the i-th evaporation variable at time t;

[0023] A steady-state detection function is constructed according to the multivariable steady-state evaluation index; wherein the steady-state detection function is expressed as:

[0024]

[0025] Wherein, SS() represents a steady-state detection function; X(t) represents a vector of evaporation variables of the aluminum oxide during the production evaporation process; AnoD() represents a steady-state anomaly detection algorithm; the steady-state anomaly detection algorithm includes:

[0026] Combining the steady-state correlation coefficient matrix with the standardized historical evaporation variables to construct an extended variable set;

[0027] The extended variable set is used to train a random forest classifier; wherein the output layer of the random forest classifier adopts the multivariate steady-state evaluation index as a judgment criterion.

[0028] Furthermore, adaptive evaporation variable coordination is performed in the evaporation process based on the steady-state detection result; wherein the process of adaptive evaporation variable coordination in the evaporation process includes: obtaining the raw liquid parameters and evaporation variables at the current moment; setting the control target of the evaporation process; wherein the control target includes: maximum product concentration and maximum evaporation efficiency; using an evaporation variable optimization algorithm to obtain optimized evaporation variables; wherein the steps of the evaporation variable optimization algorithm include:

[0029] Step 1: taking the evaporation variable as the initial optimal evaporation variable;

[0030] Step 2: evaluating the correlation of the initial optimal evaporation variables through the steady-state correlation coefficient matrix;

[0031] Step 3: Take the highest value of the correlation as the parent, perform crossover and mutation operations to generate new evaporated variables;

[0032] Step 4: updating the initial optimal evaporation variable according to the new evaporation variable;

[0033] Step 5: Repeat steps 2 to 4 until the stop condition is met to obtain the optimized evaporation variable.

[0034] A steady-state detection is performed on the optimized evaporation variable; when the optimized evaporation variable is in a steady state and can achieve the control target, the optimal evaporation variable is obtained; otherwise, the evaporation variable optimization algorithm is repeated.

[0035] A steady-state detection system for an evaporation process in aluminum oxide production, comprising: a monitoring unit, a data acquisition unit, a data processing unit, a steady-state variable analysis unit, a steady-state detection unit, a variable control unit, and a feedback unit;

[0036] Wherein, the monitoring unit is used to monitor the aluminum oxide during the production evaporation process;

[0037] The data acquisition unit is used to collect various data of the alumina production evaporation process;

[0038] The data processing unit is used to process the data collected by the data collection unit; wherein, the data processing unit includes: processing abnormal values and missing values in the relevant historical data to obtain first relevant historical data; normalizing the first relevant historical data to obtain second relevant historical data; and standardizing the second relevant historical data to obtain standardized relevant historical data.

[0039] The steady-state variable analysis unit is used to perform correlation analysis on the variables of the aluminum oxide during the production evaporation process; wherein, the steady-state variable analysis unit includes: establishing an initial matrix based on standardized relevant historical data; the initial matrix is expressed as: in, Expressed as the standardized historical stock solution parameter at the mth time point; SV mn Expressed as the nth standardized historical evaporation variable at the mth time point; Expressed as the normalized historical product concentration at the mth time point;

[0040] Furthermore, each row of data in the initial matrix is substituted into the discrete equation; wherein the discrete equation formula is:

[0041]

[0042] in, Expressed as the normalized historical product concentration of row i in the initial matrix; It is represented by the normalized historical stock solution parameter of the i-th row in the initial matrix; SV i It is represented as the normalized historical evaporation variable vector of the i-th row in the initial matrix; f() is represented as a nonlinear analytical function; E() is represented as the environmental function of the aluminum oxide in the production evaporation process;

[0043] Furthermore, the correlation coefficient vector of each data in the initial matrix is solved according to the discrete equation, and the evaporation variable correlation matrix is established;

[0044] Further, the evaporation variable correlation matrix is sorted by significance to obtain a significance sorting result of the evaporation variable;

[0045] Furthermore, steady-state variable selection is performed based on the significance ranking result of the evaporation variables combined with the marking content of the initial matrix;

[0046] Furthermore, the steady-state correlation coefficient matrix is obtained by using a correlation analysis method according to the result of the steady-state variable selection.

[0047] The steady-state detection unit is used to perform steady-state detection on various variables in the evaporation process of alumina production; wherein the steady-state detection unit includes:

[0048] A multivariate steady-state evaluation index corresponding to a timestamp is obtained based on the marked standardized related historical data; wherein the multivariate steady-state evaluation index is expressed as:

[0049]

[0050] Where SI(t) represents the steady-state index at time t; n represents the total number of evaporation variables; X i (t) represents the parameter value of the i-th evaporation variable at time t; σ i (t) represents the mean value of the ith evaporation variable at time t; μ i (t) represents the variance of the i-th evaporation variable at time t;

[0051] A steady-state detection function is constructed according to the multivariable steady-state evaluation index; wherein the steady-state detection function is expressed as:

[0052]

[0053] Wherein, SS() represents a steady-state detection function; X(t) represents a vector of evaporation variables of the aluminum oxide during the production evaporation process; AnoD() represents a steady-state anomaly detection algorithm; the steady-state anomaly detection algorithm includes:

[0054] Combining the steady-state correlation coefficient matrix with the standardized historical evaporation variables to construct an extended variable set;

[0055] The extended variable set is used to train a random forest classifier; wherein the output layer of the random forest classifier adopts the multivariate steady-state evaluation index as a judgment criterion.

