Method, device and equipment for optimizing sewage treatment plant operation cost and storage medium

Abstract

The application provides a sewage treatment plant operation cost optimization method, device, equipment and storage medium, and is applied to the sewage treatment technical field. The method obtains basic data of a sewage treatment plant, determines corresponding operation rule parameters based on the basic data, inputs the basic data and the operation rule parameters into a pipe network model, obtains a target function value of the sewage treatment plant, and the target function value is used to indicate the operation cost condition of the sewage treatment plant. In the case that the target function value does not satisfy a preset condition, the operation rule parameters are updated, a new target function value is determined based on the new operation rule parameters, and the process is repeated until the target function value satisfies the preset condition. The target operation rule parameters in the pipe network model are obtained, the target operation rule parameters are the operation rule parameters corresponding to the case that the target function value satisfies the preset condition, the sewage treatment plant operation cost corresponding to the target operation rule parameters is optimal, the operation cost is reduced, and the economic benefit is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of sewage treatment, and particularly relates to a sewage treatment plant operation cost optimization method, device, equipment and storage medium. BACKGROUND

[0002] A sewage treatment plant is an important facility for treating urban domestic sewage and industrial wastewater. Its purpose is to remove pollutants in wastewater to meet discharge standards or reuse requirements. The quality of sewage changes over time and with the seasons, which affects the treatment capacity and operating costs of the sewage treatment plant. Therefore, a sewage treatment plant operation cost optimization method is needed to ensure the economic and efficient operation of the sewage treatment plant.

[0003] In the prior art, sewage treatment plant operation cost optimization mainly focuses on the optimization of the operation rules of a single or multiple pump stations, without fully considering the impact of pump station operation on the end water quality. Although the optimization of internal process energy consumption of the sewage treatment plant can reduce the internal operating costs of the sewage treatment plant, it does not involve the control of the water quality at the front end of the pipe network.

[0004] However, the optimization process of the sewage treatment plant operation cost in the prior art does not fully consider the impact of water quality on cost, lacks cost control of coordinated operation of the plant and network, and excessively relies on manual experience, resulting in low efficiency and high operating costs. SUMMARY

[0005] The application provides a sewage treatment plant operation cost optimization method, device, equipment and storage medium to solve the defects of low efficiency and high operating costs in the prior art sewage treatment plant operation cost optimization method.

[0006] In a first aspect, the application provides a sewage treatment plant operation cost optimization method, which comprises:

[0007] Obtaining basic data of a sewage treatment plant, and determining corresponding operation rule parameters based on the basic data;

[0008] Inputting the basic data and the operation rule parameters into a pipe network model to obtain a target function value of the sewage treatment plant, the target function value being used to indicate the operating cost situation of the sewage treatment plant;

[0009] In the case where the target function value does not meet a preset condition, updating the operation rule parameters, and determining a new target function value based on the new operation rule parameters until the target function value meets the preset condition, wherein the preset condition is used to indicate that the target function value converges;

[0010] Obtaining a target operation rule parameter in the pipe network model, the target operation rule parameter being an operation rule parameter corresponding to a target function value satisfying a preset condition, and the target operation rule parameter corresponding to an optimal operation cost of the sewage treatment plant.

[0011] Optionally, the inputting the basic data and the operation rule parameter into the pipe network model to obtain the target function value of the sewage treatment plant comprises:

[0012] Controlling the pipe network model to simulate water quality changes of the sewage treatment plant based on the basic data and the operation rule parameter to obtain corresponding inlet water quality parameters;

[0013] According to the inlet water quality parameters, determining a concentration value of a target index, and determining a target function value of the sewage treatment plant according to the concentration value of the target index.

[0014] Optionally, the basic data comprises a plurality of pipelines and a plurality of pump stations, and the determining the corresponding operation rule parameter based on the basic data comprises:

[0015] Determining regional distribution information of the sewage treatment plant, the regional distribution information comprising a plurality of drainage areas;

[0016] For any one of the plurality of drainage areas, performing merging processing on the pipelines in the drainage area according to the plurality of pipelines and the plurality of pump stations to obtain an association relationship between the pipelines and the pump stations after the merging processing;

[0017] Determining the corresponding operation rule parameter according to the basic data and the association relationship;

[0018] The controlling the pipe network model to simulate water quality changes of the sewage treatment plant based on the basic data and the operation rule parameter to obtain corresponding inlet water quality parameters comprises:

[0019] Updating model parameters of the pipe network model based on the operation rule parameter;

[0020] Controlling the updated pipe network model to simulate water quality changes of the sewage treatment plant based on the basic data to obtain corresponding inlet water quality parameters.

[0021] Optionally, the controlling the updated pipe network model to simulate water quality changes of the sewage treatment plant based on the basic data to obtain corresponding inlet water quality parameters comprises:

[0022] Controlling the updated pipe network model to simulate a water quality change process of the sewage treatment plant based on the basic data to obtain predicted data output by the pipe network model;

[0023] comparing the predicted data with actual detection data to determine a performance index of the pipe network model;

[0024] when the performance index is greater than a preset value, determining that the predicted data is a hydraulic parameter of the target sewage treatment plant, and determining an influent water quality parameter of the target sewage treatment plant according to the hydraulic parameter.

[0025] Optionally, the determining a concentration value of a target index according to the influent water quality parameter, and determining a target function value of the sewage treatment plant according to the concentration value of the target index, comprises:

[0026] performing a sediment simulation process according to the influent water quality parameter to determine a suspended solid concentration of the sewage treatment plant;

[0027] determining a target index concentration value of the sewage treatment plant according to the suspended solid concentration, the target index concentration value being used to indicate a content of an organic pollutant in a water body of the sewage treatment plant;

[0028] calculating an operation cost of the sewage treatment plant according to the target index concentration value;

[0029] determining a target function value of an operation cost of the target sewage treatment plant according to the operation cost.

