Condensate water disinfection and purification device for central air conditioner

Abstract

The invention relates to the technical field of water treatment, and discloses a central air-conditioning condensate water disinfection and purification device which comprises a treatment tank main body, an ozone generator and an ozone generator real-time regulation and control system. According to the system, a demand coefficient is dynamically obtained based on residual ozone concentration and ultraviolet absorbance through a pollution load evaluation module; the ozone mass transfer efficiency evaluation module calculates a mass transfer efficiency coefficient based on the tail gas and inlet gas ozone concentration and the gas flow; the matching evaluation module combines the demand coefficient, the mass transfer efficiency coefficient and the water temperature influence factor to generate a matching factor; the generator health monitoring module evaluates a health degree coefficient based on parameters such as air inlet pressure; and the power comprehensive decision-making module fuses the health degree coefficient and the matching factor, and outputs a target power set value through a multi-parameter regulation and control model. The intelligent sensing and dynamic optimization of ozone feeding are realized, the problems of traditional control stiffness, low mass transfer efficiency and insufficient equipment guarantee are solved, and the energy utilization rate and the operation reliability are improved while the disinfection effect is ensured.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of water treatment, and particularly relates to a central air conditioning condensate water disinfection and purification device. BACKGROUND

[0002] A central air conditioning system generates a large amount of condensate water during operation. This water carries dust and microorganisms (such as legionella and mold) in the air duct during formation, becoming a potential source of pollution, and if not properly treated, it may cause public health and safety risks.

[0003] Currently, the treatment methods for condensate water mainly include direct discharge, simple reuse, or the use of traditional disinfection methods. Among them, the chemical agent method has the risk of producing harmful by-products; the ultraviolet disinfection method has no continuous sterilization ability, and the effect is easily affected by water quality.

[0004] Ozone disinfection technology is considered an ideal solution due to its high efficiency, broad spectrum, and no secondary pollution. However, existing ozone treatment devices still have significant shortcomings when applied to condensate water scenarios, which constitutes the core problem that the present application needs to solve:

[0005] Rigid control strategy: Most devices use fixed-power ozone generators, which cannot dynamically adjust according to the real-time changing pollution load (such as water quality, water quantity) of condensate water, resulting in either incomplete disinfection or excessive ozone, causing energy waste.

[0006] Low process efficiency: The dissolution efficiency (mass transfer efficiency) of ozone in water is affected by water temperature and mixing conditions, and the existing system lacks real-time evaluation and optimization of this efficiency, resulting in low ozone utilization rate.

[0007] Lack of equipment protection: There is a lack of real-time monitoring and protection of key operating parameters (such as temperature, pressure, humidity) of the ozone generator itself, and the safety and reliability of long-term operation of the equipment are difficult to guarantee.

[0008] Therefore, there is an urgent need in the field for a central air conditioning condensate water disinfection and purification device that can realize intelligent sensing, real-time evaluation, and precise regulation, to ensure disinfection effect while improving operation efficiency and equipment safety. SUMMARY

[0009] The purpose of the embodiments of the present application is to provide a central air conditioning condensate water disinfection and purification device to solve the above problems.

[0010] The application is achieved in the following way, a central air conditioning condensate water disinfection and purification device, comprising a treatment tank main body, and an ozone generator installed on the side wall of the treatment tank main body, the output end of the ozone generator is connected with the aeration pipeline at the bottom of the treatment tank main body, and further comprising: an ozone generator real-time regulation system connected with the electric control module of the ozone generator, the ozone generator real-time regulation system comprises: a pollution load evaluation module, based on the residual ozone concentration and ultraviolet light absorbance in the treated water, the demand coefficient is obtained through an ozone demand model; an ozone mass transfer efficiency evaluation module, based on the tail gas ozone concentration, the inlet gas ozone concentration and the ozone gas flow, the ozone mass transfer efficiency coefficient is obtained through an ozone mass transfer efficiency evaluation model; a matching evaluation module, based on the demand coefficient and the ozone mass transfer efficiency coefficient under the current water temperature, the matching factor is obtained through a matching model; a generator health monitoring module, based on the inlet gas pressure, the discharge chamber temperature and the inlet gas humidity of the ozone generator, the health degree coefficient is obtained through a device safety state model; and a power comprehensive decision module, based on the health degree coefficient and the matching factor, the target power setting value is output through a multi-parameter fusion regulation model.

