Reverse osmosis chemical water production energy-saving optimization method and system based on data analysis

Abstract

The invention relates to the technical field of energy-saving optimization, in particular to a reverse osmosis chemical water production energy-saving optimization method and system based on data analysis, and the method comprises the steps: carrying out the search processing of collected target product water quality characteristic data and different reverse osmosis membrane type performance characteristic data, and generating target reverse osmosis membrane type data; the method comprises the following steps: acquiring production characteristic data of different reverse osmosis membrane types, performing data matching with the production characteristic data of different reverse osmosis membrane types to generate standard production characteristic data of the water production equipment, performing difference processing on the standard production characteristic data and the acquired real-time production characteristic data of the water production equipment to generate real-time production deviation data of the water production equipment, and performing numerical comparison on the real-time production deviation data and a set deviation threshold value; generating real-time running state analysis data of the water production equipment, and if the data is normal, ending the operation; if yes, real-time abnormal production characteristic data of the water production equipment is generated, real-time running state adjustment data of the water production equipment is constructed, meanwhile, production data adjustment operation of the water production equipment is executed, and accurate regulation and control of the water production equipment and energy-saving optimization of the reverse osmosis water production process are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving optimization, and in particular to a reverse osmosis chemical water production energy-saving optimization method and system based on data analysis. Background Art

[0002] Reverse osmosis technology is an important water production method widely used in industrial production and domestic water treatment. In the reverse osmosis chemical water production process, a large amount of energy is consumed, and the existing technology often does not conduct real-time analysis of the production data of the water production process, resulting in more unnecessary energy consumption.

[0003] The Chinese invention patent with publication number CN111681126B introduces an intelligent control system and method based on energy efficiency evaluation of water treatment booster pump groups. The first parameter of the water treatment plant is collected in real time through the data acquisition module and transmitted to the human-computer interaction module for dynamic display and stored in the data storage module; the first parameter in the data storage module is called and the preset second parameter is obtained from the human-computer interaction module; data analysis is performed on the second parameter, with the highest pump group operating efficiency as the goal, the optimal operating condition corresponding to the booster pump group is optimized, and the optimization result is stored in the data storage module and output to the human-computer interaction module for display; the pump group control module regulates the booster pump group according to the optimization result; the intelligent control of the booster pump is realized, while ensuring the energy-saving, optimization and automatic operation of the booster pump group of the water treatment plant, the intelligence level of the water treatment plant is improved, but the accuracy of the analysis result cannot be guaranteed by only retrieving parameters matching the collected parameters from the preset parameters and performing data analysis. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In order to solve the deficiencies in the background technology, the present invention provides a reverse osmosis chemical water production energy-saving optimization method and system based on data analysis, which can realize precise control of water production equipment and energy-saving optimization of the reverse osmosis water production process.

[0006] (II) Technical solution

[0007] A reverse osmosis chemical water production energy-saving optimization method based on data analysis comprises the following steps:

[0008] S1. Collect water quality characteristic data of target product water;

[0009] S2, performing a target reverse osmosis membrane type data search process on the target product water quality characteristic data and the performance characteristic data of different reverse osmosis membrane types, and generating target reverse osmosis membrane type data according to the matching results;

[0010] S3. Establish production characteristic data of different reverse osmosis membrane types;

[0011] S4, performing reverse osmosis membrane type matching processing on the target reverse osmosis membrane type data and the production characteristic data of different reverse osmosis membrane types to generate standard production characteristic data of water production equipment;

[0012] S5, collecting real-time production characteristic data of the water production equipment, performing difference processing on the standard production characteristic data of the water production equipment and the real-time production characteristic data of the water production equipment, and generating real-time production deviation data of the water production equipment;

[0013] S6, setting a deviation threshold, comparing the real-time production deviation data of the water production equipment with the deviation threshold, generating real-time operation status analysis data of the water production equipment according to the comparison result, and ending the real-time operation status analysis operation of the water production equipment if the real-time operation status analysis data of the water production equipment is normal;

[0014] S7. If the real-time operation status analysis data of the water-making equipment is abnormal, generate real-time abnormal production characteristic data of the water-making equipment, construct real-time operation status adjustment data of the water-making equipment based on the real-time abnormal production characteristic data of the water-making equipment and the standard production characteristic data of the water-making equipment, and execute the water-making equipment production data adjustment operation.

[0015] The present invention searches and processes the collected target product water quality characteristic data and the performance characteristic data of different reverse osmosis membrane types to generate target reverse osmosis membrane type data, performs reverse osmosis membrane type matching processing with the established production characteristic data of different reverse osmosis membrane types, generates standard production characteristic data of water-making equipment, collects real-time production characteristic data of water-making equipment, performs difference processing on the standard production characteristic data of water-making equipment and the real-time production characteristic data of water-making equipment, generates real-time production deviation data of water-making equipment, and performs numerical comparison with a set deviation threshold value, generates real-time operation status analysis data of water-making equipment according to the numerical comparison result, and if it is normal, terminates the real-time operation status analysis operation of water-making equipment; if it is abnormal, generates real-time abnormal production characteristic data of water-making equipment, constructs real-time operation status adjustment data of water-making equipment, and executes the production data adjustment operation of water-making equipment at the same time, so as to realize precise regulation of water-making equipment and energy-saving optimization of reverse osmosis water-making process.

[0016] Preferably, the specific steps for collecting target product water quality characteristic data are as follows:

[0017] S11. Collect the product water quality characteristic data that the company needs to produce online through the product water data management platform to generate target product water quality characteristic data A. The product water quality characteristic data includes but is not limited to product water conductivity, product water hardness, product water pH and product water suspended matter content.

[0018] Preferably, the target product water quality characteristic data and the performance characteristic data of different reverse osmosis membrane types are subjected to target reverse osmosis membrane type data search processing, and the specific steps of generating target reverse osmosis membrane type data according to the matching results are as follows:

[0019] S21. Establish performance characteristic data set B of different reverse osmosis membrane types = {b1, b2, ..., b i ,…,b k}, where b i represents the performance characteristic data of the i-th type of reverse osmosis membrane, k represents the total number of performance characteristic data of different types of reverse osmosis membranes, the reverse osmosis membrane types include but are not limited to cellulose acetate membrane, polyamide membrane, polysulfone porous filter membrane and composite membrane, the performance characteristic data include but are not limited to water flux, antimicrobial performance, chemical stability and desalination performance.

