Wastewater zero discharge treatment method

Abstract

The invention relates to a wastewater zero discharge treatment method, which belongs to the field of water treatment and purification, and comprises the following steps: adopting a discharge treatment mechanism comprising an activated carbon adsorption device, an electrodialysis device and a membrane distillation device; traversing numerical value combinations of specific values of three parameters including the set activated carbon concentration, the set current numerical value and the set film body thickness to obtain value combinations of the three parameters, and intelligently predicting a return water proportion and a concentrated and crystallized solid volume corresponding to each three-parameter value combination; and combining the values of the corresponding three parameters of which the return water proportion exceeds the limit to serve as zero-emission processing configuration data. According to the invention, aiming at the technical problem that in the prior art, the zero-emission processing configuration data can be analyzed only through repeated and tedious debugging tests, the emission processing analysis model with a customized structural design can be introduced to intelligently predict the return water proportion and the concentrated and crystallized solid volume corresponding to each three-parameter value combination; therefore, the technical problem is solved.

Description

Technical Field

[0001] The present invention relates to the field of water treatment and purification, and in particular to a wastewater zero-discharge treatment method. Background Art

[0002] Zero wastewater discharge refers to the process of highly concentrating the salt content and pollutants in industrial water produced by a factory. All (over 99%) of the wastewater after this concentration is recycled, or filtered out of water-insoluble substances using a filter press and then recycled, with no waste liquid leaving the factory. The salts and pollutants in the water are concentrated and crystallized, or the waste residue from the filter press is discharged as a solid and sent to a landfill at a waste treatment plant or recycled as a useful chemical raw material. Achieving zero wastewater discharge typically requires at least two key steps: concentrated water pretreatment and concentrated crystallization to obtain return water and concentrated crystallized solids, respectively.

[0003] For example, Chinese invention patent publication CN108117223A proposes a zero-discharge treatment method for saline wastewater, comprising the following steps: (1) pretreatment; (2) reverse osmosis treatment; (3) biochemical treatment; (4) electrodialysis concentration; and (5) cyclic crystallization. Compared with the prior art, the zero-discharge salt separation process for saline wastewater provided by the present invention achieves zero or near-zero discharge of coal chemical wastewater while improving salt recovery rates and recovering high-quality sodium sulfate, mirabilite, and sodium chloride, achieving comprehensive utilization of crystallized salt. The membrane treatment unit has a stable process, a long operating cycle, low costs, and good economic efficiency for the entire process.

[0004] For example, the Chinese invention patent publication CN110563227A proposes a wastewater zero-discharge treatment device, which includes a wastewater pool, a wastewater pool gas source recoil cleaning integrated machine, an intelligent gas source recoil filtration cleaning dirt collection integrated machine, a terahertz phonon resonance ring, a water softening device, a fine filtration device, a primary precipitation filtration device and a secondary precipitation filtration device. The patent of this invention provides a suitable wastewater zero-discharge treatment solution for industrial equipment according to local conditions. The present invention can be applied to almost all salt-containing wastewater treatment. The present invention has the lowest investment, the lowest energy consumption, and a simple system, meets the requirements of zero discharge of industrial wastewater, and has no secondary pollution, and is easy to promote and apply in the field of salt-containing industrial wastewater treatment. At the same time, the present invention is a purely physical wastewater zero-discharge treatment solution, which avoids pollution from chemical agents added in wastewater treatment.

[0005] However, the above technical solution only involves a detailed description of the specific steps or specific structures of zero wastewater discharge treatment. In fact, if the real zero wastewater discharge treatment effect is achieved, that is, the percentage of return water obtained accounts for more than 99% of the initially treated wastewater, it is necessary to repeatedly and tediously debug and test the various configuration parameters corresponding to each specific step or each specific structure, and finally determine the combination of various configuration parameters that can achieve the zero wastewater discharge treatment effect based on the field discharge treatment results. Obviously, this repeated and tedious debugging and testing steps to find the optimal configuration parameter combination consumes a lot of labor costs, time costs and economic costs. At the same time, in the debugging and testing process, it is inevitable that unqualified wastewater discharge will be caused. Summary of the Invention

[0006] In order to solve the technical problems in the prior art, the present invention provides a method for treating wastewater with zero discharge. On the basis of introducing a discharge treatment analysis model with customized structural design and a plurality of basic information selected in a targeted manner, the method adopts a discharge treatment mechanism including an activated carbon adsorption device, an electrodialysis device and a membrane distillation device, and sequentially performs activated carbon adsorption operation, electrodialysis operation and distillation and membrane separation operation on the industrial wastewater generated by the target steel plant. In order to obtain a numerical combination of specific values of the three parameters of set activated carbon concentration, set current value and set membrane thickness that can meet the conditions for zero discharge treatment of wastewater, the above three parameters are traversed to obtain the values of each of the three parameters. Parameter value combination, each three-parameter value combination is input into the emission treatment analysis model in sequence to obtain the return water use ratio and concentrated crystallized solid volume corresponding to each three-parameter value combination, and the three-parameter value combination with the corresponding return water use ratio greater than 99% is used as the emission treatment agency's zero emission treatment configuration data for industrial wastewater generated by the target steel plant, so as to facilitate the subsequent execution of priority parameter configuration, so that there is no need to perform a large number of emission effect test treatments to directly obtain the specific value combination of the three parameters that meet the wastewater zero emission treatment conditions, while improving treatment efficiency and reducing treatment costs, and avoiding unqualified wastewater discharge caused by the testing process.

