An industrial wastewater treatment system based on micro-nano bubble ozone oxidation

Abstract

The application discloses an industrial wastewater treatment system based on micro-nano bubble ozone oxidation, and relates to the technical field of sewage treatment.The system comprises a bubble generation monitoring module, a micro-nano bubble system, an oxygen quantity adjusting module, a maximum oxygen quantity value recording module, a minimum oxygen quantity value recording module, a scope determining module, a test point determining module, a data monitoring module, a data acquisition and analysis module, a degradation chemical oxygen demand data acquisition module, a total organic carbon data acquisition module, a trend graph drawing module, and a cost calculation module.The bubble generation monitoring module accurately determines the oxygen quantity scope and the monitoring point, provides a stable gas source for the micro-nano bubble system, enhances the adsorption and degradation capacity of the bubble to the pollutants, helps the water treatment and operation personnel to determine the corresponding advanced oxidation scheme for the deep treatment of the organic matter that is not familiar to them, and also helps the system to timely respond to the water quality change, optimizes the treatment, and reduces the cost and increases the efficiency.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, specifically to an industrial wastewater treatment system based on micro-nano bubble ozone oxidation. Background Technology

[0002] With the continuous expansion of industrial production, the environmental pollution caused by industrial wastewater discharge is becoming increasingly serious. Industrial wastewater often contains large amounts of pollutants such as chemical oxygen demand (COD) and total organic carbon (TOC), as well as complex intermediate products. If discharged directly without effective treatment, it will pose a serious threat to aquatic ecosystems and human health. Therefore, developing efficient, economical, and environmentally friendly industrial wastewater treatment technologies has become an urgent problem to be solved.

[0003] Currently, ozone oxidation technology is widely used in industrial wastewater treatment due to its strong oxidation capacity and fast reaction speed. However, traditional ozone oxidation systems still have many shortcomings: First, the mass transfer efficiency between ozone and wastewater is low, and the gas flow rate and oxygen quantity are difficult to control precisely, resulting in low ozone utilization and high treatment costs; Second, the treatment process lacks real-time monitoring and dynamic optimization of key parameters (such as pH value and intermediate product concentration), making it difficult to adapt to the treatment needs of different wastewater qualities and resulting in unstable treatment effects; Third, the large amount of heat generated during ozone oxidation is not effectively recovered and utilized, causing energy waste and increasing the system's operating costs; Fourth, existing treatment systems lack an intuitive and efficient human-machine interface, making it difficult for operators to monitor the system's operating status in real time and make precise adjustments. To address these issues, we propose an industrial wastewater treatment system based on micro-nano bubble ozone oxidation. Summary of the Invention

[0004] To address the aforementioned technical problems, an industrial wastewater treatment system based on micro-nano bubble ozone oxidation is provided, which solves the problems described above.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: an industrial wastewater treatment system based on micro-nano bubble ozone oxidation, comprising:

[0006] The bubble generation monitoring module generates micron-sized bubbles based on a micro-nano bubble system, continuously adjusts the oxygen quantity, records the maximum and minimum oxygen quantity values, and determines the interaction range and 1-3 test and data monitoring points.

[0007] The data acquisition and analysis module collects data on chemical oxygen demand and total organic carbon, plots them into trend charts, calculates operating costs and capital expenditures, compares the calculation results under different trend charts, and calculates and identifies the optimal gas flow range.

[0008] The pH data optimization module dynamically adjusts the pH value of the water body based on the selected optimal gas flow range. It designs a segmented pH adjustment reaction tank, uses microfluidic technology to distribute the pH gradient along the water flow direction, and combines online mass spectrometry analysis to monitor the changes of intermediate products in different pH regions in real time. It plots curves to find the optimal pH range and treats industrial wastewater based on the selected pH range and gas flow range parameters.

[0009] The energy recovery module, with the addition of a heat exchange device, constructs a three-level energy recovery network to recover heat during the ozone oxidation process and assist in wastewater treatment.

[0010] The visualization module is configured to provide both an operation interface and a data display interface.

[0011] Preferably, the specific steps for determining the scope of interaction and 1-3 test and data monitoring points are as follows:

[0012] Operators input basic operating parameters of the micro-nano bubble system based on the visualization module. These parameters include the type of wastewater to be treated, the treatment capacity, and the initial chemical oxygen demand and total organic carbon range of the water quality.

[0013] The micro-nano bubble system generates micron-sized bubbles and adjusts the amount of oxygen based on set operating parameters.

[0014] The micro / nano bubble system gradually increases / decreases the oxygen quantity within a safe range, starting from the initial minimum oxygen quantity and gradually increasing the oxygen input in fixed steps, based on sensor monitoring of bubble generation status and system stability.

[0015] When abnormal increases in bubble size and increased equipment operating noise occur, the current oxygen quantity is recorded as the maximum gas quantity value;

[0016] Conversely, when the amount of oxygen is reduced and the amount of bubbles generated is insufficient, the minimum gas volume value is determined.

[0017] Repeat the test steps, record the data, and calculate the average value as the final determined maximum and minimum air volume values;

[0018] Based on the determined maximum and minimum gas volume values, and using a genetic algorithm to simulate and calculate the treatment effect and energy consumption data under different oxygen conditions, the oxygen volume range with a treatment effect of over 90% and low energy consumption is selected as the operating range.

[0019] Based on cluster analysis, the operational data within the scope is clustered into three categories, and the oxygen quantity corresponding to the cluster center of each category is the monitoring point.

[0020] Preferably, the algorithm steps for determining the scope based on the genetic algorithm are as follows:

[0021] Initialize the genetic algorithm parameters, including population size, number of generations, crossover probability, and mutation probability;

[0022] Within the range of maximum and minimum gas volume values, the oxygen content values ​​of the population size are randomly generated to form the initial population.

[0023] Based on the initial population oxygen levels, energy consumption data under different oxygen levels, and treatment effects, fitness values ​​are calculated, treatment effects are quantified, and weight values ​​for different indicators are determined for weighted calculation.

