Circulating water crystallization system multi-objective collaborative optimization method based on water quality fluctuation

Abstract

The invention relates to the technical field of thermal power plant circulating water treatment and control, in particular to a circulating water crystallization system multi-objective collaborative optimization method, device and equipment based on water quality fluctuation and a computer readable storage medium. Establishing a water quality fluctuation probability model and dividing different water quality working condition intervals; constructing a first operation cost function of the calcium carbonate induced crystallization system and a second operation cost function of the calcium sulfate induced crystallization system; constructing a multi-objective optimization model based on the first operation cost function and the second operation cost function; and solving the multi-objective optimization model by adopting an intelligent optimization algorithm, outputting an operation parameter combination, and generating an annual economic operation scheduling strategy. By establishing an accurate subsystem cost function and constructing a multi-objective optimization model with total cost minimization as an objective, optimal operation parameters are dynamically searched under all working conditions of water quality fluctuation, and the processing stability and the cooperation efficiency of the system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of circulating water treatment and control in thermal power plants, and in particular to a circulating water crystallization system multi-objective collaborative optimization method and device based on water quality fluctuation, an electronic device, and a computer readable storage medium. BACKGROUND

[0002] The circulating water system of a thermal power plant (for example, Qinbei Power Plant) faces severe water quality fluctuation problems in actual operation, and the total hardness of the circulating water thereof varies dramatically between 3000-6000 mg / L. In order to cope with high-hardness water quality and improve the concentration ratio of the system, the existing technology usually needs to simultaneously run a calcium carbonate induced crystallization system and a calcium sulfate induced crystallization system.

[0003] At present, for the operation and management of the above-mentioned dual-system coupling, a fixed parameter operation mode or an experience adjustment mode relying on the experience of operating personnel is generally adopted. These traditional modes lack self-adaptive ability to dynamic changes in the water quality, resulting in unstable system treatment effect, high cost of reagents and power consumption, and low collaborative efficiency between the two sub-systems, making it difficult to achieve economic optimal operation of the circulating water system throughout the year. The existing technology lacks a dynamic multi-objective collaborative optimization method and intelligent scheduling strategy that can effectively cope with dynamic water quality fluctuations and optimize the two crystallization systems.

[0004] In summary, the operation of the dual-crystallization system in the existing circulating water treatment lacks self-adaptive optimization ability to dynamic water quality fluctuations, resulting in low system collaborative efficiency and high annual operation cost. Therefore, there is an urgent need for a multi-objective collaborative optimization and intelligent scheduling method that can dynamically optimize the operation parameters of the calcium carbonate and calcium sulfate dual-crystallization system according to the water quality conditions, in order to solve the key technical problem of achieving the lowest total operation cost of the system under the premise of ensuring the treatment effect. SUMMARY

[0005] The present application aims to at least partially solve one of the technical problems in the related art.

[0006] To this end, a first object of the present application is to propose a circulating water crystallization system multi-objective collaborative optimization method based on water quality fluctuation, in order to solve the problems of the existing technical means lacking self-adaptive optimization ability to dynamic water quality fluctuations, resulting in low system collaborative efficiency and high annual operation cost, etc.

[0007] A second object of the present application is to propose a device.

[0008] A third object of the present application is to propose an electronic device.

[0009] A fourth object of the present application is to propose a computer readable storage medium.

[0010] To achieve the above object, the first aspect of the present application proposes a multi-objective collaborative optimization method for a circulating water crystallization system based on water quality fluctuation, comprising: collecting water quality historical data of the circulating water system throughout the year, statistically analyzing the water quality historical data, establishing a water quality fluctuation probability model, and dividing different water quality working condition intervals; constructing a first operating cost function of a calcium carbonate induced crystallization system and a second operating cost function of a calcium sulfate induced crystallization system; based on the first operating cost function and the second operating cost function, constructing a multi-objective optimization model with system operating parameters as constraint conditions; solving the multi-objective optimization model by using an intelligent optimization algorithm, outputting operating parameter combinations corresponding to each of the water quality working condition intervals, and generating an economic operation scheduling strategy throughout the year based on the operating parameter combinations.

