Microcosmic chemical net system and method for sensing and controlling intermediate compounding process in real time in dye chemical industry

Abstract

The invention relates to a microscopic chemical network system and method for sensing and controlling the compounding process of an intermediate in real time in dye chemical industry. The microscopic chemical network system comprises a first-stage real-time sensing-chemical decision-compounding control module, a second-stage real-time sensing-chemical decision-compounding control module and a third-stage real-time sensing-chemical decision-compounding control module. The closed-loop control method of chemical decision feedforward prediction and feedback correction is utilized to perform double measurement and double control on each stage of intermediate compound liquid and raw materials, firstly, microscopic chemical and macroscopic physical information of the inlet liquid and the raw materials is obtained based on a real-time sensing module, and then the theoretical addition amount of the raw materials is predicted by utilizing an optimal control model of a chemical decision module; a raw material liquid inlet valve is dynamically linked through a compounding control module to accurately control the adding rate, a reaction process chemical decision module circularly analyzes and judges the deviation between a real-time sensing value of a target species in a compound liquid and a model predicted value, and a model algorithm is self-adjusted to correct the deviation value, so that the compounding quality of an intermediate is improved, and the yield of byproducts is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of dye chemical clean production, and in particular to a microscopic chemical network system and method for real-time sensing and controlling an intermediate compounding process in dye chemical industry. Background Art

[0002] The concentration or total amount of species in process industries is closely linked to the thermodynamics and kinetics of chemical reactions, directly influencing the likelihood and extent of unit reactions. Furthermore, deviations from theoretical values for species or concentrations in one production unit are propagated to downstream units and even throughout the entire process, further amplifying these deviations and leading to reduced conversion efficiency and pollutant production far exceeding theoretically calculated values.

[0003] my country's dye industry has developed rapidly, and its output has ranked first in the world for more than 10 consecutive years. However, in recent years, it has gradually entered a mature stage. The industry development is facing problems such as overcapacity and environmental protection. The industry urgently needs green and high-quality transformation and development, and has entered a stage of structural transformation. Disperse dyes, as the only dye variety that can be dyed and printed on polyester fibers (polyester), have become the largest subcategory of all dyes. The company's disperse violet 93:1 process includes three systems: one is the medium D beating system, the second is the diazotization system, and the third is the coupling system. The beating system and the diazotization system provide intermediates, and the coupling system is a synthetic product. The intermediates in the medium D beating process are complex, the reaction process is long, and the proportion of compounding liquid is large, which is the focus of the disperse violet synthesis process.

[0004] In the company's actual production process, both D and diazonium salts are artificially synthesized, high-concentration organic raw materials with dynamically changing solid content and purity. The company's continuous production process requires dynamic compounding of multi-stage intermediates. Currently, the laboratory only conducts offline testing and analysis (5-6 times per day, 1-2 hours each) of the acidity of the circulating acid in the D pulping system and the purity of D in the artificially synthesized raw materials. There is a lack of testing and analysis of the microchemical processes involved in the compounding of the multi-stage intermediates prior to coupling synthesis, and there is no way to intervene in or manipulate the microchemical reaction processes, including compounding of D, additives, and diazonium salts. Because the company's high-concentration intermediate compounding process cannot dynamically and precisely control the amount of raw materials added, and the total amount of D and diazonium salt at any given time is difficult to maintain a stable and reasonable ratio, the reaction efficiency of the disperse violet coupling process is reduced, product quality is deteriorating, and by-products are increased. Summary of the Invention

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

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a microscopic chemical network system for real-time perception and control of intermediate compounding processes in dye chemical industry, based on the set primary intermediate compounding reaction process, secondary intermediate compounding reaction process and tertiary intermediate compounding reaction process, including:

[0008] The first-level module includes a first-level real-time perception module, a first-level chemical decision model, and a first-level compounding control module. First, the first-level module is used for the first-level intermediate compounding process. The first-level real-time perception module is used to quickly obtain the microscopic chemical information (high-concentration D purity and solid content) of the artificial synthetic raw material 1-1 (N,N-diethyl-3-acetamidoaniline, abbreviated as: D), quickly obtain the microscopic chemical information (low-concentration D content, sulfuric acid content) and macroscopic physical information (total amount of solution in the tank) of the first-level inlet liquid; then, the first-level D and acid solution in the chemical decision module are accurately added using the optimal control model. It is used to predict the theoretical addition amount of the first-level compounding organic alkaline raw material 1-1 (D in a high-concentration and high-purity molten state) and the raw material 2-1 acid solution for maintaining the stable compounding reaction, that is, the feedforward prediction theoretical addition amount value; finally, the first-level compounding control module is used to dynamically chain the dual-control raw material 1-1 and 1-2 electric control valves to accurately control the addition rate. During the first-level intermediate compounding reaction process, the first-level model of the chemical decision module cyclically analyzes and determines the deviation between the actual value of the intermediate real-time perception and the theoretical value predicted by the model, and corrects the deviation through the self-adjustment model algorithm, that is, the feedback correction theoretical addition amount value; until the theoretical limit is reached before the first-level intermediate compound liquid flows out;

