Ferric sulfide autotrophic denitrification simulation method based on continuous flow water treatment system

Abstract

The invention relates to the technical field of water treatment, and discloses a ferrous sulfide autotrophic denitrification simulation method based on a continuous flow water treatment system, which quantifies the dynamic law of the ferrous sulfide autotrophic process by constructing a plurality of differential equations of a plurality of state variables. Furthermore, the preset activated sludge model is subjected to expansion treatment through a plurality of differential equations, so that function expansion from conventional denitrification simulation to sulfur-iron synergistic autotrophic denitrification simulation is realized, and the model can accurately predict the operation state of the continuous flow treatment system. Furthermore, the target activated sludge model after function expansion is utilized to simulate the ferrous sulfide autotrophic denitrification process, dynamic change data of a plurality of state variables can be quickly output, the precision of ferrous sulfide synergistic autotrophic denitrification simulation is improved, a basis is provided for optimization of operating parameters of a continuous flow treatment system, and the method is suitable for popularization and application. And the stable operation of the continuous flow treatment system can be guaranteed, meanwhile, the engineering debugging period is shortened, and the operation cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water treatment, in particular to a sulfur-iron autotrophic denitrification simulation method based on a continuous flow water treatment system. BACKGROUND

[0003] The current mainstream denitrification technologies include physical and chemical methods (such as ion exchange, reverse osmosis) and biological methods (such as heterotrophic denitrification and autotrophic denitrification). Although the physical and chemical methods have high denitrification efficiency, they are expensive to operate and easy to cause secondary pollution, making them difficult to be applied on a large scale. The biological methods are the preferred technology for industrial and municipal wastewater denitrification due to their low cost and environmental friendliness. Among them, the heterotrophic denitrification requires the addition of carbon sources such as methanol and acetic acid, which not only increases the operating cost (carbon source cost accounts for 40%-60% of the total treatment cost), but also easily leads to excessive COD in the effluent. The autotrophic denitrification uses inorganic carbon (such as CO2) as the carbon source, and does not require the addition of organic carbon, which can effectively reduce the operating cost and has become a research hotspot in recent years. The autotrophic denitrification mainly includes sulfur autotrophic denitrification and iron autotrophic denitrification: the sulfur autotrophic bacteria use sulfide, elemental sulfur, etc. as the electron donor to reduce nitrate to nitrogen gas, while generating sulfate; the iron autotrophic bacteria use ferrous ions as the electron donor to reduce nitrate to nitrogen gas, while generating ferric ions. However, the single sulfur autotrophic or iron autotrophic denitrification simulation method has obvious defects. In order to solve the problems of single autotrophic denitrification, researchers proposed the concept of "sulfur-iron synergistic autotrophic denitrification", which realizes the complementary advantages through the synergistic effect of sulfur autotrophic bacteria and iron autotrophic bacteria. However, the existing sulfur-iron synergistic system simulation method has large errors and low simulation accuracy, which further leads to the problems of poor stability and low simulation accuracy of the sulfur-iron synergistic autotrophic denitrification simulation method. SUMMARY

[0004] The present application provides a sulfur-iron autotrophic denitrification simulation method based on a continuous flow water treatment system, to solve the problems of large errors and low simulation accuracy of the existing sulfur-iron synergistic system simulation method, which further leads to the problems of poor stability and low simulation accuracy of the sulfur-iron synergistic autotrophic denitrification simulation method.

[0005] In a first aspect, the present application provides a sulfur-iron autotrophic denitrification simulation method based on a continuous flow water treatment system, which comprises: obtaining a plurality of state variables of the sulfur-iron autotrophic denitrification process occurring in the continuous flow water treatment system and a plurality of kinetic parameters corresponding to the plurality of state variables; constructing a plurality of differential equations of the plurality of state variables according to the plurality of kinetic parameters; extending the pre-set activated sludge model by using the plurality of differential equations to obtain a target activated sludge model; and simulating the sulfur-iron autotrophic denitrification process by using the target activated sludge model to obtain a sulfur-iron autotrophic denitrification target simulation data set.

[0006] The application provides a sulfur-iron autotrophic denitrification simulation method based on a continuous flow water treatment system, a plurality of kinetic parameters corresponding to a plurality of state variables of a sulfur-iron autotrophic denitrification process occurring in the continuous flow water treatment system, a plurality of differential equations of the plurality of state variables are constructed, the dynamic law of the sulfur-iron autotrophic process is quantified, a mathematical carrier is provided for model expansion, and the subsequent simulation can reflect the core characteristics of the sulfur-iron autotrophic process. Further, the plurality of differential equations are used to expand and process a preset activated sludge model, the function expansion from the conventional denitrification simulation to the sulfur-iron autotrophic denitrification simulation is realized, the model can accurately predict the running state of the continuous flow water treatment system, and the dependence of engineering design on experience is eliminated. Further, the target activated sludge model after the function expansion is used to simulate the sulfur-iron autotrophic denitrification process, the dynamic change data of the plurality of state variables can be quickly output, the precision of the sulfur-iron autotrophic denitrification simulation is improved, a basis is provided for the optimization of the running parameters of the continuous flow water treatment system, the stable running of the continuous flow water treatment system is helped to be ensured, meanwhile, the engineering debugging period is shortened, and the running cost is reduced.

[0007] In an optional implementation, the plurality of differential equations of the plurality of state variables are constructed according to the plurality of kinetic parameters, including: According to the plurality of kinetic parameters, a continuous flow dilution term corresponding to the continuous flow water treatment system is determined, the denitrification of the plurality of state variables in the sulfur-iron autotrophic denitrification process is analyzed according to the plurality of kinetic parameters, and a plurality of reaction terms of the plurality of state variables are determined, and the plurality of differential equations are constructed according to the plurality of reaction terms and the continuous flow dilution term based on a plurality of material conservation conditions of the plurality of state variables.

[0008] The sulfur-iron autotrophic denitrification simulation method based on the continuous flow water treatment system provided by the application determines the continuous flow dilution term corresponding to the continuous flow water treatment system through the plurality of kinetic parameters, ensures that the differential equation can reflect the characteristics of continuous flow influent continuous supplement and effluent continuous removal, and further helps to improve the fitting degree of the differential equation to the continuous flow water treatment system, and avoids the disconnection between the static simulation and the actual continuous flow running. Further, the denitrification of the plurality of state variables is analyzed according to the plurality of kinetic parameters, the core reactions such as substrate consumption / generation and microbial growth / death in the sulfur-iron autotrophic process are accurately quantified, and it is ensured that the reaction term can reflect the exclusive mechanism of the sulfur-iron autotrophic process. Further, the reaction term and the dilution term are integrated through the conservation logic, it is ensured that the differential equation can accurately reflect the balance among the input, conversion and output of the material in the sulfur-iron autotrophic process, and the scientificity and reliability of the differential equation are improved.

[0009] In an alternative embodiment, the plurality of state variables comprises a plurality of substrate-like material variables, a plurality of microbial biomass variables and an environmental factor variable; according to the plurality of kinetic parameters, the denitrification of the plurality of state variables in the sulfur-iron autotrophic denitrification process is analyzed, and a plurality of reaction terms of the plurality of state variables are determined, including: According to the plurality of kinetic parameters, the metabolic generation and metabolic consumption of the plurality of substrate-like material variables in the sulfur-iron autotrophic denitrification process are analyzed respectively, and a plurality of first reaction terms of the plurality of substrate-like material variables are determined; based on a plurality of preset constraint conditions of the plurality of microbial biomass variables, according to the plurality of kinetic parameters, the denitrification of the plurality of microbial biomass variables in the sulfur-iron autotrophic denitrification process is analyzed, and a plurality of second reaction terms of the plurality of microbial biomass variables are determined; according to the plurality of kinetic parameters, the denitrification of the environmental factor variable in the sulfur-iron autotrophic denitrification process is analyzed, and a third reaction term of the environmental factor variable is determined; according to the plurality of first reaction terms, the plurality of second reaction terms and the third reaction term, a plurality of reaction terms are determined.