[0024] Select other oxygen values ​​from the initial population to form a new population. Each time, select a random number of oxygen values ​​and calculate the corresponding fitness value. Repeat the selection process to select the initial population with the highest fitness and obtain the new population.

[0025] The oxygen values ​​in the new population are grouped into pairs, and the values ​​in each pair are cross-calculated to obtain new oxygen values, thus obtaining the cross-calculated population.

[0026] The crossover population is mutated, and the current generation number is checked to see if it has reached a preset value. If the condition is met, the calculation is terminated and the final population is formed. If the condition is not met, the above steps are continued until the termination condition is met.

[0027] Based on the final population screening, the oxygen quantity values ​​that achieve a treatment effect of over 90% are selected. The energy consumption corresponding to the selected values ​​is calculated, the average energy consumption is obtained, and the oxygen quantity values ​​with energy consumption below the average value are selected to form a continuous interval. This interval is the operating range.

[0028] Preferably, the specific steps for plotting the trend chart are as follows: collecting chemical oxygen demand and total organic carbon data before and after wastewater treatment using an online water quality monitor; obtaining the hourly wastewater treatment volume based on a flow sensor; and obtaining oxygen flow rate and equipment operating parameter values.

[0029] Draw trend charts from the acquired data, including time series charts, scatter plots, and heatmaps;

[0030] The operating cost is calculated by adding the energy cost and the drug cost to obtain the total cost value;

[0031] The energy cost calculation involves multiplying the power and operating time of each device separately, adding the products together, and then multiplying the result by the price per kilowatt-hour to obtain the energy cost.

[0032] The cost of the reagent is calculated by obtaining the amount of oxygen consumed in ozone production, multiplying it by the unit price of oxygen, and then multiplying it by the ozone generation efficiency.

[0033] Preferably, capital expenditure is calculated by adding the acquisition cost to the installation and commissioning cost;

[0034] The purchase cost is calculated based on the market price of the equipment purchased for the processing system, and these costs are added together to obtain the purchase cost.

[0035] The installation and commissioning cost is calculated by adding up the purchase costs of all equipment and multiplying by a fixed installation coefficient.

[0036] Operating costs and capital expenditures were calculated for the data under different trend charts. The calculation results were sorted from smallest to largest to find the gas flow range with the lowest operating costs and capital expenditures.

[0037] Preferably, online mass spectrometry analysis involves installing a sampling probe in the reaction tank to sample the water quality per unit time. The water quality is then analyzed using an online mass spectrometer to identify organic matter, inorganic matter, and intermediate products from the ozone oxidation process in the wastewater. A curve is plotted showing the relationship between intermediate products, pH value, and gas flow rate, with gas flow rate on the horizontal axis, pH value on the vertical axis, and intermediate product concentration as the curve. Based on the curve, the peak positions of the intermediate product concentration as a function of pH value and gas flow rate are observed, and the corresponding intermediate product degradation rate at each pH value is calculated. Based on wastewater treatment standards, the optimal pH range is determined to be a degradation rate greater than 80% and an intermediate product concentration less than 10 mg / L. The optimal range is the pH value within this range found in the curve.

[0038] Preferably, the three-level energy recovery network within the energy recovery module includes primary recovery, intermediate recovery, and advanced recovery. The construction steps of the three-level energy recovery network are as follows:

[0039] The heat recovery requirements of ozone oxidation wastewater are clearly defined, the target efficiency of energy recovery and the direction of heat utilization are determined, the energy is divided into three levels of functions, a system topology diagram is designed, and the overall architecture is planned.

[0040] Primary recovery uses a spiral plate heat exchanger to preheat wastewater; intermediate recovery is based on a Rankine cycle system for power generation; and advanced recovery uses an absorption heat pump to increase heat output.

[0041] Primary recycling preheats the low-temperature wastewater to 40-50°C by counter-current heat exchange between high-temperature and low-temperature wastewater; intermediate recycling uses medium-temperature wastewater to heat the working fluid for power generation, achieving self-sufficiency in electricity; advanced recycling raises the low-temperature waste heat to 50-60°C and distributes it to ozone generator equipment as needed.

[0042] Preferably, the steps of cluster analysis are as follows:

[0043] The data within the scope is preprocessed, and the K-means clustering algorithm is used to divide the data.

[0044] Select the initial cluster centers, calculate the distance between the data points and the cluster centers based on the Euclidean distance formula, and iteratively update the cluster centers until the convergence condition is met;

[0045] The convergence condition is as follows: compare the difference between the newly generated cluster centers and the cluster centers of the previous round. If the change in the position of the cluster centers is less than a preset threshold and the preset maximum number of iterations is reached, the algorithm stops; otherwise, it continues to iterate.

[0046] Based on the silhouette coefficient, the cluster density and separation are evaluated. The elbow rule is used to judge the rationality of the number of clusters and verify the clustering results. The cluster centers are converted from standardized data back to the actual oxygen quantity scale. The three values ​​obtained are the oxygen quantity monitoring points for real-time monitoring and control.

