Micro-chemical network system and method for real-time sensing and manipulation of intermediate compounding process in dye chemical industry

Abstract

The present application relates to a kind of dye chemical real-time perception and the microcosmic chemical net system and method of intermediate compounding process, including primary real-time perception-chemical decision-making-compounding control module, secondary real-time perception-chemical decision-making-compounding control module, tertiary real-time perception-chemical decision-making-compounding control module.The present application utilizes the closed-loop control method of chemical decision-making feedforward prediction feedback correction to double measurement double control for each level intermediate compounding liquid and raw material, first based on real-time perception module obtains the microcosmic chemistry and macroscopic physical information of liquid and raw material, then the optimal control model of chemical decision-making module is used to predict the theoretical amount of raw material, then through compounding control module dynamic interlocking raw material liquid valve precision control addition rate, reaction process chemical decision-making module cyclic analysis judges the deviation of target species real-time perception value in compounding liquid and model predicted value, and self-adjusting model algorithm corrects deviation, improves intermediate compounding quality, reduces byproduct production amount.

Description

Technical Field

[0001] This invention relates to the field of clean production technology in dye chemicals, specifically a microscopic chemical network system and method for real-time sensing and control of intermediate compounding processes in dye chemicals. Background Technology

[0002] In process industries, the concentration or total amount of species is closely related to the thermodynamics and kinetics of chemical reactions, directly affecting whether a unit reaction can occur or the extent of the reaction. Furthermore, deviations in species or concentration from theoretical values ​​in one production unit are propagated to downstream production units and even to all production units throughout the entire process, further amplifying the deviation and leading to reduced conversion efficiency and pollutant generation far exceeding theoretical calculations.

[0003] my country's dye industry has developed rapidly, ranking first in the world in terms of output for more than a decade. However, in recent years, as it has gradually entered a mature stage, the industry faces problems such as overcapacity and environmental issues, urgently requiring green and high-quality transformation and development, and entering a structural transformation phase. Disperse dyes, as the only dye varieties that can be used for dyeing and printing on polyester fibers, have become the largest sub-category in terms of output among all dyes. The 93:1 process flow for disperse violet involves three systems: a medium-D pulping system, a diazotization system, and a coupling system. The pulping and diazotization systems provide intermediates, while the coupling system synthesizes the product. The medium-D pulping process involves complex intermediates, a long reaction process, and a large proportion of compounded solutions, making it the key part of the disperse violet synthesis process.

[0004] In the actual production process, both D and diazonium salts are artificially synthesized high-concentration organic raw materials with constantly changing solid content and purity. The continuous production process requires dynamic compounding of multiple intermediates. Currently, the laboratory only performs offline analysis (5-6 times / day, 1-2 hours each time) on the acidity of the circulating acid solution in the D pulping system and the purity of D in the artificially synthesized raw materials. It lacks analysis of the microscopic chemical processes involved in the compounding of multiple intermediates before coupling, and cannot intervene in or control these microscopic chemical reactions, including the compounding of D, additives, and diazonium salts. Because the company cannot dynamically and precisely control the amount of raw materials added during the high-concentration intermediate compounding process, and the total amount of D and diazonium salts at any given time is difficult to maintain a stable and reasonable ratio, the reaction efficiency of the disperse violet coupling process decreases, product quality declines, and byproducts increase. Summary of the Invention

[0005] The purpose of this invention is to provide a microscopic chemical network system and method for real-time sensing and control of intermediate compounding processes in dye chemical industry, so as to solve the problems of reduced reaction efficiency, decreased product quality, and increased by-products mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a microscopic chemical network system for real-time sensing and controlling the compounding process of intermediates in dye chemical industry. Based on the set primary intermediate compounding reaction process, secondary intermediate compounding reaction process, and tertiary intermediate compounding reaction process, it includes:

[0008] A primary module, the primary module includes a primary real-time sensing module, a first-level chemical decision-making model, and a primary compounding control module. First, the primary module is used for the first-level intermediate compounding process. The primary real-time sensing module is used for the dual measurement of quickly obtaining the microscopic chemical information (high-concentration purity and solid content of medium D) of the synthetic raw material 1-1 (N,N-diethyl-3-acetamidoaniline, abbreviated as: medium D), quickly obtaining the microscopic chemical information (content of low-concentration medium D, sulfuric acid content) and macroscopic physical information (total amount of solution in the tank) of the primary incoming liquid; then using the optimal control model for precise addition of primary medium D and acid solution in the chemical decision-making module to predict the theoretical addition amount of the primary compounded organic basic raw material 1-1 (high-concentration and high-purity molten medium D) and the raw material 2-1 acid solution to maintain the stable progress of the compounding reaction, that is, the feedforward prediction theoretical addition amount value; finally, the primary compounding control module is used for dynamically interlocking and dual-controlling the electric control valves of raw materials 1-1 and 1-2 to precisely control the addition rate. During the primary intermediate compounding reaction process, the first-level model of the chemical decision-making module circularly analyzes and judges the deviation between the actual value of intermediate real-time sensing and the theoretical value predicted by the model, and corrects the deviation amount through self-adjusting the model algorithm, that is, the feedback correction theoretical addition amount value; until the theoretical limit is reached before the outflow of the primary intermediate compounding liquid;

[0009] A secondary module, the secondary module includes a secondary real-time sensing module, a second-level chemical decision-making model, and a secondary compounding control module. First, the secondary module is used for the second-level intermediate compounding process. The secondary real-time sensing module is used for the dual measurement of quickly obtaining the microscopic chemical information (purity and solid content of the auxiliary agent) of the synthetic raw material 2-1 (saponin, urea, abbreviated as: auxiliary agent), quickly obtaining the microscopic chemical information (content of medium D, sulfuric acid content) and macroscopic physical information (total amount of solution in the tank) of the secondary incoming liquid; then using the optimal control model for precise addition of secondary auxiliary agent and acid solution in the chemical decision-making module to predict the theoretical addition amount of the secondary compounded organic basic raw material 2-1 (high-concentration mixed auxiliary agent) and the 2-2 acid solution to maintain the stable progress of the compounding reaction, that is, the feedforward prediction theoretical addition amount value; finally, the secondary compounding control module is used for dynamically interlocking and dual-controlling the electric control valves of raw materials 2-1 and 2-2 to precisely control the addition rate. During the secondary intermediate compounding reaction process, the second-level model of the chemical decision-making module circularly analyzes and judges the deviation between the actual value of intermediate real-time sensing and the theoretical value predicted by the model, and corrects the deviation amount through self-adjusting the model algorithm, that is, the feedback correction theoretical addition amount value; until the theoretical limit is reached before the outflow of the secondary intermediate compounding liquid;