[0010] The sulfur-iron autotrophic denitrification simulation method based on the continuous flow water treatment system provided by the present application can determine the source and destination of each substrate-like material variable by analyzing the metabolic generation or consumption of the substrate-like material variable, thereby ensuring that the subsequent simulation can accurately reflect the supporting effect of the substrate on the sulfur-iron autotrophic denitrification. Further, by analyzing the denitrification of the microbial biomass variable in combination with the plurality of preset constraint conditions of the plurality of microbial biomass variables, the determined second reaction term can reflect the growth or decline law of the biomass, thereby ensuring that the subsequent simulation can reflect the driving effect of the microbial population on the denitrification process. Further, by analyzing the denitrification of the environmental factor variable, the correlation between the environmental factor and the denitrification process is quantified, thereby ensuring that the subsequent simulation can cope with environmental changes in actual operation. Finally, by integrating the three types of reaction terms to form a complete reaction description system, it is ensured that the subsequent differential equation can comprehensively cover the interaction between the substrate, microorganism and environment in the sulfur-iron autotrophic denitrification process, thereby improving the completeness of the simulation.

[0011] In an alternative embodiment, the plurality of substrate-like material variables comprises a plurality of substrate-like material variables, a plurality of microbial biomass variables and an environmental factor variable; according to the plurality of kinetic parameters, the metabolic generation and metabolic consumption of the plurality of substrate-like material variables in the sulfur-iron autotrophic denitrification process are analyzed respectively, and a plurality of first reaction terms of the plurality of substrate-like material variables are determined, including: According to a plurality of dynamic parameters, the metabolism of nitrate variables in the sulfur-iron autotrophic denitrification process is analyzed, and the first metabolic consumption of the nitrate variables is determined; according to a plurality of dynamic parameters, the metabolism of sulfate variables in the sulfur-iron autotrophic denitrification process is analyzed, and the first metabolic generation of the sulfate variables is determined; according to a plurality of dynamic parameters, the metabolism of sulfide variables and ferrous ion variables in the sulfur-iron autotrophic denitrification process is analyzed respectively, and the second metabolic generation of the sulfide variables, the second metabolic consumption of the sulfide variables, the third metabolic generation of the ferrous ion variables and the third metabolic consumption of the ferrous ion variables are determined; and according to the first metabolic consumption, the first metabolic generation, the second metabolic generation, the second metabolic consumption, the third metabolic generation and the third metabolic consumption, a plurality of first reaction terms are determined.

[0012] The simulation method for sulfur-iron autotrophic denitrification based on the continuous flow water treatment system provided by the application accurately quantifies the contribution of sulfur-iron autotrophic bacteria to the consumption of nitrate by analyzing the metabolism of nitrate variables in the sulfur-iron autotrophic denitrification process, ensures that the subsequent constructed differential equation of nitrate can accurately reflect the core process of denitrification, and improves the accuracy of the simulation of the nitrate removal effect. Further, by analyzing the metabolism of sulfate variables, the generation rule of sulfate is determined, which avoids the inhibition of microbial activity due to excessive sulfate and ensures the stable operation of the continuous flow water treatment system. Further, by analyzing the metabolism of sulfide variables and ferrous ion variables, the dynamic changes of the sulfide variables and the ferrous ion variables are determined, which ensures that the subsequent constructed differential equations of sulfide and ferrous ion can reflect the matching relationship between the addition of pyrite and the demand of microorganisms, and avoids the stagnation of denitrification caused by the lack of microbial variables. Further, by combining various metabolic conditions to form a plurality of first reaction terms, it is ensured that the subsequent substrate differential equations can reflect the unique change rule of each substrate, and the ability to capture the details of substrate conversion in the simulation is improved.

[0013] In an optional implementation, based on a plurality of material balance conditions of a plurality of state variables, a plurality of differential equations are constructed according to a plurality of reaction terms and a continuous flow dilution term, including: Based on a plurality of first material balance conditions of a plurality of substrate material variables, a differential equation of nitrate, a differential equation of sulfate, a differential equation of sulfide and a differential equation of ferrous ion are constructed according to a continuous flow dilution term and a plurality of first reaction terms; based on a plurality of preset constraint conditions and a plurality of second material balance conditions of a plurality of microbial biomass variables, a biomass differential equation of sulfur autotrophic bacteria and a biomass differential equation of iron autotrophic bacteria are constructed according to a continuous flow dilution term and a plurality of second reaction terms; based on a response condition of an environmental factor variable, an environmental factor value differential equation is constructed according to a continuous flow dilution term and a third reaction term.

[0014] The simulation method for sulfur-iron autotrophic denitrification based on a continuous flow water treatment system provided by the application ensures that the nitrate differential equation, the sulfate differential equation, the sulfide differential equation and the ferrous ion differential equation can reflect the coupling effect of reaction consumption / generation and continuous flow dilution, and improves the consistency of the substrate simulation result and the actual continuous flow operation. Further, by combining a plurality of preset constraint conditions and a plurality of second material conservation conditions, it is ensured that the biomass simulation is maintained in a reasonable range, and the simulation failure and actual operation risk caused by abnormal biomass are avoided. Further, by quantifying the influence of microbial metabolism on environmental factors in response to conditions, it is ensured that the environmental factor differential equation can reflect the process of metabolic driving environmental change and continuous flow dilution adjustment, and the authenticity of the environment simulation is improved.

[0015] In an optional embodiment, the sulfur-iron autotrophic denitrification process is simulated by using a target activated sludge model to obtain a sulfur-iron autotrophic denitrification target simulation data set, which includes: A plurality of dynamic parameter values of a plurality of state variables of the continuous flow water treatment system during the sulfur-iron autotrophic denitrification process are obtained. Based on the plurality of dynamic parameter values, the target activated sludge model is solved by using the Runge-Kutta method to obtain a sulfur-iron autotrophic denitrification initial simulation data set. The sulfur-iron autotrophic denitrification initial simulation data set is screened to obtain the sulfur-iron autotrophic denitrification target simulation data set.

[0016] The simulation method for sulfur-iron autotrophic denitrification based on a continuous flow water treatment system provided by the application provides accurate numerical input for model solving by obtaining a plurality of dynamic parameter values, avoids the distortion of simulation results caused by parameter value deviation, and ensures that the subsequent solving can be consistent with the operating parameters of the actual system. Further, the model is solved by the Runge-Kutta method to ensure that the initial data set can smoothly reflect the dynamic changes of each state variable in the corresponding time period. Further, by screening the sulfur-iron autotrophic denitrification initial simulation data set, it is ensured that the sulfur-iron autotrophic denitrification target simulation data set can directly support the operation optimization of the continuous flow water treatment system, and the engineering application value of the data is improved.

[0017] In a second aspect, the application provides a simulation device for sulfur-iron autotrophic denitrification based on a continuous flow water treatment system, which comprises: The acquisition module is configured to acquire a plurality of state variables of a sulfur-iron autotrophic denitrification process of a continuous flow water treatment system and a plurality of dynamic parameters corresponding to the plurality of state variables; the construction module is configured to construct a plurality of differential equations of the plurality of state variables according to the plurality of dynamic parameters; the processing module is configured to extend a preset activated sludge model by using the plurality of differential equations to obtain a target activated sludge model; and the simulation module is configured to simulate the sulfur-iron autotrophic denitrification process by using the target activated sludge model to obtain a sulfur-iron autotrophic denitrification target simulation data set.

[0018] In a third aspect, the present application provides an electronic device, comprising a memory and a processor, the memory and the processor are connected to each other in communication, the memory stores computer instructions, and the processor executes the computer instructions to perform the sulfur-iron autotrophic denitrification simulation method based on the continuous flow water treatment system of the first aspect or any of the corresponding embodiments thereof.

[0019] In a fourth aspect, the present application provides a computer readable storage medium, the computer readable storage medium stores computer instructions, and the computer instructions are used to make a computer execute the sulfur-iron autotrophic denitrification simulation method based on the continuous flow water treatment system of the first aspect or any of the corresponding embodiments thereof.