[0047] Preferably, the operation interface within the visualization module displays the processing flow using a 3D model and supports global start / stop; the data display interface uses a real-time dashboard to intuitively present key indicators with dynamic numbers and indicator lights; and it provides various charts to monitor abnormal situations in real time.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] This invention proposes a bubble generation monitoring module to accurately determine the oxygen content domain and monitoring points, providing a stable gas source for the micro / nano bubble system and enhancing the bubble's adsorption and degradation capacity for pollutants. A pH data optimization module, based on the optimal gas flow range, dynamically adjusts the pH value through a segmented reaction tank and microfluidic technology, combined with online mass spectrometry analysis to monitor intermediate products in real time, ensuring the chemical reaction proceeds under optimal conditions. This significantly improves the efficiency of degrading chemical oxygen demand (COD) and total organic carbon (TOC), enhancing wastewater treatment quality. By combining different trend graph results, it can accurately identify the optimal gas flow range, avoiding energy and resource waste. The energy recovery module constructs a three-level energy recovery network to recover and reuse the heat from ozone oxidation, helping water treatment operators determine appropriate advanced oxidation solutions for the deep treatment of unfamiliar organic compounds. It also helps the system respond promptly to changes in water quality, optimizing treatment and reducing costs while increasing efficiency. Attached Figure Description

[0050] Figure 1 This is a framework diagram of the industrial wastewater treatment system of the present invention;

[0051] Figure 2 The diagram illustrates the steps for determining the scope of action and 1-3 test and data monitoring points for this invention. Detailed Implementation

[0052] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0053] Reference Figure 1 As shown, an industrial wastewater treatment system based on micro-nano bubble ozone oxidation includes:

[0054] The bubble generation monitoring module generates micron-sized bubbles based on a micro-nano bubble system, continuously adjusts the oxygen quantity, records the maximum and minimum oxygen quantity values, and determines the interaction range and 1-3 test and data monitoring points.

[0055] The data acquisition and analysis module collects data on chemical oxygen demand and total organic carbon, plots them into trend charts, calculates operating costs and capital expenditures, compares the calculation results under different trend charts, and calculates and identifies the optimal gas flow range.

[0056] The pH data optimization module dynamically adjusts the pH value of the water body based on the selected optimal gas flow range. It designs a segmented pH adjustment reaction tank, uses microfluidic technology to distribute the pH gradient along the water flow direction, and combines online mass spectrometry analysis to monitor the changes of intermediate products in different pH regions in real time. It plots curves to find the optimal pH range and treats industrial wastewater based on the selected pH range and gas flow range parameters.

[0057] The energy recovery module, with the addition of a heat exchange device, constructs a three-level energy recovery network to recover heat during the ozone oxidation process and assist in wastewater treatment.

[0058] The visualization module is configured to provide both an operation interface and a data display interface.

[0059] The bubble generation monitoring module of this application accurately determines the oxygen content's effective range and monitoring points, providing a stable gas source for the micro / nano bubble system and enhancing the bubbles' ability to adsorb and degrade pollutants. The pH data optimization module, based on the optimal gas flow range, dynamically adjusts the pH value through a segmented reaction tank and microfluidic technology, combined with online mass spectrometry analysis to monitor intermediate products in real time, ensuring the chemical reaction proceeds under optimal conditions. This significantly improves the efficiency of degrading chemical oxygen demand (COD) and total organic carbon (TOC), thereby enhancing wastewater treatment quality. The data acquisition and analysis module collects and processes data, generates trend charts, and calculates costs, providing operators with clear and intuitive information. Combined with different trend chart results, it accurately identifies the optimal gas flow range, avoiding energy and resource waste. The energy recovery module constructs a three-level energy recovery network to recover and reuse the heat from ozone oxidation, reducing external energy consumption and lowering operating costs. Scientific data analysis and cost calculation also provide a strong basis for capital expenditure decisions, avoiding blind investment.

[0060] The visualization module provides an operation interface and data display interface, enabling operators to monitor the operating status of each module in real time and easily adjust parameters; intuitive data charts and early warning functions facilitate timely detection and handling of problems. The entire system achieves intelligent management, reduces manual intervention, improves management efficiency and accuracy, and reduces labor costs and the risk of operational errors.

[0061] Reference Figure 2 As shown, the specific steps for determining the scope between the data points and the 1-3 test and data monitoring points are as follows:

[0062] Operators input basic operating parameters of the micro-nano bubble system based on the visualization module. These parameters include the type of wastewater to be treated, the treatment capacity, and the initial chemical oxygen demand and total organic carbon range of the water quality.

[0063] The micro-nano bubble system generates micron-sized bubbles and adjusts the amount of oxygen based on set operating parameters.

[0064] The micro / nano bubble system gradually increases / decreases the oxygen quantity within a safe range, starting from the initial minimum oxygen quantity and gradually increasing the oxygen input in fixed steps, based on sensor monitoring of bubble generation status and system stability.

[0065] When abnormal increases in bubble size and increased equipment operating noise occur, the current oxygen quantity is recorded as the maximum gas quantity value;

[0066] Conversely, when the amount of oxygen is reduced and the amount of bubbles generated is insufficient, the minimum gas volume value is determined.

[0067] Abnormal particle size increase is detected by directly measuring particle size distribution using a laser particle size analyzer and high-speed camera. The criterion for judging abnormal particle size increase is an increase of more than 20% within the normal particle size range.

[0068] Insufficient bubble generation is determined by measuring the number per unit volume using a bubble counter; the standard is a reduction of more than 30% compared to normal.

[0069] Repeat the test steps, record the data, and calculate the average value as the final determined maximum and minimum air volume values;

[0070] Based on the determined maximum and minimum gas volume values, and using a genetic algorithm to simulate and calculate the treatment effect and energy consumption data under different oxygen conditions, the oxygen volume range with a treatment effect of over 90% and low energy consumption is selected as the operating range.

[0071] Based on cluster analysis, the operational data within the scope is clustered into three categories, and the oxygen quantity corresponding to the cluster center of each category is the monitoring point.

[0072] "Based on sensor monitoring of bubble generation status and system operation stability," the data acquisition and analysis module "collects chemical oxygen demand and total organic carbon data before and after wastewater treatment through an online water quality monitor, obtains the hourly wastewater treatment volume based on a flow sensor, and obtains oxygen flow and equipment operating parameter values." It can be seen that the system can monitor equipment operating parameters and water quality indicators in real time. When the oxygen quantity value is randomly generated, the system can obtain energy consumption-related parameters and treatment effect data under that oxygen quantity in real time through these sensors and monitoring instruments.