[0007] According to the present invention, a method for treating wastewater with zero discharge is provided, the method comprising:

[0008] An emission treatment mechanism including an activated carbon adsorption device, an electrodialysis device, and a membrane distillation device is used to sequentially perform activated carbon adsorption, electrodialysis, distillation, and membrane separation operations on the industrial wastewater generated by the target steel plant to obtain return water and concentrated crystalline solids. The activated carbon adsorption device uses activated carbon with a set activated carbon concentration in g / L to separate dissolved organic carbon from the industrial wastewater. The electrodialysis device uses a DC power supply with a set current value in mA to separate inorganic salts from the industrial wastewater. The membrane distillation device uses a reverse osmosis membrane with a set membrane thickness in millimeters to separate organic salts and macromolecular organic matter from the industrial wastewater.

[0009] Obtain multiple operating scenario data of the target steel plant;

[0010] Traversing through specific value combinations of the three parameters of a set activated carbon concentration, a set current value, and a set membrane thickness to obtain various three-parameter value combinations, inputting each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model to obtain the return water use ratio and concentrated crystalline solid volume corresponding to each three-parameter value combination;

[0011] The corresponding three-parameter value combination with a return water usage ratio greater than 99% is used as the zero-discharge treatment configuration data of the emission treatment agency for the industrial wastewater generated by the target steel plant.

[0012] Compared with the prior art, the present invention has at least the following five key inventive features:

[0013] First invention point: For an industrial wastewater discharge treatment scenario in which an emission treatment mechanism including an activated carbon adsorption device, an electrodialysis device, and a membrane distillation device is used to sequentially perform activated carbon adsorption operations, electrodialysis operations, and distillation and membrane separation operations on industrial wastewater generated by a target steel plant, in order to obtain a numerical combination of specific values of three parameters: a set activated carbon concentration, a set current value, and a set membrane thickness that can meet the conditions for zero wastewater discharge treatment, the above three parameters are traversed and valued to obtain each three-parameter value combination, and each three-parameter value combination is input into an emission treatment analysis model in sequence to obtain the corresponding return water ratio and concentrated crystallized solid volume for each three-parameter value combination, and the corresponding three-parameter value combination with a return water ratio greater than 99% is used as the zero discharge treatment configuration data of the emission treatment mechanism for the industrial wastewater generated by the target steel plant, so as to facilitate the subsequent execution of priority parameter configuration, thereby directly obtaining the numerical combination of specific values of the three parameters that meet the conditions for zero wastewater discharge treatment without having to perform a large number of emission effect test processes;

[0014] Second inventive point: Different target steel mills have emission treatment analysis models with different customized structures. Specifically, the emission treatment analysis model is a deep neural network that has completed various training cycles. The deep neural network includes a single input layer, a single output layer, and multiple hidden layers. The number of deep neural network training cycles is positively correlated with the target steel mill's steel production per unit time. The structural customization of the emission treatment analysis model ensures the reliability and stability of the emission treatment effect data corresponding to each three-parameter value combination, namely, the proportion of return water use and the volume of concentrated crystalline solids.

[0015] Third invention point: In order to intelligently predict the emission treatment effect data corresponding to each three-parameter value combination, a plurality of basic information is selected in a targeted manner, specifically including each three-parameter value combination, a plurality of operating scenario data of the target steel plant, the production flow of the target steel plant's industrial wastewater, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant. The plurality of operating scenario data of the target steel plant are the target steel plant's steel output per unit time, floor area, number of workers, steelmaking furnace operating temperature, rolling mill operating temperature, pressure of the rolling rollers used for steel rolling, casting speed of the continuous casting machine during the casting process, upper limit value of the carbon content of the steel body, and lower limit value of the carbon content of the steel body. The targeted selection of the above-mentioned plurality of basic information further ensures the reliability and stability of the emission treatment effect data corresponding to each three-parameter value combination, namely, the proportion of return water use and the volume of concentrated crystalline solids;

[0016] Fourth invention point: In each training of the deep neural network, the return water usage ratio and the concentrated crystallized solid volume corresponding to a known three-parameter value combination are used as two output contents of the deep neural network, and the three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, and the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant are used as multiple input contents of the deep neural network to complete the current training of the deep neural network, thereby ensuring the training effect of each training of the deep neural network;

[0017] The fifth invention point: When the numerical values of multiple operating scenario data of the target steel plant change, the multiple operating scenario data of the target steel plant after the numerical changes are input into the emission treatment analysis model to obtain new zero-emission treatment configuration data for industrial wastewater generated by the target steel plant corresponding to the multiple operating scenario data of the target steel plant after the numerical changes, thereby realizing dynamic update of the zero-emission treatment configuration data based on changes in the emission environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The embodiments of the present invention will be described below with reference to the accompanying drawings, in which:

[0019] Figure 1 Schematic diagram of the working scenario of the zero-discharge wastewater treatment method according to the present invention.

[0020] Figure 2 This is a flowchart of the steps of a wastewater zero-discharge treatment method according to Example 1 of the present invention.

[0021] Figure 3 This is a flowchart of the steps of a wastewater zero-discharge treatment method according to Example 2 of the present invention.

[0022] Figure 4 This is a flowchart of the steps of a wastewater zero-discharge treatment method according to Example 3 of the present invention.

[0023] Figure 5 This is a flowchart of the steps of a wastewater zero-discharge treatment method according to Example 4 of the present invention.

[0024] Figure 6 This is a flowchart of the steps of a wastewater zero-discharge treatment method according to Example 5 of the present invention.

[0025] Figure 7 This is a flowchart of the steps of a wastewater zero-discharge treatment method according to Example 6 of the present invention. DETAILED DESCRIPTION

[0026] like Figure 1 As shown, a schematic diagram of a working scenario of a wastewater zero discharge treatment method according to the present invention is given.

