A method for optimizing control of a marine heat exchanger

Abstract

The application discloses a ship heat exchanger control optimization method, and particularly relates to the technical field of ship heat exchange. First, a ship heat exchanger control management database is constructed, then data generated in the ship heat exchange process is collected, a multi-physical field coupling model is further constructed, and the physical field coupling model is analyzed. According to the abnormal analysis result, a control mechanism is triggered, and control optimization is performed through an optimization model. The obtained control optimization result is evaluated, and the abnormal evaluation result is fed back to an administrator terminal for human-computer interaction. Through the heat exchanger control multi-physical field coupling module, the mutual influence among the operation of the heat exchanger and the power system, the air conditioning system and the cooling system is comprehensively considered, and then the control parameters of the heat exchanger can be accurately adjusted according to different working conditions, so that the heat exchanger is always in the best operation state, and the stability of the heat exchanger operation is improved.

Description

Technical Field

[0001] This invention relates to the field of ship heat exchange technology, specifically to a method for controlling and optimizing ship heat exchangers. Background Technology

[0002] In the field of marine engineering, marine heat exchangers play a vital role in marine systems. They are responsible for the heat transfer between different media to ensure the stable operation of marine power systems, cooling systems, and other thermal management-related systems. Therefore, optimizing their performance is of paramount importance.

[0003] Existing control methods often focus only on the operating parameters of the heat exchanger itself, such as inlet and outlet temperatures and pressures. However, a ship is a complex system, and the operation of the heat exchanger is closely related to the power system, air conditioning system, and cooling system. For example, changes in the load of the power system will directly affect the heat load of the heat exchanger. In addition, in terms of dealing with the mutual influence between systems, each system often operates independently and lacks an effective coordination mechanism, making it difficult to make reasonable parameter adjustments according to actual needs, resulting in unreasonable energy consumption and reduced equipment performance.

[0004] However, existing ship heat exchanger control methods can no longer meet the requirements for efficient, energy-saving, and safe operation of ships. For example, existing control methods only focus on the operating parameters of the heat exchanger itself, while ignoring the coordination with other ship systems. If the control method cannot comprehensively consider the mutual influence between these systems, it is difficult to achieve the optimized operation of the entire ship thermal management system. The independent operation of each system and the lack of an effective coordination mechanism make it impossible to achieve synergistic optimization between systems, leading to energy waste and increased equipment wear. Therefore, a ship heat exchanger control optimization method based on in-depth research on the above-mentioned coordinated control is needed to improve the overall performance of the ship thermal management system. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for optimizing the control of a ship heat exchanger to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the control of a ship heat exchanger, comprising:

[0007] S1: Through big data technology, collect and store standard data for ship heat exchanger control, and simultaneously receive data generated during the ship heat exchanger control process to build a ship heat exchanger control and management database;

[0008] S2: By using sensor technology, data generated during the ship's heat exchange process is collected to obtain and analyze various parameters for heat exchange control;

[0009] S3: Using big data analysis technology, multiphysics coupling models are constructed for the various parameters of heat exchange control obtained in S2, resulting in coupling models of power system-heat exchanger, air conditioning system-heat exchanger, and cooling system-heat exchanger.

[0010] S4: Analyze the multiphysics coupling model obtained in S3 using big data analysis technology, trigger the control mechanism based on the anomaly analysis results, and optimize the control by optimizing the model to obtain the optimized control result;

[0011] S5: Through big data analytics, the results of the optimized control are evaluated, and any abnormal evaluation results are fed back to the administrator terminal for human-computer interaction.

[0012] The technical effects and advantages of this invention are as follows:

[0013] 1. This invention collects data generated during the ship's heat exchange process using various sensor technologies, and analyzes various parameters for heat exchange control. This allows for precise adjustment of the control parameters of the ship's heat exchanger. By continuously optimizing these control parameters, the heat exchanger can maintain high heat exchange efficiency under different operating conditions, thereby reducing energy consumption.

[0014] 2. This invention uses a heat exchanger control multi-physics coupling module to comprehensively consider the interaction between the operation of the heat exchanger and the power system, air conditioning system, and cooling system. This allows for precise adjustment of the heat exchanger's control parameters according to different operating conditions, ensuring that it is always in the optimal operating state and improving the stability of the heat exchanger's operation.