[0056] The variable control unit is used to adjust the variable in a steady state;

[0057] The feedback unit is used to feed back parameter changes and evaporation conditions during the alumina production evaporation process to relevant staff.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The present invention proposes a method for analyzing the correlation of steady-state variables in the evaporation process of alumina production, which is used to analyze collected historical data. The method obtains the correlation characteristics of variables in the initial data matrix through discrete equations, and performs significance sorting based on the correlation characteristics of the variables to obtain the correlation differences between the variables, and selects steady-state variables in combination with the initial matrix. The selected steady-state variables are subjected to correlation analysis to obtain a final steady-state variable correlation coefficient matrix. The method can accurately analyze the interaction relationship between the various variables in the evaporation process of alumina production, ensure that the selected variables are conducive to improving the accuracy of subsequent steady-state detection, and ensure the quality of alumina production.

[0060] 2. The present invention proposes a steady-state detection function; this function performs steady-state detection on evaporation variables at different time points through a steady-state anomaly detection algorithm, and establishes a multivariate steady-state evaluation index based on historical data in the function, which accurately evaluates the evaporation variables; this method can not only provide an accurate assessment of the steady state of the evaporation variables, but also accurately understand the changes in the evaporation variables at specific times, thereby ensuring the quality of alumina production.

[0061] 3. The present invention proposes a method for adaptively coordinating evaporation variables in an evaporation process, which can adaptively adjust the evaporation variables based on steady-state detection results; this method achieves the control target without losing stability by adjusting the evaporation variables, and obtains the optimal evaporation variables through multiple iterative optimizations to meet the set control targets, thereby ensuring the quality of alumina production. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flow chart of a steady-state detection method for an alumina production evaporation process provided by an embodiment of the present invention;

[0063] Figure 2 A structural diagram of a steady-state detection system for an aluminum oxide production evaporation process provided by an embodiment of the present invention;

[0064] Figure 3 A schematic diagram of a four-effect evaporation process according to an embodiment of the present invention;

[0065] Figure 4 A schematic diagram of variable data for the evaporation process of aluminum hydride production provided by an embodiment of the present invention;

[0066] Figure 5 A schematic diagram of a single variable steady-state detection result provided by an embodiment of the present invention;

[0067] Figure 6 A schematic diagram of steady-state detection results under the action of multiple variables provided by an embodiment of the present invention;

[0068] Figure 7 A flow chart of the adaptive evaporation variable coordination method provided by an embodiment of the present invention;

[0069] Figure 8 A schematic diagram of steady-state detection results after coordination of evaporation variables provided by an embodiment of the present invention.

[0070] In the figure: 101, first condensate tank; 102, second condensate tank; 103, third condensate tank; 104, fourth condensate tank; 201, first evaporator; 202, second evaporator; 203, third evaporator; 204, fourth evaporator; 301, first preheater; 302, second preheater; 303, third preheater; 401, first flash evaporator; 402, second flash evaporator 2; 403, third flash evaporator; 501, condenser; 601, raw steam; 701, raw liquid. DETAILED DESCRIPTION

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0072] Alumina, as an important inorganic compound, has a white crystalline structure and has high hardness and melting point.

[0073] Due to its excellent physical and chemical properties, alumina has been widely used in industry, science, and medicine. For example, in the industrial field, alumina is the main component for manufacturing high-temperature resistant materials such as refractory ceramics, refractory bricks and refractory coatings, and is used for lining and protection of various industrial furnaces and metal smelting equipment. In the scientific field, alumina is an ideal carrier for many catalysts, used to support and disperse active components and improve the efficiency and stability of catalysts. In the medical field, alumina is used as an important component of artificial joints and dental filling materials because of its biocompatibility and corrosion resistance.

[0074] The alumina production process typically includes several key steps: extraction and pretreatment of alumina raw materials, preparation, refining, and processing of aluminum hydroxide. The refining process is crucial to its production. The goal of refining is to remove impurities and further purify the alumina to meet specific quality standards and application requirements. Evaporation and concentration are the primary tasks in the refining process. Evaporators are often used to heat and evaporate the alumina solution, removing water and concentrating the solution to the desired concentration.

[0075] During the alumina production evaporation process, the outlet material concentration is detected with a significant lag, making timely detection impossible. Current technology uses steady-state detection of evaporation variables to reflect the evaporation status of the alumina. However, this detection process has some drawbacks. Therefore, the present invention proposes a steady-state detection method and system for the alumina production evaporation process. The method and system proposed in the present invention are described in detail below using two embodiments.