[0030] Optionally, the calculating the operation cost of the sewage treatment plant according to the target index concentration value comprises:

[0031] determining a unit power consumption cost of the sewage treatment plant according to a total nitrogen value in the influent water quality parameter and the target index concentration value;

[0032] determining a unit chemical consumption cost of the sewage treatment plant according to the total nitrogen value in the influent water quality parameter and the target index concentration value;

[0033] determining the operation cost of the sewage treatment plant according to the unit power consumption cost and the unit chemical consumption cost.

[0034] Optionally, the operation rule parameter comprises a pump station operation duration and a pump station start-stop frequency, and the determining the target function value of the operation cost of the target sewage treatment plant comprises:

[0035] calculating a scheduling cost of the sewage treatment plant according to the pump station operation duration and the pump station start-stop frequency for any one of the plurality of pump stations;

[0036] determining the target function value of the sewage treatment plant according to the operation cost and the scheduling cost.

[0037] In a second aspect, the present application provides a sewage treatment plant operation cost optimization device, the device comprising:

[0038] an acquisition module configured to acquire basic data of a sewage treatment plant;

[0039] a determination module configured to determine corresponding operation rule parameters based on the basic data;

[0040] a processing module configured to input the basic data and the operation rule parameters into a pipe network model to obtain a target function value of the sewage treatment plant, the target function value being used to indicate an operation cost situation of the sewage treatment plant;

[0041] The processing module is further configured to update the operation rule parameters in a case where the target function value does not satisfy a preset condition, and determine a new target function value based on new operation rule parameters until the target function value satisfies the preset condition, wherein the preset condition is used to indicate that the target function value converges.

[0042] The acquisition module is further configured to acquire target operation rule parameters in the pipe network model, the target operation rule parameters being operation rule parameters corresponding to a case where a target function value satisfies a preset condition, and the target operation rule parameters corresponding to an optimal sewage treatment plant operation cost.

[0043] Optionally, the processing module is further configured to control the pipe network model to simulate water quality changes of the sewage treatment plant based on the basic data and the operation rule parameters to obtain corresponding influent water quality parameters.

[0044] The determination module is further configured to determine a concentration value of a target index according to the influent water quality parameters, and determine the target function value of the sewage treatment plant according to the concentration value of the target index.

[0045] Optionally, the determination module is further configured to determine regional distribution information of the sewage treatment plant, the regional distribution information comprising: a plurality of drainage areas.

[0046] The processing module is further configured to, for any one of the plurality of drainage areas, perform merging processing on pipes in the drainage area according to the plurality of pipes and the plurality of pump stations to obtain an association relationship between the merged pipes and pump stations.

[0047] The determination module is further configured to determine corresponding operation rule parameters according to the basic data and the association relationship.

[0048] Optionally, the processing module is further configured to update model parameters of the pipe network model based on the operation rule parameters.

[0049] The processing module is further configured to control the updated pipe network model to simulate water quality changes of the sewage treatment plant based on the basic data, to obtain corresponding influent water quality parameters.

[0050] Optionally, the processing module is further configured to control the updated pipe network model to simulate a water quality change process of the sewage treatment plant based on the basic data, to obtain predicted data output by the pipe network model.

[0051] The determining module is further configured to compare the predicted data with actual detection data, and determine a performance index of the pipe network model.

[0052] The determining module is further configured to determine, when the performance index is greater than a preset value, that the predicted data is a hydraulic parameter of the target sewage treatment plant, and determine influent water quality parameters of the target sewage treatment plant according to the hydraulic parameter.

[0053] Optionally, the sewage treatment plant operation cost optimization apparatus further includes a calculation module.

[0054] The determining module is further configured to perform sediment simulation processing according to the influent water quality parameters, to determine a sewage suspended solid concentration of the sewage treatment plant.

[0055] The determining module is further configured to determine a target index concentration value of the sewage treatment plant according to the suspended solid concentration, the target index concentration value being used to indicate an organic pollutant content in water of the sewage treatment plant.

[0056] The calculation module is configured to calculate an operation cost of the sewage treatment plant according to the target index concentration value.

[0057] The determining module is further configured to determine a target function value of the operation cost of the target sewage treatment plant according to the operation cost.

[0058] Optionally, the determining module is further configured to determine a unit power consumption cost of the sewage treatment plant according to a total nitrogen value in the influent water quality parameters and the target index concentration value.

[0059] The determining module is further configured to determine a unit drug consumption cost of the sewage treatment plant according to the total nitrogen value in the influent water quality parameters and the target index concentration value.

[0060] The determining module is further configured to determine the operation cost of the sewage treatment plant according to the unit power consumption cost and the unit drug consumption cost.

[0061] Optionally, the calculation module is further configured to calculate, for any one of the plurality of pump stations, a scheduling cost of the sewage treatment plant according to a pump station operation duration and a pump station start-stop frequency.

[0062] The determining module is further configured to determine the objective function value of the wastewater treatment plant based on the operating cost and the scheduling cost.

[0063] Thirdly, this application provides a wastewater treatment plant operating cost optimization device, comprising:

[0064] Memory;

[0065] processor;

[0066] The memory stores computer-executed instructions;

[0067] The processor executes computer execution instructions stored in the memory to implement the wastewater treatment plant operation cost optimization method as described in the first aspect and various possible implementations of the first aspect above.

[0068] Fourthly, this application provides a computer storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the wastewater treatment plant operating cost optimization method as described in the first aspect and various possible implementations of the first aspect above.

