Intelligent control method for parameters of rectifying column for waste liquid treatment

Abstract

The application provides a kind of distillation column parameter intelligent control method for waste liquid treatment, it is related to distillation column intelligent control technical field. Including: real-time collection of influence data in waste liquid treatment process;According to the influence data analysis energy utilization efficiency and mass transfer efficiency in waste liquid distillation purification process, calculate the reliability factor of key equipment, construct linear relationship;According to linear relationship and equipment reliability factor, construct distillation purification mode, use improved adaptive genetic algorithm to optimize equipment operating parameter ratio;The optimal operating parameter ratio is converted into control command and executes control command;Real-time monitoring of product quality data and equipment operating state data in waste liquid treatment process, analyze the deviation value of actual data and expected target, and adjust the operating parameter ratio of each equipment according to actual situation.The application constructs distillation purification mode, uses improved adaptive genetic algorithm to optimize the operating parameter ratio of each equipment, realizes waste liquid distillation intelligent control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control of rectifying column, and particularly to a rectifying column parameter intelligent control method for waste liquid treatment. BACKGROUND

[0002] With the increasingly stringent environmental regulations, there are higher and higher restrictions on the discharge of industrial waste liquid. If such organic solvents as DMF and DMAC are not properly treated before being discharged, they will cause serious pollution to environmental media such as water and soil.

[0003] The conventional rectification process is usually used for the treatment of DMF waste liquid in industry, and the solvent and water are separated by heating rectification. The traditional rectifying column relies on manual setting of parameters such as reflux ratio, column bottom temperature and feed rate, without considering dynamic factors such as DMF concentration fluctuation in waste liquid and environmental temperature change, resulting in low rectification efficiency. The conventional process is not optimized for the separation process according to the differences in physical properties of DMF and DMAC, and when mixed waste liquid is rectified, the two solvents are easily cross-entrained, and the remaining solvent is discharged with the waste water at the bottom of the column, which not only wastes resources but also increases the pressure of subsequent treatment. The operating parameters cannot be dynamically adjusted according to the measured data of the purity of the product at the top of the column and the concentration of the residual liquid at the column bottom, resulting in the system being in a non-optimal state for a long time. SUMMARY

[0004] The present application provides a rectifying column parameter intelligent control method for waste liquid treatment to solve the defects of low rectification efficiency and waste of resources in the prior art.

[0005] In one aspect, the present application provides a rectifying column parameter intelligent control method for waste liquid treatment, comprising:

[0006] S1: collecting influence data in real time in the DMF waste liquid treatment process, the influence data comprising waste liquid characteristic data, equipment operating state data and environmental parameter data;

[0007] S2: analyzing the energy utilization efficiency and mass transfer efficiency in the DMF waste liquid rectification and purification process according to the influence data, calculating the key equipment reliability factor, and analyzing the linear relationship between the energy utilization efficiency, mass transfer efficiency and equipment operating parameters;

[0008] S3: constructing a DMF rectification and purification mode with high recovery rate and high purity according to the linear relationship and the equipment reliability factor, and optimizing the ratio of each equipment operating parameter using an improved adaptive genetic algorithm;

[0009] S4: converting the optimal operating parameter ratio of each equipment into a control instruction, and controlling each equipment in the waste liquid treatment system to perform the collection and storage of DMF waste liquid and rectification and purification according to the control instruction;

[0010] S5: Real-time monitoring of product quality data and equipment operation state data in the DMF waste liquid treatment process, analyzing the deviation value of the actual data and the expected target, and adjusting the operation parameter ratio of each device according to the actual situation.

[0011] According to the distillation column parameter intelligent control method for waste liquid treatment provided by the application, in step S1, the waste liquid characteristic data includes DMF waste liquid concentration, DMAC waste liquid concentration, mass fraction of each component in the DMF mixed waste liquid, and impurity content in the waste liquid.

[0012] According to the distillation column parameter intelligent control method for waste liquid treatment provided by the application, in step S2, the process of analyzing the energy utilization efficiency and the mass transfer efficiency includes:

[0013] The total mass of the waste liquid entering the feed heater per unit time, the steam mass after evaporation in the evaporation tank, the DMF steam mass separated by the DMF gas-liquid separator, and the target component mass produced by the top of each stage of the concentration tower, the distillation column and the deacidification tower are collected.

[0014] According to the ratio of the total output mass of the target component per unit time to the initial total mass of the target component in the waste liquid, the recovery rate of DMF is obtained, and the energy utilization efficiency is calculated according to the energy consumption data of the reboiler and the feed heater.

[0015] The temperature and concentration distribution data of each tray of the first-stage concentration tower, the second-stage concentration tower, the third-stage concentration tower, the four-stage distillation column and the deacidification tower are collected, the concentration difference and temperature difference of the target component between adjacent trays are calculated, and the mass transfer efficiency is obtained combined with the gas phase and liquid phase flow rate in the tower.

[0016] According to the distillation column parameter intelligent control method for waste liquid treatment provided by the application, in step S2, the process of calculating the reliability factor of the key equipment includes:

[0017] The design life, the used life, the number of fault repairs in the past year and the current operating parameter deviation value of the key equipment are taken as input parameters to construct the equipment reliability evaluation model.

[0018] The weights of each input parameter are determined by the analytic hierarchy process, the design life weight ω1, the used life weight ω2, the fault repair frequency weight ω3 and the current operating parameter deviation value weight ω4 are designed, and the reliability factor of each equipment is calculated by weighted summation.