[0009] The secondary module includes a secondary real-time perception module, a chemical decision module secondary model and a secondary compounding control module. First, the secondary module is used for the second-level intermediate compounding process. The secondary real-time perception module is used to quickly obtain the microscopic chemical information (auxiliary purity and solid content) of the artificial synthetic raw material 2-1 (peregal, urea, referred to as: auxiliary agent), quickly obtain the microscopic chemical information (medium D content, sulfuric acid content) and macroscopic physical information (total amount of solution in the tank) of the secondary inlet liquid; then the secondary auxiliary agent and acid solution in the chemical decision module are accurately added to the optimal control model for prediction. The theoretical addition amount of the secondary compounding organic alkaline raw material 2-1 (high concentration mixing auxiliary agent) and the 2-2 acid solution to maintain the stable compounding reaction is measured, that is, the feedforward prediction theoretical addition amount value; finally, the secondary compounding control module is used to dynamically chain the dual-control raw material 2-1 and 2-2 electric control valves to accurately control the addition rate, and in the secondary intermediate compounding reaction process, the secondary model of the chemical decision module cyclically analyzes and determines the deviation between the real-time perception actual value of the intermediate and the theoretical value predicted by the model, and corrects the deviation through the self-adjusting model algorithm, that is, the feedforward correction theoretical addition amount value; until the theoretical limit is reached before the secondary intermediate compound liquid flows out;

[0010] The three-level module includes a three-level real-time perception module, a three-level model of a chemical decision module, and a three-level compounding control module. First, the three-level real-time perception module is used to quickly obtain the microscopic chemical information (diazonium salt purity and solid content) of the artificial synthetic raw material 3-1 (6-chloro-2,4-dinitrobenzenediazonium sulfate, referred to as: diazonium salt), and quickly obtain the microscopic chemical information (middle D content, sulfuric acid content) and macroscopic physical information (total amount of solution in the tank) of the three-level inlet liquid; then the three-level intermediate compounding optimal model in the chemical decision module is used to predict the three-level intermediate. The organic alkaline raw material 3-1 (high concentration diazonium salt) is compounded and the theoretical amount of 3-2 acid solution is added to maintain the stability of the compounding reaction, that is, the feedforward prediction theoretical addition value; finally, the three-level compounding control module is used to dynamically chain the dual-control raw materials 3-1 and 3-2 electric control valves to accurately control the addition rate, and in the three-level intermediate compounding reaction process, the three-level model of the chemical decision-making module cyclically analyzes and determines the deviation between the real-time perception actual value of the intermediate and the theoretical value predicted by the model, and self-adjusts the model algorithm to correct the deviation, that is, the feedforward correction theoretical addition value; until the three-level intermediate compounding effluent reaches the theoretical limit.

[0011] As a further solution of the present invention: During the first-stage intermediate compounding reaction process, the first-stage real-time sensing module before the reaction is used to sense in real time the microscopic chemical information (purity, solid content) of D in the artificial synthetic raw material 1-1 in the raw material tank, the microscopic chemical information (D content, sulfuric acid content) and macroscopic physical information (total amount of solution) of the liquid in the tank before the reaction, and upload the data to the first-stage model of the chemical decision module. The first-stage D and acid solution precise addition optimal control model is used to predict the theoretical addition amount of the raw materials 1-1 and 1-2 acid solution, and then the first-stage compounding control module dynamically links the raw materials 1-1 and 1-2 electric control valves to accurately control the addition rate; The first-level real-time perception module in the reaction is used to perceive in real time the rate of change of the D content in the first-level compounding reaction tank over time and the acidity stability. During this period, the first-level model of the chemical decision module is used to cyclically analyze and determine the deviation between the actual value of the intermediate real-time perception and the theoretical value predicted by the model, and self-adjust the model algorithm to correct the deviation until the first-level intermediate compound liquid reaches the theoretical limit. The first-level compounding control module is then used to control the electric control valves corresponding to the raw materials 1-1 and 1-2 to close in a chain manner; when the reaction stops, the first-level compounding control module opens the drain valve of the first-level compounding reaction tank to ensure that the D content and acidity in the first-level compounding outflow liquid reach the theoretical limit.