[0069] This application provides a method, apparatus, equipment, and storage medium for optimizing the operating costs of a wastewater treatment plant. The method involves acquiring basic data of the wastewater treatment plant and determining corresponding operating rule parameters based on this data; inputting the basic data and operating rule parameters into a pipeline network model to obtain the objective function value of the wastewater treatment plant, which indicates the plant's operating costs; updating the operating rule parameters if they do not meet preset conditions, and determining a new objective function value based on these new parameters, until the objective function value meets the preset conditions; and acquiring the target operating rule parameters from the pipeline network model, which are the operating rule parameters corresponding to when the objective function value meets the preset conditions. The target operating rule parameters correspond to the optimal operating costs of the wastewater treatment plant, thus reducing operating costs and improving economic efficiency. Attached Figure Description

[0070] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0071] Figure 1 A flowchart illustrating the wastewater treatment plant operating cost optimization method provided in this application. Figure 1 ;

[0072] Figure 2 A flowchart illustrating the wastewater treatment plant operating cost optimization method provided in this application. Figure 2 ;

[0073] Figure 3 A flowchart illustrating the wastewater treatment plant operating cost optimization method provided in this application. Figure 3 ;

[0074] Figure 4 A schematic diagram of the wastewater treatment plant operation cost optimization device provided in this application;

[0075] Figure 5 A schematic diagram of the wastewater treatment plant operation cost optimization equipment provided in this application.

[0076] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0077] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0078] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein.

[0079] In this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0080] Wastewater treatment plants are crucial facilities for treating urban sewage and industrial wastewater. Their primary task is to remove pollutants from wastewater, ensuring that the effluent quality meets discharge standards or is suitable for reuse. Wastewater quality fluctuates over time and seasonally, significantly impacting the treatment capacity and operating costs of wastewater treatment plants. Therefore, to ensure the economical and efficient operation of wastewater treatment plants, an operational cost optimization method needs to be developed. This method aims to effectively reduce costs through rational planning and management while maintaining wastewater treatment effectiveness, thus ensuring the economical and efficient operation of wastewater treatment plants.

[0081] In existing technologies, optimizing the operating costs of wastewater treatment plants typically focuses on optimizing the operating rules of individual or multiple pump stations. However, these methods do not adequately consider the impact of pump station operation on the final effluent quality. While optimizing the internal process energy consumption of wastewater treatment plants can effectively reduce their own operating costs, this does not involve controlling and optimizing the influent quality of the upstream pipeline network. Therefore, current technological approaches fail to comprehensively consider and optimize the operating costs of wastewater treatment plants from the source to the end of the treatment process.

[0082] However, existing technologies have some shortcomings in optimizing the operating costs of wastewater treatment plants. First, they do not adequately consider the impact of water quality changes on costs, neglecting the direct influence of water quality factors on wastewater treatment costs. Second, they lack a comprehensive plant-network coordination mechanism to control costs; that is, they do not optimize costs by treating the wastewater treatment plant and the entire wastewater collection and transportation network as a whole. Furthermore, existing optimization processes rely heavily on human experience and intuition, which is not only inefficient but may also lead to unnecessary increases in operating costs. In summary, existing technologies require more scientific and systematic methods to improve efficiency and reduce costs in achieving wastewater treatment plant cost optimization.

[0083] To address the aforementioned issues, this application proposes a method for optimizing the operating costs of wastewater treatment plants. This method optimizes the operating costs of wastewater treatment plants by comprehensively considering the impact of water quality changes on costs, achieving cost control through coordinated plant-network operation, and reducing reliance on manual experience, thereby ensuring that wastewater treatment plants can operate more economically and efficiently.

[0084] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0085] Figure 1 A flowchart illustrating a method for optimizing the operating costs of a wastewater treatment plant, provided in the embodiments of the application.Figure 1 .like Figure 1 As shown in this embodiment, the method for optimizing the operating costs of a wastewater treatment plant includes:

[0086] S101. Obtain basic data of the wastewater treatment plant and determine the corresponding operating rule parameters based on the basic data.

[0087] This involves acquiring real-time and historical basic data of wastewater treatment plants through methods such as installing online monitoring equipment and manual sampling and analysis. These data include, for example, user wastewater discharge coefficients, daily discharge patterns, basic data of wastewater pipe networks and pumping stations, wastewater pumping station operation rules, pumping station scheduling cost calculation data, wastewater treatment plant influent and effluent water quality data, and wastewater treatment cost accounting data.

[0088] Based on the basic data, the collected data is organized, the patterns of water quality changes and cost composition are analyzed, the patterns and trends in the data are identified, and the corresponding operating rule parameters are determined.

[0089] One possible approach is to collect basic data from the wastewater treatment plant as listed in Table 1, and then determine the corresponding operating rule parameters based on this data.

[0090] Table 1

[0091]

[0092] By acquiring basic data from wastewater treatment plants and determining operational parameters based on this data, the treatment efficiency and stability of wastewater treatment plants can be improved through reasonable adjustment of these parameters.

[0093] S102. Input the basic data and operating rule parameters into the pipeline network model to obtain the objective function value of the sewage treatment plant.

[0094] The process involves constructing a pipeline network model of the wastewater treatment plant based on fundamental data to accurately reflect actual wastewater flow and treatment processes. Fundamental data and operational parameters, such as influent conditions, treatment process parameters, and pump scheduling rules, are input into the network model. The network model is then used to simulate the wastewater treatment process, observing the influent quality, treatment efficiency, and effluent quality under different operational rules. Based on the simulation results, the variable costs of the wastewater treatment plant, including electricity and chemical consumption, are calculated. An objective function is constructed, and its value is calculated based on the calculated variable costs. This objective function value reflects the operating costs of the wastewater treatment plant under the current operational rules. Evaluating the impact of different operational rules on the objective function value helps identify the most cost-effective operational strategy.

[0095] By inputting basic data and operating rule parameters into the pipeline network model, the objective function value is obtained. The objective function value can be used to effectively evaluate the operating cost of the sewage treatment plant, thereby achieving beneficial effects such as cost savings, water quality compliance, and improved operating efficiency.

[0096] S103. If the objective function value does not meet the preset conditions, update the running rule parameters and determine a new objective function value based on the new running rule parameters until the objective function value meets the preset conditions.