[0020] In a fifth aspect, the present application provides a computer program product, comprising computer instructions, and the computer instructions are used to make a computer execute the sulfur-iron autotrophic denitrification simulation method based on the continuous flow water treatment system of the first aspect or any of the corresponding embodiments thereof. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0022] Figure 1 is a schematic diagram of an application scenario according to an embodiment of the present application; Figure 2 is a first flowchart of the sulfur-iron autotrophic denitrification simulation method based on the continuous flow water treatment system according to an embodiment of the present application; Figure 3A is a measured diagram of the concentration change of the sphalerite reactor effluent according to an embodiment of the present application; is a simulated diagram of the concentration change of the sphalerite reactor effluent according to an embodiment of the present application; Figure 3B is a measured diagram of the concentration change of the sphalerite reactor effluent according to an embodiment of the present application; is a simulated diagram of the concentration change of the sphalerite reactor effluent according to an embodiment of the present application; Figure 4A The effluent from the siderite reactor according to an embodiment of the present invention Schematic diagram of actual concentration change; Figure 4B The effluent from the siderite reactor according to an embodiment of the present invention Schematic diagram of concentration change simulation; Figure 5 This is a structural block diagram of a sulfur-iron autotrophic denitrification simulation device based on a continuous water treatment system according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0025] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0026] As an optional application scenario of this invention, the specific application environment architecture or specific hardware architecture on which the simulation method for autotrophic denitrification of sulfur and iron based on a continuous flow water treatment system depends is described here. For example... Figure 1 As shown, the architecture system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0027] The terminal device can be specifically a smart phone, a tablet computer, a notebook computer, a palm computer, and can also be a desktop computer, a game console, a smart television, a smart wearable device, a vehicle-mounted terminal, a VR (Virtual Reality) device, an AR (Augmented Reality) device, etc. The server 103 can be a stand-alone physical server, a server cluster or a distributed system, or a cloud server providing cloud services. The network 110 can be a wired network or a wireless network, and examples thereof include but are not limited to the Internet, an intranet, a local area network, a wide area network, a mobile communication network, and combinations thereof.

[0028] The single sulfur autotrophic or iron autotrophic denitrification simulation method has obvious defects: 1. Defects of sulfur autotrophic denitrification: Excessive sulfide is easy to cause the effluent sulfide to exceed the standard, and sulfate continues to accumulate during the reaction. When the sulfate concentration exceeds 100 mg / L, the activity of sulfur autotrophic bacteria will be significantly inhibited (inhibition rate of 30%-50%); in addition, the reaction is sensitive to pH, and the optimal pH is 7.0-7.5. A pH fluctuation of more than 0.5 will cause the denitrification efficiency to decrease by more than 20%.

[0029] 2. Defects of iron autotrophic denitrification: Ferrous ions are easy to be oxidized to trivalent iron and form hydroxide precipitate, resulting in low utilization rate of ferrous ions (only 60%-70%); and the growth rate of iron autotrophic bacteria is slow (specific growth rate of 0.2-0.3 d -1 ), and the biomass is difficult to maintain, so the system has weak impact resistance.

[0030] To solve the defects of single autotrophic denitrification, researchers proposed the concept of "sulfur-iron synergistic autotrophic denitrification", which realized the complementary advantages through the synergistic effect of sulfur autotrophic bacteria and iron autotrophic bacteria: sulfur autotrophic bacteria have a fast growth rate and can quickly remove high-concentration nitrate; iron autotrophic bacteria do not need to produce sulfate, which can alleviate the problem of sulfate accumulation; at the same time, the trivalent iron produced by iron autotrophic bacteria can react with sulfide produced by sulfur autotrophic bacteria to form iron sulfide precipitate, avoiding excessive sulfide. However, the existing sulfur-iron synergistic simulation method still has the following problems: 1. Poor stability: Lack of dynamic control of biomass, microorganisms are easy to be lost or over-proliferated, resulting in fluctuation of denitrification efficiency (fluctuation range of 15%-25%); 2. Uneven substrate supply: Sulfur and iron minerals are the common source of ferrous ions and sulfide, and their dissolution rate is significantly affected by environmental factors such as pH and temperature, which easily leads to insufficient or excessive substrate supply; 3. Low simulation accuracy: Traditional activated sludge models (such as ASM1, ASM3) only cover heterotrophic bacteria and nitrifying bacteria, do not involve sulfur autotrophic bacteria, iron autotrophic bacteria and pyrite dissolution process, and cannot accurately predict the system running state, resulting in long optimization period (usually 3-6 months) and high cost for engineering design. In the prior art, research on sulfur-iron synergistic autotrophic denitrification is mostly focused on reactor structure optimization or process parameter adjustment, lacking of model expansion and simulation method construction: in related technology one, sulfur-iron synergy is realized by connecting two reactors, but model simulation is not involved, and long-term running effect cannot be predicted; in related technology two, the sulfur-iron ore dosage is optimized, but the sulfate inhibition and pH dynamic change are not considered, and the model applicability is limited; in related technology three, a simplified kinetic model is established, but only three state variables (nitrate, sulfide and ferrous iron) are included, and biomass and pH are not involved, and the simulation accuracy is low (error > 10%).

[0031] Therefore, it is urgent to develop a technical scheme integrating "sulfur-iron synergistic reaction system + expanded ASM model", to realize accurate prediction of the long-term running state of the system by constructing a simulation model containing multiple state variables and multiple kinetic processes, and to ensure stable denitrification efficiency and balanced substrate supply through optimization of system structure and control strategy, solving the defects of the prior art.

[0032] According to the embodiment of the present application, a sulfur-iron autotrophic denitrification simulation method based on a continuous flow water treatment system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in a different order.

[0033] In this embodiment, a sulfur-iron autotrophic denitrification simulation method based on a continuous flow water treatment system is provided, which can be used in the mobile terminal mentioned above, such as mobile phone, tablet computer, etc. Figure 2 The flowchart of the sulfur-iron autotrophic denitrification simulation method based on a continuous flow water treatment system according to the embodiment of the present application is shown in FIG. Figure 2 The flowchart includes the following steps: Step S201, obtaining multiple state variables and multiple kinetic parameters corresponding to the multiple state variables of the sulfur-iron autotrophic denitrification process of the continuous flow water treatment system.

[0034] In an optional embodiment, the continuous flow water treatment system represents a closed-loop treatment device designed based on the sulfur-iron autotrophic denitrification process, which continuously flows into the influent, continuously reacts and continuously discharges the effluent, mainly including a reactor main body, an influent unit, a stirring unit and a substrate supplementing unit.

[0035] The reactor body is a cylindrical closed structure, the effective volume is 5-500L, the inner wall is made of 304 stainless steel or organic glass material, and the bottom is provided with an inclined sludge discharge port; the hydraulic retention time (HRT) of the reactor body is 8-24h, the operating temperature is controlled at 20-30℃, and the initial pH is adjusted to 7.0-7.5.

[0036] Further, the water inlet unit includes a water inlet tank, a peristaltic pump and a water distribution pipe, the water inlet tank is used to store nitrate-containing wastewater, the peristaltic pump controls the water inlet flow rate to be 0.1-50L / h, and the water distribution pipe is designed in a ring-shaped porous manner to ensure uniform distribution of the water inlet in the reactor; the nitrate concentration of the water inlet of the nitrate-containing wastewater is 10-40mgN / L, the sulfate concentration of the water inlet is 10-30mg / L, and the COD of the water inlet is ≤50mg / L.

[0037] Further, the stirring unit is a mechanical stirring device, the stirring speed is 100-200rpm, which is used to maintain uniform mixing in the reactor and avoid substrate deposition and microbial stratification; the water inlet unit and the substrate supplement unit are linked to adjust the water inlet flow rate, the pyrite addition amount and the pH adjusting agent (NaHCO3 or H2SO4) addition amount in real time; the substrate supplement unit is a pyrite addition device, the particle size of the pyrite is 0.5-2mm, and the addition amount is 0.1-1kg / m 3 The reactor volume is used to continuously supplement ferrous ions and sulfides.

[0038] In an optional embodiment, the sulfur-iron autotrophic denitrification process represents a biological denitrification process in a continuous flow water treatment system, in which sulfur autotrophic bacteria and iron autotrophic bacteria cooperate to reduce nitrate in wastewater to nitrogen gas (N2) under the condition of inorganic carbon (such as HCO3-) as a carbon source, with sulfide (S2-) and ferrous ion (Fe2+) generated by dissolution of pyrite as electron donors.

[0039] In the process, the sulfur autotrophic bacteria oxidize sulfide to generate sulfate (SO42-), the iron autotrophic bacteria oxidize ferrous ions to generate ferric ions (Fe3+), and the ferric ions generated by the iron autotrophic bacteria can react with the sulfide generated by the sulfur autotrophic bacteria to generate iron sulfide (FeS) precipitate, which can alleviate the problem of sulfate accumulation inhibition, and further form a sulfur-iron cooperative and complementary denitrification mechanism.

