High-salinity wastewater treatment process optimization method and system based on analogue simulation

Abstract

The invention relates to the technical field of high-salinity wastewater treatment, in particular to a high-salinity wastewater treatment process optimization method and system based on analogue simulation, and the method comprises the steps: obtaining an initial pollution factor set based on initial high-salinity wastewater, the initial pollution factor set comprises a plurality of initial pollution factors, and the initial factors comprise pollutant names and pollutant concentrations; the method comprises the steps that an initial pollution factor set is obtained, an initial treatment measure set used for optimizing the initial pollution factor set is obtained, target treatment measures are determined through the initial pollution factor set and the initial treatment measure set, optimized high-salinity wastewater and treatment parameters are obtained based on the target treatment measures and the initial high-salinity wastewater, and the treatment parameters comprise the high-salinity wastewater volume and salinity content set. And obtaining a plurality of parameter fitting nodes based on the wastewater treatment unit and the treatment parameters, and determining the optimized treatment parameters by using the plurality of parameter fitting nodes. According to the invention, the accuracy of setting the parameters required for treating the high-salinity wastewater can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-salt wastewater treatment, and in particular to a high-salt wastewater treatment process optimization method and system based on simulation. Background Art

[0002] High-salinity wastewater, typically derived from the chemical and pharmaceutical industries, is characterized by high salinity, high chemical oxygen demand, and high biological toxicity. Proper treatment of high-salinity wastewater not only enables resource recovery and reuse but also reduces its environmental impact. However, accurately setting the parameters required for high-salinity wastewater treatment remains a pressing challenge.

[0003] At present, the parameters required for treating high-salt wastewater are mostly set based on experience.

[0004] While the above method can achieve the desired parameters for treating high-salinity wastewater, it fails to consider the characteristics of high-salinity wastewater and the interplay between mechanisms. Therefore, accurately setting the parameters required for treating high-salinity wastewater has become an urgent issue. Summary of the Invention

[0005] The present invention provides a method for optimizing a high-salinity wastewater treatment process based on simulation and a computer-readable storage medium, the main purpose of which is to improve the accuracy of setting parameters required for treating high-salinity wastewater.

[0006] To achieve the above objectives, the present invention provides a high-salt wastewater treatment process optimization method based on simulation, comprising:

[0007] receiving a process optimization instruction, and determining a process optimization environment based on the process optimization instruction, wherein the process optimization environment includes initial high-salinity wastewater to be treated and a wastewater treatment unit, and the wastewater treatment unit includes a heating unit, a crystallization unit, a steam recovery unit, a thickening unit, and a centrifugation unit;

[0008] Obtaining an initial pollution factor set based on the initial high-salt wastewater, wherein the initial pollution factor set includes multiple initial pollution factors, and the initial factors include pollutant names and pollutant concentrations; obtaining an initial treatment measure set for optimizing the initial pollution factor set, wherein the initial treatment measure set includes multiple initial treatment measures; and confirming a target treatment measure using the initial pollution factor set and the initial treatment measure set;

[0009] Obtaining optimized high-salinity wastewater based on target treatment measures and initial high-salinity wastewater, obtaining treatment parameters for the optimized high-salinity wastewater, wherein the treatment parameters include: a high-salinity wastewater volume and a salt content set, and obtaining multiple parameter fitting nodes based on the wastewater treatment unit and the treatment parameters;

[0010] Determining optimized processing parameters using the plurality of parameter fitting nodes;

[0011] The optimized treatment parameters are utilized to extract salt from the optimized high-salinity wastewater to obtain extracted salt, thereby achieving treatment of the initial high-salinity wastewater.

[0012] Optionally, the determining target treatment measures using the initial pollution factor set and the initial treatment measure set includes:

[0013] For each initial action in the initial action set, perform the following operations:

[0014] Obtaining an analysis pollution factor set using the initial treatment measures and the initial pollution factor set, wherein the analysis pollution factor set includes a plurality of analysis pollution factors, and the analysis pollution factors include analysis pollutant names and analysis pollutant concentrations;

[0015] Obtain an analysis pollutant threshold based on the analysis pollutant name corresponding to the analysis pollution factor, compare the analysis pollutant concentration corresponding to the analysis pollution factor with the analysis pollutant threshold, confirm that the analysis pollutant concentration corresponding to each analysis pollution factor in the analysis pollution factor set is less than or equal to the analysis pollutant threshold corresponding to the analysis pollutant name, and then match the initial pollution factors in the initial pollution factor set with the analysis pollution factors in the analysis pollution factor set based on the pollutant name corresponding to the initial factor to obtain a pollution removal evaluation node set, wherein the pollution removal evaluation node set includes multiple pollution removal evaluation nodes, and the pollution removal evaluation nodes correspond to the initial factors one-to-one;

[0016] The pollution removal evaluation node sets are aggregated to obtain a plurality of pollution removal evaluation node sets, wherein the pollution removal evaluation node sets correspond to the initial treatment measures one by one, and the target treatment measures are obtained by using the plurality of pollution removal evaluation node sets.

[0017] Optionally, the acquiring target treatment measures by using the plurality of pollution removal evaluation node sets includes:

[0018] Obtaining an initial pollution weight value set based on the initial pollution factor set and a pre-constructed hierarchical analysis method, wherein the initial pollution weight value set includes multiple initial pollution weight values, and the initial pollution weight values ​​correspond one to one to the initial pollution factors;

[0019] Obtaining a pollution removal energy consumption value corresponding to the pollution removal assessment node set, and calculating a pollution removal assessment value using the pollution removal energy consumption value, the initial pollution weight value set, the pollution removal assessment node set, and a pre-constructed pollution removal assessment relationship;

[0020] The pollution removal evaluation values ​​are summarized to obtain a pollution removal evaluation value set, and a target treatment measure is determined based on the pollution removal evaluation value set, wherein the target treatment measure is an initial treatment measure corresponding to the maximum pollution removal evaluation value in the pollution removal evaluation value set.

[0021] Optionally, the pollution removal evaluation relationship is as follows:

[0022]

[0023] Wherein, P represents the pollution removal evaluation value, α and β are preset coefficients, H represents the pollution removal energy consumption value, and n 0i 、n 1i They represent the analysis pollutant concentration and pollutant concentration corresponding to the i-th pollution removal assessment node in the pollution removal assessment node set, ω i It represents the initial pollution weight value corresponding to the i-th pollution removal evaluation node in the pollution removal evaluation node set in the initial pollution weight value set, and n represents that there are n pollution removal evaluation nodes in the pollution removal evaluation node set.

[0024] Optionally, obtaining a plurality of parameter fitting nodes based on the wastewater treatment unit and the treatment parameters includes:

[0025] Extract the initial treatment units from the wastewater treatment units in sequence, wherein the initial treatment units are heating units, crystallization units, steam recovery units, thickening units or centrifugation units, and perform the following operations on the extracted initial treatment units:

[0026] Obtain a parameter adjustment range set of an initial processing unit, wherein the parameter adjustment range set includes one or more parameter adjustment ranges, and perform the following operations on each parameter adjustment range in the parameter adjustment range set:

[0027] Using a preset extraction value, extracting a first adjustment parameter set from the parameter adjustment range, wherein the first adjustment parameter set includes multiple first adjustment parameter values, and the number corresponding to the first adjustment parameter values ​​is the extraction value, summarizing the first adjustment parameter sets to obtain multiple first adjustment parameter sets, and using the multiple first adjustment parameter sets in a combined form to obtain a first adjustment parameter group set, wherein the first adjustment parameter group set includes multiple first adjustment parameter groups, and the first adjustment parameter groups include multiple first adjustment parameter values, and the first adjustment parameter values ​​correspond one-to-one to the first adjustment parameter sets;

[0028] Summarizing the first adjustment parameter sets to obtain a plurality of first adjustment parameter sets, and using the plurality of first adjustment parameter sets in combination to obtain an initial adjustment parameter set, wherein the initial adjustment parameter set includes a plurality of initial adjustment parameter groups, and the initial adjustment parameter group includes a plurality of first adjustment parameter groups, and the first adjustment parameter groups correspond to the initial processing units in a one-to-one manner;

[0029] A plurality of parameter fitting nodes are obtained based on the initial adjustment parameter set and the processing parameters.

