CN-121979039-A - Water treatment facilities multidimensional management system based on wisdom water affair cloud platform

CN121979039ACN 121979039 ACN121979039 ACN 121979039ACN-121979039-A

Abstract

The invention relates to the technical field of multidimensional management of water treatment equipment, and discloses a multidimensional management system of water treatment equipment based on an intelligent water service cloud platform, which comprises the following components: and calculating a dynamic alpha factor in real time through on-site off-gas detection data and a clear water reference mass transfer coefficient, and inverting to obtain continuous parameters for quantifying the gas-liquid interface state based on the monotonic relation between the bubble stagnation cap and the mass transfer resistance. The real-time sensitivity of the parameter to the air quantity of the blower is further calculated, a nonlinear model of the air quantity and mass transfer is constructed, and the target air quantity with minimum energy consumption is solved under the condition that the constraint condition of the process oxygen demand is met. And finally, issuing the target air quantity as a control instruction, and outputting interface recovery time according to the time change rate of the air-liquid interface parameter, so as to realize the dynamic energy saving and interface state collaborative management of the water treatment aeration system.

Inventors

WANG YUMIN
ZHANG HONGWEI
QIAN SHENGCAI
LI JIAMIN
JIANG ZIMING
WANG FENGLING

Assignees

青岛思普润水务环境科技有限公司

Dates

Publication Date: 20260505
Application Date: 20260120

Claims (8)