[0011] Preferably, the water quality historical data includes total hardness, calcium ion concentration, magnesium ion concentration, pH value, and water temperature.

[0012] Preferably, variables of the first operating cost function include calcium ion removal rate, alkali dosage, and operating power consumption of the calcium carbonate induced crystallization system; and variables of the second operating cost function include influent calcium ion concentration, crystallization efficiency, and operating power consumption of the calcium sulfate induced crystallization system.

[0013] Preferably, the constraint conditions include fluctuation range of water quality parameters, removal rate of calcium ions by the system, treatment capacity range of each crystallization system, and safety range of reagent dosage.

[0014] Preferably, solving the multi-objective optimization model by using an intelligent optimization algorithm comprises: encoding objective functions and constraint conditions in the multi-objective optimization model into a format that can be processed by the intelligent optimization algorithm; performing global optimization search in a solution space composed of all operating parameters by using the intelligent optimization algorithm, the intelligent optimization algorithm being a genetic algorithm or a particle swarm optimization algorithm; finally outputting an operating parameter combination that satisfies all constraint conditions and minimizes total operating cost.

[0015] Preferably, the combination of operation parameters is used to guide the coordinated operation control of the dual system, wherein: the optimal calcium ion removal rate set point of the calcium carbonate induced crystallization system is used to adjust the alkali dosage and the hydraulic load of the system in real time; the start-stop control timing of the calcium sulfate induced crystallization system is determined according to whether the calcium ion concentration of the influent exceeds the preset economic operation threshold; the reagent addition rate is the respective addition rate of the alkali of the calcium carbonate induced crystallization system and the seed or additive of the calcium sulfate induced crystallization system; and the water pump operation frequency is the frequency set value of the water pump for controlling the circulating water flowing through the two crystallization systems.

[0016] Preferably, it further comprises: dynamically feeding back and adjusting the economic operation scheduling strategy according to the real-time collected circulating water quality data.

[0017] To achieve the above purpose, the second aspect of the present application proposes a circulating water crystallization system multi-objective coordinated optimization device based on water quality fluctuation, comprising: A data acquisition module collects the water quality historical data of the circulating water system throughout the year, statistically analyzes the water quality historical data, establishes a water quality fluctuation probability model, and divides different water quality working condition intervals; A function construction module constructs a first operation cost function of the calcium carbonate induced crystallization system and a second operation cost function of the calcium sulfate induced crystallization system; A model construction module constructs a multi-objective optimization model based on the first operation cost function and the second operation cost function, with system operation parameters as constraint conditions; A strategy generation module solves the multi-objective optimization model by using an intelligent optimization algorithm, outputs the combination of operation parameters corresponding to each water quality working condition interval, and generates an economic operation scheduling strategy throughout the year based on the combination of operation parameters.

[0018] To achieve the above purpose, the third aspect of the present application proposes an electronic device, comprising: a processor, and a memory in communication connection with the processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the method of any one of the above.

[0019] To achieve the above purpose, the fourth aspect of the present application proposes a computer readable storage medium, comprising computer execution instructions stored in the computer readable storage medium, and the computer execution instructions are executed by a processor to implement the method of any one of the above.

[0020] The application provides a circulating water crystallization system multi-objective collaborative optimization method based on water quality fluctuation, which can dynamically find optimal operation parameters under the whole working condition of water quality fluctuation by establishing an accurate subsystem cost function and constructing a multi-objective optimization model with the target of minimizing total cost, overcomes the waste of reagent and power consumption under the traditional fixed or experience operation mode, and thus realizes the economic optimal operation throughout the year. Based on the divided water quality working condition interval, the optimal operation parameter combination is generated in a targeted manner, so that the calcium carbonate induced crystallization system and the calcium sulfate induced crystallization system can be adaptively and collaboratively scheduled according to the water quality of the inlet water, and the problems of unstable treatment effect and poor cooperation of the double systems are avoided. The intelligent algorithms such as genetic algorithm and particle swarm optimization are used to solve complex optimization problems, and an executable annual working condition economic operation strategy diagram and a scheduling manual are formed, so that the traditional operation mode depending on artificial experience is upgraded to intelligent decision based on data and models, and the scientificity and automation level of operation management are greatly improved.