[0012] As a further solution of the present invention: In the second-stage intermediate compounding reaction process, the second-stage real-time sensing module before the reaction is used to sense in real time the microscopic chemical information (purity, solid content) of the artificial synthetic raw material 2-1 auxiliary agent in the raw material tank, the microscopic chemical information (D content, sulfuric acid content) and macroscopic physical information (total amount of solution) of the liquid in the tank before the reaction, and upload the data to the second-stage model of the chemical decision module, the second-stage auxiliary agent and acid solution precise addition optimal control model is used to predict the theoretical addition amount of raw materials 2-1 and 2-2 acid solution, and then the addition rate is precisely controlled by the compounding control module through the dynamic linkage of the raw materials 2-1 and 2-2 electric control valves; in the reaction, The secondary real-time perception module is used to detect the rate of change of the D content and the auxiliary agent content in the secondary compounding reaction tank over time and the acidity stability. During this period, the secondary model of the chemical decision module is used to cyclically analyze and determine the deviation between the actual value of the intermediate real-time perception and the theoretical value predicted by the model, and self-adjust the model algorithm to correct the deviation until the secondary intermediate compound liquid reaches the theoretical limit. The secondary compounding control module is then used to control the electric control valves corresponding to the raw materials 2-1 and 2-2 to be closed; 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 outflow liquid all reach the theoretical limit.

[0013] As a further solution of the present invention: in the third-level intermediate compounding reaction process, the three-level real-time sensing module before the reaction is used to sense in real time the microscopic chemical information (purity, solid content) of the artificial synthetic raw material 3-1 diazonium salt in the raw material tank, the microscopic chemical information (D content, sulfuric acid content) and macroscopic physical information (total amount of solution) of the liquid in the tank before the reaction, and upload the data to the three-level model of the chemical decision module. The three-level diazonium salt and acid solution precise addition optimal control model is used to predict the theoretical addition amount of raw materials 3-1 and 3-2 acid solution, and then the addition rate is accurately controlled by the three-level compounding control module dynamically linked to the raw materials 3-1 and 3-2 electric control valves; in the reaction The three-level real-time perception module is used to perceive the D content and the rate of change of the diazonium salt content over time and the acidity stability in the three-level compounding reaction tank. During this period, the three-level model of the chemical decision module is used to cyclically analyze and determine the deviation between the actual value of the intermediate real-time perception and the theoretical value predicted by the model, and self-adjust the model algorithm to correct the deviation until the three-level intermediate compound liquid reaches the theoretical limit. Then, the three-level compounding control module is used to control the electric control valves corresponding to the raw materials 3-1 and 3-2 to be closed; when the reaction stops, the three-level compounding control module opens the drain valve of the three-level compounding reaction tank to ensure that the total amount of D, the total amount of diazonium salt and the acidity in the three-level compounding outflow liquid reach the theoretical limit.

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

[0015] As a further solution of the present invention: the optimal theoretical limit of the endpoint of the second-stage intermediate compounding reaction process is: the D content range is 2.8±0.2%, the auxiliary agent content is 0.35% (0.2% of PEG-1+0.15% of urea), and the sulfuric acid content is 4.0±0.2%.

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

[0017] In a second aspect, the present invention provides a microscopic chemical network method for real-time sensing and controlling the intermediate compounding process in dye chemical industry, comprising the following steps:

[0018] S1. The circulating acid solution containing a certain residual amount of species such as D is introduced into the intermediate compounding reaction system and circulated in the corresponding reaction tanks in the primary, secondary and tertiary compounding processes in sequence;

[0019] S2. The circulating acid first enters the first-stage intermediate compounding reaction process. The first-stage real-time sensing module obtains microscopic chemical and macroscopic physical information of the first-stage compounding process liquid. The first-stage intermediate D and acid precise addition optimal control model based on chemical decision-making uses feedforward prediction and feedback correction to determine the amount of raw material to be added. The first-stage compounding control module then dynamically links to achieve precise addition, and the reaction ends when the theoretical limit is reached.

[0020] S3. The primary effluent then enters the secondary intermediate compounding reaction process. The secondary real-time sensing module obtains microscopic chemical and macroscopic physical information about the secondary compounding process liquid. The optimal control model for the precise addition of secondary additives and acid solution using chemical decision-making uses feedforward prediction and feedback correction to determine the amount of raw material to be added. This is then dynamically linked through the secondary compounding control module to achieve precise addition, terminating the reaction when the theoretical limit is reached.