[0030] Optionally, the acquiring a plurality of parameter fitting nodes based on the initial adjustment parameter set and the processing parameters includes:

[0031] Perform the following operations for each initial adjustment parameter group in the initial adjustment parameter group set:

[0032] Using the initial adjustment parameter group to set the wastewater treatment unit to obtain a target wastewater treatment unit, constructing a simulated high-salt wastewater based on the treatment parameters, and obtaining evaluation energy consumption parameters using the target wastewater treatment unit and the simulated high-salt wastewater, wherein the evaluation energy consumption parameters include purification time and purified salt amount;

[0033] Calculating an estimated purification energy consumption value using the estimated energy consumption parameter and a pre-constructed purification energy consumption relationship, associating the estimated purification energy consumption value with an initial adjustment parameter group to obtain a purification evaluation node, and summarizing the purification evaluation nodes to obtain a purification evaluation node set;

[0034] Using the preset verification value, a plurality of verification evaluation nodes are randomly extracted from the purified evaluation node set to obtain a verification node set. The verification node set is eliminated from the purified evaluation node set to obtain a fitting evaluation node set. A fitting surface is obtained based on the fitting evaluation node set and a pre-built surface fitting model. Using the verification node set, a plurality of verification energy consumption values ​​are extracted from the fitting surface. The verification evaluation value is calculated based on the evaluation purification energy consumption value and the plurality of verification energy consumption values ​​corresponding to each verification evaluation node in the verification node set. The calculation formula is as follows:

[0035]

[0036] Wherein, Y represents the verification evaluation value, m represents the verification value, and N j0 、N j1 Respectively represent the j-th evaluation and purification energy consumption value in multiple verification and evaluation nodes and the verification energy consumption value corresponding to the j-th evaluation and purification energy consumption value in multiple verification and evaluation nodes;

[0037] Comparing the verification evaluation value with a preset verification evaluation threshold, if the verification evaluation value is greater than or equal to the verification evaluation threshold, obtaining an optimized fitting model for fitting the surface, using the optimized fitting model as a surface fitting model, and returning to the step of obtaining a fitting surface based on the fitting evaluation node set and the pre-built surface fitting model until the verification evaluation value is less than the verification evaluation threshold;

[0038] Otherwise, energy consumption interception gradients are sequentially extracted from the preset energy consumption interception gradient set, and the following operations are performed on the extracted energy consumption interception gradients:

[0039] Based on the energy consumption interception gradient, node statistics are performed in the fitting surface to obtain the number of statistical nodes. When the number of statistical nodes is greater than or equal to the preset node value, the energy consumption interception gradient is used to confirm the updated parameter range set in the fitting surface, and the updated parameter range set is used to obtain multiple parameter fitting nodes.

[0040] Optionally, the purification energy consumption relationship is as follows:

[0041]

[0042] Wherein, C represents the estimated purification energy consumption value, y represents the amount of purified salt, γ, δ are all preset coefficients, p(s1,s2,…,s q ) k represents the power of the kth initial treatment unit in the wastewater treatment unit, s1 and s2 represent the first and second first adjustment parameter values ​​corresponding to the kth initial treatment unit, respectively, q represents the parameter adjustment range corresponding to the kth initial treatment unit, and t k represents the usage time of the kth initial processing unit, and T represents the purification time.

[0043] Optionally, the determining the optimization processing parameters using the multiple parameter fitting nodes includes:

[0044] Obtaining an updated fitting node set based on the multiple parameter fitting nodes, obtaining an updated fitting surface using the updated fitting node set, obtaining an updated node value using the energy consumption interception gradient and the updated fitting surface, counting the number of parameter fitting nodes in the multiple parameter fitting nodes to obtain an evaluation node value, calculating a ratio of the update node value to the evaluation node value to obtain a credibility evaluation value, comparing the credibility evaluation value with a preset credibility evaluation threshold, and if the credibility evaluation value is greater than or equal to the credibility evaluation threshold, identifying an initial processing parameter in the updated fitting surface, and confirming that the initial processing parameter is a preset optimization processing parameter, wherein the initial processing parameter is a parameter fitting node corresponding to a minimum evaluation and purification energy consumption value in the updated fitting surface;

[0045] Otherwise, the updated fitting node set is added to the fitting evaluation node set to obtain the target evaluation node set. The target evaluation node set is used as the fitting evaluation node set, and the step of obtaining the fitting surface based on the fitting evaluation node set and the pre-built surface fitting model is returned until the optimization processing parameters are confirmed.

[0046] Optionally, confirming that the initial processing parameters are preset optimized processing parameters includes:

[0047] The optimized purification energy consumption value is obtained by using the initial processing parameters. Based on the initial processing parameters, the fitted purification energy consumption value is extracted from the updated fitting surface. The error ratio is calculated based on the optimized purification energy consumption value and the fitted purification energy consumption value. The calculation formula is as follows:

[0048]

[0049] Among them, B represents the error ratio, W1 represents the optimized purification energy consumption value, and W0 represents the fitted purification energy consumption value;

[0050] The error ratio is compared with a preset ratio threshold, and if the error ratio is less than or equal to the ratio threshold, the initial processing parameters are confirmed to be optimized processing parameters.

[0051] To achieve the above objectives, the present invention further provides a high-salt wastewater treatment process optimization system based on simulation, comprising:

[0052] a process environment confirmation module, configured to receive a process optimization instruction and confirm a process optimization environment based on the process optimization instruction, wherein the process optimization environment includes initial high-salinity wastewater to be treated and a wastewater treatment unit, and the wastewater treatment unit includes a heating unit, a crystallization unit, a steam recovery unit, a thickening unit, and a centrifugation unit;

[0053] a high-salt wastewater pretreatment module, configured to obtain an initial pollution factor set based on the initial high-salt wastewater, wherein the initial pollution factor set includes a plurality of initial pollution factors, and the initial factors include pollutant names and pollutant concentrations; obtain an initial treatment measure set for optimizing the initial pollution factor set, wherein the initial treatment measure set includes a plurality of initial treatment measures; and use the initial pollution factor set and the initial treatment measure set to identify target treatment measures;

[0054] A process parameter pre-confirmation module is used to obtain optimized high-salinity wastewater based on target treatment measures and initial high-salinity wastewater, and obtain treatment parameters for optimized high-salinity wastewater, wherein the treatment parameters include: high-salinity wastewater volume and salt content set, and obtain multiple parameter fitting nodes based on wastewater treatment units and treatment parameters;

[0055] A process parameter confirmation and high-salt wastewater treatment module is used to confirm the optimized treatment parameters using the multiple parameter fitting nodes;

[0056] The optimized treatment parameters are utilized to extract salt from the optimized high-salinity wastewater to obtain extracted salt, thereby achieving treatment of the initial high-salinity wastewater.

[0057] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0058] A memory storing at least one instruction; and a processor executing the instruction stored in the memory to implement the above-mentioned high-salt wastewater treatment process optimization method based on simulation.

[0059] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned high-salt wastewater treatment process optimization method based on simulation.