[0011] Further technical solutions, the multi-parameter fusion regulation model is: , wherein, is the target power setting value, is the rated power of the ozone generator, is the health degree coefficient, is the matching factor.

[0012] Further technical solutions, the specific steps of obtaining the health degree coefficient through the device safety state model based on the inlet gas pressure, the discharge chamber temperature and the inlet gas humidity of the ozone generator are: the absolute value of the difference between the current inlet gas pressure of the ozone generator and the best inlet gas pressure is processed by ratio, and after limiting the upper limit to 1 by using the min function, the pressure deviation index is obtained; the current discharge chamber temperature and the inlet gas humidity are processed by maximum-minimum normalization to obtain the discharge chamber temperature index and the inlet gas humidity index; the pressure deviation index, the discharge chamber temperature index and the inlet gas humidity index are weighted and fused to obtain the comprehensive health degree coefficient , the health degree coefficient is negatively correlated with the pressure deviation index, the discharge chamber temperature index and the inlet gas humidity index.

[0013] Further technical solutions, the specific steps of obtaining the matching factor through the matching model based on the demand coefficient and the ozone mass transfer efficiency coefficient under the current water temperature are: based on the current water temperature, the water temperature influence factor is calculated by using a water temperature influence model; based on the demand coefficient, the ozone mass transfer efficiency coefficient and the water temperature influence factor, the final matching factor is obtained through a matching fusion model , the matching factor is positively correlated with the demand coefficient and the ozone mass transfer efficiency coefficient, and negatively correlated with the water temperature influence factor.

[0014] Further technical solutions, the specific steps of calculating the water temperature influence factor based on the current water temperature and using the water temperature influence model are as follows: the absolute value of the difference between the current water temperature and the optimal water temperature is processed by ratio with the difference between the upper limit of water temperature and the lower limit of water temperature, and after limiting the upper limit to 1 by using the min function, the water temperature influence factor is obtained.

[0015] Further technical solutions, the specific steps of obtaining the demand coefficient based on the residual ozone concentration in the treated water and the ultraviolet light absorbance and through the ozone demand model are as follows: the residual ozone concentration index is obtained by processing the current residual ozone concentration and the target residual ozone concentration by ratio and limiting the upper limit to 1 by using the min function; the ultraviolet light absorbance index is obtained by processing the current ultraviolet light absorbance value and the reference ultraviolet light absorbance value by ratio and limiting the upper limit to 1 by using the min function; the residual ozone concentration index and the ultraviolet light absorbance index are weighted and fused to obtain the demand coefficient , the demand coefficient is positively correlated with the ultraviolet light absorbance index and negatively correlated with the residual ozone concentration index.

[0016] Further technical solutions, the specific steps of obtaining the ozone mass transfer efficiency coefficient based on the tail gas ozone concentration, the inlet gas ozone concentration and the ozone gas flow and through the ozone mass transfer efficiency evaluation model are as follows: based on the inlet gas ozone concentration, the ozone gas flow and the tail gas ozone concentration, the ozone mass dissolved into the water in unit time is calculated; the theoretical mass transfer efficiency is obtained by processing the dissolved ozone mass and the total ozone mass passed in unit time by ratio to obtain the ozone mass transfer efficiency coefficient .

[0017] Compared with the prior art, the beneficial effects of the present application are:

[0018] The central air conditioning condensate water disinfection and purification device provided by the present application realizes dynamic and accurate regulation of the ozone dosage through the real-time regulation system of the ozone generator integrated with pollution load evaluation, mass transfer efficiency evaluation, health monitoring and power decision modules, effectively solves the problems of lack of flexibility of control strategy, low ozone mass transfer efficiency and insufficient equipment safety in the prior art, can dynamically adjust the power of the ozone generator according to the real-time pollution load of the condensate water and the equipment operation state, ensures the disinfection effect while avoiding excessive ozone dosage, improves the energy utilization efficiency, and enhances the reliability and safety of equipment operation. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The flowchart of the real-time regulation system of the ozone generator provided by the present application

[0020] Figure 2 The structural schematic diagram of the central air conditioning condensate water disinfection and purification device provided by the present application.

[0021] In the drawings: 1, processing tank body; 2, ozone generator. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0023] The specific implementation of the present application is described in detail below in combination with specific examples.