[0015] 3. This invention uses a heat exchanger control optimization module to analyze a multi-physics coupling model. Once big data analysis technology detects abnormal results and triggers the control mechanism, the system can quickly take emergency measures. At the same time, the Lagrange function optimization model can avoid getting trapped in local optima, ensuring that the control strategy found is the optimal solution in the entire feasible domain, thereby achieving control optimization of the ship's heat exchanger. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0017] Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 As shown, the present invention provides a ship heat exchanger control optimization system, including a ship heat exchanger control management database, a heat exchanger control multi-parameter acquisition module, a heat exchanger control multi-physics coupling module, a heat exchanger control optimization module, and a heat exchanger control optimization feedback module.

[0020] The ship heat exchanger control and management database is connected to all other modules. The heat exchanger control multi-parameter acquisition module is connected to the heat exchanger control multi-physics coupling module. The heat exchanger control optimization module is connected to both the heat exchanger control multi-physics coupling module and the heat exchanger control optimization feedback module.

[0021] Ship heat exchanger control and management database: Through big data technology, collect and store standard data for ship heat exchanger control, and receive data generated during the ship heat exchanger control process to build a ship heat exchanger control and management database;

[0022] Heat exchanger control multi-parameter acquisition module: Through sensor technology, it collects data generated during the ship's heat exchange process, obtains and analyzes various parameters for heat exchange control, and transmits them to the heat exchanger control multi-physics coupling module;

[0023] Multiphysics coupling module for heat exchanger control: Through big data analysis technology, a multiphysics coupling model is constructed for the various parameters of heat exchange control obtained by the multi-parameter acquisition module, and then transmitted to the heat exchanger control optimization module;

[0024] This embodiment specifically describes the heat exchanger control multiphysics coupling module, which includes a power system-heat exchanger coupling unit, an air conditioning system-heat exchanger coupling unit, and a cooling system-heat exchanger coupling unit. The power system-heat exchanger coupling unit is used to analyze the power system-heat exchanger coupling parameters, the air conditioning system-heat exchanger coupling unit is used to analyze the air conditioning system-heat exchanger coupling parameters, and the cooling system-heat exchanger coupling unit is used to analyze the cooling system-heat exchanger coupling parameters.

[0025] Heat exchanger control optimization module: Through big data analysis technology, the multi-physics coupling model is analyzed, the control mechanism is triggered based on the anomaly analysis results, the control is optimized through the optimization model, and the control optimization results are transmitted to the heat exchanger control optimization feedback module.

[0026] Heat exchanger control optimization feedback module: It evaluates the results of control optimization through big data analysis technology and feeds back abnormal evaluation results to the administrator terminal for human-computer interaction.

[0027] Please see Figure 2 As shown, a method for optimizing the control of a ship heat exchanger includes: S1: Collecting and storing standard data for ship heat exchanger control using big data technology, and simultaneously receiving data generated during the ship heat exchanger control process to construct a ship heat exchanger control management database; S2: Collecting data generated during the ship heat exchange process using sensor technology to obtain and analyze various parameters for heat exchange control; S3: Constructing multi-physics coupling models for the various parameters of heat exchange control obtained in S2 using big data analysis technology, obtaining coupling models for the power system-heat exchanger, air conditioning system-heat exchanger, and cooling system-heat exchanger; S4: Analyzing the multi-physics coupling models obtained in S3 using big data analysis technology, triggering a control mechanism based on the anomaly analysis results, and simultaneously optimizing the control through the optimization model to obtain the optimized control result; S5: Evaluating the optimized control result using big data analysis technology, and feeding back the anomaly evaluation result to the administrator terminal for human-computer interaction.