[0040] In an optional embodiment, a plurality of state variables are used to reflect the dynamic changes of the sulfur-iron autotrophic denitrification process, as shown in Table 1 below: Table 1, a plurality of state variables ​​​​​​​

[0041] In an optional embodiment, the plurality of kinetic parameters represent key parameters for calculating dynamic changes of the support state variables, which are determined based on a sulfur-iron autotrophic denitrification mechanism, system hardware design, and experimental data. Among them, the core kinetic parameters are shown in Table 2: Table 2, core kinetic parameters

[0042] Step S202, constructing a plurality of differential equations of a plurality of state variables according to a plurality of kinetic parameters.

[0043] In an optional embodiment, based on the specific mechanism of the sulfur-iron autotrophic denitrification process, taking the material conservation of the continuous flow water treatment system as the core logic, the dynamic change law of the plurality of state variables is quantified through the plurality of obtained kinetic parameters, and then a plurality of differential equations capable of accurately describing substrate consumption / generation, microbial growth / death, and environmental factor response are finally constructed.

[0044] Step S203, extending the preset activated sludge model by using the plurality of differential equations to obtain a target activated sludge model.

[0045] In an optional embodiment, the preset activated sludge model represents a traditional, widely used standardized mathematical model in conventional wastewater biological treatment scenarios, which can be an ASM series model. The essence is to quantify the metabolic processes of conventional microorganisms such as heterotrophic bacteria and nitrifying bacteria, and the conversion law of conventional substrates such as dissolved organic matter, ammonia nitrogen, and nitrate, to realize the simulation of the running state of the activated sludge system. Among them, the ASM series model can be ASM1, ASM2d, ASM3, etc.

[0046] In an optional embodiment, based on the plurality of differential equations constructed, the function and application scope of the preset activated sludge model can be expanded through module addition, parameter supplement, and logic integration, and finally the target activated sludge model capable of accurately adapting to the sulfur-iron autotrophic denitrification scenario is obtained.

[0047] Step S204, simulating the sulfur-iron autotrophic denitrification process by using the target activated sludge model to obtain a sulfur-iron autotrophic denitrification target simulation data set.

[0048] In an optional embodiment, simulating the sulfur-iron autotrophic denitrification process by using the target activated sludge model can screen out dynamic change data of the plurality of state variables that meet the engineering actual constraints, and then form the corresponding sulfur-iron autotrophic denitrification target simulation data set.

[0049] The simulation method for sulfur-iron autotrophic denitrification based on a continuous flow water treatment system provided by the embodiment quantifies the dynamic law of the sulfur-iron autotrophic process by constructing a plurality of differential equations of a plurality of state variables corresponding to a plurality of kinetic parameters of the sulfur-iron autotrophic denitrification process in the continuous flow water treatment system, provides a mathematical carrier for model expansion, and guarantees that the subsequent simulation can reflect the core characteristics of sulfur-iron autotrophy. Further, the plurality of differential equations are used to expand the preset activated sludge model, realize the functional expansion from the conventional denitrification simulation to the sulfur-iron autotrophic denitrification simulation, and enable the model to accurately predict the running state of the continuous flow water treatment system and get rid of the dependence of engineering design on experience. Further, the target activated sludge model after the functional expansion is used to simulate the sulfur-iron autotrophic denitrification process, which can quickly output the dynamic change data of a plurality of state variables, improve the precision of the sulfur-iron autotrophic denitrification simulation, provide a basis for the optimization of the running parameters of the continuous flow water treatment system, help guarantee the stable running of the continuous flow water treatment system, shorten the engineering debugging period, and reduce the running cost.

[0050] In some optional embodiments, the step S202 includes: In step S2021, a continuous flow dilution term corresponding to the continuous flow water treatment system is determined according to the plurality of kinetic parameters.

[0051] In an optional embodiment, the continuous flow dilution term represents a mathematical expression term for quantifying the influence of the continuous supply of substances by influent and the continuous removal of substances by effluent on the concentration of each state variable of the sulfur-iron autotrophic denitrification process, and its essence is to reflect the dilution effect of the dynamic input and output characteristics of the continuous flow on the substance concentration, so as to ensure that the differential equation can be consistent with the actual continuous flow running scenario, rather than a static batch system.

[0052] In an optional embodiment, the influent flow rate of the continuous flow water treatment system can be extracted from the plurality of kinetic parameters. and the effective volume of the reactor .

[0053] Further, the proportion of the substances in the reactor being diluted by the influent per unit time can be calculated according to the influent flow rate and the effective volume of the reactor, and the corresponding continuous flow dilution term can be further determined in combination with the proportion .

[0054] In step S2022, the denitrification of a plurality of state variables in the sulfur-iron autotrophic denitrification process is analyzed according to the plurality of kinetic parameters, and a plurality of reaction terms of the plurality of state variables are determined.

[0055] In an optional embodiment, the plurality of state variables include a plurality of substrate material variables, a plurality of microbial biomass variables, and an environmental factor variable.

[0056] The plurality of substrate material variables are used to directly reflect the dynamic conversion rule of the substrate in the denitrification process; the plurality of microbial biomass variables are used to reflect the quantity change of the microbial population in the denitrification process; and the environmental factor variable is used to reflect the coupling response relationship between the environmental factor and the denitrification metabolism.

[0057] In an optional embodiment, the reaction term represents a mathematical expression term for quantifying the core conversion behavior of each state variable in the denitrification metabolism, growth and decline, or environmental response process based on the kinetic parameters, and is used to determine whether the differential equation can accurately reflect the specific mechanism of the sulfur-iron autotrophic denitrification.

[0058] Specifically, the above step S2022 includes: Step a1, according to the plurality of kinetic parameters, respectively analyzing the metabolic generation and metabolic consumption of the plurality of substrate material variables in the sulfur-iron autotrophic denitrification process, and determining a plurality of first reaction terms of the plurality of substrate material variables.

[0059] The plurality of substrate material variables can include nitrate variable, sulfate variable, sulfide variable and ferrous ion variable.

[0060] In an optional embodiment, the generation source and consumption direction of the plurality of substrate material variables in the sulfur-iron autotrophic denitrification are quantified by the kinetic parameters, and finally the first reaction term of each substrate variable can be determined.

[0061] In some optional embodiments, the above step a1 includes: Step a11, according to the plurality of kinetic parameters, analyzing the metabolism of the nitrate variable in the sulfur-iron autotrophic denitrification process, and determining the first metabolic consumption of the nitrate variable.

[0062] Step a12, according to the plurality of kinetic parameters, analyzing the metabolism of the sulfate variable in the sulfur-iron autotrophic denitrification process, and determining the first metabolic generation of the sulfate variable.

[0063] Step a13, according to the plurality of kinetic parameters, respectively analyzing the metabolism of the sulfide variable and the ferrous ion variable in the sulfur-iron autotrophic denitrification process, and determining the second metabolic generation of the sulfide variable, the second metabolic consumption of the sulfide variable, the third metabolic generation of the ferrous ion variable and the third metabolic consumption of the ferrous ion variable.

[0064] Step a14, according to the first metabolic consumption, the first metabolic generation, the second metabolic generation, the second metabolic consumption, the third metabolic generation and the third metabolic consumption, determining a plurality of first reaction terms.

[0065] In an alternative embodiment, nitrate consumption originates from denitrification by sulfur-autotrophic and iron-autotrophic bacteria.

[0066] For example, the Monod equation can be used in combination with pH inhibitors and sulfate inhibitors to calculate the specific growth rate of sulfur autotrophs, as shown in the following relationship (1): (1) In the formula: This indicates the specific growth rate of sulfur-autotrophic bacteria, in units of... ; The pH inhibition factor is represented by a Gaussian function and is calculated as shown in the following equation (2): (2) Furthermore, The sulfate inhibitor is represented by the following relationship (3): (3) Furthermore, the specific growth rate of iron-autotrophic bacteria can be calculated using the Monod equation combined with pH inhibition factors, as shown in the following relationship (4): (4) In the formula: This indicates that iron-autotrophic bacteria grow faster than iron-autotrophic bacteria.