[0024] As shown in Figure 1 and Figure 2 , a central air conditioning condensate water disinfection and purification device provided by an embodiment of the present application includes a processing tank body 1 and an ozone generator 2 installed on the side wall of the processing tank body 1, the output end of the ozone generator 2 is connected with an aeration pipeline at the bottom of the processing tank body 1, and further includes: an ozone generator real-time regulation system connected with the electric control module of the ozone generator 2, the ozone generator real-time regulation system includes: a pollution load evaluation module, based on the residual ozone concentration and ultraviolet light absorbance in the treated water, a demand coefficient is obtained through an ozone demand model; an ozone mass transfer efficiency evaluation module, based on the tail gas ozone concentration, the inlet gas ozone concentration and the ozone gas flow, an ozone mass transfer efficiency coefficient is obtained through an ozone mass transfer efficiency evaluation model; a matching evaluation module, based on the demand coefficient and the ozone mass transfer efficiency coefficient at the current water temperature, a matching factor is obtained through a matching model; a generator health monitoring module, based on the inlet gas pressure, the discharge chamber temperature and the inlet gas humidity of the ozone generator, a health degree coefficient is obtained through a device safety state model; and a power comprehensive decision module, based on the health degree coefficient and the matching factor, a target power set value is output through a multi-parameter fusion regulation model.

[0025] In the embodiments of the present application, the pollution load evaluation module can be understood as a module for dynamically reflecting the change of water quality pollution degree, which can be realized in various ways. For example, a sensor array is used to detect the chemical oxygen demand (COD) and total organic carbon (TOC) in the treated water, and a demand coefficient is generated by a data fitting algorithm; or a fluorescence spectrum analysis technique is used to quantitatively analyze the microbial metabolites in the water body, so as to derive the demand coefficient. The main purpose of this module is to provide a dynamic adjustment basis according to the change of water quality. The ozone mass transfer efficiency evaluation module can be understood as a module for quantifying the dissolution efficiency of ozone in water, which can be realized by different technical means. For example, a gas-liquid two-phase flow simulation method is used to calculate the dissolution rate of ozone under different flow conditions; or the change trend of dissolved oxygen concentration is monitored online to indirectly calculate the ozone mass transfer efficiency coefficient. The main function of this module is to optimize the gas input parameters to improve the utilization rate of ozone. The matching evaluation module can be understood as a module for integrating water quality conditions and mass transfer effects, which can be realized in various ways. For example, a fuzzy logic algorithm is used to weight and fuse the water quality parameters and mass transfer efficiency to generate a matching factor; or a neural network model is used to train the historical operation data to predict the best matching factor under the current working condition. The main function of this module is to ensure the adaptability between ozone supply and demand. The generator health monitoring module can be understood as a module for real-time evaluation of the running state of the equipment, which can be realized in various ways. For example, a vibration sensor is used to monitor the mechanical stability of the discharge chamber, and a thermal imaging technique is used to evaluate the temperature distribution uniformity; or a combination of humidity sensor and pressure sensor is used to construct a multi-dimensional feature vector of the device running state. The main function of this module is to prevent equipment failure and ensure long-term operation reliability. The power comprehensive decision-making module can be understood as a module for dynamically adjusting the power of the ozone generator, which can be realized in various strategies. For example, a hierarchical control strategy is used to divide different power intervals according to the health degree coefficient and the matching factor; or a reinforcement learning algorithm is used to optimize the power setting value through continuous trial and error. The main function of this module is to meet the disinfection demand while considering equipment safety and energy efficiency.

[0026] The treatment tank body 1 is used to accommodate the condensate water to be treated as the core bearing structure. The ozone generator 2 is installed on the side wall of the treatment tank body 1 and connected with the aeration pipe at the bottom of the treatment tank body 1 through the output end, so as to introduce ozone gas into the water for disinfection and purification. In order to realize dynamic adjustment, improve ozone utilization rate and ensure reliable operation of the equipment, the ozone generator real-time control system is introduced to solve the problems of rigid control strategy, low ozone mass transfer efficiency and insufficient equipment running safety.

[0027] The pollution load evaluation module calculates the demand coefficient by monitoring the residual ozone concentration and ultraviolet light absorbance in the treated water in combination with an ozone demand model. The residual ozone concentration reflects the current disinfection effect, and the ultraviolet light absorbance represents the content of organic matter in the water, both of which dynamically reflect the change in the pollution degree of the water quality. Thus, the system can adjust the ozone output according to the actual disinfection demand to avoid the problems of incomplete disinfection or excessive ozone caused by fixed power.