[0027] The specific technical process of the present invention is as follows:

[0028] Technical Process A: Design of a custom-built emission treatment mechanism for the target steel plant, comprising activated carbon adsorption devices, electrodialysis devices, and membrane distillation devices;

[0029] Specifically, the activated carbon adsorption device, the electrodialysis device, and the membrane distillation device sequentially perform activated carbon adsorption operations, electrodialysis operations, and distillation and membrane separation operations on the industrial wastewater generated by the target steel plant to obtain return water and concentrated crystalline solids;

[0030] More specifically, the activated carbon adsorption device uses activated carbon with a set activated carbon concentration in g / L to separate dissolved organic carbon from industrial wastewater, the electrodialysis device uses a DC power supply with a set current value in mA to separate inorganic salts from industrial wastewater, and the membrane distillation device uses a reverse osmosis membrane with a set membrane thickness in millimeters to separate organic salts and macromolecular organic matter from industrial wastewater.

[0031] Here, the specific numerical combination of the three parameters of activated carbon concentration, current value, and membrane thickness is uncertain. It is necessary to optimize the numerical combination to achieve a true zero-discharge wastewater treatment effect. The value range of the activated carbon concentration is set between 0-5g / L, the value range of the current value is set between 5-600mA, and the value range of the membrane thickness is set between 0.1-0.5mm. It is necessary to perform the optimization of the numerical combination within the limits of these numerical ranges.

[0032] In the prior art, it is necessary to repeatedly and tediously debug and test these three configuration parameters, and finally determine the combination of each configuration parameter that can achieve the zero discharge treatment effect of wastewater based on the field discharge treatment results. Obviously, this repeated and tedious debugging and testing step of finding the optimal configuration parameter combination consumes a lot of labor cost, time cost and economic cost. At the same time, unqualified wastewater discharge will inevitably be caused during the debugging and testing process. The present invention will use an intelligent prediction model to predict the various wastewater discharge treatment effects obtained by applying each configuration parameter combination to the customized structure discharge treatment mechanism of the target steel plant, and then directly obtain the configuration parameter combination whose wastewater discharge treatment effect meets the wastewater zero discharge treatment requirements, so as to facilitate the subsequent field configuration operation of the customized structure discharge treatment mechanism to achieve zero discharge treatment of wastewater in the target steel plant.

[0033] Technical Process B: Design a customized emission treatment analysis model to intelligently predict the emission treatment effect data corresponding to each three-parameter value combination;

[0034] For example, the emission treatment analysis model is a deep neural network after each training, the deep neural network includes a single input layer, a single output layer, and multiple hidden layers, and the number of times the deep neural network is trained is positively correlated with the steel production per unit time of the target steel plant, so that emission treatment analysis models with different customized structures are designed for different target steel plants;

[0035] For example, in each training session of the deep neural network, the return water usage ratio and the concentrated crystallized solid volume corresponding to a known three-parameter value combination are used as two output contents of the deep neural network, and the three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, and the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant are used as multiple input contents of the deep neural network to complete the current training of the deep neural network, thereby ensuring the training effect of each training session of the deep neural network;

[0036] In this way, through the structural customization of the above-mentioned emission treatment analysis model, the reliability and stability of the emission treatment effect data corresponding to each three-parameter value combination, namely the return water usage ratio and the concentrated crystallized solid volume, are guaranteed;

[0037] Technical Process C: To intelligently predict the emission treatment effect data corresponding to each three-parameter value combination, multiple basic information is selected;

[0038] For example, the multiple basic information specifically includes each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow of industrial wastewater of the target steel plant, and the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant;

[0039] like Figure 1 As shown, the multiple basic information includes three types of information. The first type is the three-parameter value combination used for the current test, the second type is multiple operating scenario data of the target steel plant, and the third type is various industrial wastewater-related data of the target steel plant, including the production flow of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant;

[0040] For further example, the multiple operation scenario data of the target steel plant are the target steel plant's steel output per unit time, floor area, number of workers, steelmaking furnace operating temperature, rolling mill operating temperature, pressure of rolling rolls used for steel rolling, casting speed of the continuous casting machine during casting, upper limit value of steel body carbon content, and lower limit value of steel body carbon content;

[0041] In this way, through the targeted selection of the above-mentioned multiple basic information, the reliability and stability of the discharge treatment effect data corresponding to each three-parameter value combination, namely the proportion of return water use and the volume of concentrated crystallized solids, are further guaranteed;

[0042] Technical Process D: Using the emission treatment analysis model designed for the target steel plant in Technical Process B, and based on the multiple basic information selected in Technical Process C, the emission treatment mechanism designed for the target steel plant in Technical Process A is used to perform intelligent prediction of the emission treatment effect data corresponding to each specific value combination of the three parameters: activated carbon concentration, current value, and membrane thickness.

[0043] like Figure 1 As shown, for the three parameter value combinations used in the current test, the corresponding emission treatment effect data is intelligently predicted, including two data items: the first item is the proportion of return water use, and the second item is the volume of concentrated crystallized solids;

[0044] Technical process E: Based on the emission treatment effect data corresponding to each specific numerical combination obtained in technical process D, obtain the specific numerical combination for the wastewater discharge treatment effect to meet the zero wastewater discharge treatment requirements;

[0045] Specifically, the emission treatment effect data corresponding to each specific numerical combination is the return water usage ratio and concentrated crystallized solid volume obtained by applying the specific numerical combination to the emission treatment mechanism. The corresponding specific numerical combination with a return water usage ratio greater than 99% is used as the zero-emission treatment configuration data of the emission treatment mechanism for the industrial wastewater generated by the target steel plant, so as to facilitate the subsequent execution of priority parameter configuration;

[0046] Technical Process F: When the values of multiple operating scenario data of the target steel plant change, the multiple operating scenario data of the target steel plant after the values change are input into the emission treatment analysis model to obtain new zero-discharge treatment configuration data for industrial wastewater generated by the target steel plant corresponding to the multiple operating scenario data of the target steel plant after the values change;

[0047] In this way, when the emission environment of the target steel plant changes, the zero emission treatment configuration data can be dynamically updated based on the change in the emission environment.