[0067] Furthermore, the first metabolic consumption of nitrate can be determined by combining the specific growth rates of sulfur-autotrophic bacteria and iron-autotrophic bacteria, thus forming the first reaction term for nitrate, as follows: .in, This indicates the rate at which sulfur-autotrophic bacteria consume nitrates; This indicates the rate at which iron-autotrophic bacteria consume nitrates.

[0068] In an optional embodiment, sulfate production originates from sulfur autotrophic denitrification (sulfide oxidation to sulfate). Therefore, by analyzing the metabolism of sulfate variables during sulfur-iron autotrophic denitrification, the first metabolic production of sulfate variables can be determined, and the first reaction term of sulfate variables can be formed, as follows: Where 2.86 is the stoichiometric coefficient (1 mol). 1.25 mol is required for reduction. Oxidation produces 1.25 mol of the product. This translates to a mass ratio of 2.86 mg SO4 / mg N.

[0069] In one alternative embodiment, the sulfides are generated from the dissolution of pyrite and consumed by denitrification by sulfur autotrophic bacteria.

[0070] For example, the rate of sulfide formation from the dissolution of pyrite can be calculated using the following relationship (5): (5) wherein: represents the sulfide production rate from pyrite dissolution, with the unit of mgS / (L·d); represents the total molar mass of S in FeS2(2×32 g / mol); represents the molar mass of FeS2; represents the pyrite dissolution rate, with the unit of mgFeS2 / (L·d), as shown in the following relationship (6): (6) wherein: represents the critical pH of pyrite dissolution, with the value of 3.8-4.2.

[0071] Further, in combination with the sulfide rate and the sulfur autotrophic bacteria denitrification, the second metabolic production and the second metabolic consumption of the sulfide variable can be further determined, and the corresponding first reaction term is: 1.71S, wherein 1.71 represents the stoichiometric coefficient (1 mgN corresponds to 1.71 mgS consumption).

[0072] In an optional embodiment, the ferrous ion is generated from pyrite dissolution and consumed from iron autotrophic bacteria denitrification.

[0073] For example, the ferrous rate generated from pyrite dissolution can be calculated by using the following relationship (7): (7) wherein: represents the ferrous rate generated from pyrite dissolution, with the unit of mgFe / (L·d); and 56 represents the molar mass of Fe (g / mol).

[0074] Further, in combination with the ferrous rate generated from pyrite dissolution and the sulfur autotrophic bacteria denitrification, the third metabolic production and the third metabolic consumption of the ferrous ion variable can be further determined, and the corresponding first reaction term is: 1.71S, wherein 1.71 represents the stoichiometric coefficient (1 mgN corresponds to 3.29 mgFe consumption).

[0075] Step a2, based on the plurality of preset constraint conditions of the plurality of microbial biomass variables, analyzing the denitrification of the plurality of microbial biomass variables in the sulfur iron autotrophic denitrification process according to the plurality of kinetic parameters, and determining the plurality of second reaction terms of the plurality of microbial biomass variables.

[0076] Wherein, the plurality of microbial biomass variables can include a sulfur autotrophic bacteria biomass variable and an iron autotrophic bacteria biomass variable .

[0077] In an alternative embodiment, under the constraints of the plurality of preset constraints of the plurality of microbial biomass variables, the growth and decay dynamics of the sulfur autotrophic biomass variable and the iron autotrophic biomass variable in the denitrification process can be quantified using the microbial metabolism and decay kinetics parameters, and then the second reaction term of the two types of biomass variables is determined.

[0078] For example, the changes in the sulfur autotrophic biomass variable and the iron autotrophic biomass variable are caused by growth and decay, and are affected by the effluent loss, and the biomass needs to be controlled in the range of 1-4 g / L, i.e. the plurality of preset constraints are all to control the biomass in the range of 1-4 g / L.

[0079] Further, when the sulfur autotrophic biomass variable , the second reaction term of the sulfur autotrophic biomass variable is , wherein represents the generation rate of the sulfur autotrophic biomass variable, represents the decay rate of the sulfur autotrophic biomass variable; when the sulfur autotrophic biomass variable , the second reaction term of the sulfur autotrophic biomass variable is .

[0080] Further, when the iron autotrophic biomass variable , the second reaction term of the iron autotrophic biomass variable is , wherein represents the generation rate of the iron autotrophic biomass variable, represents the decay rate of the iron autotrophic biomass variable; when the iron autotrophic biomass variable , the second reaction term of the iron autotrophic biomass variable is .

[0081] Step a3, according to the plurality of kinetic parameters, the denitrification of the environmental factor variable in the sulfur-iron autotrophic denitrification process is analyzed, and the third reaction term of the environmental factor variable is determined.

[0082] In an alternative embodiment, the change in pH is caused by the consumption of H + by the sulfur autotrophic bacteria and the production of H + by the iron autotrophic bacteria, therefore, using the microbial metabolism and environmental response kinetics parameters, the influence of the consumption of H + by the sulfur autotrophic bacteria and the production of H + by the iron autotrophic bacteria on the pH value can be quantified, and then the third reaction term of the environmental factor variable (pH) can be determined.

[0083] For example, the consumption rate of H + by the sulfur autotrophic bacteria and the production rate of H +The third reaction term of the environmental factor variable (pH) can be further determined according to the production rate: . Wherein, represents the pH change coefficient, and the value is 0.001-0.0015.

[0084] Step a4, determining a plurality of reaction terms according to a plurality of first reaction terms, a plurality of second reaction terms and a third reaction term.

[0085] Specifically, the integrated plurality of first reaction terms, plurality of second reaction terms and third reaction term can form a plurality of reaction terms covering all state variables.

[0086] Step S2023, based on a plurality of material balance conditions of a plurality of state variables, a plurality of reaction terms and a continuous flow dilution term are constructed.

[0087] In an optional embodiment, the plurality of material balance conditions represent the constraint criteria for establishing the quantitative substrate concentration change rule based on the input, conversion and output balance of the material in the continuous flow water treatment system, which is used to ensure that the differential equation of each substrate variable can accurately reflect the dynamic balance between its generation / consumption and dilution loss in the sulfur-iron autotrophic denitrification process, thereby avoiding the disconnection between the simulation results and the actual metabolic rules due to the lack of material balance relationship.

[0088] In an optional embodiment, the plurality of reaction terms and the continuous flow dilution term obtained are integrated based on the material balance conditions of different state variables, and then the differential equation of each state variable is constructed, thereby realizing the mathematical quantification of the sulfur-iron autotrophic denitrification process.

[0089] Specifically, the above step S2023 includes: Step S20231, based on a plurality of first material balance conditions of a plurality of substrate material variables, a plurality of first reaction terms and a plurality of continuous flow dilution terms are constructed to construct a nitrate differential equation, a sulfate differential equation, a sulfide differential equation and a ferrous ion differential equation.

[0090] In an optional embodiment, the material balance condition of the nitrate variable is constrained by "the change rate of the nitrate concentration=-the consumption rate of the sulfur / iron autotrophic bacteria (no generation, the reaction term is negative)+(the inflow nitrate concentration-the current concentration)×the dilution rate", which ensures that the subsequent simulation can reflect the balance of the continuous consumption of nitrate as an electron acceptor and the supplement of the inflow.

[0091] Further, based on the material balance condition of the nitrate variable, combined with the continuous flow dilution term and the first reaction term of the nitrate variable, the corresponding nitrate differential equation can be constructed, as shown in the following relationship (8): (8) In the formula: represents the time rate of change of nitrate concentration (unit: mgN / (L·d)); represents the influent nitrate concentration; represents the influent dilution term.

[0092] In an optional embodiment, the material balance condition of the sulfate variable is ensured by the constraint "the rate of change of sulfate concentration = + the rate of generation of sulfur autotrophic bacteria (no consumption, the reaction term is positive) + (the influent sulfate concentration - the current concentration) x the dilution rate", which ensures that the subsequent simulation can reflect the balance of the continuous generation of sulfate as a metabolic byproduct and the removal of the effluent.

[0093] Further, on the basis of the material balance condition of the sulfate variable, combined with the continuous flow dilution term and the first reaction term of the sulfate variable, the corresponding differential equation of the sulfate can be constructed, as shown in the following relationship (9): (9) In the formula: represents the time rate of change of sulfate concentration (unit: mg / (L·d)); represents the influent sulfate concentration; represents the influent dilution term.

