Seawater desalination system, method, and storage medium

Abstract

This invention discloses a seawater desalination system, method, and storage medium, belonging to the field of seawater desalination technology. The system includes a heat source module, a multi-effect distillation module, and an intelligent control center. The heat source module exchanges heat with waste heat through liquid cooling to obtain a driving heat source with a stable temperature within the target range. The multi-effect distillation module receives the driving heat source and seawater, achieving multiple uses of thermal energy and seawater desalination through cascade evaporation and vapor-liquid separation, and condenses the secondary steam from the final effect into fresh water for storage. The intelligent control center collects multi-dimensional dynamic data in real time, automatically optimizes and dynamically adjusts key operating parameters to achieve real-time coordinated optimization of total energy efficiency and water production rate even under continuously changing conditions of the driving heat source and seawater. This invention solves the problem of dynamically matching waste heat grade with desalination requirements through deep coupling of the liquid cooling system and low-temperature multi-effect distillation technology, achieving efficient and stable waste heat recovery and continuous adaptive operation of the system.

Description

Technical Field

[0001] This invention relates to the field of seawater desalination technology, and particularly to seawater desalination systems, methods, and storage media. It especially relates to intelligent seawater desalination based on data center waste heat recovery. Background Technology

[0002] Currently, existing technologies deeply couple data center waste heat with seawater desalination processes. For example, prior art with publication number CN117875782A discloses a seawater desalination system that utilizes waste heat from underwater servers. The system absorbs heat from the servers through heat-conducting components to heat the circulating fluid, which is then heated and evaporated via a heat exchanger. After condensation, fresh water is obtained, thus preliminarily verifying the feasibility of using waste heat for seawater desalination.

[0003] Regarding seawater desalination, existing technologies include Low Temperature Multi-effect Distillation (LT-MED). Its core principle is to allow heated seawater to pass through multiple evaporators (called "effects") connected in series with progressively decreasing pressure. The steam generated in the previous effect is used as the heat source for the next effect, achieving multiple evaporations and condensations, thereby producing freshwater that is many times the amount of steam input.

[0004] However, existing waste heat-driven seawater desalination technologies have not yet solved two core problems: the efficient matching of fluctuating waste heat with the low-temperature multi-effect distillation module, and the multi-objective synergistic optimization of water production rate and energy efficiency under all operating conditions. Therefore, an integrated solution that can achieve stable waste heat recovery and adaptive and efficient system operation is still needed. Summary of the Invention

[0005] Purpose of the invention: In view of the above problems, the purpose of this invention is to provide a seawater desalination system, method and storage medium.

[0006] Technical solution: In a first aspect, the present invention provides a seawater desalination system, comprising:

[0007] The heat source module is configured to perform liquid-cooled heat exchange with waste heat to obtain a driving heat source with a stable temperature within the target range;

[0008] A multi-effect distillation module is configured to receive the driving heat source and seawater, realize multiple utilization of heat energy and seawater desalination in the process of cascade evaporation and vapor-liquid separation, and condense the final secondary steam into fresh water for storage.

[0009] as well as

[0010] The intelligent control center is configured to collect multi-dimensional dynamic data in real time, automatically optimize and dynamically adjust key operating parameters, so as to always achieve real-time coordinated optimization of total energy efficiency and water production rate under the continuous changes of driving heat source and seawater conditions.

[0011] The multidimensional dynamic data includes power grid status, server load, seawater parameters, and internal operating conditions; the key operating parameters include heat source temperature, seawater feed rate, and operating pressure of each effect.

[0012] Optionally, the target range is matched with the optimal operating temperature range for low-temperature multi-effect distillation.

[0013] Optionally, the target temperature range is 60°C to 80°C, with fluctuations ≤ ±1°C.

[0014] Optionally, the intelligent control center has a built-in adaptive multi-objective Bayesian optimization algorithm module, which is used to perform the following steps:

[0015] The simultaneous optimization objectives are to minimize the overall energy consumption per unit water production and maximize the water production rate under given heat source conditions. Online calculations are then performed.

[0016] ;

[0017] In the formula, To optimize the operation objects of the algorithm; For the multi-effect distillation module Effective evaporation pressure setpoint; To drive the heat source flow; The condenser cooling water flow rate is given; the constraint condition is... ,in Let j be the j-th inequality constraint. This is a real-time parameter vector collected and aggregated by the monitoring center within the current control cycle;

[0018] Based on the optimization calculation results, coordinated control commands are generated.

[0019] Optionally, the adaptive multi-objective Bayesian optimization algorithm module collects the state changes of each actuator after executing the control command to form a new real-time parameter vector and feeds it back to trigger a new round of optimization loop.

[0020] Optionally, each optimization cycle has a dual objective of minimizing equivalent energy consumption and maximizing water production rate, with the objective function being:

[0021] ;

[0022] ;

[0023] In the formula, To minimize equivalent energy consumption; Maximize water production rate; The electrical power consumed by all pumps; For the power consumption of other auxiliary equipment; The driving thermal power input from the heat source module; This is the conversion factor for converting electrical energy into primary energy. This is the conversion factor for converting thermal energy into primary energy.