[0021] S4. The secondary effluent finally enters the tertiary intermediate compounding reaction process. The tertiary real-time sensing module acquires microscopic chemical and macroscopic physical information about the liquid during the tertiary compounding process. The chemically determined optimal control model for the precise addition of diazonium salt and dilute acid uses feedforward prediction and feedback correction to determine the amount of raw material to be added. The tertiary compounding control module then dynamically links to achieve precise addition, terminating the reaction when the theoretical limit is reached. The tertiary effluent then enters the coupling system of the next reaction unit.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] In the present invention, the real-time perception module obtains the microscopic chemical and macroscopic physical information of the influent and raw materials, and the optimal control model of the chemical decision module is used to predict the theoretical addition amount of the raw materials. Then, the compounding control module dynamically links the raw material inlet valve to accurately control the addition rate. The reaction process chemical decision module cyclically analyzes and judges the deviation between the real-time perception value of the target species in the compounded liquid and the model prediction value, and self-adjusts the model algorithm to correct the deviation, so that the effluent after multi-stage treatment reaches the corresponding theoretical limit. It effectively improves the quality of the intermediates in the compounding process, provides technical support for scientifically controlling the ratio of D and diazonium salt, effectively increasing the conversion efficiency of the coupling process, significantly improving the quality of disperse purple products, and reducing the amount of pollutant COD generated at the source, meeting the urgent production needs of enterprises to reduce pollution, reduce carbon emissions, improve quality and increase efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a connection diagram of the real-time perception-chemical decision-making-compounding control module of the present invention;

[0025] Figure 2 The present invention provides a curve diagram of the purity change of D in Example 1;

[0026] Figure 3 The present invention provides a curve diagram of the change of the solid content of D in Example 1. DETAILED DESCRIPTION

[0027] 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.

[0028] Example 1:

[0029] See also Figure 1 In an embodiment of the present invention, a microscopic chemical network system for real-time sensing and controlling the intermediate compounding process of dye chemical industry is provided, based on the set primary intermediate compounding reaction process, secondary intermediate compounding reaction process and tertiary intermediate compounding reaction process, and includes a primary module, a secondary module and a tertiary module:

[0030] The first-level module includes a first-level real-time perception module, a first-level model of a chemical decision module, and a first-level compounding control module. First, the first-level module is used for the first-level intermediate compounding process. The first-level real-time perception module is used to quickly obtain the microscopic chemical information (purity and solid content) of the artificial synthetic raw material 1 (high-concentration D), quickly obtain the microscopic chemical information (low-concentration D content, sulfuric acid content) of the first-level influent, and the macroscopic physical information (total amount of solution in the tank) of the first-level compounding process. Then, the first-level optimal control model in the chemical decision module is used to predict the first-level compounded organic alkaline raw material 1-1 ( The theoretical addition amount of 1-2 acid solution in a high-concentration, high-purity molten state (D) and maintaining the stable progress of the compounding reaction is the feedforward predicted theoretical addition amount value; the last-level compounding control module is used to dynamically chain the dual-control raw material 1-1 and 1-2 electric control valves to accurately control the addition rate. In the first-level intermediate compounding reaction process, the first-level model of the chemical decision module cyclically analyzes and determines the deviation between the actual value of the intermediate real-time perception and the theoretical value predicted by the model, and corrects the deviation through the self-adjusting model algorithm, that is, the feedforward correction theoretical addition amount value; until the theoretical limit is reached before the first-level intermediate compounding liquid flows out;

[0031] The secondary module includes a secondary real-time perception module, a secondary model of a chemical decision module, and a secondary compounding control module. First, the secondary module is used in the second-level intermediate compounding process. The secondary real-time perception module is used to quickly obtain the microscopic chemical information (purity and solid content) of the artificially prepared raw material 2-1 (auxiliary agent), the microscopic chemical information (medium D content, sulfuric acid content) and the macroscopic physical information (total amount of solution in the tank) of the secondary inlet liquid; then the secondary optimal control model in the chemical decision module is used to predict the theoretical addition amount of the secondary compounded organic alkaline raw material 2-1 (high-concentration mixed auxiliary agent) and the 2-2 acid solution to maintain the stable compounding reaction, that is, the feedforward prediction theoretical addition amount value; finally, the secondary compounding control module is used to dynamically link the dual-control raw material 2-2 and 2-3 electric control valves to accurately control the addition rate. In the secondary intermediate compounding reaction process, the chemical decision module cyclically analyzes and determines the deviation between the actual value of the intermediate real-time perception and the theoretical value predicted by the model, and corrects the deviation through the self-adjustment model algorithm, that is, the feedforward correction theoretical addition amount value; until the theoretical limit is reached before the secondary intermediate compound liquid flows out;

[0032] The three-level module includes a three-level real-time perception module, a three-level model of a chemical decision module, and a three-level compounding control module. First, the three-level real-time perception module is used to quickly obtain the microchemical information (purity and solid content) of the artificial synthetic raw material 3-1 (diazonium salt), the microchemical information (medium D content, sulfuric acid content) of the three-level inlet liquid, and the macroscopic physical information (total amount of solution in the tank). Then, the three-level optimal control model in the chemical decision module is used to predict the theoretical addition amount of the three-level compounding organic alkaline raw material 3-1 (high-concentration diazonium salt) and the 3-2 acid solution to maintain the stable compounding reaction, that is, the feedforward prediction of the theoretical addition amount value. Finally, the three-level compounding control module is used to dynamically link the dual control of the raw materials 3-1 and 3-2 electric control valves to accurately control the addition rate. During the three-level intermediate compounding reaction process, the chemical decision module cyclically analyzes and determines the deviation between the actual value of the intermediate real-time perception and the theoretical value predicted by the model, and self-adjusts the model algorithm to correct the deviation, that is, the feedback correction theoretical addition amount value; until the three-level intermediate compounding outflow reaches the theoretical limit.