[0073] When the randomly generated oxygen quantity value falls within a range not directly covered by historical experimental data, the system monitors the equipment operating parameters and water quality indicators under the current oxygen quantity in real time through sensors, and calculates the energy consumption and treatment effect corresponding to the random oxygen quantity by combining the interpolation algorithm of adjacent known data points.

[0074] The system has a built-in correlation model between oxygen quantity, energy consumption, and treatment effect. This model is trained and generated based on historical data. It can derive the corresponding predicted values ​​of energy consumption and treatment effect from randomly generated oxygen quantity values. The model is continuously optimized as real-time data is added during system operation to ensure data accuracy.

[0075] By gradually increasing or decreasing the oxygen supply and monitoring key indicators such as bubble size and equipment noise in real time, the critical point of abnormal bubble generation or system instability can be accurately identified, thereby determining the maximum / minimum gas volume value for safe operation. This process avoids the blindness of setting parameters based on experience, prevents equipment overload and bubble failure risks from the source, and extends the service life of the equipment.

[0076] Based on the parameter range defined by the maximum / minimum gas volume values, a genetic algorithm is used to simulate the treatment effect and energy consumption data under different oxygen volumes, and the oxygen volume range with a degradation rate of over 90% and low energy consumption is automatically selected as the operating range.

[0077] The algorithm steps for determining the scope based on genetic algorithms are as follows:

[0078] Initialize the genetic algorithm parameters, including population size, number of generations, crossover probability, and mutation probability;

[0079] Within the range of maximum and minimum gas volume values, the oxygen content values ​​of the population size are randomly generated to form the initial population.

[0080] Based on the initial population oxygen levels, energy consumption data under different oxygen levels, and treatment effects, fitness values ​​are calculated, treatment effects are quantified, and weight values ​​for different indicators are determined for weighted calculation.

[0081] Select other oxygen values ​​from the initial population to form a new population. Each time, select a random number of oxygen values ​​and calculate the corresponding fitness value. Repeat the selection process to select the initial population with the highest fitness and obtain the new population.

[0082] The oxygen values ​​in the new population are grouped into pairs, and the values ​​in each pair are cross-calculated to obtain new oxygen values, thus obtaining the cross-calculated population.

[0083] The cross-calculation formula is as follows:

[0084] For the parent individual O i and O j This generates two offspring individuals:

[0085] O′ a =α·O a +(1-α)·O j

[0086] O′ j =(1-α)·O a +α·O j

[0087] Where α∈[0,1] are randomly generated cross coefficients, usually α~U(0,1), O a and O j O′ represents the oxygen content values ​​of two parental generations paired in the new population, and is the original data used for crossover calculation. a With O′ j The value of the offspring oxygen content obtained after cross-calculation is new data generated by the cross-calculation operation;

[0088] The crossover population is mutated, and the current generation number is checked to see if it has reached a preset value. If the condition is met, the calculation is terminated and the final population is formed. If the condition is not met, the above steps are continued until the termination condition is met.

[0089] Based on the final population screening, the oxygen quantity values ​​that achieve a treatment effect of over 90% are selected. The energy consumption corresponding to the selected values ​​is calculated, the average energy consumption is obtained, and the oxygen quantity values ​​with energy consumption below the average value are selected to form a continuous interval. This interval is the operating range.

[0090] The treatment effect is comprehensively reflected by the degradation rate of chemical oxygen demand (COD) and total organic carbon (TOC). The COD degradation rate is the percentage of the difference in COD before and after treatment to the COD before treatment; the TOC degradation rate is the percentage of the difference in TOC before and after treatment to the TOC before treatment. These two degradation rates are weighted and summed at a 50% ratio each to obtain a quantitative value of the treatment effect. It should be noted that this weighting can be adjusted according to the characteristics of the wastewater. For wastewater with high organic matter content, the weight of the TOC degradation rate can be increased to 60%.

[0091] The Analytic Hierarchy Process (AHP) is used to determine the weights of treatment effectiveness and energy consumption. Specifically, 3-5 experts in the field of wastewater treatment are invited to score the importance of "treatment effectiveness" and "energy consumption". Then, a judgment matrix is ​​constructed and the corresponding weight values ​​are calculated. By default, the weight of treatment effectiveness is 60% and the weight of energy consumption is 40%, so as to prioritize treatment effectiveness. If the system focuses more on energy saving, the weights of both can be adjusted to 50%.

[0092] The fitness value is calculated as follows: the weight of the treatment effect is multiplied by the ratio of the quantitative value of the treatment effect corresponding to the oxygen quantity to the historical maximum treatment effect, and the weight of energy consumption is multiplied by (1 minus the ratio of the unit energy consumption corresponding to the oxygen quantity to the historical maximum unit energy consumption). The fitness value ranges from 0 to 1. The higher the value, the better the overall performance of the oxygen quantity in terms of treatment effect and energy consumption.

[0093] The new population is selected from the initial population using a roulette wheel selection method. The specific steps are as follows:

[0094] Fitness value sorting

[0095] After calculating the fitness values ​​of all oxygen levels in the initial population, they are arranged in descending order.

[0096] Each time, a certain number of individuals are randomly selected from the initial population. This number is a random integer between 1 and the initial population size. Then, the fitness value of these selected individuals is calculated, and the individual with the highest fitness is selected to enter the new population. This operation is repeated until the number of individuals in the new population is the same as the initial population size to ensure the stability of the population size.

[0097] This random selection method can prevent the algorithm from getting stuck in local optima and ensure the diversity of the population; at the same time, prioritizing the retention of individuals with high fitness can speed up the convergence of the algorithm to the optimal solution.

[0098] Through the above process, the system can screen out oxygen levels with lower energy consumption while ensuring processing effectiveness, providing a high-quality population base for subsequent crossover and mutation operations, and ultimately achieving precise determination of the operational domain.

[0099] The specific steps for creating a trend chart are as follows: collect chemical oxygen demand and total organic carbon data before and after wastewater treatment using an online water quality monitor; obtain the hourly wastewater treatment volume based on a flow sensor; and obtain oxygen flow rate and equipment operating parameter values.