[0076] Example 1:

[0077] In the embodiment of this application, the proposed method and system are combined to achieve the purpose of the present invention; first, Figure 1 The specific steps of the method proposed in the present invention are given in the following, including: S10. Obtaining relevant historical data of alumina during multiple production evaporation processes according to timestamps; S20. Preprocessing the relevant historical data to obtain standardized relevant historical data; S30. Marking the evaporation variables in the standardized relevant historical data; S40. Analyzing the evaporation variables and calculating the steady-state correlation coefficient; S50. Performing steady-state detection; S60. Adaptively adjusting the evaporation variables. Correspondingly, in Figure 2 The structure of the steady-state detection system for the evaporation process of aluminum oxide production proposed by the present invention is given in the text, which includes: a monitoring unit, a data acquisition unit, a data processing unit, a steady-state variable analysis unit, a steady-state detection unit, a variable control unit and a feedback unit. Figure 1 The method steps can be Figure 2 The units in the implement the corresponding functions. Finally, the following content will explain the process of combining the two.

[0078] Specifically, the evaporation process of alumina production can be monitored in real time by the monitoring unit of the system; wherein, the monitoring unit mainly monitors data through multiple types of sensors, including: temperature sensors, concentration sensors, flow meters and pressure sensors; wherein, temperature sensors are used to monitor the temperature inside the evaporation equipment (evaporator, heater and cooler, etc.), the process temperature of the solution or mixture entering or leaving the evaporation equipment, and the temperature of the heat transfer medium (such as hot water and steam, etc.); alumina production usually involves dissolving alumina or other substances in a solvent to form a solution, and concentration sensors are used to monitor the concentration of alumina in these solutions; in alumina production, the solution is one of the liquids that often needs to flow, and the flow meter is used to measure the flow rate of the solution in the pipeline or equipment; in addition to the solution, the heat transfer medium such as water or steam also plays an important role in alumina production and also needs to be measured by a flow meter; when heating or cooling the evaporation equipment, hot water, steam or other liquids are usually used as the heat transfer medium. Pressure sensors are used to monitor the pressure changes of these media to ensure their normal flow and usage status in the equipment.

[0079] Specifically, the data acquisition unit collects data during the evaporation process of alumina production through the sensor of the monitoring unit and saves the data for subsequent analysis; the function of this unit corresponds to the above-mentioned step S10;

[0080] The data collected by the data collection unit has a time series characteristic; each piece of data is timestamped according to the set collection time so that relevant staff can query it;

[0081] Specifically, the data processing unit of the system processes the collected relevant historical data, corresponding to the above-mentioned step S20; wherein the relevant historical data includes: historical raw solution parameters (temperature, concentration and flow rate, etc.), historical evaporation variables (evaporator inlet solution flow rate, evaporator outlet concentrated solution flow rate, evaporator inlet solution temperature, evaporator outlet concentrated solution temperature, evaporator internal steam pressure, evaporator external ambient temperature, evaporator inlet solution concentration and evaporator outlet concentrated solution concentration, etc.), historical evaporation efficiency and historical product concentration at the outlet of the final flash evaporator;

[0082] The data processing process of the data processing unit includes: processing abnormal values and missing values in the relevant historical data to obtain first relevant historical data; wherein, the processing of abnormal values or missing values can adopt deletion method and interpolation method to perform data correction; normalizing the first relevant historical data to obtain second relevant historical data; and standardizing the second relevant historical data to obtain the standardized relevant historical data.

[0083] Refer to Table 1, which lists some of the processed data;

[0084] Table 1. Some historical data related to standardization

[0085]

[0086] Table 1 lists some preprocessed historical data at different time points, mainly including eight variable parameters: first evaporator discharge, evaporation mother liquor, first evaporator flow rate, first evaporator pressure, causticizing raw liquid, evaporation raw liquid tank temperature, heat exchanger outlet water temperature, and causticizing tank temperature. Among them, the first evaporator is the material quantity parameter output by the discharge pipe of the first evaporator during the production evaporation process.

[0087] The data processing unit of the system described in the embodiment of the present application processes the collected relevant historical data; wherein, data processing includes: outlier and missing value processing can help eliminate errors or incomplete parts in the data, thereby improving the quality and accuracy of the data, thereby avoiding large errors in subsequent steady-state detection; normalization eliminates the complexity of data interpretation and analysis introduced by unit differences. This makes the data easier to compare and analyze, ensuring the consistency and comparability of the data; standardization can eliminate the effects introduced by dimensional differences between different variables; in the evaporation process of alumina production, the parameters involved may include temperature, pressure, and concentration, etc., and the dimensions of these parameters may be different. Standardization scales each variable to the same scale to ensure that they have similar importance in the analysis, avoiding certain variables from having too much influence on the test results due to their numerical values.

