Method, system, and media for optimization of alumina evaporator scab cleaning decisions

Abstract

The application discloses a method, system and medium for optimizing a scum cleaning decision of an alumina evaporator, and relates to the technical field of decision optimization. The method comprises the following steps: determining a precipitation rate model and a re-diffusion rate model of the alumina evaporator, and establishing a scum mechanism model based on the precipitation rate model and the re-diffusion rate model; combining the scum mechanism model and a preset cleaning maintenance cost to establish a first evaporation benefit model which is continuous in time; converting the first evaporation benefit model into a second evaporation benefit model which is periodic; and dynamically solving the second evaporation model to obtain a target cleaning plan. The technical scheme provided by the application improves the operation efficiency and economic benefits of the evaporator.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of decision optimization, in particular to a method and system for optimizing the decision of cleaning the scaling of an alumina evaporator, and a medium. BACKGROUND

[0002] In the production of alumina, the evaporator as a key equipment, its heat transfer efficiency directly affects the energy consumption and cost of production. However, with the production, the inner wall of the evaporator will gradually form scaling (fouling) composed of silicate and sodium salt and other substances, which not only reduces the heat transfer efficiency, but also increases the frequency and cost of equipment maintenance.

[0003] The traditional cleaning method is usually based on fixed period or experience, lack of systematicness and scientific basis, often leading to over-cleaning or insufficient cleaning, affecting the production efficiency and economic benefits, therefore, an effective dynamic decision optimization method for cleaning the scaling of alumina evaporator is urgently needed, so as to improve the operation efficiency and economic benefits of the evaporator. SUMMARY

[0004] The present application provides a method and system for optimizing the decision of cleaning the scaling of an alumina evaporator, which improves the operation efficiency and economic benefits of the evaporator.

[0005] In a first aspect, the present application provides a method for optimizing the decision of cleaning the scaling of an alumina evaporator, the method comprising:

[0006] determining a precipitation rate model and a re-diffusion rate model of the alumina evaporator, and establishing a scaling mechanism model based on the precipitation rate model and the re-diffusion rate model;

[0007] establishing a first evaporation benefit model in time sequence by combining the scaling mechanism model and a preset cleaning and maintenance cost;

[0008] converting the first evaporation benefit model into a second evaporation benefit model in periodicity;

[0009] dynamically solving the second evaporation model to obtain a target cleaning plan.

[0010] By adopting the technical scheme, the scaling mechanism model of the alumina evaporator is established through the deposition rate model and the re-diffusion rate model, the formation mechanism of the scaling inside the evaporator can be accurately described, on the basis, the first evaporation benefit model which is time continuous is established combined with the preset cleaning and maintenance cost, the first evaporation benefit model is converted into the second evaporation benefit model which is periodical, then the second evaporation benefit model is dynamically solved, and finally the optimal target cleaning plan of the alumina evaporator is obtained, compared with the traditional experience cleaning method, the technical scheme realizes scientific optimization of the scaling cleaning plan of the evaporator, the cleaning times can be reduced and the energy consumption can be reduced, and meanwhile the operation efficiency of the evaporator and the economic benefits are improved.

[0011] Optionally, the deposition rate model of the alumina evaporator is determined by:

[0012] The concentration of the dirt material at the interface between the deposition layer and the heat exchanger of the alumina evaporator, the temperature of the liquid-solid interface, and the concentration of the dirt material when saturated are obtained.

[0013] The deposition rate model is determined based on the concentration of the dirt material at the interface between the deposition layer and the heat exchanger, the temperature of the liquid-solid interface, and the concentration of the dirt material when saturated.

[0014] The deposition rate model is:

[0015]

[0016] In the formula, A' is the Arrhenius constant, E is the activation energy, R is the gas universal constant, T is the temperature of the liquid-solid interface, C is the concentration of the dirt material at the interface between the deposition layer and the heat exchanger, C is the concentration of the dirt material when saturated, and n is the order of chemical reaction. The deposition rate, A' is the Arrhenius constant, E is the activation energy, R T The gas universal constant, T s The temperature of the liquid-solid interface, C s The concentration of the dirt material at the interface between the deposition layer and the heat exchanger, C cat T s The concentration of the dirt material when saturated, and n is the order of chemical reaction.

[0017] By adopting the technical scheme, the concentration of the dirt material at the interface between the deposition layer and the heat exchanger of the alumina evaporator, the temperature of the liquid-solid interface, and the concentration of the dirt material when saturated are obtained, and the scaling deposition rate model is established according to the data, the model considers the influence of various factors such as the interface concentration, the temperature and the saturation concentration on the deposition rate.

[0018] The scaling generation mechanism model established by combining various parameters can more accurately predict the generation and accumulation process of the scaling.

[0019] Optionally, the re-diffusion rate model of the alumina evaporator is determined by:

[0020] The shear stress of the fluid on the solid surface is constructed based on the friction coefficient and the fluid density of the alumina evaporator.

[0021] The shear stress is: In the formula, τ w is the shear stress, f is the friction coefficient, p is the fluid density, and u is the flow rate; a re-diffusion rate model of the aluminum oxide evaporator is constructed based on the shear stress;

[0022] The re-diffusion rate model is: is the re-diffusion rate, B0 is a preset constant, is a precipitated structure function.

[0023] By using the above technical solution, a shear stress model of fluid on a solid surface is constructed according to the friction coefficient and the fluid density of the aluminum oxide evaporator, and further, a re-diffusion rate model of fouling is established based on the shear stress model. The re-diffusion model considers the influence of the flow rate and friction of the fluid in the evaporator on the re-diffusion of fouling. Compared with a simple preset re-diffusion rate, the re-diffusion mechanism model reflecting the actual process condition of the present solution can dynamically predict the re-growth process of fouling and more accurately describe the overall mechanism of fouling accumulation.

