Multi-objective optimization analysis method and system for visual flash boiling waste heat recovery

Abstract

The invention belongs to the technical field of energy utilization, and particularly discloses a multi-objective optimization analysis method and system for visual flash boiling waste heat recovery. The method comprises the following steps: constructing a visual flash boiling experiment data acquisition device, and acquiring monitoring parameters in a flash process; constructing a VOF model used for describing the flash evaporation process and an improved Lee model; by taking the VOF and the improved Lee model as constraint conditions and taking the heat recovery efficiency, the water resource recovery rate and the economic benefit as fitness values, constructing a multi-objective optimization model based on an MOARO algorithm; and optimizing the multi-objective optimization model by adopting an ARO algorithm, updating individual positions until termination conditions are met, and outputting the optimal liquid level height, inlet superheat degree, vacuum degree and slurry flow of the flash chamber. The method integrates the VOF model and the improved Lee model to more accurately describe the flash evaporation process, and has the characteristics of high heat recovery efficiency and high water resource recovery rate.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy utilization, and more specifically, relates to a multi-objective optimization analysis method and system for visual flash boiling waste heat recovery. Background Art

[0002] At present, the research on flash technology at home and abroad mainly focuses on pool flash, droplet flash, and spray flash. The research forms adopted are basically the method of mutual verification between theoretical analysis and experimental results, and corresponding empirical relations and flash mathematical models have been summarized. However, due to problems such as the conversion flow of different phases in the specific flash process, the process is extremely complex, and the existing models all have problems such as strong pertinence and poor versatility. Moreover, the current industrial flash systems at home and abroad cannot achieve visualization, cannot clearly and intuitively study the influence of factors such as liquid level height on flash, and cannot meet the laboratory measurement standards.

[0003] In addition, traditional flash systems have problems of low heat recovery efficiency, low water resource recovery rate, and limited economic benefits in industrial applications. Summary of the Invention

[0004] In view of the above defects or improvement requirements of the prior art, the present invention provides a multi-objective optimization analysis method and system for visual flash boiling waste heat recovery. By integrating the VOF (Volume of Fluid) model and the improved Lee model to describe the flash process, and using the ARO (Artificial Raindrop Optimization) algorithm to optimize the design of flash conditions, the heat recovery efficiency, water resource recovery rate, and economic benefits are improved.

[0005] To achieve the above object, according to one aspect of the present invention, a multi-objective optimization analysis method for visual flash boiling waste heat recovery is proposed, including the following steps:

[0006] Step 1: Construct a visual flash boiling experimental data acquisition device and collect monitoring parameters during the flash process;

[0007] Step 2: Based on the monitoring parameters, construct a VOF and an improved Lee model for describing the flash process;

[0008] Step 3: Use the VOF and the improved Lee model as constraint conditions, and the heat recovery efficiency, water resource recovery rate, and economic benefits as fitness values to construct a multi-objective optimization model based on the MOARO algorithm;

[0009] Step 4: Optimize the multi-objective optimization model using the ARO algorithm, update the individual positions until the termination condition is met, and output the optimal solution, i.e., the optimal liquid level height, inlet superheat, vacuum degree, and slurry flow rate of the flash chamber.

[0010] As a further preference, in Step 2, fit the variation of the saturation temperature of pure water with pressure, add the variation of the saturation temperature of pure water with pressure to the Lee model, and fit according to the local pressure-driven phase change process to obtain an improved Lee model:

[0011] T = 31.35p 0.1526 + 191.1

[0012]

[0013] In the formula, is the mass change source term caused by phase change, coeff is the evaporation coefficient, α 1 is the size of the liquid volume fraction, ρ 1 is the size of the liquid density (kg / m 3 ), T sat is the set phase change temperature, and p is the size of the pressure.

[0014] As a further preference, in Step 2, the VOF model includes:

[0015] Mass conservation equation:

[0016]

[0017] Momentum conservation equation:

[0018]

[0019] Energy conservation equation:

[0020]

[0021] Volume fraction transport equation:

[0022]

[0023] In the formula, ρ is the local mixed density, ρ v is the local gas density, V is the local velocity vector, p is the local pressure, α v is the local gas volume fraction, μ is the dynamic viscosity coefficient, g is the gravitational acceleration vector, E is the internal energy, λ is the effective thermal conductivity, T is the local temperature, is the energy change source term caused by phase change.

[0024] As a further preference, in step three, the multi-objective optimization model based on the MOARO algorithm includes:

[0025] F(x) = w 1 η heat (x) + w 2 η water (x) + w 3 B(x)

[0026] In the formula, w 1 、w 2 and w 3 are weight coefficients, x is the decision variable vector (L, T in , P vac , Q), where L is the liquid level height, T in is the inlet superheat degree, P vac is the vacuum degree, and Q is the slurry flow rate.