[0088] Specifically, according to Figure 1 Step S30 of the process is to mark the variables in the processed data; wherein the marking content includes: 1 represents a stable state; 0 represents an unstable state; the marking process is pre-marked by relevant staff through observation of the data;

[0089] Specifically, the steady-state correlation analysis of the marked data is performed by the steady-state variable analysis unit of the system, corresponding to the above-mentioned step S40; wherein the specific analysis process includes:

[0090] An initial matrix is established based on standardized relevant historical data; the initial matrix is expressed as: in, Expressed as the standardized historical stock solution parameter at the mth time point; SV mn Expressed as the nth standardized historical evaporation variable at the mth time point; Expressed as the normalized historical product concentration at the mth time point;

[0091] Substitute each row of data in the initial matrix into the discrete equation; wherein the discrete equation formula is:

[0092]

[0093] in, Expressed as the normalized historical product concentration of row i in the initial matrix; It is represented by the normalized historical stock solution parameter of the i-th row in the initial matrix; SV i It is represented as the normalized historical evaporation variable vector of the i-th row in the initial matrix; f() is represented as a nonlinear analytical function; E() is represented as the environmental function of the aluminum oxide in the production evaporation process;

[0094] Among them, an environmental function is added to the above discrete equation, which is used to simulate the environmental conditions of the alumina production evaporation model; in the embodiment of the present application, the alumina production evaporation model can be composed of 4 evaporators, 3 flash evaporators, 3 preheaters and 4 condensate tanks to realize a four-effect evaporation process; refer to Figure 3 , the figure shows the process diagram of four-effect evaporation; among them, Figure 3 The process includes:

[0095] (1) Feed and liquid process

[0096] The raw liquid 701 is fed from the raw material tank via a feed pump into the third evaporator 203 and the fourth evaporator 204, respectively. The outlet of the fourth evaporator 204 is pumped into the third preheater 303 by a feed pump. In the third preheater 303, the liquid is heated to a temperature close to the boiling point of the third evaporator 203 before entering the third evaporator 203. Subsequently, the liquid enters the first evaporator 201 and the second evaporator 202 in the same manner. The liquid is continuously circulated through each evaporator via a circulation pump. The outlet of the first evaporator 201, driven by pressure differential, enters the first flash evaporator 401, the second flash evaporator 402, and the third flash evaporator 403, in sequence.

[0097] (2) Steam process

[0098] Raw steam 601 enters the first evaporator 201. The secondary steam generated by the first evaporator 201 enters the second evaporator 201 and the first preheater 301, respectively. Similarly, the secondary steam from the second evaporator 202 enters the third evaporator 203 and the second preheater 302, respectively. The secondary steam from the third evaporator 203 enters the fourth evaporator 204 and the third preheater 303, respectively. The secondary steam from the fourth evaporator 204 enters the vacuum pump via the condenser 501. The steam generated by evaporation in each condensate tank enters the corresponding evaporator for reuse. Part of the steam generated by the first condensate tank 101 enters the third condensate tank 103. The secondary steam from the first flash evaporator 401, the second flash evaporator 402, and the third flash evaporator 403 enter the first preheater 301, the second preheater 302, and the third preheater 303, respectively.

[0099] (3) Condensate process

[0100] The condensed water generated by the first evaporator 201 is pumped to the condensed water monitoring station through a qualified water pump after self-evaporation. The condensed water in the second condensed water tank 102 enters the third condensed water tank 103, recovers heat from self-evaporation to the third evaporator 203, and is pumped to the condensed water monitoring station by an unqualified condensed water pump. The condensed water generated by the fourth evaporator 204 is directly pumped to the condensed water monitoring station by a qualified condensed water pump.

[0101] Specifically, the environmental function includes four modules: an evaporator module, a flash evaporator module, a preheater module, and a condensed water tank module;

[0102] The evaporator module is represented as: Among them, T evap,i Expressed as the temperature of evaporator i; Q in,i Expressed as the feed heat of evaporator i; Q steam,i Expressed as the steam heat output of evaporator i; Q out,i Expressed as other heat loss or output of evaporator i; Cp i Expressed as the specific heat capacity of evaporator i material; m i Expressed as the mass of the material in evaporator i;

[0103] The flash evaporator module is represented as: Among them, M liquid,j Expressed as the mass of the liquid phase of flash evaporator j; M vapor,j Expressed as the mass of the gas phase in flash evaporator j; Expressed as the steam mass flow rate of flash evaporator j;

[0104] The preheater module is represented by: Q preheat,k =U k *A k *(T steam -T feed,k )(k=1,2,3); where Q preheat,k Expressed as the heat transfer rate of the preheater k; U k Expressed as the heat transfer coefficient of the preheater k; A k Expressed as the heat transfer area of preheater k; T steam Expressed as the temperature of steam; T feed,k Expressed as the feed temperature of preheater k;

[0105] The condensate tank module is represented by: M condensed,l =f condensation_rat (T steam,l )(l=1,2,3,4); where M condensed,l Expressed as the condensate production rate of the condensate tank l; f condensation_rat () is the condensed water production rate function; T steam,l Expressed as the flow rate of the condensate tank l;

[0106] By combining the modules of the above components, an integrated environmental model can be established to describe the dynamic evolution of the entire alumina production evaporation process and solved using the Runge-Kutta method as f().