[0028] The ozone mass transfer efficiency evaluation module obtains the ozone mass transfer efficiency coefficient through an ozone mass transfer efficiency evaluation model based on the tail gas ozone concentration, the inlet gas ozone concentration and the ozone gas flow. Specifically, the module quantifies the dissolution efficiency of ozone in water to provide a basis for optimizing the gas input parameters, thereby improving the ozone utilization rate and solving the problem of low process efficiency.

[0029] The matching evaluation module obtains the matching factor through a matching model in combination with the demand coefficient and the ozone mass transfer efficiency coefficient at the current water temperature. Further, the module comprehensively considers the water quality conditions and the mass transfer effect to ensure the precise adaptation of ozone supply and demand and to strengthen the dynamic adjustment capability of the system.

[0030] The generator health monitoring module obtains the health degree coefficient through a device safety state model by monitoring the inlet gas pressure, the discharge chamber temperature and the inlet gas humidity of the ozone generator. Specifically, the module evaluates the running state of the device in real time to prevent faults caused by overheating, overpressure or excessively high humidity, thereby ensuring the long-term safe operation of the device.

[0031] The power comprehensive decision module outputs the target power set value through a multi-parameter fusion regulation and control model based on the health degree coefficient and the matching factor. Thus, the system can meet the disinfection demand while taking into account the device safety and energy efficiency to achieve precise regulation and control.

[0032] The above modules closely cooperate to form a closed-loop intelligent regulation and control mechanism. The pollution load evaluation and the mass transfer efficiency evaluation provide real-time input of water quality and efficiency, the matching evaluation makes a multi-parameter comprehensive decision, the health monitoring ensures the reliability of the device, and the power decision performs precise regulation and control. The overall technical solution effectively realizes intelligent perception, real-time evaluation and dynamic optimization of the condensate water disinfection process and comprehensively solves the core problems of rigid control strategy, low mass transfer efficiency and lack of device protection.

[0033] As shown in Figure 1 , as a preferred embodiment of the present application, the multi-parameter fusion regulation and control model is: , wherein, is the target power set value, is the rated power of the ozone generator, is the health degree coefficient, is the matching factor.

[0034] Specifically, the multi-parameter fusion regulation model refers to a mathematical model for dynamically adjusting the power of the ozone generator based on multiple key parameters. It can accurately output the target power by collecting real-time data on the operating state of the equipment and the treatment conditions of the condensed water, combined with a pre-set calculation formula. In practical applications, this model can be implemented using an embedded control system or an industrial computer system, with the purpose of dynamically regulating the power output to ensure that the ozone generator operates efficiently while avoiding energy waste and the risk of equipment overload.

[0035] In detail, the above-mentioned central air conditioning condensed water disinfection and purification device realizes intelligent optimization control of the power of the ozone generator through a multi-parameter fusion regulation model. First, the rated power serves as a reference value to ensure that the target power is always within the safe operating range of the equipment, thereby avoiding the risk of equipment damage caused by excessively high power. Second, the health coefficient dynamically reflects the real-time safety state of the equipment by comprehensively evaluating key operating parameters such as the inlet pressure of the ozone generator, the discharge chamber temperature, and the inlet humidity. When the health coefficient decreases, the target power decreases accordingly, effectively protecting the equipment from abnormal operating conditions. In addition, the matching factor dynamically reflects changes in the condensed water pollution load and ozone dissolution efficiency by integrating the demand coefficient and the ozone mass transfer efficiency coefficient at the current water temperature. When the matching factor changes, the target power adjusts accordingly to ensure that the power is increased to guarantee the disinfection effect under high pollution load or low mass transfer efficiency conditions, while the power is reduced to save energy under favorable conditions. By combining the health coefficient and the matching factor with the rated power, this model not only solves the problems of insufficient disinfection or energy waste caused by fixed power regulation, but also significantly improves the intelligence level and operating efficiency of the system.

[0036] In summary, the above technical solution successfully realizes dynamic optimization and regulation of the power of the ozone generator by introducing a multi-parameter fusion regulation model, which not only ensures the stability of the disinfection effect of the condensed water, but also improves the safety and economy of equipment operation.

[0037] As shown in Figure 1 As a preferred embodiment of the present application, the health coefficient is obtained by the specific steps of:

[0038] The absolute value of the difference between the current inlet pressure of the ozone generator and the optimal inlet pressure is processed by ratio with the optimal inlet pressure, and after limiting the upper limit to 1 using the min function, the pressure deviation index is obtained;

[0039] The pressure deviation index refers to quantifying the degree of pressure influence by calculating the relative deviation between the current intake pressure and the optimal intake pressure, which can be realized by the ratio operation of the absolute difference value and the optimal pressure combined with the limiting processing, aiming to eliminate the interference of the range difference of the absolute pressure, and prevent the extreme deviation value from distorting the evaluation results.

