A method and system for adaptive optimization of a defoamer component

Abstract

The application discloses a defoaming agent component self-adaptive optimization method and system, relates to the technical field of defoaming agent optimization, and comprises the following steps: collecting original production environment data, and performing pretreatment to obtain a production environment data set; adjusting the defoaming agent proportion by using an efficient defoaming agent formula to obtain an enhanced defoaming agent, activating the enhanced defoaming agent through multi-frequency ultrasonic waves, accurately putting the activated enhanced defoaming agent into a foam generation position, and monitoring foam change conditions in real time to generate defoaming effect evaluation data; performing parameter correction and optimization on the defoaming agent component effect relationship by using the defoaming effect evaluation data, obtaining the optimized defoaming agent component effect relationship, adjusting the required adjustment amount of the defoaming agent component and the put-in amount in the next round of production, and generating a new defoaming agent component. The defoaming efficiency and response speed are further improved.

Description

Technical Field

[0001] This invention relates to the field of defoamer optimization technology, and in particular to an adaptive optimization method and system for defoamer components. Background Technology

[0002] With the continuous improvement of industrial production levels, foam issues are encountered in many manufacturing and chemical processes. The presence of foam not only affects product quality but may also lead to equipment damage or increase the difficulty of cleaning and maintenance. Therefore, the use of defoamers has become an inevitable choice in various industrial production processes. The basic function of defoamers is to reduce foam formation by breaking down the foam film, thereby effectively inhibiting foam expansion.

[0003] Existing technologies for optimizing the formulation and dosage of defoamers rely on static formulation adjustment methods, often neglecting real-time changes in the production environment, such as temperature, humidity, air pressure, and raw material flow rate. Although some methods incorporate environmental data for adjustment, they still lack a dynamic response mechanism to environmental changes during the production process. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an adaptive optimization method for defoamer components, which solves the problem of lack of dynamic adjustment of defoamer ratio.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides an adaptive optimization method for defoamer components, comprising,

[0008] Collect raw production environment data and preprocess it to obtain a production environment dataset;

[0009] Statistical analysis was performed on the production environment dataset. Regression analysis was used to establish the relationship between the effects of defoamer components. Through parameter fitting, the defoamer adjustment amount was output.

[0010] Based on the defoamer adjustment amount and combined with production environment monitoring data, the defoamer ratio is optimized through an intelligent adjustment mechanism to obtain a preliminary defoamer formula, and a high-efficiency defoamer formula is generated through parameter adjustment;

[0011] By adjusting the defoamer ratio using a high-efficiency defoamer formula, an enhanced defoamer is obtained. This enhanced defoamer is then activated using multi-frequency ultrasound. The activated enhanced defoamer is then precisely applied to the foam-generating location, and the foam changes are monitored in real time to generate defoaming effect evaluation data.

[0012] The defoaming effect evaluation data is used to correct and optimize the relationship between the defoaming agent components, resulting in an optimized relationship between the defoaming agent components and the dosage required for the next round of production, thus generating new defoaming agent components.

[0013] As a preferred embodiment of the adaptive optimization method for defoamer components described in this invention, the specific steps for collecting raw production environment data and preprocessing it to obtain a production environment dataset are as follows.

[0014] Collect data on temperature, humidity, air pressure, raw material flow rate, and formula fluctuations in the production environment to obtain raw production environment data;

[0015] The raw production environment data is denoised, missing values ​​are filled in, and normalized to obtain the environment dataset;

[0016] The environmental dataset is integrated and features are extracted to obtain an environmental data feature set. Then, through statistical analysis and correlation mining, environmental data features are generated.

[0017] Cluster analysis is performed on the environmental data features to screen out the effective environmental features, and these effective environmental features are then standardized and integrated into a production environment dataset.

[0018] As a preferred embodiment of the adaptive optimization method for defoamer components described in this invention, the specific steps for statistically analyzing the production environment dataset and establishing the relationship between the effects of defoamer components using regression analysis are as follows.

[0019] Variable selection and regression analysis were performed on the production environment dataset to obtain the environmental characteristic influencing factors.

[0020] Statistical analysis of environmental characteristic influencing factors was conducted to screen out environmental characteristics related to the effect of defoamer and generate an effective environmental characteristic dataset.