[0027] As a further preference, in step three, the heat recovery efficiency is:

[0028]

[0029] In the formula, Q condensed is the heat of the condensate water, Q input is the heat input into the flash system;

[0030] The water resource recovery rate is:

[0031]

[0032] In the formula, m condensed is the mass of the condensate water, m input is the mass of the water input into the flash system;

[0033] The economic benefit is:

[0034] B = R - C

[0035] where R is the income and C is the cost.

[0036] As a further preference, the optimization calculation in step four includes the following steps:

[0037] (41) Each search individual tends to update its own position to another search individual randomly selected from the population and increase the perturbation, so as to ensure the global search ability of the ARO algorithm;

[0038] (42) In the ARO algorithm, each iteration is regarded as a rabbit generating d holes around it along each dimension of the search space and always randomly selecting one of all the holes to hide in, so as to reduce the probability of being preyed on, and updating the position of the rabbit according to the fitness;

[0039] (43) Determine the strategy of the algorithm according to the energy level of the rabbit;

[0040] (44) The Pareto optimal solutions obtained by using the MOARO algorithm are a set of solution sets that meet the requirements of multi-objective optimization.

[0041] According to another aspect of the present invention, there is also provided a multi-objective optimization analysis system for visualizing flash boiling waste heat recovery, including:

[0042] A visual flash boiling experimental data acquisition device for acquiring monitoring parameters during the flash process;

[0043] An optimization controller module for constructing a VOF and an improved Lee model for describing the flash process based on the monitoring parameters, and using the VOF and the improved Lee model as constraint conditions, and the heat recovery efficiency, water resource recovery rate and economic benefit as fitness values to construct a multi-objective optimization model based on the MOARO algorithm; using the ARO algorithm to optimize the multi-objective optimization model, updating the individual position until the termination condition is met, and outputting the optimal solution, that is, the optimal liquid level height, inlet superheat degree, vacuum degree and slurry flow rate of the flash chamber.

[0044] As a further preference, the visual flash boiling experimental data acquisition device includes:

[0045] A flash chamber, on which a high-speed CCD camera is provided;

[0046] A water tank, which is respectively connected to the flash chamber through a water inlet pipe and a water return pipe, thermometers are provided on both the water inlet pipe and the water return pipe, and a glass rotameter is also provided on the water inlet pipe;

[0047] An evaporation and condensation chamber, which is connected to the flash chamber through a vapor channel, a thermometer is provided on the vapor channel, and a condensate water pipe is provided in the evaporation and condensation chamber;

[0048] A condensate water tank, which is connected to both ends of the condensate water pipe through a refrigerant inlet pipe and a refrigerant outlet pipe to form a closed circulation loop;

[0049] A vacuum pump, which is respectively connected to the evaporation and condensation chamber and the condensate water tank to provide a set vacuum environment for the evaporation and condensation chamber and the condensate water tank.

[0050] As a further preference, the flash chamber includes a cylindrical body and a conical body disposed at the bottom of the cylindrical body. The side wall of the cylindrical body is provided with a water inlet and a water outlet. The bottom of the conical body is provided with a sewage outlet. The sewage outlet is communicated with the water tank through a sewage pipeline. The sewage pipeline is provided with a sewage valve. A water tank heater is further arranged in the water tank.

[0051] As a further preference, a condensate water pump is further arranged inside the refrigerant water tank for pumping the condensate water in the refrigerant water tank into the condensate water pipe in the evaporation and condensation chamber through a refrigerant water inlet pipe.

[0052] Generally speaking, compared with the prior art by the above technical solutions conceived by the present invention, the following technical advantages are mainly possessed:

[0053] 1. By integrating the VOF model and the improved Lee model, the system of the present invention can more accurately describe and simulate the liquid-phase and gas-phase behaviors and phase change phenomena during the flash process. This accurate physical description enables the system to optimize the flash working conditions, thereby significantly improving the heat recovery efficiency and water resource recovery rate. The optimized flash process can make more effective use of waste heat, reduce energy waste, and at the same time recover more water resources, which is of great significance for environmental protection and resource conservation.

[0054] 2. By the multi-objective optimization algorithm (MOARO), the present invention comprehensively considers the heat recovery efficiency, water resource recovery rate and economic benefits to find the optimal flash working conditions. This method not only focuses on improving the recovery efficiency of energy and resources, but also takes into account costs and revenues, making the entire flash process more economically feasible and profitable. By optimizing parameters such as liquid level height, inlet superheat degree, vacuum degree and slurry flow rate, the system can reduce operating costs and increase product value, thereby enhancing the overall economic benefits.