[0010] The three-level module comprises a three-level real-time sensing module, a three-level chemical decision-making module, and a three-level compounding control module. First, the three-level real-time sensing module rapidly acquires microscopic chemical information (diazonium salt purity and solid content) of the synthetic raw material 3-1 (6-chloro-2,4-dinitrodiazobenzene sulfate, abbreviated as: diazonium salt), and rapidly acquires microscopic chemical information (D content, sulfuric acid content) and macroscopic physical information (total volume of solution in the tank) of the three-level liquid entry. Then, the optimal compounding model of the three-level intermediates in the chemical decision-making module is used to predict the three-level... The compounding process involves adding organic alkaline raw material 3-1 (high-concentration diazonium salt) and maintaining the stability of the compounding reaction by adding the theoretical amount of acid solution 3-2, i.e., the feedforward predicted theoretical addition amount. Finally, the three-stage compounding control module is used to dynamically and interlock the electronically controlled valves of raw materials 3-1 and 3-2 to precisely control the addition rate. During the three-stage intermediate compounding reaction process, the chemical decision module's three-stage model cyclically analyzes and judges the deviation between the actual value and the theoretical value predicted by the model in real time, and performs self-adjustment model algorithm to correct the deviation, i.e., the feedback correction theoretical addition amount. This continues until the effluent from the three-stage intermediate compounding reaches the theoretical limit.

[0011] As a further aspect of the present invention: in the first-stage intermediate compounding reaction process, before the reaction, the first-stage real-time sensing module is used to sense the microscopic chemical information (purity, solid content) of D in the artificially synthesized raw material 1-1 in the raw material tank, the microscopic chemical information (D content, sulfuric acid content) of the liquid in the tank before the reaction, and the macroscopic physical information (total solution volume), and upload the data to the first-stage model of the chemical decision module. The first-stage optimal control model for precise addition of D and acid is used to predict the theoretical addition amount of raw material 1-1 and acid 1-2, and then the addition rate is precisely controlled by the dynamic interlocking of the electric control valves of raw material 1-1 and 1-2 through the first-stage compounding control module. The primary real-time sensing module is used to sense the rate of change of D content and acidity stability in the primary compounding reaction tank over time. During this process, the primary model of the chemical decision module is used to cyclically analyze and judge the deviation between the actual real-time sensing value of the intermediate and the theoretical value predicted by the model, and to self-adjust the model algorithm to correct the deviation until the primary intermediate compound liquid reaches the theoretical limit. Then, the primary compounding control module is used to interlock and close the electronically controlled valves corresponding to raw materials 1-1 and 1-2. When the reaction stops, the primary compounding control module opens the drain valve of the primary compounding reaction tank to ensure that the D content and acidity in the primary compounding effluent both reach the theoretical limit.

[0012] As a further aspect of the present invention: in the second-stage intermediate compounding reaction process, the secondary real-time sensing module is used to sense the microscopic chemical information (purity, solid content) of the synthetic raw material 2-1 auxiliary agent in the raw material tank, the microscopic chemical information (D content, sulfuric acid content) of the liquid in the tank before the reaction, and the macroscopic physical information (total solution volume), and uploads the data to the secondary model of the chemical decision module. The optimal control model for precise addition of the secondary auxiliary agent and acid is used to predict the theoretical addition amount of raw material 2-1 and acid 2-2, and then the addition rate is precisely controlled by the dynamic interlocking of the electric control valves of raw material 2-1 and 2-2 through the compounding control module; during the reaction... The secondary real-time sensing module is used to monitor the rate of change of D content and additive content over time, as well as acidity stability in the secondary compounding reaction tank. During this process, the secondary model of the chemical decision module is used to cyclically analyze and judge the deviation between the actual real-time sensing value of the intermediate and the theoretical value predicted by the model, and to self-adjust the model algorithm to correct the deviation until the secondary intermediate compound liquid reaches the theoretical limit. Then, the secondary compounding control module is used to interlock and control the corresponding electronic control valves of raw materials 2-1 and 2-2 to close. When the reaction stops, the secondary compounding control module opens the drain valve of the secondary compounding reaction tank to ensure that the D content, additive content, and acidity in the secondary compound effluent all reach the theoretical limit.

[0013] As a further aspect of the present invention: in the third-stage intermediate compounding reaction process, before the reaction, the third-stage real-time sensing module is used to sense the microscopic chemical information (purity, solid content) of the artificially synthesized raw material 3-1 diazonium salt in the raw material tank, the microscopic chemical information (D content, sulfuric acid content) of the liquid in the tank before the reaction, and the macroscopic physical information (total solution volume), and uploads the data to the third-stage chemical decision module model. The optimal control model for precise addition of the third-stage diazonium salt and acid solution is used to predict the theoretical addition amount of raw material 3-1 and acid solution 3-2, and then the addition rate is precisely controlled by the dynamic interlocking of the electric control valves of raw material 3-1 and 3-2 through the third-stage compounding control module; during the reaction... The three-level real-time sensing module is used to sense the rate of change of D content, diazonium salt content, and acidity stability in the three-level compound reaction tank over time. During this period, the three-level chemical decision module model is used to cyclically analyze and judge the deviation between the actual real-time sensing value of the intermediate and the theoretical value predicted by the model, and to self-adjust the model algorithm to correct the deviation until the three-level intermediate compound liquid reaches the theoretical limit. Then, the three-level compound control module is used to interlock and control the corresponding electric control valves of raw materials 3-1 and 3-2 to close. At the end of the reaction, the three-level compound control module opens the drain valve of the three-level compound reaction tank to ensure that the total D content, total diazonium salt content, and acidity in the three-level compound effluent all reach the theoretical limit.

[0014] As a further aspect of the present invention: the optimal theoretical limit for the endpoint of the first-stage intermediate compounding reaction process is: the D content range is 8.2±0.2% and the sulfuric acid content is 3.0±0.2%.

[0015] As a further aspect of the present invention: the optimal theoretical limit for the endpoint of the second-stage intermediate compounding reaction process is: the content of D is 2.8±0.2%, the content of auxiliary agent is 0.35% (0.2% of Pingping plus 0.15% of urea), and the content of sulfuric acid is 4.0±0.2%.