[0060] The present invention is to solve the problems described in the background technology. An embodiment of the present invention receives a process optimization instruction and confirms a process optimization environment based on the process optimization instruction, wherein the process optimization environment includes the initial high-salt wastewater to be treated and a wastewater treatment unit, and the wastewater treatment unit includes a heating unit, a crystallization unit, a steam recovery unit, a thickening unit and a centrifugal unit. It can be seen that the present invention considers using a steam recovery unit to realize steam recovery for the crystallization unit, thereby improving the energy utilization rate when treating high-salt wastewater, and obtaining an initial pollution factor set based on the initial high-salt wastewater, wherein the initial pollution factor set includes multiple initial pollution factors, and the initial factors include pollutant names and pollutant concentrations, and obtaining an initial treatment measure set for optimizing the initial pollution factor set, wherein the initial treatment measure set includes multiple initial treatment measures, and using the initial pollution factor set and the initial treatment measure set to confirm the target treatment measure, it can be seen that the embodiment of the present invention considers the characteristics of the pollutants in the initial high-salt wastewater before obtaining the target treatment measure. Here, the characteristics of the pollutants As the initial factor, and then, by processing different initial treatment measures, the target treatment measure with the lowest energy consumption can be confirmed, and when evaluating the initial treatment measures, the weights corresponding to different pollutants are also considered, thereby improving the accuracy of obtaining the pollution removal evaluation value, obtaining optimized high-salt wastewater based on the target treatment measure and the initial high-salt wastewater, obtaining the treatment parameters for optimizing the high-salt wastewater, wherein the treatment parameters include: high-salt wastewater volume and salt content set, obtaining multiple parameter fitting nodes based on the wastewater treatment unit and the treatment parameters, and using the multiple parameter fitting nodes to confirm the optimized treatment parameters. It can be seen that the embodiment of the present invention considers dividing the range of parameters involved in different units before obtaining multiple parameter fitting nodes, and obtains a parameter group for achieving the treatment of high-salt wastewater in the form of a combination, by constructing and verifying the fitting surface between the evaluation purification energy consumption value and the parameter group, improving the accuracy of the parameter group acquisition, and by verifying the initial treatment parameters confirmed in the fitting surface, further improving the accuracy of the confirmed parameter group. Therefore, the present invention can improve the accuracy of setting the parameters required for treating high-salt wastewater. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram of a process flow for optimizing a high-salt wastewater treatment process based on simulation provided by one embodiment of the present invention;

[0062] Figure 2 A functional module diagram of a high-salt wastewater treatment process optimization system based on simulation provided by one embodiment of the present invention;

[0063] Figure 3 A schematic structural diagram of an electronic device for implementing the high-salt wastewater treatment process optimization method based on simulation provided in one embodiment of the present invention.

[0064] Description of reference numerals:

[0065] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0066] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0067] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0068] The embodiment of the present application provides a method for optimizing a high-salt wastewater treatment process based on simulation. The execution subject of the method for optimizing a high-salt wastewater treatment process based on simulation includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for optimizing a high-salt wastewater treatment process based on simulation can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0069] Reference Figure 1 FIG. 1 is a flow chart of a method for optimizing a high-salinity wastewater treatment process based on simulation according to an embodiment of the present invention. In this embodiment, the method for optimizing a high-salinity wastewater treatment process based on simulation includes:

[0070] S1. Receive a process optimization instruction, and confirm a process optimization environment based on the process optimization instruction, wherein the process optimization environment includes initial high-salt wastewater to be treated and a wastewater treatment unit, and the wastewater treatment unit includes a heating unit, a crystallization unit, a steam recovery unit, a thickening unit, and a centrifugal unit.

[0071] It should be explained that process optimization instructions refer to instructions for optimizing process parameters for treating high-salt wastewater, and process optimization environment refers to the necessary environment for optimizing parameters of the high-salt wastewater treatment process. Initial high-salt wastewater refers to high-salt wastewater to be treated. Wastewater treatment unit refers to software or equipment that can simulate wastewater treatment. Generally speaking, a heating unit refers to a unit that can heat high-salt wastewater for the purpose of increasing its concentration. Optionally, a heater is used as the heating unit. A crystallization unit is a unit that precipitates salt from wastewater to form solid salt by evaporation and crystallization. Optionally, an evaporation crystallizer is used as the crystallization unit. A steam recovery unit is a unit that recovers and reuses secondary steam generated during the evaporation process. Optionally, a steam compressor is used as the steam recovery unit. A thickening unit refers to a unit used to treat the crystal slurry generated during the crystallization process and increase the concentration of solid particles in the crystal slurry. Optionally, a thickener is used as the thickening unit. The main function of the centrifugal unit is to separate the crystallized solid salt from the mother liquor by centrifugal force. Optionally, a centrifuge is used as the centrifugal unit. The main purpose of the embodiment of the present invention is to improve the accuracy of setting process parameters when treating high-salinity wastewater, thereby reducing the energy consumption required for treating high-salinity wastewater.

[0072] For example, in order to reduce the energy consumption required for recycling high-salt wastewater, the recycling personnel set up simulation experiments based on the characteristics corresponding to the high-salt wastewater to improve the accuracy of the parameter settings involved in the high-salt wastewater, thereby reducing the energy consumption required for recycling this type of high-salt wastewater. Therefore, the recycling personnel issue the process optimization instructions and confirm the process optimization environment.

[0073] S2. Based on the initial high-salt wastewater, an initial pollution factor set is obtained, wherein the initial pollution factor set includes multiple initial pollution factors, and the initial factors include pollutant names and pollutant concentrations; an initial treatment measure set is obtained for optimizing the initial pollution factor set, wherein the initial treatment measure set includes multiple initial treatment measures; and target treatment measures are confirmed using the initial pollution factor set and the initial treatment measure set.

[0074] It should be explained that the initial pollution factor refers to the pollutants to be treated included in the initial high-salt wastewater, and the pollutant name refers to the name of the pollutant contained in the initial high-salt wastewater. For example: heavy metal ions. The pollutant concentration refers to the concentration of the pollutant corresponding to the pollutant name. The initial treatment measures refer to a method or a series of methods for removing or reducing the pollutants concentrated in the initial pollution factor. Optionally, the initial treatment measures are obtained by empirical methods, and the same effects can be achieved by using other technologies, which will not be repeated here. For example: in order to reduce the concentration of organic pollutants in the initial high-salt wastewater, activated carbon adsorption or biological pretreatment can be used to remove or reduce the concentration of organic pollutants in the initial high-salt wastewater. Optionally, after confirming the name of the pollutant to be detected in the initial high-salt wastewater, the corresponding detection method can be used to detect the corresponding pollutants in the initial high-salt wastewater. For example, now that it is clear that the pollutant present in the initial high-salt wastewater is nitrate, the initial high-salt wastewater is directly measured by ion chromatography to obtain the concentration of nitrate.

[0075] Furthermore, the use of the initial pollution factor set and the initial treatment measure set to determine the target treatment measure includes:

[0076] For each initial action in the initial action set, perform the following operations:

[0077] Obtaining an analysis pollution factor set using the initial treatment measures and the initial pollution factor set, wherein the analysis pollution factor set includes a plurality of analysis pollution factors, and the analysis pollution factors include analysis pollutant names and analysis pollutant concentrations;

[0078] Obtain an analysis pollutant threshold based on the analysis pollutant name corresponding to the analysis pollution factor, compare the analysis pollutant concentration corresponding to the analysis pollution factor with the analysis pollutant threshold, confirm that the analysis pollutant concentration corresponding to each analysis pollution factor in the analysis pollution factor set is less than or equal to the analysis pollutant threshold corresponding to the analysis pollutant name, and then match the initial pollution factors in the initial pollution factor set with the analysis pollution factors in the analysis pollution factor set based on the pollutant name corresponding to the initial factor to obtain a pollution removal evaluation node set, wherein the pollution removal evaluation node set includes multiple pollution removal evaluation nodes, and the pollution removal evaluation nodes correspond to the initial factors one-to-one;

[0079] The pollution removal evaluation node sets are aggregated to obtain a plurality of pollution removal evaluation node sets, wherein the pollution removal evaluation node sets correspond to the initial treatment measures one by one, and the target treatment measures are obtained by using the plurality of pollution removal evaluation node sets.

[0080] It should be understood that the analytical pollution factor set refers to the set of treated pollutants obtained after the pollutants in the initial high-salt wastewater are treated using initial treatment measures. The analytical pollutant name and the analytical pollutant concentration have the same effects as the pollutant name and the pollutant concentration, respectively, and will not be repeated here. The analytical pollutant threshold refers to the numerical value corresponding to the analytical pollutant name. Here, the analytical pollutant threshold characterizes the maximum value at which this pollutant will not affect the subsequent extraction of salt from the initial high-salt wastewater, and the analytical pollutant threshold is related to the analytical pollutant name. Optionally, the analytical pollutant threshold is set by an empirical method. When the analytical pollutant concentration corresponding to each analytical pollution factor in the analytical pollution factor set is less than or equal to the analytical pollutant threshold corresponding to the analytical pollutant name, it indicates that the use of this initial treatment measure can effectively reduce or remove pollutants in the initial high-salt wastewater.