[0055] 3. The design of the present invention takes into account various factors in actual operation, including the material strength, service life, pressure fluctuation, etc. of the flash chamber, making the optimization result not only optimal in theory but also feasible in practical application. By using a high-speed CCD camera to monitor the flash phenomenon in real time and combining the data of a thermometer, a pressure gauge and a flowmeter, the system can provide real-time and visual analysis of the flash process, which enhances the monitoring ability and control accuracy of the system. In addition, the design of the system allows adjustment of control parameters as needed, providing flexibility and adaptability, enabling it to adapt to different operating conditions and requirements, and enhancing the reliability and practicability of the system.

[0056] 3. The system of the present invention has the advantages of high production efficiency, fast processing speed, continuous production, suitability for large-scale production, flexible control parameters that can be adjusted according to needs, etc. It can be visualized and can clearly capture the real-time flashing phenomenon. It can not only achieve flowing flashing but also static flashing. In addition, in order to achieve visualization, the material strength of the flash chamber of the experimental platform is low and the service life is short. This experimental system uses a high-speed CCD camera to realize the visualization of the flashing phenomenon; at the same time, the cylindrical dual-material structure of the flash chamber of this system can make up for the shortcomings of low material strength and short life of the flash chamber. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a flowchart of a multi-objective optimization analysis method for visual flashing boiling waste heat recovery according to an embodiment of the present invention;

[0058] Figure 2 is a schematic structural diagram of a visual flashing boiling waste heat recovery system according to an embodiment of the present invention;

[0059] Figure 3 is a schematic structural diagram of the flash chamber according to an embodiment of the present invention;

[0060] Figure 4 is a cloud map of the water component distribution at different inlet superheats according to an embodiment of the present invention;

[0061] Figure 5 is a cloud map of the water component distribution at different liquid levels according to an embodiment of the present invention.

[0062] In all the drawings, the same reference numerals represent the same technical features, specifically: 1 - evaporation and condensation chamber, 2 - vacuum pipeline, 3 - water turbine pipeline, 4 - vacuum pump, 5 - water inlet pipe, 6 - glass rotor flowmeter, 7 - temperature digital control display screen, 8 - water pump, 9 - sewage pipeline, 10 - water tank, 11 - sewage valve, 12 - drain pipe, 13 - drain valve, 14 - condensate water pump, 15 - refrigerant water tank, 16 - water turbine pipeline valve, 17 - refrigerant outlet pipe, 18 - refrigerant inlet pipe, 19 - first thermometer, 20 - flash chamber, 21 - pressure gauge, 23 - third thermometer, 24 - second thermometer, 26 - water inlet, 27 -, 28 - water outlet, 29 - pressure gauge interface, 30 - vapor outlet, 31 - water tank heater. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0064] Embodiment 1

[0065] As Figure 1 shown, a multi-objective optimization analysis method for visual flash boiling waste heat recovery provided by an embodiment of the present invention includes the following steps:

[0066] Step 1, construct a visual flash boiling experimental data acquisition device and collect the monitoring parameters during the flash process. In this step, the monitoring parameters include temperature data, pressure data, flow rate data, liquid level data, visual data and vacuum data. Among them,

[0067] The temperature data includes:

[0068] Inlet superheat (Tin): the difference between the temperature of the fluid at the flash inlet and the saturation temperature at this pressure; the temperature in the flash chamber: the temperatures at different positions in the flash chamber, used to monitor the temperature distribution during the flash process; the steam temperature: the temperature of the steam generated after flashing, used for the calculation of the heat recovery efficiency.

[0069] The pressure data includes:

[0070] The pressure in the flash chamber (Pvac): the pressure in the flash chamber, which is crucial for controlling the flash process and phase change phenomenon; the saturation pressure: the saturation pressure corresponding to the temperature in the flash chamber, used for calculating and monitoring the phase change conditions during the flash process.

[0071] The flow rate data includes: slurry flow rate (Q): the flow rate of the slurry entering the flash chamber, which affects the flash rate and heat recovery efficiency; the condensate flow rate: the flow rate of the condensed water, used for the calculation of the water resource recovery rate.

[0072] The liquid level data includes: liquid level height (L): the height of the liquid level in the flash chamber, which affects the thermodynamic conditions and phase change rate of the flash process.

[0073] Visual data: images taken by a high-speed CCD camera: providing intuitive visual information about the flash phenomenon, such as the formation, rising and bursting processes of bubbles, and the change of the liquid level, etc.

[0074] The vacuum data includes: the degree of vacuum in the flash chamber.