[0040] The current discharge chamber temperature and the intake humidity are subjected to maximum-minimum normalization processing to obtain the discharge chamber temperature index and the intake humidity index.

[0041] The discharge chamber temperature index and the intake humidity index refer to the indicators generated by standardizing the relative position of the measured value within the safe range, which can be realized by the maximum-minimum normalization method, aiming to unify the dimensions of different physical quantities, so that the changes of the environmental parameters can be objectively reflected.

[0042] The pressure deviation index, the discharge chamber temperature index and the intake humidity index are subjected to weighted fusion to obtain the comprehensive health degree coefficient The health degree coefficient is negatively correlated with the pressure deviation index, the discharge chamber temperature index and the intake humidity index, and the health degree coefficient refers to the comprehensive evaluation index dynamically adjusted based on the degree of deviation of multiple operating parameters from the safe range, which can be realized by the weighted fusion formula, aiming to allocate the weights of each parameter according to the characteristics of the equipment, and adapt to the risk priority under different working conditions.

[0043] The weighted fusion is wherein, is the health degree coefficient, is the pressure deviation index, is the discharge chamber temperature index, is the intake humidity index, , and are weight coefficients with the value range of 0-1, and .

[0044] Specifically, the above scheme builds a dynamic evaluation model of health degree coefficient by systematizing and quantifying the deviation degree of key operating parameters of the ozone generator. First, for the inlet air pressure, the ratio processing combined with the amplitude limiting operation is adopted to accurately capture the potential threat of pressure abnormality to discharge stability, ensuring that the pressure fluctuation can be accurately reflected within a reasonable range. Second, for the discharge chamber temperature and inlet air humidity, the normalized processing is used to generate standardized indicators, making the deterioration degree of different physical quantities comparable and providing a basis for subsequent fusion. Finally, the weighted fusion formula is used to integrate the influence of multiple parameters, and the method of 1 minus each exponential term and weighted summation is used to directly reflect the negative impact of parameter deterioration on health degree. At the same time, the adjustability of the weight coefficient, which is a parameter used to adjust the importance of different factors in decision-making, is used to optimize the evaluation sensitivity. The weight coefficient can be assigned through a pre-set strategy or a dynamic algorithm. This process not only solves the problem of inaccurate safety state monitoring of the device, but also provides a reliable device state basis for power regulation, avoiding the one-sidedness of single parameter monitoring.

[0045] In summary, the above technical scheme quantifies the deviation degree of key operating parameters of the ozone generator to achieve dynamic evaluation of the health state of the device, thereby effectively preventing device failure and ensuring long-term operation reliability.

[0046] As shown in Figure 1 , as a preferred embodiment of the present application, the specific steps for obtaining the matching factor based on the demand coefficient and the ozone mass transfer efficiency coefficient under the current water temperature are as follows:

[0047] Based on the current water temperature, the water temperature influence factor is calculated using the water temperature influence model;

[0048] The absolute value of the difference between the current water temperature and the optimal water temperature is compared with the difference between the upper limit of water temperature and the lower limit of water temperature, and after limiting the upper limit to 1 using the min function, the water temperature influence factor is obtained;

[0049] The water temperature influence factor is a parameter that converts real-time water temperature data into a quantitative indicator through the water temperature influence model. It can be realized by ratio processing and amplitude limiting mechanism, and the purpose is to sensitively capture the negative impact of water temperature fluctuation on ozone dissolution efficiency.

[0050] Based on the demand coefficient, the ozone mass transfer efficiency coefficient and the water temperature influence factor, the final matching factor is obtained through the matching fusion model The matching factor is positively correlated with the demand coefficient and the ozone mass transfer efficiency coefficient, and is negatively correlated with the water temperature influence factor. The matching factor can be understood as a dynamic integration of the calculation results of the water temperature influence factor and the key operation parameters. The matching factor can be generated by a weighted fusion formula of the demand coefficient, the ozone mass transfer efficiency coefficient and the water temperature influence factor. The purpose is to ensure that the power setting value can respond to the water quality demand and adapt to the change of the physical environment. In practical application, the matching fusion model refers to a mathematical expression form which comprehensively considers the demand coefficient, the ozone mass transfer efficiency coefficient and the water temperature influence factor. The matching fusion model can be realized by using the geometric mean combined with linear compensation. The purpose is to balance the contribution of each parameter and offset the mass transfer efficiency decay caused by the increase of the water temperature.