[0028] S1: Through big data technology, collect and store standard data for ship heat exchanger control, and simultaneously receive data generated during the ship heat exchanger control process to build a ship heat exchanger control and management database;

[0029] This embodiment requires specific explanation of the standard data for ship heat exchanger control, including but not limited to the design operating status parameters of the heat exchanger in the ship heat exchanger control system, the inlet and outlet temperature ranges of the hot fluid and cooling medium, and the range of heat transfer efficiency that the heat exchanger should achieve under specific operating conditions; control variables required in the ship heat exchanger control process, such as the opening degree of the heat exchanger medium flow regulating valve, the frequency of the cooling pump, and the opening degree of the air conditioning system expansion valve; and data generated in the ship heat exchanger control process, including but not limited to real-time monitoring of the inlet and outlet temperatures of the hot fluid and cooling medium, and the real-time flow rates of the cooling medium and hot fluid.

[0030] S2: Data generated during the ship's heat exchange process is collected using sensor technology to obtain and analyze various parameters for heat exchange control. These parameters include coupling parameters between the power system and the heat exchanger, the air conditioning system and the heat exchanger, and the cooling system and the heat exchanger. The coupling parameters between the power system and the heat exchanger include the output power of the ship's main engine, the amount of heat that the heat exchanger can dissipate under maximum design conditions, and the amount of heat that the heat exchanger can dissipate under maximum design conditions. The coupling parameters between the air conditioning system and the heat exchanger include the heat exchanger outlet temperature, the air conditioning system reference efficiency, the reference temperature at which the cooling efficiency decreases under standard conditions, and the cooling water temperature. The coupling parameters between the cooling system and the heat exchanger include the hot fluid inlet temperature, the hot fluid outlet temperature, the cold fluid inlet temperature, and the cold fluid outlet temperature.

[0031] S3: Using big data analytics, multiphysics coupling models are constructed for the various parameters of heat exchange control obtained in S2, resulting in coupling models for the power system-heat exchanger, the air conditioning system-heat exchanger, and the cooling system-heat exchanger. This includes the following steps:

[0032] S3.1: Power System-Heat Exchanger Coupled Model: First, the output power P of the main engine is measured in real time by a power sensor installed on the ship's main engine. eng Then, the host load P is recorded through sensors and a data acquisition system. eng and heat load Q of heat exchanger HX Historical data was used to fit a linear regression model to obtain the intercept term β0, which represents the base heat load of the heat exchanger when the main engine load is zero. The slope β1, i.e., the coupling coefficient, represents the change in heat load of the heat exchanger when the main engine load increases by 1MW (e.g., a value of 0.85kW / MW). Secondly, through big data analysis technology, the power system-heat exchanger coupling model was obtained: Q HX =β0+β1×P eng +ε, where ε represents the error term; finally, the heat exchanger heat dissipation redundancy index R is obtained. HX R HX =(Q HX,max -Q HX ) / Q HX,max Q HX,max This indicates the amount of heat that the heat exchanger can dissipate under maximum design operating conditions;

[0033] In this embodiment, it should be specifically noted that in a ship's heat exchanger system, the main engine load typically refers to the load of the ship's main propulsion engine. The main propulsion engine is the core equipment providing forward power to the ship, and is usually a diesel engine, gas turbine, or steam turbine. The main engine load represents the current output mechanical power of the main engine, typically measured in megawatts (MW) or kilowatts (kW). The difference between the predicted and actual values ​​of the error term model is called the residual, which is expressed in the linear regression model as ε = Q. HX -(β0+β1×P eng ) obtained; Q HX,max This indicates the maximum heat dissipation capacity of the heat exchanger, that is, the amount of heat that the heat exchanger can dissipate under maximum design conditions (unit: kW or MW).

[0034] This embodiment specifically explains that the ship's heat exchangers (such as coolers, condensers, etc.) are used to manage the heat of the main engine and other equipment to ensure that the system operates within a suitable temperature range; the operation of the main engine generates a large amount of heat, which needs to be dissipated into the environment (such as seawater or air) through the heat exchanger; the cooling system of the main engine is the main source of the heat load of the heat exchanger, so there is a direct relationship between the main engine load and the heat load of the heat exchanger.