[0048] It can be seen that compared with the existing technology, the present invention does not need to perform a large number of discharge effect test processes to directly obtain the specific numerical combination of the three parameters that meet the wastewater zero discharge treatment conditions, while improving the treatment efficiency and reducing the treatment cost, and avoiding the unqualified wastewater discharge caused by the testing process.

[0049] The key points of the present invention are: traversing and combining the specific values of the three parameters of set activated carbon concentration, set current value and set membrane thickness within their respective value ranges to obtain each specific value combination, intelligent prediction of the wastewater discharge treatment effect of each specific value combination, direct acquisition of specific value combinations that meet the wastewater zero discharge treatment conditions, customized structural design of the emission treatment analysis model and targeted selection of multiple basic information.

[0050] Hereinafter, the zero-discharge wastewater treatment method of the present invention will be specifically described by way of examples.

[0051] Example 1

[0052] Figure 2 This is a flowchart of the steps of a wastewater zero-discharge treatment method according to Example 1 of the present invention.

[0053] like Figure 2 As shown, the wastewater zero discharge treatment method includes the following specific steps:

[0054] Step 201: Using an emission treatment mechanism including an activated carbon adsorption device, an electrodialysis device, and a membrane distillation device, the industrial wastewater generated by the target steel plant is sequentially subjected to activated carbon adsorption, electrodialysis, distillation, and membrane separation operations to obtain return water and concentrated crystalline solids. The activated carbon adsorption device uses activated carbon with a set activated carbon concentration in g / L to separate dissolved organic carbon from the industrial wastewater. The electrodialysis device uses a DC power supply with a set current value in mA to separate inorganic salts from the industrial wastewater. The membrane distillation device uses a reverse osmosis membrane with a set membrane thickness in millimeters to separate organic salts and macromolecular organic matter from the industrial wastewater.

[0055] For example, the emission treatment mechanism may further include a parameter configuration interface for connecting to the activated carbon adsorption device, the electrodialysis device, and the membrane distillation device, respectively, so as to configure specific values of the three parameters of the activated carbon adsorption device, the electrodialysis device, and the membrane distillation device, respectively, namely, the set activated carbon concentration, the set current value, and the set membrane thickness;

[0056] Step 202: Acquire multiple operation scenario data of the target steel plant;

[0057] Specifically, the multiple operating scenario data of the target steel plant reflect the emission environment of the target steel plant. When the value of any of the multiple operating scenario data of the target steel plant changes, it can be determined that the emission environment of the target steel plant has changed. Obviously, under different emission environments, even if the same emission treatment mechanism is used, the specific value combination of the above three parameters to achieve zero wastewater discharge treatment may be different;

[0058] In a subsequent step of the present invention, when the values of the multiple operation scenario data of the target steel plant change, the multiple operation scenario data of the target steel plant after the value change are input into the emission treatment analysis model to obtain new zero-emission treatment configuration data for industrial wastewater generated by the target steel plant corresponding to the multiple operation scenario data of the target steel plant after the value change. In this way, when the emission environment of the target steel plant changes, the zero-emission treatment configuration data can be dynamically updated based on the change in the emission environment.

[0059] Step 203: Traverse the specific value combinations of the three parameters of the set activated carbon concentration, the set current value, and the set membrane thickness to obtain various three-parameter value combinations, and input each three-parameter value combination, multiple operation scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model to obtain the return water use ratio and concentrated crystallized solid volume corresponding to each three-parameter value combination;

[0060] Specifically, the traversal of the specific values of the above three parameters needs to be performed within their respective value ranges;

[0061] Step 204: using the corresponding three-parameter value combination with a return water usage ratio greater than 99% as zero-discharge treatment configuration data for the discharge treatment agency for the industrial wastewater generated by the target steel plant;

[0062] Specifically, the corresponding return water usage ratio is greater than 99% to meet the requirement of zero-discharge treatment of industrial wastewater. Obviously, there may be more than one combination of three-parameter values for the corresponding return water usage ratio to be greater than 99%.

[0063] Among them, the multiple operating scenario data of the target steel plant include the target steel plant's steel output per unit time, floor space, number of workers, steelmaking furnace operating temperature, rolling mill operating temperature, rolling roll pressure, billet drawing speed during the continuous casting process, upper limit value of steel body carbon content, and lower limit value of steel body carbon content;

[0064] The activated carbon concentration is set to a value range of 0-5 g / L, the current value is set to a value range of 5-600 mA, and the membrane thickness is set to a value range of 0.1-0.5 mm.

[0065] The return water ratio corresponding to each three-parameter value combination is the percentage of return water obtained after the discharge treatment mechanism is configured using the three-parameter value combination to perform discharge treatment on the industrial wastewater generated by the target steel plant;

[0066] The volume of concentrated crystalline solids corresponding to each three-parameter value combination is the volume of concentrated crystalline solids obtained after the discharge treatment mechanism configured with the three-parameter value combination performs discharge treatment on the industrial wastewater generated by the target steel plant;

[0067] In this way, most of the industrial wastewater that has undergone zero-discharge treatment is reused as return water, and a small portion of it is discharged as solids, which are concentrated and crystallized and sent to a waste treatment plant for landfill or recycled as useful chemical raw materials, achieving a zero-discharge treatment effect for the target steel plant without any waste liquid being discharged.