[0051] The matching fusion model is: wherein, is the matching factor, is the demand coefficient, is the ozone mass transfer efficiency coefficient, is the water temperature influence factor.

[0052] The above scheme realizes the real-time optimization calculation of the matching factor by dynamically integrating the water temperature influence factor and the key operation parameters, so that the power setting value of the ozone generator can accurately respond to the interference of the water temperature change on the ozone mass transfer efficiency. In the process, first, the water temperature influence factor is generated according to the current water temperature. The special part of this step is to directly associate the degree of water temperature deviation from the optimal range through ratio processing and amplitude limiting mechanism, avoiding the rigid problem of fixed threshold setting. Secondly, the matching factor is generated based on the demand coefficient, the ozone mass transfer efficiency coefficient and the water temperature influence factor. This process integrates the relationship among the three through the matching fusion model. The core is that the positive correlation between the demand coefficient and the mass transfer efficiency coefficient and the matching factor ensures that the power setting value is reasonably improved when the pollution load is high or the ozone dissolution efficiency is good, and the negative correlation of the water temperature influence factor dynamically offsets the mass transfer efficiency decay caused by the increase of the water temperature. In addition, the geometric mean of the demand coefficient and the mass transfer efficiency coefficient is used in the matching fusion model formula. The cleverness is to balance the contribution of the two to avoid the dominance of a single parameter, and to multiply the term to directly offset the negative impact of the water temperature, so as to realize the efficiency and stability of the system operation. On this basis, the above scheme also combines other module functions of the real-time regulation system of the ozone generator, such as the ozone mass transfer efficiency evaluation module and the generator health monitoring module, to jointly solve the problems of low ozone utilization rate and fluctuation of disinfection effect caused by the change of the water temperature, so as to ensure the disinfection effect while avoiding energy waste.

[0053] As shown in Figure 1 , as a preferred embodiment of the present application, the specific steps of obtaining the demand coefficient through the ozone demand model based on the residual ozone concentration in the treated water and the ultraviolet absorbance are as follows:

[0054] The current residual ozone concentration is compared with the target residual ozone concentration, and after the min function is limited to 1, the residual ozone concentration index is obtained;

[0055] The residual ozone concentration index is a quantitative index obtained by comparing the current residual ozone concentration with the target residual ozone concentration and limiting the amplitude, which can be realized by a linear amplification deviation sensitivity algorithm, aiming to accurately indicate whether the ozone is excessive or insufficient.

[0056] The current ultraviolet absorbance value is compared with the reference ultraviolet absorbance value, and after the min function is limited to 1, the ultraviolet absorbance index is obtained;

[0057] The ultraviolet absorbance index is a quantitative index obtained by comparing the current ultraviolet absorbance value with the reference ultraviolet absorbance value and limiting the amplitude, which can be realized by a relative pollution intensity conversion algorithm, aiming to stably represent the water pollution load.

[0058] The residual ozone concentration index and the ultraviolet absorbance index are weighted and fused to obtain a demand coefficient The demand coefficient is positively correlated with the ultraviolet absorbance index and negatively correlated with the residual ozone concentration index;

[0059] The weighted fusion formula is: , wherein is the demand coefficient, is the ultraviolet absorbance index, is the residual ozone concentration index, is the ultraviolet absorbance weight coefficient, The value range is 0-1. The weight coefficient in the weighted fusion formula is used to dynamically adjust the contribution proportion of the two indexes, which can be realized by a strategy based on adaptive adjustment of water quality conditions, or by a preset strategy or dynamic algorithm for assignment, aiming to ensure that the demand coefficient can be accurately output under different water quality conditions.