[0081] Furthermore, each initial factor in the initial pollution factor set includes a pollutant name, and each analytical pollution factor in the analytical pollution factor set includes an analytical pollutant name. Therefore, the pollutant name can be used to match the analytical pollution factors in the analytical pollution factor set. When there is no analytical pollution factor in the analytical pollution factor set whose analytical pollutant name is the same as the pollutant name corresponding to the initial factor, it is determined that the pollutant in the initial factor is removed, and the analytical pollutant concentration is set to 0 at the pollution removal evaluation node. Generally, each pollution removal evaluation node in the pollution removal evaluation node set includes an initial factor and an analytical pollution factor, and the pollutant name corresponding to the initial factor is the same as the analytical pollutant name corresponding to the analytical pollution factor.

[0082] It is understandable that the method of obtaining target treatment measures by using the plurality of pollution removal evaluation node sets includes:

[0083] Obtaining an initial pollution weight value set based on the initial pollution factor set and a pre-constructed hierarchical analysis method, wherein the initial pollution weight value set includes multiple initial pollution weight values, and the initial pollution weight values ​​correspond one to one to the initial pollution factors;

[0084] Obtaining a pollution removal energy consumption value corresponding to the pollution removal assessment node set, and calculating a pollution removal assessment value using the pollution removal energy consumption value, the initial pollution weight value set, the pollution removal assessment node set, and a pre-constructed pollution removal assessment relationship;

[0085] The pollution removal evaluation values ​​are summarized to obtain a pollution removal evaluation value set, and a target treatment measure is determined based on the pollution removal evaluation value set, wherein the target treatment measure is an initial treatment measure corresponding to the maximum pollution removal evaluation value in the pollution removal evaluation value set.

[0086] It should be explained that the initial pollution weight value refers to the weight value used to evaluate the importance of this type of pollutant when removing it. For example, the pollutants corresponding to the initial high-salt wastewater include heavy metals and inorganic salts. However, the presence of heavy metals will seriously affect the subsequent salt extraction of the initial high-salt wastewater. Therefore, the weights of heavy metals and inorganic salts are obtained respectively through a pre-set evaluation index set and the analytic hierarchy process. Here, the weights of heavy metals and inorganic salts are both the initial pollution weight values. Before using the analytic hierarchy process to perform weight evaluation on the initial pollution factor set, it also includes: obtaining an evaluation index set for the analytic hierarchy process, and the technology of evaluating each initial pollution factor in the initial pollution factor set through the evaluation index set and the analytic hierarchy process is an existing technology and will not be repeated here. Optionally, the evaluation index set includes: environmental impact indicators and health risk indicators, wherein the environmental impact indicators include: water pollution potential, ecotoxicity and persistence, and the health risk indicators include acute toxicity, chronic toxicity and bioaccumulation. Among them, the water pollution potential refers to the assessment value of the eutrophication and dissolved oxygen consumption of water bodies by pollutants. Optionally, the chemical oxygen demand is used as the water pollution potential. Ecotoxicity refers to the median lethal concentration or median effect concentration for aquatic organisms. Persistence refers to the degradation half-life of pollutants in the environment. Acute toxicity refers to the lethal dose or carcinogenicity for short-term exposure to humans. Chronic toxicity refers to the harmful effects on organisms after long-term exposure to pollutants. Optionally, the carcinogenicity of the relevant pollutants can be obtained by searching in existing databases. Bioaccumulation refers to the ability of pollutants to enrich in organisms and amplify through the food chain. Optionally, the biomagnification factor is used as the bioaccumulation.

[0087] It is understood that the pollution removal energy consumption value refers to the energy consumption value of the initial treatment measure corresponding to the pollution removal assessment node set. Optionally, the pollution removal energy consumption value can be obtained through theoretical analysis. For example, if pH adjustment is used as the initial treatment measure, the pollution removal energy consumption value can be obtained by multiplying the unit energy consumption of the agent used for pH adjustment by the amount of the agent used. Other technologies can achieve the same effect and are not further described here.

[0088] Furthermore, the pollution removal evaluation equation is as follows:

[0089]

[0090] Wherein, P represents the pollution removal evaluation value, α and β are preset coefficients, H represents the pollution removal energy consumption value, and n 0i 、n 1i They represent the analysis pollutant concentration and pollutant concentration corresponding to the i-th pollution removal assessment node in the pollution removal assessment node set, ω iIt represents the initial pollution weight value corresponding to the i-th pollution removal evaluation node in the pollution removal evaluation node set in the initial pollution weight value set, and n represents that there are n pollution removal evaluation nodes in the pollution removal evaluation node set.

[0091] S3. Obtain optimized high-salt wastewater based on the target treatment measures and the initial high-salt wastewater, and obtain treatment parameters for the optimized high-salt wastewater, wherein the treatment parameters include: a set of high-salt wastewater volume and salt content, and obtain multiple parameter fitting nodes based on the wastewater treatment unit and the treatment parameters.

[0092] It should be explained that optimized high-salinity wastewater refers to the high-salinity wastewater obtained after treating the initial high-salinity wastewater using the target treatment measure. High-salinity wastewater volume refers to the volume of optimized high-salinity wastewater, and salt content set refers to the set of ion contents used to characterize different salts in the optimized high-salinity wastewater. For example, if the optimized high-salinity wastewater includes sulfate ions, sodium ions, and chloride ions, then the salt content set refers to the set of sulfate ion concentrations, sodium ion concentrations, and chloride ion concentrations.

[0093] It is understandable that in the embodiment of the present invention, the treatment process for optimizing high-salt wastewater is as follows: after a certain amount of optimized high-salt wastewater is introduced into the heating unit, the introduced optimized high-salt wastewater is heated by the heating unit after setting parameters to obtain heated high-salt wastewater, the heated high-salt wastewater is introduced into the crystallization unit after setting parameters, and the heated high-salt wastewater is initially crystallized by the crystallization unit after setting parameters to obtain initial high-salt wastewater and high-salt steam, the high-salt steam is introduced into the steam recovery unit after setting parameters to obtain heating steam, and the heating unit is heated by the heating steam. Here, it means using heating steam and the heating unit to heat the optimized high-salt wastewater that needs to be heated, introducing the initial high-salt wastewater into the thickening unit after setting parameters, and using the thickening unit to concentrate the initial high-salt wastewater to obtain high-salt slurry, introducing the high-salt slurry into the centrifugal unit after setting parameters, and using the centrifugal unit to extract the high-salt slurry to obtain recovered high salt. Generally speaking, the process or technology for treating the initial high-salt wastewater in the embodiment of the present invention is prior art and will not be repeated here.

[0094] It is understandable that the acquisition of multiple parameter fitting nodes based on the wastewater treatment unit and treatment parameters includes:

[0095] Extract the initial treatment units from the wastewater treatment units in sequence, wherein the initial treatment units are heating units, crystallization units, steam recovery units, thickening units or centrifugation units, and perform the following operations on the extracted initial treatment units:

[0096] Obtain a parameter adjustment range set of an initial processing unit, wherein the parameter adjustment range set includes one or more parameter adjustment ranges, and perform the following operations on each parameter adjustment range in the parameter adjustment range set:

[0097] Using a preset extraction value, extracting a first adjustment parameter set from the parameter adjustment range, wherein the first adjustment parameter set includes multiple first adjustment parameter values, and the number corresponding to the first adjustment parameter values ​​is the extraction value, summarizing the first adjustment parameter sets to obtain multiple first adjustment parameter sets, and using the multiple first adjustment parameter sets in a combined form to obtain a first adjustment parameter group set, wherein the first adjustment parameter group set includes multiple first adjustment parameter groups, and the first adjustment parameter groups include multiple first adjustment parameter values, and the first adjustment parameter values ​​correspond one-to-one to the first adjustment parameter sets;

[0098] Summarizing the first adjustment parameter sets to obtain a plurality of first adjustment parameter sets, and using the plurality of first adjustment parameter sets in combination to obtain an initial adjustment parameter set, wherein the initial adjustment parameter set includes a plurality of initial adjustment parameter groups, and the initial adjustment parameter group includes a plurality of first adjustment parameter groups, and the first adjustment parameter groups correspond to the initial processing units in a one-to-one manner;

[0099] A plurality of parameter fitting nodes are obtained based on the initial adjustment parameter set and the processing parameters.