[0021] Statistical analysis and correlation mining were performed on the effective environmental feature dataset to obtain the relationship between the effects of defoamer components.

[0022] As a preferred embodiment of the adaptive optimization method for defoamer components described in this invention, the defoamer adjustment amount is obtained by parameter fitting and adjustment calculation of the relationship between the effects of defoamer components, and by optimizing the ratio between defoamer components and dosage based on environmental characteristic influencing factors.

[0023] As a preferred embodiment of the adaptive optimization method for defoamer components described in this invention, the steps include: optimizing the defoamer ratio through an intelligent adjustment mechanism based on the defoamer adjustment amount and combined with production environment monitoring data to obtain a preliminary defoamer formula; and generating a high-efficiency defoamer formula through parameter adjustment. The specific steps are as follows.

[0024] The real-time monitoring data of the production environment is input into the intelligent adjustment mechanism, and combined with the defoamer adjustment amount, the ratio of each component is automatically calculated to generate a preliminary defoamer formula;

[0025] By utilizing defoamer adjustment amounts, defoaming effect evaluation data, and environmental factors, the proportions, dosages, and physical performance parameters of defoamer components are adjusted to optimize the initial defoamer formulation and generate a high-efficiency defoamer formulation.

[0026] As a preferred embodiment of the adaptive optimization method for defoamer components described in this invention, the step of adjusting the defoamer ratio using a high-efficiency defoamer formulation to obtain an enhanced defoamer, and then activating it using multi-frequency ultrasound, includes the following specific steps.

[0027] By adjusting the proportions and concentrations of each component in a high-efficiency defoamer formulation, an enhanced defoamer can be obtained.

[0028] By utilizing the cavitation effect of ultrasound at different frequencies and the molecular excitation of ultrasound at different frequencies, an activated enhanced defoamer is obtained.

[0029] As a preferred embodiment of the adaptive optimization method for defoamer components described in this invention, the defoaming effect evaluation data is obtained by real-time monitoring of the foam generation location, adjusting the angle, flow rate, and direction of the dispensing device, accurately dispensing the activated enhanced defoamer to the foam generation location, and monitoring foam changes in real time.

[0030] As a preferred embodiment of the adaptive optimization method for defoamer components described in this invention, the steps of using defoaming effect evaluation data to correct and optimize the relationship between defoamer component effects to obtain the optimized relationship between defoamer component effects are as follows:

[0031] The defoaming effect evaluation data is sorted, cleaned, and standardized to obtain the cleaned defoaming evaluation data.

[0032] Factor analysis was performed on the defoaming effect evaluation data after cleaning, and the proportion of defoamer components was dynamically adjusted to obtain the optimized relationship between the defoamer component effects.

[0033] As a preferred embodiment of the adaptive optimization method for defoamer components described in this invention, the specific steps for adjusting the required adjustment amount of the defoamer components and dosage in the next round of production to generate new defoamer components are as follows.

[0034] Based on the optimized relationship between the effects of the defoamer components, the proportions and dosages of each defoamer component were recalculated to obtain the optimized defoamer formulation.

[0035] By optimizing the defoamer formulation, the dosage of each component was recalculated, and the defoamer was prepared in proportion according to the calculation results to generate a new defoamer.

[0036] Secondly, the present invention provides an adaptive optimization system for defoamer components, comprising,

[0037] The data acquisition module is used to collect raw production environment data and preprocess it to obtain a production environment dataset.

[0038] The regression analysis module is used for statistical analysis of production environment datasets. It uses regression analysis to establish the relationship between the effects of defoamer components and outputs the defoamer adjustment amount through parameter fitting.

[0039] The formulation optimization module is used to optimize the defoamer formulation based on the defoamer adjustment amount and combined with production environment monitoring data, through an intelligent adjustment mechanism to obtain a preliminary defoamer formula, and generate a high-efficiency defoamer formula through parameter adjustment;

[0040] The defoaming activation module is used to adjust the defoamer ratio using a high-efficiency defoamer formula to obtain an enhanced defoamer. It is then activated by multi-frequency ultrasound, and the activated enhanced defoamer is precisely delivered to the foam generation location. The module also monitors the foam changes in real time and generates defoaming effect evaluation data.