[0019] According to the distillation column parameter intelligent control method for waste liquid treatment provided by the application, in step S2, the specific steps of constructing the linear relationship between the energy utilization efficiency, the mass transfer efficiency and the equipment operation parameter are:

[0020] The variables required for constructing the linear relationship are determined, the independent variables are the feed circulating pump rate, the reboiler temperature of each concentration tower and rectification tower, the reflux pump flow rate of each stage, and the deacidification tower reboiler temperature, and the dependent variables are the energy utilization efficiency and the mass transfer efficiency.

[0021] The independent variables and dependent variables are subjected to standardization conversion, redundant parameters are removed through correlation analysis, and simplified variable data are obtained.

[0022] According to the simplified variable data, a multivariate linear regression method is used to construct a relationship model of the energy utilization efficiency, the mass transfer efficiency and the equipment operation parameter, the intercept term and the regression coefficient in the model are solved through the least square method, and a preliminary model of the linear relationship among the three is established.

[0023] The influence data are divided into a training set and a test set, the coefficient is calculated to measure the fitting degree of the model, whether the fitting degree is greater than a preset threshold value is judged, if yes, the significant parameters are screened through the stepwise regression method, the energy utilization efficiency predicted by the model is substituted into the energy balance equation, otherwise, the regression coefficient weight is corrected, and the linear relationship between the energy utilization efficiency, the mass transfer efficiency and the equipment operation parameter is constructed.

[0024] According to the waste liquid treatment rectification tower parameter intelligent control method provided by the application, in step S3, the specific steps of constructing the DMF rectification purification mode with high recovery rate and high purity are as follows:

[0025] Taking the minimum total energy consumption as the objective function, and taking the DMF recovery rate and the product purity as constraint conditions, the objective function is constructed, and the objective function is represented as:

[0026]

[0027] According to the linear relationship and the reliable factor, the parameter adjustment range is corrected.

[0028] Taking the linear relationship as a link, the mapping relationship between the equipment operation parameters and the recovery rate, the purity and the energy consumption is established, and the sensitivity of parameter adjustment is corrected through the equipment reliability factor.

[0029] Taking the equipment operation parameters as optimization variables of the algorithm, and taking the total energy consumption as the objective function, under the premise of meeting the recovery rate, the purity, the reliability boundary and the conservation constraint, the preliminary parameter matching of the energy utilization efficiency and the mass transfer efficiency is found, and the preliminary rectification purification mode is formed.

[0030] According to the waste liquid treatment rectification tower parameter intelligent control method provided by the application, the gradient descent method is used to calculate the optimal solution meeting the conservation constraint, and the iteration formula is:

[0031]

[0032] In the formula, a is a learning rate, ∇Etotal is a gradient vector of total energy consumption, P k P(k) is a vector of device operating parameters at the kth iteration, k+1 P(k+1) is an updated device operating parameter vector at the k+1th iteration.

[0033] According to the intelligent control method for parameters of a rectifying column for waste liquid treatment provided by the application, in step S3, the specific steps of using the improved adaptive genetic algorithm to optimize the matching of each device operating parameter are as follows:

[0034] Each parameter combination is coded as a chromosome, a real number coding is used to code the standardized value of each gene corresponding to a parameter, a certain number of initial populations are randomly generated to cover different combinations in the parameter range, and an initial solution set for algorithm optimization is formed.

[0035] Taking minimization of total system energy consumption as a core target, the energy utilization efficiency and the mass transfer efficiency are converted into energy consumption correction terms in combination with linear relationships, and a fitness function of the initial solution set is designed.

[0036] The current population is optimized and iterated according to the fitness function.

[0037] For each population after optimization and iteration, the current optimal chromosome is decoded into actual parameter values, which are substituted into the energy conservation and mass conservation equations for verification, and it is determined whether the verification deviation exceeds a preset threshold value. If yes, the penalty weight of the corresponding constraint is increased in the next generation iteration.

[0038] When the change rate of the optimal fitness value of the population of multiple generations in succession is less than a set threshold value, the iteration is terminated, the final optimal chromosome is decoded into actual operating parameter values of each device, and the matching of the device operating parameters is obtained.

[0039] According to the intelligent control method for parameters of a rectifying column for waste liquid treatment provided by the application, the specific steps of optimizing and iterating the current population are as follows:

[0040] Parent individuals are selected from the current population, and chromosomes with high fitness are more likely to be selected.

[0041] The crossover operation is performed, the parent gene fragments are exchanged through arithmetic crossover, and excellent parameter combinations are retained.

[0042] When the mutation operation is performed and the gene values are randomly disturbed, the disturbance amplitude is limited according to the device reliability factor, an elite reservation strategy is introduced, and part of the optimal chromosomes of each generation are directly entered into the next generation.

[0043] According to the intelligent control method for parameters of a rectifying column for waste liquid treatment provided by the application, the specific steps of converting the optimal operating parameter matching of each device into a control instruction are as follows:

[0044] The device operation parameter matching is analyzed, the target parameter values of each device are determined, a parameter-device correspondence table is formed, and the table is used as a basis for instruction conversion.

[0045] According to the parameter-device correspondence table, the target parameters are converted into control signal format target parameters according to the device type.

[0046] The control signal format target parameters are time-sequentially classified and sorted, time stamps and timeout thresholds are added, and the priority of the execution classification instruction is determined.

