Desulfurization wastewater accurate dosing method and system based on intelligent algorithm

Abstract

The invention discloses a desulfurization wastewater precise dosing method and system based on an intelligent algorithm, and the method comprises the steps: carrying out the feed-forward control of the dosage of sodium hypochlorite in a wastewater buffer pool based on a first model of the desulfurization wastewater precise dosing system according to the incoming water flow, aeration rate, inflow water COD, hydraulic retention time and sulfite content of the wastewater buffer pool, correcting the dosage of sodium hypochlorite in the wastewater buffer pool according to the effluent COD and sulfate content of the wastewater buffer pool; based on a second model of the accurate desulfurization wastewater dosing system, the dosing amount of the triple box is controlled according to the incoming water quality index of the wastewater buffer pool, the sludge reflux amount, the suspended matter concentration of the settling box and the reaction pH value of the flocculation box; and the dosage of the triple box is corrected according to the pH value, the suspended matter concentration and the heavy metal content of the effluent of the triple box. The method and the system can accurately control the dosage of the desulfurization wastewater.

Description

Technical Field

[0001] The present invention belongs to the technical field of water treatment and relates to a precise dosing method and system for desulfurization wastewater based on an intelligent algorithm. Background Art

[0002] Treatment of desulfurization wastewater from thermal power plants is a crucial component of environmental protection. Traditional desulfurization wastewater treatment processes are complex, weakly acidic, and difficult to adjust manually. They also involve a wide variety of reagents with poor stability. Current desulfurization wastewater treatment processes are plagued by drawbacks such as overdosing or underdosing, resulting in unstable removal efficiency for heavy metals and COD in the wastewater. In severe cases, this can even impact the desulfurization wastewater's ability to meet discharge standards.

[0003] In traditional desulfurization wastewater treatment, the chemical dosing stage is crucial. Excessive chemical addition can lead to a range of adverse effects. Firstly, it increases treatment costs, as chemicals themselves represent a cost investment, and excessive use can result in unexpected financial expenditures. For example, when using flocculants, if the dosage far exceeds the required amount, chemical waste results. Secondly, excessive chemical addition can lead to new pollution problems. Excessive chemical addition in the wastewater can cause complex reactions with other substances, producing difficult-to-treat byproducts and potentially impacting the stability of subsequent wastewater treatment processes. For example, excessive neutralizers can alter the wastewater's pH, preventing the subsequent heavy metal precipitation step from achieving the desired effect. Insufficient chemical addition often results in suboptimal desulfurization wastewater treatment. For example, if insufficient precipitant is added, heavy metal ions in the wastewater cannot be fully precipitated, resulting in excessive heavy metal levels in the treated wastewater. This can prevent the wastewater from meeting discharge standards and pollute the environment.

[0004] In summary, the drawbacks of the existing desulfurization wastewater dosing system, such as excessive or insufficient dosing, have seriously affected the effect, cost and environmental protection of wastewater treatment. In addition, the dosing system also has problems such as high operating failure rate, time-consuming dosing and high labor cost, which urgently need to be improved. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method and system for precise dosing of desulfurization wastewater based on an intelligent algorithm. The method and system can accurately control the dosing amount of desulfurization wastewater at a low cost.

[0006] To achieve the above objectives, the present invention discloses a method for precise dosing of desulfurization wastewater based on an intelligent algorithm, comprising:

[0007] Obtaining the inlet flow, aeration volume, inlet COD, hydraulic retention time, and sulfite content of the wastewater buffer tank, obtaining the effluent COD and sulfate content of the wastewater buffer tank, and based on the first model of the desulfurization wastewater precision dosing system, performing feedforward control on the sodium hypochlorite dosing amount of the wastewater buffer tank according to the inlet flow, aeration volume, inlet COD, hydraulic retention time, and sulfite content of the wastewater buffer tank, and correcting the sodium hypochlorite dosing amount of the wastewater buffer tank according to the effluent COD and sulfate content of the wastewater buffer tank;

[0008] The inlet water quality indicators of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank, and the reaction pH value of the flocculation tank are obtained, and the pH value, suspended solids concentration and heavy metal content of the effluent of the triple tank are obtained. Based on the second model of the desulfurization wastewater precision dosing system, the dosing amount of the triple tank is controlled according to the inlet water quality indicators of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank, and the reaction pH value of the flocculation tank; the dosing amount of the triple tank is corrected according to the pH value, suspended solids concentration and heavy metal content of the effluent of the triple tank.