[0024] Optionally, the time-continuous first evaporation yield model is established based on the fouling mechanism model and a preset cleaning and maintenance cost, and includes:

[0025] Based on the fouling mechanism model and the pipe parameters connected to the aluminum oxide evaporator, a total heat transfer coefficient of the aluminum oxide evaporator is calculated;

[0026] Based on the total heat transfer coefficient, a total heat transfer amount is calculated;

[0027] Based on the total heat transfer amount and a preset cleaning and maintenance cost, a time-continuous first evaporation yield model is established; the first evaporation yield model is: In the formula, M n is the total yield under the scheme n, T is the total time, Q t' is the total heat transfer amount at t', ΔH t' is the heat enthalpy of water in the feed liquid, e is the yield per ton of water, m is the number of cleanings, and w is the preset cleaning and maintenance cost.

[0028] By adopting the technical scheme, the total heat transfer coefficient of the alumina evaporator is calculated based on the pre-established fouling mechanism model and the pipeline parameters connected with the evaporator, the total heat transfer amount is calculated based on this, and the time-continuous first evaporation benefit model is established by combining the preset cleaning and maintenance cost, which considers the comprehensive influence of the fouling degree, the heat transfer parameter and the cleaning cost on the evaporation benefit. Compared with the simple empirical method preset benefit model, the first evaporation benefit model established by combining the fouling mechanism, the heat transfer parameter and the economic factor in the scheme can more accurately describe the dynamically changing economic benefit in the evaporation process.

[0029] Optionally, the total heat transfer coefficient of the alumina evaporator is calculated based on the fouling mechanism model and the pipeline parameters connected with the alumina evaporator, and the total heat transfer coefficient of the alumina evaporator is calculated.

[0030] The fouling mechanism model and the pipeline parameters of the alumina evaporator are substituted into the heat transfer coefficient calculation formula to obtain the total heat transfer coefficient of the alumina evaporator.

[0031] The heat transfer coefficient calculation formula is:

[0032] Among them, the K t' is the total heat transfer coefficient, the α1 is the condensation convection heat transfer coefficient of steam, the α2 is the falling film side convection heat transfer coefficient, the λ b is the thermal conductivity of the pipe wall material, the δ b is the pipe wall thickness, the R f (t') is the fouling mechanism model.

[0033] By adopting the technical scheme, the total heat transfer coefficient of the alumina evaporator is calculated by substituting the pre-established fouling mechanism model and the evaporator pipeline parameters into the heat transfer coefficient calculation formula, which considers the condensation heat transfer coefficient, the convection heat transfer coefficient, the pipe wall thermal conductivity, the pipe wall thickness and the fouling mechanism model, can accurately describe the influence of the fouling degree on the heat transfer performance, and by combining the heat transfer calculation formula of various influencing factors, the changing heat transfer parameter in the fouling process can be dynamically calculated, so that the heat transfer law can be more accurately described.

[0034] Optionally, the first evaporation benefit model is converted into a periodic second evaporation benefit model, which includes dividing the time in the first evaporation benefit model according to a preset period to obtain a plurality of fixed periods, and establishing a periodic second evaporation benefit model.

[0035] The second evaporation benefit model is:

[0036]

[0037] Among them, c represents the fixed period number, the period time range of the number c is from (c-1)Ts +1 day to the cth s day, n c denotes the cleaning plan of the cth period, n c ∈{0,1,2,...,T s}, u c is a label of whether the alumina evaporator is cleaned in this period, A is the heat transfer area, is the heat transfer temperature difference, ΔH is the heat enthalpy of water in the feed liquid, is the degree of fouling on the day in the cth period, t'∈[1,T s ].

[0038] By adopting the technical scheme, the time in the first evaporation benefit model established is divided according to a preset period to obtain a plurality of fixed periods. Then, a second evaporation benefit model of a period is established based on the fixed period. The second benefit model considers factors influencing the benefit, such as a period number, a corresponding cleaning plan, a heat transfer parameter, a degree of fouling, and the like. By establishing the periodic benefit model, the influence of different periods and different cleaning plans on the evaporation benefit can be quantitatively evaluated, and the problem of an infinite time domain is converted into a solvable form.

[0039] Optionally, the second evaporation model is dynamically solved to obtain a target cleaning plan, including:

[0040] determining historical data of the alumina evaporator, and predicting a first cleaning plan corresponding to each period time in the whole period based on the historical data;

[0041] dynamically solving the second evaporation model, and obtaining a second cleaning plan corresponding to each period time;

[0042] judging whether the first cleaning plan corresponding to each period time is consistent with the second cleaning plan;

[0043] if the first cleaning plan is consistent with the second cleaning plan, the first cleaning plan is taken as the target cleaning plan;

[0044] if the first cleaning plan is not consistent with the second cleaning plan, the second cleaning plan is taken as the target cleaning plan.

[0045] By adopting the technical scheme, the first cleaning plan and the second cleaning plan corresponding to each period time are compared. If the two plans are consistent, it indicates that the historical experience and the current optimization result are consistent. In this case, the first cleaning plan is taken as the target cleaning plan. In this case, the decision has high reliability because it not only conforms to the historical law but also meets the current optimization requirement. If the two plans are not consistent, the second cleaning plan is selected as the target cleaning plan because the second cleaning plan is based on the latest production data and optimization result and can better adapt to the current production condition and market environment.

[0046] In a second aspect of the present application, a scumming cleaning decision optimization system of an alumina evaporator is provided, and the system comprises:

[0047] A scumming mechanism model establishing module is configured to determine a precipitation rate model and a re-diffusion rate model of the alumina evaporator, and establish a scumming mechanism model based on the precipitation rate model and the re-diffusion rate model;

[0048] A first benefit model establishing module is configured to establish a time-continuous first evaporation benefit model in combination with the scumming mechanism model and a preset cleaning maintenance cost;

[0049] A second benefit model establishing module is configured to convert the first evaporation benefit model into a periodic second evaporation benefit model; and a cleaning plan determining module is configured to dynamically solve the second evaporation model to obtain a target cleaning plan.

[0050] In a third aspect of the present application, a computer storage medium is provided, and the computer storage medium stores a plurality of instructions suitable for being loaded and executed by a processor to perform the above-mentioned method steps.

[0051] In a fourth aspect of the present application, an electronic device is provided, and the electronic device comprises a processor and a memory; wherein the memory stores a computer program suitable for being loaded and executed by the processor to perform the above-mentioned method steps.