[0016] As a further aspect of the present invention: the optimal theoretical limit for the endpoint of the third-stage intermediate compounding reaction process is: total D content: diazonium salt content = 1:1 (molar ratio) = 206.14:325.94 (mass ratio) = 0.33t / h:0.52t / h (flow rate ratio), and the sulfuric acid content is 2.5±0.2%.

[0017] In a second aspect, the present invention provides a method for real-time sensing and manipulation of intermediate compounding processes in dye chemical industry using a microscopic chemical network, comprising the following steps;

[0018] S1. A circulating acid solution containing a certain amount of residual D and other species is introduced into the intermediate compounding reaction system and flows sequentially in the corresponding reaction tanks during the first, second and third compounding processes;

[0019] S2. The circulating acid first enters the first-stage intermediate compounding reaction process. Based on the first-stage real-time sensing module, the microscopic chemical information and macroscopic physical information of the liquid in the first-stage compounding process are obtained. The feedforward prediction and feedback correction of the optimal control model for precise addition of acid in the first-stage intermediate D and acid are used to determine the amount of raw materials to be added. Then, the precise addition is achieved through dynamic interlocking of the first-stage compounding control module. The reaction ends when the theoretical limit is reached.

[0020] S3. The first-stage effluent then enters the second-stage intermediate compounding reaction process. Based on the second-stage real-time sensing module, the microscopic chemical information and macroscopic physical information of the liquid in the second-stage compounding process are obtained. The feedforward prediction and feedback correction of the optimal control model for precise addition of chemical decision-making secondary auxiliary agent and acid are used to determine the amount of raw materials to be added. Then, the precise addition is achieved through dynamic interlocking of the second-stage compounding control module. The reaction ends when the theoretical limit is reached.

[0021] S4. The secondary effluent finally enters the tertiary intermediate compounding reaction process. Based on the real-time sensing module of the tertiary stage, the microscopic chemical information and macroscopic physical information of the liquid in the tertiary compounding process are obtained. The feedforward prediction and feedback correction of the optimal control model for the precise addition of tertiary diazonium salt and dilute acid are used to determine the amount of raw materials to be added. Then, the precise addition is achieved through dynamic interlocking of the tertiary compounding control module. The reaction ends when the theoretical limit is reached. The tertiary effluent enters the coupling system of the next reaction unit.

[0022] Compared with the prior art, the beneficial effects of the present invention are:

[0023] In this invention, a real-time sensing module acquires microscopic chemical and macroscopic physical information of the influent and raw materials. The optimal control model of the chemical decision module predicts the theoretical amount of raw materials to be added. Then, the addition rate is precisely controlled by the dynamically interlocking raw material inlet valve through the compounding control module. During the reaction process, the chemical decision module cyclically analyzes and judges the deviation between the real-time sensing value and the model prediction value of the target species in the compound solution, and self-adjusts the model algorithm to correct the deviation, ensuring that the effluent after multi-stage treatment reaches the corresponding theoretical limit. This effectively improves the quality of intermediates in the compounding process, providing technical support for the scientific control of the ratio of nitrogen (D) and diazonium salts, effectively increasing the conversion efficiency of the coupling process, significantly improving the quality of disperse violet products, and reducing COD generation at the source, thus meeting the urgent production needs of enterprises for pollution reduction, carbon reduction, quality improvement, and efficiency enhancement. Attached Figure Description

[0024] Figure 1 This is a schematic diagram showing the connection of the real-time sensing-chemical decision-combination control module of the present invention;

[0025] Figure 2 This invention provides a graph showing the change in the purity of D in Example 1.

[0026] Figure 3 The present invention provides a graph showing the change in D solid content in Example 1. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0028] Example 1:

[0029] Please see Figure 1 In this embodiment of the invention, a microscopic chemical network system for real-time sensing and control of intermediate compounding processes in dye chemistry is provided. Based on a set primary intermediate compounding reaction process, a secondary intermediate compounding reaction process, and a tertiary intermediate compounding reaction process, it includes a primary module, a secondary module, and a tertiary module:

[0030] The primary module comprises a primary real-time sensing module, a chemical decision-making module, a primary model, and a primary compounding control module. Firstly, the primary module is used for the first-stage intermediate compounding process. The primary real-time sensing module rapidly acquires microscopic chemical information (purity and solid content) of the synthetic raw material 1 (high concentration of D), and rapidly acquires microscopic chemical information (low concentration of D content and sulfuric acid content) and macroscopic physical information (total volume of solution in the tank) of the first-stage influent. Then, the primary optimal control model in the chemical decision-making module is used to predict the primary compounding process of organic alkaline raw material 1-1 (…). The theoretical addition amount of D) in the high-concentration, high-purity molten state and the 1-2 acid solution that maintains the stability of the compounding reaction is the feedforward predicted theoretical addition amount; the last-stage compounding control module is used to dynamically and interlock the dual control valves of raw materials 1-1 and 1-2 to precisely control the addition rate. In the first-stage intermediate compounding reaction process, the chemical decision module's first-stage model cyclically analyzes and judges the deviation between the actual value of the intermediate and the theoretical value predicted by the model in real time, and corrects the deviation through the self-adjusting model algorithm, i.e., the feedback correction theoretical addition amount; until the theoretical limit is reached before the first-stage intermediate compounding liquid flows out;

[0031] The secondary module comprises a secondary real-time sensing module, a chemical decision module, a secondary model, and a secondary compounding control module. Firstly, the secondary module is used for the secondary intermediate compounding process. The secondary real-time sensing module rapidly acquires microscopic chemical information (purity and solid content) of the artificially prepared raw material 2-1 (auxiliary agent), and rapidly acquires microscopic chemical information (medium D content, sulfuric acid content) and macroscopic physical information (total volume of solution in the tank) of the secondary influent. Then, the secondary optimal control model in the chemical decision module is used to predict the theoretical addition amount of the organic alkaline raw material 2-1 (high-concentration mixed auxiliary agent) and the 2-2 acid solution to maintain stable compounding reaction; this is the feedforward predicted theoretical addition amount. Finally, the secondary compounding control module dynamically and interlocks the electronically controlled valves of raw materials 2-1 and 2-2 to precisely control the addition rate. During the secondary intermediate compounding reaction, the chemical decision module cyclically analyzes and judges the deviation between the actual real-time sensing value and the model's predicted theoretical value, and corrects the deviation through a self-adjusting model algorithm; this is the feedback corrective theoretical addition amount; until the secondary intermediate compound solution reaches the theoretical limit before effluent.