[0009] The further improvement of the intelligent algorithm-based precise dosing method for desulfurization wastewater of the present invention is:

[0010] Furthermore, the heavy metals include at least one of total cadmium, total arsenic, total mercury and total lead.

[0011] Furthermore, the dosage of the triple box includes the dosage of lime milk, the dosage of organic sulfur, the dosage of flocculant and the dosage of coagulant aid.

[0012] Furthermore, the first model of the desulfurization wastewater precision dosing system is constructed based on the LSTM neural network.

[0013] Furthermore, the second model of the desulfurization wastewater precision dosing system is constructed based on the XGBoost neural network.

[0014] Furthermore, it also includes:

[0015] The prediction effects of the first model of the desulfurization wastewater precise dosing system and the second model of the desulfurization wastewater precise dosing system were evaluated by precision, recall, specificity, F score and accuracy.

[0016] The present invention discloses a precise dosing system for desulfurization wastewater based on an intelligent algorithm, comprising:

[0017] A first control module is configured to obtain the inflow flow, aeration volume, influent COD, hydraulic retention time, and sulfite content of the wastewater buffer tank, obtain the effluent COD and sulfate content of the wastewater buffer tank, perform feedforward control on the sodium hypochlorite dosage of the wastewater buffer tank based on the inflow flow, aeration volume, influent COD, hydraulic retention time, and sulfite content of the wastewater buffer tank, and correct the sodium hypochlorite dosage of the wastewater buffer tank based on the effluent COD and sulfate content of the wastewater buffer tank;

[0018] The second control module is used to obtain the inlet water quality indicators of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank and the reaction pH value of the flocculation tank, and obtain the pH value, suspended solids concentration and heavy metal content of the effluent of the triple tank. Based on the second model of the desulfurization wastewater precision dosing system, the dosing amount of the triple tank is controlled according to the inlet water quality indicators of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank and the reaction pH value of the flocculation tank; the dosing amount of the triple tank is corrected according to the pH value, suspended solids concentration and heavy metal content of the effluent of the triple tank.

[0019] The further improvement of the intelligent algorithm-based desulfurization wastewater precise dosing system of the present invention is:

[0020] Furthermore, the heavy metals include at least one of total cadmium, total arsenic, total mercury and total lead;

[0021] The dosage of the triple box includes the dosage of lime milk, the dosage of organic sulfur, the dosage of flocculant and the dosage of coagulant aid.

[0022] Furthermore, the first model of the desulfurization wastewater precision dosing system is constructed based on the LSTM neural network.

[0023] Furthermore, the second model of the desulfurization wastewater precision dosing system is constructed based on the XGBoost neural network.

[0024] The present invention has the following beneficial effects:

[0025] The intelligent algorithm-based precise dosing method and system for desulfurization wastewater, described in this invention, employs feedforward control and feedback correction based on the first and second models of the precise dosing system for desulfurization wastewater. This method accurately controls the sodium hypochlorite dosage in the wastewater buffer tank and the dosage in the triple tank, thereby improving system processing efficiency. Furthermore, the precise dosing system for desulfurization wastewater enables remote monitoring and automated operation, reducing manual intervention, extending equipment life, improving production safety, and reducing dosing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0027] Figure 1 is a flow chart of the method of the present invention;

[0028] Figure 2 This is the control principle diagram of sodium hypochlorite dosage;