[0035] S3.2: Air Conditioning System-Heat Exchanger Coupled Model: First, the heat exchanger outlet temperature T is obtained through sensor technology. HX,out Then, the baseline efficiency η of the air conditioning system is obtained through standard data from the ship's heat exchanger control. AC,0 The reference temperature T for the degradation of cooling efficiency under standard operating conditions base Finally, through big data analysis technology, a coupling model of the air conditioning system and heat exchanger was obtained: η AC The actual cooling efficiency of the air conditioning system is expressed as a decimal, where λ represents the cooling efficiency decay coefficient (e.g., 0.03 / ℃), meaning that for every 1℃ increase in temperature, the cooling efficiency decreases by 0.03. The cooling water temperature T is recorded using sensors and a data acquisition system. water and the cooling efficiency η of the air conditioning system AC Historical data is input into the cooling efficiency attenuation coefficient λ model:

[0036] This embodiment specifically explains how, based on parameters such as cooling water temperature and flow rate, the cooling efficiency of the air conditioning system and the heat dissipation capacity of the heat exchanger are predicted in real time, and the T... HX,out Controlled within the optimal range (e.g., 30℃≤T) HX,out ≤35℃), dynamically adjust the heat exchanger operating parameters (such as cooling water flow rate and temperature), and balance η by increasing the cooling water flow rate. AC And cooling water pump energy consumption.

[0037] S3.3: Cooling System-Heat Exchanger Coupled Model: First, using sensor technology, the inlet temperature T of the hot fluid is obtained at the inlet and outlet of the heat exchanger, respectively. h,in Hot fluid outlet temperature T h,out Cold fluid inlet temperature T c,in and the cold fluid outlet temperature T c,out By using standard data for ship heat exchanger control, the number of heat exchanger zones n is obtained, leading to a cooling system-heat exchanger coupling model. H coup The heat exchanger capacity transfer matching degree is represented by ΔT, where i represents the i-th partition. LM ΔT represents the actual logarithmic mean temperature difference. des ΔT represents the temperature difference under standard operating conditions. LM Including the logarithmic mean temperature difference ΔT between countercurrent hot and cold fluids LM n Logarithmic mean temperature difference ΔT between hot and cold fluids flowing in countercurrent LM b ,

[0038]

[0039] This embodiment requires specific explanation of H. coup By comparing the actual temperature difference ΔT LM Temperature difference ΔT under standard operating conditions des Real-time quantification of the deviation in energy transfer efficiency of heat exchangers; in the field of marine engineering, when the main engine load changes abruptly or the seawater temperature changes drastically, H coup It can detect heat transfer imbalance and trigger control compensation.

[0040] S4: Analyze the multiphysics coupling model obtained in S3 using big data analytics. Trigger a control mechanism based on the anomaly analysis results. Simultaneously, optimize the control by optimizing the model to obtain the optimized result. This includes the following steps:

[0041] S4.1: Within the preset monitoring period of the ship's heat exchanger, firstly, using big data analysis technology, the multiphysics coupling model is analyzed to obtain the heat exchanger heat dissipation redundancy anomaly index AR(R). HX ), Air conditioning system cooling efficiency anomaly index AR(η) AC ) and the heat exchanger capacity transfer mismatch index AR(H) coup ), AR(R HX )=(R HX,0 -R HX ) / R HX,0 R HX and R HX,0 AR(η) represents the heat exchanger heat dissipation redundancy index and the corresponding threshold.AC )=(η AC,0 -η AC ) / η AC,0 η AC,0 η represents the baseline efficiency of the air conditioning system. AC AR(H) represents the actual cooling efficiency of the air conditioning system. coup )=|H coup -H coup,0 | / H coup,0 H coup H represents the heat exchanger's capacity transfer matching degree. coup,0 Indicate the corresponding setting value; then AR(R) HX AR(η) AC ) and AR(H coup Each result is compared with its corresponding threshold. If any abnormal result occurs, a control mechanism is triggered. Abnormal results include AR(R) HX AR(R) is greater than the corresponding threshold. HX ) 0 (e.g., 10%), AR(η) AC ) greater than the corresponding threshold AR(η) AC ) 0 (e.g., 10%) and AR(H) coup ) greater than the corresponding threshold AR(H coup ) 0 (e.g., deviation of ±15%)