[0097] The process involves evaluating the objective function value under the current operating rules after the pipeline network model has run and simulated. The objective function value represents the operating cost of the wastewater treatment plant. Pre-defined conditions for the objective function value are established, such as a percentage cost reduction or a specific cost threshold, as optimization criteria. Finally, it is determined whether the current objective function value meets the pre-defined conditions, i.e., whether the algorithm has converged.

[0098] If the objective function value does not meet the preset conditions, for example, by adjusting the operating rule parameters using a particle swarm optimization algorithm, the updated operating rule parameters are input into the model, and the simulation is repeated to calculate the new objective function value. This process is repeated iteratively to gradually optimize the operating rules until the objective function value meets the preset conditions.

[0099] In each iteration, record the optimal operating rule and the corresponding objective function value. Check the magnitude of change in the objective function value during consecutive iterations; if the magnitude of change is less than a set threshold, the algorithm is considered to have converged. Once the objective function value meets the preset conditions, determine the final operating rule parameters.

[0100] By continuously updating the operating rule parameters and calculating new objective function values ​​until the objective function values ​​meet preset conditions, the final operating rule parameters are determined. This can effectively reduce the operating costs of wastewater treatment plants, improve operating efficiency, ensure water quality meets standards, and achieve the goal of sustainable operation.

[0101] S104. Obtain the target operation rule parameters in the pipeline network model. The target operation rule parameters are the operation rule parameters corresponding to the objective function value when the preset conditions are met.

[0102] This involves obtaining the target operating rule parameters from the pipeline network model. These target operating rule parameters are optimized versions of the target operating rule parameters, representing the operating rule parameters corresponding to when the objective function value meets preset conditions. Applying these optimized target operating rule parameters to actual operation optimizes the operating cost of the wastewater treatment plant.

[0103] By obtaining the target operation rule parameters in the pipeline network model, that is, the operation rule parameters corresponding to the objective function value meeting the preset conditions, and applying the target operation rule parameters to actual operation, the operating costs of sewage treatment plants can be effectively reduced, the operating efficiency improved, the water quality met, and the goal of sustainable operation achieved.

[0104] This embodiment proposes a method for optimizing the operating cost of wastewater treatment plants. This method involves acquiring basic data of the wastewater treatment plant and determining corresponding operating rule parameters based on this data. The basic data and operating rule parameters are then input into a pipeline network model to obtain the objective function value of the wastewater treatment plant. This objective function value indicates the operating cost of the wastewater treatment plant. If the objective function value does not meet preset conditions, the operating rule parameters are updated, and a new objective function value is determined based on the new operating rule parameters until the objective function value meets the preset conditions. Finally, the target operating rule parameters in the pipeline network model are obtained. These target operating rule parameters correspond to the operating rule parameters when the objective function value meets the preset conditions. The wastewater treatment plant operating cost corresponding to the target operating rule parameters is optimal, thus reducing operating costs and improving economic efficiency.

[0105] Figure 2 This is a flowchart illustrating a method for optimizing the operating costs of a wastewater treatment plant, as provided in an embodiment of this application. Figure 1 This embodiment is... Figure 2 Based on the examples, a detailed explanation of the method for optimizing the operating costs of wastewater treatment plants is provided. For example... Figure 3 As shown in this embodiment, the method for optimizing the operating costs of a wastewater treatment plant includes:

[0106] S201. Obtain basic data of the wastewater treatment plant and determine the regional distribution information of the wastewater treatment plant. The regional distribution information includes: multiple drainage zones.

[0107] This involves collecting real-time and historical basic data from wastewater treatment plants through methods such as installing online monitoring equipment and manual sampling and analysis. This includes basic information such as geographic information, population density, influent and effluent water quality data, treatment capacity, energy consumption data, and operating costs.

[0108] The geographical area served by the wastewater treatment plant was surveyed to determine the distribution of drainage zones. Based on factors such as wastewater pipeline layout, user distribution, and topography, the service area was divided into multiple drainage zones.

[0109] S202. For any one of the multiple drainage zones, based on multiple pipelines and multiple pumping stations, merge the pipelines within the drainage zone to obtain the relationship between the merged pipelines and pumping stations.

[0110] Specifically, for any one of the multiple drainage zones, data such as the amount of wastewater generated, water quality characteristics, and discharge patterns of each drainage zone are integrated and analyzed.

[0111] Information on all pipelines within the drainage area is collected, including their location, diameter, and length. Information on all pumping stations within the drainage area is also collected, including their location, power, and efficiency. The connection relationships between pipelines and pumping stations are analyzed, such as which pipelines connect to specific pumping stations. Similarity analysis is performed on multiple pipelines within the drainage area to identify pipeline groups with similar characteristics. Based on the results of the similarity analysis, principles for pipeline merging are established. According to these principles, similar pipelines are merged to obtain the associated relationships between the merged pipelines and pumping stations, thereby simplifying the pipeline network structure.

[0112] By merging multiple pipelines within the drainage area and obtaining the relationship between the merged pipelines and pumping stations, the pipeline network structure can be effectively simplified, the amount of data processed by the model can be reduced, and the model processing efficiency can be improved.

[0113] S203. Determine the corresponding operating rule parameters based on the basic data and the relationships.

[0114] S204. Based on the operating rule parameters, update the model parameters of the pipeline network model.

[0115] This process involves in-depth analysis of collected fundamental data, including wastewater discharge volume, water quality parameters, and pump station operation data. It clarifies the relationships between pipelines, pump stations, and wastewater treatment plants, as well as their impact on water quality and quantity. Based on the fundamental data and these relationships, it determines pump station operation rules parameters, such as operating time, start-up and shutdown frequency, and pump station scheduling strategies. These operation rules parameters are then mapped onto the pipeline network model, and the model parameters are updated accordingly.

[0116] S205. The updated pipeline model is controlled to simulate the water quality change process of the sewage treatment plant based on the basic data, and the predicted data output by the pipeline model is obtained.

[0117] The process involves inputting collected basic data, such as wastewater discharge data, pipeline characteristics, and pump station operation information, into the updated model. The updated pipeline model then simulates the water quality changes at the wastewater treatment plant over a future period based on this basic data, yielding predicted data output by the model, such as water quality trends and flow rate changes.