[0107] Specifically, the correlation coefficient vector of each data in the initial matrix is solved according to the discrete equation, and the evaporation variable correlation matrix is established; refer to Table 2, which provides a table of the evaporation variable correlation matrix in this application example;

[0108] Table 2. Values of the correlation matrix of evaporation variables

[0109] Evaporator temperature Feed temperature Steam production Feed mass flow rate Evaporator temperature 1.00 0.85 0.60 0.72 Feed temperature 0.85 1.00 0.55 0.68 Steam production 0.60 0.55 1.00 0.45 Feed mass flow rate 0.72 0.68 0.45 1.00

[0110] Specifically, the significance ranking of the evaporation variable correlation matrix is performed to obtain the significance ranking result of the evaporation variable; the significance calculation process based on the data in Table 2 is as follows:

[0111] Evaporator temperature: 1.00+0.85+0.60+0.72=3.17;

[0112] Feed temperature: 0.85+1.00+0.55+0.68=3.08;

[0113] Steam volume: 0.72+0.68+0.45+1.00=2.85;

[0114] Feed mass flow rate: 0.60 + 0.55 + 0.100 + 0.45 = 2.60;

[0115] The results of the above significance ranking are given in Table 3;

[0116] Table 3. Significance ranking results

[0117] Ranking Evaporation variable Sum of correlation coefficients 1 Evaporator temperature 3.17 2 Feed temperature 3.08 3 Steam production 2.85 4 Feed mass flow rate 2.60

[0118] Table 3 describes the four evaporation variables: evaporator temperature, feed temperature, steam production, and feed mass flow rate. The sum of the correlation coefficients for evaporator temperature is 3.17, the highest, and therefore ranks first in the significance ranking. The sum of the correlation coefficients for feed temperature is 3.08, closely following and ranking second. The sum of the correlation coefficients for steam production is 2.85, ranking third. The sum of the correlation coefficients for feed mass flow rate is 2.60, ranking fourth.

[0119] Specifically, steady-state variables are selected based on the significance ranking results of the evaporation variables combined with the marking content of the initial matrix; wherein the selection of the steady-state variables can be screened by a threshold method;

[0120] Specifically, the steady-state correlation coefficient matrix is obtained by using a correlation analysis method according to the result of the steady-state variable selection.

[0121] In an embodiment of the present application, a method for analyzing the correlation of steady-state variables in the evaporation process of aluminum oxide production is adopted to analyze the collected historical data; the method obtains the variable correlation characteristics in the initial data matrix through discrete equations, and performs significance sorting according to the variable correlation characteristics to obtain the correlation differences between the variables, and selects steady-state variables based on the initial matrix; the selected steady-state variables are subjected to correlation analysis to obtain the final steady-state variable correlation coefficient matrix; the method can accurately analyze the interaction relationship between the various variables in the evaporation process of aluminum oxide production, and ensure that the selected variables are conducive to improving the accuracy of subsequent steady-state detection.

[0122] Specifically, according to the correlation analysis of the steady-state variables in the historical data, the influence state between the variables in the evaporation process of alumina production can be obtained; next, steady-state detection is performed according to the analysis results, and this function is realized by the steady-state detection unit of the system. Figure 1 In step S50, the process is as follows:

[0123] Specifically, a multivariate steady-state evaluation index corresponding to the timestamp is obtained based on the marked standardized related historical data; wherein the multivariate steady-state evaluation index is expressed as:

[0124]

[0125] Where SI(t) represents the steady-state index at time t; n represents the total number of evaporation variables; X i (t) represents the parameter value of the i-th evaporation variable at time t; σ i (t) represents the mean value of the ith evaporation variable at time t; μ i (t) represents the variance of the i-th evaporation variable at time t;

[0126] In order to achieve steady-state detection of various variables in the evaporation process of alumina production, the embodiment of the present application adopts a multivariable steady-state evaluation index for evaluation; the multivariable steady-state evaluation index is calculated based on different time nodes in historical data, thereby achieving more accurate variable monitoring and steady-state detection.

[0127] Specifically, a steady-state detection function is constructed according to the multivariable steady-state evaluation index; wherein the steady-state detection function is expressed as:

[0128]

[0129] Wherein, SS() represents a steady-state detection function; X(t) represents a vector of evaporation variables of the aluminum oxide during the production evaporation process; AnoD() represents a steady-state anomaly detection algorithm;

[0130] Combining the steady-state correlation coefficient matrix with the standardized historical evaporation variables to construct an extended variable set;

[0131] Using the expanded variable set to train a random forest classifier; wherein the output layer of the random forest classifier uses the multivariate steady-state evaluation index as a judgment criterion;

[0132] In the embodiment of the present application, three main operating parameters, namely steam flow, pressure and raw liquid flow, are selected as the basis for process steady-state judgment. The actual evaporation process is sampled every 5 minutes, and 240 sets of data are collected within 20 consecutive hours. The steady-state detection unit is used to perform steady-state judgment; wherein, referring to Figure 4 ,exist Figure 4 The actual values of the new steam flow, pressure and raw liquid flow during the actual measurement are given in Figure 5 The steady-state test results of the above three variables are given in Figure 5 A non-zero vertical coordinate indicates that the moment is in a steady state (in order to distinguish the three detection results, the two monostable detection results are respectively increased by 0.2 and 0.3), and 0 indicates that the corresponding moment is in an unstable state.

[0133] In order to further illustrate the steady-state detection results of the evaporation process of alumina production under the interaction of multiple variables, Figure 6 The test results within 20 hours are given.