[0029] Figure 3 This is the control principle diagram of the dosage of the triple box. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0032] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0033] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0034] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0035] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0036] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0037] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0038] The desulfurization wastewater triple-tank treatment system includes a desulfurization wastewater tank, a wastewater buffer tank, a neutralization tank, a sedimentation tank, a flocculation tank, a clarifier, and a clear water tank. The neutralization tank, sedimentation tank, and flocculation tank form a triple-tank system. During operation, sodium hypochlorite is added to the wastewater buffer tank, along with air, to degrade organic matter and oxidized sulfites in the desulfurization wastewater. Lime milk is added to the neutralization tank to adjust the pH of the desulfurization wastewater to 9.0-9.5, converting heavy metals in the desulfurization wastewater into hydroxide precipitates and eliminating the formation of Mg(OH)2 precipitates. To prevent scaling in the wastewater treatment system equipment, a portion of the active sludge from the bottom of the clarifier is pumped back into the neutralization tank to serve as seeds for gypsum crystallization, reducing the solution's calcium sulfate saturation. Organic sulfur is added to the sedimentation tank, causing heavy metal ions that did not precipitate as hydroxides in the neutralization tank to precipitate as sulfides.

[0039] Example 1

[0040] In order to improve the efficiency of chemical use in the desulfurization wastewater triple-tank treatment system and reduce chemical costs and sludge treatment costs, an intelligent algorithm is used to scientifically predict the dosage of each chemical in the desulfurization wastewater triple-tank treatment system to improve the system treatment efficiency.

[0041] To achieve the above goals, refer to Figure 1 、 Figure 2 and Figure 3 The present invention discloses a method for precise dosing of desulfurization wastewater based on an intelligent algorithm, comprising the following steps:

[0042] The inlet flow, aeration volume, inlet COD, hydraulic retention time, and sulfite content of the wastewater buffer tank are obtained, and the effluent COD and sulfate content of the wastewater buffer tank are obtained. Based on the first model of the desulfurization wastewater precise dosing system, the sodium hypochlorite dosing of the wastewater buffer tank is feedforward controlled according to the inlet flow, aeration volume, inlet COD, hydraulic retention time, and sulfite content of the wastewater buffer tank. The sodium hypochlorite dosing of the wastewater buffer tank is corrected according to the effluent COD and sulfate content of the wastewater buffer tank. During control, the sodium hypochlorite dosing is accurately added by controlling the dosing metering pump.

[0043] The inlet water quality index of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank and the reaction pH value of the flocculation tank are obtained, and the pH value, suspended solids concentration and heavy metal content of the effluent of the triple tank are obtained. Based on the second model of the desulfurization wastewater precision dosing system, the dosing amount of the triple tank is controlled according to the inlet water quality index of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank and the reaction pH value of the flocculation tank. The dosing amount of the triple tank includes the dosing amount of lime milk, the dosing amount of organic sulfur, the dosing amount of flocculant and the dosing amount of coagulant aid. The dosing amount of the triple tank is corrected according to the pH value, suspended solids concentration and heavy metal content of the effluent of the triple tank, thereby improving the use efficiency of the agent while reducing the cost of the agent, wherein the heavy metals include at least total cadmium, total arsenic, total mercury and total lead.

[0044] The first model of the desulfurization wastewater precise dosing system is constructed based on the LSTM neural network;

[0045] The second model of the desulfurization wastewater precision dosing system is constructed based on the XGBoost neural network.

[0046] In this embodiment, the prediction effects of the first model of the desulfurization wastewater precise dosing system and the second model of the desulfurization wastewater precise dosing system are evaluated by precision, recall, specificity, F score and accuracy.

[0047] The precision is:

[0048]

[0049] The accuracy is:

[0050]

[0051] The recall rate Recall is:

[0052]

[0053] The F-score is:

[0054]

[0055] Specificity is:

[0056]

[0057] Among them, TP is the number of samples in the positive class that are correctly predicted as positive, FP is the number of samples in the negative class that are incorrectly predicted as positive, FN is the number of samples in the positive class that are incorrectly predicted as negative, and TN is the number of samples in the negative class that are correctly predicted as negative.