[0100] It should be explained that the parameter adjustment range refers to the range in which parameters can be adjusted in the initial processing unit. For example, the parameters that can be adjusted in the heating unit are temperature and pressure, and the parameter adjustment range set corresponding to the heating unit includes the temperature range and the pressure range. Generally speaking, after setting the extraction value, a uniform extraction algorithm can be used to extract multiple first adjustment parameter values ​​with the same spacing in the parameter adjustment range. The same effect can be achieved by using other technologies, which will not be repeated here. Optionally, the parameter adjustment range set of the initial processing unit is obtained by empirical method. The same effect can be achieved by using other technologies, which will not be repeated here.

[0101] Furthermore, each initial adjustment parameter group in the initial adjustment parameter group set includes parameters required by each initial treatment unit in the wastewater treatment unit.

[0102] It should be explained that the step of obtaining a plurality of parameter fitting nodes based on the initial adjustment parameter set and processing parameters includes:

[0103] Perform the following operations for each initial adjustment parameter group in the initial adjustment parameter group set:

[0104] Using the initial adjustment parameter group to set the wastewater treatment unit to obtain a target wastewater treatment unit, constructing a simulated high-salt wastewater based on the treatment parameters, and obtaining evaluation energy consumption parameters using the target wastewater treatment unit and the simulated high-salt wastewater, wherein the evaluation energy consumption parameters include purification time and purified salt amount;

[0105] Calculating an estimated purification energy consumption value using the estimated energy consumption parameter and a pre-constructed purification energy consumption relationship, associating the estimated purification energy consumption value with an initial adjustment parameter group to obtain a purification evaluation node, and summarizing the purification evaluation nodes to obtain a purification evaluation node set;

[0106] Using the preset verification value, a plurality of verification evaluation nodes are randomly extracted from the purified evaluation node set to obtain a verification node set. The verification node set is eliminated from the purified evaluation node set to obtain a fitting evaluation node set. A fitting surface is obtained based on the fitting evaluation node set and a pre-built surface fitting model. Using the verification node set, a plurality of verification energy consumption values ​​are extracted from the fitting surface. The verification evaluation value is calculated based on the evaluation purification energy consumption value and the plurality of verification energy consumption values ​​corresponding to each verification evaluation node in the verification node set. The calculation formula is as follows:

[0107]

[0108] Wherein, Y represents the verification evaluation value, m represents the verification value, and N j0 、N j1 Respectively represent the j-th evaluation and purification energy consumption value in multiple verification and evaluation nodes and the verification energy consumption value corresponding to the j-th evaluation and purification energy consumption value in multiple verification and evaluation nodes;

[0109] Comparing the verification evaluation value with a preset verification evaluation threshold, if the verification evaluation value is greater than or equal to the verification evaluation threshold, obtaining an optimized fitting model for fitting the surface, using the optimized fitting model as a surface fitting model, and returning to the step of obtaining a fitting surface based on the fitting evaluation node set and the pre-built surface fitting model until the verification evaluation value is less than the verification evaluation threshold;

[0110] Otherwise, energy consumption interception gradients are sequentially extracted from the preset energy consumption interception gradient set, and the following operations are performed on the extracted energy consumption interception gradients:

[0111] Based on the energy consumption interception gradient, node statistics are performed in the fitting surface to obtain the number of statistical nodes. When the number of statistical nodes is greater than or equal to the preset node value, the energy consumption interception gradient is used to confirm the updated parameter range set in the fitting surface, and the updated parameter range set is used to obtain multiple parameter fitting nodes.

[0112] It is understood that setting the wastewater treatment unit using the initial adjustment parameter group refers to setting the initial treatment unit corresponding to the first adjustment parameter in the wastewater treatment unit using the first adjustment parameter group in the initial adjustment parameter group. The target wastewater treatment unit refers to the wastewater treatment unit after the parameters corresponding to each initial treatment unit have been set. Simulated high-salt wastewater refers to high-salt wastewater simulated according to the treatment parameters. The technology of using software to simulate high-salt wastewater in combination with treatment parameters is existing technology and will not be described in detail here. The amount of purified salt refers to the mass of salt that can be extracted using the target wastewater treatment unit and the simulated high-salt wastewater. For example, if the salt in the simulated high-salt wastewater is sodium chloride, the amount of purified salt refers to the mass of sodium chloride that can be obtained using the target wastewater treatment unit. The purification time refers to the time taken to complete the treatment of the simulated high-salt wastewater using the target wastewater treatment unit. Optionally, a multinomial regression model can be used as the surface fitting model. Other techniques can achieve the same effect and will not be described in detail here. The fitting surface refers to a surface constructed with the evaluated purification energy consumption value as the dependent variable and the initial adjustment parameter group as the independent variable.

[0113] Furthermore, the purpose of obtaining a validation node set is to evaluate the difference between the constructed fitting surface and the true value. When the validation evaluation value is less than the validation evaluation threshold, it indicates that the difference between the constructed fitting surface and the true value is small, that is, the constructed fitting surface can be used to characterize the relationship between the purification energy consumption value and the initial adjustment parameter set. Generally speaking, the effect of the optimization fitting model is the same as that of the surface fitting model, and will not be repeated here.

[0114] It is understandable that the energy consumption interception gradient refers to the value of the dependent variable in the fitting surface, and the energy consumption interception gradient can be set manually. For example, a fitting energy consumption range is obtained in the fitting surface, and a plurality of energy consumption interception gradients are extracted from the fitting energy consumption range using a preset interception ratio. The fitting energy consumption range refers to the range formed by the minimum assessed purification energy consumption value and the maximum assessed purification energy consumption value in the fitting surface. For example: the fitting energy consumption range is 50 to 60, and the preset interception ratio is 0.1, then 51, 52, 53, 54, 55, 56, 57, 58, 59 and 60 can be extracted from the fitting energy consumption range using the interception ratio.

[0115] It should be explained that, based on each energy consumption interception gradient in the preset energy consumption interception gradient set, node statistics are performed on the fitting surface to obtain the number of statistical nodes, which means using the energy consumption interception gradient to construct a plane in the fitting surface, and the evaluation and purification energy consumption value corresponding to this plane is the energy consumption interception gradient, and the number of purified evaluation nodes of the fitting surface under the plane is counted to obtain the statistical number of nodes. The method of obtaining the parameter fitting nodes is the same as the method of obtaining the initial adjustment parameter set, which will not be repeated here. The fitted fitting surface may not necessarily represent the actual situation. Therefore, in the embodiment of the present invention, taking the updated parameter range set with a statistical node number greater than or equal to the node value can improve the accuracy of the obtained updated parameter range set. Here, the updated parameter range set refers to the set of ranges corresponding to each parameter in the fitting surface under the plane.

[0116] It should be understood that the purification energy consumption relationship is as follows:

[0117]

[0118] Wherein, C represents the estimated purification energy consumption value, y represents the amount of purified salt, γ, δ are all preset coefficients, p(s1,s2,…,s q ) k represents the power of the kth initial treatment unit in the wastewater treatment unit, s1 and s2 represent the first and second first adjustment parameter values ​​corresponding to the kth initial treatment unit, respectively, q represents the parameter adjustment range corresponding to the kth initial treatment unit, and t k represents the usage time of the t-th initial processing unit, and T represents the purification time.

[0119] S4. Utilize the plurality of parameter fitting nodes to determine the optimization processing parameters.