[0118] By simulating the water quality change process in the updated pipeline network model and obtaining the predicted data output by the pipeline network model, we can understand the results of water quality changes after water treatment.

[0119] S206. Compare the predicted data with the actual test data to determine the performance indicators of the pipeline network model.

[0120] This process involves collecting actual water quality monitoring data from wastewater treatment plants during the simulation period, such as BOD5, SS, and TN data. The model's predicted data is then compared with the actual monitoring data to analyze the consistency and deviations between the two. Based on the deviations between the model's predicted data and the actual monitoring data, performance indicators for the pipeline network model are determined, such as the Nash efficiency coefficient, root mean square error, or mean absolute error.

[0121] By comparing predicted data with actual detection data, the performance indicators of the pipeline network model are determined, the performance indicators are analyzed, and the advantages and disadvantages of the pipeline network model are identified, thereby effectively improving the prediction accuracy of the model.

[0122] S207. When the performance indicators are greater than the preset values, the predicted data shall be determined as the hydraulic parameters of the target wastewater treatment plant, and the influent water quality parameters of the target wastewater treatment plant shall be determined based on the hydraulic parameters.

[0123] Specifically, if the performance indicators exceed the preset values, the prediction accuracy of the pipeline network model is considered to meet the requirements. The predicted data that meet the performance indicator requirements are used as the hydraulic parameters of the target wastewater treatment plant, and the influent water quality parameters of the target wastewater treatment plant are predicted based on the hydraulic parameters.

[0124] By determining the hydraulic parameters of the target wastewater treatment plant when its performance indicators exceed preset values, and then determining the influent water quality parameters based on these hydraulic parameters, the accuracy of the influent water quality parameters can be effectively improved, thereby providing more accurate decision support for optimizing the operating costs of the wastewater treatment plant.

[0125] S208. Based on the influent water quality parameters, determine the concentration values ​​of the target indicators, and based on the concentration values ​​of the target indicators, determine the objective function value of the wastewater treatment plant.

[0126] Specifically, based on the wastewater treatment plant's treatment objectives and discharge standards, target indicators to be controlled are selected. For example, the concentration value of the target indicator is used to indicate the content of organic pollutants in the wastewater treatment plant's water. In particular, the concentration value of organic pollutants in the wastewater treatment plant's water is determined based on the influent water quality parameters.

[0127] S209. If the objective function value does not meet the preset conditions, update the running rule parameters and determine a new objective function value based on the new running rule parameters until the objective function value meets the preset conditions.

[0128] In cases where the objective function value does not meet the preset conditions, i.e., the objective function value does not converge, the operating rule parameters are updated. After the operating rule parameters change, the sediment transport efficiency of the sewage treatment plant will be affected, which in turn will affect the influent water quality of the sewage treatment plant.

[0129] One possible approach is to use a particle swarm optimization algorithm to update the operating rule parameters, thereby optimizing pump station scheduling and reducing the operating costs of wastewater treatment plants.

[0130] Treating a single pump station as a particle, its velocity update equation is as follows:

[0131]

[0132]

[0133] in, For particle update speed, The current velocity of the particle. The current position of the particle. For the updated position, , The learning factor can be referenced to the influence of the particle optimal solution and the global optimal solution. , Using random numbers between (0,1) can prevent premature convergence. Let be the optimal solution for the i-th particle during the time interval l. This represents the optimal solution for the global particle within time l. The particle position has two dimensions: pump station runtime and pump station start / stop frequency variation. The range and initial rate of variation can be determined based on actual conditions. The updated model parameters have been rewritten, including pump station start / stop times and the number of pumps in operation.

[0134] If the global optimal solution If the objective function remains constant or changes very little throughout multiple iterations, this can be considered a sign of convergence, and the iteration process will end. If the condition is not met, the operating rule parameters are updated, and a new round of calculations is performed based on the new operating rule parameters to determine a new objective function value.

[0135] S210. Obtain the target operation rule parameters in the pipeline network model. The target operation rule parameters are the operation rule parameters corresponding to the objective function value when the preset conditions are met.

[0136] Step S210 is the same as step S104, and will not be repeated here.

[0137] This embodiment proposes a method for optimizing the operating costs of wastewater treatment plants. This method involves collecting basic data from the wastewater treatment plant, dividing drainage areas, simplifying the pipe network structure, setting operational rule parameters, and updating the model to simulate water quality changes. Through model performance evaluation, hydraulic and water quality parameters are determined, and the objective function value is calculated and optimized. Finally, the method iteratively adjusts the parameters until preset conditions are met, obtaining the optimal operational rule parameters and achieving optimization of both cost and efficiency of the wastewater treatment plant.

[0138] Figure 3 This is a flowchart illustrating a method for optimizing the operating costs of a wastewater treatment plant, as provided in an embodiment of this application. Figure 1 This embodiment is... Figure 3 Based on the examples, a possible implementation of the method for determining the objective function value of a wastewater treatment plant based on the concentration value of the target indicator is described in detail. For example... Figure 4 As shown in this embodiment, the method for optimizing the operating costs of a wastewater treatment plant includes:

[0139] S301. Based on the incoming water quality parameters, conduct sediment simulation treatment to determine the suspended solids concentration in the wastewater of the wastewater treatment plant.

[0140] Based on the incoming water quality parameters, a suitable mathematical model or simulation software is selected to simulate sediments, analyze the physical and chemical properties of sediments in the wastewater, and understand their distribution and behavior in the wastewater. Based on the simulation results, the concentration of suspended solids in the wastewater at the wastewater treatment plant is determined.

[0141] By simulating sediment based on influent water quality parameters and determining the concentration of suspended solids in the wastewater, data support is provided for subsequent calculations of the wastewater treatment plant's operating costs.