[0068] The emission treatment analysis model is a deep neural network that has completed various training cycles. The deep neural network includes a single input layer, a single output layer, and multiple hidden layers. The number of training cycles of the deep neural network is positively correlated with the steel production per unit time of the target steel plant.

[0069] Specifically, the number of deep neural network training times is positively correlated with the steel output per unit time of the target steel plant, including: the steel output per unit time of the target steel plant is 500,000 tons, the corresponding number of deep neural network training times is 500 times, the steel output per unit time of the target steel plant is 800,000 tons, the corresponding number of deep neural network training times is 800 times, the steel output per unit time of the target steel plant is 1.2 million tons, the corresponding number of deep neural network training times is 1,200 times, the steel output per unit time of the target steel plant is 1.8 million tons, the corresponding number of deep neural network training times is 1,800 times, and so on;

[0070] And wherein, in each training execution of the deep neural network, the return water usage ratio and the concentrated crystalline solid volume corresponding to a certain known three-parameter value combination are used as the two output contents of the deep neural network, and the said three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant are used as multiple input contents of the deep neural network to complete this training of the deep neural network.

[0071] Example 2

[0072] Figure 3 This is a flowchart of the steps of a wastewater zero-discharge treatment method according to Example 2 of the present invention.

[0073] like Figure 3 As shown, Figure 2 Unlike the embodiment in , after using the corresponding three-parameter value combination with a return water usage ratio greater than 99% as the zero-discharge treatment configuration data of the discharge treatment mechanism for the industrial wastewater generated by the target steel plant, that is, after step S204, the method further includes:

[0074] Step S205: using the zero emission treatment configuration data to configure the activated carbon adsorption device, electrodialysis device, and membrane distillation device of the emission treatment mechanism to set specific values of three parameters: activated carbon concentration, current value, and membrane thickness;

[0075] Specifically, the parameter configuration interface in the emission treatment mechanism can be selected to connect to the activated carbon adsorption device, the electrodialysis device and the membrane distillation device respectively, so as to realize the configuration of the specific values of the three parameters of the activated carbon concentration, the current value and the membrane thickness corresponding to the activated carbon adsorption device, the electrodialysis device and the membrane distillation device respectively;

[0076] Among them, the configuration of using zero-emission treatment configuration data to set the specific values of the three parameters of activated carbon concentration, current value and membrane thickness for the activated carbon adsorption device, electrodialysis device and membrane distillation device of the emission treatment mechanism includes: when there is more than one zero-emission treatment configuration data, selecting any zero-emission treatment configuration data to set the specific values of the three parameters of activated carbon concentration, current value and membrane thickness for the activated carbon adsorption device, electrodialysis device and membrane distillation device of the emission treatment mechanism.

[0077] Example 3

[0078] Figure 4 This is a flowchart of the steps of a wastewater zero-discharge treatment method according to Example 3 of the present invention.

[0079] like Figure 4 As shown, Figure 2 Unlike the embodiment in , after using the corresponding three-parameter value combination with a return water usage ratio greater than 99% as the zero-discharge treatment configuration data of the discharge treatment mechanism for the industrial wastewater generated by the target steel plant, that is, after step S204, the method further includes:

[0080] Step S206: receiving zero-discharge treatment configuration data, and wirelessly transmitting the zero-discharge treatment configuration data to a remote wastewater zero-discharge management server via a wireless communication network;

[0081] For example, receiving zero-discharge treatment configuration data and wirelessly transmitting the zero-discharge treatment configuration data to a remote wastewater zero-discharge management server via a wireless communication network includes: the wireless communication network is based on a frequency division duplex communication mechanism or a time division duplex communication mechanism.

[0082] Example 4

[0083] Figure 5 This is a flowchart of the steps of a wastewater zero-discharge treatment method according to Example 4 of the present invention.

[0084] like Figure 5 As shown, Figure 2 Unlike the embodiment in , after using the corresponding three-parameter value combination with a return water usage ratio greater than 99% as the zero-discharge treatment configuration data of the discharge treatment mechanism for the industrial wastewater generated by the target steel plant, that is, after step S204, the method further includes:

[0085] Step S207: receiving zero-emission processing configuration data, and using an on-site display mechanism to complete on-site display of the zero-emission processing configuration data;

[0086] For example, receiving the zero-emission processing configuration data and using an on-site display mechanism to display the zero-emission processing configuration data on-site includes: the on-site display mechanism is an LCD display array, an LED display array, or a giant display screen.

[0087] Example 5

[0088] Figure 6 This is a flowchart of the steps of a wastewater zero-discharge treatment method according to Example 5 of the present invention.

[0089] like Figure 6 As shown, Figure 2 Unlike the embodiment in , after using the corresponding three-parameter value combination with a return water usage ratio greater than 99% as the zero-discharge treatment configuration data of the discharge treatment mechanism for the industrial wastewater generated by the target steel plant, that is, after step S204, the method further includes:

[0090] Step S208: When the values of the multiple operating scenario data of the target steel plant change, the multiple operating scenario data of the target steel plant after the values change are input into the emission treatment analysis model to obtain new zero-discharge treatment configuration data for industrial wastewater generated by the target steel plant corresponding to the multiple operating scenario data of the target steel plant after the values change;

[0091] Specifically, when multiple operating scenario data of the target steel plant undergo numerical changes, the multiple operating scenario data of the target steel plant after the numerical changes are input into the emission treatment analysis model to obtain new zero-emission treatment configuration data for industrial wastewater generated by the target steel plant corresponding to the multiple operating scenario data of the target steel plant after the numerical changes, including: if more than one operating scenario data among the multiple operating scenario data of the target steel plant undergoes numerical changes, it can be determined that the multiple operating scenario data of the target steel plant undergo numerical changes, and the zero-emission treatment configuration data needs to be re-analyzed.