[0094] In an optional embodiment, the material balance condition of the sulfide variable is ensured by the constraint "the rate of change of sulfide concentration = (the rate of generation of pyrite dissolution - the rate of consumption of sulfur autotrophic bacteria) (the difference between generation and consumption of the reaction term) + (the influent sulfide concentration ≈ 0 - the current concentration) x the dilution rate", which ensures that the subsequent simulation can reflect the closed-loop balance between the generation, consumption and loss of sulfide.

[0095] Further, on the basis of the material balance condition of the sulfide variable, combined with the continuous flow dilution term and the first reaction term of the sulfide variable, the corresponding differential equation of the sulfide can be constructed, as shown in the following relationship (10): (10) In the formula: represents the time rate of change of sulfide concentration (unit: mgS / (L·d)); represents the influent sulfide concentration; represents the influent dilution term.

[0096] In an optional embodiment, the material balance condition of the ferrous ion variable is ensured by the constraint "the rate of change of ferrous ion concentration = (the rate of generation of pyrite dissolution - the rate of consumption of iron autotrophic bacteria) (the difference between generation and consumption of the reaction term) + (the influent ferrous ion concentration ≈ 0 - the current concentration) x the dilution rate", which ensures that the subsequent simulation can reflect the similar closed-loop balance rule of ferrous ion and sulfide.

[0097] Furthermore, based on the material conservation condition of the ferrous ion variable, and combining the continuous flow dilution term and the first reaction term of the ferrous ion variable, the corresponding ferrous ion differential equation can be constructed as shown in the following relation (11): (11) In the formula: The rate of change of ferrous ion concentration over time (unit: mgFe / (L·d)); Indicates the concentration of ferrous iron in the influent; This indicates the influent dilution item.

[0098] Step S20232: Based on multiple preset constraints and multiple second material conservation conditions for multiple microbial biomass variables, and according to the continuous flow dilution term and multiple second reaction terms, construct the differential equations for sulfur autotrophic bacteria biomass and iron autotrophic bacteria biomass.

[0099] Among them, multiple microbial biomass variables may include sulfur autotrophic bacteria biomass variables and iron autotrophic bacteria biomass variables.

[0100] In an optional embodiment, when the biomass of sulfur-autotrophic bacteria changes When combining the continuous flow dilution term and the second reaction term of the sulfur autotrophic bacteria biomass variable, the corresponding differential equation for sulfur autotrophic bacteria biomass can be constructed, as shown in the following relationship (12): (12) Furthermore, when the biomass of sulfur-autotrophic bacteria changes At that time, the corresponding differential equation for sulfur autotrophic bacteria biomass is shown in the following relationship (13): (13) In the formula: The time-varying rate of sulfur autotrophic bacteria biomass (unit: g / (L·d)); This indicates the rate at which sulfur autotrophic bacteria biomass is lost with the effluent.

[0101] In an optional embodiment, when the biomass of iron autotrophic bacteria changes When combining the continuous flow dilution term and the second reaction term of the iron autotrophic bacteria biomass variable, the corresponding iron autotrophic bacteria biomass differential equation can be constructed, as shown in the following relationship (14): (14) Furthermore, when the biomass of iron-autotrophic bacteria changes At that time, the corresponding differential equation for the biomass of iron autotrophic bacteria is shown in the following relationship (15): (15) In the formula: The time-varying rate of iron autotrophic bacteria biomass (unit: g / (L·d)); This indicates the rate at which iron autotrophic bacteria biomass is lost with the effluent.

[0102] Step S20233: Based on the response conditions of environmental factor variables, construct differential equations for environmental factor values ​​according to the continuous flow dilution term and the third reaction term.

[0103] In an optional embodiment, the response condition of the environmental factor variable is used to quantify the change in the environmental factor variable pH value during the sulfur-iron autotrophic denitrification process due to the combined effects of microbial metabolism and continuous flow dilution, expressed as: pH value change rate = biological driving term (third reaction term) + physical regulation term (continuous flow dilution term).

[0104] Furthermore, based on the response conditions of the environmental factor variables, and combining the continuous flow dilution term and the third reaction term of the environmental factor variables, the corresponding differential equations for the environmental factor values ​​can be constructed, as shown in the following relation (16): (16) In the formula: The rate of change of pH over time (unit: ); This indicates that sulfur-autotrophic bacteria consume H+. + rate; This indicates that iron-autotrophic bacteria produce H. + rate; This indicates the pH value of the influent.

[0105] In some optional implementations, step S204 above includes: Step S2041: Obtain the values ​​of multiple kinetic parameters of multiple state variables in the continuous flow water treatment system during the sulfur-iron autotrophic denitrification process.

[0106] In one optional embodiment, the specific values ​​of multiple kinetic parameters corresponding to multiple state variables can be extracted and determined from the hardware design parameters of the continuous water treatment system, the parameters of the autotrophic denitrification mechanism of sulfur and iron, and engineering operation experience.

[0107] For example, it can be determined based on experimental data or literature review. , Values ​​for core parameters (recommended values:) , , (pH_k=0.0012).

[0108] Furthermore, initial values ​​can be set based on system startup conditions (recommended initial values: , , , , , , pH = 7.2).

[0109] Further, the simulation duration is set to 50 days, and the number of time nodes is 500 (time step 0.1 day), ensuring smooth curves without lag.

[0110] In step S2042, the target activated sludge model is solved based on the plurality of kinetic parameter values by using the Runge-Kutta method to obtain the initial simulation data set of the sulfur-iron autotrophic denitrification.

[0111] In an optional embodiment, the Runge-Kutta method represents an important class of implicit or explicit iterative methods for solving nonlinear ordinary differential equations. In this embodiment, the method is used to convert complex differential equations into state variable concentration values at discrete time nodes through multi-step recursion, thereby achieving high-precision simulation of the dynamic changes of state variables such as nitrate, biomass, and pH within the preset simulation period.

[0112] In an optional embodiment, the plurality of kinetic parameter values obtained are input into the target activated sludge model, and the Runge-Kutta method is used to solve the seven state variable differential equations included in the target activated sludge model. The concentration of each state variable at each time node within the preset simulation period can be calculated, and the corresponding initial simulation data set of the sulfur-iron autotrophic denitrification is formed.

[0113] Further, in the solving process, the Runge-Kutta method can adjust the solving result through adaptive step length based on the pre-set tolerance requirement.

[0114] In step S2043, the initial simulation data set of the sulfur-iron autotrophic denitrification is screened to obtain the target simulation data set of the sulfur-iron autotrophic denitrification.

[0115] In an optional embodiment, by removing the abnormal values and invalid values in the initial simulation data set, and screening the valid data that meet the operation constraints of the continuous flow water treatment system, the target simulation data set that can be directly used for engineering guidance can be finally formed.

[0116] In an example, a technical solution integrating a "sulfur-iron synergistic reaction system + extended ASM model" is provided, which is divided into a "continuous flow water treatment system" and an "extended ASM simulation method". The two parts work together to achieve efficient denitrification and precise simulation of nitrate-containing wastewater. Specifically, it includes: I. Continuous flow water treatment system, mainly including reactor main body, water inlet unit, stirring unit, and substrate supplementing unit, the functions and parameters of each unit are as follows: 1. Reactor main body.

[0117] (1) Structure: cylindrical closed structure, effective volume 5500L (laboratory scale 5-50L, engineering scale 100-500L), top exhaust port (to exclude nitrogen produced by denitrification), bottom inclined sludge discharge port (inclination 30°, facilitating residual sludge discharge); (2) Material: laboratory scale uses organic glass (for easy observation of internal reaction), engineering scale uses 304 stainless steel (corrosion resistant, service life ≥10 years); (3) Operating parameters: HRT = 3-5h, temperature = 20-30℃ (controlled by water bath or heating rod), dissolved oxygen (DO) ≤0.5mg / L.

[0118] 2. Influent unit.

[0119] (1) Influent tank: volume is 2-3 times the effective volume of the reactor, with liquid level sensor (to control the stable water level); peristaltic pump: flow adjustment range 0.1-50L / h, accuracy ±2%, real-time flow adjustment can be achieved through PLC control; (2) Water distribution pipe: ring-shaped multi-hole design (hole diameter 2-3mm, hole spacing 5-10mm), ensuring uniform distribution of influent across the reactor cross-section, avoiding excessive local substrate concentration.