[0021] Additional aspects and advantages of the application will be set forth in part in the description that follows, and in part will become apparent to those having ordinary skill in the art upon examination of the following or can be learned from practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0022] The above and / or additional aspects and advantages of the application will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which: Figure 1 A flow chart of a first specific embodiment of a circulating water crystallization system multi-objective collaborative optimization method based on water quality fluctuation provided by the application; Figure 2 A structural block diagram of a circulating water crystallization system multi-objective collaborative optimization device based on water quality fluctuation provided by the embodiment of the application. DETAILED DESCRIPTION

[0023] The core of the application is to provide a circulating water crystallization system multi-objective collaborative optimization method, device, electronic equipment and computer readable storage medium based on water quality fluctuation, which can dynamically find optimal operation parameters under the whole working condition of water quality fluctuation by establishing an accurate subsystem cost function and constructing a multi-objective optimization model with the target of minimizing total cost, and improve the system processing stability and collaborative efficiency.

[0024] In order to make the person skilled in the art better understand the application scheme, the application will be further described in detail below in combination with the drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the application.

[0025] Please refer toFigure 1 , Figure 1 The flowchart illustrates a first specific embodiment of a multi-objective collaborative optimization method for a circulating water crystallization system based on water quality fluctuations provided by this invention; the specific operation steps are as follows: Step S101: Collect the historical water quality data of the circulating water system throughout the year, perform statistical analysis on the historical water quality data, establish a water quality fluctuation probability model, and divide different water quality operating condition intervals. Step S102: Construct the first operating cost function for the calcium carbonate-induced crystallization system and the second operating cost function for the calcium sulfate-induced crystallization system; Step S103: Based on the first operating cost function and the second operating cost function, and with the system operating parameters as constraints, construct a multi-objective optimization model; Step S104: Use an intelligent optimization algorithm to solve the multi-objective optimization model, output the combination of operating parameters corresponding to each of the water quality operating conditions, and generate an economic operation scheduling strategy for the whole year based on the combination of operating parameters.

[0026] Based on the above embodiments, this embodiment will provide a detailed description of step S101: In one embodiment, historical water quality data includes total hardness, calcium ion concentration, magnesium ion concentration, pH value, and water temperature.

[0027] Specifically, based on the historical water quality data of the power plant's circulating water system throughout the year, such as total hardness, calcium and magnesium ion concentration, pH, and temperature, statistical analysis results were generated through data acquisition and preprocessing methods. Based on the statistical analysis results, different water quality operating condition intervals were generated by establishing a water quality fluctuation probability model.

[0028] Based on the above embodiments, this embodiment will provide a detailed description of step S102: In one embodiment, the variables of the first operating cost function include the calcium ion removal rate, alkali dosage, and operating power consumption of the calcium carbonate-induced crystallization system; the variables of the second operating cost function include the influent calcium ion concentration, crystallization efficiency, and operating power consumption of the calcium sulfate-induced crystallization system.

[0029] Specifically, based on calcium ion removal rate, alkali dosage, power consumption, and water quality parameters, an operating cost function for the calcium carbonate induced crystallization system is established. The method generates the operating cost function of the calcium carbonate induced crystallization system. ; Based on the influent calcium ion concentration, crystallization efficiency, power consumption, and water quality parameters, an operating cost function for the calcium sulfate-induced crystallization system was established. The method generates the operating cost function of the calcium sulfate-induced crystallization system. .

[0030] Based on the above embodiments, this embodiment explains step S103 in detail: In one embodiment, the constraints include: fluctuation range of water quality parameters, removal rate of calcium ions by the system, processing capacity range of each crystallization system, and safe range of reagent addition.