[0032] The three-level module comprises a three-level real-time sensing module, a chemical decision-making module, a three-level model, and a three-level compounding control module. First, the three-level real-time sensing module rapidly acquires microscopic chemical information (purity and solid content) of the synthetic raw material 3-1 (diazonium salt), and simultaneously acquires microscopic chemical information (D content and sulfuric acid content) and macroscopic physical information (total volume of solution in the tank) of the tertiary intermediate. Then, the three-level optimal control model in the chemical decision-making module predicts the theoretical addition amount of the organic alkaline raw material 3-1 (high-concentration diazonium salt) and the 3-2 acid solution to maintain stable compounding reaction; this is the feedforward predicted theoretical addition amount. Finally, the three-level compounding control module dynamically controls the addition rate of the 3-1 and 3-2 electrically controlled valves through a dual-control system. During the tertiary intermediate compounding reaction, the chemical decision-making module cyclically analyzes and judges the deviation between the actual real-time sensing value and the model's predicted theoretical value, and performs self-adjustment of the model algorithm to correct the deviation; this is the feedback corrective theoretical addition amount, until the tertiary intermediate compounding effluent reaches the theoretical limit.

[0033] Specifically, in this invention, the microscopic chemical and macroscopic physical information of the influent and raw materials is acquired by a real-time sensing module. The optimal control model of the chemical decision module predicts the theoretical amount of raw materials to be added. Then, the addition rate is precisely controlled by the dynamically interlocking raw material inlet valve through the compounding control module. During the reaction process, the chemical decision module cyclically analyzes and judges the deviation between the real-time sensing value and the model prediction value of the target species in the compound solution, and self-adjusts the model algorithm to correct the deviation, ensuring that the effluent after multi-stage treatment reaches the corresponding theoretical limit. This effectively improves the quality of intermediates in the compounding process, providing technical support for the scientific control of the ratio of nitrogen (D) and diazonium salts, effectively increasing the conversion efficiency of the coupling process, significantly improving the quality of disperse violet products, and reducing COD generation at the source, thus meeting the urgent production needs of enterprises for pollution reduction, carbon reduction, quality improvement, and efficiency enhancement.

[0034] Preferably, in the first-stage intermediate compounding reaction process, the first-stage real-time sensing module is used to sense the microscopic chemical information (purity, solid content) of D in the synthetic raw material 1-1 in the raw material tank, as well as the microscopic chemical information (D content, sulfuric acid content) and macroscopic physical information (total solution volume) of the liquid in the tank before the reaction, and uploads the data to the chemical decision module. The first-stage optimal control model is used to predict the theoretical addition amount of raw material 1-1 and acid solution 1-2, and then the addition rate is precisely controlled by the dynamic interlocking of the electric control valves of raw material 1-1 and 1-2 through the first-stage compounding control module; during the reaction, the first-stage real-time sensing module... The module is used to sense the rate of change of D content and acidity stability in the primary compounding reaction tank over time in real time. During this period, the chemical decision module is used to cyclically analyze and judge the deviation between the actual value of the intermediate and the theoretical value predicted by the model, and to self-adjust the model algorithm to correct the deviation until the primary intermediate compound liquid reaches the theoretical limit. Then, the primary compounding control module is used to interlock and control the corresponding electric control valves of raw materials 1-1 and 1-2 to close. When the reaction stops, the primary compounding control module opens the drain valve of the primary compounding reaction tank to ensure that the D content and acidity in the primary compounding effluent both reach the theoretical limit.

[0035] Preferably, in the second-stage intermediate compounding reaction process, the second-stage real-time sensing module is used to sense the microscopic chemical information (purity, solid content) of the artificially prepared raw material 2-1 auxiliary agent in the raw material tank, the microscopic chemical information (D content, sulfuric acid content) of the liquid in the tank before the reaction, and the macroscopic physical information (total solution volume) in real time before the reaction, and uploads the data to the chemical decision module. The second-stage optimal control model is used to predict the theoretical addition amount of raw material 2-1 and 2-2 acid solution, and then the addition rate is precisely controlled by the dynamic interlocking of the electric control valves of raw material 2-1 and 2-2 through the compounding control module; during the reaction, the second-stage real-time sensing module is used for... The reaction tank monitors the rate of change of D content and additive content over time, as well as acidity stability. During this period, the chemical decision module is used to cyclically analyze and judge the deviation between the actual value and the theoretical value predicted by the model in real time, and to self-adjust the model algorithm to correct the deviation until the secondary intermediate compound liquid reaches the theoretical limit. Then, the secondary compound control module is used to interlock and control the corresponding electric control valves of raw materials 2-1 and 2-2 to close. When the reaction stops, the secondary compound control module opens the drain valve of the secondary compound reaction tank to ensure that the D content, additive content and acidity in the secondary compound effluent all reach the theoretical limit.

[0036] Preferably, in the third-stage intermediate compounding reaction process, the three-stage real-time sensing module is used to sense the microscopic chemical information (purity, solid content) of the artificially synthesized diazonium salt 3-1 in the raw material tank, the microscopic chemical information (D content, sulfuric acid content) of the liquid in the tank before the reaction, and the macroscopic physical information (total solution volume), and uploads the data to the chemical decision module. The three-stage optimal control model is used to predict the theoretical addition amount of raw material 3-1 and acid 3-2, and then the addition rate is precisely controlled by the dynamic interlocking of the electric control valves of raw material 3-1 and 3-2 through the three-stage compounding control module. During the reaction, the three-stage real-time sensing module is used to sense... The chemical decision module is used to analyze and judge the deviation between the actual value and the theoretical value predicted by the model in real time, and to self-adjust the model algorithm to correct the deviation until the compound liquid of the tertiary intermediate reaches the theoretical limit. Then, the tertiary compound control module is used to interlock and control the electric valves corresponding to raw materials 3-1 and 3-2 to close. When the reaction stops, the tertiary compound control module opens the drain valve of the tertiary compound reaction tank to ensure that the total amount of D, the total amount of diazonium salt and the acidity in the tertiary compound effluent all reach the theoretical limit.

[0037] Preferably, the optimal theoretical limit for the endpoint of the first-stage intermediate compounding reaction process is: a D content range of 8.2±0.2% and a sulfuric acid content of 3.0±0.2%.