[0092] Example 6

[0093] Figure 7 This is a flowchart of the steps of a wastewater zero-discharge treatment method according to Example 6 of the present invention.

[0094] like Figure 7 As shown, Figure 2 Different from the embodiment in the embodiment, after obtaining multiple operation scenario data of the target steel plant, that is, after step S202, the method further includes:

[0095] Step S209: performing training on the deep neural network to obtain a deep neural network after each training and outputting it as an emission treatment analysis model, wherein the number of times the deep neural network is trained is positively correlated with the steel production per unit time of the target steel plant;

[0096] Specifically, each training is performed on the deep neural network to obtain the deep neural network after each training and output it as the emission treatment analysis model. The number of trainings of the deep neural network is positively correlated with the steel output per unit time of the target steel plant, including: the steel output per unit time of the target steel plant is 500,000 tons, and the corresponding number of trainings of the deep neural network is 500 times; the steel output per unit time of the target steel plant is 800,000 tons, and the corresponding number of trainings of the deep neural network is 800 times; the steel output per unit time of the target steel plant is 1.2 million tons, and the corresponding number of trainings of the deep neural network is 1,200 times; the steel output per unit time of the target steel plant is 1.8 million tons, and the corresponding number of trainings of the deep neural network is 1,800 times, and so on.

[0097] Next, various method embodiments of the present invention will be described in detail.

[0098] In the wastewater zero discharge treatment method according to various method embodiments of the present invention:

[0099] Obtain the target steel plant's steel output per unit time, floor area, number of workers, steelmaking furnace operating temperature, rolling mill operating temperature, rolling roll pressure, billet drawing speed during the continuous casting process, upper limit value of steel carbon content, and lower limit value of steel carbon content as multiple operating scenario data for the target steel plant, including: the target steel plant's upper limit value of steel carbon content is 0.35%, and the target steel plant's lower limit value of steel carbon content is 0.25%;

[0100] The multiple operating scenario data of the target steel plant here reflect the operating scenarios of the target steel plant. If the values of more than one operating scenario data change, it can be determined that the multiple operating scenario data of the target steel plant have changed, and the zero-emission treatment configuration data needs to be re-analyzed;

[0101] Among them, the emission treatment analysis model is a deep neural network after completing each training. The deep neural network includes a single input layer, a single output layer and multiple hidden layers, and the number of times the deep neural network is trained is positively correlated with the steel production per unit time of the target steel plant, including: the more steel production per unit time of the target steel plant, the more times the deep neural network corresponding to the steel production per unit time of the target steel plant is trained.

[0102] And in the wastewater zero discharge treatment method according to each method embodiment of the present invention:

[0103] Inputting each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model to obtain the return water use ratio and the concentrated crystalline solid volume corresponding to each three-parameter value combination, including: inputting each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model, and running the emission treatment analysis model to obtain the return water use ratio and the concentrated crystalline solid volume corresponding to each three-parameter value combination output by the emission treatment analysis model;

[0104] Specifically, the MATLAB toolbox can be selected to complete the simulation and testing of the data processing process of inputting each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model, and then running the emission treatment analysis model to obtain the return water usage ratio and concentrated crystallized solid volume corresponding to each three-parameter value combination output by the emission treatment analysis model;

[0105] wherein, after inputting each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model, the emission treatment analysis model is run to obtain the return water use ratio and concentrated crystallized solid volume corresponding to each three-parameter value combination output by the emission treatment analysis model, including: performing numerical normalization processing on each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant, and then inputting them into the emission treatment analysis model in parallel;

[0106] wherein, after inputting each three-parameter value combination, multiple operation scenario data of the target steel plant, the production flow of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model, the emission treatment analysis model is run to obtain the return water use ratio and concentrated crystalline solid volume corresponding to each three-parameter value combination output by the emission treatment analysis model, further comprising: the return water use ratio and concentrated crystalline solid volume corresponding to each three-parameter value combination output by the emission treatment analysis model are both numerical representations after numerical normalization processing;

[0107] wherein each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant are respectively subjected to numerical normalization processing and then input into the emission treatment analysis model in parallel, including: the numerical normalization processing is a binary numerical conversion processing;

[0108] wherein each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant are numerically normalized and then input into the emission treatment analysis model in parallel, further comprising: using a numerical conversion device to perform numerical normalization on each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant;

[0109] wherein each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant are numerically normalized and then inputted in parallel into the emission treatment analysis model further comprising: employing a parallel control device for inputting each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant, which have been subjected to numerical normalization, in parallel into the emission treatment analysis model;

[0110] The method comprises: using a parallel control device to input each three-parameter value combination after numerical normalization processing, multiple operation scenario data of the target steel plant, the production flow of industrial wastewater of the target steel plant, and the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model in parallel, including: the parallel control device is connected to the numerical conversion device;

[0111] The method further comprises: using a parallel control device to input each three-parameter value combination after numerical normalization processing, multiple operation scenario data of the target steel plant, the production flow of industrial wastewater of the target steel plant, and the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model in parallel, further comprising: the parallel control device and the numerical conversion device share the same serial configuration interface;

[0112] For example, the parallel control device and the numerical value conversion device share the same serial configuration interface, which includes: the parallel control device and the numerical value conversion device share the same IIC configuration interface, and the parallel control device and the numerical value conversion device have different configuration addresses respectively;

[0113] And wherein, a parallel control device is used to input each three-parameter value combination after numerical normalization processing, multiple operating scenario data of the target steel plant, the production flow of industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant in parallel into the emission treatment analysis model, which also includes: using different models of CPLD chips to respectively implement the parallel control device and the numerical conversion device.