[0052] To sum up, the one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0053] The present application establishes a scumming mechanism model of the alumina evaporator through a precipitation rate model and a re-diffusion rate model, can accurately describe the formation mechanism of the internal scumming of the evaporator, and on this basis, establishes a time-continuous first evaporation benefit model in combination with a preset cleaning maintenance cost, converts the first evaporation benefit model into a periodic second evaporation benefit model, then dynamically solves the second evaporation benefit model, and finally obtains an optimal target cleaning plan of the alumina evaporator. Compared with a traditional experience cleaning method, the technical solution realizes scientific optimization of the scumming cleaning plan of the evaporator, can reduce the cleaning times and reduce energy consumption, and at the same time improves the operation efficiency and economic benefits of the evaporator. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 FIG. 1 is a flowchart of a scumming cleaning decision optimization method of an alumina evaporator provided in an embodiment of the present application;

[0055] Figure 2 FIG. 2 is a schematic diagram of a slow time-varying process of scumming;

[0056] Figure 3is a decision optimization process schematic diagram in a scheduling period provided by an embodiment of the present application.

[0057] Figure 4 is a module schematic diagram of an alumina evaporator scab cleaning decision optimization system provided by an embodiment of the present application.

[0058] Figure 5 is a structure schematic diagram of an electronic device provided by an embodiment of the present application.

[0059] Legend: 500, electronic device; 501, processor; 502, communication bus; 503, user interface; 504, network interface; 505, memory. DETAILED DESCRIPTION

[0060] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in conjunction with the drawings in the embodiments of the specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments.

[0061] In the description of the embodiments of the present application, the words such as "for example" or "for instance" are used to represent an example, illustration or description. Any embodiment or design scheme described as "for example" or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words such as "for example" or "for instance" are intended to present the relevant concept in a specific manner.

[0062] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are used for description purposes only, and should not be interpreted as indicating or implying relative importance or implicitly indicating the indicated technical features. Therefore, the features defined with "first" and "second" can explicitly or implicitly include one or more of the features. The terms "include", "contain", "have" and their variants mean "include but are not limited to", unless otherwise specifically emphasized.

[0063] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments.

[0064] Please refer to Figure 1The application discloses a flowchart of a scab cleaning decision optimization method of an alumina evaporator, and the method can be realized by means of a computer program, a single-chip microcomputer or an alumina evaporator scab cleaning decision optimization system. The computer program can be integrated in an intelligent device or run as an independent tool application. Specifically, the method comprises steps 10 to 40.

[0065] Step 10: determining a precipitation rate model and a re-diffusion rate model of the alumina evaporator, and establishing a scab mechanism model based on the precipitation rate model and the re-diffusion rate model.

[0066] The alumina evaporator in the embodiment of the application refers to an evaporation device used in an alumina production process, and its main function is to concentrate an alumina solution. In the process, due to the increase of the solution concentration and the change of the temperature and flow rate, silicon salts and sodium salts and other substances are precipitated from the solution and attached to the inner wall of the evaporator to form scabs.

[0067] The precipitation rate model in the embodiment of the application refers to the deposition rate of the dirt substances on the heat exchange surface of the evaporator; and the re-diffusion rate model in the embodiment of the application refers to the rate of the deposited dirt substances falling off due to fluid scouring.

[0068] Specifically, the scab refers to hard solid substances attached to various production devices. The dirt substances are attached to the surface of the device, significantly reduce the heat transfer coefficient of the heat exchange device, reduce the effective heat transfer area of the device, reduce the production capacity, damage the device and affect the stable operation of the process. Therefore, the embodiment of the application establishes a scab mechanism model, aims to provide a theoretical basis for subsequent optimization decision, and makes the cleaning decision more accurate and efficient. First, relevant parameters of the alumina evaporator are acquired, including the concentration of the dirt substances at the interface between the deposition layer and the heat exchanger, the temperature at the liquid-solid interface and the concentration of the dirt substances when saturated, and the like. Then, the precipitation rate model is established based on the parameters. Next, in order to describe the falling process of the deposited dirt substances, the embodiment constructs a re-diffusion rate model. The shear stress model of the fluid on the solid surface is constructed based on the friction coefficient and the fluid density of the alumina evaporator. Then, the re-diffusion rate model is constructed based on the shear stress. Finally, the scab mechanism model is established based on the precipitation rate model and the re-diffusion rate model. The scab mechanism model established in this way can more accurately reflect the formation and change process of the scab on the inner wall of the alumina evaporator.

[0069] In the alumina evaporation process, with the increase of solution concentration and the change of temperature and flow rate, silicon salt and sodium salt and other substances are precipitated from the solution and attached to the inner wall of the evaporator, gradually forming hardened scab. The formation of dirt can be divided into two parts: deposition and detachment. On the one hand, the dirt in the flow will deposit on the heat transfer surface, and on the other hand, the dirt will also fall off due to the scouring of the flow fluid, so the net fouling rate can be expressed as the difference between the deposition rate and the resuspension rate, the net fouling rate: In the formula is the deposition rate, is the resuspension rate, the deposition rate of dirt is related to the liquid-solid phase interface temperature, the formation of deposition, etc., and the resuspension rate is related to the viscosity of the dirt, the flow rate in the pipe, the shear force and other factors.

[0070] As Figure 2 shown, it is a slow time-varying process diagram of scab provided by the embodiment of the application, as Figure 2 shown, it can be observed from the curve that the scab rate presents a slow change trend with time, especially in the early stage of the curve, the scab process is relatively slow and stable. This phenomenon shows that the fouling process has a significant slow time-varying characteristic; the volatility of the curve reflects the dynamic balance of the deposition and resuspension process. The scab material will also partially fall off due to the scouring of the fluid while depositing, and the net fouling rate is determined by the difference between the deposition rate and the resuspension rate; in the initial stage of the operation of the evaporator, the scab material accumulates slowly, and the curve slope is small, indicating that the fouling rate is low; the obvious falling point in the middle of the curve represents a cleaning process, which removes a large amount of dirt, so that the amount of dirt instantaneously decreases.