[0031] Specifically, based on the generated calcium carbonate-induced crystallization system operation cost function and the calcium sulfate-induced crystallization system operation cost function , by adding the two and taking the minimum as the target, the objective function is constructed: ; Based on the fluctuation range of water quality, the requirement of calcium ion removal rate, the upper and lower limits of system processing capacity, and the safe range of reagent addition, the complete constraint condition set of the optimization model is generated by setting the constraint conditions.

[0032] Based on the above embodiments, this embodiment explains step S104 in detail: In one embodiment, the objective function and the constraint conditions in the multi-objective optimization model are coded into a format that can be processed by the intelligent optimization algorithm; The intelligent optimization algorithm is used to perform global optimization search in the solution space composed of all operating parameters, and the intelligent optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm; Finally, the operating parameter combination that satisfies all the constraints and minimizes the total operating cost is output.

[0033] The operating parameter combination is used to guide the collaborative operation control of the double system, wherein: the optimal calcium ion removal rate set point of the calcium carbonate-induced crystallization system is used to adjust the alkali reagent addition amount and the hydraulic load of the system in real time; the start-stop control timing of the calcium sulfate-induced crystallization system is determined according to whether the influent calcium ion concentration exceeds the preset economic operation threshold; the reagent addition rate is the addition rate of the alkali reagent of the calcium carbonate-induced crystallization system and the seed or additive of the calcium sulfate-induced crystallization system; and the water pump operating frequency is the frequency setting value of the water pump that controls the circulating water flowing through the two crystallization systems.

[0034] Specifically, based on the divided different water quality condition intervals and the constructed objective function min(C_total) and constraint condition set, the optimal operating parameter combination (including the calcium carbonate system optimal calcium removal rate set point, the calcium sulfate system start-stop timing, the reagent addition rate, the water pump operating frequency, etc.) under different water quality intervals is generated by using the genetic algorithm or the particle swarm optimization algorithm.

[0035] Based on the generated optimal operating parameter combination under different water quality intervals, the economic operation strategy map and dispatch manual for the whole year are formed, and the economic operation strategy and dispatch manual for the whole year are finally generated to realize intelligent dispatching.

[0036] It also includes dynamic feedback and adjustment of the economic operation dispatching strategy according to real-time collected circulating water quality data.

[0037] Specifically, a real-time data acquisition and feedback mechanism is established, and real-time water quality parameters collected by online water quality monitoring instruments are input into the water quality fluctuation probability model to realize real-time identification and matching of the current water quality working condition interval. If the identification result changes, the optimal operating parameter combination corresponding to the new interval or the multi-objective optimization model is called to dynamically update the economic operation dispatching strategy currently executed.

[0038] The embodiment provides a circulating water crystallization system multi-objective collaborative optimization method based on water quality fluctuation. By establishing an accurate subsystem cost function and constructing a multi-objective optimization model with the goal of minimizing total cost, the optimal operating parameters can be dynamically found under the whole working condition of water quality fluctuation, and the waste of reagent and power consumption under the traditional fixed or experience operation mode is overcome, so that the economic optimal operation for the whole year is realized. Based on the divided water quality working condition interval, the optimal operating parameter combination is generated, so that the calcium carbonate induced crystallization system and the calcium sulfate induced crystallization system can be adaptively and collaboratively dispatched according to the water quality of the inlet water, and the problems of unstable treatment effect and poor cooperation of the double systems are avoided. By using intelligent algorithms such as genetic algorithm and particle swarm optimization to solve complex optimization problems, an executable economic operation strategy map and dispatch manual for the whole year are formed, the traditional operation mode depending on artificial experience is upgraded to intelligent decision-making based on data and models, and the scientificity and automation level of operation management are greatly improved.