[0120] It should be explained that the use of the plurality of parameter fitting nodes to determine the optimization processing parameters includes:

[0121] Obtaining an updated fitting node set based on the multiple parameter fitting nodes, obtaining an updated fitting surface using the updated fitting node set, obtaining an updated node value using the energy consumption interception gradient and the updated fitting surface, counting the number of parameter fitting nodes in the multiple parameter fitting nodes to obtain an evaluation node value, calculating a ratio of the update node value to the evaluation node value to obtain a credibility evaluation value, comparing the credibility evaluation value with a preset credibility evaluation threshold, and if the credibility evaluation value is greater than or equal to the credibility evaluation threshold, identifying an initial processing parameter in the updated fitting surface, and confirming that the initial processing parameter is a preset optimization processing parameter, wherein the initial processing parameter is a parameter fitting node corresponding to a minimum evaluation and purification energy consumption value in the updated fitting surface;

[0122] Otherwise, the updated fitting node set is added to the fitting evaluation node set to obtain the target evaluation node set. The target evaluation node set is used as the fitting evaluation node set, and the step of obtaining the fitting surface based on the fitting evaluation node set and the pre-built surface fitting model is returned until the optimization processing parameters are confirmed.

[0123] It is understood that the method for obtaining the updated fitting node set is the same as the method for obtaining the fitting evaluation node set, and can achieve the same effects, so it will not be repeated here. The method for obtaining the updated fitting surface using the updated fitting node set is the same as the method for obtaining the fitting surface, so it will not be repeated here. The method for obtaining the updated node value is the same as the method for obtaining the number of statistical nodes, so it will not be repeated here.

[0124] It should be explained that the confirmation that the initial processing parameters are preset optimized processing parameters includes:

[0125] The optimized purification energy consumption value is obtained by using the initial processing parameters. Based on the initial processing parameters, the fitted purification energy consumption value is extracted from the updated fitting surface. The error ratio is calculated based on the optimized purification energy consumption value and the fitted purification energy consumption value. The calculation formula is as follows:

[0126]

[0127] Among them, B represents the error ratio, W1 represents the optimized purification energy consumption value, and W0 represents the fitted purification energy consumption value;

[0128] The error ratio is compared with a preset ratio threshold, and if the error ratio is less than or equal to the ratio threshold, the initial processing parameters are confirmed to be optimized processing parameters.

[0129] Furthermore, the method for obtaining the optimized purification energy consumption value is the same as that for the evaluated purification energy consumption value, and will not be repeated here. The fitted purification energy consumption value refers to the energy consumption value corresponding to the initial processing parameters in the updated fitting surface. The difference between the fitted purification energy consumption value and the optimized purification energy consumption value is that the optimized purification energy consumption value is the actual value, while the fitted purification energy consumption value is the ideal value or the fitted value on the surface.

[0130] S5. Using the optimized treatment parameters to extract salt from the optimized high-salt wastewater to obtain extracted salt, thereby achieving treatment of the initial high-salt wastewater.

[0131] It should be explained that the extracted salt is the salt obtained after setting the corresponding parameters of the wastewater treatment unit to the optimized treatment parameters and using the wastewater treatment unit with the set parameters to treat the optimized high-salt wastewater.

[0132] The present invention is to solve the problems described in the background technology. An embodiment of the present invention receives a process optimization instruction and confirms a process optimization environment based on the process optimization instruction, wherein the process optimization environment includes the initial high-salt wastewater to be treated and a wastewater treatment unit, and the wastewater treatment unit includes a heating unit, a crystallization unit, a steam recovery unit, a thickening unit and a centrifugal unit. It can be seen that the present invention considers using a steam recovery unit to realize steam recovery for the crystallization unit, thereby improving the energy utilization rate when treating high-salt wastewater, and obtaining an initial pollution factor set based on the initial high-salt wastewater, wherein the initial pollution factor set includes multiple initial pollution factors, and the initial factors include pollutant names and pollutant concentrations, and obtaining an initial treatment measure set for optimizing the initial pollution factor set, wherein the initial treatment measure set includes multiple initial treatment measures, and using the initial pollution factor set and the initial treatment measure set to confirm the target treatment measure, it can be seen that the embodiment of the present invention considers the characteristics of the pollutants in the initial high-salt wastewater before obtaining the target treatment measure. Here, the characteristics of the pollutants As the initial factor, and then, by processing different initial treatment measures, the target treatment measure with the lowest energy consumption can be confirmed, and when evaluating the initial treatment measures, the weights corresponding to different pollutants are also considered, thereby improving the accuracy of obtaining the pollution removal evaluation value, obtaining optimized high-salt wastewater based on the target treatment measure and the initial high-salt wastewater, obtaining the treatment parameters for optimizing the high-salt wastewater, wherein the treatment parameters include: high-salt wastewater volume and salt content set, obtaining multiple parameter fitting nodes based on the wastewater treatment unit and the treatment parameters, and using the multiple parameter fitting nodes to confirm the optimized treatment parameters. It can be seen that the embodiment of the present invention considers dividing the range of parameters involved in different units before obtaining multiple parameter fitting nodes, and obtains a parameter group for achieving the treatment of high-salt wastewater in the form of a combination, by constructing and verifying the fitting surface between the evaluation purification energy consumption value and the parameter group, improving the accuracy of the parameter group acquisition, and by verifying the initial treatment parameters confirmed in the fitting surface, further improving the accuracy of the confirmed parameter group. Therefore, the present invention can improve the accuracy of setting the parameters required for treating high-salt wastewater.

[0133] like Figure 2 1 is a functional module diagram of a high-salt wastewater treatment process optimization system based on simulation provided by one embodiment of the present invention.

[0134] The high-salt wastewater treatment process optimization system 100 based on simulation simulation of the present invention can be installed in an electronic device. According to the functions to be implemented, the high-salt wastewater treatment process optimization system 100 based on simulation simulation may include a process environment confirmation module 101, a high-salt wastewater pretreatment module 102, a process parameter pre-confirmation module 103 and a process parameter confirmation and high-salt wastewater treatment module 104. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0135] The process environment confirmation module 101 is used to receive a process optimization instruction and confirm a process optimization environment based on the process optimization instruction, wherein the process optimization environment includes initial high-salinity wastewater to be treated and a wastewater treatment unit, and the wastewater treatment unit includes a heating unit, a crystallization unit, a steam recovery unit, a thickening unit and a centrifugation unit;

[0136] The high-salt wastewater pretreatment module 102 is configured to obtain an initial pollution factor set based on the initial high-salt wastewater, wherein the initial pollution factor set includes multiple initial pollution factors, and the initial factors include pollutant names and pollutant concentrations, obtain an initial treatment measure set for optimizing the initial pollution factor set, wherein the initial treatment measure set includes multiple initial treatment measures, and identify target treatment measures using the initial pollution factor set and the initial treatment measure set;

[0137] The process parameter pre-confirmation module 103 is used to obtain optimized high-salinity wastewater based on the target treatment measures and the initial high-salinity wastewater, and obtain treatment parameters for the optimized high-salinity wastewater, wherein the treatment parameters include: a set of high-salinity wastewater volume and salt content, and obtain multiple parameter fitting nodes based on the wastewater treatment unit and the treatment parameters;

[0138] The process parameter confirmation and high-salt wastewater treatment module 104 is used to confirm the optimized treatment parameters using the multiple parameter fitting nodes;

[0139] The optimized treatment parameters are utilized to extract salt from the optimized high-salinity wastewater to obtain extracted salt, thereby achieving treatment of the initial high-salinity wastewater.

[0140] In detail, the modules in the high-salt wastewater treatment process optimization system 100 based on simulation in the embodiment of the present invention are used in the same manner as above. Figure 1 The same technical means are used as the high-salt wastewater treatment process optimization method based on simulation described in, and can produce the same technical effects, so they will not be repeated here.

[0141] like Figure 31 is a schematic diagram of the structure of an electronic device for implementing a high-salt wastewater treatment process optimization method based on simulation according to an embodiment of the present invention.

[0142] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a high-salinity wastewater treatment process optimization method program based on simulation.

[0143] Wherein, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example: SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. The memory 11 can be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 can also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed on the electronic device 1, such as the code of a high-salt wastewater treatment process optimization method program based on simulation, but can also be used to temporarily store data that has been output or is to be output.