[0071] On the basis of the above embodiment, as an optional embodiment, the step of determining the deposition rate model of the alumina evaporator can further include the following steps:

[0072] Step 1011: obtaining the concentration of dirt at the deposition layer and heat exchanger interface of the alumina evaporator, the temperature of the liquid-solid interface, and the concentration of the dirt when saturated.

[0073] Specifically, these parameters can be obtained through various methods. For the concentration of fouling substances at the interface between the deposited layer and the heat exchanger, an online monitoring system can be used to measure it in real time. This system can use conductivity or optical sensors installed at key locations in the evaporator to continuously monitor the concentration of fouling substances at the interface. The temperature of the liquid-solid interface can be measured by thermocouples or infrared temperature sensors, which can be placed at multiple locations on the inner wall of the evaporator to obtain more comprehensive temperature distribution data. For the concentration of fouling substances at saturation, it can be obtained through laboratory analysis or by consulting relevant physical property databases, or it can be calculated theoretically by establishing a material balance model. To improve the accuracy and reliability of parameter acquisition, the present embodiment uses data fusion technology. The data from different sensors and measurement methods are analyzed comprehensively, and algorithms such as Kalman filtering are used to eliminate noise and outliers, thereby obtaining more accurate parameter estimates. At the same time, considering the dynamic characteristics of the evaporation process, the present embodiment also introduces an adaptive sampling strategy. In the stage of rapid scabbing, the sampling frequency is increased; in the stage of slow scabbing, the sampling frequency is appropriately reduced, which ensures the timeliness of the data and avoids the generation of redundant data.

[0074] Step 1012: determining the precipitation rate model based on the concentration of fouling substances at the interface between the deposited layer and the heat exchanger, the temperature of the liquid-solid interface, and the concentration of fouling substances at saturation.

[0075] Specifically, the present embodiment uses a combination of the Arrhenius equation and mass transfer theory to construct the precipitation rate model. First, considering that the fouling process is essentially a chemical reaction, its rate is closely related to temperature and concentration. Therefore, the Arrhenius equation is introduced to describe the effect of temperature on reaction rate. At the same time, considering the mass transfer process of fouling substances from the solution to the solid surface, the concentration difference term is introduced to describe the mass transfer driving force. By integrating these factors, the expression of the precipitation rate model is obtained. First, according to the transport law of physical chemistry, we have: According to the Arrhenius theorem, K r = A' exp(-E / R T T S ), combining the above two algorithms, the precipitation rate model is obtained: In the formula, is the precipitation rate, A' is the Arrhenius constant, E is the activation energy, R T is the gas universal constant, T s is the temperature of the liquid-solid interface, C s is the concentration of fouling substances at the interface between the deposited layer and the heat exchanger, C cat is the concentration of fouling substances at T s saturation, and n is the order of chemical reaction.

[0076] On the basis of the above embodiment, as an optional embodiment, the step of determining the re-diffusion rate model of the alumina evaporator can further include the following steps:

[0077] Step 1021: Based on the friction coefficient of the alumina evaporator and the fluid density, the shear stress of the fluid on the solid surface is constructed.

[0078] Specifically, the shear stress model is established by combining the fluid mechanics theory with the special working conditions of the alumina evaporator. First, the friction coefficient and the fluid density of the alumina evaporator are obtained. The friction coefficient is obtained by experiment or by consulting relevant literature, and it is related to the roughness of the inner wall of the evaporator, the viscosity of the fluid and other factors. The fluid density can be measured in real time by an online densimeter, or obtained by interpolation according to the temperature and concentration. Then, the fluid flow rate is introduced, which can be measured by a flowmeter and converted to obtain the shear stress model. The shear stress (N / m2) of the fluid on the solid surface is related to the Reynolds number of the fluid and the roughness of the wall, that is: τ w f = 0.0792Re -0.25 , where f is the friction coefficient, Re is the Reynolds number, p is the fluid density, and u is the flow rate.

[0079] Step 1022: Based on the shear stress, the re-diffusion rate model of the alumina evaporator is constructed.

[0080] Specifically, the dirt is detached due to the scouring of the material flow fluid, and the detachment process is related to the shear action at the heat exchange surface and the structure function ψ of the sediment, where B0 is a predetermined constant.

[0081] When is a constant independent of time, is proportional to R f , the net fouling rate can be expressed as: When the boundary condition t = 0, R f = 0 is taken, and integration is performed at both ends, the asymptotic line relationship is obtained: From the above analysis, the steady-state asymptotic value R * is positively correlated with the temperature T S of the liquid-solid interface, and τ = Ψ / B0τ w is negatively correlated with the fluid flow rate u. Therefore, the fouling mechanism model of the evaporation process can be described as follows: where k1, k2, and k3 are constants related to the properties of steam and material liquid; C(t) represents the concentration of the material liquid, and G(t) and θ(t) represent the flow rate and temperature of the steam.

[0082] Step 20: combine the fouling mechanism model and the preset cleaning maintenance cost to establish a time-continuous first evaporation benefit model.

[0083] Specifically, cleaning the evaporator can improve the heat transfer efficiency of the evaporation process, but cleaning also incurs labor costs. For the same evaporator, it is generally believed that the cleaning workload and difficulty are fixed within a relatively long period of time. Therefore, it can be assumed that the single cleaning maintenance cost of this evaporator is a fixed value within a certain planning period, i.e., the preset cleaning maintenance cost, denoted by w; and the profit of evaporating one ton of water is e. For an evaporation system, the net profit within a certain time range can be represented as the profit of evaporated water minus the cleaning cost. First, the total heat transfer coefficient of the alumina evaporator is calculated based on the fouling mechanism model and the pipe parameters connected to the alumina evaporator, then the total heat transfer amount is calculated based on the total heat transfer coefficient, and finally, the time-continuous first evaporation benefit model is established according to the total heat transfer amount and the preset cleaning maintenance cost.

[0084] By establishing the first evaporation benefit model, the fouling mechanism model can be directly linked to economic benefits, so that the cleaning decision can be based on a solid theoretical foundation. Secondly, by introducing dynamic pricing and fuzzy cost mechanisms, the model has strong adaptability and robustness and can cope with various uncertainties in actual production.