[0039] Based on the above embodiment, the circulating water crystallization system multi-objective collaborative optimization method based on water quality fluctuation is described as follows: First step: data acquisition and working condition division: First, the historical operation data of the target thermal power plant (for example, Qinbei Power Plant) circulating water system for the whole year are collected, mainly including the total hardness, calcium ion concentration, magnesium ion concentration, pH value, temperature and other key water quality parameters of the circulating water. By statistically analyzing these historical data, the change law and probability distribution of the water quality parameters are revealed, so as to establish a water quality fluctuation probability model. Based on the model, the complex and changeable water quality state for the whole year is divided into several representative water quality working condition intervals, for example, the division can be made according to different concentration ranges of total hardness or seasonal variation characteristics, to provide a basis for subsequent fine optimization.

[0040] Second step: Establishing subsystem cost model: For the calcium carbonate-induced crystallization system and the calcium sulfate-induced crystallization system running simultaneously on site, their operation cost models are established respectively. For the calcium carbonate-induced crystallization system, the operation cost function mainly considers the influence of calcium ion removal rate, alkali dosage, system power consumption and influent water quality parameters. For the calcium sulfate-induced crystallization system, the operation cost function is mainly related to influent calcium ion concentration, crystallization efficiency, system power consumption and related water quality parameters. The two cost functions aim to quantitatively describe the economic performance of each subsystem under different operating conditions.

[0041] Third step: Building multi-objective optimization model: The operation cost functions of the above two subsystems are added to minimize the total system operation cost as the core optimization objective to build a multi-objective optimization model. At the same time, comprehensive constraints are set, including but not limited to: expected fluctuation range of water quality parameters, lower limit of calcium ion removal rate necessary to meet the effluent water quality or concentration ratio requirement, upper and lower limits of the processing capacity of the two crystallization systems, and safe and effective concentration range of various reagents (such as alkali, seed crystals, etc.).

[0042] Fourth step: Optimization solution and strategy generation: Intelligent optimization algorithms (such as genetic algorithm or particle swarm optimization algorithm) are used to solve the model built in the third step, which includes objective functions and constraints. The algorithm searches in the solution space composed of various operating parameters (such as removal rate set value, dosage rate, start-stop state, etc.), and finally outputs the optimal operating parameter combination corresponding to each water quality condition interval divided in the second step. This parameter combination specifically guides the best calcium ion removal rate set point of the calcium carbonate system, the start-stop control timing of the calcium sulfate system, the accurate dosage rate of various reagents, and the operating frequency of related water pumps, etc.

[0043] Fifth step: Forming annual scheduling strategy and implementing dynamic feedback: All optimal operating parameter combinations obtained in the fourth step are integrated and formatted to generate a complete annual working condition economic operation strategy diagram or scheduling manual for guiding daily operation. At the same time, a real-time data acquisition and feedback mechanism is established to continuously obtain real-time data of circulating water quality through online monitoring instruments. The system matches real-time data with the divided water quality condition intervals, and if it identifies that the current water quality has switched to another condition interval, it automatically calls or calculates the corresponding optimal operating parameters to dynamically adjust the current actual operating settings of the system, thereby realizing intelligent and adaptive optimization scheduling based on real-time water quality fluctuations.

[0044] Through the above steps, the embodiment realizes a complete technical closed loop from historical data analysis, model construction, offline optimization to online dynamic scheduling, and effectively solves the problems of uneconomic and unstable collaborative operation of the double-crystal system under severe water quality fluctuations.

[0045] Please refer to Figure 2 , Figure 2 A structure block diagram of a circulating water crystallization system multi-objective collaborative optimization device based on water quality fluctuations is provided for the embodiment of the application; the specific device can include: A data acquisition module 100 collects water quality historical data of the circulating water system throughout the year, statistically analyzes the water quality historical data, establishes a water quality fluctuation probability model, and divides different water quality working condition intervals; A function construction module 200 constructs a first operation cost function of a calcium carbonate induced crystallization system and a second operation cost function of a calcium sulfate induced crystallization system; A model construction module 300 constructs a multi-objective optimization model based on the first operation cost function and the second operation cost function, with system operation parameters as constraint conditions; A strategy generation module 400 solves the multi-objective optimization model by using an intelligent optimization algorithm, outputs a combination of operation parameters corresponding to each of the water quality working condition intervals, and generates an economic operation scheduling strategy throughout the year based on the combination of operation parameters.