[0024] In a second aspect, the present invention provides a server including a liquid cooling unit, the liquid cooling unit being used for integration with a heat source module in the seawater desalination system described in the first aspect.

[0025] The liquid cooling unit uses a surface-mount microchannel cold plate to directly contact the server chip in order to capture the chip's waste heat.

[0026] Thirdly, a seawater desalination method of the present invention includes the following steps:

[0027] Liquid cooling heat exchange is performed with waste heat to obtain a driving heat source with a stable temperature within the target range;

[0028] The thermal energy of the driving heat source is used to carry out cascade evaporation and vapor-liquid separation of seawater, so as to realize the multiple use of thermal energy and seawater desalination.

[0029] The final-effect secondary steam is condensed into fresh water and stored;

[0030] The system collects multi-dimensional dynamic data in real time, automatically optimizes and dynamically adjusts key operating parameters to achieve real-time coordinated optimization of total energy efficiency and water production rate under continuously changing driving heat source and seawater conditions. The multi-dimensional dynamic data includes power grid status, server load, seawater parameters and internal operating conditions. The key operating parameters include heat source temperature, seawater feed rate and operating pressure of each effect.

[0031] Fourthly, an optimization method of the present invention, used in the seawater desalination system described in the first aspect, includes the following steps:

[0032] Construct a real-time parameter vector representing the current operating condition ;

[0033] Real-time parameter vector As input, the built-in algorithm model based on adaptive multi-objective Bayesian optimization is invoked to perform online calculations with the simultaneous optimization objectives of minimizing the comprehensive energy consumption per unit water production and maximizing the water production rate under given heat source conditions.

[0034] Based on the optimization calculation results, generate coordinated control commands;

[0035] Control commands are issued to each implementing agency;

[0036] Collect the state changes of each actuator after it executes the control command to form a new real-time parameter vector, and feed back to trigger a new round of optimization cycle.

[0037] Fifthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the optimization method as described in the fourth aspect.

[0038] The beneficial effects of this invention are:

[0039] The significant advantages of this invention compared to existing technologies are:

[0040] 1. This invention establishes a flexible matching and precise control mechanism for fluctuating waste heat from data centers between 80°C and 90°C through deep coupling of liquid cooling system and low-temperature multi-effect distillation technology. It can adaptively adjust the plate heat exchange parameters and pressure distribution of each effect to ensure that the driving hot water at 60°C to 80°C always matches the optimal operating temperature range of low-temperature multi-effect distillation. This solves the problem of dynamic matching between waste heat grade and desalination requirements, and realizes efficient and stable waste heat recovery and continuous adaptive operation of the system.

[0041] 2. Simultaneously, a dual-objective collaborative optimization function with minimizing equivalent energy consumption and maximizing water production rate as its core is introduced. Under the fluctuation of all operating conditions, the operating parameters are optimized in real time to drive the cascade utilization efficiency of the driving heat source to maximize the GOR approximation efficiency number N. This overcomes the bottleneck of the difficulty in balancing water production rate and energy efficiency, realizes the efficient conversion of waste heat into high-value desalination driving energy, and significantly improves the overall energy utilization efficiency and comprehensive economic benefits of the system. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0043] Figure 2 This is a flowchart of the adaptive multi-objective Bayesian optimization algorithm used in this invention;

[0044] Figure 3 This is a comparison chart of the power output fluctuation of the driving heat source in this invention and conventional technology;

[0045] Figure 4 This is a comparison chart showing the improvement in water production rate between the present invention and traditional technologies.

[0046] Figure 5 This is a comparison chart of the heat source temperature stability of the present invention and traditional technologies. Detailed Implementation

[0047] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and not intended to limit the scope of the invention. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the embodiments of the present invention, and not all structures.

[0048] In the following description, specific details such as target system architecture and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0049] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0050] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0051] Furthermore, in the description of this application and the appended claims, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0052] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include the target features, structures, or characteristics described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0053] This invention discloses an intelligent seawater desalination system based on data center waste heat recovery. See [link to relevant documentation]. Figure 1 The system provides power to the server cluster in the heat source module through the data center power supply unit. The server cluster generates heat to generate hot water at 60°C to 80°C through the liquid cooling unit. The external seawater supply unit inputs seawater at 25°C to 30°C into the multi-effect distillation module for evaporation and separation. Finally, the fresh water is collected through the condensation collection unit and the seawater is concentrated to 6% to 8%. The entire process is coordinated and monitored by the intelligent control center to achieve dynamic optimization of the entire process from waste heat recovery to freshwater production.

[0054] The following is in conjunction with the appendix Figures 1 to 5 The embodiments of the present invention will be described in further detail below.