[0142] One possible approach is to perform sediment simulation treatment without considering the erosion, transport, and deposition processes of sediments. This requires coupling sediment movement equations to further improve the sediment simulation function. Based on the influent water quality parameters, sediment simulation treatment is performed to determine the suspended solids concentration in the wastewater treatment plant.

[0143] S302. Determine the target index concentration value of the wastewater treatment plant based on the suspended solids concentration.

[0144] Among them, the target index concentration value of the wastewater treatment plant is determined based on the suspended solids concentration. The target index concentration value is used to indicate the content of organic pollutants in the water body of the wastewater treatment plant.

[0145] One possible implementation involves transporting wastewater with suspended solids concentrations approximately 0.7 to 1.0 times that of sediment. Concentration. Based on the suspended solids concentration and the multiple relationship between the suspended solids concentration and the organic pollutant concentration in the wastewater, the concentration of suspended solids in the wastewater treatment plant water body is determined. concentration.

[0146] S303. Determine the unit power consumption cost of the wastewater treatment plant based on the total nitrogen value and target index concentration value in the incoming water quality parameters.

[0147] The electricity cost of a wastewater treatment plant mainly includes the electricity consumption for aeration, wastewater pump lifting, recirculation, mixing, and sludge dewatering. The electricity cost varies under different water quality conditions. The unit electricity cost of the wastewater treatment plant is calculated based on the total nitrogen value and target indicator concentration values ​​in the influent water quality parameters.

[0148] One possible approach is to determine the total nitrogen (TN) value and target indicators in the incoming water quality parameters. Concentration values, and based on total nitrogen (TN) and target indicators. Concentration values ​​are used to determine the unit electricity cost of a wastewater treatment plant.

[0149] Specifically, for example, the total nitrogen (TN) value in the incoming water quality parameters is 30 mg / L, and the target index is... With a concentration of 75 mg / L, the unit electricity cost of the wastewater treatment plant can be determined to be 0.165 yuan according to Table 2.

[0150] Table 2 shows the electricity cost under different influent water quality conditions provided in this embodiment:

[0151] Table 2

[0152]

[0153] S304. Determine the unit chemical consumption cost of the wastewater treatment plant based on the total nitrogen value and target index concentration value in the incoming water quality parameters.

[0154] The main components of chemical consumption costs in wastewater treatment plants include chemical phosphorus removal, carbon source addition, sludge treatment conditioning agents, and disinfectants. Chemical consumption costs vary under different water quality conditions. The unit chemical consumption cost of a wastewater treatment plant is calculated based on the total nitrogen value and target indicator concentration values ​​in the influent water quality parameters.

[0155] One possible approach is to determine the total nitrogen (TN) value and target indicators in the incoming water quality parameters. Concentration values, and based on total nitrogen (TN) and target indicators. Concentration values ​​determine the unit chemical consumption cost of a wastewater treatment plant.

[0156] Specifically, for example, the total nitrogen (TN) value in the incoming water quality parameters is 30 mg / L, and the target index is... With a concentration of 75 mg / L, the unit power consumption cost of the wastewater treatment plant can be determined to be 0.234 yuan according to Table 2.

[0157] Table 3 shows the cost of medicines under different influent water quality conditions provided in this embodiment:

[0158] Table 3

[0159]

[0160] S305. Determine the operating cost of the wastewater treatment plant based on the unit electricity consumption cost and the unit chemical consumption cost.

[0161] The operating cost of a wastewater treatment plant is determined based on unit electricity consumption cost and unit chemical consumption cost. Specifically, the following company's method is used to calculate the dimensionless operating cost of the wastewater treatment plant. :

[0162]

[0163] in, Let y be the dimensionless operating cost of the wastewater treatment plant of the y-th water plant. The operating cost of a wastewater treatment plant under the current water quality conditions. The operating cost of a wastewater treatment plant under optimized water quality conditions.

[0164] One possible implementation method, specifically, is the influent of the current wastewater treatment plant. The average concentration was 100 mg / L, and the influent of the wastewater treatment plant was optimized after the pump station scheduling was improved. With a concentration of 75 mg / L and an average TN concentration of 30 mg / L, based on Tables 2 and 3, the unit electricity cost under the current water quality is determined to be 0.173, and the unit chemical cost is determined to be 0.132. The operating cost of the wastewater treatment plant under the current water quality is... Based on Tables 2 and 3, the optimized unit electricity cost for water quality is 0.165, and the unit chemical cost is 0.234. This represents the operating cost of the wastewater treatment plant under the current water quality conditions. .

[0165] The dimensionless operating cost of a wastewater treatment plant is determined based on unit electricity consumption cost and unit chemical consumption cost. for: .

[0166] After optimization, the operating cost of the wastewater treatment plant is 76% of the cost before optimization. In other words, by optimizing the operating rules parameters of the wastewater treatment plant, the operating cost of the wastewater treatment plant has been reduced by 24% compared with the original cost.

[0167] S306. For any one of the multiple pump stations, calculate the scheduling cost of the sewage treatment plant based on the pump station's operating time and the frequency of pump station start-up and shutdown.

[0168] The pumps used in the pumping station are fixed-frequency pumps. During dry weather, the water level in the forebay of the pumping station changes relatively little, so the power consumption of the pumps per unit time can be approximated as constant. The main factors affecting the cost of the scheduling scheme are the daily operating time of the pumping station and the frequency of pumping station start-up and shutdown. That is, the operating rule parameters are: pumping station operating time and pumping station start-up and shutdown frequency. For any one of the multiple pumping stations, the scheduling cost of the sewage treatment plant is calculated based on the pumping station operating time and pumping station start-up and shutdown frequency.

[0169] One possible implementation method is to determine the operating rule parameters of the pump station under different operating conditions according to Table 4. The operating rule parameters include: pump station running time and pump station start-up and shutdown frequency.