[0046] The circulating water crystallization system multi-objective collaborative optimization device based on water quality fluctuations is used to realize the foregoing circulating water crystallization system multi-objective collaborative optimization method based on water quality fluctuations, and therefore the specific embodiments in the circulating water crystallization system multi-objective collaborative optimization device based on water quality fluctuations can refer to the embodiment part of the circulating water crystallization system multi-objective collaborative optimization method based on water quality fluctuations in the foregoing, for example, the data acquisition module 100, the function construction module 200, the model construction module 300, and the strategy generation module 400 are respectively used to realize steps S101, S102, S103, and S104 in the foregoing circulating water crystallization system multi-objective collaborative optimization method based on water quality fluctuations, and therefore the specific embodiments can refer to the descriptions of the respective parts of the embodiments, and will not be described herein again.

[0047] In order to realize the foregoing embodiments, the application further provides an electronic device, including a processor and a memory in communication connection with the processor; the memory stores computer execution instructions; and the processor executes the computer execution instructions stored in the memory to realize the method provided in the foregoing embodiments.

[0048] To achieve the above-mentioned embodiments, the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a processor to implement the method provided by the foregoing embodiments.

[0049] To achieve the above-mentioned embodiments, the present application further provides a computer program product, comprising a computer program, wherein the computer program is executed by a processor to implement the method provided by the foregoing embodiments.

[0050] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in the present application comply with relevant laws and regulations and do not violate public order and good customs.

[0051] It should be noted that the personal information from the user should be collected for legal and reasonable purposes, and should not be shared or sold outside these legal uses. In addition, such collection / sharing should be carried out after the user's informed consent is received, including but not limited to informing the user to read the user agreement / user notice before the user uses the function, and signing the agreement / authorization including authorization of relevant user information. In addition, any necessary steps should be taken to protect and ensure access to such personal information data, and to ensure that other people with access to personal information data comply with their privacy policy and processes.

[0052] The present application is expected to provide embodiments in which the user can selectively prevent the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk is minimized by limiting data collection and deleting data. In addition, such personal information is de-identified, if applicable, to protect the privacy of the user.

[0053] In the foregoing embodiment description, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples, without contradiction.

[0054] Moreover, the terms "first", "second", "third", etc. are used herein only to describe different steps or categories of steps in a claim for patent purposes, and are not to be construed as indicating or implying relative importance of one step to another or a quantity of steps. Thus, features defined with "first", "second" or "third" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "plurality" is at least two, for example, two, three, etc., unless otherwise explicitly and specifically limited.

[0055] Any process or method descriptions or blocks in flow charts herein and elsewhere can be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process, and alternate implementations are possible. In some embodiments, the processes or methods described in flow charts herein and elsewhere can be tailored by reordering steps and / or adding or omitting one or more of the described steps, and the order of the steps can be changed, including according to the functionality involved, and the steps can be performed in substantially simultaneous with, or in reverse order, depending on the functionality involved, as will be understood by those of ordinary skill in the art.

[0056] Logic and / or steps represented in flow charts herein and elsewhere, for example, can be embodied in computer-readable media, which can be any available media that can be accessed by a general purpose or special purpose computing system, including a processor-based system, computer, or other system that can fetch the instructions from the instruction-executing system, device, or apparatus and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction-execution system, apparatus, or device. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, an article of manufacture that can be used with a machine, or a machine-readable signal. Specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical), and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a machine-readable format.

[0057] It should be understood that parts of the present application can be realized in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be realized as software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if realized in hardware, and in another embodiment, any one or a combination of the following technologies known in the art can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0058] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing relevant hardware, and the programs can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0059] In addition, each functional unit in each embodiment of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The above-mentioned integrated module can be realized in the form of hardware or in the form of a software function module. The integrated module, if realized in the form of a software function module and sold or used as an independent product, can also be stored in a computer readable storage medium.

[0060] The above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-mentioned embodiments within the scope of the present application.