[0060] The scheme realizes the accurate quantification of dynamic changes of pollution load by constructing a two-parameter collaborative evaluation framework of residual ozone concentration and ultraviolet absorbance. On this basis, the current residual ozone concentration is processed by ratio and limited amplitude compared with the target value, which can linearly amplify the sensitivity of concentration deviation and avoid the instability of regulation caused by exponential overrun. The current ultraviolet absorbance is processed by ratio and limited amplitude compared with the reference value, which can convert the absolute absorbance into a relative pollution intensity index and eliminate the interference of extreme water quality fluctuations. The demand coefficient is generated by weighted fusion of the above two indexes, in which the ultraviolet absorbance index is positively correlated, which is designed because the increase of pollution load requires higher ozone demand, and the residual ozone concentration index is negatively correlated, which reflects the logic that the demand decreases when the residual ozone is sufficient. This two-way correlation mechanism enables the demand coefficient to adaptively balance the pollution degree and the current disinfection state. In addition, by using a specific weighted fusion formula, the dominant representation of ultraviolet absorbance on pollution load is retained, and the feedback effect of residual ozone concentration on real-time state is also considered, thereby providing reliable input for power regulation.

[0061] Through the above technical scheme, the problem of inaccurate demand coefficient calculation is solved, the accuracy of the matching factor is ensured, and the accuracy of the ozone generator power regulation is improved, thereby realizing the optimal balance between disinfection effect and energy utilization.

[0062] As shown in Figure 1 , as a preferred embodiment of the present application, the specific steps of obtaining the ozone mass transfer efficiency coefficient based on the tail gas ozone concentration, the inlet gas ozone concentration and the ozone gas flow rate through the ozone mass transfer efficiency evaluation model are as follows:

[0063] Based on the inlet gas ozone concentration, the ozone gas flow rate and the tail gas ozone concentration, the ozone mass dissolved into the water per unit time is calculated;

[0064] The dissolved ozone mass is processed by ratio with the total ozone mass passed in per unit time to obtain the theoretical mass transfer efficiency as the ozone mass transfer efficiency coefficient .

[0065] The theoretical mass transfer efficiency calculation formula is: , wherein, is the theoretical mass transfer efficiency, is the ozone gas flow rate, is the inlet gas ozone concentration, is the tail gas ozone concentration.

[0066] Specifically, the ozone mass transfer efficiency evaluation model refers to a mathematical model for quantifying the dissolution efficiency of ozone in water, which can be dynamically calculated by real-time acquisition of tail gas ozone concentration, inlet gas ozone concentration, and ozone gas flow, combined with physical and chemical principles. In practical application, this model can be realized by an algorithm based on the law of conservation of mass, or an empirical formula can be established through experimental calibration, the purpose of which is to provide a dynamic evaluation mechanism that can accurately reflect the ozone mass transfer efficiency.

[0067] The dissolved ozone mass refers to the amount of ozone actually dissolved in the water per unit time, which can be calculated by the difference between the inlet gas ozone concentration, ozone gas flow, and tail gas ozone concentration. Specifically, the introduction of this feature aims to quantify the actual utilization rate of ozone, thereby providing a reliable basis for subsequent power regulation. The theoretical mass transfer efficiency refers to the ratio of the dissolved ozone mass to the total mass of ozone input, which reflects the utilization efficiency of ozone under specific conditions, and can be directly calculated by the simplified formula to ensure the efficiency and real-time performance of the calculation process.

[0068] In detail, the above technical solution accurately evaluates the ozone mass transfer efficiency by real-time monitoring of the inlet gas ozone concentration, ozone gas flow, and tail gas ozone concentration, and accurately calculating the dissolved ozone mass per unit time. The collection and calculation of the inlet gas ozone concentration, ozone gas flow, and tail gas ozone concentration directly reflect the difference between ozone input and output, and can quantify the actual dissolved ozone mass, avoiding the dependence on the assumption of fixed mass transfer efficiency. At the same time, the theoretical mass transfer efficiency coefficient is obtained by taking the ratio of the dissolved ozone mass to the total mass of ozone input, which directly reflects the utilization efficiency of ozone and provides a quantifiable basis for subsequent power regulation. In addition, the theoretical mass transfer efficiency calculation formula is simplified as , which not only eliminates the redundancy of the gas flow parameter, but also ensures the simplicity and efficiency of the calculation process, facilitating real-time response of the system to changes in water conditions.

[0069] On this basis, the above technical solution and other modules in the real-time regulation system of the ozone generator work together to form a complete power regulation logic. For example, the ozone mass transfer efficiency coefficient, as a key input of the matching model, optimizes the target power setting value of the ozone generator in cooperation with other parameters, thereby effectively solving the problem of inaccurate mass transfer efficiency evaluation, making the utilization of ozone resources more efficient and the disinfection effect more reliable.