[0085] On the basis of the above embodiment, as an optional embodiment, the step of combining the fouling mechanism model and the preset cleaning maintenance cost to establish a time-continuous first evaporation benefit model can further include the following steps:

[0086] Step 201: calculate the total heat transfer coefficient of the alumina evaporator based on the fouling mechanism model and the pipe parameters connected to the alumina evaporator.

[0087] Specifically, the present application adopts a heat resistance series model in heat transfer theory, and establishes a total heat transfer coefficient calculation model in combination with the special working conditions of the alumina evaporator. The heat transfer coefficient of the falling film evaporator, as an important parameter for measuring the production performance of the evaporator, refers to the amount of heat passing through a unit area per unit time. In addition to being related to the structure of the evaporator equipment itself, the heat transfer coefficient of the evaporator is also affected by the material properties parameters, such as viscosity, density, tension, liquid film thickness, etc. Changes in many process control parameters will also change the total heat transfer coefficient, such as effective temperature difference, liquid flow rate, etc.

[0088] First, the parameters of the pipeline connected to the alumina evaporator are obtained, including the thermal conductivity of the pipe wall material and the pipe wall thickness. These parameters can be directly obtained from the equipment design specifications. Next, the condensation convection heat transfer coefficient of the steam and the falling film side convection heat transfer coefficient are estimated by online monitoring or an empirical model. Then, the scumming thermal resistance is obtained from the previously established scumming mechanism model. Based on these parameters, the total heat transfer coefficient of the alumina evaporator is obtained by substituting into the heat transfer coefficient calculation formula: where K t' is the total heat transfer coefficient, α1 is the condensation convection heat transfer coefficient of the steam, α2 is the falling film side convection heat transfer coefficient, λ b is the thermal conductivity of the pipe wall material, δ b is the pipe wall thickness, R f (t’) is the scumming mechanism model. R f is the scumming thermal resistance. Here, only the relationship between the scumming thermal resistance and time is considered, and the remaining parameters are determined according to the material of the evaporator.

[0089] Step 202: Calculate the total heat transfer based on the total heat transfer coefficient.

[0090] Specifically, for the total heat transfer, according to the heat exchange principle, it can be obtained by the product of the heat transfer coefficient, the heat transfer area and the heat transfer temperature difference: Q t' = K t' AΔt t' , where Q t' is the total heat transfer at t', ΔH t' is the heat enthalpy of water in the feed liquid, and A is the heat transfer area.

[0091] Step 203: Based on the total heat transfer and the preset cleaning and maintenance cost, a time-continuous first evaporation benefit model is established.

[0092] Specifically, the present application adopts the net present value theory, and combines the characteristics of alumina evaporation production to construct a time-continuous first evaporation benefit model: where M n is the total benefit under scheme n, T is the total time, Q t' is the total heat transfer at t', ΔH t' is the heat enthalpy of water in the feed liquid, e is the benefit per ton of water, m is the number of cleanings, and w is the preset cleaning and maintenance cost. This model comprehensively considers the benefits and cleaning costs of evaporation production, and can directly reflect the long-term economic benefits under different cleaning strategies.

[0093] Step 30: Convert the first evaporation benefit model into a periodic second evaporation benefit model.

[0094] Specifically, in the scheduling of the cleaning operation of the alumina evaporator, first, an optimization decision is made in the first time period, i.e., from t = 1 to t = T s , and then the optimization decision is made in the next period until the entire time range that needs to be optimized for cleaning is completed. It is worth noting that each period here is fixed, rather than sliding forward every day, which is designed to better utilize the prediction accuracy that improves over time. For the rolling period strategy, when a cleaning decision is made, the window rolls forward, and the length of the time range to be optimized does not change. For the time range to be optimized, the overall prediction accuracy does not improve as new, more distant dates are added. If a fixed period is chosen, for a certain scheduling period, as time progresses, we are closer and closer to all future dates, and the prediction information is more and more accurate; for past dates, more and more historical data can be obtained, and historical data is absolutely accurate without prediction error. That is, for this fixed period, we become more and more knowledgeable, and as uncertainty decreases and certain information accumulates, the decisions made will also become more optimized.

[0095] The embodiment adopts the method of discretization and period division, combines the actual operation characteristics of alumina evaporation production, and constructs a periodic revenue model. First, according to production practice and management needs, a suitable decision period length is obtained and determined, and the continuous time in the first evaporation revenue model is divided into multiple fixed periods according to the preset period. Let c (c = 1, 2,..., C) represent the number of each fixed period, and the period numbered c contains the time range from the (c-1)T s +1 day to the cT s day. Therefore, the degree of scarring on each day can be represented by the periodic symbol: R f (t') = R f ((c-1)T s +t'), t' ∈ [1, T s ]. The heat transfer coefficient and the heat transfer temperature difference can also be represented by the periodic symbol, which are and According to the actual operation and maintenance needs of the evaporation station on site, for a 15-day period design, an evaporator will be cleaned at most once in a period, or not cleaned at all. Cleaning twice or more in a period can be directly judged as unreasonable according to experience, and the efficiency loss of cleaning reduction cannot make up for such a high frequency of cleaning cost. Therefore, the cleaning scheduling plan n for the period numbered c can be simplified as: n c (n c ∈ {0, 1, 2,..., T s}). It represents the number of cleaning date. n c = 0 means that the evaporator will not be scheduled for cleaning in this period. Based on this, the fouling degree equation for each day in period c can be simplified as the following periodic form:

[0096] If n c ≠ 0:

[0097]

[0098] Else:

[0099]

[0100] If the decision for the evaporator in this fixed period is to clean, that is, n c ≠ 0, then the time period from t = (c - 1)T s + 1 to t = cT s will be divided into two parts by the cleaning date, the fouling state on the cleaning day is The fouling degree on the last day of the previous period of this period is This fouling degree is also the initial fouling degree of this period. If the evaporator is not cleaned in this scheduling period, that is, n c = 0, then the fouling will continue to accumulate in this period. The net benefit in scheduling period c when the cleaning decision is n c , that is, the second evaporation benefit model, is:

[0101] Where c represents the fixed period number,

[0102]

[0103] The period number c is the time range from (c - 1)T s + 1 day to cT s day, n c represents the cleaning plan of period number c, n c ∈ {0, 1, 2,..., T s}, u c is the label of whether the alumina evaporator is cleaned in this period, A is the heat transfer area, is the heat transfer temperature difference, ΔH is the heat enthalpy of water in the feed liquid, is the fouling degree of the day in the period number c, t' ∈ [1, T s ].