[0033] Specifically, in the present invention, the real-time perception module obtains the microscopic chemical and macroscopic physical information of the influent and raw materials, and the optimal control model of the chemical decision module is used to predict the theoretical addition amount of the raw materials. Then, the compounding control module dynamically links the raw material inlet valve to accurately control the addition rate. The reaction process chemical decision module cyclically analyzes and judges the deviation between the real-time perception value of the target species in the compounded liquid and the model prediction value, and self-adjusts the model algorithm to correct the deviation, so that the effluent after multi-stage treatment reaches the corresponding theoretical limit. It effectively improves the quality of the intermediates in the compounding process, provides technical support for scientifically controlling the ratio of D and diazonium salt, effectively increasing the conversion efficiency of the coupling process, significantly improving the quality of disperse purple products, and reducing the amount of pollutant COD generated at the source, meeting the urgent production needs of enterprises to reduce pollution, reduce carbon emissions, improve quality and increase efficiency.

[0034] Preferably, in the first-stage intermediate compounding reaction process, the first-stage real-time sensing module before the reaction is used to sense in real time the microscopic chemical information (purity, solid content) of D in the artificial 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 amount of solution) of the liquid in the tank before the reaction, and upload the data to the chemical decision module. The first-stage optimal control model is used to predict the theoretical addition amount of the raw materials 1-1 and 1-2 acid solution, and then the first-stage compounding control module dynamically links the raw materials 1-1 and 1-2 electric control valves to accurately control the addition rate; the first-stage real-time sensing module during the reaction The module is used to perceive in real time the rate of change of the D content in the first-level compounding reaction tank over time and the acidity stability. During this period, the chemical decision-making module is used to cyclically analyze and determine the deviation between the actual value perceived in real time by the intermediate and the theoretical value predicted by the model, and self-adjust the model algorithm to correct the deviation until the first-level intermediate compounding liquid reaches the theoretical limit. Then, the first-level compounding control module is used to control the electric control valves corresponding to raw materials 1-1 and 1-2 to be closed; when the reaction stops, the first-level compounding control module opens the drain valve of the first-level compounding reaction tank to ensure that the D content and acidity in the first-level compounding outflow reach the theoretical limit.

[0035] Preferably, in the second-stage intermediate compounding reaction process, the secondary real-time sensing module before the reaction is used to sense in real time 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 (medium D content, sulfuric acid content) and macroscopic physical information (total amount of solution) of the liquid in the tank before the reaction, and upload the data to the chemical decision module. The secondary optimal control model is used to predict the theoretical addition amount of raw materials 2-1 and 2-2 acid solution, and then the addition rate is accurately controlled by the dynamic linkage of the raw materials 2-1 and 2-2 electric control valves through the compounding control module; the secondary real-time sensing module during the reaction is used for the secondary The rate of change of D content and auxiliary agent content in the first-stage compounding reaction tank over time and the stability of acidity, during which the chemical decision-making module is used to cyclically analyze and judge the deviation between the actual value perceived in real time by the intermediate and the theoretical value predicted by the model, and self-adjust the model algorithm to correct the deviation until the secondary intermediate compounding liquid reaches the theoretical limit, and then the secondary compounding control module is used to control the electric control valves corresponding to raw materials 2-1 and 2-2 to be closed; 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 outflow liquid reach the theoretical limit.

[0036] Preferably, in the third-stage intermediate compounding reaction process, the three-stage real-time sensing module before the reaction is used to sense in real time the microscopic chemical information (purity, solid content) of the artificial synthetic raw material 3-1 diazonium salt in the raw material tank, the microscopic chemical information (medium D content, sulfuric acid content) and macroscopic physical information (total amount of solution) of the liquid in the tank before the reaction, and upload the data to the chemical decision module. The three-stage optimal control model is used to predict the theoretical addition amount of the raw materials 3-1 and 3-2 acid solution, and then the addition rate is accurately controlled by the three-stage compounding control module through the dynamic linkage of the raw materials 3-1 and 3-2 electric control valves; the three-stage real-time sensing module during the reaction is used to sense The rate of change of D content and diazonium salt content in the tertiary compounding reaction tank over time and the acidity stability are known. During this period, the chemical decision-making module is used to cyclically analyze and judge the deviation between the actual value perceived by the intermediate in real time and the theoretical value predicted by the model, and self-adjust the model algorithm to correct the deviation until the tertiary intermediate compounding liquid reaches the theoretical limit. Then, the tertiary compounding control module is used to control the electric control valves corresponding to the raw materials 3-1 and 3-2 to be closed; the tertiary compounding control module opens the drain valve of the tertiary compounding reaction tank at the end of the reaction to ensure that the total amount of D, the total amount of diazonium salt and the acidity in the tertiary compounding outflow liquid reach the theoretical limit.