[0058] It should be noted that before using the first model of the precise dosing system for desulfurization wastewater and the second model of the precise dosing system for desulfurization wastewater, the first model of the precise dosing system for desulfurization wastewater and the second model of the precise dosing system for desulfurization wastewater need to be trained first. After training, historical data is obtained, and the historical data is preprocessed using a clustering analysis algorithm and then normalized. 80% of the normalized data is used to construct a training set, and the remaining 20% of the normalized data is used to construct a test set.

[0059] The LSTM algorithm specifically includes:

[0060] 11) Forget Gate f t Calculation formula:

[0061] f t =σ(W f ·[h t-1 ,x t ]+b f )

[0062] Among them, σ is the sigmoid activation function, W f is the forget gate weight matrix, [h t-1 ,x i ] is the connection between the hidden state of the previous moment and the current input, b f is the forget gate bias term.

[0063] 12) Input gate i t Calculation formula:

[0064] i t =σ(W i ·[h t-1 ,x t ]+b i )

[0065]

[0066] Among them, C t is a new candidate memory unit, W i and W c is the weight matrix between the input gate and the candidate memory unit, b i and b c is the bias term between the input gate and the candidate memory unit.

[0067] 13) Current memory cell state C t Calculation formula:

[0068]

[0069] Among them, C t-1 is the memory unit state at the previous moment.

[0070] 14) Output gate o t Calculation formula:

[0071] o t =σ(W o ·[h t-1 ,x t ]+b o )

[0072] h t =o t tanh(C t )

[0073] Among them, h t is the hidden state at the current moment, W o and b o is the weight matrix and bias term of the output gate.

[0074] The process of the XGBoost algorithm is:

[0075] 21) When initializing the model, the XGBoost algorithm needs to set the initial prediction value, calculate the pseudo residual in each iteration, and build a new tree to fit the pseudo residual, and then update the model's prediction value. The iterative process is repeated until the number of trees or the loss function no longer decreases significantly.

[0076] 22) The XGBoost algorithm needs to minimize the objective function:

[0077]

[0078] in, Is the loss function, which represents the predicted value y i and the true value The error between Ω(f k ) is a regularization term used to control model complexity and prevent overfitting.

[0079] 23) After approximating the objective function through Taylor expansion, the optimization problem is transformed into:

[0080]

[0081] in, and are the first and second order derivatives of the loss function, respectively.

[0082] The present invention has the following characteristics:

[0083] The present invention uses artificial intelligence algorithms to achieve precise dosing in desulfurization wastewater treatment. By constructing an intelligent treatment model and using historical water quality monitoring index data and historical dosing data to train the algorithm model, the optimal dosing amount of the current desulfurization wastewater can be predicted, achieving a fast, convenient and efficient dosing process to ensure that the wastewater meets discharge standards.

[0084] This invention uses precise dosing to ensure that wastewater meets discharge standards, avoids reagent waste during operation, and reduces operating costs. Furthermore, precise dosing of desulfurization wastewater allows for remote monitoring and automated operation, reducing manual intervention, extending equipment life, and improving production safety.

[0085] The present invention realizes real-time data collection, analysis and prediction through the intelligent monitoring system, so that thermal power plants can adjust equipment operation more accurately, optimize treatment processes, and achieve more efficient environmental protection and energy utilization.

[0086] Example 2

[0087] The precise dosing system for desulfurization wastewater based on intelligent algorithms of the present invention comprises:

[0088] A first control module is configured to obtain the inflow flow, aeration volume, influent COD, hydraulic retention time, and sulfite content of the wastewater buffer tank, obtain the effluent COD and sulfate content of the wastewater buffer tank, perform feedforward control on the sodium hypochlorite dosage of the wastewater buffer tank based on the inflow flow, aeration volume, influent COD, hydraulic retention time, and sulfite content of the wastewater buffer tank, and correct the sodium hypochlorite dosage of the wastewater buffer tank based on the effluent COD and sulfate content of the wastewater buffer tank;

[0089] The second control module is used to obtain the inlet water quality indicators of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank and the reaction pH value of the flocculation tank, and obtain the pH value, suspended solids concentration and heavy metal content of the effluent of the triple tank. Based on the second model of the desulfurization wastewater precision dosing system, the dosing amount of the triple tank is controlled according to the inlet water quality indicators of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank and the reaction pH value of the flocculation tank; the dosing amount of the triple tank is corrected according to the pH value, suspended solids concentration and heavy metal content of the effluent of the triple tank.