[0144] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and lines, and executing the programs or modules stored in the memory 11 (such as a high-salt wastewater treatment process optimization method program based on simulation, etc.), as well as calling the data stored in the memory 11, to perform various functions of the electronic device 1 and process data.

[0145] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0146] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0147] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management system, thereby implementing functions such as charge management, discharge management, and power consumption management through the power management system. The power source may further include any components such as one or more DC or AC power sources, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may further include a variety of sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be described in detail here.

[0148] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0149] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0150] The high-salinity wastewater treatment process optimization method program based on simulation stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve the following:

[0151] receiving a process optimization instruction, and determining a process optimization environment based on the process optimization instruction, wherein the process optimization environment includes initial high-salinity wastewater to be treated and a wastewater treatment unit, and the wastewater treatment unit includes a heating unit, a crystallization unit, a steam recovery unit, a thickening unit, and a centrifugation unit;

[0152] Obtaining an initial pollution factor set based on the initial high-salt wastewater, wherein the initial pollution factor set includes multiple initial pollution factors, and the initial factors include pollutant names and pollutant concentrations; obtaining an initial treatment measure set for optimizing the initial pollution factor set, wherein the initial treatment measure set includes multiple initial treatment measures; and confirming a target treatment measure using the initial pollution factor set and the initial treatment measure set;

[0153] Obtaining optimized high-salinity wastewater based on target treatment measures and initial high-salinity wastewater, obtaining treatment parameters for the optimized high-salinity wastewater, wherein the treatment parameters include: a high-salinity wastewater volume and a salt content set, and obtaining multiple parameter fitting nodes based on the wastewater treatment unit and the treatment parameters;

[0154] Determining optimized processing parameters using the plurality of parameter fitting nodes;

[0155] The optimized treatment parameters are utilized to extract salt from the optimized high-salinity wastewater to obtain extracted salt, thereby achieving treatment of the initial high-salinity wastewater.

[0156] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0157] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0158] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0159] receiving a process optimization instruction, and determining a process optimization environment based on the process optimization instruction, wherein the process optimization environment includes initial high-salinity wastewater to be treated and a wastewater treatment unit, and the wastewater treatment unit includes a heating unit, a crystallization unit, a steam recovery unit, a thickening unit, and a centrifugation unit;

[0160] Obtaining an initial pollution factor set based on the initial high-salt wastewater, wherein the initial pollution factor set includes multiple initial pollution factors, and the initial factors include pollutant names and pollutant concentrations; obtaining an initial treatment measure set for optimizing the initial pollution factor set, wherein the initial treatment measure set includes multiple initial treatment measures; and confirming a target treatment measure using the initial pollution factor set and the initial treatment measure set;

[0161] Obtaining optimized high-salinity wastewater based on target treatment measures and initial high-salinity wastewater, obtaining treatment parameters for the optimized high-salinity wastewater, wherein the treatment parameters include: a high-salinity wastewater volume and a salt content set, and obtaining multiple parameter fitting nodes based on the wastewater treatment unit and the treatment parameters;

[0162] Determining optimized processing parameters using the plurality of parameter fitting nodes;

[0163] The optimized treatment parameters are utilized to extract salt from the optimized high-salinity wastewater to obtain extracted salt, thereby achieving treatment of the initial high-salinity wastewater.

[0164] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only exemplary, and actual implementations may have other division methods.

[0165] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0166] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0167] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A high-salt wastewater treatment process optimization method based on simulation, characterized in that: The method comprises: receiving a process optimization instruction, and determining a process optimization environment based on the process optimization instruction, wherein the process optimization environment includes initial high-salinity wastewater to be treated and a wastewater treatment unit, and the wastewater treatment unit includes a heating unit, a crystallization unit, a steam recovery unit, a thickening unit, and a centrifugation unit; Obtaining an initial pollution factor set based on the initial high-salt wastewater, wherein the initial pollution factor set includes multiple initial pollution factors, and the initial factors include pollutant names and pollutant concentrations; obtaining an initial treatment measure set for optimizing the initial pollution factor set, wherein the initial treatment measure set includes multiple initial treatment measures; and confirming a target treatment measure using the initial pollution factor set and the initial treatment measure set; Obtaining optimized high-salinity wastewater based on target treatment measures and initial high-salinity wastewater, obtaining treatment parameters for the optimized high-salinity wastewater, wherein the treatment parameters include: a high-salinity wastewater volume and a salt content set, and obtaining multiple parameter fitting nodes based on the wastewater treatment unit and the treatment parameters; Determining optimized processing parameters using the plurality of parameter fitting nodes; The optimized treatment parameters are utilized to extract salt from the optimized high-salinity wastewater to obtain extracted salt, thereby achieving treatment of the initial high-salinity wastewater.

2. The high-salt wastewater treatment process optimization method based on simulation according to claim 1, characterized in that: The method of using the initial pollution factor set and the initial treatment measure set to determine the target treatment measure includes: For each initial action in the initial action set, perform the following operations: Obtaining an analysis pollution factor set using the initial treatment measures and the initial pollution factor set, wherein the analysis pollution factor set includes a plurality of analysis pollution factors, and the analysis pollution factors include analysis pollutant names and analysis pollutant concentrations; Obtain an analysis pollutant threshold based on the analysis pollutant name corresponding to the analysis pollution factor, compare the analysis pollutant concentration corresponding to the analysis pollution factor with the analysis pollutant threshold, confirm that the analysis pollutant concentration corresponding to each analysis pollution factor in the analysis pollution factor set is less than or equal to the analysis pollutant threshold corresponding to the analysis pollutant name, and then match the initial pollution factors in the initial pollution factor set with the analysis pollution factors in the analysis pollution factor set based on the pollutant name corresponding to the initial factor to obtain a pollution removal evaluation node set, wherein the pollution removal evaluation node set includes multiple pollution removal evaluation nodes, and the pollution removal evaluation nodes correspond to the initial factors one-to-one; The pollution removal evaluation node sets are aggregated to obtain a plurality of pollution removal evaluation node sets, wherein the pollution removal evaluation node sets correspond to the initial treatment measures one by one, and the target treatment measures are obtained by using the plurality of pollution removal evaluation node sets.

3. The high-salt wastewater treatment process optimization method based on simulation according to claim 2, characterized in that: The obtaining target treatment measures by using the plurality of pollution removal evaluation node sets includes: Obtaining an initial pollution weight value set based on the initial pollution factor set and a pre-constructed hierarchical analysis method, wherein the initial pollution weight value set includes multiple initial pollution weight values, and the initial pollution weight values ​​correspond one to one to the initial pollution factors; Obtaining a pollution removal energy consumption value corresponding to the pollution removal assessment node set, and calculating a pollution removal assessment value using the pollution removal energy consumption value, the initial pollution weight value set, the pollution removal assessment node set, and a pre-constructed pollution removal assessment relationship; The pollution removal evaluation values ​​are summarized to obtain a pollution removal evaluation value set, and a target treatment measure is determined based on the pollution removal evaluation value set, wherein the target treatment measure is an initial treatment measure corresponding to the maximum pollution removal evaluation value in the pollution removal evaluation value set.

4. The high-salt wastewater treatment process optimization method based on simulation according to claim 3 is characterized in that: The pollution removal evaluation relationship is as follows: Wherein, P represents the pollution removal evaluation value, α and β are preset coefficients, h represents the pollution removal energy consumption value, and n 0i 、n 1i They represent the analysis pollutant concentration and pollutant concentration corresponding to the i-th pollution removal assessment node in the pollution removal assessment node set, ω i It represents the initial pollution weight value corresponding to the i-th pollution removal evaluation node in the pollution removal evaluation node set in the initial pollution weight value set, and n represents that there are n pollution removal evaluation nodes in the pollution removal evaluation node set.

