Multi-target collaborative wastewater treatment system

Abstract

The invention belongs to the field of wastewater treatment, and particularly relates to a multi-target collaborative wastewater treatment system which is composed of a pretreatment unit, a biological treatment unit, an advanced oxidation treatment unit and an intelligent control system, the pretreatment unit adopts a hydrolytic acidification technology, and refractory organics in wastewater are effectively degraded; the biological treatment unit adopts a BS sectional type MBR (Membrane Bioreactor) technology, and improves the biological treatment efficiency and stability by optimizing biological reaction conditions; the advanced oxidation treatment unit is used for carrying out advanced treatment on residual refractory substances to ensure that the effluent quality reaches a high standard, and the intelligent control system adopts a multi-target particle swarm optimization (MOPSO) algorithm to automatically adjust operation parameters of each treatment unit so as to realize multi-target collaborative optimization. The advanced biological treatment technology is combined with a physical and chemical treatment unit, and an intelligent control strategy is applied, so that the energy consumption in the treatment process is effectively reduced, and the treatment efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of wastewater treatment, and in particular relates to a multi-objective coordinated wastewater treatment system. Background Art

[0002] With the rapid advancement of urbanization, garbage transfer stations are important hubs for urban garbage collection and treatment, and are hubs connecting garbage sources and terminal treatment systems. There are many sources of wastewater generated by garbage transfer stations, and the high-concentration, complex-component wastewater they generate poses a severe challenge to the environment. Traditional wastewater treatment systems often fall into the dual dilemma of meeting water quality standards and controlling energy consumption, and it is difficult to balance efficient purification and cost-effectiveness. These systems mostly use a single wastewater treatment technology path, lack flexibility and intelligence, resulting in low resource recovery rates and high overall operating costs. Therefore, the present invention provides a multi-objective collaborative wastewater treatment system with intelligent control capabilities to achieve a wastewater treatment system with high water quality standards, low energy consumption, and maximized resource recovery. Summary of the invention

[0003] The present invention provides a multi-objective collaborative wastewater treatment system, which is used to solve the contradiction between water quality compliance and energy efficiency in traditional wastewater treatment systems, and the technical problem that it is impossible to simultaneously achieve high-standard purification of effluent water quality in the process of urban sewage treatment, significantly reduce energy consumption, and maximize resource recovery.

[0004] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present invention is as follows: a multi-objective collaborative wastewater treatment system, consisting of a pretreatment unit, a biological treatment unit, an advanced oxidation treatment unit and an intelligent control system. The pretreatment unit adopts acidification technology to effectively degrade the difficult-to-degrade organic matter in the wastewater; the biological treatment unit adopts BS segmented MBR technology to improve the biological treatment efficiency and stability by optimizing the biological reaction conditions; the advanced oxidation treatment unit performs deep treatment on the remaining difficult-to-degrade substances to ensure that the effluent water quality meets high standards. The intelligent control system adopts the multi-objective particle swarm optimization algorithm MOPSO to automatically adjust the operating parameters of each treatment unit to achieve multi-objective collaborative optimization.

[0005] Furthermore, the pretreatment unit adopts hydrolysis acidification technology and a composite anaerobic hydrolysis acidification reactor, including a sludge suspension zone, a sludge-water separation zone, and a biofilm zone; the operating control parameters of the composite anaerobic hydrolysis acidification reactor are: sludge mass concentration: 6-8g / L, operating pH 7-8, temperature: 24-26°C, and hydraulic retention time 3-4h.

[0006] Furthermore, during the acidification adjustment process of the pretreatment unit, the hydraulic retention time h, the organic matter consumption, i.e., the chemical oxygen demand COD, and the volatile fatty acid VFA are calculated:

[0007] Hydraulic retention time (h) = organic matter concentration in wastewater (mg / L) × reaction rate constant (h^-1);

[0008] Organic matter consumption (mg / L) = influent COD - effluent COD;

[0009] VFA production (mg / L) = ∑ (each VFA concentration × volume coefficient).

[0010] The acidification results were evaluated by assessing hydraulic retention time, organic matter consumption, and VFA production.