[0100] Draw trend charts from the acquired data, including time series charts, scatter plots, and heatmaps;

[0101] The operating cost is calculated by adding the energy cost and the drug cost to obtain the total cost value;

[0102] The energy cost calculation involves multiplying the power and operating time of each device separately, adding the products together, and then multiplying the result by the price per kilowatt-hour to obtain the energy cost.

[0103] The cost of the reagent is calculated by obtaining the amount of oxygen consumed in ozone production, multiplying it by the unit price of oxygen, and then multiplying it by the ozone generation efficiency.

[0104] Capital expenditure is calculated by adding the purchase cost to the installation and commissioning cost.

[0105] The purchase cost is calculated based on the market price of the equipment purchased for the processing system, and these costs are added together to obtain the purchase cost.

[0106] The installation and commissioning cost is calculated by adding up the purchase costs of all equipment and multiplying by a fixed installation coefficient.

[0107] The formula for calculating operating costs is as follows:

[0108] TOC = C 能耗 +C 药剂

[0109] Where TOC represents operating costs, and C... 能耗 For energy consumption costs, C 药剂 For the cost of the medicine;

[0110] Energy cost C 能耗 The calculation formula is:

[0111]

[0112] Where P i Let t be the power (kW) of the i-th device. i E is the runtime (h) of the i-th device. 单价 Let n be the price per kilowatt-hour (yuan / kWh), and n be the total number of devices.

[0113] Drug cost C 药剂 The calculation formula is:

[0114] C 药剂 =Q 氧气 ×P 氧气单价 ×η 臭氧生成效率

[0115] Q 氧气 The amount of oxygen consumed to produce ozone (m 3 or kg), P 氧气单价 Oxygen unit price (yuan / m³) 3 (or yuan / kg), η 臭氧生成效率 The ozone generation efficiency coefficient (the unit needs to be determined based on the process, such as g ozone / m³) 3 oxygen);

[0116] The formula for calculating capital expenditures is:

[0117] TCE = C 购置 +C 安装调试

[0118] Where TCE represents capital expenditure, and C 购置 For purchase costs, C 安装调试 For installation and debugging costs;

[0119] Purchase cost C 购置 The calculation formula is:

[0120]

[0121] Where P 设备j Let m be the purchase price (in yuan) of the j-th equipment, and m be the number of equipment types (or the total number of units, which needs to be adjusted according to the actual statistical criteria).

[0122] Installation and commissioning cost C 安装调试 The calculation formula is:

[0123] C 安装调试 =C 购置 ×α

[0124] α is a fixed installation factor (dimensionless, such as a percentage, for example, 10% means α = 0.1);

[0125] Ozone generation efficiency: In actual processes, ozone generation efficiency is expressed as "the amount of ozone generated per unit of oxygen" (e.g., g ozone / m³). 3 For oxygen, the multiplication and division relationships in the formula need to be adjusted according to the law of conservation of mass.

[0126] Installation factor: α needs to be determined based on industry standards or historical data, and is 5% to 15% of the purchase cost;

[0127] Operating costs and capital expenditures were calculated for the data under different trend charts. The calculation results were sorted from smallest to largest to find the gas flow range with the lowest operating costs and capital expenditures.

[0128] In the "Cost Calculation of Reagents", this application mentions "the amount of oxygen consumed in the production of ozone", which clearly indicates that the generation of ozone depends on oxygen as a raw material. This shows that there is a step in the system where oxygen is converted into ozone, and the ozone generator is the core equipment to realize this conversion (which is in line with the conventional process logic of ozone preparation: ozone is generated by using oxygen electrolysis or silent discharge through the ozone generator). The oxygen supply end of the micro-nano bubble system is connected to the ozone generator by a pipeline. After the oxygen is converted into ozone by the ozone generator, it enters the micro-nano bubble system to generate ozone-containing micro-nano bubbles.

[0129] The core function of the micro-nano bubble system is to generate micron-sized bubbles for ozone oxidation. Ozone oxidation requires ozone as an oxidant. Based on the calculations of ozone generation efficiency and oxygen consumption in the data acquisition and analysis module, it can be seen that the bubbles participating in the reaction in the micro-nano bubble system actually contain ozone components. Their source is the product of oxygen converted by the ozone generator. Therefore, the connection between the two should be: the gas source input end of the micro-nano bubble system is connected to the output end of the ozone generator through a pipe, and the ozone generator receives oxygen from the gas source system through another pipe, forming a gas supply chain of "oxygen → ozone generator → micro-nano bubble system".

[0130] When the voltage of the micro-nano bubble generator is stable within the range of 380V±5%, the standard deviation of the bubble particle size distribution can be controlled within 8μm. At this time, the gas-liquid mass transfer efficiency is increased by more than 20% compared with the voltage fluctuation. If only the oxygen quantity is fixed and the voltage deviates from this range, even if the gas quantity is the same, the bubble breaking efficiency will decrease by 15%-20%, resulting in a decrease of about 10% in the pollutant removal rate. The uniformity of bubble particle size distribution and the generator voltage have a significant impact on the treatment effect.

[0131] Online mass spectrometry analysis involves installing a sampling probe inside the reaction tank to sample water quality per unit time. The analysis is then performed using an online mass spectrometer to identify organic matter, inorganic matter, and intermediate products from the ozone oxidation process in the wastewater. A graph showing the relationship between intermediate products, pH value, and gas flow rate is plotted, with gas flow rate on the horizontal axis, pH value on the vertical axis, and intermediate product concentration as the curve. Based on the graph, the peak positions of intermediate product concentration as a function of pH value and gas flow rate are observed, and the corresponding intermediate product degradation rate at each pH value is calculated. Based on wastewater treatment standards, the optimal pH range is determined to be a degradation rate greater than 80% and an intermediate product concentration less than 10 mg / L. The optimal range is the pH value within this range found on the graph.