[0055] In one embodiment, see Figure 1 The heat source module and the liquid cooling unit of the server in the server cluster are integrated in structure and control function. The server liquid cooling unit serves as the waste heat capture end, and the heat source module serves as the waste heat conversion and output end. The two are thermally coupled through a plate heat exchanger and circulation pipeline, and are coordinated and scheduled by the same intelligent control center. The waste heat generated by the server chip is transferred to the primary cooling medium through a microchannel cold plate, and then to the secondary working medium water through a plate heat exchanger. This ensures that the secondary working medium water is stably output as the 60°C to 80°C driving hot water required by LT-MED, thereby realizing thermal coupling and information linkage between the waste heat capture end and the desalination utilization end. The heat source module is used to capture the waste heat of the chip and convert it into a stable and controllable driving heat source. In one possible implementation, the waste heat capture adopts a patch-type microchannel cold plate in direct thermal contact with the server chip; the heat source module includes three stages: the waste heat capture stage, the heat transfer and enhancement stage, and the heat source standardization output stage.

[0056] In the waste heat capture stage, microchannels with equivalent diameters of 0.5 mm to 2 mm are processed inside the cold plate. They are attached to the top surface of the chip through a high thermal conductivity interface material and a closed circulation pipeline is used. A non-conductive coolant with high specific heat capacity and low electrical conductivity is used as the medium and is driven by a variable frequency pump to force convection within the microchannels.

[0057] As an example, when the chip load reaches 80°C to 90°C, heat is efficiently conducted to the coolant via the cold plate; that is, the coolant can capture the high heat flux density waste heat at the chip level (80°C to 90°C) generated during high-load server operation in real time. Furthermore, the captured heat power can be calculated using the following method:

[0058] ;

[0059] In the formula, For the effective heat power captured; The instantaneous mass flow rate of the cooling medium flowing through the server's liquid cooling unit; The physical properties of the cooling medium; This refers to the temperature difference of the coolant before and after it absorbs heat as it flows through the server chip. Since this exemplary description aims to capture high heat flux density waste heat of 80°C to 90°C, therefore... This usually refers to the difference between the coolant outlet temperature and the inlet temperature, or the difference between the actual outlet temperature and the ambient temperature reference.

[0060] During the heat transfer and enhancement stage, the high specific heat capacity cooling medium circulating within the microchannel continuously absorbs waste heat from the chip. At this point, the temperature of the high specific heat capacity cooling medium rises significantly, forming an intermediate heat transfer medium carrying stable thermal energy. As an example, deionized water is used as the high specific heat capacity cooling medium, and its mass flow rate... At a flow rate of 0.4 kg / s, 15 kW of waste heat can be converted into a temperature rise of approximately 9°C, raising the medium temperature from 55°C to 64°C, thus forming a stable high-temperature heat transfer fluid that is transported downstream.

[0061] Among them, the high specific heat capacity cooling medium is represented by deionized water, which has a specific heat capacity of 4.18 kJ / (kg·℃). It can efficiently carry the waste heat of server chips from 80℃ to 90℃, and has low electrical conductivity and non-corrosive properties. It can also be replaced with antifreeze ethylene glycol solution or high thermal conductivity nanofluid according to the deployment environment to meet the needs of different scenarios.

[0062] During the standardized output stage of the heat source, the heated cooling medium flows through the primary side of a high-efficiency plate heat exchanger connected between the microchannel cold plate outlet circuit and the hot water supply circuit of the multi-effect distillation module. This efficiently transfers heat to the pre-treated working medium water on the system side (i.e., the hot water supply circuit of the multi-effect distillation module). Through intelligent flow control and multi-stage heat exchange regulation—specifically, by adjusting the coolant flow rate via a variable frequency pump and controlling the secondary water flow rate via an electric regulating valve—and combined with segmented heat exchange or bypass regulation of the plate heat exchanger, the output water temperature of the working medium water on the system side is adjusted. Precisely stabilized within the target temperature range of 60℃ to 80℃, this provides a continuous, controllable, and appropriately temperature-controlled driving heat source for subsequent multi-effect distillation modules, realizing a complete conversion chain from discrete waste heat from data centers to systematically usable thermal energy. This is illustrated by the following formula:

[0063] ;

[0064] In the formula, Specific heat capacity of the cooling medium, representing the temperature rise per unit mass of coolant. The amount of heat required to be absorbed; For capture efficiency; Power consumption of the server cluster (W); The temperature at which the coolant flows out after absorbing heat through the server. This is the temperature at which the coolant flows into the server before absorbing heat. The coefficient of performance (COP) of a data center liquid cooling system is the electrical energy consumed to remove one unit of waste heat. A value closer to 0 indicates higher heat dissipation efficiency. To characterize the effective thermal energy ratio in waste heat, i.e., waste heat utilization rate, it represents the proportion of total electrical energy consumed by the server that, excluding the portion used for active cooling, is converted into waste heat and can be recycled. The effective thermal power is the heat that is ultimately extracted after heat exchange and transmission to drive the subsequent multi-effect distillation module. This refers to the flow rate of the coolant circulating in the microchannel.

[0065] The working medium water is makeup water that has undergone softening and dechlorination pretreatment to prevent scaling on the heat exchange surface.