[0041] The effect evaluation module is used to correct and optimize the relationship between the effects of defoamer components using defoaming effect evaluation data, obtain the optimized relationship between the effects of defoamer components, adjust the required adjustment amount of defoamer components and dosage in the next round of production, and generate new defoamer components.

[0042] The beneficial effects of this invention are as follows: By using regression analysis and parameter fitting, the adjustment amount of defoamer is optimized, and the ratio and dosage can be automatically adjusted according to changes in the production environment, thereby improving the stability and efficiency of the defoaming effect. Utilizing multi-frequency ultrasound to activate the defoamer enhances its defoaming performance and allows for precise delivery to the foam-generating location, further improving defoaming efficiency and response speed. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 The flowchart shows the adaptive optimization method for defoamer components.

[0045] Figure 2A schematic diagram of an adaptive optimization system for defoamer components.

[0046] Figure 3 This is a flowchart of the data processing process in the production environment.

[0047] Figure 4 Flowchart for optimizing the formulation of defoamer. Detailed Implementation

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0049] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0051] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides an adaptive optimization method for defoamer components, comprising the following steps:

[0052] S1. Collect raw production environment data and preprocess it to obtain a production environment dataset.

[0053] S1.1 Collect data on temperature, humidity, air pressure, raw material flow rate, and formula fluctuations in the production environment to obtain raw production environment data.

[0054] It should be noted that temperature, humidity, air pressure, raw material flow rate, and formula fluctuation data are recorded in real time using sensors and data acquisition devices. Temperature and humidity sensors accurately measure the temperature and humidity in the production environment; air pressure sensors provide air pressure information; raw material flow rate sensors monitor the speed of raw material flow in real time; and formula fluctuation data is collected by dedicated equipment to reflect changes in the formula during production. All of this data is collected synchronously and transmitted to the data storage unit to form the original production environment dataset.

[0055] It should be noted that temperature and humidity data are collected in real time by temperature and humidity sensors installed at key locations in the production environment to measure the temperature and humidity in the air; air pressure data is collected by air pressure sensors, typically located at ventilation openings or enclosed areas of the production environment, to monitor air pressure changes in real time; raw material flow rate data is collected by flow sensors installed inside raw material pipelines to monitor the flow speed of raw materials; formula fluctuation data is recorded by formula management equipment to track formula adjustments and the addition ratio of each component in real time during the production process. All this data is collected and stored synchronously by sensors and control equipment to form the raw production environment data.

[0056] S1.2. The original production environment data is denoised, missing values ​​are filled in and normalized to obtain the environment dataset.

[0057] It should be noted that noise processing is performed on the raw production environment data to remove outliers caused by external interference. Common denoising methods include median filtering, which effectively smooths the data and reduces the impact of sudden noise on the analysis results. Missing values ​​in the raw production environment data are imputed; common imputation methods include nearest-neighbor interpolation to ensure data integrity and consistency. The processed raw production environment data is then normalized and scaled according to a unified standard to eliminate the influence between different data units, ensuring consistency across numerical ranges, resulting in the environmental dataset.

[0058] S1.3. Integrate and extract features from the environmental dataset to obtain an environmental data feature set, and generate environmental data features through statistical analysis and correlation mining.

[0059] It should be noted that various environmental datasets are integrated according to time, space, and other dimensions to form a multi-dimensional dataset. Feature extraction methods are used to extract relevant features from the environmental dataset, typically including mean, variance, maximum, minimum, skewness, and kurtosis statistics, to reflect the basic characteristics of the environmental data feature set. Statistical analysis methods are then used to identify the correlations between different features, employing correlation coefficient analysis and regression analysis to mine associations, thereby discovering environmental factors affecting defoaming effects and generating environmental data features.

[0060] S1.4 Perform cluster analysis on the environmental data characteristics, screen out the effective environmental characteristics, and integrate the effective environmental characteristics into a production environment dataset through standardization.