[0011] Furthermore, the biological treatment unit includes three stages: a primary screening stage, a staged biological reaction stage, and a membrane separation stage. The primary screening stage uses a grit chamber to remove large particles and suspended matter. The staged biological reaction stage uses a BS staged bioreactor, which is divided into three staged areas: an anaerobic stage, an anoxic stage, and an aerobic stage. Each area is optimized according to the treatment target and the growth characteristics of the microorganisms. The anaerobic stage is used for denitrification and hydrolysis of difficult-to-degrade organic matter; the anoxic stage further performs denitrification reaction; and the aerobic stage performs organic matter degradation and nitrification reaction.

[0012] Furthermore, the growth characteristics of the microorganism are reflected by the growth rate of the microorganism, and the growth rate of the microorganism is: μ = μ max *S / (Ks+S), where μ is the specific growth rate of the microorganism, μ max is the maximum specific growth rate, S is the substrate concentration, and Ks is the half-saturation constant.

[0013] Furthermore, the wastewater treated in the segmented biological reaction stage enters the membrane separation stage. The membrane assembly uses physical screening and adsorption to retain suspended matter and colloids in the wastewater on the membrane surface or in the membrane pores. The treated water is discharged from the system through the membrane pores to achieve the purpose of solid-liquid separation. The quality of the membrane separation process is measured by the membrane flux. The membrane flux calculation formula is J=V / (A*t), where J is the membrane flux, V is the volume of water passing through the membrane assembly, A is the effective area of ​​the membrane, and t is time.

[0014] Furthermore, the advanced oxidation treatment unit utilizes free radicals generated by the strong oxidant hydrogen peroxide to perform deep oxidation decomposition on the remaining refractory substances.

[0015] Furthermore, the multi-objective particle swarm optimization algorithm MOPSO process is as follows:

[0016] (1) Initialization setting parameters: determine the size of the particle swarm N, the number of iterations T, the dimension of the particles, that is, the number of processing parameters, the initial position and velocity of the particles, and the inertia weight;

[0017] Initialize particles: randomly generate the initial positions of each particle, which correspond to the operating parameters of each treatment unit in the wastewater treatment system;

[0018] Initialization archive set: The archive set is used to store the Pareto optimal solution found and is empty at the beginning;

[0019] (2) Fitness evaluation

[0020] Calculate the objective function value for each particle:

[0021] Outlet water quality optimization f1: Minimize the total concentration of pollutants in the effluent;

[0022] Energy consumption minimization f2: total energy consumption during calculation;

[0023] Maximizing resource recovery rate f3: Calculate the total amount of recovered resources or the recovery rate;

[0024] Minimize operating costs f4: Comprehensively consider chemical consumption, equipment maintenance, and personnel costs;

[0025] For the maximization objective, it is converted into a minimization problem for unified processing;

[0026] (3) Non-dominated sorting

[0027] The non-dominated sorting algorithm is used to sort all particles in the current particle swarm and identify the non-dominated solution, i.e., the Pareto frontier.

[0028] At the same time, if the archive set is not empty, the solutions in the archive set are also non-dominated sorted together with the current particle swarm to update the archive set;

[0029] (4) Archive Update

[0030] Add the non-dominated solutions in the current particle swarm to the archive set. If the archive set exceeds the preset size, delete it according to the crowding distance diversity preservation strategy to maintain the size and diversity of the archive set.

[0031] (5) Particle Update

[0032] Speed ​​update: Update the particle speed according to the particle's current speed, individual historical optimal position and global optimal position;

[0033] Position update: Use the updated velocity to adjust the particle's position to ensure that the new position is within a reasonable parameter range;

[0034] Pbest update: For each particle, if the new position is on the Pareto front or better than its current Pbest in at least one objective, update the Pbest.

[0035] (6) Iteration continues

[0036] Repeat steps (2) to (5) until the preset number of iterations T is reached or the archive set does not change significantly after multiple iterations.

[0037] (7) Output results

[0038] At the end of the algorithm, the solutions stored in the archive set are the Pareto optimal solution set found.

[0039] Furthermore, the multi-objective particle swarm optimization algorithm MOPSO is specifically designed as follows: Outlet water quality optimization:

[0040] (1) Objective: To minimize the pollutant concentrations of COD, BOD, ammonia nitrogen, total phosphorus, and total nitrogen in the effluent.

[0041] (2) Constraints

[0042] Dealing with efficiency constraints.