[0104] For the actual running evaporator station, if it needs to be cleaned, it often needs to make a decision in advance and make a reservation to prepare. It is assumed that T c (Tc <T s If the minimum time required to prepare is to be scheduled in advance, then this introduces a constraint on the cleaning schedule, called the preparation constraint, which is expressed as follows:

[0105] n c ∈{T c ,T c +1,...,T s}

[0106] However, the net profit within a period cannot be... This is directly used as the optimization target for evaporative scaling and cleaning. Because the scaling process is continuous, although cleaning is divided into cycles, the scaling process is not independent. When deciding whether to perform scaling and cleaning in an optimization cycle, the effect of the cleaning action is not limited to that cycle; its effect persists. As stated in the formula above, the initial scaling degree of the cleaning cycle numbered c is... In reality, this represents the final scaling degree of the previous cycle (numbered c-1). The scaling cleanup in this cycle will affect the initial scaling degree of the next cycle, a fact that cannot be changed through further decisions in the next cycle. Therefore, decisions in one cycle constrain the optimization range of the next cycle. Since this correlation exists between every two consecutive cycles, when making decisions for a particular cycle, its impact on the next cycle must be considered. That is, the heat transfer efficiency resulting from the initial scaling degree of the current cycle is calculated, using historical expected values ​​for the heat transfer temperature difference, feed liquid enthalpy, and cleaning schedule. This achieves overall optimization across all cycles. Therefore, the economic benefit resulting from the initial scaling degree of the current cycle is defined as:

[0107]

[0108] In summary, the objective function for decision optimization of cleaning cycle c can be modeled as follows:

[0109]

[0110] Step 40: Dynamically solve the second evaporation model to obtain the target cleaning plan.

[0111] Specifically, since the decision-making is made in the early stage of the cycle, the data for the latter part of the cycle is unknown, so the Transformer model is used in the embodiments of the present application to predict future data. Based on the previously constructed optimization model, at the beginning of the scheduling cycle, the optimization cleaning strategy for the entire cycle is formulated according to the current information and the predicted information, which is called the "preliminary plan". As time goes on, at the end of the next day, the operating conditions, heat transfer temperature difference and enthalpy value of the material fluid of the previous day become fixed historical data. As the time span of the remaining days shortens, the model can obtain more accurate prediction data. With the update of historical and predicted data, there is an opportunity to review and optimize the cleaning strategy. If the optimized strategy is consistent with the "preliminary plan" formulated before, the original plan is continued to be executed. However, if there is a deviation, considering that the new strategy is formulated based on more accurate data, it should be more favorable in probability, therefore, it is meaningful to adjust to this new strategy. This daily review and adjustment process will continue until one of the predetermined end conditions is reached. In this way, it can be ensured that the cleaning strategy remains optimal throughout the cycle while adapting to various changes that may occur in actual operation.

[0112] In a feasible embodiment, the embodiments first determine the historical data of the alumina evaporator, including but not limited to the past scaling speed, cleaning frequency, production load, environmental conditions and other related information. Based on these historical data, a prediction model is constructed using machine learning algorithms such as long short-term memory network LSTM or random forest, etc. to predict the first cleaning plan corresponding to each cycle time in the entire cycle. This prediction process takes into account long-term trends such as seasonal changes and equipment aging, providing a reference benchmark for cleaning decisions based on historical experience. At the same time, the embodiments dynamically solve the second evaporation model to obtain the second cleaning plan corresponding to each cycle time. This process uses real-time collected production data such as the current scaling condition, production load, market demand, etc. to determine the optimal cleaning time by solving an optimization problem. Compare the first cleaning plan and the second cleaning plan corresponding to each cycle time. If the two plans are consistent, it means that the historical experience and the current optimization result are consistent, at this time, the first cleaning plan is taken as the target cleaning plan, in this case, the decision has high credibility, because it not only conforms to the historical law, but also meets the current optimization requirements. If the two plans are inconsistent, the second cleaning plan is selected as the target cleaning plan, because the second cleaning plan is based on the latest production data and optimization results, and can better adapt to the current production conditions and market environment.

[0113] As Figure 3 shown, a decision optimization process diagram in a scheduling cycle provided by the embodiments of the present application is shown, which comprises the following steps: Figure 3It can be seen that, in the decision preparation stage, if the decision result of the first day is the 22nd day, the decision premise is the 21st day, indicating that the optimal time solved by the current model is the 22nd day, but since the preparation constraint is 21 days, it means that the cleaning arrangement of the decision must be after the 21st day, and since there are four days in total after the 21st day that can be used for cleaning, we do not immediately execute the current decision result, and further wait for the next decision. When making a decision on the third day, the historical data is updated, and the data of the first and second days is increased, which can make the data model more accurate, thereby affecting the decision result. Figure 3 Three decision results are displayed in total. When the preparation constraint and the decision result coincide, it means that this result is executed to avoid missing the optimal solution. Secondly, there is only one scheme or no scheme after the preparation constraint, indicating that cleaning or no cleaning can only be performed on the last day.

[0114] See Figure 4 A module schematic diagram of an alumina evaporator scab cleaning decision optimization system provided by the embodiment of the application. The alumina evaporator scab cleaning decision optimization system can include a scab mechanism model establishing module, a first benefit model establishing module, a second benefit model establishing module, and a cleaning plan determining module, wherein:

[0115] The scab mechanism model establishing module is configured to determine a deposition rate model and a re-diffusion rate model of the alumina evaporator, and establish a scab mechanism model based on the deposition rate model and the re-diffusion rate model.

[0116] The first benefit model establishing module is configured to establish a time-continuous first evaporation benefit model by combining the scab mechanism model and a preset cleaning maintenance cost.

[0117] The second benefit model establishing module is configured to convert the first evaporation benefit model into a periodic second evaporation benefit model. The cleaning plan determining module is configured to dynamically solve the second evaporation model to obtain a target cleaning plan.