[0047] The classification instruction is issued and real-time feedback is received, the matching degree of the actual parameters of the device and the instruction is monitored, the fine adjustment instruction is sent when the deviation exceeds the limit, the machine is stopped and an alarm is given when the abnormality continues, and the instruction execution report is generated according to the period.

[0048] The application provides a kind of distillation column parameter intelligent control method for waste liquid treatment, by constructing the linear relationship between energy utilization efficiency, mass transfer efficiency and equipment operation parameter, according to linear relationship and equipment reliability factor, construct high recovery rate, high purity DMF rectification purification mode, use improved adaptive genetic algorithm to optimize each equipment operation parameter matching, realize waste liquid rectification intelligent control.The beneficial effects obtained are as follows:

[0049] Through the combination of sensors and laboratory analysis, as well as various monitoring devices, meteorological sensors and other multiple ways, the data of waste liquid characteristics, equipment operating state and environmental parameters are comprehensively and real-time collected, so that the whole system has a detailed and accurate understanding of the treatment process, laying a solid foundation for subsequent accurate control, and avoiding decision-making errors caused by data missing or inaccuracy.

[0050] The analysis of energy utilization efficiency and mass transfer efficiency, as well as the calculation of key equipment reliability factor and the construction of linear relationship, make the operation of each link quantitatively presented, accurately grasp the internal law in the rectification and purification process, and clearly know the influence of different device parameters on the overall effect, so that the subsequent mode construction and parameter optimization have scientific and reliable basis.

[0051] The construction of high recovery rate and high purity rectification purification mode and the optimization of parameter matching by improved adaptive genetic algorithm can meet the key indicators such as recovery rate and purity, while realizing the lowest total energy consumption of the system, effectively reducing the operating cost and improving the economic efficiency of resource utilization.Moreover, it focuses on solving the problem of multi-tower parameter coordination, ensuring the efficient and coordinated operation of the whole distillation system from feeding to tower treatment to discharging, and avoiding the situation of local optimization but overall poor performance.

[0052] The optimal parameter ratio is converted into control instructions to control the operation of each device, ensuring that the entire waste liquid treatment process is stably carried out according to the optimal setting, ensuring that each link is closely connected and orderly, and whether the waste liquid is collected, heated, distilled, or the product is refluxed and stored, etc. can be efficiently completed, improving the processing efficiency.

[0053] The mechanism of real-time monitoring and dynamic adjustment of parameters according to the deviation can timely respond to actual problems such as product quality fluctuations and equipment failures, which can not only ensure the stable quality of the final product and realize the efficient recycling of DMF resources, but also maintain the stability of the treatment process and reduce the adverse effects caused by failures or abnormal parameters, thereby improving the intelligent level of waste liquid treatment and resource recycling efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0055] Figure 1 is a flowchart of a distillation column parameter intelligent control method for waste liquid treatment provided by an embodiment of the present application;

[0056] Figure 2 is a structural diagram of a distillation column parameter intelligent control method for waste liquid treatment provided by an embodiment of the present application Figure One ;

[0057] Figure 3 is a structural diagram of a distillation column parameter intelligent control method for waste liquid treatment provided by an embodiment of the present application Figure Two ;

[0058] Figure 4 is a structural diagram of a distillation column parameter intelligent control method for waste liquid treatment provided by an embodiment of the present application Figure Three . DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely in the following combined with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0060] The following will be described in combination with Figures 1-4The application discloses an intelligent control method for parameters of a rectifying tower for waste liquid treatment.

[0061] Figure 1 Figure 1 is a flowchart of an intelligent control method for parameters of a rectifying tower for waste liquid treatment.

[0062] The specific structure of the rectifying tower comprises a pretreatment unit, a concentration unit, a rectification refining unit and a post-treatment and storage unit.

[0063] The pretreatment unit comprises a feeder and a vapor-liquid separation tank; raw materials are heated by a feeding heater, transported by a feeding circulating pump, preliminarily vaporized in an evaporation tank, separated by a DMF gas-liquid separator, and then fed into a subsequent concentration tower; and liquid phase can be returned or transported by an intermediate tank and an intermediate pump to provide adaptive materials for rectification.

[0064] The vapor-liquid separation tank separates a vapor-liquid mixture, and the vapor-liquid mixture is separated into gas phase and liquid phase; the liquid phase is transported by a vapor-liquid separation tank water pump, and a water circulating pump is used to realize water system circulation and maintain system water balance and energy utilization.

[0065] The concentration unit comprises a first-stage concentration tower, a second-stage concentration tower and a third-stage concentration tower.

[0066] The first-stage concentration tower is the core, a tower reboiler provides heat, and gas-liquid mass transfer is realized in the tower; a tower top liquid tank collects condensed liquid of the gas phase at the top of the tower, a first-stage reflux pump punches back part of the condensed liquid into the tower as reflux to maintain the rectification environment in the tower; and a first-stage water pump transports the bottom of the tower to the second-stage concentration, and a first-stage discharge pump is used for external sending or sequence transfer of the material at the bottom of the tower.

[0067] The second-stage concentration tower is heated by a second-stage tower reboiler, a second-stage tower top liquid tank collects condensed liquid, a second-stage reflux pump is used for reflux, a second-stage water pump is used for external discharge or reuse, and a second-stage discharge pump is used for sending the material to the third-stage concentration to further enrich DMF.

[0068] The third-stage concentration tower is heated by a third-stage concentration tower reboiler, a third-stage tower top liquid tank collects condensed liquid, a third-stage reflux pump is used for reflux, a third-stage water pump and a third-stage discharge pump are used in cooperation to send the high-concentration DMF material to a downstream rectifying tower, and a T203 tail gas condenser is used to treat the tail gas at the top of the tower.