1. Water treatment facilities multidimensional management system based on wisdom water affair cloud platform, characterized by comprising: The data mapping module is configured to calculate a dynamic alpha factor in real time based on the on-site acquired off-gas detection data and the clear water reference mass transfer coefficient; The characteristic inversion module is configured to invert the dynamic alpha factor into a local bubble interface stagnation area fraction by utilizing a monotonic coupling mechanism of a bubble stagnation cap and mass transfer resistance, wherein the local bubble interface stagnation area fraction is a continuous scalar which quantifies the ratio of an interface rheological lock to an immovable area in the total interface of the bubble group; The decision optimization module is configured to calculate the real-time sensitivity of the stagnation area fraction of the local bubble interface to the air quantity of the blower, embed the real-time sensitivity into a nonlinear coupling model of air quantity and mass transfer, and solve the target air quantity with minimum energy consumption under the constraint condition of meeting the process oxygen demand; And the multidimensional control module is configured to send the target air quantity as an equipment control instruction and output interface recovery time according to the time change rate of the local bubble interface stagnation area fraction.
2. The system of claim 1, wherein the real-time calculation of the dynamic alpha factor based on the on-site collected off-gas detection data and the clear water reference mass transfer coefficient comprises: Dividing the product of the air volume flow and the absolute pressure of the gas by the product of the ideal gas constant and the absolute temperature of the gas, and multiplying the quotient by the difference between one mole fraction and the mole fraction of the water vapor to obtain the dry basis molar flow of the inlet gas; multiplying the molar flow of the inlet air dry basis by the difference between the molar fraction of the inlet air oxygen and the molar fraction of the outlet air oxygen to obtain an oxygen transfer rate; And obtaining the dynamic alpha factor by dividing the oxygen transfer rate by the continuous product of the standard total mass transfer coefficient of the clear water, the aeration reaction volume and the difference between the saturated dissolved oxygen mass concentration and the liquid-phase actually-measured dissolved oxygen mass concentration.
3. The smart water cloud platform-based multidimensional management system of water treatment facilities of claim 2, wherein the monotonic coupling mechanism of bubble stagnation caps and mass transfer resistance comprises: To quantify the geometric duty cycle in which the bubble surface is constrained to be immobile, dividing the difference obtained by subtracting the cosine of the polar angle of the stagnation cap by two to obtain the area ratio of the stagnation cap; In order to characterize nonlinear attenuation of the limited transfer capacity of an interface, performing power operation by taking a difference obtained by subtracting the area proportion of a stagnation cap as a base number and taking an interfacial viscoelastic effect index parameter as an index to obtain a power value, and multiplying the power value by a single bubble interface mass transfer coefficient under a clean water condition to obtain the single bubble interface mass transfer coefficient under a sewage condition; To define the mass transfer resistance intensity of the gas-liquid interface, dividing the mass transfer resistance intensity by the mass transfer coefficient of a single bubble interface under the sewage condition to obtain the mass transfer resistance of the gas-liquid interface; Based on the positive derivative relation of the gas-liquid interface mass transfer resistance to the stagnation cap area ratio, a monotonic coupling mechanism is established, wherein the monotonic coupling mechanism is caused by the increase of the stagnation cap area ratio and the gas-liquid interface mass transfer resistance.
4. The system of claim 3, wherein inverting the dynamic alpha factor to a local bubble interface stagnation area fraction by utilizing a monotonic coupling mechanism of bubble stagnation caps and mass transfer resistances comprises: in order to convert the dynamic alpha factor into the mass transfer characterization quantity under the sewage working condition, multiplying the dynamic alpha factor by the clear water reference total mass transfer coefficient to obtain the total mass transfer coefficient under the sewage working condition serving as the inversion left side quantity; Based on a monotonic coupling mechanism, establishing quantitative mapping of the total mass transfer coefficient and the local bubble interface stagnation area fraction, wherein the quantitative mapping is that the total mass transfer coefficient under the sewage working condition is equal to the standard total mass transfer coefficient of clear water multiplied by a power value which is obtained by subtracting the local bubble interface stagnation area fraction and takes the interface viscoelastic effect index parameter as an index as a base number; Based on quantitative mapping, a power value obtained by performing power operation by taking a subtraction of a dynamic alpha factor as a base and taking a quotient obtained by dividing an interface viscoelastic effect index parameter as an index is utilized to obtain the local bubble interface stagnation area fraction by means of solution.
5. The intelligent water service cloud platform-based multidimensional management system for water treatment equipment of claim 4, wherein calculating real-time sensitivity of local bubble interface stagnation area fraction to blower air volume comprises: Based on the dependence of the dynamic alpha factor on the air volume flow, dividing the continuous product of the difference of the absolute gas pressure, the first subtraction water vapor mole fraction and the difference of the inlet air oxygen mole fraction and the outlet air oxygen mole fraction by the continuous product of the difference of the ideal gas constant, the absolute gas temperature, the clear water reference total mass transfer coefficient, the aeration reaction volume, the saturated dissolved oxygen mass concentration and the liquid phase measured dissolved oxygen mass concentration to obtain the derivative of the dynamic alpha factor relative to the air quantity of the blower; Based on a chained derivation rule, performing power operation by taking a dynamic alpha factor as a base to obtain an intermediate power value by taking a difference obtained by subtracting one from a quotient obtained by dividing an interfacial viscoelastic effect index parameter as an index; Dividing the negative one by the interface viscoelastic effect index parameter to obtain a weighting coefficient; And obtaining the real-time sensitivity of the local bubble interface stagnation area fraction relative to the air quantity of the air blower by using the continuous product of the weighting coefficient, the intermediate power value and the derivative of the dynamic alpha factor relative to the air quantity of the air blower.
6. The multidimensional management system of water treatment equipment based on an intelligent water service cloud platform as claimed in claim 5, wherein the nonlinear coupling model of the real-time sensitivity embedded air volume and mass transfer solves the target air volume with minimized energy consumption under the constraint condition of meeting the process oxygen demand, and comprises the following steps: dividing the oxygen transfer rate of the process oxygen demand by the product of the difference of the saturated dissolved oxygen mass concentration minus the measured dissolved oxygen mass concentration of the liquid phase and the aeration reaction volume to obtain the mass transfer requirement of a unit volume; Obtaining an interface stagnation area fraction at any air quantity by using the product of the local air bubble interface stagnation area fraction at the current moment and the real-time sensitivity of the local air bubble interface stagnation area fraction relative to the air quantity of the air blower and the difference of the air quantity of the air blower subtracted from the air quantity of the air blower at the current moment; and obtaining an interface state item by subtracting the interface stagnation area fraction under any air quantity.
7. The intelligent water service cloud platform-based multidimensional management system of water treatment equipment of claim 6, wherein the real-time sensitivity is embedded into a nonlinear coupling model of air volume and mass transfer, and the target air volume with minimized energy consumption is solved under the constraint condition of meeting the process oxygen demand, and the system further comprises: Multiplying the clear water reference coefficient by the power of a clear water reference air volume index of the air volume of the blower, multiplying by a power value taking an interface state item as a base and taking an interface viscoelastic effect index parameter as an index, and subtracting the mass transfer requirement of a unit volume to construct a supply and requirement difference function; The method comprises the steps of multiplying a clear water reference coefficient by the difference between a first intermediate term and a second intermediate term to obtain a first derivative of a difference function, wherein the first intermediate term is the quotient obtained by multiplying the clear water reference air volume index of the air blower by the clear water reference air volume index of the air blower, subtracting the first power and multiplying the first power by the interfacial viscoelastic effect index parameter of an interfacial state term, the second intermediate term is the interfacial viscoelastic effect index parameter, multiplying the real-time sensitivity of the local bubble interfacial stagnation area fraction relative to the air blower air volume by the clear water reference air volume index of the air blower, multiplying the first power by the interfacial viscoelastic effect index parameter of the interfacial state term, subtracting the first power by the air blower air volume of the last iteration step by the quotient obtained by dividing the value of the difference function between the supply and the demand of the last iteration step by the value of the difference function, updating the air blower air volume until the supply and demand difference function converges to zero, and determining the obtained minimum non-negative solution as the target air volume with minimum energy consumption.
8. The system of claim 7, wherein the system issues the target air volume as the device control command and outputs the interface recovery time according to the time change rate of the local bubble interface stagnation area fraction, and the system comprises: Directly setting the target air volume with minimized energy consumption as the set air volume of the air blower, and transmitting the set air volume of the air blower to an air blower execution unit for equipment control; dividing the difference obtained by subtracting the local bubble interface stagnation area fraction at the last sampling time from the local bubble interface stagnation area fraction at the current time by the time interval of two adjacent samplings to obtain the time change rate of the local bubble interface stagnation area fraction; the difference obtained by subtracting the interface reference upper limit value set by the system from the local bubble interface stagnation area fraction at the current moment is used as a dividend; Calculating the opposite number of the time change rate of the stagnation area fraction of the local bubble interface, comparing the opposite number with 10 -6 , and selecting the larger value of the opposite number as the divisor; dividing the divisor by the divisor to obtain the interface recovery time and outputting.