[0132] The intermediate product identification method involves installing sampling probes in different areas of a segmented pH-adjusting reaction tank, automatically collecting water samples at fixed time intervals, controlling the sampling volume through microfluidic technology to avoid interference with the reaction system, and removing particulate impurities from the water samples to ensure analytical accuracy.

[0133] After the collected water sample enters the online mass spectrometer, organic matter, inorganic matter and intermediate products are ionized by electrospray ionization or atmospheric pressure chemical ionization technology to form charged ions. Under the action of electric and magnetic fields, the ions are separated according to their mass-to-charge ratio. The detector records the intensity signal of the ions to form a mass spectrum.

[0134] By combining the built-in standard substance mass spectrometry database (NIST database), the detected ion mass-to-charge ratio is matched with the characteristic peaks of known compounds. At the same time, the chemical structure of intermediate products is identified by combining retention time and fragment ion information. For unknown products, their fragment patterns can be analyzed by secondary mass spectrometry to infer their molecular structure.

[0135] Intermediate products include phenols, peroxides, and oxygen compounds;

[0136] The collected raw data is filtered to remove outliers (such as data jumps caused by instrument malfunctions), and a three-dimensional dataset is established according to the correspondence between "gas flow rate - pH value - intermediate product concentration". For example, when the gas flow rate is Q1, the intermediate product concentration is recorded at different pH values ​​(such as pH = 3, 4, 5, etc.); similarly, when the pH value is fixed at a certain value, the concentration changes corresponding to different gas flow rates are recorded to ensure that there is matching concentration data for each parameter combination.

[0137] Steps for drawing a curve graph

[0138] Coordinate axis setting: The gas flow rate is set as the horizontal axis (unit such as L / h), and the pH value is set as the vertical axis (unit is dimensionless) to form a two-dimensional coordinate plane;

[0139] Curve representation: The concentration of intermediate products is used as the third dimension, and the concentration is represented by the density of the curves, the depth of the color, or the thickness of the lines (for example, the higher the concentration, the darker the line color).

[0140] Within the same coordinate system, curves showing the changes in intermediate product concentration at different pH values ​​are plotted in order of increasing or decreasing gas flow rate, forming a complete "flow rate-pH-concentration" correlation graph that visually demonstrates the dynamic relationship among the three.

[0141] Mark the peak position of the intermediate product concentration (i.e. the point of highest concentration) on the graph, and indicate the corresponding gas flow rate and pH value to provide a visual reference for subsequent calculation of degradation rate and determination of the optimal range.

[0142] This application applies online mass spectrometry analysis to the ozone oxidation process in wastewater treatment. The sampling and pretreatment technologies are mature; multi-point sampling within a segmented reaction tank can be achieved using automated sampling probes (such as a high-precision syringe pump sampling system), and fixed-time interval sampling can be completed through program control. Microfluidic technology has been widely used for the precise manipulation of trace samples, effectively controlling the sampling volume (typically down to microliters or even nanoliters) and avoiding interference with the reaction system. The removal of particulate matter from water samples can be achieved through online filtration modules (such as 0.22 μm filter membranes). These technologies have been standardized and applied in fields such as environmental monitoring and chemical reaction analysis.

[0143] Electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) are currently the mainstream soft ionization technologies, which can efficiently ionize organic matter, inorganic matter, and intermediate products (such as carboxylic acids, aldehydes, ketones, phenols, etc.) in water. They are suitable for the analysis of polar and moderately polar compounds and have become routine methods in the field of water quality testing. Mass spectrometry is a very mature technology for separating ions by mass-to-charge ratio (m / z) (such as quadrupole mass spectrometry and time-of-flight mass spectrometry). It can accurately distinguish different components in complex mixtures, and the detection limit can reach the ppb or even ppt level, which fully meets the needs of trace analysis of intermediate products.

[0144] Standard mass spectrometry databases such as NIST contain standard mass spectra of millions of compounds, which can be used to quickly match known intermediates using information such as mass-to-charge ratio and fragment ion patterns. For unknown products, secondary mass spectrometry (MS / MS) can be used to fragment the parent ion and infer the molecular structure (such as functional group type and carbon chain length) based on the mass and intensity of the fragment ions. This method has been widely used in organic chemistry and environmental science for the identification of unknowns. In addition, combining retention time (such as in LC-MS systems coupled with liquid chromatography) can further improve the accuracy of identification. Such coupled devices are already commercially available and widely used in the current technology.

[0145] The energy recovery module has a three-level energy recovery network, which includes primary recovery, intermediate recovery, and advanced recovery. The construction steps of the three-level energy recovery network are as follows:

[0146] The heat recovery requirements of ozone oxidation wastewater are clearly defined, the target efficiency of energy recovery and the direction of heat utilization are determined, the energy is divided into three levels of functions, a system topology diagram is designed, and the overall architecture is planned.

[0147] Primary recovery uses a spiral plate heat exchanger to preheat wastewater; intermediate recovery is based on a Rankine cycle system for power generation; and advanced recovery uses an absorption heat pump to increase heat output.

[0148] Primary recycling preheats the low-temperature wastewater to 40-50°C by counter-current heat exchange between high-temperature and low-temperature wastewater; intermediate recycling uses medium-temperature wastewater to heat the working fluid for power generation, achieving self-sufficiency in electricity; advanced recycling raises the low-temperature waste heat to 50-60°C and distributes it to ozone generator equipment as needed.

[0149] Waste heat recovery from steam condensate is achieved through heat exchange between different systems. For the primary recovery in this application, high-temperature wastewater comes from wastewater after ozone oxidation reaction, as it carries a large amount of heat after the reaction; low-temperature wastewater is the original wastewater to be treated or wastewater that has undergone preliminary treatment but is still at a low temperature. In terms of achieving countercurrent heat exchange, a spiral plate heat exchanger is used, with high-temperature wastewater and low-temperature wastewater entering from different channels of the heat exchanger respectively, exchanging heat in a countercurrent manner. After heat exchange, the temperature of high-temperature wastewater decreases and it enters the subsequent treatment process, entering the intermediate recovery stage as the source of medium-temperature wastewater; low-temperature wastewater is preheated to 40-50℃ and then enters the subsequent micro-nano bubble system or other treatment units, improving treatment efficiency and reducing energy consumption.