[0114] In addition, the present invention may also cite the following technical contents to further demonstrate the outstanding substantial progress of the present invention:

[0115] The emission treatment analysis model is a deep neural network after each training, the deep neural network includes a single input layer, a single output layer, and multiple hidden layers, and the number of times the deep neural network is trained is positively correlated with the steel production per unit time of the target steel plant, including: the number of hidden layers in the deep neural network is positively correlated with the steel production per unit time of the target steel plant;

[0116] For example, a positive correlation between the number of hidden layers in the deep neural network and the steel output per unit time of the target steel plant includes: the steel output per unit time of the target steel plant is 500,000 tons, the number of hidden layers in the deep neural network is 3, the steel output per unit time of the target steel plant is 800,000 tons, the number of hidden layers in the deep neural network is 4, the steel output per unit time of the target steel plant is 1.2 million tons, the number of hidden layers in the deep neural network is 5, the steel output per unit time of the target steel plant is 1.8 million tons, the number of hidden layers in the deep neural network is 6, and so on;

[0117] The positive correlation between the number of hidden layers in the deep neural network and the steel output per unit time of the target steel plant further includes: using a data mapping formula to represent a data mapping relationship of the positive correlation between the number of hidden layers in the deep neural network and the steel output per unit time of the target steel plant;

[0118] The data mapping relationship of using a data mapping formula to express the positive correlation between the number of hidden layers in the deep neural network and the steel production per unit time of the target steel plant includes: in the data mapping formula, the steel production per unit time of the target steel plant is input data of the data mapping formula;

[0119] And wherein, the data mapping relationship expressing the positive correlation between the number of hidden layers in the deep neural network and the steel production per unit time of the target steel plant using a data mapping formula includes: in the data mapping formula, the number of hidden layers in the deep neural network corresponding to the steel production per unit time of the target steel plant is the output data of the data mapping formula.

[0120] The foregoing description of the exemplary embodiments of the present invention has been provided for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations will be apparent to those skilled in the art. The exemplary embodiments were chosen and described in order to best illustrate the principles of the invention and its practical application, thereby enabling others skilled in the art to understand the various embodiments and variations of the invention as they may be adapted for the specific application contemplated. It is intended that the scope of the invention be defined by the appended claims and their equivalents.

Claims

1. A method for treating wastewater with zero discharge, characterized in that: The method comprises: An emission treatment mechanism including an activated carbon adsorption device, an electrodialysis device, and a membrane distillation device is used to sequentially perform activated carbon adsorption, electrodialysis, distillation, and membrane separation operations on the industrial wastewater generated by the target steel plant to obtain return water and concentrated crystalline solids. The activated carbon adsorption device uses activated carbon with a set activated carbon concentration in g / L to separate dissolved organic carbon from the industrial wastewater. The electrodialysis device uses a DC power supply with a set current value in mA to separate inorganic salts from the industrial wastewater. The membrane distillation device uses a reverse osmosis membrane with a set membrane thickness in millimeters to separate organic salts and macromolecular organic matter from the industrial wastewater. Obtain multiple operating scenario data of the target steel plant; Traversing through specific value combinations of the three parameters of a set activated carbon concentration, a set current value, and a set membrane thickness to obtain various three-parameter value combinations, inputting each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model to obtain the return water use ratio and concentrated crystalline solid volume corresponding to each three-parameter value combination; The corresponding three-parameter value combination with a return water usage ratio greater than 99% is used as the zero-discharge treatment configuration data of the emission treatment agency for the industrial wastewater generated by the target steel plant.

2. The zero-discharge wastewater treatment method according to claim 1, wherein: The multiple operation scenario data of the target steel plant include the target steel plant's steel output per unit time, floor space, number of workers, steelmaking furnace operating temperature, rolling mill operating temperature, rolling roll pressure, billet drawing speed during the continuous casting process, upper limit value of steel body carbon content, and lower limit value of steel body carbon content; The activated carbon concentration is set to a value range of 0-5 g / L, the current value is set to a value range of 5-600 mA, and the membrane thickness is set to a value range of 0.1-0.5 mm. The return water ratio corresponding to each three-parameter value combination is the percentage of return water obtained after the discharge treatment mechanism is configured using the three-parameter value combination to perform discharge treatment on the industrial wastewater generated by the target steel plant; The volume of concentrated crystalline solid corresponding to each three-parameter value combination is the volume of concentrated crystalline solid obtained after the discharge treatment mechanism is configured using the three-parameter value combination to perform discharge treatment on the industrial wastewater generated by the target steel plant.

3. The zero-discharge wastewater treatment method according to claim 2, wherein: The emission treatment analysis model is a deep neural network after each training cycle. The deep neural network includes a single input layer, a single output layer, and multiple hidden layers. The number of times the deep neural network is trained is positively correlated with the steel production per unit time of the target steel plant. Among them, in each training execution of the deep neural network, the return water usage ratio and the concentrated crystalline solid volume corresponding to a known three-parameter value combination are used as the two output contents of the deep neural network, and the said three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant are used as multiple input contents of the deep neural network to complete this training of the deep neural network.

4. The zero-discharge wastewater treatment method according to claim 3, wherein: After using the corresponding three-parameter value combination with a return water usage ratio greater than 99% as zero-discharge treatment configuration data of the discharge treatment mechanism for the industrial wastewater generated by the target steel plant, the method further includes: Using the zero emission treatment configuration data, the activated carbon adsorption device, electrodialysis device and membrane distillation device of the emission treatment mechanism are configured to set specific values of the three parameters: activated carbon concentration, current value and membrane thickness; Among them, the configuration of using zero-emission treatment configuration data to set the specific values of the three parameters of activated carbon concentration, current value and membrane thickness for the activated carbon adsorption device, electrodialysis device and membrane distillation device of the emission treatment mechanism includes: when there is more than one zero-emission treatment configuration data, selecting any zero-emission treatment configuration data to set the specific values of the three parameters of activated carbon concentration, current value and membrane thickness for the activated carbon adsorption device, electrodialysis device and membrane distillation device of the emission treatment mechanism.