Description

Water treatment facilities multidimensional management system based on wisdom water affair cloud platform Technical Field The invention relates to the technical field of multidimensional management of water treatment equipment, in particular to a multidimensional management system of water treatment equipment based on an intelligent water service cloud platform. Background Currently, an aeration system is generally adopted by a water treatment plant as a key link of biochemical treatment, and air is supplied by an air blower to maintain the concentration of dissolved oxygen required by microbial metabolism. With the rapid development of intelligent water affairs, a plurality of stations are connected into a cloud platform, so that the on-line acquisition and visualization of operation data such as air quantity, dissolved oxygen, electricity consumption and the like are realized. However, although the informatization degree is continuously improved, the aeration efficiency is still one of the links with the highest energy consumption and the most complicated control. Most of the existing systems are adjusted based on fixed experience parameters (such as experience alpha factor or air quantity-power curve), and the actual oxygen transfer efficiency of the aeration tank under the condition of sewage component change cannot be reflected, so that energy consumption optimization and process control are misaligned. Under conditions of high-load sewage or industrial mixed wastewater, the influent water often contains surface active substances and extracellular polymers, which can significantly change the physical properties of the bubble interface. Under the influence, even if the blast volume is maintained stable, nonlinear phenomena such as hysteresis, drifting or abnormal stability and the like can occur in the change of the dissolved oxygen, so that the data model of the cloud platform misjudges the running state of the aeration system, and the energy efficiency and the water outlet stability are further influenced. The traditional oxygen transfer efficiency evaluation method mainly depends on experimental results under the clean water condition, and calculates an oxygen transfer coefficient under the sewage condition through fixing a correction coefficient. Such methods ignore differences in interface contamination, bubble age, and turbulence update rates. In actual operation, various organic or colloidal components in the sewage can form a stable film on the surface of the bubbles, so that the rising speed of the bubbles and the mass transfer resistance of a gas-liquid interface are changed. Because the interface effect occurs at a microscopic scale, the traditional monitoring equipment (such as a dissolved oxygen probe or a flowmeter) cannot be directly captured, and the cloud platform can only indirectly judge through macroscopic quantities (such as energy of a fan and water inlet and outlet dissolved oxygen difference), so that fitting distortion of the data model on oxygen transfer efficiency is caused. Furthermore, the operating conditions of the aeration system are highly dynamic. Air volume adjustment, sludge concentration change and inflow fluctuation can cause rapid changes of bubble distribution, bubble diameter spectrum and oxygen transfer rate. Most of algorithms of the existing intelligent water service platform are linear or static models, and small changes of interface states along with time and space cannot be identified in real time. As a result, when the aeration system is subject to surface contamination, a change in bubble group structure, or a decrease in air diffusion efficiency, the system cannot be identified at the first time, which results in not only waste of energy but also fluctuation in microbial activity and a decrease in treatment efficiency. Disclosure of Invention The invention provides a multidimensional management system of water treatment equipment based on an intelligent water service cloud platform, which solves the technical problem of how to realize real-time quantitative expression of air bubble interface state change on the intelligent water service cloud platform in a sewage aeration system so as to accurately reflect dynamic change of oxygen transfer efficiency and optimize air quantity control and energy consumption management according to the dynamic change. The invention provides a water treatment equipment multidimensional management system based on an intelligent water service cloud platform, which comprises the following components: The data mapping module is configured to calculate a dynamic alpha factor in real time based on the on-site acquired off-gas detection data and the clear water reference mass transfer coefficient; The characteristic inversion module is configured to invert the dynamic alpha factor into a local bubble interface stagnation area fraction by utilizing a monotonic coupling mechanism of a bubble stagnation cap and mass transfer resistan