[0066] In this system, the plate heat exchanger is fed with a heated cooling medium on the primary side and with the working medium (water) to be heated on the secondary side. The intelligent control center adjusts the frequency of the cooling medium circulation pump and the opening of the bypass valve to control the output hot water temperature. Stable temperature control within the range of 60℃ to 80℃, with fluctuations ≤ ±1℃, matching the optimal operating temperature range for low-temperature multi-effect distillation.

[0067] In one embodiment, see Figure 1 The core desalination unit is a multi-effect distillation module, employing an N-effect low-temperature multi-effect distillation process. Specifically, it involves connecting N evaporators in series, operating within a low-temperature range of 60℃ to 80℃ in a tiered evaporation desalination technology. The first effect uses 60℃ to 80℃ hot water from a heat source module to heat seawater and produce steam. Subsequent effects directly utilize the steam generated in the previous effect as a heat source, eliminating the need for additional heating and achieving multiple heat reuses. Ultimately, one unit of waste heat can produce nearly N units of fresh water, perfectly matching the grade and temperature range of data center waste heat.

[0068] Specifically, the first effect, which is the first-stage evaporator of the N-effect low-temperature multi-effect distillation process, is driven by hot water from the heat source module. Subsequent effects are driven by steam generated by the previous effect. The first effect, as the initial heating unit, receives a heat source of 60°C to 80°C and introduces it into the shell-side heat exchange tubes to heat the original seawater to the saturation temperature at the corresponding pressure to generate primary steam. The cooled hot water after heat exchange is returned to the heat source module for reheating. The second to N-1 effects adopt a reverse heat exchange logic. The steam generated by the previous effect is introduced into the heat exchange tubes of this effect as a heat source to heat the concentrated seawater from the previous effect and then condenses into fresh water. At the same time, the newly generated secondary steam in this effect continues to be sent to the next effect without the need for an additional external heat source. After receiving the steam heat exchange from the N-1 effect, the remaining uncondensed secondary steam is sent to the condensation and collection unit. The salinity of the concentrated seawater increases with each effect and is finally discharged at a concentration of 6% to 8%. The entire process achieves continuous evaporation at low temperature through stepped pressure reduction, and the heat is reused N times.

[0069] In other words, the multi-effect distillation module achieves tiered evaporation and desalination of seawater through a continuous thermodynamic cycle. During the raw material input stage, raw seawater with a temperature of 25℃ to 30℃ and a salinity of approximately 3.5% is introduced into the multi-effect distillation module through the seawater inlet. Simultaneously, it receives a driving heat source from the heat source module at a temperature of 60℃ to 80℃ as the initial heat source. In the first-effect evaporation and heat transfer stage, the driving heat source flows into the heat exchange tube bundle of the first-effect evaporator, transferring its heat to the raw seawater outside the tube bundle. This heat transfer process strictly adheres to the law of conservation of energy; the heat energy released by the driving heat source is equal to the sum of the latent heat of vaporization absorbed by the seawater evaporation, the sensible heat absorbed by the heating of the concentrated brine, and the heat dissipation losses of the equipment. It should be noted that, considering that each effect's outer shell is covered with a 50mm thick aluminum silicate insulation layer and operates in a closed environment, the measured proportion of environmental heat dissipation and minor heat loss from the pipelines to the total heat transfer is ≤2%, having a negligible impact on the overall energy efficiency calculation. Therefore, this type of secondary heat loss is ignored here, and only the core energy conversion term is retained to ensure the model's solution efficiency.

[0070] The heat transfer process follows the principle of heat balance, and its quantitative relationship is described by the heat balance equation of the i-th effect:

[0071] ;

[0072] In the formula, Let be the heat transfer rate of the i-th effect; , Let be the flow rate and temperature of the driving heat source in the i-th effect, respectively. For the first Effectively drives the inlet temperature of the heat source. For the first The outlet temperature of the effective driving heat source, h represents the heating fluid (Hot water), and i represents the i-th effect evaporator; Specifically refers to the driving heat source in the i-th effect, that is, the heat source medium at 60°C to 80°C flowing into the heat exchange tube bundle of that effect; , These are the flow rate and temperature of the brine at the bottom of the i-th effect, respectively; , These are the flow rate and temperature of the brine at the bottom of the (i-1)th effect, respectively; The specific heat capacity that drives the heat source; The specific heat capacity of seawater; For the i-th effective pressure The latent heat of vaporization below.

[0073] During the inter-effect transfer and cascade evaporation stages, the initial steam generated in the first effect is introduced into the second-effect evaporator as a heat source, releasing latent heat through its own condensation. · While heating the concentrated seawater from the first effect, a second steam is generated. Similarly, the steam generated in the i-th effect is introduced into the (i+1)-th effect as a heat source for condensation and heat release, while the i-th effect itself absorbs the heat released by the condensation of steam in the (i-1)-th effect, i.e., the heat transfer of the i-th effect. This is to achieve seawater evaporation. This refers to the mass flow rate of secondary steam generated by the evaporation of seawater in the i-th effect evaporator. This process is repeated in each subsequent effect, creating a counter-current flow of steam and concentrated brine between effects, achieving multiple stages of thermal energy utilization. The steam production rate (water production rate component) of each effect can be calculated from the heat transfer and latent heat of vaporization of that effect to obtain the steam production rate (water production rate component) of the i-th effect.