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

[0038] Preferably, the optimal theoretical limits for the endpoint of the second-stage intermediate compounding reaction process are: the D content range is 2.8±0.2%, the auxiliary agent content is 0.35% (0.2% of PEG-1+0.15% of urea), and the sulfuric acid content is 4.0±0.2%.

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

[0040] A microscopic chemical network method for real-time sensing and controlling the intermediate compounding process in dye chemical industry comprises the following steps:

[0041] S1. The circulating acid solution containing a certain residual amount of species such as D is introduced into the intermediate compounding reaction system and circulated in the corresponding reaction tanks in the primary, secondary and tertiary compounding processes in sequence;

[0042] S2. The circulating acid first enters the first-stage intermediate compounding reaction process. The first-stage real-time sensing module obtains microscopic chemical and macroscopic physical information of the first-stage compounding process liquid. The first-stage intermediate D and acid precise addition optimal control model based on chemical decision-making uses feedforward prediction and feedback correction to determine the amount of raw material to be added. The first-stage compounding control module then dynamically links to achieve precise addition, and the reaction ends when the theoretical limit is reached.

[0043] S3. The primary effluent then enters the secondary intermediate compounding reaction process. The secondary real-time sensing module obtains microscopic chemical and macroscopic physical information about the secondary compounding process liquid. The optimal control model for the precise addition of secondary additives and acid solution using chemical decision-making uses feedforward prediction and feedback correction to determine the amount of raw material to be added. This is then dynamically linked through the secondary compounding control module to achieve precise addition, terminating the reaction when the theoretical limit is reached.

[0044] S4. The secondary effluent finally enters the tertiary intermediate compounding reaction process. The tertiary real-time sensing module acquires microscopic chemical and macroscopic physical information about the compounding process liquid. The chemically determined optimal control model for the precise addition of the tertiary diazonium salt and acid solution uses feedforward prediction and feedback correction to determine the amount of raw material to be added. The tertiary compounding control module then dynamically links to achieve precise addition, terminating the reaction when the theoretical limit is reached. The tertiary effluent then enters the coupling system of the next reaction unit.