[0020] S22, performing a target reverse osmosis membrane type data search process on the target product water quality characteristic data A and the different reverse osmosis membrane type performance characteristic data in the different reverse osmosis membrane type performance characteristic data sets by using a water wave optimization algorithm, searching for different reverse osmosis membrane type performance characteristic data that successfully matches the target product water quality characteristic data A, and performing data identification on the reverse osmosis membrane type corresponding to the different reverse osmosis membrane type performance characteristic data, to generate target reverse osmosis membrane type data C;

[0021] S221. Construct a reverse osmosis membrane type search water wave population, set the reverse osmosis membrane type search water wave population size to M, the current number of iterations to t, and the maximum number of iterations to t. max and the search space dimension of performance characteristic data of different reverse osmosis membrane types is P;

[0022] The performance characteristic data sets of different reverse osmosis membrane types are used as the search space for performance characteristic data of different reverse osmosis membrane types. M performance characteristic data of different reverse osmosis membrane types are randomly generated in the search space for performance characteristic data of different reverse osmosis membrane types. Each performance characteristic data of different reverse osmosis membrane types corresponds to a reverse osmosis membrane type search water wave individual. The initial position set of the reverse osmosis membrane type search water wave population is initialized as X={X1,X2,…,X i ,…,X M}, where X i represents the initial position of the search water wave individual of the i-th reverse osmosis membrane type;

[0023] S222, calculating the fitness function value of each reverse osmosis membrane type search water wave individual in the reverse osmosis membrane type search water wave population, arranging each reverse osmosis membrane type search water wave individual in the reverse osmosis membrane type search water wave population from large to small according to the fitness function value, and selecting the reverse osmosis membrane type search water wave individual with the highest fitness function value as the current optimal individual; the fitness function formula is as follows:

[0024]

[0025] Among them, Fit i represents the fitness function value of the i-th reverse osmosis membrane type search water wave individual; x i It represents the ability of the i-th reverse osmosis membrane type search water wave individual to process raw water with different reverse osmosis membrane type performance characteristic data so that the raw water reaches the target product water quality characteristic data; φ represents the correction value;

[0026] S223, each reverse osmosis membrane type search water wave individual in the reverse osmosis membrane type search water wave population performs a propagation operation in the search space of the performance characteristic data of different reverse osmosis membrane types to update the position; the position update formula is as follows:

[0027]

[0028] in, and X ij They represent the positions of the i-th reverse osmosis membrane type search water wave individual after and before the position update, rand1 represents a random number uniformly distributed between [-1,1], and λ i Indicates the wavelength of the water wave individual searched for the i-th reverse osmosis membrane type, ub j and lb j They represent the upper and lower bounds of the search space of performance characteristic data of different reverse osmosis membrane types in the jth dimension respectively;

[0029] S224, if the fitness function value of the reverse osmosis membrane type search water wave individual after position update is greater than the fitness function value of the current optimal individual, the reverse osmosis membrane type search water wave individual is used to replace the current optimal individual, and the breaking wave operation is performed in the search space of the performance characteristic data of different reverse osmosis membrane types to update the position; the position update formula is as follows:

[0030]

[0031] Among them, rand2 represents a Gaussian random number that obeys (0,1), and α represents the breaking wave coefficient;

[0032] If the fitness function value of the reverse osmosis membrane type search water wave individual after the position update is less than or equal to the fitness function value of the current optimal individual, the wave height h of the reverse osmosis membrane type search water wave individual is subtracted by 1;

[0033] S225, if the wave height h of the reverse osmosis membrane type search water wave individual is equal to 0, the reverse osmosis membrane type search water wave individual performs a refraction operation in the search space of the performance characteristic data of different reverse osmosis membrane types to update the position; otherwise, directly enter S226; the position update formula is as follows:

[0034]

[0035] Among them, X best represents the position of the current optimal individual, N(μ,σ) represents a Gaussian random number with mean μ and variance σ;

[0036] S226, calculating the fitness function value of each reverse osmosis membrane type search water wave individual in the reverse osmosis membrane type search water wave population after the position is updated, if the fitness function value of the reverse osmosis membrane type search water wave individual after the position is updated is less than the original fitness function value, then the new position is used to replace the original position; otherwise, the original position is retained;

[0037] Rearranging the reverse osmosis membrane type search water wave individuals in the reverse osmosis membrane type search water wave population from large to small according to the fitness function value, and selecting the reverse osmosis membrane type search water wave individual with the highest fitness function value as the new current optimal individual;

[0038] S227, updating the wavelength of each individual reverse osmosis membrane type search water wave in the reverse osmosis membrane type search water wave population; the wavelength update formula is as follows:

[0039]

[0040] in, and λ i They represent the wavelengths of the search water wave of the i-th reverse osmosis membrane type after and before the update, respectively. min and Fit max Respectively represent the minimum value and the maximum value of the fitness function of each reverse osmosis membrane type search water wave individual in the reverse osmosis membrane type search water wave population;

[0041] S228, determine whether the current number of iterations t is less than the maximum number of iterations t max , if the current number of iterations t is less than the maximum number of iterations t max, then the current iteration number t is increased by 1, and the process returns to S223; otherwise, the current optimal individual is taken as the global optimal solution, and the performance characteristic data of different reverse osmosis membrane types corresponding to the global optimal solution are retained;

[0042] S229, data identification is performed on the reverse osmosis membrane types corresponding to the retained performance characteristic data of different reverse osmosis membrane types to generate target reverse osmosis membrane type data C;

[0043] S23. If the type of the reverse osmosis membrane installed in the water production equipment matches the target reverse osmosis membrane type data C, directly enter S3; otherwise, perform a reverse osmosis membrane replacement operation on the reverse osmosis membrane installed in the water production equipment based on the target reverse osmosis membrane type data C, and enter S3 after the reverse osmosis membrane replacement operation is completed.