[0061] It should be noted that cluster analysis is applied to group environmental data features to identify potential structures and patterns within the environmental data feature set. Based on the similarity of environmental data features, related features are grouped together, and effective environmental features closely related to defoaming effects are screened. Through correlation analysis, regression analysis, and statistical significance tests, environmental features with significant impacts on defoaming effects, such as temperature, humidity, air pressure, and flow rate, are identified. Cluster analysis removes redundant information, retaining only features that affect the production process. The screened effective environmental features are standardized to convert them to a uniform scale, eliminating the influence of dimensional differences between features. The standardized effective environmental features are then integrated into a production environment dataset.

[0062] It should be noted that the similarity of environmental data features is obtained through cluster analysis. Various environmental data features are integrated according to dimensions such as time and space to form a multi-dimensional dataset. Clustering algorithms are then used to group these data, classifying them into different groups based on the similarity between features. By calculating the distance or similarity between features, cluster analysis can identify potential structures and patterns in the data, thereby filtering out effective environmental features that impact the production process.

[0063] S2. Perform statistical analysis on the production environment dataset, use regression analysis to establish the relationship between the effects of defoamer components, and output the defoamer adjustment amount through parameter fitting.

[0064] S2.1 Perform variable selection and regression analysis on the production environment dataset to obtain the environmental characteristic influencing factors.

[0065] It should be noted that redundant or irrelevant features are removed through variable selection. This process typically employs statistical methods such as correlation coefficient analysis to identify environmental variables that have a positive impact on defoaming performance. Regression analysis is then used to model the selected environmental features, analyzing the quantitative relationship between the environmental features and the defoaming effect. The regression analysis results reveal the degree of contribution of different environmental features to the defoaming effect, thus yielding the environmental feature influencing factors.

[0066] S2.2 Statistical analysis of environmental characteristic influencing factors is conducted to screen out environmental characteristics related to the defoamer effect and generate the relationship between the defoamer component effect.

[0067] It should be noted that correlation analysis was applied to assess the relationship between various effective environmental characteristics and the defoamer's effect. Through statistical analysis, effective environmental characteristics beneficial to the defoamer's effect were screened. The analysis results will reveal which environmental characteristics play a role in the defoaming process. The screened effective environmental characteristics were then integrated to form the relationship between the defoamer components and their effects.

[0068] S2.3 Perform statistical analysis and correlation mining on the effective environmental feature dataset to generate the relationship between the effects of defoamer components.

[0069] It should be noted that statistical analysis and correlation mining were performed on the relationship between the effects of defoamer components. Basic statistics of effective environmental characteristics were calculated through descriptive statistical analysis. Multivariate regression analysis was then used to further explore the nonlinear relationship between environmental characteristics and defoamer effects, generating the relationship between the effects of defoamer components.

[0070] S2.4 Perform parameter fitting and adjustment calculations on the relationship between the effects of defoamer components, and optimize the ratio of defoamer components to dosage based on environmental characteristic influencing factors, and output the defoamer adjustment amount.

[0071] It should be noted that, based on the relationship between the effects of defoamer components, a fitting method is used to determine the mathematical relationship between effective environmental characteristics and the defoamer effect. By combining the weights of environmental characteristic influencing factors, the ratio of defoamer components to dosage is adjusted to ensure that the impact of each effective environmental characteristic is reflected. Through this process, the ratio of defoamer components to dosage can be optimized, enabling the defoamer components and dosage to achieve the best defoaming effect in the actual production environment, and outputting the defoamer adjustment amount.

[0072] It should be noted that the weights of the environmental characteristic influencing factors are obtained through regression analysis and weighted calculation. Regression analysis is used to establish a quantitative relationship between environmental characteristics and the defoamer's effectiveness, determining the degree of influence of each environmental characteristic. Based on the regression analysis results, the regression coefficients of each characteristic are calculated; the larger the regression coefficient, the greater the characteristic's influence on the defoamer's effectiveness. Using the regression coefficients, a weight can be assigned to each environmental characteristic; a higher weight indicates a greater contribution to the defoamer's effectiveness, ultimately yielding the weights of each environmental characteristic influencing factor.

[0073] S3. Based on the defoamer adjustment amount and combined with production environment monitoring data, the defoamer ratio is optimized through an intelligent adjustment mechanism to obtain a preliminary defoamer formula, and a high-efficiency defoamer formula is generated through parameter adjustment.