Claims

1. A multi-objective collaborative optimization method for a circulating water crystallization system based on water quality fluctuations, characterized in that, include: Collect the historical water quality data of the circulating water system throughout the year, perform statistical analysis on the historical water quality data, establish a water quality fluctuation probability model, and divide different water quality operating condition intervals. Construct a first operating cost function for a calcium carbonate-induced crystallization system and a second operating cost function for a calcium sulfate-induced crystallization system; Based on the first and second operating cost functions, a multi-objective optimization model is constructed with system operating parameters as constraints. The multi-objective optimization model is solved using an intelligent optimization algorithm, outputting the combination of operating parameters corresponding to each of the water quality operating conditions, and generating an annual economic operation scheduling strategy based on the combination of operating parameters.

2. The multi-objective collaborative optimization method for a circulating water crystallization system based on water quality fluctuations according to claim 1, characterized in that, The historical water quality data includes total hardness, calcium ion concentration, magnesium ion concentration, pH value, and water temperature.

3. The multi-objective collaborative optimization method for a circulating water crystallization system based on water quality fluctuations according to claim 1, characterized in that, The variables of the first operating cost function include the calcium ion removal rate, alkali dosage, and operating power consumption of the calcium carbonate induced crystallization system; the variables of the second operating cost function include the influent calcium ion concentration, crystallization efficiency, and operating power consumption of the calcium sulfate induced crystallization system.

4. The multi-objective collaborative optimization method for a circulating water crystallization system based on water quality fluctuations according to claim 1, characterized in that, The constraints include: the fluctuation range of water quality parameters, the system's calcium ion removal rate, the treatment capacity range of each crystallization system, and the safe range of reagent dosage.

5. The multi-objective collaborative optimization method for a circulating water crystallization system based on water quality fluctuations according to claim 1, characterized in that, The process of solving the multi-objective optimization model using an intelligent optimization algorithm includes: The objective function and constraints in the multi-objective optimization model are encoded into a format that the intelligent optimization algorithm can process. The intelligent optimization algorithm is used to perform a global optimization search in the solution space consisting of all operating parameters. The intelligent optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm. The final output is the combination of operating parameters that satisfies all constraints and minimizes the total operating cost.

6. The multi-objective collaborative optimization method for a circulating water crystallization system based on water quality fluctuations according to claim 1, characterized in that, The combination of operating parameters is used to guide the coordinated operation control of the two systems, wherein: the optimal calcium ion removal rate setpoint of the calcium carbonate induced crystallization system is used to adjust the alkali dosage and hydraulic load of the system in real time; the start-up and shutdown control timing of the calcium sulfate induced crystallization system is determined based on whether the calcium ion concentration in the influent exceeds the preset economic operating threshold; the reagent dosing acceleration rate is the respective dosing acceleration rate of the alkali in the calcium carbonate induced crystallization system and the seed crystal or auxiliary agent in the calcium sulfate induced crystallization system; and the pump operating frequency is the set value of the frequency of the pumps controlling the circulation water flowing through the two crystallization systems.

7. The multi-objective collaborative optimization method for a circulating water crystallization system based on water quality fluctuations according to claim 1, characterized in that, Also includes: Based on real-time collected circulating water quality data, the economic operation scheduling strategy is dynamically fed back and adjusted.

8. A multi-objective collaborative optimization device for a circulating water crystallization system based on water quality fluctuations, characterized in that, include: The data acquisition module collects the annual historical water quality data of the circulating water system, performs statistical analysis on the historical water quality data, establishes a water quality fluctuation probability model, and divides different water quality operating condition intervals. The function construction module constructs the first operating cost function for the calcium carbonate-induced crystallization system and the second operating cost function for the calcium sulfate-induced crystallization system; The model building module constructs a multi-objective optimization model based on the first and second operating cost functions and with system operating parameters as constraints. The strategy generation module uses an intelligent optimization algorithm to solve the multi-objective optimization model, outputs the combination of operating parameters corresponding to each of the water quality operating conditions, and generates an economical operation scheduling strategy for the whole year based on the combination of operating parameters.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

Images