Claims

1. A microscopic chemical network system for real-time sensing and controlling the intermediate compounding process in dye chemical industry, characterized by: Based on the set primary intermediate compound reaction process, secondary intermediate compound reaction process and tertiary intermediate compound reaction process, it includes: The first-level module includes a first-level real-time perception module, a first-level chemical decision model and a first-level compounding control module. First, the first-level module is used for the first-level intermediate compounding process. The first-level real-time perception module is used to quickly obtain the microscopic chemical information of the artificial synthesis raw material 1-1, and quickly obtain the microscopic chemical information and macroscopic physical information of the first-level inlet liquid. The microscopic chemical information of the artificial synthesis raw material 1-1 includes the D purity and solid content of the artificial synthesis raw material 1-1, the microscopic chemical information of the first-level inlet liquid includes the D content and sulfuric acid content of the first-level inlet liquid, and the macroscopic physical information of the first-level inlet liquid includes the total amount of solution in the tank of the first-level inlet liquid; then use The first-level optimal control model in the chemical decision module predicts the theoretical addition amount of the first-level compounded organic alkaline raw material 1-1 and the acid solution of the raw material 1-2 for maintaining the stable compounding reaction, that is, the feedforward predicted theoretical addition amount value; finally, the first-level compounding control module is used to dynamically chain the dual-control electric control valves of the raw material 1-1 and the raw material 1-2 to accurately control the addition rate. During the first-level intermediate compounding reaction process, the first-level model of the chemical decision module cyclically analyzes and determines the deviation between the real-time perception actual value of the intermediate 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 value; until the theoretical limit is reached before the first-level intermediate compound liquid flows out; The secondary module includes a secondary real-time perception module, a chemical decision-making secondary model and a secondary compounding control module. First, the secondary module is used for the second-level intermediate compounding process. The secondary real-time perception module is used to quickly obtain the microscopic chemical information of the artificially prepared raw material 2-1, and quickly obtain the microscopic chemical information and macroscopic physical information of the secondary inlet liquid. The microscopic chemical information of the artificially prepared raw material 2-1 includes the purity and solid content of the auxiliary agent of the artificially prepared raw material 2-1, the microscopic chemical information of the secondary inlet liquid includes the D content and sulfuric acid content of the secondary inlet liquid, and the macroscopic physical information of the secondary inlet liquid includes the total amount of solution in the tank of the secondary inlet liquid; then use The secondary optimal control model in the chemical decision module is used to predict the theoretical addition amount of the secondary compounded organic alkaline raw material 2-1 and the acid solution 2-2 to maintain the stable compounding reaction, that is, the feedforward model predicts the theoretical addition amount value; finally, the secondary compounding control module is used to dynamically chain the dual-control electric control valves of raw material 2-1 and raw material 2-2 to accurately control the addition rate. During the secondary intermediate compounding reaction process, the secondary model of the chemical decision module cyclically analyzes and determines the deviation between the actual value of the intermediate real-time perception 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 value; until the theoretical limit is reached before the secondary intermediate compound liquid flows out; The three-level module includes a three-level real-time perception module, a three-level chemical decision-making model and a three-level compounding control module. First, the three-level real-time perception module is used to quickly obtain the microscopic chemical information of the artificial synthesis raw material 3-1, and quickly obtain the microscopic chemical information and macroscopic physical information of the three-level inlet liquid. The microscopic chemical information of the artificial synthesis raw material 3-1 includes the purity and solid content of the diazonium salt of the artificial synthesis raw material 3-, the microscopic chemical information of the three-level inlet liquid includes the D content and sulfuric acid content of the three-level inlet liquid, and the macroscopic physical information of the three-level inlet liquid includes the total amount of solution in the tank of the three-level inlet liquid; then the chemical decision module is used to quickly obtain the microscopic chemical information of the three-level inlet liquid and the macroscopic physical information of the three-level inlet liquid. The three-level optimal control model is used to predict the theoretical addition amount of the three-level compounding organic alkaline raw material 3-1 and the acid solution 3-2 to maintain the stable compounding reaction, that is, the feedforward prediction theoretical addition amount value; finally, the three-level compounding control module is used to dynamically chain the dual-control raw material 3-1 and raw material 3-2 electric control valves to accurately control the addition rate, and in the three-level intermediate compounding reaction process, the three-level model of the chemical decision module cyclically analyzes and determines the deviation between the real-time perception actual value of the intermediate and the theoretical value predicted by the model, and self-adjusts the model algorithm to correct the deviation, that is, the feedback correction theoretical addition amount value; until the three-level intermediate compounding outflow reaches the theoretical limit.

2. The microscopic chemical network system for real-time sensing and controlling the intermediate compounding process 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 before the reaction is used to sense in real time the microscopic chemical information of the artificial synthetic raw material 1-1 in the raw material tank, as well as the microscopic chemical information and macroscopic physical information of the liquid in the tank before the reaction. The microscopic chemical information of the artificial synthetic raw material 1-1 in the raw material tank includes the D purity and solid content of the artificial synthetic raw material 1-1 in the raw material tank, 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, and the macroscopic physical information of the liquid in the tank before the reaction is the total amount of the solution of the liquid in the tank before the reaction, and the data is uploaded to the first-stage model of the chemical decision module. The first-stage D and acid solution precise addition optimal control model is used to predict the theoretical addition amount of raw material 1-1 and raw material 1-2, and then through The first-level compounding control module dynamically links the electric control valves of raw materials 1-1 and 1-2 to accurately control the addition rate; during the reaction, the first-level real-time perception module is used to perceive in real time the rate of change of the D content in the first-level compounding reaction tank over time and the acidity stability. During this period, the first-level model of the chemical decision module is used to cyclically analyze and judge the deviation between the actual value of the real-time perception of the intermediate and the theoretical value predicted by the model, and self-adjust the model algorithm to correct the deviation until the first-level intermediate compound liquid reaches the theoretical limit. The first-level compounding control module is then used to control the electric control valves corresponding to raw materials 1-1 and 1-2 to be closed; when the reaction stops, the first-level compounding control module opens the drain valve of the first-level compounding reaction tank to ensure that the D content and acidity in the first-level compounding outflow liquid reach the theoretical limit.

3. The microscopic chemical network system for real-time sensing and controlling the intermediate compounding process in dye chemical industry according to claim 1 is characterized by: In the second-stage intermediate compounding reaction process, the secondary real-time sensing module before the reaction is used to sense in real time 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 information 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, and the macroscopic physical information of the liquid in the tank before the reaction includes the total amount of solution of the liquid in the tank before the reaction, and uploads the data to the secondary model of the chemical decision module. The secondary auxiliary agent and acid solution precise addition optimal control model is used to predict the theoretical addition amount of raw materials 2-1 and 2-1, and then through compounding The control module dynamically links 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 perception module is used to determine the rate of change of the D content and the auxiliary agent content in the secondary compounding reaction tank over time and the acidity stability. During this period, the secondary model of the chemical decision module is used to cyclically analyze and determine the deviation between the actual value of the intermediate real-time perception and the theoretical value predicted by the model, and self-adjust the model algorithm to correct the deviation until the secondary intermediate compound liquid reaches the theoretical limit. The secondary compounding control module is then used to 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 outflow liquid reach the theoretical limit.