[0074] It should be noted that in existing technologies, the formulation of defoamers typically relies on empirical methods. During operation, the component ratios and dosages of the defoamer are mostly based on manually set standard ratios, failing to incorporate real-time changes in the production environment and feedback on the defoaming effect. Typically, these ratios are static and lack dynamic optimization and real-time adjustment mechanisms.

[0075] This invention combines defoamer dosage adjustment, production environment monitoring data, and defoaming effect, utilizing an intelligent adjustment mechanism to dynamically optimize the defoamer ratio. It can automatically adjust the defoamer ratio in real time based on changes in the production environment and feedback on the defoaming effect to generate an initial defoamer formula. Further parameter adjustments generate a high-efficiency defoamer formula, achieving a more precise and efficient defoaming effect. This process emphasizes a data-driven and real-time feedback adjustment mechanism, overcoming the limitations of static formulas in existing technologies.

[0076] S3.1 Based on real-time monitoring data of the production environment and the defoaming effect, combined with the defoamer adjustment amount, the data is input into the intelligent adjustment mechanism. Based on the defoamer adjustment amount and the production environment monitoring data, the ratio of each component is automatically calculated to generate a preliminary defoamer formula.

[0077] It should be noted that, based on real-time monitoring data of the production environment and the defoaming effect, combined with the adjustment amount of defoamer, the production environment monitoring data is input into the intelligent adjustment mechanism. The intelligent adjustment mechanism automatically calculates the optimal ratio of each defoamer component based on the relationship between the production environment data and the defoamer adjustment amount. By analyzing the interactions of each component, the mechanism can adjust the ratio to cope with changes in the real-time production environment, ensuring maximum defoaming effect and generating a preliminary defoamer formula.

[0078] S3.2. Using defoamer adjustment amount, defoaming effect evaluation data and environmental factors, adjust the proportion of defoamer components, dosage and physical performance parameters, optimize the initial defoamer formula, and generate a high-efficiency defoamer formula.

[0079] It should be noted that the rate of decrease in foam height from the time the defoamer is added until the effect becomes apparent, and the final level of foam height maintained, should be observed. After the foam height reaches a stable state, the fluctuations in the value should be observed. The observed decreasing trend and stable state should be compared with the expected defoaming effect target to determine the effectiveness of the defoaming effect. The performance of the initial defoamer formulation in the actual production environment should be evaluated in conjunction with environmental factors such as temperature, humidity, air pressure, and raw material flow rate. The proportions of the defoamer components should be adjusted according to the contribution weights of each component provided by the defoamer adjustment amount. The total dosage of defoamer should be optimized according to the severity of foam formation. Considering the transport characteristics of the defoamer in pipelines, the physical performance parameters of the initial defoamer formulation should be adjusted to ensure that the flowability meets the requirements of precise dosing equipment. By comprehensively adjusting the proportions of defoamer components, dosage, and physical performance parameters, the defoamer formulation can be better adapted to real-time production conditions, resulting in a highly efficient defoamer formulation.

[0080] S4. Adjust the defoamer ratio using a high-efficiency defoamer formula to obtain an enhanced defoamer, which is then activated using multi-frequency ultrasound. The activated enhanced defoamer is then precisely applied to the foam generation location, and the foam changes are monitored in real time to generate defoaming effect evaluation data.

[0081] It should be noted that in existing technologies, the formulation adjustment of defoamers typically relies on a fixed formula, and the defoaming effect is improved through empirical methods or static formula adjustments. For defoamer activation, most methods use single-frequency ultrasound, which fails to fully utilize the enhancing effect of multi-frequency ultrasound. Defoamer dosing generally depends on manual operation, failing to adjust in real time according to foam changes, resulting in a typically delayed assessment of the defoaming effect and a lack of immediate feedback.

[0082] This invention optimizes the formulation of defoamers using a high-efficiency defoamer formula to generate an enhanced defoamer. Multi-frequency ultrasound is used for activation to enhance the defoamer's effectiveness, and precise activation is achieved through cavitation and molecular excitation of ultrasound at different frequencies. The activated defoamer is then precisely applied to the foam-generating location using a controlled device, with real-time monitoring of foam changes during application. This process generates defoaming effect evaluation data, and the application method and dosage are adjusted based on real-time feedback to ensure optimal defoaming performance, overcoming the lag and shortcomings of existing methods.