[0074] ;

[0075] Total water production rate That is, the sum of the water production of each effect:

[0076] ;

[0077] Furthermore, the MED low-temperature effect unit of the multi-effect distillation apparatus integrates an evaporation chamber and steam separators for each effect. The vapor-liquid mixture generated in the evaporation chamber tangentially enters the inverted conical steam separator cylinder below. Centrifugal force throws the denser concentrated brine against the cylinder wall, causing it to sink and flow back to the bottom of the evaporation chamber. The less dense saturated steam rises and, after being intercepted by a wire mesh demister at the top, is transported as clean steam with a purity ≥99.5% from the top outlet pipe to the next effect as a heat source. This achieves continuous separation and transport with no power and low resistance. This effectively separates the steam and concentrated brine, ensuring the purity of the heat source steam entering the next effect, while simultaneously increasing the seawater concentration effect by effect.

[0078] In the product output stage, the pure secondary steam with a temperature of 60℃ to 80℃ separated by the final-effect steam separator is transported to the condenser for condensation and then flows into the freshwater collector. The concentrated brine produced in each effect, after being separated by the steam separator, is collected centrally through an internal loop and ultimately discharged continuously from the multi-effect distillation unit as concentrated brine with a concentration of 6% to 8%. This is achieved through stepped pressure reduction, not solely through temperature transfer. The pressure of each effect decreases progressively. Although the initial temperature of the driving heat source is only 60℃ to 80℃, the boiling point of seawater decreases under the low-pressure environment of the final effect. The remaining low-grade heat from the previous effect allows the seawater to continuously boil and generate steam within the 60℃ to 80℃ range, ensuring that the final-effect steam temperature meets the standard and achieving stepped reuse of thermal energy. The core energy efficiency of the multi-effect distillation unit is measured by the water production ratio, defined as the ratio of the total latent heat contained in the produced freshwater to the driving heat energy input to the system. The calculation formula is:

[0079] ;

[0080] In the formula, It is a key indicator for measuring thermal energy utilization efficiency, and its value is close to the efficiency factor. With thermal efficiency The product; For freshwater production; The average latent heat of vaporization; The mass flow rate of the heating medium; This is the specific heat capacity of water; The inlet temperature of the heating medium; This refers to the outlet temperature of the heating medium. For thermal efficiency.

[0081] In one embodiment, see Figure 1 The secondary steam from the last effect of the multi-effect distillation module is received by the condensation and collection unit, specifically the mass flow rate of the secondary steam from the last effect. With saturation temperature The former determines the condensing heat load and water production, while the latter determines the cooling demand and waste heat recovery value.

[0082] In this embodiment, the condensation and collection unit includes a condenser and a freshwater collector. Secondary steam is condensed into freshwater in the condenser and stored in the freshwater collector (which can be a freshwater collection tank). The condensation and collection unit module achieves the final acquisition and stable storage of freshwater through a continuous physical processing and automatic control flow, specifically including: a steam input stage, receiving secondary steam at a temperature of 60°C to 80°C from the final effect output of the multi-effect distillation module; a condensation stage, introducing the secondary steam into a partitioned condenser, where indirect heat exchange occurs through a cooling medium flowing on the other side of the tube wall, causing the steam to completely transform into liquid freshwater; and a collection and storage stage, introducing the condensed freshwater into a freshwater collector equipped with a level sensor for safe storage.

[0083] Based on the above energy transfer principle, the condensation load of the condensation collection unit and the required cooling medium flow rate can be calculated using the following energy balance formula:

[0084] ;

[0085] In the formula, This refers to the condensing heat load, which is the waste heat power that the condenser needs to handle. The mass flow rate of the secondary steam output from the Nth effect of the multi-effect distillation module; The latent heat of vaporization at the Nth effect corresponding pressure; Specific heat capacity of the cooling medium; , These are the temperatures of the cooling medium entering and exiting the condenser, respectively. To control the mass flow rate of cooling water, during the output control phase, the water level in the tank is monitored in real time by a level sensor, and the level data is fed back to the intelligent control center. This ensures a stable water supply while automatically maintaining the water level in the tank within a preset safe operating range, thus completing the reliable conversion and controlled storage of steam into usable liquid fresh water.

[0086] In one embodiment, see Figure 1 and Figure 2 The intelligent control center incorporates an Adaptive Multi-Objective Bayesian Optimization (AMBO) algorithm module. The intelligent control center is connected to the monitoring center via signal transmission, enabling closed-loop operation of "perception-modeling-optimization-control." First, the monitoring center uses three types of sensors to collect real-time, multi-dimensional parameters required for system operation, constructing a real-time parameter vector representing the current operating condition. .

[0087] The external condition sensor group includes a power supply status sensor installed at the power grid connection point. This refers to the grid power, used to monitor whether the voltage, current, and power are stable. This is for seawater salinity, and is used in temperature and salinity sensors installed at seawater inlets to monitor the temperature and salinity parameters of the raw seawater in real time.