[0075] Other relevant parameters: specific enthalpy of flash steam (hvapor): used to calculate the heat recovery efficiency; latent heat of vaporization (Lv): used to calculate the energy change during the phase change process; specific enthalpy of water before and after flashing: used to calculate the energy change during the flashing process.

[0076] These monitoring data are collected in real time through corresponding sensors and devices, and then transmitted to a computer system for processing and analysis. Through these data, the dynamic changes of the flashing process can be monitored in real time, the flashing efficiency can be evaluated, and the flashing conditions can be optimized and adjusted.

[0077] Step 2: Based on the monitoring parameters, construct a VOF and an improved Lee model for describing the flashing process. Specifically:

[0078] The VOF model includes:

[0079] Mass conservation equation:

[0080]

[0081] Momentum conservation equation:

[0082]

[0083] Energy conservation equation:

[0084]

[0085] Volume fraction transport equation:

[0086]

[0087] In the formula, ρ is the local mixture density, ρ v is the local gas-phase density, V is the local velocity vector, p is the local pressure, α v is the local gas-phase volume fraction, μ is the dynamic viscosity coefficient, g is the gravitational acceleration vector, E is the internal energy, λ is the effective thermal conductivity, T is the local temperature, is the energy change source term caused by the phase change.

[0088] Fit the magnitude of the change in the saturation temperature of pure water with pressure, add the magnitude of the change in the saturation temperature of pure water with pressure to the Lee model, and fit according to the local pressure driving the phase change process to obtain the improved Lee model:

[0089] T = 31.35p 0.1526 + 191.1

[0090]

[0091] In the formula, is the source term of mass change caused by phase change, coeff is the evaporation coefficient, α 1 is the size of the liquid volume fraction, ρ 1 is the size of the liquid density (kg / m 3 ), T sat is the set phase change temperature, and p is the size of the pressure.

[0092] Step 3: Taking the VOF and the improved Lee model as constraint conditions, and the heat recovery efficiency, water resource recovery rate, and economic benefit as fitness values, construct a multi-objective optimization model based on the MOARO algorithm. In this step, the heat recovery efficiency is:

[0093]

[0094] In the formula, Q condensed is the heat of the condensed water, and Q input is the heat input into the flash system;

[0095] The water resource recovery rate is:

[0096]

[0097] In the formula, m condensed is the mass of the condensed water, and m input is the mass of the water input into the flash system;

[0098] The economic benefit is:

[0099] B = R - C

[0100] where R is the income and C is the cost.

[0101] The multi-objective optimization model based on the MOARO algorithm includes:

[0102] F(x) = w 1 η heat (x) + w 2 η water (x) + w 3 B(x)

[0103] In the formula, w 1 , w 2 and w 3 are weight coefficients, and x is the decision variable vector (L, T in , P vac , Q), where L is the liquid level height, T in is the inlet superheat degree, P vac is the vacuum degree, and Q is the slurry flow rate.

[0104] Step 4: Optimize the multi-objective optimization model using the ARO algorithm, update the individual positions until the termination condition is met, and output the optimal solution, i.e., the optimal liquid level height, inlet superheat degree, vacuum degree, and slurry flow rate of the flash chamber.

[0105] In this step, the optimization calculation includes the following steps:

[0106] (41) Each search individual tends to update its own position towards another search individual randomly selected from the population and add perturbations to ensure the global search ability of the ARO algorithm;

[0107] (42) In the ARO algorithm, each iteration is regarded as a rabbit creating d holes around it along each dimension of the search space and always randomly selecting one of all the holes to hide in to reduce the probability of being preyed upon, and updating the position of the rabbit according to the fitness;

[0108] (43) Determine the strategy of the algorithm according to the energy level of the rabbit;

[0109] (44) The Pareto optimal solution obtained by using the MOARO algorithm is a set of solution sets that meet the requirements of multi-objective optimization.

[0110] Among them, obtaining the optimal solution includes:

[0111] The Pareto optimal solution obtained by using the MOARO algorithm is a set of solution sets that meet the requirements of multi-objective optimization and contains multiple objective parameter decision-making schemes. However, in actual engineering, only one decision-making scheme is needed. Therefore, it is necessary to select an optimal solution from the Pareto solution set. Starting from the simplicity, the ideal point method is used to select the optimal decision-making scheme. The implementation process is as follows:

[0112] Step1: Calculate the distance between all solutions on the Pareto front and the ideal point, which can be calculated by the following formula:

[0113]

[0114] where D i is the distance between the average value of all points and the ideal point, (x i , y i , z i ) are the coordinates of the optimal Pareto boundary point, and (x Epoint , y Epoint , z Epoint ) are the coordinates of the ideal point.