[0083] S4.1. By adjusting the proportion and concentration of each component of the defoamer using a high-efficiency defoamer formula, an enhanced defoamer is obtained.

[0084] It should be noted that, based on the relationship between the effects of the defoamer components, the contribution of each component to the defoaming effect is evaluated. Based on production environment data and defoaming effect feedback, the proportions and concentrations of each component are adjusted to optimize the defoaming effect. During the adjustment process, the performance changes of the defoamer under different environmental conditions are taken into account to ensure that the synergistic effect between the components is maximized. By accurately calculating the optimal proportion of each component, an enhanced defoamer is finally obtained.

[0085] S4.2. By utilizing the cavitation effect of ultrasound at different frequencies and the molecular excitation of ultrasound at different frequencies to enhance the defoamer, an activated enhanced defoamer is obtained.

[0086] It should be explained that by utilizing the cavitation effect of ultrasound at different frequencies, microbubbles are generated in the defoamer by adjusting the frequency of the ultrasound. These microbubbles rapidly expand and burst under the action of ultrasound, thereby producing a strong local high temperature and high pressure effect, enhancing the activity of the defoamer. Furthermore, by utilizing the molecular excitation effect of ultrasound at different frequencies, the molecules in the defoamer are excited, increasing the reactivity and further enhancing the defoaming effect. Through the combination of ultrasonic cavitation and molecular excitation, an activated and enhanced defoamer is obtained.

[0087] S4.3 By monitoring the foam generation location in real time, adjusting the angle, flow rate, and direction of the dispensing equipment, the activated enhanced defoamer is precisely dispensed to the foam generation location, and foam changes are monitored in real time to generate defoaming effect evaluation data.

[0088] It should be noted that by monitoring the location of foam generation in real time, the angle, flow rate, and dispensing direction of the dispensing equipment are precisely adjusted to ensure that the activated enhanced defoamer is dispensed at the most suitable location. Through these adjustments, the enhanced defoamer can act directly on the foam generation source and quickly exert its defoaming effect. The real-time monitoring equipment continuously tracks changes in the foam, capturing and recording characteristics such as foam height and stability, generating defoaming effect evaluation data.

[0089] S5. Using the defoaming effect evaluation data, the parameters of the defoamer component effect relationship are corrected and optimized to obtain the optimized defoamer component effect relationship. The required adjustment amount of defoamer component and dosage in the next round of production is then adjusted to generate new defoamer components.

[0090] S5.1 Organize, clean, and standardize the defoaming effect evaluation data to obtain the cleaned defoaming evaluation data.

[0091] It should be noted that the data is sorted by time, batch, and other dimensions to remove redundancy and irrelevant information; outliers are detected and removed using statistical methods, and missing data are filled using the mean; the min-max standardization method is used to convert the data into a uniform dimension and range to ensure data consistency and quality, resulting in defoaming evaluation data after cleaning.

[0092] S5.2. Perform factor analysis on the defoaming effect evaluation data after cleaning, identify the various factors affecting the defoaming effect, and adjust the proportion of defoamer components to obtain the optimized relationship between the defoamer components and their effects.

[0093] It should be noted that factor analysis was performed on the defoaming effect evaluation data after cleaning to identify key factors affecting the defoaming effect, including environmental characteristics such as temperature, humidity, and air pressure, as well as the various components of the defoamer. Based on the analysis results, the proportions of each component of the defoamer were adjusted to optimize the component-effect relationship of the defoamer, ensuring that the defoamer can achieve the best effect under different production conditions, thus obtaining the optimized component-effect relationship of the defoamer.

[0094] S5.3 Based on the optimized relationship between the defoamer components, recalculate the proportion and dosage of each defoamer component to obtain the optimized defoamer ratio.

[0095] It should be noted that the optimized defoamer component effect relationship is used to analyze the impact of each defoamer component on the defoaming effect. Based on the contribution of each defoamer component to the defoaming effect, the proportion and dosage of each defoamer component are recalculated to ensure that each component achieves the best defoaming effect in the final formulation. Through this calculation, the optimized defoamer formulation is obtained.