5. The zero-discharge wastewater treatment method according to claim 3, characterized in that: After using the corresponding three-parameter value combination with a return water usage ratio greater than 99% as zero-discharge treatment configuration data of the discharge treatment mechanism for the industrial wastewater generated by the target steel plant, the method further includes: Receive zero-discharge treatment configuration data and wirelessly transmit the zero-discharge treatment configuration data to a remote wastewater zero-discharge management server via a wireless communication network.

6. The zero-discharge wastewater treatment method according to claim 3, characterized in that: After using the corresponding three-parameter value combination with a return water usage ratio greater than 99% as zero-discharge treatment configuration data of the discharge treatment mechanism for the industrial wastewater generated by the target steel plant, the method further includes: Receive zero emission treatment configuration data and use a field display mechanism to complete field display of the zero emission treatment configuration data.

7. The zero-discharge wastewater treatment method according to claim 3, wherein: After using the corresponding three-parameter value combination with a return water usage ratio greater than 99% as zero-discharge treatment configuration data of the discharge treatment mechanism for the industrial wastewater generated by the target steel plant, the method further includes: When the numerical values of multiple operating scenario data of the target steel plant change, the multiple operating scenario data of the target steel plant after the numerical changes are input into the emission treatment analysis model to obtain new zero-emission treatment configuration data for industrial wastewater generated by the target steel plant corresponding to the multiple operating scenario data of the target steel plant after the numerical changes.

8. The zero-discharge wastewater treatment method according to claim 3, wherein: After obtaining a plurality of operation scenario data of the target steel plant, the method further includes: The deep neural network is trained each time to obtain the deep neural network after each training and output as the emission treatment analysis model. The number of times the deep neural network is trained is positively correlated with the steel production per unit time of the target steel plant.

9. The zero-discharge wastewater treatment method according to any one of claims 3 to 8, characterized in that: Obtain the target steel plant's steel output per unit time, floor area, number of workers, steelmaking furnace operating temperature, rolling mill operating temperature, rolling roll pressure, billet drawing speed during the continuous casting process, upper limit value of steel carbon content, and lower limit value of steel carbon content as multiple operating scenario data for the target steel plant, including: the target steel plant's upper limit value of steel carbon content is 0.35%, and the target steel plant's lower limit value of steel carbon content is 0.25%; Among them, the emission treatment analysis model is a deep neural network after completing each training. The deep neural network includes a single input layer, a single output layer and multiple hidden layers, and the number of times the deep neural network is trained is positively correlated with the steel production per unit time of the target steel plant, including: the more steel production per unit time of the target steel plant, the more times the deep neural network corresponding to the steel production per unit time of the target steel plant is trained.

10. The zero-discharge wastewater treatment method according to any one of claims 3 to 8, characterized in that: Inputting each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model to obtain the return water use ratio and the concentrated crystalline solid volume corresponding to each three-parameter value combination, including: inputting each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model, and running the emission treatment analysis model to obtain the return water use ratio and the concentrated crystalline solid volume corresponding to each three-parameter value combination output by the emission treatment analysis model; wherein, after inputting each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model, the emission treatment analysis model is run to obtain the return water use ratio and concentrated crystallized solid volume corresponding to each three-parameter value combination output by the emission treatment analysis model, including: performing numerical normalization processing on each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant, and then inputting them into the emission treatment analysis model in parallel; wherein, after inputting each three-parameter value combination, multiple operation scenario data of the target steel plant, the production flow of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model, the emission treatment analysis model is run to obtain the return water use ratio and concentrated crystalline solid volume corresponding to each three-parameter value combination output by the emission treatment analysis model, further comprising: the return water use ratio and concentrated crystalline solid volume corresponding to each three-parameter value combination output by the emission treatment analysis model are both numerical representations after numerical normalization processing; wherein each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant are respectively subjected to numerical normalization processing and then input into the emission treatment analysis model in parallel, including: the numerical normalization processing is a binary numerical conversion processing; wherein each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant are numerically normalized and then input into the emission treatment analysis model in parallel, further comprising: using a numerical conversion device to perform numerical normalization on each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant; wherein each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant are numerically normalized and then inputted in parallel into the emission treatment analysis model further comprising: employing a parallel control device for inputting each three-parameter value combination, multiple operating scenario data of the target steel plant, the production flow rate of the industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant, which have been subjected to numerical normalization, in parallel into the emission treatment analysis model; The method comprises: using a parallel control device to input each three-parameter value combination after numerical normalization processing, multiple operation scenario data of the target steel plant, the production flow of industrial wastewater of the target steel plant, and the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model in parallel, including: connecting the parallel control device to the numerical conversion device; The method further comprises: using a parallel control device to input each three-parameter value combination after numerical normalization processing, multiple operation scenario data of the target steel plant, the production flow of industrial wastewater of the target steel plant, and the organic carbon concentration, inorganic salt concentration, organic salt concentration, and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant into the emission treatment analysis model in parallel, further comprising: the parallel control device and the numerical conversion device share the same serial configuration interface; Among them, a parallel control device is used to input each three-parameter value combination after numerical normalization processing, multiple operating scenario data of the target steel plant, the production flow of industrial wastewater of the target steel plant, the organic carbon concentration, inorganic salt concentration, organic salt concentration and macromolecular organic matter concentration of the industrial wastewater generated by the target steel plant in parallel into the emission treatment analysis model, and also includes: using different types of CPLD chips to respectively realize the parallel control device and the numerical conversion device.

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