[0043] Furthermore, the effluent after treatment by the advanced oxidation treatment unit requires disinfection and neutralization treatment steps to ensure that the effluent quality meets the discharge standards or reuse water quality requirements.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] (1) The present invention uses an intelligent control system to monitor the changes in wastewater quality and the operating status of the system in real time, and automatically adjusts the operating parameters of each treatment unit according to a preset multi-objective optimization strategy. The system introduces a multi-objective particle swarm optimization algorithm (MOPSO), which can balance and optimize multiple objectives (such as effluent quality, energy consumption, resource recovery rate, etc.) to find the optimal treatment solution. Through real-time monitoring and dynamic adjustment, the intelligent control system can ensure that the system always operates in the optimal state, achieving high efficiency, low consumption and resource utilization of the wastewater treatment process.

[0046] (2) The present invention adopts a combination of advanced biological treatment technology and physical and chemical treatment units, as well as the application of intelligent control strategies, which effectively reduces energy consumption during the treatment process and improves treatment efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of the multi-objective collaborative wastewater treatment system of the present invention. DETAILED DESCRIPTION

[0048] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0049] A multi-objective collaborative wastewater treatment system consists of a pretreatment unit, a biological treatment unit, an advanced oxidation treatment unit and an intelligent control system. The pretreatment unit adopts acidification technology to effectively degrade the difficult-to-degrade organic matter in the wastewater; the biological treatment unit adopts BS segmented MBR technology to improve the biological treatment efficiency and stability by optimizing the biological reaction conditions; the advanced oxidation treatment unit performs in-depth treatment on the remaining difficult-to-degrade substances to ensure that the effluent water quality meets high standards. The intelligent control system adopts the multi-objective particle swarm optimization algorithm MOPSO to automatically adjust the operating parameters of each treatment unit to achieve multi-objective collaborative optimization.

[0050] Pretreatment unit: The hydrolysis acidification technology is used to create favorable conditions for the subsequent biological treatment process. The acidification process can not only effectively reduce the burden of the biological treatment unit, but also improve the treatment efficiency and stability of the entire system.

[0051] Hydrolysis and acidification process: a composite anaerobic hydrolysis and acidification reactor is used, including a sludge suspension zone, a sludge-water separation zone, and a biofilm zone; the operation control parameters of the composite anaerobic hydrolysis and acidification reactor are: sludge mass concentration: 6-8g / L, operating pH 7-8, temperature: 24-26°C, and hydraulic retention time 3-4h.

[0052] During the regulation process, it is necessary to calculate the hydraulic retention time h, organic matter consumption (chemical oxygen demand) COD and volatile fatty acids (VFA):

[0053] Hydraulic retention time (h) = organic matter concentration in wastewater (mg / L) × reaction rate constant (h^-1).

[0054] Organic matter consumption (mg / L) = inlet COD - outlet COD.

[0055] VFA production (mg / L) = ∑ (each VFA concentration × volume coefficient).

[0056] The acidification results were evaluated by assessing hydraulic retention time, organic matter consumption, and VFA production.

[0057] Biological treatment unit (BS segmented MBR technology): The biological treatment unit is the core part of the wastewater treatment system, and its performance directly determines the quality of the effluent. The BS segmented MBR technology is adopted, which combines the dual advantages of biological reaction and membrane separation. By optimizing the biological reaction conditions (such as temperature, pH value, dissolved oxygen concentration, etc.), it realizes the efficient proliferation and metabolic activities of microorganisms. The BS segmented design further improves the flexibility and pertinence of biological treatment, and can perform segmented treatment according to the characteristics of different wastewaters, thereby achieving better treatment effects. In addition, the application of MBR technology also realizes mud-water separation, reduces the amount of sludge generated, and reduces the difficulty and cost of subsequent treatment.

[0058] The biological treatment unit includes three stages: primary screening stage, segmented biological reaction stage and membrane separation stage. The primary screening stage adopts a grit chamber to remove large particles and suspended solids. The segmented biological reaction stage adopts a BS segmented bioreactor, which is divided into three segmented areas: anaerobic section, anoxic section and aerobic section. Each area is optimized according to the treatment target and the growth characteristics of microorganisms. The anaerobic section is used for denitrification and hydrolysis of difficult-to-degrade organic matter; the anoxic section further carries out denitrification reaction; and the aerobic section carries out organic matter degradation and nitrification reaction.

[0059] The growth characteristics of microorganisms are reflected by their growth rate. The growth rate of microorganisms: μ = μ max *S / (Ks+S), where μ is the specific growth rate of the microorganism, μ max is the maximum specific growth rate, S is the substrate concentration, and Ks is the half-saturation constant. Each segmented area provides a suitable growth environment for microorganisms by adjusting conditions such as temperature, pH value, and dissolved oxygen concentration, thereby promoting efficient proliferation and metabolic activities of microorganisms.