[0038] Preferably, the optimal theoretical limit for the endpoint of the secondary intermediate compounding reaction process is: a D content range of 2.8±0.2%, an auxiliary agent content of 0.35% (0.2% Pingping plus 0.15% urea), and a sulfuric acid content of 4.0±0.2%.

[0039] Preferably, the optimal theoretical limit for the endpoint of the third-stage intermediate compounding reaction process is: total D content: diazonium salt content = 1:1 (molar ratio) = 206.14:325.94 (mass ratio) = 0.33t / h:0.52t / h (flow rate ratio), and the sulfuric acid content is 2.5±0.2%.

[0040] A microscopic chemical network method for real-time sensing and manipulation of intermediate compounding processes in dye chemical industry includes the following steps;

[0041] S1. A circulating acid solution containing a certain amount of residual D and other species is introduced into the intermediate compounding reaction system and flows sequentially in the corresponding reaction tanks during the first, second and third compounding processes;

[0042] S2. The circulating acid first enters the first-stage intermediate compounding reaction process. Based on the first-stage real-time sensing module, the microscopic chemical information and macroscopic physical information of the liquid in the first-stage compounding process are obtained. The feedforward prediction and feedback correction of the optimal control model for precise addition of acid in the first-stage intermediate D and acid are used to determine the amount of raw materials to be added. Then, the precise addition is achieved through dynamic interlocking of the first-stage compounding control module. The reaction ends when the theoretical limit is reached.

[0043] S3. The first-stage effluent then enters the second-stage intermediate compounding reaction process. Based on the second-stage real-time sensing module, the microscopic chemical information and macroscopic physical information of the liquid in the second-stage compounding process are obtained. The feedforward prediction and feedback correction of the optimal control model for precise addition of chemical decision-making secondary auxiliary agent and acid are used to determine the amount of raw materials to be added. Then, the precise addition is achieved through dynamic interlocking of the second-stage compounding control module. The reaction ends when the theoretical limit is reached.

[0044] S4. The secondary effluent finally enters the tertiary intermediate compounding reaction process. Based on the real-time sensing module of the tertiary stage, the microscopic chemical and macroscopic physical information of the liquid in the tertiary compounding process is obtained. The feedforward prediction and feedback correction of the optimal control model for the precise addition of the tertiary diazonium salt and acid solution are used to determine the amount of raw materials to be added. Then, the precise addition is achieved through dynamic interlocking of the tertiary compounding control module. The reaction ends when the theoretical limit is reached. The tertiary effluent enters the coupling system of the next reaction unit.

[0045]

Example

[0046] According to the chemical reaction equation for the 93:1 coupling of disperse violet (see Table 1), the molar ratio of D and diazonium salt in the chemical reaction is 1:1, and the mass ratio is 206.14:325.94. However, in the actual production process of the enterprise, both D and diazonium salt are artificially synthesized high-concentration organic raw materials with constantly changing solid content and purity. The continuous production process of the enterprise requires dynamic compounding of multiple intermediates, including the first stage of precise addition of D for intermediate compounding, the second stage of precise addition of auxiliary agents for intermediate compounding, and the third stage of addition of diazonium salt for intermediate compounding. This ensures that the compounded solution at each stage can reach the predetermined target limit before effluent, and ultimately ensures that the total amount of D and the total amount of diazonium salt remain in a stable and reasonable ratio at any time before entering the coupling process reaction.

[0047] Table 1 Basic Information on Microscopic Chemical Reactions

[0048]

[0049] Table 2 shows the microscopic chemical and macroscopic physical information of the circulating acid solution, raw material 1, raw material 2, raw material 3, and dilute acid solution in the primary, secondary, and tertiary intermediate compounding process. The purity and solid content variation curves of D in raw material 1 are shown in Table 2. Figure 2-3 .

[0050] Table 2 Microscopic chemical and macroscopic physical information of species

[0051]

[0052] 1. Prediction of the addition amount of D and dilute acid in the primary intermediate compound model

[0053] 1.1 Data from the Level 1 Real-Time Sensing Module

[0054] The content of D (C1), sulfuric acid (H1), and total solution volume (M1) in the circulating acid solution, as well as the purity and solid content of D in raw material 1, are constantly changing. Assume that the real-time sensing values ​​before primary compounding are: D content C1 = 1.0%, sulfuric acid content H1 = 4.2%, total solution volume M1 = 10000 kg; D purity in raw material P1 = 95%, solid content S1 = 90%.

[0055] 1.2 Prediction and Correction of First-Level Model for Chemical Decision Making

[0056] Because the optimal theoretical limits for the endpoint of the primary intermediate compounding reaction are: a D content range of 8.2 ± 0.2% and a sulfuric acid content of 3.0 ± 0.2%, for the D content, it is only necessary to solve for the D content at the endpoint of the primary reaction being 8.0% ≤ C. 出1 The amount of raw material 1 added, m1, under the condition of ≤8.4%;

[0057] Right now: Therefore, the theoretically optimal control model for precise addition of D in Level 1 is:

[0058] Solve the system of simultaneous equations: (1)

[0059] (2)

[0060] Substituting the real-time sensing data from "1.1" into the table: then

[0061] (1)

[0062] (2)

[0063] Therefore, when both (1) and (2) are met, the theoretical amount of m1 to be added is: 903.23kg≤m1≤954.84kg.

[0064] That is: ΔC 理论 =(C 出1 -C1)~m1

[0065] Assuming the amount of raw material 1 added, m1, is 920 kg, the D content at the final stage of its primary compounding should be:

[0066] theory

[0067] If, according to real-time sensing, the concentration of C rises to 8.10% and then stops increasing, that is, the actual C... 出1 =8.10%

[0068] The concentration deviation is then: ΔC = (theoretical C) 出1 -Actual C 出1 = 8.12% - 8.10% = 0.02%

[0069] Based on this, the corrective action amount for the addition of raw material 1 can be calculated as follows:

[0070]

[0071] Therefore, the theoretical m1 = 920 kg, the actual m1 = 920 + 2.82 = 922.82 kg, and the model correction absorption... Chemical decision cycle analysis judges self-correction, and in the first stage, the optimal control model is accurately added to D:

[0072] Model after one correction: Actual m1 = K1 × Theoretical m1

[0073] Model after secondary correction: Actual m1 = K2 × Theoretical m1

[0074] ……………………………………………………

[0075] Model after N corrections: Actual m1 = K N ×Theoretical m1

[0076] The final optimal control model for precise addition of D in the first stage is obtained under the condition that the effluent meets the limiting requirements:

[0077]

[0078] Secondly, since D is an alkaline organic compound, a neutralization reaction occurs during the addition process, which leads to a decrease in the acidity of the compound solution. In order to maintain the stability of the primary compounding reaction, the amount of dilute acid replenished is dynamically adjusted according to the acidity changes caused by the amount of D added to maintain the solution at a certain acidity value: 3.0±0.2%.