[0088] The heat source status sensor group includes temperature sensors and load sensors installed on the data center server cluster to monitor chip temperature and server computing load. To collect server chip temperature, To assess server utilization and evaluate the quality and stability of waste heat sources.

[0089] The system's internal status sensor group includes a temperature sensor installed at the outlet of the heat source module, pressure and temperature sensors installed in each effect of the multi-effect distillation module, and a liquid level sensor installed in the freshwater collection tank. To collect the outlet water temperature of the heat source module, This represents the freshwater tank level, used to monitor the operational status of key nodes within the system. The final real-time parameter vector is represented as follows:

[0090] ;

[0091] In the formula, This is a real-time parameter vector collected and aggregated by the monitoring center within the current control cycle; Input power or power supply status parameters to the power grid; For server chip temperature; Server load rate; This represents the original seawater inlet salinity, where the subscript sw indicates seawater and in indicates the inlet end; This represents the salinity of the concentrated brine outlet, where 'out' indicates the outlet or discharge end. This is the original seawater feed rate; The output temperature of the driving heat source from the heat source module; The operating pressure of the i-th effect evaporator; Let be the temperature of the brine inside the i-th effect evaporator, where i = 1, 2, ..., N, and N is the total number of effects in the multi-effect distillation module; The water level in the freshwater collection tank; To drive the target temperature of the heat source.

[0092] The above parameters together constitute the aforementioned real-time parameter vector. It is used to characterize the real-time operating state of the system within the current control cycle and serves as the input to the AMBO algorithm for subsequent multi-objective optimization calculations and control command generation.

[0093] Next, optimization calculations are performed, using the aggregated real-time data as input, based on the real-time parameter vector. The built-in AMBO algorithm model is invoked, and online calculations are performed with the simultaneous optimization objectives of "lowest overall energy consumption per unit of water production" and "highest water production rate under given heat source conditions."

[0094] ;

[0095] In the formula, For the multi-effect distillation module The effective evaporation pressure setpoint directly affects the boiling point of the effect; To drive the heat source flow; condenser cooling water flow rate; vector It is the object of the optimization algorithm, and its value directly determines the two objective functions. and The final result is the control instruction set that needs to be optimized. The constraints are as follows: , where j = 1, 2, ..., M; Let J represent the j-th inequality constraint, and M be the total number of constraints.

[0096] Next, commands are generated and issued. Based on the optimization calculation results, a series of coordinated control commands are generated to adjust the flow rate of the circulating working fluid in the heat source module to control the output hot water temperature, adjust the frequency of the seawater feed pump in the multi-effect distillation module to control the feed rate, adjust the vacuum degree in the evaporator, and adjust the cooling water flow rate of the condenser in the condensation collection unit module.

[0097] Furthermore, closed-loop execution and feedback send control commands to each actuator; after the system operates according to the new optimized parameters, its state changes are again captured by the data acquisition device, forming a new real-time parameter vector, which is then fed back to the intelligent control center as input for the next control cycle to trigger the next round of optimization calculations. Each round of optimization calculations has a dual objective of minimizing equivalent energy consumption and maximizing water production rate, and its objective function is:

[0098] ;

[0099] ;

[0100] In the formula, To minimize equivalent energy consumption; Maximize water production rate; The control center's algorithm directly reads the sensor values ​​to determine the electrical power consumed by all pumps, expressed as follows:

[0101] ;

[0102] For the power consumption of other auxiliary equipment; The driving thermal power input from the heat source module; This is the conversion factor for converting electrical energy into primary energy. This is the conversion factor for converting thermal energy into primary energy. and It is used to uniformly quantify and sum the energy consumption of two different grades of electricity and heat under the same benchmark.

[0103] It should be noted that the intelligent control center uses the AMBO algorithm to solve the problem in each control cycle. Internally, the intelligent control center uses the AMBO algorithm to solve the above model. This algorithm uses a Gaussian process as a substitute model and can efficiently learn the objective function. and With decision variables The complex nonlinear relationships between them are actively sampled through Bayesian inference, while satisfying constraints. Under the premise of quickly searching for those that can simultaneously maximum, The smallest set of Pareto optimal solutions. In decision-making and execution, the algorithm outputs a frontier composed of numerous Pareto optimal solutions. The intelligent control center selects the optimal solution best suited to the current requirements from this set based on a pre-set real-time operating strategy. Subsequently, The abstract values ​​contained therein are converted into specific, executable physical instructions and sent to the execution mechanisms of each module, completing the final closed loop from intelligent decision-making to physical control.

[0104] In one embodiment, a seawater desalination system is disclosed, comprising:

[0105] The heat source module is configured to perform liquid-cooled heat exchange with waste heat to obtain a driving heat source with a stable temperature within the target range;

[0106] The multi-effect distillation module is configured to receive the driving heat source and seawater, realize the multiple utilization of heat energy and seawater desalination in the cascade evaporation and vapor-liquid separation, and condense the final secondary steam into fresh water for storage.