[0044] The target product water quality characteristic data and the performance characteristic data of different reverse osmosis membrane types are searched and processed by the water wave optimization algorithm, and the reverse osmosis membrane that is most suitable for producing the target product water is accurately retrieved from the database to ensure the effect and quality of the raw water filtered by the selected reverse osmosis membrane. At the same time, the water wave optimization algorithm can improve the speed and efficiency of the successful search processing and effectively reduce the response time for obtaining search results.

[0045] Preferably, the specific steps for establishing production characteristic data of different reverse osmosis membrane types are as follows:

[0046] S31, preset corresponding production characteristic data for each of the different reverse osmosis membrane type performance characteristic data in the different reverse osmosis membrane type performance characteristic data set through the product water preparation data entry interface, and obtain the different reverse osmosis membrane type production characteristic data set D = {d1, d2, ..., d i ,…,d k}, where d i It represents the production characteristic data of the i-th different reverse osmosis membrane type, and the production characteristic data includes raw water temperature, inlet and outlet flow rate and inlet pressure.

[0047] Preferably, the target reverse osmosis membrane type data and the production characteristic data of different reverse osmosis membrane types are matched with each other to generate the standard production characteristic data of water production equipment in the following specific steps:

[0048] S41. Traverse all the production characteristic data of different reverse osmosis membrane types in the production characteristic data sets of different reverse osmosis membrane types through an iterative deepening search algorithm, search for the production characteristic data of different reverse osmosis membrane types that match the target reverse osmosis membrane type data and perform data identification, and generate a standard production characteristic data set E = (e1, e2, e3) for water production equipment, wherein e1, e2 and e3 respectively represent the standard raw water temperature, standard inlet and outlet flow rates and standard inlet pressure when the water production equipment produces the target product water.

[0049] The target reverse osmosis membrane type data and the production characteristic data of different reverse osmosis membrane types are matched with each other through an iterative deepening search algorithm, so that the production characteristic data of different reverse osmosis membrane types that match the target reverse osmosis membrane type data are quickly and accurately searched out, providing a data basis for the subsequent analysis of the real-time operating status of the water production equipment.

[0050] Preferably, the specific steps of collecting the real-time production characteristic data of the water production equipment, performing difference processing on the standard production characteristic data of the water production equipment and the real-time production characteristic data of the water production equipment, and generating the real-time production deviation data of the water production equipment are as follows:

[0051] S51, collecting real-time production data of the current water production equipment during operation through different sensors, and obtaining a real-time production feature data set E'=(e'1, e'2, e'3) of the water production equipment, wherein e'1, e'2 and e'3 respectively represent the real-time raw water temperature, real-time inlet and outlet flow and real-time inlet pressure of the current water production equipment during operation, and the different sensors include a temperature sensor, a pressure sensor and a flow sensor;

[0052] S52, using a difference formula to perform numerical difference processing on each standard production characteristic data of water production equipment in the standard production characteristic data set of water production equipment and the real-time production characteristic data of water production equipment with corresponding digital subscripts in the real-time production characteristic data set of water production equipment, to generate a real-time production deviation data set of water production equipment. in, as well as They respectively represent the raw water temperature deviation, inlet and outlet flow deviation, and inlet pressure deviation when the water production equipment produces the target product water.

[0053] Preferably, a deviation threshold is set, and the real-time production deviation data of the water production equipment is numerically compared with the deviation threshold, and the real-time operation status analysis data of the water production equipment is generated according to the numerical comparison result. If the real-time operation status analysis data of the water production equipment is normal, the specific steps of ending the real-time operation status analysis operation of the water production equipment are as follows:

[0054] S61, setting the raw water temperature deviation threshold to η1, the inlet and outlet flow deviation threshold to η2, and the inlet pressure deviation threshold to η3;

[0055] like It means that the real-time raw water temperature of the water-making equipment when producing the target product water is in a normal state; otherwise, it means that the real-time raw water temperature of the water-making equipment when producing the target product water is in an abnormal state;

[0056] like It means that the real-time inlet and outlet flow rates of the water production equipment when producing the target product water are in a normal state; otherwise, it means that the real-time inlet and outlet flow rates of the water production equipment when producing the target product water are in an abnormal state;

[0057] like It means that the real-time inlet pressure of the water production equipment when producing the target product water is in a normal state; otherwise, it means that the real-time inlet pressure of the water production equipment when producing the target product water is in an abnormal state;

[0058] S62. If the real-time raw water temperature, real-time import and export flow rate and real-time import pressure of the water-making equipment when producing the target product water are all in normal state, then the output of the real-time operation status analysis data of the water-making equipment is normal, and the real-time operation status analysis data of the water-making equipment is pushed to the real-time supervision platform of the water-making equipment through the Internet of Things communication network, and the real-time operation status analysis operation of the water-making equipment is ended; otherwise, the output of the real-time operation status analysis data of the water-making equipment is abnormal.

[0059] The real-time production characteristic data of the water-making equipment and the standard production characteristic data of the water-making equipment are subtracted by a difference formula to generate real-time production deviation data of the water-making equipment, which are numerically compared with the set deviation threshold value to scientifically analyze the real-time operation status of the water-making equipment. At the same time, in the numerical comparison process, the raw water temperature, inlet and outlet flow rates and inlet pressure of the water-making equipment are analyzed separately to achieve real-time monitoring of multiple production data of the water-making equipment, ensure that the energy consumption during the operation of the water-making equipment is kept within a reasonable range, and effectively avoid additional energy consumption in the production process.