[0120] 3. Agitation unit (1) Type: mechanical stirring (paddle type), paddle diameter is 1 / 3-1 / 2 of the reactor inner diameter; (2) Speed: 100-200rpm, ensuring uniform mixing in the reactor (mixing time ≤5min), avoiding pyrite particle deposition (deposition rate ≤5%).

[0121] 4. Substrate replenishment unit (1) Pyrite characteristics: particle size 5-10mm (too small to be easily lost, too large to have slow dissolution rate), purity ≥90% (FeS2 content ≥90%).

[0122] II. Extended ASM model.

[0123] The extended ASM model is based on the traditional ASM3 model framework, innovatively introducing sulfur autotrophic bacteria, iron autotrophic bacteria, and pyrite dissolution process, constructing kinetic equations for 7 state variables, and achieving accurate simulation of the system.

[0124] 1. State variable definitions are shown in Table 1 above.

[0125] 2. Core kinetic parameters are shown in Table 2 above.

[0126] 3. Differential equation set construction.

[0127] The core of the extended ASM model is to establish the continuous flow differential equations of each state variable, considering the joint action of "reaction term" and "influent-outflow dilution term", and the specific equations are shown in the above relationship (8) to (16).

[0128] III. Simulation method: based on the above-mentioned extended ASM model, the present example provides a complete simulation method, including 6 steps, as follows: Step S1: model parameter initialization.

[0129] (1) Kinetic parameter initialization: according to experimental data or literature research, determine the values of core parameters such as , (recommended values: , , , pH_k = 0.0012); (2) Initial state variable initialization: according to the system startup condition, set the initial value (recommended initial value: , , , , , , pH = 7.2); (3) Time parameter initialization: simulation duration is 50 days, time node number is 500 (time step is 0.1 day), to ensure smooth curve without carding.

[0130] Step S2: construct the differential equation set. According to the differential equations shown in the above relationship (8) to (16), construct the complete differential equation set. Note that all parameter units need to be unified (such as time unit is day, concentration unit is mg / L or g / L); Inhibitor factor (I_pH, I_SO4) needs to be embedded in growth rate formula to ensure the accuracy of kinetic process.

[0131] Step S3: solve the differential equation set. Use Python's scipy.integrate.solve_ivp function to solve the differential equation set, with the following specific settings: (1) Solution method: RK45 (Runge-Kutta method), suitable for differential equation set with small rigidity, high calculation efficiency; (2) Tolerance setting: relative tolerance rtol = 1e-6, absolute tolerance atol = 1e-8, to ensure calculation accuracy and avoid curve carding; (3) Boundary condition processing: when the state variable approaches the constraint threshold, trigger the concentration constraint mechanism to adjust the differential equation rate.

[0132] Step S4: result post-processing.

[0133] (1) Data retention: the state variable data of the 500 time nodes obtained by solving is retained to 1 significant figure (e.g. , ) ; (2) Outlier rejection: if there is data anomaly (e.g. negative concentration, pH exceeding 6.0-8.0), the parameter setting (e.g. , ) needs to be checked and re-solved; (3) Data export: the processed data is exported as an Excel file, containing 8 columns of "time (days), , , , , s , , pH", for subsequent analysis.

[0134] Step S5: result visualization and verification.

[0135] (1) Visualization setting: two groups of curves are drawn using the matplotlib library.

[0136] Compared with the prior art, the present example has the following significant beneficial effects: (1) High removal rate: the 50-day simulation results show that when the influent nitrate concentration is 25 mgN / L, the effluent concentration is stable at below 2 mgN / L, with a removal rate of ≥92%, meeting the first-level A discharge standard (nitrate ≤15 mgN / L) of GB18918-2002 and the sensitive area discharge standard (≤10 mgN / L); (2) Strong stability: the biomass is controlled at about 4 g / L through "growth-decline" balance, with a fluctuation range of ≤5%; the pH is maintained at 6.8-7.5 through real-time adjustment, with a fluctuation range of ≤0.3; the sulfate and ferrous ions are always ≥5 mg / L, with no substrate deficiency problem; the system has strong impact resistance, and when the influent nitrate concentration suddenly increases from 20 mgN / L to 40 mgN / L, it only takes 5 days to recover to stability, with denitrification efficiency restored to over 90%.

[0137] (3) Low operating cost: no carbon source needs to be added; compared with traditional heterotrophic denitrification (carbon source cost 0.5-1.0 yuan / m³ of water), the present invention uses pyrite as the substrate, with pyrite addition cost of only 0.1-0.2 yuan / m³ of water, reducing carbon source cost by 70%-80%; (4) High simulation accuracy and strong practicality: the extended ASM model includes 7 state variables and multiple kinetic processes, with a relative error of ≤20% between the simulation results and the actual operation data, better than the existing simplified model (error >50%); the engineering commissioning period is shortened from 3-6 months to 1-2 weeks, significantly reducing the commissioning cost.

[0138] (5) Curve smoothing: By setting 500 time nodes and small tolerance, ensure that key curves such as nitrate, biomass, sulfate, etc. are smooth without stalls, and long-term operation trends can be accurately predicted.

[0139] In an optional embodiment, to further verify the effectiveness of the technical solutions provided in the above examples, a specific implementation is carried out in combination with a laboratory-scale continuous flow system (reactor effective volume 10L), and the detailed steps are as follows: 1. The experimental device is selected as a cylindrical organic glass with a height of 1200mm, a diameter of 100mm, and an effective volume of 9.42L. Three reactors are divided into three layers according to the different fillers, and the lower layer is limestone with a particle size of 10-20mm and a thickness of 200mm. The middle layer A, B and C columns are respectively fine sand with a particle size of 0.125-0.25mm, pyrite with a particle size of 5-20mm, and a thickness of 400mm. The upper layer is zeolite with a particle size of 5-8mm and a thickness of 400mm. The total thickness of the three layers of fillers is 1000mm. Each cylindrical device is provided with three water outlets, which are respectively located at a distance of 50mm from the bottom of the layer of fillers, facilitating the collection of water samples in each layer. The water sampling points of the fillers from top to bottom of the reactor are marked as S1, S2 and S3. Among them, the water outlet of the lowermost layer is guided to the waste liquid barrel through the water outlet hose, and the water level of the water outlet is level with the zeolite layer of the reactor, so as to ensure that the fillers are in a water-filled state. The water inlet of each reactor is filtered to the upper part of the corresponding reactor filler from the water storage barrel through the peristaltic pump according to the set flow rate, and the water inlet is uniformly and continuously watered. The water outlet is uniformly and continuously discharged.

[0140] 2. Model parameter setting.

[0141] (1) Kinetic parameters: , , , pH_k = 0.0012, , , , , , , , , , , , , , ; (2) Initial state variables: , , , , , , pH = 7.2; (3) Time parameter: simulation duration 50 days, time nodes 500, time step 0.1 day.

[0142] 4. System operation and simulation: in the siderite group water body , the concentration was reduced from 20 mg / L to about 5 mg / L, and the experimental data and simulation results are shown in Figure 3A and Figure 3B , respectively. The final concentration of the siderite group was maintained at about 30.8 mg / L -1 . The experimental data and simulation results are shown in Figure 4A and Figure 4B , respectively.

[0143] In this embodiment, a sulfur-iron autotrophic denitrification simulation device based on a continuous flow water treatment system is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and has been described above. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware is also possible and contemplated.

[0144] This embodiment provides a sulfur-iron autotrophic denitrification simulation device based on a continuous flow water treatment system, as shown in Figure 5 , which comprises: The acquisition module 501 is configured to acquire a plurality of state variables and a plurality of kinetic parameters corresponding to the plurality of state variables of the sulfur-iron autotrophic denitrification process in the continuous flow water treatment system.

[0145] The construction module 502 is configured to construct a plurality of differential equations of the plurality of state variables according to the plurality of kinetic parameters.

[0146] The processing module 503 is configured to extend the predetermined activated sludge model by using the plurality of differential equations to obtain a target activated sludge model.

[0147] The simulation module 504 is configured to simulate the sulfur-iron autotrophic denitrification process by using the target activated sludge model to obtain a sulfur-iron autotrophic denitrification target simulation data set.