5. The high-salt wastewater treatment process optimization method based on simulation according to claim 4 is characterized in that: The method of obtaining multiple parameter fitting nodes based on the wastewater treatment unit and the treatment parameters includes: Extract the initial treatment units from the wastewater treatment units in sequence, wherein the initial treatment units are heating units, crystallization units, steam recovery units, thickening units or centrifugation units, and perform the following operations on the extracted initial treatment units: Obtain a parameter adjustment range set of an initial processing unit, wherein the parameter adjustment range set includes one or more parameter adjustment ranges, and perform the following operations on each parameter adjustment range in the parameter adjustment range set: Using a preset extraction value, extracting a first adjustment parameter set from the parameter adjustment range, wherein the first adjustment parameter set includes multiple first adjustment parameter values, and the number corresponding to the first adjustment parameter values ​​is the extraction value, summarizing the first adjustment parameter sets to obtain multiple first adjustment parameter sets, and using the multiple first adjustment parameter sets in a combined form to obtain a first adjustment parameter group set, wherein the first adjustment parameter group set includes multiple first adjustment parameter groups, and the first adjustment parameter groups include multiple first adjustment parameter values, and the first adjustment parameter values ​​correspond one-to-one to the first adjustment parameter sets; Summarizing the first adjustment parameter sets to obtain a plurality of first adjustment parameter sets, and using the plurality of first adjustment parameter sets in combination to obtain an initial adjustment parameter set, wherein the initial adjustment parameter set includes a plurality of initial adjustment parameter groups, and the initial adjustment parameter group includes a plurality of first adjustment parameter groups, and the first adjustment parameter groups correspond to the initial processing units in a one-to-one manner; A plurality of parameter fitting nodes are obtained based on the initial adjustment parameter set and the processing parameters.

6. The high-salt wastewater treatment process optimization method based on simulation according to claim 5, characterized in that: The obtaining of a plurality of parameter fitting nodes based on the initial adjustment parameter set and the processing parameters includes: For each initial adjustment parameter group in the initial adjustment parameter group set, perform the following operations: Using the initial adjustment parameter group to set the wastewater treatment unit to obtain a target wastewater treatment unit, constructing a simulated high-salt wastewater based on the treatment parameters, and obtaining evaluation energy consumption parameters using the target wastewater treatment unit and the simulated high-salt wastewater, wherein the evaluation energy consumption parameters include purification time and purified salt amount; Calculating an estimated purification energy consumption value using the estimated energy consumption parameter and a pre-constructed purification energy consumption relationship, associating the estimated purification energy consumption value with an initial adjustment parameter group to obtain a purification evaluation node, and summarizing the purification evaluation nodes to obtain a purification evaluation node set; Using the preset verification value, a plurality of verification evaluation nodes are randomly extracted from the purified evaluation node set to obtain a verification node set. The verification node set is eliminated from the purified evaluation node set to obtain a fitting evaluation node set. A fitting surface is obtained based on the fitting evaluation node set and a pre-built surface fitting model. Using the verification node set, a plurality of verification energy consumption values ​​are extracted from the fitting surface. The verification evaluation value is calculated based on the evaluation purification energy consumption value and the plurality of verification energy consumption values ​​corresponding to each verification evaluation node in the verification node set. The calculation formula is as follows: Wherein, Y represents the verification evaluation value, m represents the verification value, and N j0 、N j1 Respectively represent the j-th evaluation and purification energy consumption value in multiple verification and evaluation nodes and the verification energy consumption value corresponding to the j-th evaluation and purification energy consumption value in multiple verification and evaluation nodes; Comparing the verification evaluation value with a preset verification evaluation threshold, if the verification evaluation value is greater than or equal to the verification evaluation threshold, obtaining an optimized fitting model for fitting the surface, using the optimized fitting model as a surface fitting model, and returning to the step of obtaining a fitting surface based on the fitting evaluation node set and the pre-built surface fitting model until the verification evaluation value is less than the verification evaluation threshold; Otherwise, energy consumption interception gradients are sequentially extracted from the preset energy consumption interception gradient set, and the following operations are performed on the extracted energy consumption interception gradients: Based on the energy consumption interception gradient, node statistics are performed in the fitting surface to obtain the number of statistical nodes. When the number of statistical nodes is greater than or equal to the preset node value, the energy consumption interception gradient is used to confirm the updated parameter range set in the fitting surface, and the updated parameter range set is used to obtain multiple parameter fitting nodes.

7. The high-salt wastewater treatment process optimization method based on simulation according to claim 6, characterized in that: The purification energy consumption relationship is as follows: Wherein, C represents the estimated purification energy consumption value, y represents the amount of purified salt, γ, δ are all preset coefficients, p(s1,s2,…,s q ) k represents the power of the kth initial treatment unit in the wastewater treatment unit, s1 and s2 represent the first and second first adjustment parameter values ​​corresponding to the kth initial treatment unit, respectively, q represents the parameter adjustment range corresponding to the kth initial treatment unit, and t k represents the usage time of the kth initial processing unit, and T represents the purification time.

8. The high-salt wastewater treatment process optimization method based on simulation according to claim 7, characterized in that: The step of determining the optimization processing parameters by using the plurality of parameter fitting nodes includes: Obtaining an updated fitting node set based on the multiple parameter fitting nodes, obtaining an updated fitting surface using the updated fitting node set, obtaining an updated node value using the energy consumption interception gradient and the updated fitting surface, counting the number of parameter fitting nodes in the multiple parameter fitting nodes to obtain an evaluation node value, calculating a ratio of the update node value to the evaluation node value to obtain a credibility evaluation value, comparing the credibility evaluation value with a preset credibility evaluation threshold, and if the credibility evaluation value is greater than or equal to the credibility evaluation threshold, identifying an initial processing parameter in the updated fitting surface, and confirming that the initial processing parameter is a preset optimization processing parameter, wherein the initial processing parameter is a parameter fitting node corresponding to a minimum evaluation and purification energy consumption value in the updated fitting surface; Otherwise, the updated fitting node set is added to the fitting evaluation node set to obtain the target evaluation node set. The target evaluation node set is used as the fitting evaluation node set, and the step of obtaining the fitting surface based on the fitting evaluation node set and the pre-built surface fitting model is returned until the optimization processing parameters are confirmed.

9. The high-salt wastewater treatment process optimization method based on simulation according to claim 8, characterized in that: The confirming that the initial processing parameters are preset optimized processing parameters includes: The optimized purification energy consumption value is obtained by using the initial processing parameters. Based on the initial processing parameters, the fitted purification energy consumption value is extracted from the updated fitting surface. The error ratio is calculated based on the optimized purification energy consumption value and the fitted purification energy consumption value. The calculation formula is as follows: Among them, B represents the error ratio, W1 represents the optimized purification energy consumption value, and W0 represents the fitted purification energy consumption value; The error ratio is compared with a preset ratio threshold, and if the error ratio is less than or equal to the ratio threshold, the initial processing parameters are confirmed to be optimized processing parameters.

10. A high-salt wastewater treatment process optimization system based on simulation, characterized in that: The system comprises: a process environment confirmation module, configured to receive a process optimization instruction and confirm a process optimization environment based on the process optimization instruction, wherein the process optimization environment includes initial high-salinity wastewater to be treated and a wastewater treatment unit, and the wastewater treatment unit includes a heating unit, a crystallization unit, a steam recovery unit, a thickening unit, and a centrifugation unit; a high-salt wastewater pretreatment module, configured to obtain an initial pollution factor set based on the initial high-salt wastewater, wherein the initial pollution factor set includes a plurality of initial pollution factors, and the initial factors include pollutant names and pollutant concentrations; obtain an initial treatment measure set for optimizing the initial pollution factor set, wherein the initial treatment measure set includes a plurality of initial treatment measures; and use the initial pollution factor set and the initial treatment measure set to identify target treatment measures; A process parameter pre-confirmation module is used to obtain optimized high-salinity wastewater based on target treatment measures and initial high-salinity wastewater, and obtain treatment parameters for optimized high-salinity wastewater, wherein the treatment parameters include: high-salinity wastewater volume and salt content set, and obtain multiple parameter fitting nodes based on wastewater treatment units and treatment parameters; A process parameter confirmation and high-salt wastewater treatment module is used to confirm the optimized treatment parameters using the multiple parameter fitting nodes; The optimized treatment parameters are utilized to extract salt from the optimized high-salinity wastewater to obtain extracted salt, thereby achieving treatment of the initial high-salinity wastewater.

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