[0069] The rectification refining unit comprises a four-tower rectifying tower, a deacidification tower, formic acid decomposition and recovery.

[0070] The four-tower rectifying tower is heated by a rectifying tower reboiler, deep rectification is realized in the tower, a rectifying tower top condenser is used to condense the gas phase at the top of the tower, a rectifying tower top liquid tank is used to collect condensed liquid, and a fourth-stage reflux pump is used for reflux to maintain rectification; a fourth-stage water pump and a fourth-stage discharge pump are respectively used for external discharge of light components at the top of the tower and external sending of high-purity DMF at the bottom of the tower.

[0071] The deacidification tower reboiler in the deacidification tower is heated to process acidic DMF materials. The DMF deacidification pump transports the materials and removes acidic components through distillation. The gas phase at the top of the tower is condensed, and the DMF at the bottom of the tower is sent to subsequent purification.

[0072] Formic acid decomposition and recovery: The formic acid decomposition tower is equipped with a reboiler for heating and a condenser for condensation to decompose formic acid and other impurities. The formic acid condensate tank collects the decomposed liquid phase, realizing the removal of impurities and the recovery of resources, and ensuring the purity of DMF.

[0073] The post-processing and storage unit includes: pure DMF processing. The refined DMF is cooled by a pure DMF cooler. The upper and lower reflux pumps of DMF work together to create an internal reflux environment. The DMF condenser and DMF condensate tank ensure the condensation process. The DMF discharge pump sends the pure DMF to external storage or packaging. The defective intermediate tank temporarily stores unqualified products for further processing.

[0074] The entire DMF distillation process is as follows: The feedstock is heated by the feed heater, conveyed by the feed circulation pump, and vaporized in the evaporator. The vapor phase is separated by the DMF gas-liquid separator, and the liquid phase is regulated by an intermediate tank / intermediate pump to prepare feed for the concentration tower. Simultaneously, the vapor-liquid separator separates the vapor and liquid phases, and the outlet pump and water circulation pump maintain the water system circulation. The vapor phase feed enters the primary concentration tower, where the reboiler provides heating. Gas-liquid mass transfer occurs within the tower, and the condensate from the top of the tower is refluxed by the reflux pump. The bottom product is pumped to the secondary concentration tower via the outlet pump. The secondary concentration tower further concentrates the product, and the effluent from the secondary concentration tower discharges light components. The outlet product from the secondary concentration tower is pumped to the tertiary concentration tower. The tertiary concentration tower further concentrates the product, and the reflux pump maintains the reflux. The product is pumped to the distillation tower via the outlet pump. The material enters the fourth column of the distillation column, where the reboiler provides heat. The vapor phase at the top of the column is condensed by the top condenser and then refluxed by the fourth column's reflux pump. High-purity DMF from the bottom of the column is discharged by the fourth column's discharge pump. Acidic materials enter the deacidification column, where the reboiler provides heat and the DMF deacidification pump delivers the material. After deacidification, the material is further refined. If formic acid impurities are present, they are decomposed in the formic acid decomposition column, and the formic acid condensate tank collects the byproducts to ensure DMF purity. After refining, the DMF is cooled by the pure DMF cooler, and the internal reflux is regulated by the DMF upper / lower reflux pump. The reflux is ensured by the DMF condenser and DMF condensate tank, and finally discharged by the DMF discharge pump. Substandard products are sent to the substandard intermediate tank for further processing.

[0075] like Figure 1 As shown in the embodiment of the present invention, an intelligent control method for parameters of a distillation column for waste liquid treatment is provided. The method includes:

[0076] By collecting and storing DMF (dimethylformamide) and DMAC (dimethylacetamide) waste liquids, and then purifying them through a waste liquid treatment system by distillation, the finished DMF / DMAC products are extracted and recycled as products, thus achieving resource recycling. The above uses DMF waste liquid to replace the DMF / DMAC mixed waste liquid, which will be explained in detail below.

[0077] S1: Collecting the influence data in the DMF / DMAC waste liquid treatment process in real time, including waste liquid characteristic data, equipment running state data and environmental parameter data.

[0078] Waste liquid characteristic data is obtained by combining sensors with laboratory analysis, including DMF waste liquid concentration, DMAC waste liquid concentration, mass fraction of each component in DMF / DMAC mixed waste liquid, and impurity content in waste liquid. Equipment running state data is tracked in real time by using monitoring devices installed on each equipment, including steam-water separation tank water outlet pump, water circulating pump, primary concentration tower, 1-tower reboiler, 1-tower reflux pump, 1-tower water outlet pump, 1-tower discharge pump, secondary concentration tower, 2-tower reboiler, 2-tower reflux pump, 2-tower water outlet pump, 2-tower discharge pump, tertiary concentration tower, tertiary concentration tower reboiler, 3-tower reflux pump, 3-tower water outlet pump, 3-tower discharge pump, rectification tower 4-tower, rectification tower reboiler, 4-tower reflux pump, 4-tower water outlet pump, 4-tower discharge pump, deacidification tower, deacidification tower reboiler, DMF deacidification pump, pure DMF cooler, DMF upper reflux pump, DMF lower reflux pump, DMF discharge pump, formic acid decomposition tower, formic acid decomposition tower reboiler, formic acid decomposition tower condenser, feed heating device, feed circulating pump, evaporation tank, DMF gas-liquid separator, and recovery pot. Corresponding equipment running state data includes flow rate and rotating speed of each pump body, temperature and pressure of each tower body, heating power of each reboiler, cooling efficiency of each condenser, liquid level and pressure of steam-water separation tank, and liquid level and component concentration of each stage tower top liquid tank. Environmental parameter data of waste liquid treatment area is collected in real time by using meteorological sensors, including environmental temperature, humidity and atmospheric pressure, and auxiliary environmental data such as heating medium temperature of feed heating device and steam pressure of recovery pot.