[0060] After the treatment in the stage of segmented biological reaction, the wastewater enters the membrane separation stage. The membrane assembly uses physical screening and adsorption to retain suspended solids and colloids in the wastewater on the membrane surface or in the membrane pores. The treated water is discharged from the system through the membrane pores to achieve the purpose of solid-liquid separation. The quality of the membrane separation process is measured by the membrane flux. The membrane flux calculation formula is J = V / (A*t), where J is the membrane flux, V is the volume of water passing through the membrane assembly, A is the effective area of ​​the membrane, and t is time. The membrane separation process also removes pollutants such as microorganisms and macromolecular organic matter, improving the quality of the effluent.

[0061] Multi-objective particle swarm optimization algorithm (MOPSO): MOPSO algorithm is an optimization algorithm based on swarm intelligence. It simulates the behavior of bird flocks foraging and seeks the global optimal solution through information sharing and collaboration between particles. In wastewater treatment systems, MOPSO algorithm is used to solve multi-objective optimization problems, that is, to consider multiple objectives such as effluent quality, energy consumption and resource recovery rate at the same time. The algorithm continuously adjusts the operating parameters of each treatment unit through iterative calculation to seek the best balance between these objectives.

[0062] The algorithm process is as follows:

[0063] (1) Initialization setting parameters: determine the size of the particle swarm N, the number of iterations T, the dimension of the particles, that is, the number of processing parameters, the initial position and velocity of the particles, and the inertia weight;

[0064] Initialize particles: randomly generate the initial positions of each particle, which correspond to the operating parameters of each treatment unit in the wastewater treatment system;

[0065] Initialization archive set: The archive set is used to store the Pareto optimal solution found and is empty at the beginning;

[0066] (2) Fitness evaluation

[0067] For each particle, the wastewater treatment process is simulated according to its position (i.e., treatment parameter combination).

[0068] Calculate the objective function value for each particle:

[0069] Outlet water quality optimization f1: Minimize the total concentration of pollutants in the effluent;

[0070] Energy consumption minimization f2: total energy consumption during calculation;

[0071] Maximizing resource recovery rate f3: Calculate the total amount of recovered resources or the recovery rate;

[0072] Minimize operating costs f4: Comprehensively consider chemical consumption, equipment maintenance, and personnel costs;

[0073] For the maximization objective, it is converted into a minimization problem for unified processing;

[0074] (3) Non-dominated sorting

[0075] The non-dominated sorting algorithm is used to sort all particles in the current particle swarm and identify the non-dominated solution, i.e., the Pareto frontier.

[0076] At the same time, if the archive set is not empty, the solutions in the archive set are also non-dominated sorted together with the current particle swarm to update the archive set;

[0077] (4) Archive Update

[0078] Add the non-dominated solutions in the current particle swarm to the archive set. If the archive set exceeds the preset size, delete it according to the crowding distance diversity preservation strategy to maintain the size and diversity of the archive set.

[0079] (5) Particle Update

[0080] Speed ​​update: Update the particle speed according to the particle's current speed, individual historical optimal position and global optimal position;

[0081] Position update: Use the updated velocity to adjust the particle's position to ensure that the new position is within a reasonable parameter range;

[0082] Pbest update: For each particle, if the new position is on the Pareto front or better than its current Pbest in at least one objective, update the Pbest.

[0083] (6) Iteration continues

[0084] Repeat steps (2) to (5) until the preset number of iterations T is reached or the archive set does not change significantly after multiple iterations.

[0085] (7) Output results

[0086] At the end of the algorithm, the solutions stored in the archive set are the Pareto optimal solution set found.

[0087] The specific design process of the multi-objective particle swarm optimization algorithm MOPSO is as follows: Outlet water quality optimization:

[0088] (1) Objective: To minimize the pollutant concentrations of COD, BOD, ammonia nitrogen, total phosphorus, and total nitrogen in the effluent.

[0089] Objective function: C i is the concentration of the ith pollutant, w i is its weight, n is the number of pollutant types;

[0090] Minimize energy consumption:

[0091] Objective function: Where E j is the energy consumption of the jth processing unit or device, m is the number of processing units or devices;

[0092] Maximize resource recovery rate:

[0093] Objective: To improve the recovery rate of useful substances such as nitrogen, phosphorus and metals in wastewater.