[0079] 2. Secondary intermediate compound model for predicting the dosage of auxiliaries, dilute acids, and their addition amounts.

[0080] 2.1 Data from the Level 2 Real-Time Sensing Module

[0081] D content (C) in primary effluent 出1 ), sulfuric acid content (H) 出1 ) and total solution volume (M 出1The content of additive 2 (C2) in raw material 2 is constantly changing. Assuming the real-time sensing value before secondary compounding is: D content C 出1 =8.2%, sulfuric acid content H 出1 =3.0%, total solution volume M 出1 =4000kg; Auxiliary agent content C2 = 98%.

[0082] 2.2 Prediction and Correction of Second-Level Chemical Decision Model

[0083] Because the optimal theoretical limit for the endpoint of the secondary intermediate compound reaction process is: the content of D is 2.8±0.2%, the content of the auxiliary agent is 0.2%+0.15%=0.35%, and the content of sulfuric acid is 4.0±0.2%. For D and the auxiliary agent, it is necessary to solve for the following to satisfy the endpoint of the secondary reaction: (1) the content of D is 2.6%≤C 出2 ≤3.0% and sulfuric acid content of 2.6% ≤H 出2 The amount of dilute acid added under conditions of ≤3.0% m 2-1 (3) The amount of raw material 2 added under the condition of 0.35% auxiliary agent content m 2-2 ;

[0084] First, solve the system of simultaneous equations:

[0085] (1) (2)

[0086] Therefore, the theoretically optimal control model for precise addition of dilute acid when the D content in the secondary compound effluent is (2.8±0.2%) is:

[0087]

[0088] Solve the system of equations (1-1):

[0089] (1-2)

[0090] Substituting the real-time sensing data from "2.1" into the table: then

[0091] (1-1)

[0092] (1-2)

[0093] Therefore, when requirement (1) is met, m 2-1 The theoretical addition amount is: 6933.33 kg ≤ m 2-1 ≤8615.38kg.

[0094] The theoretically optimal control model for precise addition of dilute acid when the sulfuric acid content of the secondary compound effluent is (4.0±0.2%) is:

[0095]

[0096] Solve the system of equations (2): (2-1)

[0097] (2-2)

[0098] Substituting the real-time sensing data from "2.1" into the table: then

[0099] (2-1)

[0100] (2-2)

[0101] Therefore, when requirements (1) and (2) are satisfied, m 2-1 The theoretical addition amount is: 6933.33 kg ≤ m 2-1 ≤8615.38kg.

[0102] Assuming the amount of dilution added is m 2-1 Given a value of 8000 kg, determine the amount of raw material 2 to be added, m, under the condition that the additive content is 0.35% to satisfy the second-order reaction endpoint. 2-2 ;

[0103] The theoretically optimal control model for the precise addition of secondary compound additives is:

[0104]

[0105] Solve equation (3)

[0106] Substituting the real-time sensing data from "2.1" and (1) into the table: then

[0107] (3)

[0108] Therefore, when requirement (3) is met, m 2-2 The theoretical addition amount is: m 2-2 =444.44kg.

[0109] Referring to the chemical decision-making cycle analysis in "2.1", a self-correction process was performed, ultimately obtaining (1) where the D content is 2.6% ≤ C, meeting the secondary compounding restriction requirements. 出2 ≤3.0% and sulfuric acid content of 2.6% ≤H 出2 The amount of dilute acid added under conditions of ≤3.0% m 2-1 (3) The amount of raw material 2 added under the condition of 0.35% auxiliary agent content m2-2

[0110] Dilution addition optimal control model: (1)

[0111] (2) Optimal control model for additive addition: (3)

[0112] 3. Prediction of Diazonium Salt and Dilute Acid Addition Amounts Using a Tertiary Intermediate Compound Model

[0113] 3.1 Data from the Level 3 Real-Time Sensing Module

[0114] D content in secondary effluent (C) 出2 ), sulfuric acid content (H) 出2 ) and total solution volume (M 出2 The content of diazonium salt (C3) in raw material 3 is constantly changing. It is assumed that the real-time sensing value before the three-stage compounding is the D content C. 出2 =2.8%, sulfuric acid content H 出2 =4.0%, total solution volume M 出2 =12000kg; Diazonium salt content C3=46%.

[0115] 3.2 Prediction and Correction of Three-Level Chemical Decision Model

[0116] Because the optimal theoretical limit for the final reaction of the primary intermediate compounding process is: the optimal theoretical limit for the final reaction of the tertiary intermediate compounding process is: total D : diazonium salt = 1 : 1 (molar ratio) = 206.14 : 325.94 (mass ratio) = 0.33 t / h : 0.52 t / h (flow rate ratio). Therefore, for diazonium salt, we only need to solve for the amount of raw material 3 added (m3) under the condition that the total D : diazonium salt = 0.33 t : 0.52 t per hour at the final reaction of the tertiary intermediate.

[0117] The theoretically optimal control model for the precise addition of three-stage compound diazonium salts is:

[0118] Right now:

[0119] Solve the equation

[0120] Substitute the real-time sensing data from "3.1" into the table:

[0121] but,

[0122] Therefore, the theoretical addition amount of diazonium salt m3 is 1150.99 kg when the requirements of the three-level compounding are met.

[0123] Referring to the chemical decision-making cycle analysis in "2.1", a self-correcting mechanism was developed, ultimately yielding the optimal control model for precise addition of tertiary diazonium salts under the condition that the effluent meets the limiting requirements:

[0124]

[0125] Secondly, since diazonium salts are alkaline organic compounds, a neutralization reaction occurs during the addition process, leading to a decrease in the acidity of the compound solution. In order to maintain the stability of the three-stage compounding reaction, the amount of dilute acid replenishment is dynamically adjusted according to the acidity changes caused by the amount of diazonium salt added, so as to keep the solution at a certain acidity value of 2.5±0.2%.