[0060] Preferably, if the real-time operation status analysis data of the water production equipment is abnormal, the real-time abnormal production characteristic data of the water production equipment is generated, the real-time operation status adjustment data of the water production equipment is constructed based on the real-time abnormal production characteristic data of the water production equipment and the standard production characteristic data of the water production equipment, and the specific steps of executing the water production equipment production data adjustment operation are as follows:

[0061] S71. If the real-time operation status analysis data of the water production equipment is abnormal, the real-time production deviation data of the water production equipment in the abnormal state in the real-time production deviation data of the water production equipment is marked to generate the real-time abnormal production characteristic data F of the water production equipment.yichang ;

[0062] S72, the real-time abnormal production characteristic data F of the water production equipment yichang Combined with the water production equipment standard production characteristic data set E, the real-time operation state adjustment data Q of the water production equipment is generated. yichang , E);

[0063] S73, adjusting the real-time operation status of the water production equipment through the Internet of Things communication network yichang , E) pushes it to the water production equipment production data adjustment platform and executes the water production equipment production data adjustment operation.

[0064] The present invention also includes a reverse osmosis chemical water production energy-saving optimization system based on data analysis, including a target product water quality characteristic data acquisition module, a target reverse osmosis membrane type search module, a production characteristic data establishment module for different reverse osmosis membrane types, a water production equipment standard production characteristic data matching module, a water production equipment real-time production deviation data calculation module, a water production equipment real-time operation status analysis module, and a water production equipment real-time operation status adjustment data construction module;

[0065] The target product water quality characteristic data collection module collects the product water quality characteristic data that the current enterprise needs to produce online through the product water data management platform to generate the target product water quality characteristic data;

[0066] The target reverse osmosis membrane type search module performs target reverse osmosis membrane type data search processing on the target product water quality characteristic data and the performance characteristic data of different reverse osmosis membrane types through a water wave optimization algorithm, and generates target reverse osmosis membrane type data according to the search results;

[0067] The module for establishing production characteristic data of different reverse osmosis membrane types presets corresponding production characteristic data for the performance characteristic data of different reverse osmosis membrane types through the product water preparation data entry interface to obtain production characteristic data of different reverse osmosis membrane types;

[0068] The water production equipment standard production characteristic data matching module performs reverse osmosis membrane type matching processing on the target reverse osmosis membrane type data and the production characteristic data of different reverse osmosis membrane types through an iterative deepening search algorithm to generate standard production characteristic data of water production equipment;

[0069] The water production equipment real-time production deviation data calculation module collects the real-time production data of the current water production equipment during operation through different sensors, obtains the real-time production characteristic data of the water production equipment, and performs subtraction processing with the standard production characteristic data of the water production equipment to generate the real-time production deviation data of the water production equipment;

[0070] The water production equipment real-time operation status analysis module sets a deviation threshold, compares the real-time production deviation data of the water production equipment with the deviation threshold, generates the water production equipment real-time operation status analysis data according to the numerical comparison result, and ends the water production equipment real-time operation status analysis operation if the water production equipment real-time operation status analysis data is normal;

[0071] When the real-time operation status analysis data of the water production equipment is abnormal, the water production equipment real-time operation status adjustment data construction module will identify the real-time production deviation data of the water production equipment in the abnormal state, generate the real-time abnormal production characteristic data of the water production equipment, combine the data with the standard production characteristic data set of the water production equipment, generate the real-time operation status adjustment data of the water production equipment, and push it to the water production equipment production data adjustment platform through the Internet of Things communication network, and execute the water production equipment production data adjustment operation at the same time.

[0072] (III) Beneficial effects

[0073] 1. The present invention searches and processes the collected target product water quality characteristic data and the performance characteristic data of different reverse osmosis membrane types to generate target reverse osmosis membrane type data, and performs reverse osmosis membrane type matching processing with the established production characteristic data of different reverse osmosis membrane types to generate standard production characteristic data of water-making equipment, collects real-time production characteristic data of water-making equipment, performs difference processing on the standard production characteristic data of water-making equipment and the real-time production characteristic data of water-making equipment to generate real-time production deviation data of water-making equipment, and performs numerical comparison with a set deviation threshold value, and generates real-time operation status analysis data of water-making equipment according to the numerical comparison result. If it is normal, the real-time operation status analysis operation of the water-making equipment is terminated; if it is abnormal, the real-time abnormal production characteristic data of the water-making equipment is generated, and the real-time operation status adjustment data of the water-making equipment is constructed, and the production data adjustment operation of the water-making equipment is performed at the same time, so as to realize the precise regulation of the water-making equipment and the energy-saving optimization of the reverse osmosis water-making process.

[0074] 2. Use the water wave optimization algorithm to search and process the target product water quality characteristic data and the performance characteristic data of different reverse osmosis membrane types, accurately retrieve the most suitable reverse osmosis membrane for producing the target product water from the database, and ensure the effect and quality of the selected reverse osmosis membrane for filtering raw water. At the same time, the water wave optimization algorithm can improve the speed and efficiency of successful search processing and effectively reduce the response time for obtaining search results;

[0075] 3. Perform reverse osmosis membrane type matching processing on the target reverse osmosis membrane type data and the production characteristic data of different reverse osmosis membrane types through an iterative deepening search algorithm, and quickly and accurately search for the production characteristic data of different reverse osmosis membrane types that match the target reverse osmosis membrane type data, thereby providing a data basis for the subsequent analysis of the real-time operating status of the water production equipment;

[0076] 4. The real-time production characteristic data of the water-making equipment and the standard production characteristic data of the water-making equipment are subtracted through a difference formula to generate real-time production deviation data of the water-making equipment, and the data is numerically compared with the set deviation threshold to scientifically analyze the real-time operation status of the water-making equipment. At the same time, in the process of numerical comparison, the raw water temperature, inlet and outlet flow and inlet pressure of the water-making equipment are analyzed separately to realize real-time monitoring of multiple production data of the water-making equipment, ensure that the energy consumption during the operation of the water-making equipment is kept within a reasonable range, and effectively avoid additional energy consumption in the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0078] Figure 1 A flow chart of a reverse osmosis chemical water production energy-saving optimization method based on data analysis provided by the present invention;

[0079] Figure 2 A module schematic diagram of a reverse osmosis chemical water production energy-saving optimization system based on data analysis provided by the present invention. DETAILED DESCRIPTION

[0080] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0081] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0082] Embodiment 1 is as follows:

[0083] See also Figure 1 , a reverse osmosis chemical water production energy saving optimization method based on data analysis, comprising the following steps:

[0084] S1. Collect water quality characteristic data of target product water;

[0085] S11. Collect the product water quality characteristic data that the company needs to produce online through the product water data management platform to generate target product water quality characteristic data A. The product water quality characteristic data includes but is not limited to product water conductivity, product water hardness, product water pH and product water suspended matter content.