[0134] In an embodiment of the present application, a steady-state detection method is adopted by establishing a steady-state detection function; the function performs steady-state detection on evaporation variables at different time points using a steady-state anomaly detection algorithm, and establishes a multivariate steady-state evaluation index based on historical data in the function, which accurately evaluates the evaporation variables; this method can not only provide an accurate assessment of the steady state of the evaporation variables, but also accurately understand the changes in the evaporation variables at specific times.

[0135] Specifically, the variable control unit of the system is used to control the evaporation variable, corresponding to the above step S60; see Figure 7 The specific control process is as follows:

[0136] Obtain the current raw liquid parameters and evaporation variables; set the control target of the evaporation process;

[0137] The control objectives include: maximum product concentration and maximum evaporation efficiency; using an evaporation variable optimization algorithm to obtain optimized evaporation variables;

[0138] The steps of the evaporation variable optimization algorithm include:

[0139] Step 1: taking the evaporation variable as the initial optimal evaporation variable;

[0140] Step 2: evaluating the correlation of the initial optimal evaporation variables through the steady-state correlation coefficient matrix;

[0141] The evaluation process of step 2 includes:

[0142] According to the strength and direction of each relationship in the steady-state correlation coefficient matrix; the value of the correlation coefficient ranges from -1 to +1, close to +1 indicates a strong positive correlation, close to -1 indicates a strong negative correlation; close to 0 indicates no correlation;

[0143] The values of the initial optimal evaporation variables are combined and calculated according to the median of the steady-state correlation coefficient matrix.

[0144] Step 3: Take the highest value of the correlation as the parent, perform crossover and mutation operations to generate new evaporated variables;

[0145] Step 4: updating the initial optimal evaporation variable according to the new evaporation variable;

[0146] Step 5: Repeat steps 2 to 4 until the stop condition is met to obtain the optimized evaporation variable.

[0147] Performing steady-state detection on the optimized evaporation variable; when the optimized evaporation variable is in steady state and can achieve the control target, obtaining the optimal evaporation variable; otherwise, repeating the evaporation variable optimization algorithm;

[0148] In this embodiment, the four-effect evaporation process takes about 90 minutes for the current data. Therefore, 60 consecutive sets of evaporation variables are selected for coordination during this evaporation process. Table 4 shows a comparison between the coordination results of the variable control unit and the measured results.

[0149] Table 4. Comparison of evaporation variable coordination results and measured values

[0150]

[0151] The table shows a comparison between the coordinated results of the evaporation variables and the measured values. The coordinated evaporation variables listed in the table are representative process control variables in the entire four-effect evaporation process. It can be seen from the table that the error between the method proposed by the present invention and the actual detection values is small. For example, the measured value of the raw steam flow rate of evaporator 3 in the table is 20.050, while after coordination with the present invention, it is 19.537, with an error of 0.513; the measured value of the preheater pressure is 0.520, while after coordination with the present invention, it is 0.517, with an error of 0.003.

[0152] In addition, according to the embodiment of the present application Figure 6 The steady-state detection of the evaporation process of alumina production is used to adjust the variables adaptively; Figure 8 The results show that after coordination, the detection of the evaporation process in the continuous time period is in a stable state.

[0153] In the embodiment of the present application, a method for adaptively coordinating evaporation variables in the evaporation process is adopted to adaptively adjust the evaporation variables; this method achieves the control target without losing stability when adjusting the evaporation variables, and obtains the optimal evaporation variables through multiple iterative optimizations to meet the set control targets, thereby ensuring the quality of alumina production.

[0154] Specifically, the feedback unit of the system feeds back the parameter changes and evaporation conditions during the alumina production evaporation process to relevant staff.

[0155] In the embodiment of the present application, the method of the present invention is combined with the system to realize the steady-state detection of aluminum oxide in the four-effect evaporation process and the coordination of variables; the present invention mainly starts from the following main aspects: first, the relevant historical data of aluminum oxide in multiple production evaporation processes are collected according to the timestamp, and an evaporation variable analysis method is used to perform correlation analysis on the collected historical data; the analysis method obtains the variable correlation characteristics in the initial data matrix through discrete equations, and performs significance sorting according to the variable correlation characteristics to obtain the relevant differences between the variables, and selects the steady-state variables in combination with the initial matrix; the selection results are used to obtain the steady-state correlation coefficient matrix; the method helps to optimize the selection process of evaporation variables, ensuring that the selected variables are conducive to improving the subsequent steady-state detection Accuracy; secondly, the present invention proposes a steady-state detection method, which establishes a steady-state detection function. The function performs steady-state detection on evaporation variables at different time points through a steady-state anomaly detection algorithm, and establishes a multivariate steady-state evaluation index in the function through historical data, which can accurately evaluate the evaporation variables; this method can not only provide an accurate evaluation of the steady state of the evaporation variables, but also accurately understand the changes in the evaporation variables at a specific time; finally, a method for adaptively coordinating evaporation variables in the evaporation process is adopted to adaptively adjust the evaporation variables; this method achieves the control target while adjusting the evaporation variables without losing stability, and obtains the optimal evaporation variables through multiple iterative optimizations to meet the set control target, thereby ensuring the quality of alumina production.