[0079] The parameter control requirements of specific equipment are as follows:

[0080]

[0081] S2: Analyzing energy utilization efficiency and mass transfer efficiency in the DMF / DMAC waste liquid rectification and purification process according to influence data, calculating reliability factors of key equipment, and constructing linear relationship between energy utilization efficiency, mass transfer efficiency and equipment running parameters.

[0082] The process of analyzing energy utilization efficiency and mass transfer efficiency includes:

[0083] The total mass of the waste liquid entering the feed heater per unit time, the mass of the steam after evaporation in the evaporation tank, the mass of the DMF steam separated by the DMF gas-liquid separator, and the mass of the target component produced by the top of each concentration column, rectification column, and deacidification column are collected. The recovery rate of DMF / DMAC is obtained according to the ratio of the total output mass of the target component per unit time to the initial total mass of the target component in the waste liquid. The energy utilization efficiency is calculated by combining the energy consumption data of each reboiler and feed heater, which is represented by the formula:

[0084]

[0085] where η is the energy utilization efficiency, m is the total mass of DMF / DMAC produced per unit time, q is the combustion heat value of DMF / DMAC, E is the total energy consumption of each reboiler and feed heater per unit time. e target combustion total

[0086] The temperature and concentration distribution data of each tray in the first-stage concentration column, second-stage concentration column, third-stage concentration column, four-column rectification column, and deacidification column are collected. The concentration difference and temperature difference of the target component between adjacent trays are calculated. The mass transfer efficiency is obtained by combining the gas phase and liquid phase flow rates in the column, which is represented by the formula:

[0087]

[0088] where η is the mass transfer efficiency, ΔC is the actual concentration difference between adjacent trays, and ΔCmax is the theoretical maximum concentration difference.

[0089] The specific steps for calculating the reliability factor of key equipment are as follows:

[0090] A device reliability evaluation model is constructed using the design life, used life, number of fault repairs in the past 12 months, and current operating parameter deviation value as input parameters. The design life and used life are obtained from the equipment factory data and operation records. The number of fault repairs is calculated from the equipment maintenance records. The current operating parameter deviation value is the difference between the actual operating parameters (steam-water separation tank outlet pump flow, 1-tower reboiler temperature) and the rated parameters, expressed as a percentage of the rated parameters.

[0091] The weights of each input parameter are determined using the analytic hierarchy process. The design life weight is 0.3, the used life weight is 0.25, the number of fault repairs weight is 0.25, and the current operating parameter deviation value weight is 0.2. The reliability factor of each device is calculated by weighted summation, which is represented by the formula:

[0092] ​​​​​​​

[0093] wherein R i is the reliability factor of the ith equipment, L d is the design life of the ith equipment, L u is the used life of the ith equipment, N f is the number of failure maintenance of the ith equipment in the past 12 months, D d is the current operating parameter deviation value of the ith equipment, σ i is the standard deviation of the operating parameters of the equipment in the past 30 days.

[0094] The process of constructing the linear relationship between energy utilization efficiency, mass transfer efficiency and equipment operating parameters includes: selecting key equipment operating parameters as independent variables, including feed circulation pump rate, 1-tower reboiler temperature, 1-tower reflux pump flow, 2-tower reboiler temperature, 2-tower reflux pump flow, three-stage concentration tower reboiler temperature, 3-tower reflux pump flow, rectification tower reboiler temperature, 4-tower reflux pump flow, deacidification tower reboiler temperature, taking energy utilization efficiency and mass transfer efficiency as dependent variables, and constructing a linear model by multiple linear regression, which is expressed as:

[0095]

[0096]

[0097] wherein, , is the intercept term, - , - is the regression coefficient, is the feed circulation pump rate, T1r is the 1-tower reboiler temperature, Q1 is the 1-tower reflux pump flow, T2r is the 2-tower reboiler temperature, Q2r is the 2-tower reflux pump flow, T3r is the three-stage concentration tower reboiler temperature, Q3r is the 3-tower reflux pump flow, T4r is the rectification tower reboiler temperature, Q4r is the 4-tower reflux pump flow, and Td is the deacidification tower reboiler temperature.

[0098] The historical operation data and the linear model formula are fitted, the values of the regression coefficients are obtained by the least square method, and the cross-validation method is used to evaluate the prediction ability of the model to ensure that the prediction error of the model test set is ≤3%.

[0099] S3: According to the linear relationship and the equipment reliability factor, a high recovery rate and high purity DMF / DMAC rectification purification mode is constructed, and an improved adaptive genetic algorithm is used to optimize the ratio of each equipment operating parameter.

[0100] The specific steps of constructing the rectification purification mode are:

[0101] The total energy consumption is taken as the objective function, and the DMF / DMAC recovery rate, product purity, and are taken as constraint conditions. The objective function is represented as:

[0102]

[0103] In the formula, is the total energy consumption of the system, P i is the power of the ith device, t i is the running time of the device.

[0104] The parameter adjustment range is corrected according to the linear relationship and the reliability factor.