[0086] S2, performing a target reverse osmosis membrane type data search process on the target product water quality characteristic data and the performance characteristic data of different reverse osmosis membrane types, and generating target reverse osmosis membrane type data according to the matching results;

[0087] S21. Establish performance characteristic data set B of different reverse osmosis membrane types = {b1, b2, ..., b i ,…,b k}, where b i represents the performance characteristic data of the i-th type of reverse osmosis membrane, k represents the total number of performance characteristic data of different types of reverse osmosis membranes, the reverse osmosis membrane types include but are not limited to cellulose acetate membrane, polyamide membrane, polysulfone porous filter membrane and composite membrane, the performance characteristic data include but are not limited to water flux, antimicrobial performance, chemical stability and desalination performance.

[0088] S22, performing a target reverse osmosis membrane type data search process on the target product water quality characteristic data A and the different reverse osmosis membrane type performance characteristic data in the different reverse osmosis membrane type performance characteristic data sets by using a water wave optimization algorithm, searching for different reverse osmosis membrane type performance characteristic data that successfully matches the target product water quality characteristic data A, and performing data identification on the reverse osmosis membrane type corresponding to the different reverse osmosis membrane type performance characteristic data, to generate target reverse osmosis membrane type data C;

[0089] S221. Construct a reverse osmosis membrane type search water wave population, set the reverse osmosis membrane type search water wave population size to M, the current number of iterations to t, and the maximum number of iterations to t. max and the search space dimension of performance characteristic data of different reverse osmosis membrane types is P;

[0090] The performance characteristic data sets of different reverse osmosis membrane types are used as the search space for performance characteristic data of different reverse osmosis membrane types. M performance characteristic data of different reverse osmosis membrane types are randomly generated in the search space for performance characteristic data of different reverse osmosis membrane types. Each performance characteristic data of different reverse osmosis membrane types corresponds to a reverse osmosis membrane type search water wave individual. The initial position set of the reverse osmosis membrane type search water wave population is initialized as X={X1,X2,…,X i ,…,X M}, where X i represents the initial position of the search water wave individual of the i-th reverse osmosis membrane type;

[0091] S222, calculating the fitness function value of each reverse osmosis membrane type search water wave individual in the reverse osmosis membrane type search water wave population, arranging each reverse osmosis membrane type search water wave individual in the reverse osmosis membrane type search water wave population from large to small according to the fitness function value, and selecting the reverse osmosis membrane type search water wave individual with the highest fitness function value as the current optimal individual; the fitness function formula is as follows:

[0092]

[0093] Among them, Fit i represents the fitness function value of the i-th reverse osmosis membrane type search water wave individual; x i It represents the ability of the i-th reverse osmosis membrane type search water wave individual to process raw water with different reverse osmosis membrane type performance characteristic data so that the raw water reaches the target product water quality characteristic data; φ represents the correction value;

[0094] S223, each reverse osmosis membrane type search water wave individual in the reverse osmosis membrane type search water wave population performs a propagation operation in the search space of the performance characteristic data of different reverse osmosis membrane types to update the position; the position update formula is as follows:

[0095]

[0096] in, and X ij They represent the positions of the i-th reverse osmosis membrane type search water wave individual after and before the position update, rand1 represents a random number uniformly distributed between [-1,1], and λ i Indicates the wavelength of the water wave individual searched for the i-th reverse osmosis membrane type, ub j and lb j They represent the upper and lower bounds of the search space of performance characteristic data of different reverse osmosis membrane types in the jth dimension respectively;

[0097] S224, if the fitness function value of the reverse osmosis membrane type search water wave individual after position update is greater than the fitness function value of the current optimal individual, the reverse osmosis membrane type search water wave individual is used to replace the current optimal individual, and the breaking wave operation is performed in the search space of the performance characteristic data of different reverse osmosis membrane types to update the position; the position update formula is as follows:

[0098]

[0099] Among them, rand2 represents a Gaussian random number that obeys (0,1), and α represents the breaking wave coefficient;

[0100] If the fitness function value of the reverse osmosis membrane type search water wave individual after the position update is less than or equal to the fitness function value of the current optimal individual, the wave height h of the reverse osmosis membrane type search water wave individual is subtracted by 1;

[0101] S225. When the wave height h of the reverse osmosis membrane type search water wave individual is equal to 0, the reverse osmosis membrane type search water wave individual performs a refraction operation in the search space of the performance characteristic data of different reverse osmosis membrane types to update its position. Otherwise, directly enter S226; the position update formula is as follows:

[0102]

[0103] Among them, X best represents the position of the current optimal individual, N(μ,σ) represents a Gaussian random number with mean μ and variance σ;

[0104] S226, calculating the fitness function value of each reverse osmosis membrane type search water wave individual in the reverse osmosis membrane type search water wave population after the position is updated, if the fitness function value of the reverse osmosis membrane type search water wave individual after the position is updated is less than the original fitness function value, then the new position is used to replace the original position; otherwise, the original position is retained;

[0105] Rearranging the reverse osmosis membrane type search water wave individuals in the reverse osmosis membrane type search water wave population from large to small according to the fitness function value, and selecting the reverse osmosis membrane type search water wave individual with the highest fitness function value as the new current optimal individual;

[0106] S227, updating the wavelength of each individual reverse osmosis membrane type search water wave in the reverse osmosis membrane type search water wave population; the wavelength update formula is as follows:

[0107]

[0108] in, and λ iThey represent the wavelengths of the search water wave of the i-th reverse osmosis membrane type after and before the update, respectively. min and Fit max Respectively represent the minimum value and the maximum value of the fitness function of each reverse osmosis membrane type search water wave individual in the reverse osmosis membrane type search water wave population;