[0118] Optionally, the scab mechanism model establishing module is further configured to obtain a concentration of a fouling substance at a deposition layer and a heat exchanger interface of the alumina evaporator, a temperature of a liquid-solid interface, and a concentration of the fouling substance when saturated.

[0119] The deposition rate model is determined based on the concentration of the fouling substance at the deposition layer and the heat exchanger interface, the temperature of the liquid-solid interface, and the concentration of the fouling substance when saturated.

[0120] The deposition rate model is as follows:

[0121]

[0122] In the formula, is the precipitation rate, A' is the Arrhenius constant, E is the activation energy, R T is the gas universal constant, T s is the temperature of the liquid-solid interface, C s is the concentration of the fouling material at the interface between the deposited layer and the heat exchanger, C cat is T s is the concentration of the fouling material at the lower saturation, n is the order of the chemical reaction.

[0123] Optionally, the scalling mechanism model establishing module is further configured to construct a shear stress of the fluid on the solid surface based on a friction coefficient of the aluminum oxide evaporator and a fluid density;

[0124] The shear stress is: In the formula, τ w is the shear stress, f is the friction coefficient, ρ is the fluid density, and u is the flow rate; and a re-diffusion rate model of the aluminum oxide evaporator is constructed based on the shear stress;

[0125] The re-diffusion rate model is: is the re-diffusion rate, B0 is a preset constant, is a precipitation structure function.

[0126] Optionally, the first revenue model establishing module is further configured to calculate a total heat transfer coefficient of the aluminum oxide evaporator based on the scalling mechanism model and pipeline parameters connected to the aluminum oxide evaporator.

[0127] A total heat transfer amount is calculated based on the total heat transfer coefficient.

[0128] A time-continuous first evaporation revenue model is established based on the total heat transfer amount and a preset cleaning and maintenance cost.

[0129] The first evaporation revenue model is: In the formula, M n is the total revenue under the scheme n, T is the total time, Q t' is the total heat transfer amount at t', ΔH t' is the heat enthalpy of water in the feed liquid, e is the revenue per ton of water, m is the number of cleanings, and w is the preset cleaning and maintenance cost.

[0130] Optionally, the first revenue model establishing module is further configured to substitute the scalling mechanism model and the pipeline parameters of the aluminum oxide evaporator into a heat transfer coefficient calculation formula to obtain the total heat transfer coefficient of the aluminum oxide evaporator.

[0131] The heat transfer coefficient calculation formula is:

[0132] In the formula, the K t'λ is the overall heat transfer coefficient, α1 is the condensation convective heat transfer coefficient of steam, α2 is the falling film side convective heat transfer coefficient, and λ is the total heat transfer coefficient. b δ is the thermal conductivity of the pipe wall material. b R is the pipe wall thickness. f (t') represents the scarring mechanism model.

[0133] Optionally, the second revenue model building module is further configured to divide the time in the first evaporation revenue model according to a preset period to obtain multiple fixed periods, and to build a periodic second evaporation revenue model.

[0134] The second evaporation revenue model is as follows:

[0135]

[0136] Where c represents a fixed period number, and the period time range of period number c is from (c-1)T. s +1 day to the CT s Heaven, n c This represents the cleaning plan for period c, where n is the number of the cycle. c ∈{0,1,2,...,T s}, u c This label indicates whether the alumina evaporator has been cleaned during this cycle; A represents the heat transfer area. The temperature difference is for heat transfer, and ΔH is the enthalpy of water in the liquid. Let t' represent the degree of scarring on a given day within a period of time c, where t' ∈ [1, T]. s ].

[0137] Optionally, the cleaning plan determination module is further configured to determine the historical data of the alumina evaporator and predict the first cleaning plan corresponding to each cycle time in the entire cycle based on the historical data.

[0138] The second evaporation model is dynamically solved, and the second cleaning plan is corresponding to each cycle time.

[0139] Determine whether the first cleaning plan corresponding to each cycle time is consistent with the second cleaning plan;

[0140] If they match, the first cleaning plan will be used as the target cleaning plan;

[0141] If they are inconsistent, the second cleaning plan will be used as the target cleaning plan.

[0142] It should be noted that the system provided by the above embodiment is only used as an example to divide the above functional modules when realizing the function, and in actual application, the above functions can be completed by different functional modules according to the needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the system and method embodiments provided by the above embodiment belong to the same concept, and the specific implementation process is described in the method embodiment, which will not be described here.

[0143] The embodiment of the present application further provides a computer storage medium, which can store a plurality of instructions, the instructions being suitable for being loaded by a processor and executing the above-mentioned embodiment of the method for optimizing the scarring cleaning decision of an aluminum oxide evaporator, and the specific execution process can be referred to the specific description of the above-mentioned embodiment, which will not be described here.

[0144] Please refer to Figure 5 The present application also discloses an electronic device. Figure 5 is a structural schematic diagram of an electronic device disclosed by the embodiment of the present application. The electronic device 500 can include at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0145] The communication bus 502 is used to realize the connection and communication between the components.

[0146] The user interface 503 can include a display screen (Display) and a camera (Camera), and the optional user interface 503 can further include a standard wired interface and a wireless interface.

[0147] The network interface 504 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0148] The processor 501 can include one or more processing cores. The processor 501 connects various parts within the server through various interfaces and lines, performs various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 505, and calling data stored in the memory 505. Alternatively, the processor 501 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 501 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes operating systems, user interfaces, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 501, but can be realized by a separate chip.

[0149] The memory 505 can include a random access memory (RAM) and can also include a read-only memory (ROM). Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 505 can also be at least one storage device located away from the aforementioned processor 501. Referring to Figure 5 The memory 505 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an application program of the method for optimizing the scarring cleaning decision of an aluminum oxide evaporator.

[0150] In Figure 5The electronic device 500 shown, the user interface 503 is mainly used for providing the interface for the user to input, obtaining the data input by the user; and the processor 501 can be used to call the application program of the method for optimizing the scab cleaning decision of the aluminum oxide evaporator stored in the memory 505, and when executed by one or more processors 501, the electronic device 500 executes the method described in one or more of the above embodiments. It should be noted that for the foregoing method embodiments, in order to simply describe, they are all expressed as a combination of a series of actions, but those skilled in the art should know that the application is not limited to the action sequence described, because according to the application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the application.