[0070] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A central air conditioning condensate disinfection and purification device, comprising a treatment tank body and an ozone generator installed on the side wall of the treatment tank body, wherein the output end of the ozone generator is connected to an aeration pipe at the bottom of the treatment tank body, characterized in that, Also includes: A real-time control system for the ozone generator, connected to the electronic control module of the ozone generator, includes: The pollution load assessment module obtains the demand coefficient based on the residual ozone concentration and ultraviolet absorbance in the treated water through an ozone demand model. The ozone mass transfer efficiency assessment module obtains the ozone mass transfer efficiency coefficient based on the ozone concentration in the exhaust gas, the ozone concentration in the inlet gas, and the ozone gas flow rate through the ozone mass transfer efficiency assessment model. The matching evaluation module, based on the demand coefficient and ozone mass transfer efficiency coefficient at the current water temperature, obtains the matching factor through a matching model: The generator health monitoring module, based on the ozone generator's inlet pressure, discharge chamber temperature, and inlet humidity, obtains a health coefficient through the equipment safety status model. The power integrated decision module outputs the target power setpoint based on the health coefficient and matching factor through a multi-parameter fusion control model.

2. The central air conditioning condensate disinfection and purification device according to claim 1, characterized in that, The multi-parameter fusion control model is as follows: ,in, Set the target power value. This refers to the rated power of the ozone generator. For health score, This is the matching factor.

3. The central air conditioning condensate disinfection and purification device according to claim 2, characterized in that, The specific steps for obtaining the health coefficient based on the ozone generator's inlet pressure, discharge chamber temperature, and inlet humidity through the equipment safety status model are as follows: The absolute value of the difference between the current ozone generator's inlet pressure and the optimal inlet pressure is compared with the optimal inlet pressure. After using a min function to limit the upper limit to 1, the pressure deviation index is obtained. The current discharge chamber temperature and intake humidity are processed by maximum-minimum normalization to obtain the discharge chamber temperature index and intake humidity index. The pressure deviation index, discharge chamber temperature index, and intake air humidity index are weighted and fused to obtain a comprehensive health coefficient. The health coefficient was negatively correlated with the pressure deviation index, the discharge chamber temperature index, and the intake humidity index.

4. The central air conditioning condensate disinfection and purification device according to claim 2, characterized in that, The specific steps for obtaining the matching factor based on the demand coefficient and ozone mass transfer efficiency coefficient at the current water temperature using a matching model are as follows: Based on the current water temperature, the water temperature influence factor is calculated using the water temperature influence model. Based on the aforementioned demand coefficient, ozone mass transfer efficiency coefficient, and water temperature influence factor, the final matching factor is obtained through a matching fusion model. The matching factor is positively correlated with both the demand coefficient and the ozone mass transfer efficiency coefficient, and negatively correlated with the water temperature influence factor.

5. The central air conditioning condensate disinfection and purification device according to claim 4, characterized in that, The specific steps for calculating the water temperature influence factor based on the current water temperature using the water temperature influence model are as follows: The water temperature influence factor is obtained by comparing the absolute value of the difference between the current water temperature and the optimal water temperature with the difference between the upper and lower limits of the water temperature, and then using the min function to limit the upper limit to 1.

6. The central air conditioning condensate disinfection and purification device according to claim 4, characterized in that, The specific steps for obtaining the demand coefficient based on the residual ozone concentration and ultraviolet absorbance in the treated water using the ozone demand model are as follows: The residual ozone concentration index is obtained by comparing the current residual ozone concentration with the target residual ozone concentration and then using a min function to limit the upper limit to 1. The ultraviolet absorbance index is obtained by comparing the current ultraviolet absorbance value with the reference ultraviolet absorbance value and then using the min function to limit the upper limit to 1. The demand coefficient is obtained by weighting and fusing the residual ozone concentration index and the ultraviolet absorbance index. The demand coefficient is positively correlated with the ultraviolet absorbance index and negatively correlated with the residual ozone concentration index.

7. The central air conditioning condensate disinfection and purification device according to claim 4, characterized in that, The specific steps for obtaining the ozone mass transfer efficiency coefficient based on the exhaust gas ozone concentration, the inlet gas ozone concentration, and the ozone gas flow rate using the ozone mass transfer efficiency evaluation model are as follows: Based on the ozone concentration in the inlet air, the ozone gas flow rate, and the ozone concentration in the exhaust gas, calculate the mass of ozone dissolved into the water body per unit time. The ratio of the dissolved ozone mass to the total ozone mass introduced per unit time is used to obtain the theoretical mass transfer efficiency, which is then used as the ozone mass transfer efficiency coefficient. .

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