[0148] The sulfur-iron autotrophic denitrification simulation device based on a continuous flow water treatment system provided by the embodiments of the present application can perform the sulfur-iron autotrophic denitrification simulation method based on a continuous flow water treatment system provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method. The further function description of each module is the same as the corresponding embodiment described above, and will not be repeated here.

[0149] Figure 6 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown.

[0150] Reference will now be made in detail to Figure 6 which shows a structural schematic diagram suitable for being used to implement the electronic device in the embodiments of the present application. The electronic device can include a processor (such as a central processor, a graphic processor, etc.) 601, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 602 or programs loaded from a memory 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device are also stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0151] Generally, the following devices can be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a memory 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device with various devices is shown, but it should be understood that all the shown devices are not required to be implemented or possessed, and more or less devices can be alternatively implemented or possessed.

[0152] In particular, according to the embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, the embodiments of the present application include a computer program product including a computer program carried on a non-transitory computer readable medium, the computer program containing program codes for executing the method shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication device 609, or installed from the memory 608, or installed from the ROM 602. When the computer program is executed by the processor 601, the above-mentioned functions defined in the simulation method of sulfur-iron autotrophic denitrification based on a continuous flow water treatment system of the embodiments of the present application are performed.

[0153] Figure 6 The electronic device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present application.

[0154] The embodiments of the present application further provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded to a local storage medium through network, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned memories. It can be understood that the computer, the processor, the microprocessor controller, or the programmable hardware includes a storage component that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the simulation method of the autotrophic denitrification of ferrous iron based on the continuous flow water treatment system shown in the above embodiments is realized.

[0155] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, the operation of the computer can invoke or provide the method and / or technical solutions according to the present application. Those skilled in the art should understand that the form of computer program instructions in computer readable medium includes but is not limited to source file, executable file, installation package file, etc. Correspondingly, the way of computer program instructions executed by computer includes but is not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer readable medium can be any available computer readable storage medium or communication medium accessible to the computer.

[0156] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A method for modeling autotrophic sulfide-dependent denitrification based on a continuous flow water treatment system, characterized in that, The method comprises: obtaining a plurality of state variables of a sulfur-iron autotrophic denitrification process in a continuous flow water treatment system and a plurality of kinetic parameters corresponding to the plurality of state variables; constructing a plurality of differential equations of the plurality of state variables according to the plurality of kinetic parameters; extending a preset activated sludge model by using the plurality of differential equations to obtain a target activated sludge model; simulating the sulfur-iron autotrophic denitrification process by using the target activated sludge model to obtain sulfur-iron autotrophic denitrification target simulation data set.

2. The method of claim 1, wherein, The method comprises: determining a continuous flow dilution term corresponding to the continuous flow water treatment system according to the plurality of kinetic parameters; analyzing denitrification of the plurality of state variables in the sulfur-iron autotrophic denitrification process according to the plurality of kinetic parameters, and determining a plurality of reaction terms of the plurality of state variables; constructing a plurality of differential equations of the plurality of state variables according to the plurality of reaction terms and the continuous flow dilution term based on a plurality of material conservation conditions of the plurality of state variables.

3. The method of claim 2, wherein, The plurality of state variables comprises a plurality of substrate material variables, a plurality of microbial biomass variables and an environmental factor variable; analyzing denitrification of the plurality of state variables in the sulfur-iron autotrophic denitrification process according to the plurality of kinetic parameters, and determining a plurality of reaction terms of the plurality of state variables, comprises: analyzing metabolic generation and metabolic consumption of the plurality of substrate material variables in the sulfur-iron autotrophic denitrification process according to the plurality of kinetic parameters, and determining a plurality of first reaction terms of the plurality of substrate material variables; analyzing denitrification of the plurality of microbial biomass variables in the sulfur-iron autotrophic denitrification process according to the plurality of kinetic parameters based on a plurality of preset constraint conditions of the plurality of microbial biomass variables, and determining a plurality of second reaction terms of the plurality of microbial biomass variables; analyzing denitrification of the environmental factor variable in the sulfur-iron autotrophic denitrification process according to the plurality of kinetic parameters, and determining a third reaction term of the environmental factor variable; determining the plurality of reaction terms according to the plurality of first reaction terms, the plurality of second reaction terms and the third reaction term.

4. The method of claim 3, wherein, The plurality of substrate material variables comprises a nitrate variable, a sulfate variable, a sulfide variable and a ferrous ion variable; analyzing metabolic generation and metabolic consumption of the plurality of substrate material variables in the sulfur-iron autotrophic denitrification process according to the plurality of kinetic parameters, and determining a plurality of first reaction terms of the plurality of substrate material variables, comprises: analyzing metabolism of the nitrate variable in the sulfur-iron autotrophic denitrification process according to the plurality of kinetic parameters, and determining first metabolic consumption of the nitrate variable; analyzing metabolism of the sulfate variable in the sulfur-iron autotrophic denitrification process according to the plurality of kinetic parameters, and determining first metabolic generation of the sulfate variable; According to the plurality of kinetic parameters, the metabolism of the sulfide variable and the ferrous ion variable in the sulfur-iron autotrophic denitrification process is analyzed respectively, and second metabolic generation and second metabolic consumption of the sulfide variable and third metabolic generation and third metabolic consumption of the ferrous ion variable are determined; According to the first metabolic consumption, the first metabolic generation, the second metabolic generation, the second metabolic consumption, the third metabolic generation and the third metabolic consumption, the plurality of first reaction terms are determined.

5. The method of claim 3, wherein, Based on the plurality of material balance conditions of the plurality of state variables, the plurality of differential equations are constructed according to the plurality of reaction terms and the continuous flow dilution term, including: Based on the plurality of first material balance conditions of the plurality of substrate material variables, the nitrate differential equation, the sulfate differential equation, the sulfide differential equation and the ferrous ion differential equation are constructed according to the continuous flow dilution term and the plurality of first reaction terms; Based on the plurality of second material balance conditions of the plurality of microbial biomass variables and the plurality of preset constraint conditions, the sulfur autotrophic bacteria biomass differential equation and the iron autotrophic bacteria biomass differential equation are constructed according to the continuous flow dilution term and the plurality of second reaction terms; Based on the response condition of the environmental factor variable, the environmental factor value differential equation is constructed according to the continuous flow dilution term and the third reaction term.

6. The method of claim 1, wherein, The sulfur-iron autotrophic denitrification process is simulated by using the target activated sludge model, and a sulfur-iron autotrophic denitrification target simulation data set is obtained, including: The kinetic parameter values of the plurality of state variables of the continuous flow water treatment system in the sulfur-iron autotrophic denitrification process are obtained; Based on the plurality of kinetic parameter values, the target activated sludge model is solved by using the Runge-Kutta method, and a sulfur-iron autotrophic denitrification initial simulation data set is obtained; The sulfur-iron autotrophic denitrification initial simulation data set is filtered, and the sulfur-iron autotrophic denitrification target simulation data set is obtained.

7. A simulated apparatus for autotrophic sulfide-dependent denitrification based on a continuous flow water treatment system, characterized in that, The device comprises: An acquisition module is configured to acquire a plurality of state variables of a continuous flow water treatment system in a sulfur-iron autotrophic denitrification process and a plurality of kinetic parameters corresponding to the plurality of state variables; A construction module is configured to construct a plurality of differential equations of the plurality of state variables according to the plurality of kinetic parameters; A processing module is configured to extend a preset activated sludge model by using the plurality of differential equations to obtain a target activated sludge model; An simulation module is configured to simulate the sulfur-iron autotrophic denitrification process by using the target activated sludge model to obtain a sulfur-iron autotrophic denitrification target simulation data set.

8. An electronic device, comprising: It comprises: A memory and a processor are communicatively connected, and the memory stores computer instructions. The processor executes the computer instructions to perform the method for simulating sulfur-iron autotrophic denitrification based on a continuous flow water treatment system according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing a computer to execute the simulation method of iron-sulfur autotrophic denitrification based on a continuous flow water treatment system according to any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer readable storage medium stores computer instructions for causing a computer to execute the simulation method of iron-sulfur autotrophic denitrification based on a continuous flow water treatment system according to any one of claims 1 to 6.

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