[0126] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A microscopic chemical network system for real-time sensing and manipulation of intermediate compounding processes in dye chemical industry, characterized in that, Based on the established primary intermediate complexing reaction process, secondary intermediate complexing reaction process, and tertiary intermediate complexing reaction process, including: The primary module comprises a primary real-time sensing module, a primary chemical decision-making model, and a primary compounding control module. This primary module is initially used for the first-stage intermediate compounding process. The primary real-time sensing module rapidly acquires microscopic chemical information of the synthetic raw material 1-1, and simultaneously acquires microscopic chemical and macroscopic physical information of the primary liquid input. The microscopic chemical information of synthetic raw material 1-1 includes its D purity and solid content. The microscopic chemical information of the primary liquid input includes its D content and sulfuric acid content. The macroscopic physical information of the primary liquid input includes the total volume of solution in the tank. Then, the chemical decision-making primary model is used to predict the theoretical addition amounts of organic alkaline raw material 1-1 and acidic raw material 1-2, which maintain the stability of the compounding reaction, in the primary compounding process. This is the feedforward predicted theoretical addition amount. Finally, the primary compounding control module is used to dynamically interlock and control the electric control valves of raw material 1-1 and raw material 1-2 to precisely control the addition rate. During the primary intermediate compounding reaction process, the chemical decision-making primary model cyclically analyzes and judges the deviation between the actual value of the intermediate and the theoretical value predicted by the model in real time, and corrects the deviation through a self-adjusting model algorithm. This is the feedback correction theoretical addition amount. This continues until the theoretical limit is reached before the primary intermediate compounding liquid flows out. The secondary module includes a secondary real-time sensing module, a secondary chemical decision-making model, and a secondary compounding control module. Firstly, the secondary module is used in the second-stage intermediate compounding process. The secondary real-time sensing module is used to rapidly acquire dual measurements of the microscopic chemical information of the artificially prepared raw material 2-1, the microscopic chemical information of the secondary liquid, and the macroscopic physical information. The microscopic chemical information of the artificially prepared raw material 2-1 includes the purity and solid content of its additives. The microscopic chemical information of the secondary liquid includes the sodium (D) content and sulfuric acid content. The macroscopic physical information of the secondary liquid includes the total volume of the solution in the tank. Then, the secondary chemical decision model is used to predict the theoretical addition amounts of organic alkaline raw material 2-1 and acid solution 2-2 to maintain the stability of the compounding reaction in the secondary compounding process; that is, the feedforward model predicts the theoretical addition amount. Finally, the secondary compounding control module is used to dynamically and interlock the electric control valves of raw material 2-1 and raw material 2-2 to precisely control the addition rate. During the secondary intermediate compounding reaction process, the secondary chemical decision model cyclically analyzes and judges the deviation between the actual value of the intermediate in real time and the theoretical value predicted by the model, and corrects the deviation through the self-adjusting model algorithm; that is, the feedback correction theoretical addition amount; until the theoretical limit is reached before the secondary intermediate compounding liquid flows out. The three-level module includes a three-level real-time sensing module, a three-level chemical decision-making model, and a three-level compounding control module. Firstly, the three-level real-time sensing module is used to quickly acquire the microscopic chemical information of the synthetic raw material 3-1, and to quickly acquire the microscopic chemical information and macroscopic physical information of the tertiary liquid input. The microscopic chemical information of the synthetic raw material 3-1 includes the purity and solid content of its diazonium salt. The microscopic chemical information of the tertiary liquid input includes the D content and sulfuric acid content of the tertiary liquid input. The macroscopic physical information of the tertiary liquid input includes the total volume of the solution in the tank. Then, the three-level compounding control module is used... The three-level chemical decision-making model is used to predict the theoretical addition amount of organic alkaline raw material 3-1 and acid solution 3-2 to maintain the stability of the compounding reaction in the three-level compounding process, i.e., the feedforward predicted theoretical addition amount value; finally, the three-level compounding control module is used to dynamically and interlock the electric control valves of raw material 3-1 and raw material 3-2 to precisely control the addition rate. In the three-level intermediate compounding reaction process, the three-level chemical decision-making model cyclically analyzes and judges the deviation between the actual value of the intermediate in real time and the theoretical value predicted by the model, and performs self-adjustment model algorithm to correct the deviation amount, i.e., the feedback correction theoretical addition amount value; until the effluent of the three-level intermediate compounding reaches the theoretical limit.

2. The microscopic chemical network system for real-time sensing and control of intermediate compounding processes in dye chemical industry according to claim 1, characterized in that: In the first-stage intermediate compounding reaction process, the first-stage real-time sensing module is used to sense the microscopic chemical information of the synthetic raw material 1-1 in the raw material tank, as well as the microscopic chemical and macroscopic physical information of the liquid in the tank before the reaction. The microscopic chemical information of the synthetic raw material 1-1 in the raw material tank includes the D purity and solid content. The microscopic chemical information of the liquid in the tank before the reaction includes the D content and sulfuric acid content. The macroscopic physical information of the liquid in the tank before the reaction is the total solution volume of the liquid in the tank before the reaction. The data is uploaded to the first-stage chemical decision model. The first-stage optimal control model for precise addition of D and acid is used to predict the theoretical addition amount of raw material 1-1 and raw material 1-2, and then... The primary compounding control module dynamically interlocks the electric control valves of raw materials 1-1 and 1-2 to precisely control the addition rate. During the reaction, the primary real-time sensing module is used to sense the rate of change of D content and acidity stability in the primary compounding reaction tank over time. During this period, the primary chemical decision-making model is used to cyclically analyze and judge the deviation between the actual real-time sensing value of the intermediate and the theoretical value predicted by the model, and to self-adjust the model algorithm to correct the deviation until the primary intermediate compound liquid reaches the theoretical limit. Then, the primary compounding control module is used to interlock and control the electric control valves corresponding to raw materials 1-1 and 1-2 to close. When the reaction stops, the primary compounding control module opens the drain valve of the primary compounding reaction tank to ensure that the D content and acidity in the primary compounding effluent both reach the theoretical limit.