[0115] Step2: Select the point closest to the ideal point as the optimal point, and the optimal point can be considered as the actual optimal decision. The optimal point is calculated as follows

[0116] D opt = min(D i )

[0117] where D opt is the optimal solution.

[0118] In the above embodiment, it is also necessary to evaluate the performance of the optimized multi-objective optimization model. Among them, the coefficient of determination (R 2 ), root mean square error (RMSE), and mean square error (MSR) are three indicators commonly used to evaluate the performance of a prediction model. The meanings of the parameters are as follows: R 2 evaluates the fitting degree between the predicted value and the true value, and its value range is 0-1. The closer its value is to 1, the higher the fitting degree of the model and the better the prediction performance. Both MSE and RMSE are indicators that measure the difference between the predicted value of the model and the true value. The smaller their values, the smaller the deviation between the predicted value of the model and the actual value, and the higher the prediction accuracy of the model. The calculation formulas of the three evaluation indicators are as follows:

[0119]

[0120] where n is the total number of data in the sample dataset; and represent the predicted value of the model and the actual observed value, respectively.

[0121] According to another aspect of this embodiment, as Figure 2 and Figure 3 shown, a visualization flash boiling waste heat recovery system is also provided, which is an experimental system for analyzing the influence of factors such as superheat degree, liquid level height, residence time, etc. on characteristics such as flash evaporation rate and sensible heat-latent heat conversion rate by using a visualization low-temperature flash evaporation system for flash evaporation, including: a visualization flash boiling experimental data acquisition device for collecting monitoring parameters during the flash evaporation process; an optimization controller module for constructing a VOF and an improved Lee model for describing the flash evaporation process based on the monitoring parameters, and using the VOF and the improved Lee model as constraint conditions, and the heat recovery efficiency, water resource recovery rate, and economic benefit as fitness values to construct a multi-objective optimization model based on the MOARO algorithm; using the ARO algorithm to optimize the multi-objective optimization model, update the individual position until the termination condition is met, and output the optimal solution, that is, the optimal liquid level height, inlet superheat degree, vacuum degree, and slurry flow rate of the flash evaporation chamber.

[0122] Preferably, the visualization flash boiling experiment data acquisition device includes: a flash chamber 20, on which a high-speed CCD camera and a pressure gauge 21 are provided; a water tank 10, which is respectively communicated with the flash chamber 20 through a water inlet pipe 5 and a water return pipe 12, thermometers are provided on both the water inlet pipe 5 and the water return pipe 12, and a glass rotor flowmeter 6 is further provided on the water inlet pipe 5; an evaporation and condensation chamber 1, which is communicated with the flash chamber 20 through a vapor channel 22, a thermometer is provided on the vapor channel 22, and a condensate water pipe is provided in the evaporation and condensation chamber 1; a condensate water tank 15, which is communicated with both ends of the condensate water pipe through a refrigerant inlet pipe 18 and a refrigerant outlet pipe 17 to form a closed circulation loop; a vacuum pump 4, which is respectively communicated with the evaporation and condensation chamber 1 and the condensate water tank 15 to provide a set vacuum environment for the evaporation and condensation chamber 1 and the condensate water tank 15.

[0123] Preferably, the flash chamber 20 includes a cylindrical body and a conical body provided at the bottom of the cylindrical body. An inlet 26 and an outlet 28 are provided on the side wall of the cylindrical body. A sewage outlet 27 is provided at the bottom of the conical body. The sewage outlet 27 is communicated with the water tank 10 through a sewage pipe 9. A sewage valve is provided on the sewage pipe 9, and a water tank heater 31 is further provided in the water tank 10.

[0124] Preferably, a condensate water pump 14 is further provided inside the refrigerant water tank 15 for pumping the condensate water in the refrigerant water tank 15 into the condensate water pipe in the evaporation and condensation chamber 1 through the refrigerant inlet pipe 18.

[0125] Embodiment 2

[0126] In this embodiment, the bubble dynamics characteristics and heat-mass balance characteristics of the flash process are tested and analyzed. At the same time, the influence of influencing factors such as superheat degree, liquid level height, residence time, etc. on characteristics such as flash rate and sensible heat-latent heat conversion rate can be studied through the images captured by the high-speed camera and the real-time measured values of the thermometer and pressure gauge.

[0127] In this embodiment, the VOF + improved Lee model method with fewer empirical coefficients and less computational resources consumption in the model is used to describe the flash process.