[0151] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0152] In several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of units is only a logical function division. In actual implementation, another division mode can be adopted. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some service interface, device or unit, and can be electrical or other forms.

[0153] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0154] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0155] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0156] The above-described are only exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practicing the true principles of the present disclosure.

[0157] The present application is intended to cover any variations, uses, or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for optimizing the decision-making process for cleaning scale buildup in an alumina evaporator, characterized in that, The method includes: Determine the precipitation rate model and the re-diffusion rate model of the alumina evaporator, and establish a scaling mechanism model based on the precipitation rate model and the re-diffusion rate model; A time-continuous first evaporation benefit model is established by combining the aforementioned scarring mechanism model with the preset cleaning and maintenance costs; The step of establishing a time-continuous first evaporation revenue model by combining the scaling mechanism model with the preset cleaning and maintenance costs includes: Based on the scaling mechanism model and the pipe parameters connecting the alumina evaporator, the overall heat transfer coefficient of the alumina evaporator is calculated. Calculate the total heat transfer based on the overall heat transfer coefficient; Based on the total heat transfer and the preset cleaning and maintenance costs, a time-continuous first evaporation revenue model is established. The first evaporation revenue model is as follows: ,in, Let T be the total revenue under scheme n, and T be the total time. Let be the total heat transfer at time t′. Let t′ be the enthalpy of water in the liquid, e be the revenue per ton of water, m be the number of cleaning cycles, and w be the preset cleaning and maintenance cost. The first evaporation revenue model is converted into a periodic second evaporation revenue model; The second evaporation benefit model is dynamically solved to obtain the target cleaning plan.

2. The method for decision optimization of scaling and cleaning of alumina evaporators according to claim 1, characterized in that, The model for determining the precipitation rate of the alumina evaporator includes: The concentration of fouling substances at the interface between the deposited layer and the heat exchanger of the alumina evaporator, the temperature of the liquid-solid interface, and the concentration of fouling substances at saturation are obtained. The sedimentation rate model is determined based on the concentration of fouling material at the interface between the deposit layer and the heat exchanger, the temperature of the liquid-solid interface, and the concentration of fouling material at saturation. The precipitation rate model is as follows: In the formula, For precipitation rate, Here, Aronius constant is given, and E is the activation energy. For gases, the universal constant is... The temperature at the liquid-solid interface. This refers to the concentration of fouling substances at the interface between the deposit layer and the heat exchanger. for The concentration of scale at subsaturation, where n is the order of the chemical reaction.

3. The scaling and cleaning decision optimization method for alumina evaporators according to claim 1, characterized in that, Determine the re-diffusion rate model for the alumina evaporator, including: Based on the friction coefficient and fluid density of the alumina evaporator, the shear stress of the fluid on the solid surface is constructed; The shear stress is: In the formula, Where is the shear stress, and f is the coefficient of friction. Where is the fluid density and u is the flow velocity; A re-diffusion rate model for an alumina evaporator was constructed based on the shear stress. The re-diffusion rate model is as follows: , For the re-diffusion rate, As a preset constant, For precipitation structure functions.

4. The scaling and cleaning decision optimization method for alumina evaporators according to claim 1, characterized in that, The calculation of the overall heat transfer coefficient of the alumina evaporator based on the scaling mechanism model and the pipe parameters connecting the alumina evaporator includes: Substituting the scaling mechanism model and the pipe parameters of the alumina evaporator into the heat transfer coefficient calculation formula, the total heat transfer coefficient of the alumina evaporator is obtained. The formula for calculating the heat transfer coefficient is: ; Among them, the The overall heat transfer coefficient is... The condensation convective heat transfer coefficient of steam. The convective heat transfer coefficient on the falling film side is... The thermal conductivity of the pipe wall material. For pipe wall thickness, This is a model for the scarring mechanism.

5. The method for decision-making optimization of scaling and cleaning of alumina evaporators according to claim 4, characterized in that, The step of converting the first evaporation revenue model into a periodic second evaporation revenue model includes: The time in the first evaporation revenue model is divided according to a preset period to obtain multiple fixed periods, and a periodic second evaporation revenue model is established. The second evaporation revenue model is as follows: ； Where c represents a fixed period number, and the period time range for period number c is from Heaven to the Di sky, This represents the cleaning plan for cycle number c. , This label indicates whether the alumina evaporator has been cleaned during this cycle; A represents the heat transfer area. For heat transfer temperature difference, The enthalpy of water in the liquid feed. .

6. The scaling and cleaning decision optimization method for alumina evaporators according to claim 5, characterized in that, The dynamic solution of the second evaporation revenue model yields the target cleaning plan, including: Determine the historical data of the alumina evaporator, and predict the first cleaning plan corresponding to each cycle time in the entire cycle based on the historical data; The second evaporation revenue model is dynamically solved, and the second cleaning plan is corresponding to each cycle time. Determine whether the first cleaning plan corresponding to each cycle time is consistent with the second cleaning plan; If they match, the first cleaning plan will be used as the target cleaning plan; If they are inconsistent, the second cleaning plan will be used as the target cleaning plan.

7. A decision-making optimization system for cleaning scale buildup in an alumina evaporator, characterized in that, For implementing the scale cleaning decision optimization method for alumina evaporators as described in claim 1, the system comprises: The scaling mechanism model establishment module is used to determine the precipitation rate model and the re-diffusion rate model of the alumina evaporator, and to establish a scaling mechanism model based on the precipitation rate model and the re-diffusion rate model. The first revenue model establishment module is used to establish a time-continuous first evaporation revenue model by combining the scarring mechanism model and the preset cleaning and maintenance costs. The second revenue model building module is used to convert the first evaporation revenue model into a periodic second evaporation revenue model. The cleaning plan determination module is used to dynamically solve the second evaporation benefit model to obtain the target cleaning plan.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted to be loaded by a processor and executed as described in any one of claims 1-6.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-6.

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