[0096] S5.4. Based on the optimized defoamer ratio, recalculate the dosage of each component, and prepare the defoamer according to the calculation results to generate a new defoamer.

[0097] It should be noted that the dosage of each component should be recalculated based on the adjusted proportions. Based on the calculated dosage of each component, the defoamer should be prepared step-by-step according to the proportions, ensuring that the proportions of each component meet the optimized requirements. In practice, the dosage of each component should be accurately measured according to the required proportions, and the mixture should be thoroughly mixed to ensure the uniformity and stability of the formulation, thus generating a new defoamer.

[0098] This embodiment also provides an adaptive optimization system for defoamer components, including:

[0099] The data acquisition module is used to collect raw production environment data and preprocess it to obtain a production environment dataset.

[0100] The regression analysis module is used for statistical analysis of production environment datasets. It uses regression analysis to establish the relationship between the effects of defoamer components and outputs the defoamer adjustment amount through parameter fitting.

[0101] The formulation optimization module is used to optimize the defoamer formulation based on the defoamer adjustment amount and combined with production environment monitoring data, through an intelligent adjustment mechanism to obtain a preliminary defoamer formula, and generate a high-efficiency defoamer formula through parameter adjustment;

[0102] The defoaming activation module is used to adjust the defoamer ratio using a high-efficiency defoamer formula to obtain an enhanced defoamer. It is then activated by multi-frequency ultrasound, and the activated enhanced defoamer is precisely delivered to the foam generation location. The module also monitors the foam changes in real time and generates defoaming effect evaluation data.

[0103] The effect evaluation module is used to correct and optimize the relationship between the effects of defoamer components using defoaming effect evaluation data, obtain the optimized relationship between the effects of defoamer components, adjust the required adjustment amount of defoamer components and dosage in the next round of production, and generate new defoamer components.

[0104] This embodiment also provides a computer device applicable to the adaptive optimization method for defoamer components, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the adaptive optimization method for defoamer components as proposed in the above embodiment.

[0105] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0106] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the adaptive optimization method for defoamer components as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0107] In summary, this invention optimizes the dosage of defoamer through regression analysis and parameter fitting, automatically adjusting the ratio and dosage according to changes in the production environment, thereby improving the stability and efficiency of the defoaming effect. Furthermore, utilizing multi-frequency ultrasound to activate the defoamer enhances its defoaming performance and allows for precise delivery to the foam-generating location, further improving defoaming efficiency and response speed.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An adaptive optimization method for defoamer components, characterized in that: include, Collect raw production environment data and preprocess it to obtain a production environment dataset; Statistical analysis was performed on the production environment dataset. Regression analysis was used to establish the relationship between the effects of the defoamer components. Through parameter fitting, the defoamer adjustment amount was output. The specific steps are as follows. Variable selection and regression analysis were performed on the production environment dataset to obtain the environmental characteristic influencing factors. Statistical analysis of environmental characteristic influencing factors was conducted to screen out environmental characteristics related to the effect of defoamer and generate an effective environmental characteristic dataset. Statistical analysis and correlation mining were performed on the effective environmental feature dataset to obtain the relationship between the effects of defoamer components; By performing parameter fitting and adjustment calculations on the relationship between the effects of defoamer components, and based on environmental characteristic influencing factors, the ratio of defoamer components to dosage is optimized, and the defoamer adjustment amount is output. Based on the defoamer dosage and combined with production environment monitoring data, an intelligent adjustment mechanism is used to optimize the defoamer ratio, obtain a preliminary defoamer formula, and then generate a high-efficiency defoamer formula through parameter adjustments. The specific steps are as follows. The real-time monitoring data of the production environment is input into the intelligent adjustment mechanism, and combined with the defoamer adjustment amount, the ratio of each component is automatically calculated to generate a preliminary defoamer formula; By utilizing defoamer adjustment amount, defoaming effect evaluation data and environmental factors, the proportion of defoamer components, dosage and physical performance parameters are adjusted to optimize the initial defoamer formulation and generate a high-efficiency defoamer formulation. By adjusting the defoamer ratio using a high-efficiency defoamer formula, an enhanced defoamer is obtained. This enhanced defoamer is then activated using multi-frequency ultrasound. The activated enhanced defoamer is then precisely applied to the foam-generating location, and the foam changes are monitored in real time to generate defoaming effect evaluation data. The defoaming effect evaluation data is used to correct and optimize the relationship between the defoaming agent components, resulting in an optimized relationship between the defoaming agent components and the dosage required for the next round of production, thus generating new defoaming agent components.