[0105] The linear relationship is taken as the link to establish the mapping relationship between the operating parameters of each device and the recovery rate, purity, and energy consumption. The sensitivity of parameter adjustment is corrected through the device reliability factor, the parameter coordination problem of multiple towers is focused on, and it is ensured that the optimization of single-tower parameters does not affect the overall goal of the system.

[0106] The operating parameters of the device are taken as the optimization variables of the algorithm, and the total energy consumption is taken as the objective function. Under the premise of meeting the recovery rate, purity, reliability boundary, and conservation constraints, the preliminary parameter ratio that makes the energy utilization efficiency and mass transfer efficiency collaborative is found, and the preliminary rectification purification mode is formed.

[0107] The gradient descent method is used to solve the optimal solution that meets the conservation constraints, and the iteration formula is:

[0108]

[0109] In the formula, α is the learning rate, and is the gradient vector of the total energy consumption, P k is the vector representation of the device operating parameters at the kth iteration, which includes the current iteration operating parameter value of each device, is the current solution in the gradient descent iteration process, P k+1 is the updated device operating parameter vector at the k+1th iteration, that is, the new solution obtained after the gradient descent update operation.

[0110] The specific steps of using the improved adaptive genetic algorithm to optimize the operating parameter ratio of each device are as follows:

[0111] Each parameter combination is encoded as a chromosome, and real number coding is used to encode the standardized value of each gene corresponding to a parameter. A certain number of initial populations are randomly generated to cover different combinations in the parameter range, forming the initial solution set of the algorithm optimization.

[0112] The minimization of the total energy consumption of the system is taken as the core target, and the energy utilization efficiency and mass transfer efficiency are converted into energy consumption correction terms through the linear relationship to design the fitness function of the initial solution set.

[0113] Optimize the current population according to the fitness function for iteration.

[0114] Select parent individuals from the current population, and the chromosomes with high fitness are more likely to be selected.

[0115] Perform a crossover operation to exchange parent gene fragments through arithmetic crossover and retain excellent parameter combinations.

[0116] Perform a mutation operation to randomly disturb the gene values, limit the disturbance amplitude according to the device reliability factor, introduce an elite reservation strategy, and reserve part of the optimal chromosomes directly into the next generation.

[0117] For the population after optimization iteration, decode the current optimal chromosome into actual parameter values every certain number of iterations, substitute it into the energy conservation and mass conservation equations for verification, and determine whether the verification deviation exceeds the preset threshold. If so, increase the penalty weight of the corresponding constraint in the next generation iteration.

[0118] When the change rate of the optimal fitness value of consecutive generations is less than the set threshold, terminate the iteration, decode the final optimal chromosome into the actual operating parameter values of each device, and obtain the device operating parameter ratio.

[0119] S4: Convert the optimal operating parameter ratio of each device into control instructions, and control each device in the waste liquid treatment system to perform DMF / DMAC waste liquid collection, storage, rectification and purification operations according to the control instructions. The process of converting the control instructions includes: according to the optimal operating parameters of each device and the device control protocol, converting the parameter values into electrical signal instructions recognizable by the device. For example, for the feed circulating pump, if the optimal rate is 2.5 m 3The converted control instructions are transmitted to the control systems of each device through industrial Ethernet to control the steam-water separation tank outlet pump and the water circulating pump to operate at the optimal flow rate, ensuring that the liquid level in the steam-water separation tank is stable at 1 / 2-2 / 3 height. The classified DMF / DMAC waste liquid is transported to the feed heater by the feed circulating pump, heated at the optimal temperature, and then sent to the first concentration tower. The 1-tower reboiler is controlled to heat at the optimal temperature, and the 1-tower reflux pump is controlled to operate at the optimal flow rate to achieve reflux. The first concentration tower overhead liquid is transported to the 1-tower overhead liquid tank by the 1-tower outlet pump, and the column liquid is transported to the second concentration tower by the 1-tower outlet pump. Similarly, the supporting equipment of the second concentration tower, the third concentration tower, the fourth distillation tower, and the deacidification tower are controlled to operate at the optimal parameters. The 2-tower outlet water of the second concentration tower is treated and discharged or reused after reaching the standard. The tail gas of the third concentration tower is condensed by the T203 tail gas condenser and then enters the 3-tower overhead liquid tank. The overhead liquid of the fourth distillation tower is condensed by the distillation overhead condenser and then enters the distillation overhead liquid tank. The pure DMF produced by the deacidification tower is cooled by the pure DMF cooler, and part of it is refluxed by the DMF upper reflux pump and the DMF lower reflux pump, and part of it is transported to the DMF condensate tank by the DMF outlet pump for storage. The purity of the non-standard product is transported to the defective intermediate tank for temporary storage, and then it is reprocessed. At the same time, the formic acid decomposition tower and its supporting equipment, the formic acid decomposition tower reboiler, and the formic acid decomposition tower condenser are controlled to operate. The generated formic acid is decomposed, and the condensate is stored in the formic acid condensate tank. The intermediate product is transported to the corresponding processing unit by the intermediate tank and the intermediate pump. The waste heat generated by each device is collected by the recovery pot and used for auxiliary heating or other process links.