3. The microscopic chemical network system for real-time sensing and control of intermediate compounding processes in dye chemical industry according to claim 1, characterized in that: In the second-stage intermediate compounding reaction process, the secondary real-time sensing module is used to sense the microscopic chemical information of the artificially prepared raw material 2-1 auxiliary agent in the raw material tank, as well as the microscopic chemical and macroscopic physical information of the liquid in the tank before the reaction. The microscopic chemical information of the artificially prepared raw material 2-1 auxiliary agent includes the purity and solid content of the artificially prepared raw material 2-1 auxiliary agent. The microscopic chemical information of the liquid in the tank before the reaction includes the D content and sulfuric acid content of the liquid in the tank before the reaction. The macroscopic physical information of the liquid in the tank before the reaction includes the total solution volume of the liquid in the tank before the reaction. The data is uploaded to the secondary chemical decision model. The optimal control model for precise addition of secondary auxiliary agent and acid is used to predict the addition amount of raw material 2-1 and 2-1 theoretically, and then through compounding operation... The control module dynamically interlocks the electric control valves of raw materials 2-1 and 2-2 to precisely control the addition rate. During the reaction, the secondary real-time sensing module is used to monitor the rate of change of D content and auxiliary agent content in the secondary compounding reaction tank over time, as well as the acidity stability. During this period, the secondary chemical decision model is used to cyclically analyze and judge the deviation between the actual real-time sensing value of the intermediate and the theoretical value predicted by the model, and to self-adjust the model algorithm to correct the deviation until the secondary intermediate compound liquid reaches the theoretical limit. Then, the secondary compounding control module is used to interlock and control the electric control valves corresponding to raw material 2 and dilute acid to close. When the reaction stops, the secondary compounding control module opens the drain valve of the secondary compounding reaction tank to ensure that the D content, auxiliary agent content, and acidity in the secondary compounding effluent all reach the theoretical limit.

4. The microscopic chemical network system for real-time sensing and control of intermediate compounding processes in dye chemical industry according to claim 1, characterized in that: In the third-stage intermediate compounding reaction process, the three-stage real-time sensing module is used to sense the microscopic chemical information of the artificially synthesized raw material 3-1 diazonium salt in the raw material tank, as well as the microscopic chemical and macroscopic physical information of the liquid in the tank before the reaction. The microscopic chemical information of the diazonium salt includes its purity and solid content. The microscopic chemical information of the liquid in the tank before the reaction includes its sodium (D) content and sulfuric acid content. The macroscopic physical information of the liquid in the tank before the reaction includes its total solution volume. This data is then uploaded to the three-stage chemical decision-making model. The optimal control model for precise addition of the three-stage diazonium salt and acid is used to predict the theoretical addition amounts of raw materials 3-1 and 3-2. The three-stage compounding control module then dynamically links the raw materials. The 3-1 and 3-2 electrically controlled valves precisely control the addition rate; during the reaction, the three-level real-time sensing module is used to sense the rate of change of D content, diazonium salt content and acidity stability in the three-level compound reaction tank over time. During this period, the three-level chemical decision model is used to cyclically analyze and judge the deviation between the actual real-time sensing value of the intermediate and the theoretical value predicted by the model, and to self-adjust the model algorithm to correct the deviation until the three-level intermediate compound liquid reaches the theoretical limit. Then, the three-level compound control module is used to interlock and control the corresponding electrically controlled valves of raw materials 3-1 and 3-2 to close. At the end of the reaction, the three-level compound control module opens the drain valve of the three-level compound reaction tank to ensure that the total D content, total diazonium salt content and acidity in the three-level compound effluent all reach the theoretical limit.

5. The microscopic chemical network system for real-time sensing and control of intermediate compounding processes in dye chemical industry according to claim 2, characterized in that: The optimal theoretical limit for the endpoint of the first-stage intermediate compounding reaction process is: the D content range is 8.2±0.2% and the sulfuric acid content is 3.0±0.2%.

6. The microscopic chemical network system for real-time sensing and control of intermediate compounding processes in dye chemical industry according to claim 3, characterized in that: The optimal theoretical limits for the endpoint of the second-stage intermediate compounding reaction process are: D content range of 2.8±0.2%, auxiliary agent content of 0.35%, and sulfuric acid content of 4.0±0.2%.

7. The microscopic chemical network system for real-time sensing and control of intermediate compounding processes in dye chemical industry according to claim 4, characterized in that: The optimal theoretical limit for the endpoint of the compounding reaction of the third-level intermediate is: total amount of D: amount of diazonium salt = molar ratio 1:1 = mass ratio 206.14 : 325.94 = flow rate ratio 0.33 t / h : 0.52 t / h, and sulfuric acid content is 2.5±0.2%.

8. A microscopic chemical network method for real-time sensing and manipulation of intermediate compounding processes in dye chemical industry, applied to the system described in any one of claims 1-7, characterized in that, Includes the following steps; S1. A circulating acid solution containing a certain residual amount of species D is introduced into the intermediate compounding reaction system and flows sequentially in the corresponding reaction tanks during the first-stage, second-stage and third-stage compounding processes; S2. The circulating acid solution first enters the first-stage intermediate compounding reaction process. Based on the first-stage real-time sensing module, the microscopic chemical information and macroscopic physical information of the liquid in the first-stage compounding process are obtained. The feedforward prediction and feedback correction of the optimal control model for precise addition of acid solution in the first-stage intermediate D of chemical decision are used to determine the amount of raw materials to be added. Then, the precise addition is achieved through dynamic interlocking of the first-stage compounding control module. The reaction ends when the theoretical limit is reached. S3. The first-stage effluent then enters the second-stage intermediate compounding reaction process. Based on the second-stage real-time sensing module, the microscopic chemical information and macroscopic physical information of the liquid in the second-stage compounding process are obtained. The feedforward prediction and feedback correction of the optimal control model for precise addition of chemical decision-making secondary auxiliary agent and acid are used to determine the amount of raw materials to be added. Then, the precise addition is achieved through dynamic interlocking of the second-stage compounding control module. The reaction ends when the theoretical limit is reached. S4. The secondary effluent finally enters the tertiary intermediate compounding reaction process. Based on the real-time sensing module of the tertiary process, the microscopic chemical information and macroscopic physical information of the liquid in the tertiary compounding process are obtained. The feedforward prediction and feedback correction of the optimal control model for the precise addition of tertiary diazonium salt and dilute acid are used to determine the amount of raw materials to be added. Then, the precise addition is achieved through dynamic interlocking of the tertiary compounding control module. When the theoretical limit is reached, the reaction ends and the tertiary effluent enters the next reaction unit coupling system.

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