[0128] Under the three-dimensional model, the VOF model consists of a mass conservation equation, three momentum equations, an energy conservation equation, and a phase fraction supplementary equation, as follows:

[0129] Mass conservation equation:

[0130]

[0131] Momentum conservation equation:

[0132]

[0133] Energy conservation equation:

[0134]

[0135] Volume fraction transport equation:

[0136]

[0137] where

[0138] ρ —— local mixture density magnitude (kg / m3);

[0139] ρ v —— local gas phase density magnitude (kg / m3);

[0140] V —— local velocity vector (m / s);

[0141] p —— local pressure magnitude (Pa);

[0142] α v —— local gas phase volume fraction magnitude;

[0143] μ —— dynamic viscosity coefficient (Pa·s);

[0144] g —— gravitational acceleration vector (m / s2);

[0145] E —— internal energy (J);

[0146] λ —— effective thermal conductivity (W / (m·K));

[0147] T —— local temperature (K);

[0148] —— energy change source term due to phase change (J / (m3·s));

[0149] —— mass change source term due to phase change (kg / (m3·s))

[0150] In the Lee model, the set phase change temperature is a fixed value. During the flashing process, the pressure reduction is the direct cause of the phase change phenomenon, and the pressure fluctuation inside the flash chamber is very intense. The uncertainty brought about by the complexity of the pressure distribution inside the flash chamber is the focus of the research. Under the original fixed saturation temperature setting, the superheat degree distribution inside the flash chamber is not considered, and the water at different positions boils simultaneously, unable to simulate the boiling of the upper water body during the actual flashing process, while the lower water body does not undergo a phase change because the water pressure is too high and the pressure does not drop below the saturation pressure. Considering that the change in the saturation temperature caused by the pressure change cannot be ignored, this system fits the change in the saturation temperature of pure water with the pressure change and adds this change to the Lee model. The phase change process is driven by the local pressure. The fitting formula and the updated Lee model are as follows:

[0151] T = 31.35p 0.1526 + 191.1

[0152]

[0153] In the formula

[0154] coeff - evaporation coefficient (1 / s);

[0155] α 1 - local liquid volume fraction;

[0156] ρ 1 - local liquid density (kg / m 3 );

[0157] T sat - set phase change temperature (K);

[0158] The calculation formulas used in this experimental test system are as follows:

[0159]

[0160] In the formula: GR is the vaporization rate; η is the sensible heat conversion rate of flashing; m s is the mass of the flashing steam; h s is the specific enthalpy of the flashing steam; r is the latent heat of vaporization; m 1 is the mass of the water before flashing; h 1 is the specific enthalpy of the water before flashing; h e is the saturated water enthalpy corresponding to the pressure inside the flash chamber; m 2 is the mass of the water after flashing; h 2 is the specific enthalpy of the water after flashing. And all of them can be obtained from the experimental data.

[0161] As follows Figure 4 、 Figure 5As shown, through the pattern captured by the high-speed camera and real-time data analysis, the components and the rising height of the experimental water body can be clearly tested and analyzed. The test system of this experiment transmits real-time data through various thermometers, pressure gauges, and flow meters, uses the above calculation formula to obtain real-time experimental data, and combines the photos taken by the high-speed CCD camera to analyze the visualized experimental phenomena.

[0162] Example 3

[0163] An embodiment of the present invention provides a visualized flash boiling experimental data acquisition device, and its specific structure, connection method, and working process are as follows: A vacuum pump 4 is connected and arranged at the lower part of the evaporation and condensation chamber 1. The evaporation and condensation chamber 1 and the flash chamber 21 connected to the evaporation and condensation chamber are evacuated through the vacuum pump 4. A condensate water pipe is arranged inside the evaporation and condensation chamber 1. Refrigerant water pipes 17 and 18 are provided above and below the evaporation and condensation chamber 1, and the refrigerant water flows through the evaporation and condensation chamber 1 from bottom to top. A water inlet pipe 5 and a water return pipe 12 are respectively installed on the side wall of the flash chamber 21. The bottom of the flash chamber is designed in a conical shape to facilitate the residual water and impurities to flow out through the sewage outlet 11. The experimental water enters the flash chamber 21 through the water inlet pipe 5 for flash evaporation. The steam after flash evaporation moves upward and enters the condensate water pipe to exchange heat with the refrigerant water. The water after flash evaporation flows back into the water tank 10 through the water return pipe 12. A high-speed CCD camera is arranged outside the flash chamber 21 to capture and observe the clear and real-time flash evaporation phenomenon, and is connected to a computer for experimental data analysis.