[0150] This application utilizes high-temperature wastewater for preheating low-temperature wastewater in the primary stage of recycling to reduce energy consumption in subsequent processes; the intermediate stage of recycling uses a medium-temperature wastewater to generate electricity through an organic Rankine cycle system, achieving self-sufficiency in electricity; and the advanced stage of recycling uses an absorption heat pump to increase the temperature of low-temperature waste heat, matching the heat source requirements of ozone equipment to form a closed-loop heat circulation, covering all heat grades, significantly reducing energy costs, shortening the equipment investment payback period, reducing carbon emissions and thermal pollution, complying with sustainable development and environmental protection policy requirements, adapting to multiple operating conditions, and can be operated in combination or independently, ensuring process continuity and reducing downtime risks.

[0151] The steps of cluster analysis are as follows:

[0152] The data within the scope is preprocessed, and the K-means clustering algorithm is used to divide the data.

[0153] Select the initial cluster centers, calculate the distance between the data points and the cluster centers based on the Euclidean distance formula, and iteratively update the cluster centers until the convergence condition is met;

[0154] The convergence condition is as follows: compare the difference between the newly generated cluster centers and the cluster centers of the previous round. If the change in the position of the cluster centers is less than a preset threshold and the preset maximum number of iterations is reached, the algorithm stops; otherwise, it continues to iterate.

[0155] Based on the silhouette coefficient, the cluster density and separation are evaluated. The elbow rule is used to judge the rationality of the number of clusters and verify the clustering results. The cluster centers are converted from standardized data back to the actual oxygen quantity scale. The three values ​​obtained are the oxygen quantity monitoring points for real-time monitoring and control.

[0156] Choose the K-means algorithm

[0157] Divide the n' samples into k clusters (here k=3), and minimize the within-cluster sum of squares (WSS).

[0158] Initialize cluster centers

[0159]

[0160] in Let k be the initial cluster center (a vector in the normalized space).

[0161] Euclidean distance calculation

[0162]

[0163] Where d im Let x' be the distance from the i-th sample to the m-th cluster center (in the t-th iteration), p be the number of features, and x' be the distance from the i-th sample to the m-th cluster center. ij For standardized data, Let be the j-th feature value of the m-th cluster center during the t-th iteration;

[0164] Iterative logic: By continuously updating cluster centers and sample allocation, samples within clusters are made as close together as possible, and samples between clusters are made as far apart as possible;

[0165] Evaluation metrics: The silhouette coefficient assesses the clustering quality of a single sample, and the elbow rule helps determine the optimal number of clusters (here, k=3 is directly set based on business needs).

[0166] The visualization module's user interface displays the processing flow using a 3D model and supports global start / stop; the data display interface uses a real-time dashboard to intuitively present key indicators with dynamic numbers and indicator lights; it also provides various charts for real-time monitoring of anomalies.

[0167] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. An industrial wastewater treatment system based on micro-nano bubble ozone oxidation, characterized in that, include: The bubble generation monitoring module generates micron-sized bubbles based on a micro-nano bubble system, continuously adjusts the oxygen quantity, records the maximum and minimum oxygen quantity values, and determines the interaction range and 1-3 test and data monitoring points. The data acquisition and analysis module collects data on chemical oxygen demand and total organic carbon, plots them into trend charts, calculates operating costs and capital expenditures, compares the calculation results under different trend charts, and calculates and identifies the optimal gas flow range. The pH data optimization module dynamically adjusts the pH value of the water body based on the selected optimal gas flow range. It designs a segmented pH adjustment reaction tank, uses microfluidic technology to distribute the pH gradient along the water flow direction, and combines online mass spectrometry analysis to monitor the changes of intermediate products in different pH regions in real time. It plots curves to find the optimal pH range and treats industrial wastewater based on the selected pH range and gas flow range parameters. The energy recovery module, with the addition of a heat exchange device, constructs a three-level energy recovery network to recover heat during the ozone oxidation process and assist in wastewater treatment. The visualization module is configured to provide both an operation interface and a data display interface for presentation. The specific steps for determining the scope between them and the 1-3 test and data monitoring points are as follows: Operators input basic operating parameters of the micro-nano bubble system based on the visualization module. These parameters include the type of wastewater to be treated, the treatment capacity, and the initial chemical oxygen demand and total organic carbon range of the water quality. The micro-nano bubble system generates micron-sized bubbles and adjusts the amount of oxygen based on set operating parameters. The micro / nano bubble system gradually increases / decreases the oxygen quantity within a safe range, starting from the initial minimum oxygen quantity and gradually increasing the oxygen input in fixed steps, based on sensor monitoring of bubble generation status and system stability. When abnormal increases in bubble size and increased equipment operating noise occur, the current oxygen quantity is recorded as the maximum gas quantity value; Conversely, when the amount of oxygen is reduced and the amount of bubbles generated is insufficient, the minimum gas volume value is determined. Repeat the test steps, record the data, and calculate the average value as the final determined maximum and minimum air volume values; Based on the determined maximum and minimum gas volume values, and using a genetic algorithm to simulate and calculate the treatment effect and energy consumption data under different oxygen conditions, the oxygen volume range with a treatment effect of over 90% and low energy consumption is selected as the operating range. Based on cluster analysis, the operational data within the scope is clustered into three categories, and the oxygen quantity corresponding to the cluster center of each category is the monitoring point. The algorithm steps for determining the scope based on genetic algorithms are as follows: Initialize the genetic algorithm parameters, including population size, number of generations, crossover probability, and mutation probability; Within the range of maximum and minimum gas volume values, the oxygen content values ​​of the population size are randomly generated to form the initial population. Based on the initial population oxygen levels, energy consumption data under different oxygen levels, and treatment effects, fitness values ​​are calculated, treatment effects are quantified, and weight values ​​for different indicators are determined for weighted calculation. Select other oxygen values ​​from the initial population to form a new population. Each time, select a random number of oxygen values ​​and calculate the corresponding fitness value. Repeat the selection process to select the initial population with the highest fitness and obtain the new population. The oxygen values ​​in the new population are grouped into pairs, and the values ​​in each pair are cross-calculated to obtain new oxygen values, thus obtaining the cross-calculated population. The crossover population is mutated, and the current generation number is checked to see if it has reached a preset value. If the condition is met, the calculation is terminated and the final population is formed. If the condition is not met, the above steps are continued until the termination condition is met. Based on the final population screening, the oxygen quantity values ​​that achieve a treatment effect of over 90% are selected. The energy consumption corresponding to the selected values ​​is calculated, the average energy consumption is obtained, and the oxygen quantity values ​​with energy consumption below the average value are selected to form a continuous interval. This interval is the operating range.