[0090] In this embodiment, the heavy metals include at least one of total cadmium, total arsenic, total mercury and total lead;

[0091] The dosage of the triple box includes the dosage of lime milk, the dosage of organic sulfur, the dosage of flocculant and the dosage of coagulant aid.

[0092] In this embodiment, the first model of the desulfurization wastewater precision dosing system is constructed based on the LSTM neural network.

[0093] In this embodiment, the second model of the desulfurization wastewater precision dosing system is constructed based on the XGBoost neural network.

[0094] The division of modules in the embodiments of the present application is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present application may be integrated into a single processor, or may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0095] Example 3

[0096] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for accurately dosing desulfurization wastewater based on an intelligent algorithm are implemented, for example, including: obtaining the inflow flow, aeration volume, influent COD, hydraulic retention time, and sulfite content of the wastewater buffer tank; obtaining the effluent COD and sulfate content of the wastewater buffer tank; and dosing sodium hypochlorite in the wastewater buffer tank based on the inflow flow, aeration volume, influent COD, hydraulic retention time, and sulfite content of the wastewater buffer tank based on the first model of the desulfurization wastewater accurate dosing system. The amount of sodium hypochlorite in the wastewater buffer tank is feedforward controlled, and the amount of sodium hypochlorite added to the wastewater buffer tank is corrected according to the COD and sulfate content of the effluent from the wastewater buffer tank; the water quality index of the inlet water of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank, and the reaction pH value of the flocculation tank are obtained, and the pH value, suspended solids concentration, and heavy metal content of the effluent from the triple tank are obtained. Based on the second model of the desulfurization wastewater precision dosing system, the amount of dosing in the triple tank is controlled according to the water quality index of the inlet water of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank, and the reaction pH value of the flocculation tank; the amount of dosing in the triple tank is corrected according to the pH value, suspended solids concentration, and heavy metal content of the effluent from the triple tank. The memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory; the processor, network interface, and memory are interconnected via an internal bus, which may be an industrial standard architecture bus, a peripheral component interconnect standard bus, an extended industrial standard architecture bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the programs may include program codes, and the program codes include computer operation instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0097] Example 4

[0098] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the method for accurately dosing desulfurization wastewater based on an intelligent algorithm are implemented. For example, the steps include: obtaining the inflow flow, aeration volume, influent COD, hydraulic retention time, and sulfite content of a wastewater buffer tank; obtaining the effluent COD and sulfate content of the wastewater buffer tank; and based on a first model of the precise dosing system for desulfurization wastewater, performing feedforward control on the sodium hypochlorite dosing amount of the wastewater buffer tank according to the inflow flow, aeration volume, influent COD, hydraulic retention time, and sulfite content of the wastewater buffer tank. The sodium hypochlorite dosage of the wastewater buffer tank is corrected according to the COD and sulfate content of the effluent of the wastewater buffer tank; the water quality index of the inlet water of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank, and the reaction pH value of the flocculation tank are obtained, and the pH value, suspended solids concentration, and heavy metal content of the effluent of the triple tank are obtained. Based on the second model of the desulfurization wastewater precision dosing system, the dosage of the triple tank is controlled according to the water quality index of the inlet water of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank, and the reaction pH value of the flocculation tank; the dosage of the triple tank is corrected according to the pH value, suspended solids concentration, and heavy metal content of the effluent of the triple tank. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0099] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0100] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0101] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0103] Those skilled in the art will readily identify other embodiments of the present invention after considering the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0104] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