[0164] Figure 3 It is a structural diagram of the flash chamber. The overall structure of the flash chamber of this experimental system is a cylindrical structure. Compared with the box-shaped structure, this structure is more robust in the face of a sharp change in the internal wall pressure. The upper chamber cover and the lower conical bottom of the flash chamber are made of stainless steel, with good structural stability, compressive resistance, and corrosion resistance. The lower part is designed in a conical structure, which is more convenient for the remaining residues and residual water in the chamber to be discharged from the system through the sewage outlet 27 after the test. The water inlet 26 and the water outlet 28 are arranged on both sides of the flash chamber. The steam outlet 30 is the flash steam discharge port, and 29 is the pressure gauge interface.

[0165] A condensate water tank 15 is arranged outside the evaporation and condensation chamber 1, and the condensate water tank 15 is connected to the lower and upper ends of the evaporation and condensation chamber 1 through the refrigerant water pipes 17 and 18.

[0166] Furthermore, a condensate water pump is also arranged inside the refrigerant water tank 15 to pump the condensate water into the refrigerant water pipe and enter the evaporation and condensation chamber 1.

[0167] Furthermore, the experimental water inlet pipe 5 is horizontally arranged on the lower side wall of the flash chamber.

[0168] Furthermore, the experimental water return pipe 12 is horizontally arranged on the lower side wall of the flash chamber.

[0169] Further, the vacuum pump 4 evacuates the interior of the evaporation and condensation chamber 1 and the flash chamber 21 to an environment of approximately 20 to 50 Kpa.

[0170] The specific experimental procedure is as follows:

[0171] Turn on the vacuum pump 4, the water pump 8, the temperature counting and control display screen 7, the condensate water tank water pump 14, and the water tank heater 31. Open the valve 16 and close the valves 11 and 13.

[0172] Further, the experimental water (60°C to 90°C) in the water tank 10 is pumped into the flash chamber 21 by the water pump 8 through the water inlet pipe 5. By checking the table, it can be known that the saturated vapor pressure of water at 60°C is 19.932 Kpa. The experimental water rapidly boils and vaporizes in the flash chamber 20. The glass rotor flowmeter 6 is used to measure the experimental flow rate. The thermometer 24 is used to measure the real-time temperature of the fluid at the water inlet.

[0173] Further, after the experimental water accumulates to a certain height in the flash chamber 21, turn on the vacuum pump 4 to provide a vacuum environment for flashing. The flashed steam enters the evaporation and condensation chamber 1 through the steam channel 23. The thermometer 23 is used to measure the real-time steam temperature, and the thermometer 20 is used to measure the real-time temperature at the water outlet.

[0174] Further, the water vapor exchanges heat with the flowing condensate water in the evaporation and condensation chamber 1 in the condensate water pipe, condenses into water, then flows into the water turbine of the vacuum pump 4 through the pipe 2, and finally flows back to the condensate water tank 15 through the water turbine pipe 3.

[0175] After the experiment is completed, turn off the power supply, close the valve 16, open the valve 13, so that the remaining water in the flash chamber flows back into the water tank 10. After the reflux is sufficient, open the valve 11 to make the remaining water flow back into the water tank 10.

[0176] Those skilled in the art can easily understand that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-objective optimization analysis method for visual flash boiling waste heat recovery, characterized in that: The following methods are included: Step 1: construct a visual flash boiling experiment data acquisition device and collect monitoring parameters during the flash boiling process; Step 2: constructing a VOF and an improved Lee model for describing the flash evaporation process based on the monitoring parameters; Step 3, taking the VOF and the improved Lee model as constraint conditions, and the heat recovery efficiency, water resource recovery rate and economic benefit as fitness values, a multi-objective optimization model based on the MOARO algorithm is constructed; Step 4: Use the ARO algorithm to optimize the multi-objective optimization model, update the individual positions until the termination conditions are met, and output the optimal solution, that is, the optimal liquid level height, inlet superheat, vacuum degree and slurry flow rate of the flash chamber.

2. A multi-objective optimization analysis method for visual flash boiling waste heat recovery according to claim 1, characterized in that: In step 2, the variation of the saturation temperature of pure water with pressure is fitted, and the variation of the saturation temperature of pure water with pressure is added to the Lee model. According to the local pressure-driven phase change process, the improved Lee model is obtained: T=31.35p 0.1526 +191.1 In the formula, is the source term of mass change caused by phase change, coeff is the evaporation coefficient, α1 is the liquid volume fraction, and ρ1 is the liquid density (kg / m 3 ), T sat is the set phase change temperature, and p is the pressure.

3. The multi-objective optimization analysis method for visual flash boiling waste heat recovery according to claim 1 is characterized in that: In step 2, the VOF model includes: The mass conservation equation: Momentum conservation equation: Energy conservation equation: Volume fraction transport equation: Where ρ is the local mixing density, ρ v is the local gas density, V is the local velocity vector, p is the local pressure, α v is the local gas volume fraction, μ is the dynamic viscosity coefficient, g is the gravity acceleration vector, E is the internal energy, λ is the effective thermal conductivity, T is the local temperature, is the source term of energy change caused by phase change.