[0109] S228, determine whether the current number of iterations t is less than the maximum number of iterations t max , if the current number of iterations t is less than the maximum number of iterations t max , then the current iteration number t is increased by 1, and the process returns to S223; otherwise, the current optimal individual is taken as the global optimal solution, and the performance characteristic data of different reverse osmosis membrane types corresponding to the global optimal solution are retained;

[0110] S229, data identification is performed on the reverse osmosis membrane types corresponding to the retained performance characteristic data of different reverse osmosis membrane types to generate target reverse osmosis membrane type data C;

[0111] S23. If the type of the reverse osmosis membrane installed in the water production equipment matches the target reverse osmosis membrane type data C, directly enter S3; otherwise, perform a reverse osmosis membrane replacement operation on the reverse osmosis membrane installed in the water production equipment based on the target reverse osmosis membrane type data C, and enter S3 after the reverse osmosis membrane replacement operation is completed.

[0112] S3. Establish production characteristic data of different reverse osmosis membrane types;

[0113] S31, preset corresponding production characteristic data for each of the different reverse osmosis membrane type performance characteristic data in the different reverse osmosis membrane type performance characteristic data set through the product water preparation data entry interface, and obtain the different reverse osmosis membrane type production characteristic data set D = {d1, d2, ..., d i ,…,d k}, where d i It represents the production characteristic data of the i-th different reverse osmosis membrane type, and the production characteristic data includes raw water temperature, inlet and outlet flow rate and inlet pressure.

[0114] S4, performing reverse osmosis membrane type matching processing on the target reverse osmosis membrane type data and the production characteristic data of different reverse osmosis membrane types to generate standard production characteristic data of water production equipment;

[0115] S41. Traverse all the production characteristic data of different reverse osmosis membrane types in the production characteristic data sets of different reverse osmosis membrane types through an iterative deepening search algorithm, search for the production characteristic data of different reverse osmosis membrane types that match the target reverse osmosis membrane type data and perform data identification, and generate a standard production characteristic data set E = (e1, e2, e3) for water production equipment, wherein e1, e2 and e3 respectively represent the standard raw water temperature, standard inlet and outlet flow rates and standard inlet pressure when the water production equipment produces the target product water.

[0116] S5, collecting real-time production characteristic data of the water production equipment, performing difference processing on the standard production characteristic data of the water production equipment and the real-time production characteristic data of the water production equipment, and generating real-time production deviation data of the water production equipment;

[0117] S51, collecting real-time production data of the current water production equipment during operation through different sensors, and obtaining a real-time production feature data set E'=(e'1, e'2, e'3) of the water production equipment, wherein e'1, e'2 and e'3 respectively represent the real-time raw water temperature, real-time inlet and outlet flow and real-time inlet pressure of the current water production equipment during operation, and the different sensors include a temperature sensor, a pressure sensor and a flow sensor;

[0118] S52, using a difference formula to perform numerical difference processing on each standard production characteristic data of water production equipment in the standard production characteristic data set of water production equipment and the real-time production characteristic data of water production equipment with corresponding digital subscripts in the real-time production characteristic data set of water production equipment, to generate a real-time production deviation data set of water production equipment. in, as well as They respectively represent the raw water temperature deviation, inlet and outlet flow deviation, and inlet pressure deviation when the water production equipment produces the target product water.

[0119] S6, setting a deviation threshold, comparing the real-time production deviation data of the water production equipment with the deviation threshold, generating real-time operation status analysis data of the water production equipment according to the comparison result, and ending the real-time operation status analysis operation of the water production equipment if the real-time operation status analysis data of the water production equipment is normal;

[0120] S61, setting the raw water temperature deviation threshold to η1, the inlet and outlet flow deviation threshold to η2, and the inlet pressure deviation threshold to η3;

[0121] like It means that the real-time raw water temperature of the water-making equipment when producing the target product water is in a normal state; otherwise, it means that the real-time raw water temperature of the water-making equipment when producing the target product water is in an abnormal state;

[0122] like It means that the real-time inlet and outlet flow rates of the water production equipment when producing the target product water are in a normal state; otherwise, it means that the real-time inlet and outlet flow rates of the water production equipment when producing the target product water are in an abnormal state;

[0123] like It means that the real-time inlet pressure of the water production equipment when producing the target product water is in a normal state; otherwise, it means that the real-time inlet pressure of the water production equipment when producing the target product water is in an abnormal state;

[0124] S62. If the real-time raw water temperature, real-time import and export flow rate and real-time import pressure of the water-making equipment when producing the target product water are all in normal state, then the output of the real-time operation status analysis data of the water-making equipment is normal, and the real-time operation status analysis data of the water-making equipment is pushed to the real-time supervision platform of the water-making equipment through the Internet of Things communication network, and the real-time operation status analysis operation of the water-making equipment is ended; otherwise, the output of the real-time operation status analysis data of the water-making equipment is abnormal.

[0125] S7, if the real-time operation status analysis data of the water production equipment is abnormal, generating real-time abnormal production characteristic data of the water production equipment, constructing real-time operation status adjustment data of the water production equipment based on the real-time abnormal production characteristic data of the water production equipment and the standard production characteristic data of the water production equipment, and performing the water production equipment production data adjustment operation;

[0126] S71. If the real-time operation status analysis data of the water production equipment is abnormal, the real-time production deviation data of the water production equipment in the abnormal state in the real-time production deviation data of the water production equipment is marked to generate the real-time abnormal production characteristic data F of the water production equipment. yichang ;

[0127] S72, the real-time abnormal production characteristic data F of the water production equipment yichang Combined with the water production equipment standard production characteristic data set E, the real-time operation state adjustment data Q of the water production equipment is generated. yichang , E);

[0128] S73, adjusting the real-time operation status of the water production equipment through the Internet of Things communication network yichang , E) pushes it to the water production equipment production data adjustment platform and executes the water production equipment production data adjustment operation.