[0105] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A precise dosing method for desulfurization wastewater based on intelligent algorithm, characterized in that: include: Obtaining the inlet flow, aeration volume, inlet COD, hydraulic retention time, and sulfite content of the wastewater buffer tank, obtaining the effluent COD and sulfate content of the wastewater buffer tank, and based on the first model of the desulfurization wastewater precision dosing system, performing feedforward control on the sodium hypochlorite dosing amount of the wastewater buffer tank according to the inlet flow, aeration volume, inlet COD, hydraulic retention time, and sulfite content of the wastewater buffer tank, and correcting the sodium hypochlorite dosing amount of the wastewater buffer tank according to the effluent COD and sulfate content of the wastewater buffer tank; The inlet water quality indicators of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank, and the reaction pH value of the flocculation tank are obtained, and the pH value, suspended solids concentration and heavy metal content of the effluent of the triple tank are obtained. Based on the second model of the desulfurization wastewater precision dosing system, the dosing amount of the triple tank is controlled according to the inlet water quality indicators of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank, and the reaction pH value of the flocculation tank; the dosing amount of the triple tank is corrected according to the pH value, suspended solids concentration and heavy metal content of the effluent of the triple tank.

2. The precise dosing method for desulfurization wastewater based on intelligent algorithm according to claim 1 is characterized in that: The heavy metals include at least one of total cadmium, total arsenic, total mercury and total lead.

3. The precise dosing method for desulfurization wastewater based on intelligent algorithm according to claim 1 is characterized in that: The dosage of the triple box includes the dosage of lime milk, the dosage of organic sulfur, the dosage of flocculant and the dosage of coagulant aid.

4. The precise dosing method for desulfurization wastewater based on intelligent algorithm according to claim 1 is characterized in that: The first model of the desulfurization wastewater precise dosing system is constructed based on the LSTM neural network.

5. The precise dosing method for desulfurization wastewater based on intelligent algorithm according to claim 1 is characterized in that: The second model of the desulfurization wastewater precision dosing system is constructed based on the XGBoost neural network.

6. The precise dosing method for desulfurization wastewater based on intelligent algorithm according to claim 1 is characterized in that: Also includes: The prediction effects of the first model of the desulfurization wastewater precise dosing system and the second model of the desulfurization wastewater precise dosing system were evaluated by precision, recall, specificity, F score and accuracy.

7. A precise dosing system for desulfurization wastewater based on intelligent algorithm, characterized in that: include: A first control module is configured to obtain the inflow flow, aeration volume, influent COD, hydraulic retention time, and sulfite content of the wastewater buffer tank, obtain the effluent COD and sulfate content of the wastewater buffer tank, perform feedforward control on the sodium hypochlorite dosage of the wastewater buffer tank based on the inflow flow, aeration volume, influent COD, hydraulic retention time, and sulfite content of the wastewater buffer tank, and correct the sodium hypochlorite dosage of the wastewater buffer tank based on the effluent COD and sulfate content of the wastewater buffer tank; The second control module is used to obtain the inlet water quality indicators of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank and the reaction pH value of the flocculation tank, and obtain the pH value, suspended solids concentration and heavy metal content of the effluent of the triple tank. Based on the second model of the desulfurization wastewater precision dosing system, the dosing amount of the triple tank is controlled according to the inlet water quality indicators of the wastewater buffer tank, the sludge return flow, the suspended solids concentration of the sedimentation tank and the reaction pH value of the flocculation tank; the dosing amount of the triple tank is corrected according to the pH value, suspended solids concentration and heavy metal content of the effluent of the triple tank.

8. The precise dosing system for desulfurization wastewater based on intelligent algorithm according to claim 7 is characterized in that: The heavy metals include at least one of total cadmium, total arsenic, total mercury and total lead; The dosage of the triple box includes the dosage of lime milk, the dosage of organic sulfur, the dosage of flocculant and the dosage of coagulant aid.

9. The precise dosing system for desulfurization wastewater based on intelligent algorithm according to claim 7 is characterized in that: The first model of the desulfurization wastewater precise dosing system is constructed based on the LSTM neural network.

10. The precise dosing system for desulfurization wastewater based on intelligent algorithm according to claim 7 is characterized in that: The second model of the desulfurization wastewater precision dosing system is constructed based on the XGBoost neural network.

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