4. The multi-objective optimization analysis method for visual flash boiling waste heat recovery according to claim 1 is characterized in that: In step 3, the multi-objective optimization model based on the MOARO algorithm includes: F(x)=w1η heat (x)+w2η water (x)+w3B(x) Where w1, w2 and w3 are weight coefficients, x is the decision variable vector (L, T in ,P vac ,Q), where L is the liquid level, T in is the inlet superheat, P vac is the vacuum degree, and Q is the slurry flow rate.

5. The multi-objective optimization analysis method for visual flash boiling waste heat recovery according to claim 1 is characterized in that: In step 3, the heat recovery efficiency is: In the formula, Q condensed is the heat of condensed water, Q input is the heat input to the flash system; The water recovery rate is: In the formula, m condensed is the mass of condensed water, m input is the mass of water input to the flash system; The economic benefits are: B=RC Among them, R is revenue and C is cost.

6. The multi-objective optimization analysis method for visual flash boiling waste heat recovery according to claim 1 is characterized in that: The optimization calculation in step 4 includes the following steps: (41) Each search individual tends to update its position to another search individual randomly selected from the population and add disturbances to ensure the global search capability of the ARO algorithm; (42) In the ARO algorithm, each iteration is regarded as a rabbit generating d holes around it along each dimension of the search space, and always randomly selects one hole from all the holes to hide in order to reduce the probability of being preyed on, and updates the position of the rabbit according to the fitness; (43) The algorithm strategy is determined based on the energy of the rabbit; (44) The Pareto optimal solution obtained using the MOARO algorithm is a set of solutions that meet the requirements of multi-objective optimization.

7. A multi-objective optimization analysis system for visual flash boiling waste heat recovery, characterized in that: include: Visual flash boiling experiment data acquisition device, used to collect monitoring parameters during the flash boiling process; The optimization controller module is used to construct a VOF and an improved Lee model for describing the flash process based on the monitoring parameters, and use the VOF and the improved Lee model as constraints, and the heat recovery efficiency, water resource recovery rate and economic benefit as fitness values ​​to construct a multi-objective optimization model based on the MOARO algorithm; the ARO algorithm is used to optimize the multi-objective optimization model, update individual positions until the termination conditions are met, and output the optimal solution, that is, the optimal liquid level height, inlet superheat, vacuum degree and slurry flow rate of the flash chamber.

8. The multi-objective optimization analysis system for visual flash boiling waste heat recovery according to claim 7 is characterized in that: The visual flash boiling experiment data acquisition device comprises: A flash chamber (20), wherein the flash chamber (20) is provided with a high-speed CCD camera and a pressure gauge (21); A water tank (10), the water tank (10) being connected to the flash chamber (20) through a water inlet pipe (5) and a water outlet pipe (12), the water inlet pipe (5) and the water outlet pipe (12) being provided with thermometers, and the water inlet pipe (5) being provided with a glass rotor flowmeter (6); An evaporation condensation chamber (1), the evaporation condensation chamber (1) being in communication with the flash chamber (20) via a steam channel (22), a thermometer being provided on the steam channel (22), and a condensation water pipe being provided in the evaporation condensation chamber (1); A condensation water tank (15), the condensation water tank (15) being connected to both ends of the condensation water pipe via a refrigerant water inlet pipe (18) and a refrigerant water outlet pipe (17) to form a closed circulation loop; A vacuum pump (4), wherein the vacuum pump (4) is respectively connected to the evaporation condensation chamber (1) and the condensation water tank (15) to provide a vacuum environment set by the evaporation condensation chamber (1) and the condensation water tank (15).

9. The multi-objective optimization analysis system for visual flash boiling waste heat recovery according to claim 8 is characterized in that: The flash chamber (20) comprises a cylindrical body and a conical body arranged at the bottom of the cylindrical body, a water inlet (26) and a water outlet (28) are arranged on the side wall of the cylindrical body, a sewage outlet (27) is arranged at the bottom of the conical body, the sewage outlet (27) is connected to the water tank (10) through a sewage pipe (9), the sewage pipe (9) is provided with a sewage valve, and a water tank heater (31) is also arranged in the water tank (10).

10. The multi-objective optimization analysis system for visual flash boiling waste heat recovery according to claim 8, characterized in that: A condensation water pump (14) is also provided inside the refrigerant water tank (15) for pumping condensed water in the refrigerant water tank (15) into the condensation water pipe in the evaporation condensation chamber (1) through a refrigerant water inlet pipe (18).

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