[0094] Objective function: Where Rk is the amount of recycling of the kth resource, T k is its theoretically recyclable amount, and p is the number of resource types.

[0095] Minimize operating costs:

[0096] Objective: Comprehensively consider chemical consumption, equipment maintenance, and personnel costs.

[0097] Objective function: f4 = C chem +C maint +C labor , which represent chemical consumption cost, equipment maintenance cost and labor cost respectively.

[0098] (2) Constraints

[0099] Processing efficiency constraints:

[0100] Ensure that the effluent quality meets national or local discharge standards;

[0101] Constraints: in is the maximum permissible concentration of the ith pollutant;

[0102] Equipment capacity constraints:

[0103] The processing unit has limited processing power;

[0104] Constraints: Q in ≤Q max And Q out ≤Q max , where Q in and Q out are the inlet and outlet water flow rates of the treatment unit, Q max is its maximum processing capacity;

[0105] Operation parameter range constraints:

[0106] The parameters of aeration volume, reflux ratio and oxidant dosage are within a reasonable range.

[0107] Constraints: min(x j )≤x j ≤max(x j ), where x j is the jth operation parameter, min(x j ) and max(x j ) are their minimum and maximum values ​​respectively;

[0108] Resource recovery technology limitations:

[0109] The recycling process may be limited by technical or economic conditions;

[0110] Constraint: Recovery Rate Among them, recovery max,k is the maximum recoverable rate of the kth resource.

[0111] The effluent after treatment in the advanced oxidation treatment unit requires disinfection and neutralization treatment steps to ensure that the effluent quality meets the discharge standards or reuse water quality requirements.

[0112] The intelligent control system is responsible for real-time monitoring of wastewater quality changes and system operation status, and automatically adjusts the operating parameters of each treatment unit according to the preset multi-objective optimization strategy. The system introduces the multi-objective particle swarm optimization algorithm (MOPSO), which can balance and optimize between multiple objectives (such as effluent quality, energy consumption, resource recovery rate, etc.) to find the optimal treatment solution. Through real-time monitoring and dynamic adjustment, the intelligent control system can ensure that the system always operates in the optimal state, achieving high efficiency, low consumption and resource utilization of the wastewater treatment process.

[0113] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A multi-objective collaborative wastewater treatment system, It is characterized in that It consists of a pretreatment unit, a biological treatment unit, an advanced oxidation treatment unit and an intelligent control system. The pretreatment unit adopts hydrolysis acidification technology to effectively degrade the difficult-to-degrade organic matter in the wastewater; the biological treatment unit adopts BS segmented MBR technology to improve the biological treatment efficiency and stability by optimizing the biological reaction conditions; the advanced oxidation treatment unit performs deep treatment on the remaining difficult-to-degrade substances to ensure that the effluent water quality meets high standards. The intelligent control system adopts the multi-objective particle swarm optimization algorithm MOPSO to automatically adjust the operating parameters of each treatment unit to achieve multi-objective collaborative optimization.

2. A multi-objective coordinated wastewater treatment system according to claim 1, It is characterized in that The pretreatment unit adopts hydrolysis acidification technology and a composite anaerobic hydrolysis acidification reactor, including a sludge suspension zone, a sludge-water separation zone, and a biofilm zone; the operation control parameters of the composite anaerobic hydrolysis acidification reactor are: sludge mass concentration: 6-8g / L, operating pH 7-8, temperature: 24-26°C, and hydraulic retention time 3-4h.

3. A multi-objective coordinated wastewater treatment system according to claim 2, It is characterized in that During the acidification adjustment process of the pretreatment unit, the hydraulic retention time h, organic matter consumption, i.e. chemical oxygen demand COD, and volatile fatty acids VFA are calculated: Hydraulic retention time (h) = organic matter concentration in wastewater (mg / L) × reaction rate constant (h^-1); Organic matter consumption (mg / L) = influent COD - effluent COD; VFA production (mg / L) = ∑ (VFA concentration × volume coefficient); The acidification results were evaluated by assessing hydraulic retention time, organic matter consumption, and VFA production.