[0107] as well as

[0108] The intelligent control center is configured to collect multi-dimensional dynamic data in real time, automatically optimize and dynamically adjust key operating parameters, so as to always achieve real-time coordinated optimization of total energy efficiency and water production rate under the continuous changes of driving heat source and seawater conditions.

[0109] The multidimensional dynamic data includes power grid status, server load, seawater parameters, and internal operating conditions; key operating parameters include heat source temperature, seawater feed rate, and operating pressure of each effect.

[0110] In one embodiment, a server is disclosed, including a liquid cooling unit for integration with a heat source module in the seawater desalination system described above.

[0111] The liquid cooling unit uses a surface-mount microchannel cold plate to directly contact the server chip in order to capture the chip's waste heat.

[0112] In one embodiment, a seawater desalination method is disclosed, comprising the following steps:

[0113] Liquid cooling heat exchange is performed with waste heat to obtain a driving heat source with a stable temperature within the target range;

[0114] The thermal energy of the driving heat source is used to carry out cascade evaporation and vapor-liquid separation of seawater, so as to realize the multiple use of thermal energy and seawater desalination.

[0115] The final-effect secondary steam is condensed into fresh water and stored;

[0116] Real-time acquisition of multi-dimensional dynamic data, automatic optimization and dynamic adjustment of key operating parameters, to achieve real-time coordinated optimization of total energy efficiency and water production rate under the continuous changes of driving heat source and seawater conditions; the multi-dimensional dynamic data includes power grid status, server load, seawater parameters and internal operating conditions; key operating parameters include heat source temperature, seawater feed rate and operating pressure of each effect.

[0117] In one embodiment, an optimization method is disclosed for the seawater desalination system in the above embodiment, comprising the following steps:

[0118] Construct a real-time parameter vector representing the current operating condition ;

[0119] Real-time parameter vector As input, the built-in algorithm model based on adaptive multi-objective Bayesian optimization is invoked to perform online calculations with the simultaneous optimization objectives of minimizing the comprehensive energy consumption per unit water production and maximizing the water production rate under given heat source conditions.

[0120] Based on the optimization calculation results, generate coordinated control commands;

[0121] Control commands are issued to each implementing agency;

[0122] Collect the state changes of each actuator after it executes the control command to form a new real-time parameter vector, and feed back to trigger a new round of optimization cycle.

[0123] In one embodiment, a computer-readable storage medium is disclosed storing a computer program, characterized in that, when executed by a processor, the computer program causes the processor to perform the steps of the optimized method described above.

[0124] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0125] To verify the technical effect of the present invention, this embodiment is compared and verified under the same usage scenario and parameter benchmark: the heat dissipation of the data center rack is set to 15kW, the ambient seawater temperature is 25℃, and the salinity is 3.5%. The comparative example and the experimental example use the exact same plate heat exchanger and multi-effect distillation (MED) hardware. The comparative example adopts a traditional fixed-frequency PID control scheme, that is, the output temperature of the heat source module is fixed at 70℃, the frequency of the seawater feed pump is constant at 30Hz, the evaporator vacuum degree is maintained at 20kPa absolute pressure, and the condenser cooling water flow rate is fixed at 8m³ / h. When the server load fluctuates, the heat source outlet water temperature of the system fluctuates by more than ±2.5℃, resulting in instability of the heat transfer temperature difference between MED effects, and the standard deviation of the water production rate fluctuation is greater than 12%. In contrast, the experimental example utilized the AMBO algorithm of the intelligent control center to collect real-time data on the entire process, including chip temperature, heat source outlet water temperature, and pressure at each effect. When an increase in heat load was detected, the algorithm generated coordinated control commands through multi-objective optimization calculations: reducing the circulating working fluid flow rate of the heat source module from 5 m³ / h to 4.2 m³ / h to suppress heat source temperature drift; increasing the seawater feed pump frequency of the multi-effect distillation module from 30 Hz to 45 Hz to match the heat flux density; fine-tuning the first-effect evaporation pressure in the evaporator from 20 kPa to 18 kPa to lower the seawater boiling point; and increasing the cooling water flow rate of the condensation collection unit from 8 m³ / h to 9.5 m³ / h to maintain condensation efficiency. Through this coordinated adjustment mechanism, the system achieved a 18.2% reduction in heat source fluctuations, an 18% increase in water production rate, and a 16.5% improvement in temperature stability during 72 hours of continuous operation. Specific performance data are as follows: Figures 3 to 5 As shown.

[0126] Figure 3 In this invention, the system achieves more stable waste heat capture and output, and more precise temperature control through the coordinated optimization of the liquid cooling unit and plate heat exchanger, combined with the AMBO algorithm of the intelligent control center. Compared with traditional systems, the power output fluctuation of the driving heat source is reduced by approximately 18.2%, significantly improving the continuity and reliability of thermal energy utilization and effectively ensuring the stable operation of the multi-effect distillation module.