4. The microscopic chemical network system for real-time sensing and controlling the intermediate compounding process in dye chemical industry according to claim 1, characterized in that: The third-level intermediate compounding reaction process, the three-level real-time sensing module before the reaction is used to sense in real time the microscopic chemical information of the artificial synthetic raw material 3-1 diazonium salt in the raw material tank, as well as the microscopic chemical information and macroscopic physical information of the liquid in the tank before the reaction, the microscopic chemical information of the diazonium salt includes the purity and solid content of the diazonium salt, 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, and the macroscopic physical information of the liquid in the tank before the reaction includes the total amount of solution of the liquid in the tank before the reaction, and uploads the data to the three-level model of the chemical decision module, the three-level diazonium salt and acid solution precise addition optimal control model is used to predict the theoretical addition amount of raw materials 3-1 and 3-2, and then dynamically calculates the amount of raw materials 3-1 and 3-2 through the three-level compounding control module. The electric control valves of raw materials 3-1 and 3-2 are linked to accurately control the addition rate; the three-level real-time perception module in the reaction is used to perceive the D content, the rate of change of diazonium salt content over time and the acidity stability in the three-level compounding reaction tank. During this period, the chemical decision module is used to cyclically analyze and judge the deviation between the actual value of the real-time perception of the intermediate and the theoretical value predicted by the model, and self-adjust the model algorithm to correct the deviation until the three-level intermediate compound liquid reaches the theoretical limit, and then the three-level compounding control module is used to control the electric control valves corresponding to raw materials 3-1 and 3-2 to be closed; at the end of the reaction, the three-level compounding control module opens the drain valve of the three-level compounding reaction tank to ensure that the total amount of D, the total amount of diazonium salt and the acidity in the three-level compounding outflow liquid reach the theoretical limit.

5. The microscopic chemical network system for real-time sensing and controlling the intermediate compounding process in dye chemical industry according to claim 2, characterized in that: The optimal theoretical limits for the endpoint of the first-stage intermediate compounding reaction process are: 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 controlling the intermediate compounding process 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 controlling the intermediate compounding process in dye chemical industry according to claim 4, characterized in that: The optimal theoretical limit of the endpoint of the third-stage intermediate compounding reaction process is: total amount of D: diazonium salt amount = molar ratio 1:1 = mass ratio 206.14:325.94 = flow ratio 0.33t / h:0.52t / h, and the sulfuric acid content is 2.5±0.2%.

8. A microscopic chemical network method for real-time perception and control of intermediate compounding process in dye chemical industry, applied to the system according to any one of claims 1 to 7, characterized in that: The method includes the following steps: S1. The circulating acid solution containing a certain residual amount of species such as D is introduced into the intermediate compounding reaction system and circulated in the corresponding reaction tanks in the primary, secondary and tertiary compounding processes in sequence; S2. The circulating acid first enters the first-stage intermediate compounding reaction process. The first-stage real-time sensing module obtains microscopic chemical and macroscopic physical information of the first-stage compounding process liquid. The first-stage intermediate D and acid precise addition optimal control model based on chemical decision-making uses feedforward prediction and feedback correction to determine the amount of raw material to be added. The first-stage compounding control module then dynamically links to achieve precise addition, and the reaction ends when the theoretical limit is reached. S3. The primary effluent then enters the secondary intermediate compounding reaction process. The secondary real-time sensing module obtains microscopic chemical and macroscopic physical information about the secondary compounding process liquid. The optimal control model for the precise addition of secondary additives and acid solution using chemical decision-making uses feedforward prediction and feedback correction to determine the amount of raw material to be added. This is then dynamically linked through the secondary compounding control module to achieve precise addition, terminating the reaction when the theoretical limit is reached. S4. The secondary effluent finally enters the tertiary intermediate compounding reaction process. The tertiary real-time sensing module acquires microscopic chemical and macroscopic physical information about the liquid during the tertiary compounding process. The chemically determined optimal control model for the precise addition of diazonium salt and dilute acid uses feedforward prediction and feedback correction to determine the amount of raw material to be added. The tertiary compounding control module then dynamically links to achieve precise addition, terminating the reaction when the theoretical limit is reached. The tertiary effluent then enters the coupling system of the next reaction unit.

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