[0156] Example 2:

[0157] In Example 1, the method of the present invention is combined with the system to achieve steady-state detection of aluminum oxide during the four-effect evaporation process. In order to further illustrate the feasibility of the method proposed by the present invention, a technical description will be further provided in this embodiment of the present application. The specific detection process is basically the same as that of Example 1, and mainly includes:

[0158] Obtain relevant historical data of the alumina during multiple production evaporation processes according to the timestamp;

[0159] Further, data processing is performed on the relevant historical data to obtain standardized relevant historical data;

[0160] Furthermore, according to the observation of relevant staff, steady-state marking was performed;

[0161] Furthermore, an evaporation variable analysis is performed on the standardized related historical data to obtain an evaporation variable correlation matrix; wherein the calculation process of the evaporation variable correlation matrix includes:

[0162] An initial matrix is established based on the standardized related historical data; the initial matrix is expressed as: in, Expressed as the standardized historical stock solution parameter at the mth time point; SV mn Expressed as the nth standardized historical evaporation variable at the mth time point; Expressed as the normalized historical product concentration at the mth time point;

[0163] Substitute each row of data in the initial matrix into the discrete equation; wherein the discrete equation formula is:

[0164]

[0165] in, Expressed as the normalized historical product concentration of row i in the initial matrix; It is represented by the normalized historical stock solution parameter of the i-th row in the initial matrix; SV i It is represented as the normalized historical evaporation variable vector of the i-th row in the initial matrix; f() is represented as a nonlinear analytical function; E() is represented as the environmental function of the aluminum oxide in the production evaporation process;

[0166] Solving the correlation coefficient vector of each data in the initial matrix according to the discrete equation, and establishing the evaporation variable correlation matrix;

[0167] Further, a steady-state correlation coefficient matrix is calculated using the evaporation variable correlation matrix;

[0168] Furthermore, steady-state detection is performed; the steady-state detection process includes: obtaining a multivariate steady-state evaluation index corresponding to the timestamp based on the marked standardized related historical data;

[0169] Wherein, the multivariable steady-state evaluation index is expressed as:

[0170]

[0171] Where SI(t) represents the steady-state index at time t; n represents the total number of evaporation variables; X i (t) represents the parameter value of the i-th evaporation variable at time t; σ i (t) represents the mean value of the ith evaporation variable at time t; μ i (t) represents the variance of the i-th evaporation variable at time t;

[0172] A steady-state detection function is constructed according to the multivariable steady-state evaluation index; wherein the steady-state detection function is expressed as:

[0173]

[0174] Wherein, SS() represents a steady-state detection function; X(t) represents a vector of evaporation variables of the aluminum oxide during the production evaporation process; AnoD() represents a steady-state anomaly detection algorithm;

[0175] Furthermore, adaptive evaporation variable coordination is performed in the evaporation process based on the detection results; wherein the process of adaptive evaporation variable coordination in the evaporation process includes: obtaining the raw liquid parameters and evaporation variables at the current moment; setting the control target of the evaporation process; wherein the control target includes: maximum product concentration and maximum evaporation efficiency; using an evaporation variable optimization algorithm to obtain optimized evaporation variables; wherein the steps of the evaporation variable optimization algorithm include:

[0176] Step 1: taking the evaporation variable as the initial optimal evaporation variable;

[0177] Step 2: evaluating the correlation of the initial optimal evaporation variables through the steady-state correlation coefficient matrix;

[0178] Step 3: Take the highest value of the correlation as the parent, perform crossover and mutation operations to generate new evaporated variables;

[0179] Step 4: updating the initial optimal evaporation variable according to the new evaporation variable;

[0180] Step 5: Repeat steps 2 to 4 until the stop condition is met to obtain the optimized evaporation variable.

[0181] A steady-state detection is performed on the optimized evaporation variable; when the optimized evaporation variable is in a steady state and can achieve the control target, the optimal evaporation variable is obtained; otherwise, the evaporation variable optimization algorithm is repeated.

[0182] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A steady-state detection method for the evaporation process of aluminum oxide production, characterized in that: include: Relevant historical data of alumina during multiple production evaporation processes are obtained based on timestamps; the relevant historical data include: historical raw liquid parameters, historical evaporation variables, historical evaporation efficiency, and historical product concentration at the outlet of the final flash evaporator; data processing is performed on the relevant historical data to obtain standardized relevant historical data; the historical evaporation variables are marked; the marking content includes: 1 represents a stable state; 0 represents an unstable state; evaporation variable analysis is performed on the standardized relevant historical data to obtain an evaporation variable correlation matrix; and a steady-state correlation coefficient matrix is calculated. The process of obtaining the evaporation variable correlation matrix includes: Establish an initial matrix based on standardized relevant historical data; substitute each row of data in the initial matrix into the discrete equation; the discrete equation includes the environmental function of the alumina production evaporation process; Solving the correlation coefficient vector of each data in the initial matrix according to the discrete equation, and establishing the evaporation variable correlation matrix; A steady-state detection is performed on the current alumina production evaporation process using a steady-state correlation coefficient matrix to obtain steady-state detection results; adaptive evaporation variable coordination is performed based on the steady-state detection results; the adaptive evaporation variable coordination process includes: obtaining the current raw liquid parameters and evaporation variables; setting control targets for the evaporation process; the control targets include: maximum product concentration and maximum evaporation efficiency; and using an evaporation variable optimization algorithm to obtain optimized evaporation variables. The steps of the evaporation variable optimization algorithm include: Step 1: taking the evaporation variable as the initial optimal evaporation variable; Step 2: evaluating the correlation of the initial optimal evaporation variables through the steady-state correlation coefficient matrix; Step 3: Take the highest value of the correlation as the parent, perform crossover and mutation operations to generate new evaporated variables; Step 4: updating the initial optimal evaporation variable according to the new evaporation variable; Step 5: Repeat steps 2 to 4 until the stop condition is met to obtain the optimized evaporation variable; The optimized evaporation variable is tested for steady state; when the optimized evaporation variable is in steady state and can achieve the control target, the optimal evaporation variable is obtained; otherwise, the evaporation variable optimization algorithm is repeated.