[0170] Table 4

[0171]

[0172] The dimensionless scheduling cost of the wastewater treatment plant is calculated using the following formula, based on the pump station's operating time and start-up / shutdown frequency. :

[0173]

[0174] in, Let be the dimensionless scheduling cost of the x-th pumping station, and let a and b be the weighting coefficients for the pumping station's operating time and start / stop, respectively, with a taking a value of 0.7 and b taking a value of 0.3. Given the current operating time of the pumping station, The duration used in the dry weather scheduling rules for pumping stations. The current pump station start-up and shutdown frequency, The start-stop frequency is used for the pump station's dry weather scheduling rules. Under initial conditions... The value equals 1, depending on the pump station's operating time and start-up / shutdown frequency. Corresponding changes will occur.

[0175] S307. Determine the objective function value of the wastewater treatment plant based on operating costs and scheduling costs.

[0176] The objective function is designed to comprehensively consider both the operating costs of the wastewater treatment plant and the scheduling costs of its pumping stations. The operating costs of the wastewater treatment plant have a relatively large weight, while the scheduling costs of the pumping stations have a relatively small weight. The objective function is as follows:

[0177]

[0178] Where w is the pump station scheduling cost weight, x is the number of pump stations, and y is the number of sewage treatment plants. For the dimensionless pump station scheduling cost of sewage treatment plants, This refers to the dimensionless operating cost of a wastewater treatment plant.

[0179] This embodiment proposes a method for optimizing the operating costs of wastewater treatment plants. This method determines the suspended solids concentration (SSD) of the wastewater treatment plant by simulating influent water quality parameters, reflecting the content of organic pollutants. Based on the SSD concentration and total nitrogen value, the unit power consumption and chemical consumption costs are calculated, thereby determining the overall operating cost. Simultaneously, scheduling costs are calculated by considering the operating characteristics of the pumping stations. Finally, the operating costs and scheduling costs are combined to form an objective function value, which is used to optimize the operating efficiency and cost-effectiveness of the wastewater treatment plant.

[0180] Figure 4 A schematic diagram of a wastewater treatment plant operation cost optimization device provided in this application is shown below. Figure 5 As shown, the wastewater treatment plant operation cost optimization device 400 provided in this embodiment includes:

[0181] Module 401 is used to acquire basic data of the wastewater treatment plant;

[0182] The determining module 402 is used to determine the corresponding operating rule parameters based on the basic data;

[0183] The processing module 403 is used to input the basic data and the operating rule parameters into the pipeline network model to obtain the objective function value of the sewage treatment plant. The objective function value is used to indicate the operating cost of the sewage treatment plant.

[0184] The processing module 403 is further configured to update the running rule parameters when the objective function value does not meet the preset conditions, and determine a new objective function value based on the new running rule parameters until the objective function value meets the preset conditions, wherein the preset conditions are used to indicate that the objective function value converges;

[0185] The acquisition module 401 is further used to acquire the target operation rule parameters in the pipeline network model. The target operation rule parameters are the operation rule parameters corresponding to the objective function value satisfying the preset conditions. The sewage treatment plant operation cost corresponding to the target operation rule parameters is optimal.

[0186] Optionally, the processing module 403 is further configured to control the pipeline network model to simulate the water quality changes of the sewage treatment plant based on the basic data and operating rule parameters, so as to obtain the corresponding influent water quality parameters.

[0187] The determining module 402 is further configured to determine the concentration value of the target indicator based on the influent water quality parameters, and to determine the objective function value of the wastewater treatment plant based on the concentration value of the target indicator.

[0188] Optionally, the determining module 402 is further configured to determine the regional distribution information of the wastewater treatment plant, the regional distribution information including: multiple drainage zones;

[0189] The processing module 403 is also used to merge the pipes in any one of the multiple drainage zones according to the multiple pipes and multiple pumping stations, and obtain the relationship between the merged pipes and pumping stations.

[0190] The determining module 402 is further configured to determine the corresponding operating rule parameters based on the basic data and the correlation relationship;

[0191] Optionally, the processing module 403 is further configured to update the model parameters of the pipeline network model based on the running rule parameters;

[0192] The processing module 403 is also used to control the updated pipeline model to simulate the water quality changes of the sewage treatment plant based on the basic data, so as to obtain the corresponding influent water quality parameters.

[0193] Optionally, the processing module 403 is further configured to control the updated pipeline model to simulate the water quality change process of the sewage treatment plant based on the basic data, so as to obtain the predicted data output by the pipeline model.

[0194] The determining module 402 is also used to compare the predicted data with the actual detection data to determine the performance indicators of the pipeline network model;

[0195] The determining module 402 is further configured to determine the predicted data as the hydraulic parameters of the target wastewater treatment plant when the performance index is greater than the preset value, and to determine the influent water quality parameters of the target wastewater treatment plant based on the hydraulic parameters.

[0196] Optionally, the wastewater treatment plant operating cost optimization device further includes: a calculation module 404;

[0197] The determining module 402 is further configured to perform sediment simulation treatment based on the incoming water quality parameters to determine the suspended solids concentration in the wastewater of the wastewater treatment plant;

[0198] The determining module 402 is further configured to determine the target index concentration value of the wastewater treatment plant based on the suspended solids concentration, wherein the target index concentration value is used to indicate the content of organic pollutants in the water body of the wastewater treatment plant;

[0199] The calculation module 404 is used to calculate the operating cost of the wastewater treatment plant based on the target index concentration value;

[0200] The determining module 402 is further configured to determine the objective function value of the operating cost of the target wastewater treatment plant based on the operating cost.

[0201] Optionally, the determining module 402 is further configured to determine the unit power consumption cost of the wastewater treatment plant based on the total nitrogen value and the target index concentration value in the influent water quality parameters;

[0202] The determining module 402 is further configured to determine the unit chemical consumption cost of the wastewater treatment plant based on the total nitrogen value and the target index concentration value in the influent water quality parameters.

[0203] The determining module 402 is also used to determine the operating cost of the wastewater treatment plant based on the unit power consumption cost and the unit drug consumption cost.