[0129] Embodiment 2 is as follows:

[0130] See also Figure 2, a reverse osmosis chemical water production energy-saving optimization system based on data analysis, including a target product water quality characteristic data acquisition module, a target reverse osmosis membrane type search module, a production characteristic data establishment module of different reverse osmosis membrane types, a water production equipment standard production characteristic data matching module, a water production equipment real-time production deviation data calculation module, a water production equipment real-time operation status analysis module and a water production equipment real-time operation status adjustment data construction module;

[0131] The target product water quality characteristic data collection module collects the product water quality characteristic data that the current enterprise needs to produce online through the product water data management platform to generate the target product water quality characteristic data;

[0132] The target reverse osmosis membrane type search module performs target reverse osmosis membrane type data search processing on the target product water quality characteristic data and the performance characteristic data of different reverse osmosis membrane types through a water wave optimization algorithm, and generates target reverse osmosis membrane type data according to the search results;

[0133] The module for establishing production characteristic data of different reverse osmosis membrane types presets corresponding production characteristic data for the performance characteristic data of different reverse osmosis membrane types through the product water preparation data entry interface to obtain production characteristic data of different reverse osmosis membrane types;

[0134] The water production equipment standard production characteristic data matching module performs reverse osmosis membrane type matching processing on the target reverse osmosis membrane type data and the production characteristic data of different reverse osmosis membrane types through an iterative deepening search algorithm to generate standard production characteristic data of water production equipment;

[0135] The water production equipment real-time production deviation data calculation module collects the real-time production data of the current water production equipment during operation through different sensors, obtains the real-time production characteristic data of the water production equipment, and performs subtraction processing with the standard production characteristic data of the water production equipment to generate the real-time production deviation data of the water production equipment;

[0136] The water production equipment real-time operation status analysis module sets a deviation threshold, compares the real-time production deviation data of the water production equipment with the deviation threshold, generates the water production equipment real-time operation status analysis data according to the numerical comparison result, and ends the water production equipment real-time operation status analysis operation if the water production equipment real-time operation status analysis data is normal;

[0137] When the real-time operation status analysis data of the water production equipment is abnormal, the water production equipment real-time operation status adjustment data construction module will identify the real-time production deviation data of the water production equipment in the abnormal state, generate the real-time abnormal production characteristic data of the water production equipment, combine the data with the standard production characteristic data set of the water production equipment, generate the real-time operation status adjustment data of the water production equipment, and push it to the water production equipment production data adjustment platform through the Internet of Things communication network, and execute the water production equipment production data adjustment operation at the same time.

[0138] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. 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 invention. In this specification, the schematic representation 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 one or more embodiments or examples in a suitable manner.

[0139] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A reverse osmosis chemical water production energy-saving optimization method based on data analysis, characterized in that: The steps include: S1. Collect water quality characteristic data of target product water; S2, performing a target reverse osmosis membrane type data search process on the target product water quality characteristic data and the performance characteristic data of different reverse osmosis membrane types, and generating target reverse osmosis membrane type data according to the matching results; S3. Establish production characteristic data of different reverse osmosis membrane types; S4, performing reverse osmosis membrane type matching processing on the target reverse osmosis membrane type data and the production characteristic data of different reverse osmosis membrane types to generate standard production characteristic data of water production equipment; S5, collecting real-time production characteristic data of the water production equipment, performing difference processing on the standard production characteristic data of the water production equipment and the real-time production characteristic data of the water production equipment, and generating real-time production deviation data of the water production equipment; S6, setting a deviation threshold, comparing the real-time production deviation data of the water production equipment with the deviation threshold, generating real-time operation status analysis data of the water production equipment according to the comparison result, and ending the real-time operation status analysis operation of the water production equipment if the real-time operation status analysis data of the water production equipment is normal; S7. If the real-time operation status analysis data of the water-making equipment is abnormal, generate real-time abnormal production characteristic data of the water-making equipment, construct real-time operation status adjustment data of the water-making equipment based on the real-time abnormal production characteristic data of the water-making equipment and the standard production characteristic data of the water-making equipment, and execute the water-making equipment production data adjustment operation.

2. The method for energy-saving optimization of reverse osmosis chemical water production based on data analysis according to claim 1 is characterized in that: The S1 comprises the following steps: S11. Collect the water quality characteristic data of the product water that the enterprise currently needs to produce online through the product water data management platform to generate target product water quality characteristic data A.

3. The method for energy-saving optimization of reverse osmosis chemical water production based on data analysis according to claim 2 is characterized in that: The S2 comprises the following steps: S21. Establish performance characteristic data set B of different reverse osmosis membrane types = {b1, b2, ..., b i ,…,b k }. S22, performing a target reverse osmosis membrane type data search process on the target product water quality characteristic data A and the different reverse osmosis membrane type performance characteristic data in the different reverse osmosis membrane type performance characteristic data sets by using a water wave optimization algorithm, searching for different reverse osmosis membrane type performance characteristic data that successfully matches the target product water quality characteristic data A, and performing data identification on the reverse osmosis membrane type corresponding to the different reverse osmosis membrane type performance characteristic data, to generate target reverse osmosis membrane type data C; S23. If the type of the reverse osmosis membrane installed in the water production equipment matches the target reverse osmosis membrane type data C, directly enter S3; otherwise, perform a reverse osmosis membrane replacement operation on the reverse osmosis membrane installed in the water production equipment based on the target reverse osmosis membrane type data C, and enter S3 after the reverse osmosis membrane replacement operation is completed.