[0120] S5: Real-time monitoring of product quality data and equipment operating state data during the DMF / DMAC waste liquid treatment process, analyzing the deviation value of the actual data from the expected target, and adjusting the operating parameter ratio of each device according to the actual situation. In a specific embodiment, the monitored product quality data includes the concentration of the liquid at the top of each concentration tower, the purity of the liquid at the top of the four-tower rectification tower, the purity of the pure DMF / DMAC at the outlet of the deacidification tower, the formic acid concentration at the outlet of the formic acid decomposition tower, and the residual amount of DMF / DMAC in the residual liquid at the bottom of each tower. The monitored equipment operating state data includes the actual flow rate and rotational speed of each pump body, the actual temperature and pressure of each tower body, the actual heating power of each reboiler, the actual cooling efficiency of each condenser, and the actual liquid level and pressure of the steam-water separation tank. The process of analyzing the deviation value includes calculating the difference between the actual product purity and the expected purity, the difference between the actual recovery rate and the expected recovery rate, the difference between the actual energy consumption and the expected energy consumption, and the difference between the actual operating parameters of each device and the optimal operating parameters. The adjusted parameter ratio is converted back into control commands and transmitted to the control systems of each device to achieve dynamic optimization. The substandard products temporarily stored in the substandard intermediate tank are transported to the three-stage concentration tower or the four-tower rectification tower for re-distillation and purification after the parameters are adjusted and stabilized, ensuring that the final products all meet the recycling standards and achieving efficient recycling of DMF / DMAC resources.

[0121] In summary, the present embodiment provides an intelligent control method for the parameters of a rectification tower used for waste liquid treatment. By establishing a linear relationship between energy utilization efficiency, mass transfer efficiency, and equipment operating parameters, a high-recovery, high-purity DMF / DMAC rectification and purification mode is constructed based on the linear relationship and device reliability factors. An improved adaptive genetic algorithm is used to optimize the operating parameter ratio of each device, achieving intelligent control of waste liquid rectification. The beneficial effects achieved are:

[0122] Through the combination of sensors and laboratory analysis, as well as various monitoring devices, weather sensors, and other means, data such as waste liquid characteristics, equipment operating states, and environmental parameters are comprehensively and real-time collected, providing a detailed and accurate understanding of the treatment process and laying a solid foundation for subsequent precise control, avoiding decision-making errors caused by data missing or inaccuracy.

[0123] The analysis of energy utilization efficiency and mass transfer efficiency, the calculation of key equipment reliability factors, and the establishment of linear relationships allow the operating conditions of each link to be quantitatively presented, enabling accurate grasp of the internal laws in the rectification and purification process and clear understanding of the impact of different device parameters on the overall effect, thereby providing a scientific and reliable basis for subsequent mode construction and parameter optimization.

[0124] The rectification purification mode with high recovery rate and high purity is constructed, and the improved adaptive genetic algorithm is used to optimize the parameter ratio, so that the total energy consumption of the system is the lowest while meeting the key indicators such as recovery rate and purity, the operation cost is effectively reduced, and the economy of resource utilization is improved. Moreover, the parameter coordination problem of multiple towers is solved, the efficient coordinated operation of the whole rectification system from feeding to tower processing to discharging is ensured, and the situation of local optimization but overall poor is avoided.

[0125] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and necessary general hardware platforms, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, and the computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments or some parts of the embodiments.