[0127] Figure 4 In this invention, the system utilizes a closed-loop synergistic mechanism of heat source-multi-effect distillation-condensation collection to achieve highly efficient coupling of multi-effect cascade evaporation. By adaptively optimizing and controlling the pressure, feed rate, and hot water flow rate of each effect, the overall water production rate of each effect is improved, while ensuring reduced fluctuations in individual effects. The average improvement is controlled within 18%, demonstrating the system's balanced advantage between high-efficiency water production and low energy consumption.

[0128] Figure 5In this invention, the system maintains the hot water temperature within the target range of 60°C to 80°C through liquid cooling heat energy conversion and intelligent flow control, achieving higher temperature stability than traditional systems. Temperature fluctuations are reduced by approximately 16.5%, ensuring maximum evaporation efficiency in the first effect and reducing heat loss between subsequent effects, further enhancing the overall energy efficiency and response speed of the system.

Claims

1. A sea water desalination system, characterized by, include: The heat source module is configured to perform liquid-cooled heat exchange with waste heat to obtain a driving heat source with a stable temperature within the target range; A multi-effect distillation module is configured to receive the driving heat source and seawater, realize multiple utilization of heat energy and seawater desalination in the process of cascade evaporation and vapor-liquid separation, and condense the final secondary steam into fresh water for storage. as well as The intelligent control center is configured to collect multi-dimensional dynamic data in real time, automatically optimize and dynamically adjust key operating parameters, so as to always achieve real-time coordinated optimization of total energy efficiency and water production rate under the continuous changes of driving heat source and seawater conditions. The multidimensional dynamic data includes power grid status, server load, seawater parameters, and internal operating conditions; the key operating parameters include heat source temperature, seawater feed rate, and operating pressure of each effect.

2. The system according to claim 1, characterized in that, The target range matches the optimal operating temperature range for low-temperature multi-effect distillation.

3. The system according to claim 1, characterized in that, The target temperature range is 60℃ to 80℃, with fluctuations ≤ ±1℃.

4. The system according to claim 1, characterized in that, The intelligent control center has a built-in adaptive multi-objective Bayesian optimization algorithm module, which is used to perform the following steps: The simultaneous optimization objectives are to minimize the overall energy consumption per unit water production and maximize the water production rate under given heat source conditions. Online calculations are then performed. ； In the formula, To optimize the operation objects of the algorithm; For the multi-effect distillation module Effective evaporation pressure setpoint; To drive the heat source flow; The condenser cooling water flow rate is given; the constraint condition is... ,in Let j be the j-th inequality constraint. This is a real-time parameter vector collected and aggregated by the monitoring center within the current control cycle; Based on the optimization calculation results, coordinated control commands are generated.

5. The system according to claim 4, characterized in that, The adaptive multi-objective Bayesian optimization algorithm module collects the state changes of each actuator after executing the control command to form a new real-time parameter vector and feeds it back to trigger a new round of optimization loop.

6. The system according to claim 5, characterized in that, Each optimization cycle has two objectives: minimizing equivalent energy consumption and maximizing water production rate. The objective function is as follows: ； ； In the formula, To minimize equivalent energy consumption; Maximize water production rate; The electrical power consumed by all pumps; For the power consumption of other auxiliary equipment; The driving thermal power input from the heat source module; This is the conversion factor for converting electrical energy into primary energy. This is the conversion factor for converting thermal energy into primary energy.

7. A server, characterized in that, Includes a liquid cooling unit, which is used to integrate with the heat source module of the seawater desalination system according to any one of claims 1 to 6; The liquid cooling unit uses a surface-mount microchannel cold plate to directly contact the server chip in order to capture the chip's waste heat.

8. A method for seawater desalination, characterized in that, Includes the following steps: Liquid cooling heat exchange is performed with waste heat to obtain a driving heat source with a stable temperature within the target range; The thermal energy of the driving heat source is used to carry out cascade evaporation and vapor-liquid separation of seawater, so as to realize the multiple use of thermal energy and seawater desalination. The final-effect secondary steam is condensed into fresh water and stored; The system collects multi-dimensional dynamic data in real time, automatically optimizes and dynamically adjusts key operating parameters to achieve real-time coordinated optimization of total energy efficiency and water production rate under continuously changing driving heat source and seawater conditions. The multi-dimensional dynamic data includes power grid status, server load, seawater parameters and internal operating conditions. The key operating parameters include heat source temperature, seawater feed rate and operating pressure of each effect.

9. An optimization method for the seawater desalination system according to any one of claims 1 to 6, characterized in that, Includes the following steps: Construct a real-time parameter vector representing the current operating condition ; Real-time parameter vector As input, the built-in algorithm model based on adaptive multi-objective Bayesian optimization is invoked to perform online calculations with the simultaneous optimization objectives of minimizing the comprehensive energy consumption per unit water production and maximizing the water production rate under given heat source conditions. Based on the optimization calculation results, generate coordinated control commands; Control commands are issued to each implementing agency; Collect the state changes of each actuator after it executes the control command to form a new real-time parameter vector, and feed back to trigger a new round of optimization cycle.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the processor performs the steps of the optimization method as described in claim 9.

Images