2. The adaptive optimization method for defoamer components as described in claim 1, characterized in that: The process of collecting raw production environment data and preprocessing it to obtain a production environment dataset involves the following steps. Collect data on temperature, humidity, air pressure, raw material flow rate, and formula fluctuations in the production environment to obtain raw production environment data; The raw production environment data is denoised, missing values ​​are filled in, and normalized to obtain the environment dataset; The environmental dataset is integrated and features are extracted to obtain an environmental data feature set. Then, through statistical analysis and correlation mining, environmental data features are generated. Cluster analysis is performed on the environmental data features to screen out the effective environmental features, and these effective environmental features are then standardized and integrated into a production environment dataset.

3. The adaptive optimization method for defoamer components as described in claim 1, characterized in that: The process involves adjusting the defoamer ratio using a high-efficiency defoamer formulation to obtain an enhanced defoamer, which is then activated using multi-frequency ultrasound. The specific steps are as follows. By adjusting the proportions and concentrations of each component in a high-efficiency defoamer formulation, an enhanced defoamer can be obtained. By utilizing the cavitation effect of ultrasound at different frequencies and the molecules of ultrasound at different frequencies, an enhanced defoamer is excited, resulting in an activated enhanced defoamer.

4. The adaptive optimization method for defoamer components as described in claim 1, characterized in that: The defoaming effect evaluation data is obtained by real-time monitoring of the foam generation location, adjusting the angle, flow rate, and direction of the dispensing device to accurately deliver the activated enhanced defoamer to the foam generation location, and monitoring the foam changes in real time.

5. The adaptive optimization method for defoamer components as described in claim 4, characterized in that: The process of using defoaming effect evaluation data to correct and optimize the relationship between the effects of defoamer components, resulting in an optimized relationship, is described in the following steps. The defoaming effect evaluation data is sorted, cleaned, and standardized to obtain the cleaned defoaming evaluation data. Factor analysis was performed on the defoaming effect evaluation data after cleaning, and the proportion of defoamer components was dynamically adjusted to obtain the optimized relationship between the defoamer component effects.

6. The adaptive optimization method for defoamer components as described in claim 5, characterized in that: The adjustment of the defoamer components and dosage in the next round of production, and the generation of new defoamer components, are carried out through the following specific steps. Based on the optimized relationship between the effects of the defoamer components, the proportions and dosages of each defoamer component were recalculated to obtain the optimized defoamer formulation. By optimizing the defoamer formulation, the dosage of each component was recalculated, and the defoamer was prepared in proportion according to the calculation results to generate a new defoamer.

7. An adaptive optimization system for defoamer components, based on the adaptive optimization method for defoamer components according to any one of claims 1 to 6, characterized in that: include, The data acquisition module is used to collect raw production environment data and preprocess it to obtain a production environment dataset. The regression analysis module is used for statistical analysis of production environment datasets. It uses regression analysis to establish the relationship between the effects of defoamer components and outputs the defoamer adjustment amount through parameter fitting. The formulation optimization module is used to optimize the defoamer formulation based on the defoamer adjustment amount and combined with production environment monitoring data, through an intelligent adjustment mechanism to obtain a preliminary defoamer formula, and generate a high-efficiency defoamer formula through parameter adjustment; The defoaming activation module is used to adjust the defoamer ratio using a high-efficiency defoamer formula to obtain an enhanced defoamer. It is then activated by multi-frequency ultrasound, and the activated enhanced defoamer is precisely delivered to the foam generation location. The module also monitors the foam changes in real time and generates defoaming effect evaluation data. The effect evaluation module is used to correct and optimize the relationship between the effects of defoamer components using defoaming effect evaluation data, obtain the optimized relationship between the effects of defoamer components, adjust the required adjustment amount of defoamer components and dosage in the next round of production, and generate new defoamer components.

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