[0126] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit it. Although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for some technical features. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent control method for parameters of a rectifying column for waste liquid treatment, characterized in that, The method comprises the following steps: S1: Collecting influence data in the DMF waste liquid treatment process in real time, the influence data comprising waste liquid characteristic data, equipment operation state data and environmental parameter data; S2: Analyzing the energy utilization efficiency and mass transfer efficiency in the DMF waste liquid rectification and purification process according to the influence data, calculating the key equipment reliability factor, and analyzing the linear relationship between the energy utilization efficiency, the mass transfer efficiency and the equipment operation parameters; Collecting the total mass of the waste liquid entering the feed heater, the steam mass after evaporation in the evaporation tank, the DMF steam mass separated by the DMF gas-liquid separator, and the target component mass produced by the top of each stage of the concentration column, the rectification column and the deacidification column in unit time; According to the ratio of the total output mass of the target component in unit time to the initial total mass of the target component in the waste liquid, the recovery rate of the DMF is obtained, and the energy utilization efficiency is calculated according to the energy consumption data of the reboiler and the feed heater; Collecting the temperature and concentration distribution data of each tray of the first-stage concentration column, the second-stage concentration column, the third-stage concentration column, the four-stage rectification column and the deacidification column, calculating the concentration difference and temperature difference of the target component between adjacent trays, and combining the gas phase and liquid phase flow rates to obtain the mass transfer efficiency; Taking the design life, the used life, the number of fault repairs in the past year and the current operation parameter deviation value of the key equipment as input parameters, a device reliability evaluation model is constructed; The weights of the input parameters are determined by the analytic hierarchy process, the design life weight ω1, the used life weight ω2, the fault repair number weight ω3 and the current operation parameter deviation value weight ω4 are designed, and the reliability factor of each device is calculated by weighted summation; S3: According to the linear relationship and the equipment reliability factor, a DMF rectification and purification mode with high recovery rate and high purity is constructed, and an improved adaptive genetic algorithm is used to optimize the operation parameter matching of each device; Each parameter combination is coded as a chromosome, a real number coding is used to code the standardized value of each gene corresponding to a parameter, a certain number of initial populations are randomly generated to cover different combinations in the parameter range, and an initial solution set for algorithm optimization is formed; Taking the minimization of the total energy consumption of the system as the core target, the energy utilization efficiency and the mass transfer efficiency are converted into energy consumption correction terms by combining the linear relationship, and the fitness function of the initial solution set is designed; The current population is optimized and iterated according to the fitness function; For the population after optimization and iteration, the current optimal chromosome is decoded into actual parameter values, which are substituted into the energy conservation and mass conservation equations for verification, and it is judged whether the verification deviation exceeds the preset threshold value, and if so, the penalty weight of the corresponding constraint is increased in the next generation iteration; When the change rate of the optimal fitness value of continuous generations is less than a set threshold value, the iteration is terminated, the final optimal chromosome is decoded into the actual operation parameter values of each device, and the operation parameter matching of the device is obtained; S4: The optimal operation parameter matching of each device is converted into control instructions, and each device in the waste liquid treatment system performs the collection and storage of the DMF waste liquid and the rectification and purification according to the control instructions. S5: Real-time monitoring of product quality data and equipment operating state data in the DMF waste liquid treatment process, analyzing the deviation value of the actual data from the expected target, and adjusting the operating parameter ratio of each device according to the actual situation. 2.The intelligent control method for parameters of a rectifying column for waste liquid treatment according to claim 1, characterized in that, In step S1, the waste liquid characteristic data includes DMF waste liquid concentration, DMAC waste liquid concentration, mass fraction of each component in the DMF mixed waste liquid, and impurity content in the waste liquid. 3.The intelligent control method of parameters of a rectifying column for waste liquid treatment according to claim 1, characterized in that, In step S2, the specific steps for constructing the linear relationship between energy utilization efficiency, mass transfer efficiency and equipment operating parameters are as follows: Determine the variables required to construct the linear relationship, the independent variables are the feed circulating pump rate, the reboiler temperature of each stage of the concentration tower and the rectification tower, the reflux pump flow rate of each stage, and the reboiler temperature of the deacidification tower, and the dependent variables are the energy utilization efficiency and the mass transfer efficiency; Standardize the independent variables and dependent variables, eliminate redundant parameters through correlation analysis, and obtain simplified variable data; According to the simplified variable data, a multiple linear regression method is used to construct a relationship model between energy utilization efficiency, mass transfer efficiency and equipment operating parameters, the intercept term and regression coefficient in the model are solved by least squares method, and a preliminary model of the linear relationship among the three is established; Divide the influence data into training set and test set, calculate the coefficient to measure the goodness of fit of the model, and determine whether the goodness of fit is greater than a preset threshold value. If yes, filter significant parameters by stepwise regression method, and substitute the predicted energy utilization efficiency into the energy balance equation. Otherwise, correct the regression coefficient weight to obtain the linear relationship between energy utilization efficiency, mass transfer efficiency and equipment operating parameters. 4.The intelligent control method of parameters of a rectifying column for waste liquid treatment according to claim 1, characterized in that, In step S3, the specific steps for constructing a high recovery rate and high purity DMF rectification purification mode are as follows: Taking the minimum total energy consumption as the objective function, and taking the DMF recovery rate, product purity, and as constraint conditions, a target function is constructed, which is represented as: In the formula, Ptotal is the total energy consumption of the system, i P; is the power of the i-th device, t i is the device operating time; According to the linear relationship and the reliability factor, the parameter adjustment range is corrected. Taking the linear relationship as the link, the mapping relationship between each equipment operating parameter and the recovery rate, purity, and energy consumption is established, and the sensitivity of parameter adjustment is corrected by the equipment reliability factor. Taking the equipment operating parameters as the optimization variables of the algorithm, and taking the total energy consumption as the objective function, the preliminary parameter ratio that makes the energy utilization efficiency and mass transfer efficiency collaborative is found under the premise of meeting the recovery rate, purity, reliability boundary and conservation constraint, and a preliminary rectification purification mode is formed.

5. The intelligent control method of parameters of a rectifying column for waste liquid treatment according to claim 4, characterized in that, The optimal solution that meets the conservation constraint is calculated by gradient descent method, and the iteration formula is: where a is the learning rate, V Etotal is the gradient vector of the total energy consumption, P k P k is the vector of device operating parameters at the kth iteration, k+1 P k+1 is the updated vector of device operating parameters at the k+1th iteration. 6.The intelligent control method for parameters of a rectifying column for waste liquid treatment according to claim 1, characterized in that, The specific steps for optimizing and iterating the current population are as follows: Select parent individuals from the current population, and the probability of selecting chromosomes with high fitness is higher; Perform crossover operation to exchange parent gene fragments through arithmetic crossover and retain excellent parameter combinations; Perform mutation operation, and when randomly disturbing the gene value, limit the disturbance amplitude according to the equipment reliability factor, introduce the elite reservation strategy, and reserve part of the optimal chromosomes directly into the next generation. 7.The intelligent control method of parameters of a rectifying column for waste liquid treatment according to claim 1, characterized in that, The specific steps for converting the optimal operating parameter ratio of each device into control instructions are as follows: Analyze the equipment operating parameter ratio, determine the target parameter values of each device, form a parameter-equipment correspondence table, and use it as the basis for instruction conversion; According to the parameter-device correspondence table, the target parameter is converted into a control signal format target parameter according to the device type; The control signal format target parameter is sequentially sorted according to the time sequence, a time stamp and a timeout threshold are added, and the priority of the execution classification instruction is determined; The classification instruction is issued, and feedback is received in real time. The matching degree of the actual parameter of the device and the instruction is monitored. When the deviation exceeds the limit, a fine tuning instruction is sent. If the abnormality continues, the machine is stopped and an alarm is given. The instruction execution report is generated according to the period.

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