4. The method for energy-saving optimization of reverse osmosis chemical water production based on data analysis according to claim 3 is characterized in that: The S22 comprises the following steps: S221. Construct a reverse osmosis membrane type search water wave population, set the reverse osmosis membrane type search water wave population size to M, the current number of iterations to t, and the maximum number of iterations to t. max and the search space dimension of performance characteristic data of different reverse osmosis membrane types is P; Using the different reverse osmosis membrane type performance characteristic data sets as different reverse osmosis membrane type performance characteristic data search space, randomly generating M different reverse osmosis membrane type performance characteristic data in the different reverse osmosis membrane type performance characteristic data search space, each different reverse osmosis membrane type performance characteristic data corresponds to a reverse osmosis membrane type search water wave individual; S222, calculating the fitness function value of each reverse osmosis membrane type search water wave individual, and taking the reverse osmosis membrane type search water wave individual with the highest fitness function value as the current optimal individual; S223, each reverse osmosis membrane type searches for water wave individuals to perform a propagation operation to update the position; S224, if the fitness function value of the reverse osmosis membrane type search water wave individual after the position is updated is greater than the fitness function value of the current optimal individual, the reverse osmosis membrane type search water wave individual is used to replace the current optimal individual, and the breaking wave operation is performed to update the position; otherwise, the wave height h of the reverse osmosis membrane type search water wave individual is subtracted by 1; S225, if the wave height h of the reverse osmosis membrane type search water wave individual is equal to 0, the reverse osmosis membrane type search water wave individual performs a refraction operation to update the position; otherwise, directly enter S226; S226, calculating the fitness function value of each reverse osmosis membrane type search water wave individual after the position is updated, if the fitness function value of the reverse osmosis membrane type search water wave individual after the position is updated is less than the original fitness function value, then the new position is used to replace the original position; otherwise, the original position is retained; At the same time, the reverse osmosis membrane type search water wave individual with the highest fitness function value is selected as the new current optimal individual; S227, updating the wavelength of the individual water waves searched for each reverse osmosis membrane type; S228, determine whether t is less than t max , if t is less than t max , then t is increased by 1, and the process returns to S223; otherwise, the current optimal individual is taken as the global optimal solution, and the performance characteristic data of different reverse osmosis membrane types corresponding to the global optimal solution are retained; S229, data identification is performed on the reverse osmosis membrane types corresponding to the retained performance characteristic data of different reverse osmosis membrane types to generate target reverse osmosis membrane type data C.

5. The method for energy-saving optimization of reverse osmosis chemical water production based on data analysis according to claim 4 is characterized in that: The S3 comprises the following steps: S31. Preset corresponding production characteristic data for each of the different reverse osmosis membrane type performance characteristic data in the different reverse osmosis membrane type performance characteristic data sets through the product water preparation data entry interface to obtain different reverse osmosis membrane type production characteristic data sets.

6. The method for energy-saving optimization of reverse osmosis chemical water production based on data analysis according to claim 5, characterized in that: The S4 comprises the following steps: S41. Traverse all the production characteristic data of different reverse osmosis membrane types in the production characteristic data sets of different reverse osmosis membrane types through an iterative deepening search algorithm, search for the production characteristic data of different reverse osmosis membrane types that match the target reverse osmosis membrane type data and perform data identification, and generate a standard production characteristic data set E = (e1, e2, e3) for water production equipment, wherein e1, e2 and e3 respectively represent the standard raw water temperature, standard inlet and outlet flow rates and standard inlet pressure when the water production equipment produces the target product water.

7. The method for energy-saving optimization of reverse osmosis chemical water production based on data analysis according to claim 6, characterized in that: The S5 comprises the following steps: S51, collecting real-time production data of the current water production equipment during operation through different sensors, and obtaining a real-time production feature data set E'=(e'1, e'2, e'3) of the water production equipment, wherein e'1, e'2 and e'3 respectively represent the real-time raw water temperature, real-time inlet and outlet flow and real-time inlet pressure of the current water production equipment during operation, and the different sensors include a temperature sensor, a pressure sensor and a flow sensor; S52, using a difference formula to perform numerical difference processing on each standard production characteristic data of water production equipment in the standard production characteristic data set of water production equipment and the real-time production characteristic data of water production equipment with corresponding digital subscripts in the real-time production characteristic data set of water production equipment, to generate a real-time production deviation data set of water production equipment. in, as well as They respectively represent the raw water temperature deviation, inlet and outlet flow deviation, and inlet pressure deviation when the water production equipment produces the target product water.

8. The method for energy-saving optimization of reverse osmosis chemical water production based on data analysis according to claim 7, characterized in that: The S6 comprises the following steps: S61, setting the raw water temperature deviation threshold to η1, the inlet and outlet flow deviation threshold to η2, and the inlet pressure deviation threshold to η3; like It means that the real-time raw water temperature of the water-making equipment when producing the target product water is in a normal state; otherwise, it means that the real-time raw water temperature of the water-making equipment when producing the target product water is in an abnormal state; like It means that the real-time inlet and outlet flow rates of the water production equipment when producing the target product water are in a normal state; otherwise, it means that the real-time inlet and outlet flow rates of the water production equipment when producing the target product water are in an abnormal state; like It means that the real-time inlet pressure of the water production equipment when producing the target product water is in a normal state; otherwise, it means that the real-time inlet pressure of the water production equipment when producing the target product water is in an abnormal state; S62. If the real-time raw water temperature, real-time import and export flow rate and real-time import pressure of the water-making equipment when producing the target product water are all in normal state, then the output of the real-time operation status analysis data of the water-making equipment is normal, and the real-time operation status analysis data of the water-making equipment is pushed to the real-time supervision platform of the water-making equipment through the Internet of Things communication network, and the real-time operation status analysis operation of the water-making equipment is ended; otherwise, the output of the real-time operation status analysis data of the water-making equipment is abnormal.

9. The method for energy-saving optimization of reverse osmosis chemical water production based on data analysis according to claim 8, characterized in that: The S7 comprises the following steps: S71. If the real-time operating status analysis data of the water production equipment is abnormal, The real-time production deviation data of the water-making equipment in an abnormal state is marked to generate the real-time abnormal production characteristic data F of the water-making equipment yichang ; S72, the F yichang Combined with the data of E, the real-time operation status adjustment data of water production equipment Q is generated = (F yichang , E); S73, through the Internet of Things communication network, Q=(F yichang , E) pushes it to the water production equipment production data adjustment platform and executes the water production equipment production data adjustment operation.

10. A system for implementing the reverse osmosis chemical water production energy-saving optimization method based on data analysis as described in any one of claims 1 to 9.

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