4. A multi-objective coordinated wastewater treatment system according to claim 1, It is characterized in that The biological treatment unit includes three stages: a primary screening stage, a segmented biological reaction stage, and a membrane separation stage. The primary screening stage uses a grit chamber to remove large particles and suspended matter. The segmented biological reaction stage uses a BS segmented bioreactor, which is divided into three segmented areas: an anaerobic section, an anoxic section, and an aerobic section. Each area is optimized according to the treatment target and the growth characteristics of microorganisms. The anaerobic section is used for denitrification and hydrolysis of difficult-to-degrade organic matter; the anoxic section further performs denitrification reaction; and the aerobic section performs organic matter degradation and nitrification reaction.

5. A multi-objective coordinated wastewater treatment system according to claim 4, It is characterized in that The growth characteristics of the microorganism are reflected by the growth rate of the microorganism. The growth rate of the microorganism is: μ = μ max *S / (Ks+S), where μ is the specific growth rate of the microorganism, μ max is the maximum specific growth rate, S is the substrate concentration, and Ks is the half-saturation constant.

6. A multi-objective coordinated wastewater treatment system according to claim 4, It is characterized in that The wastewater treated in the stage of segmented biological reaction enters the membrane separation stage. The membrane assembly uses physical screening and adsorption to retain suspended matter and colloids in the wastewater on the membrane surface or in the membrane pores. The treated water is discharged from the system through the membrane pores to achieve the purpose of solid-liquid separation. The quality of the membrane separation process is measured by the membrane flux. The membrane flux calculation formula is J=V / (A*t), where J is the membrane flux, V is the volume of water passing through the membrane assembly, A is the effective area of ​​the membrane, and t is time.

7. A multi-objective coordinated wastewater treatment system according to claim 1, It is characterized in that The advanced oxidation treatment unit utilizes free radicals generated by a strong oxidant, hydrogen peroxide, to perform deep oxidation decomposition on the remaining refractory substances.

8. A multi-objective coordinated wastewater treatment system according to claim 1, It is characterized in that The multi-objective particle swarm optimization algorithm MOPSO process is as follows: (1) Initialization setting parameters: determine the size of the particle swarm N, the number of iterations T, the dimension of the particles, that is, the number of processing parameters, the initial position and velocity of the particles, and the inertia weight; Initialize particles: randomly generate the initial positions of each particle, which correspond to the operating parameters of each treatment unit in the wastewater treatment system; Initialization archive set: The archive set is used to store the Pareto optimal solution found and is empty at the beginning; (2) Fitness evaluation Calculate the objective function value for each particle: Optimization of effluent quality 1 : Minimize the total concentration of pollutants in the effluent; Minimize energy consumption 2 : Calculate the total energy consumption during the processing; Maximize resource recovery rate 3 : Calculate the total amount or recycling rate of recycled resources; Minimize operating costs f4: Comprehensively consider chemical consumption, equipment maintenance, and personnel costs; For the maximization objective, it is converted into a minimization problem for unified processing; (3) Non-dominated sorting Use the non-dominated sorting algorithm to sort all particles in the current particle swarm and identify the non-dominated solution, i.e., the Pareto frontier; At the same time, if the archive set is not empty, the solutions in the archive set are also non-dominated sorted together with the current particle swarm to update the archive set; (4) Archive Update Add the non-dominated solutions in the current particle swarm to the archive set. If the archive set exceeds the preset size, delete it according to the crowding distance diversity preservation strategy to maintain the size and diversity of the archive set. (5) Particle update Speed ​​update: Update the particle speed according to the particle's current speed, individual historical optimal position and global optimal position; Position update: Use the updated velocity to adjust the particle's position to ensure that the new position is within a reasonable parameter range; Pbest update: For each particle, if the new position is on the Pareto front or better than its current Pbest in at least one objective, update the Pbest. (6) Iteration continues Repeat steps (2) to (5) until the preset number of iterations T is reached or the archive set does not change significantly after multiple iterations; (7) Output results At the end of the algorithm, the solutions stored in the archive set are the Pareto optimal solution set found.

9. A multi-objective coordinated wastewater treatment system according to claim 8, It is characterized in that The multi-objective particle swarm optimization algorithm MOPSO is specifically designed as follows: Outlet water quality optimization: (1) Objective: To minimize the pollutant concentrations of COD, BOD, ammonia nitrogen, total phosphorus, and total nitrogen in effluent; (2) Constraints: Dealing with efficiency constraints.

10. A multi-objective coordinated wastewater treatment system according to claim 1, It is characterized in that The effluent after treatment in the advanced oxidation treatment unit requires disinfection and neutralization treatment steps to ensure that the effluent quality meets the discharge standards or reuse water quality requirements.

Images