2. The industrial wastewater treatment system based on micro-nano bubble ozone oxidation according to claim 1, characterized in that, The specific steps for creating a trend chart are as follows: collect chemical oxygen demand and total organic carbon data before and after wastewater treatment using an online water quality monitor; obtain the hourly wastewater treatment volume based on a flow sensor; and obtain oxygen flow rate and equipment operating parameter values. Draw trend charts from the acquired data, including time series charts, scatter plots, and heatmaps; The operating cost is calculated by adding the energy cost and the drug cost to obtain the total cost value; The energy cost calculation involves multiplying the power and operating time of each device separately, adding the products together, and then multiplying the result by the price per kilowatt-hour to obtain the energy cost. The cost of the reagent is calculated by obtaining the amount of oxygen consumed in ozone production, multiplying it by the unit price of oxygen, and then multiplying it by the ozone generation efficiency.

3. The industrial wastewater treatment system based on micro-nano bubble ozone oxidation according to claim 1, characterized in that, Capital expenditure is calculated by adding the purchase cost to the installation and commissioning cost. The purchase cost is calculated based on the market price of the equipment purchased for the processing system, and these costs are added together to obtain the purchase cost. The installation and commissioning cost is calculated by adding up the purchase costs of all equipment and multiplying by a fixed installation coefficient. Operating costs and capital expenditures were calculated for the data under different trend charts. The calculation results were sorted from smallest to largest to find the gas flow range with the lowest operating costs and capital expenditures.

4. The industrial wastewater treatment system based on micro-nano bubble ozone oxidation according to claim 1, characterized in that, Online mass spectrometry analysis involves installing a sampling probe inside the reaction tank to sample water quality per unit time. The analysis is then performed using an online mass spectrometer to identify organic matter, inorganic matter, and intermediate products from the ozone oxidation process in the wastewater. A graph showing the relationship between intermediate products, pH value, and gas flow rate is plotted, with gas flow rate on the horizontal axis, pH value on the vertical axis, and intermediate product concentration as the curve. Based on the graph, the peak positions of intermediate product concentration as a function of pH value and gas flow rate are observed, and the corresponding intermediate product degradation rate at each pH value is calculated. Based on wastewater treatment standards, the optimal pH range is determined to be a degradation rate greater than 80% and intermediate product concentration less than 10 mg / L. The optimal range is the pH value within this range found on the graph.

5. The industrial wastewater treatment system based on micro-nano bubble ozone oxidation according to claim 1, characterized in that, The energy recovery module has a three-level energy recovery network, which includes primary recovery, intermediate recovery, and advanced recovery. The construction steps of the three-level energy recovery network are as follows: The heat recovery requirements of ozone oxidation wastewater are clearly defined, the target efficiency of energy recovery and the direction of heat utilization are determined, the energy is divided into three levels of functions, a system topology diagram is designed, and the overall architecture is planned. Primary recovery uses a spiral plate heat exchanger to preheat wastewater; intermediate recovery is based on a Rankine cycle system for power generation; and advanced recovery uses an absorption heat pump to increase heat output. Primary recycling preheats the low-temperature wastewater to 40-50℃ by counter-current heat exchange between high-temperature and low-temperature wastewater; intermediate recycling uses medium-temperature wastewater to heat the working fluid for power generation, achieving self-sufficiency in electricity; advanced recycling raises the low-temperature waste heat to 50-60℃ and distributes it to ozone generator equipment as needed.

6. The industrial wastewater treatment system based on micro-nano bubble ozone oxidation according to claim 1, characterized in that, The steps of cluster analysis are as follows: The data within the scope is preprocessed, and the K-means clustering algorithm is used to divide the data. Select the initial cluster centers, calculate the distance between the data points and the cluster centers based on the Euclidean distance formula, and iteratively update the cluster centers until the convergence condition is met; The convergence condition is as follows: compare the difference between the newly generated cluster centers and the cluster centers of the previous round. If the change in the position of the cluster centers is less than a preset threshold and the preset maximum number of iterations is reached, the algorithm stops; otherwise, it continues to iterate. Based on the silhouette coefficient, the cluster density and separation are evaluated. The elbow rule is used to judge the rationality of the number of clusters and verify the clustering results. The cluster centers are converted from standardized data back to the actual oxygen quantity scale. The three values ​​obtained are the oxygen quantity monitoring points for real-time monitoring and control.

7. The industrial wastewater treatment system based on micro-nano bubble ozone oxidation according to claim 1, characterized in that, The visualization module's user interface displays the processing flow using a 3D model and supports global start / stop. The data display interface presents key indicators intuitively with dynamic numbers and indicator lights through a real-time dashboard; it also provides a variety of charts for real-time monitoring of abnormal situations.

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