2. The method for steady-state detection of an aluminum oxide production evaporation process according to claim 1, characterized in that: The data processing includes: Processing abnormal values and missing values in the relevant historical data to obtain first relevant historical data; Normalizing the first relevant historical data to obtain second relevant historical data; The second relevant historical data is standardized to obtain the standardized relevant historical data.

3. The method for steady-state detection of an evaporation process in aluminum oxide production according to claim 1, characterized in that: The process of calculating the steady-state correlation coefficient matrix includes: The significance ranking of the evaporation variable correlation matrix is performed to obtain the evaporation variable significance ranking result; the steady-state variable selection is performed based on the evaporation variable significance ranking result and the marking content of the initial matrix; and the steady-state correlation coefficient matrix is obtained using the correlation analysis method based on the result of the steady-state variable selection.

4. The method for steady-state detection of an evaporation process in aluminum oxide production according to claim 1, characterized in that: The steady-state detection process includes: obtaining a multivariate steady-state evaluation index corresponding to the timestamp based on the marked standardized related historical data; constructing a steady-state detection function based on the multivariate steady-state evaluation index; and using the steady-state detection function to perform steady-state detection on the alumina production evaporation process to obtain the steady-state detection result.

5. A steady-state detection system for the evaporation process of aluminum oxide production, characterized in that: The system includes: a monitoring unit, a data acquisition unit, a data processing unit, a steady-state variable analysis unit, a steady-state detection unit, a variable control unit and a feedback unit; wherein the monitoring unit is used to monitor the alumina during the production evaporation process; the data acquisition unit is used to collect various data of the alumina production evaporation process; the data processing unit is used to process the data collected by the data acquisition unit; the steady-state variable analysis unit is used to perform correlation analysis on the variables of the alumina during the production evaporation process; The steady-state variable analysis unit includes: establishing an initial matrix based on standardized related historical data; solving the correlation coefficient vector of each data in the initial matrix according to the discrete equation, and establishing an evaporation variable correlation matrix; calculating a steady-state correlation coefficient matrix based on the evaporation variable correlation matrix; The steady-state detection unit is used to perform steady-state detection on various variables in the evaporation process of alumina production; the variable control unit is used to perform steady-state adjustment on the variables; the specific process is as follows: Obtaining the current stock solution parameters and evaporation variables; setting control targets for the evaporation process; wherein the control targets include: maximum product concentration and maximum evaporation efficiency; using an evaporation variable optimization algorithm to obtain optimized evaporation variables; The steps of the evaporation variable optimization algorithm include: Step 1: taking the evaporation variable as the initial optimal evaporation variable; Step 2: evaluating the correlation of the initial optimal evaporation variables through the steady-state correlation coefficient matrix; Step 3: Take the highest value of the correlation as the parent, perform crossover and mutation operations to generate new evaporated variables; Step 4: updating the initial optimal evaporation variable according to the new evaporation variable; Step 5: Repeat steps 2 to 4 until the stop condition is met to obtain the optimized evaporation variable; The feedback unit is used to feed back parameter changes and evaporation conditions during the alumina production evaporation process to relevant staff.

6. The steady-state detection system for the evaporation process of aluminum oxide production according to claim 5, characterized in that: The data processing unit includes: Processing abnormal values and missing values in the relevant historical data to obtain first relevant historical data; Normalizing the first relevant historical data to obtain second relevant historical data; The second relevant historical data is standardized to obtain standardized relevant historical data.

7. The steady-state detection system for the evaporation process of aluminum oxide production according to claim 5, characterized in that: The steady-state correlation coefficient matrix includes: performing significance sorting on the evaporation variable correlation matrix to obtain a significance sorting result of the evaporation variable; selecting steady-state variables based on the significance sorting result of the evaporation variable combined with the marking content of the initial matrix; and obtaining the steady-state correlation coefficient matrix using a correlation analysis method based on the result of the steady-state variable selection.

8. The steady-state detection system for the evaporation process of aluminum oxide production according to claim 5, characterized in that: The steady-state detection unit includes: obtaining a multivariate steady-state evaluation index corresponding to a timestamp based on the marked standardized related historical data; constructing a steady-state detection function based on the multivariate steady-state evaluation index; and using the steady-state detection function to perform steady-state detection on the alumina production evaporation process to obtain a steady-state detection result.

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