[0204] Optionally, the calculation module 404 is also used to calculate the scheduling cost of the sewage treatment plant for any one of the plurality of pump stations based on the pump station's operating time and pump station start-up and shutdown frequency.

[0205] The determining module 402 is further configured to determine the objective function value of the wastewater treatment plant based on the operating cost and the scheduling cost.

[0206] Figure 5 A structural schematic diagram of the wastewater treatment plant operation cost optimization equipment provided in this application. (See attached diagram.) ​ As shown, this application provides a wastewater treatment plant operation cost optimization device. The edge node security application scheduling device 500 includes: a receiver 501, a transmitter 502, a processor 503, and a memory 504.

[0207] Receiver 501 is used to receive instructions and data;

[0208] Transmitter 502 is used to send commands and data;

[0209] Memory 504 is used to store instructions executed by the computer;

[0210] The processor 503 is used to execute computer execution instructions stored in the memory 504 to implement the various steps of the wastewater treatment plant operation cost optimization method in the above embodiments. For details, please refer to the relevant descriptions in the foregoing embodiments of the wastewater treatment plant operation cost optimization method.

[0211] Optionally, the memory 504 can be either standalone or integrated with the processor 503.

[0212] When the memory 504 is set up independently, the electronic device also includes a bus for connecting the memory 504 and the processor 503.

[0213] This application also provides a computer storage medium storing computer execution instructions, which, when executed by a processor, implement the wastewater treatment plant operation cost optimization method performed by the aforementioned wastewater treatment plant operation cost optimization equipment.

[0214] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0215] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0216] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for optimizing the operating costs of a wastewater treatment plant, characterized in that, The method includes: Obtain basic data of the wastewater treatment plant; the basic data includes: multiple pipelines and multiple pumping stations; Determine the regional distribution information of the wastewater treatment plant, the regional distribution information including: multiple drainage zones; For any one of the multiple drainage zones, based on the multiple pipelines and multiple pumping stations, the pipelines within the drainage zone are merged to obtain the relationship between the merged pipelines and pumping stations. The corresponding operating rule parameters are determined based on the basic data and the correlation relationships; the operating rule parameters include: pump station running time and pump station start-up and shutdown frequency. Based on the aforementioned operating rule parameters, the model parameters of the pipeline network model are updated. The updated pipeline network model is used to simulate the water quality change process of the wastewater treatment plant based on the basic data to obtain the predicted data output by the pipeline network model; the predicted data is compared with the actual detection data to determine the performance index of the pipeline network model; when the performance index is greater than the preset value, the predicted data is determined as the hydraulic parameters of the target wastewater treatment plant, and the influent water quality parameters of the target wastewater treatment plant are determined based on the hydraulic parameters. Based on the influent water quality parameters, sediment simulation treatment was performed to determine the suspended solids concentration in the wastewater of the wastewater treatment plant. The target index concentration value of the wastewater treatment plant is determined based on the suspended solids concentration, and the target index concentration value is used to indicate the content of organic pollutants in the water body of the wastewater treatment plant; The operating cost of the wastewater treatment plant is calculated based on the concentration value of the target indicator. The objective function value of the operating cost of the target wastewater treatment plant is determined based on the operating cost, and the objective function value is used to indicate the operating cost of the wastewater treatment plant. If the objective function value does not meet the preset conditions, the running rule parameters are updated, and a new objective function value is determined based on the new running rule parameters, until the objective function value meets the preset conditions, wherein the preset conditions are used to indicate that the objective function value converges; Obtain the target operation rule parameters in the pipeline network model. The target operation rule parameters are the operation rule parameters corresponding to the objective function value satisfying preset conditions. The wastewater treatment plant operating cost is optimal corresponding to the target operation rule parameters.

2. The method according to claim 1, characterized in that, The calculation of the operating cost of the wastewater treatment plant based on the target index concentration value includes: The unit power consumption cost of the wastewater treatment plant is determined based on the total nitrogen value and the target index concentration value in the influent water quality parameters. The unit chemical consumption cost of the wastewater treatment plant is determined based on the total nitrogen value and the concentration value of the target index in the influent water quality parameters. The operating cost of the wastewater treatment plant is determined based on the unit electricity consumption cost and the unit pharmaceutical consumption cost.

3. The method according to claim 1, characterized in that, The objective function value for determining the operating cost of the target wastewater treatment plant based on the operating cost includes: For any one of the multiple pump stations, the scheduling cost of the sewage treatment plant is calculated based on the pump station's operating time and the frequency of pump station start-up and shutdown. The objective function value of the wastewater treatment plant is determined based on the operating cost and the scheduling cost.

4. A device for optimizing the operating costs of a wastewater treatment plant, characterized in that, The apparatus is used to execute the wastewater treatment plant operation cost optimization method according to any one of claims 1-3, the apparatus comprising: The acquisition module is used to acquire basic data about the wastewater treatment plant. The determination module is used to determine the corresponding operating rule parameters based on the basic data; The processing module is used to input the basic data and the operating rule parameters into the pipeline network model to obtain the objective function value of the sewage treatment plant. The objective function value is used to indicate the operating cost of the sewage treatment plant. The processing module is further configured to update the running rule parameters when the objective function value does not meet the preset conditions, and determine a new objective function value based on the new running rule parameters, until the objective function value meets the preset conditions, wherein the preset conditions are used to indicate that the objective function value converges; The acquisition module is also used to acquire the target operation rule parameters in the pipeline network model. The target operation rule parameters are the operation rule parameters corresponding to the objective function value satisfying preset conditions. The wastewater treatment plant operating cost corresponding to the target operation rule parameters is optimal.

5. A wastewater treatment plant operating cost optimization device, characterized in that, include: Memory; processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the wastewater treatment plant operation cost optimization method as described in any one of claims 1-3.

6. A computer storage medium, characterized in that, The computer storage medium stores computer execution instructions, which, when executed by a processor, are used to implement the wastewater treatment plant operation cost optimization method as described in any one of claims 1-3.

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