[0042] S4.2: Based on the abnormal result triggering control mechanism in S4.1, an optimization model L is constructed using the Lagrangian function for optimization. w1, w2, and w3 represent the corresponding weights, which are dynamically adjusted based on historical data and actual ship navigation conditions. For example, at high speeds, priority is given to ensuring main engine cooling (w1 = 0.7); when berthed in port, priority is given to maintaining air conditioning comfort (w2 = 0.6), etc. j Let (X) represent the constraint function, and g1(X) = T out -T max ≤0,T out T represents the outlet temperature of the heat exchanger. max Representing the maximum permissible temperature, g2(X) = f in -f max ≤0, f in f represents the inlet pressure of the heat exchanger. max This represents the maximum permissible pressure, g3(X) = m c -m max ≤0,m c Indicates cooling water flow rate, m max Indicates the maximum allowed flow rate, γ j Denotes the Lagrange multiplier of the j-th constraint function;

[0043] This embodiment needs to specifically explain that the Lagrangian function is an important tool in the field of mathematical optimization for solving constrained optimization problems. It transforms the originally complex constrained optimization problem into an unconstrained optimization problem by combining the constraints with the objective function. Different constraints correspond to different physical limitations, resulting in different Lagrange multipliers. In the field of marine engineering, the Lagrangian function can be used to balance multiple objectives such as cooling efficiency, energy matching degree, and heat dissipation redundancy, while satisfying constraints such as flow rate and temperature.

[0044] S4.3: Obtain the required control variables from the ship's heat exchanger control management database, and use a sequential quadratic programming algorithm (such as SQP) to iteratively solve the optimization model L obtained in S4.2, maximizing the control variable dataset U. opt U opt =[U1,U2,...,U N ], U N Let U represent the Nth control variable, where N represents the number of control variables. The optimal control parameters are obtained by setting the gradient of the optimization model L to 0. For example, the control variables include the opening degree of the heat exchanger medium flow regulating valve, the cooling pump frequency, and the opening degree of the air conditioning system expansion valve; then, based on U... opt Based on the current state, the calculated adjustment amount is used to generate corresponding control commands, such as U. opt The opening of the heat exchanger medium flow regulating valve should be set to 85%, but it is currently at 70%, so it needs to be increased by 15%.

[0045] This embodiment specifically explains that Sequential Quadratic Programming (SQP) is an iterative algorithm for solving nonlinear constrained optimization problems. It approximates the optimal solution to the original problem by solving a quadratic programming (QP) subproblem in each iteration. The SQP algorithm performs well in handling optimization problems with complex constraints and is therefore widely used in solving for optimal control parameters in control systems.

[0046] S5: Utilizing big data analytics, the optimized control results are evaluated, and anomaly assessments are fed back to the administrator terminal for human-computer interaction. Data monitoring technology is used to monitor the heat exchanger's heat dissipation redundancy anomaly index (AR(R)) in real time. HX ), Air conditioning system cooling efficiency anomaly index AR(η) AC ) and the heat exchanger capacity transfer mismatch index AR(H) coup If abnormal results occur within M consecutive preset control cycles, the calibration feedback mechanism of the optimization model L will be triggered and sent to the administrator terminal.

[0047] This embodiment specifically explains that the abnormal evaluation results are fed back to the administrator terminal in real time, enabling the administrator to understand the system's operating status in a timely manner. Based on the feedback results, the parameters or structure of model L are automatically adjusted and optimized to improve the model's accuracy and adaptability, forming a closed-loop control system of "control-evaluation-feedback-calibration," which allows the control strategy of the ship's heat exchanger to be continuously optimized.

[0048] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0049] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the control of a ship heat exchanger, characterized in that: include: S1: Collect and store standard control data for ship heat exchangers, and simultaneously receive data generated during the control process of ship heat exchangers to build a ship heat exchanger control management database; S2: Data generated during the ship's heat exchange process is collected using sensor technology to obtain and analyze various parameters for heat exchange control. These parameters include: power system-heat exchanger coupling parameters, air conditioning system-heat exchanger coupling parameters, and cooling system-heat exchanger coupling parameters. The power system-heat exchanger coupling parameters include the output power of the ship's main engine, the amount of heat that the heat exchanger can dissipate under maximum design conditions, and the amount of heat that the heat exchanger can dissipate under maximum design conditions. The air conditioning system-heat exchanger coupling parameters include the heat exchanger outlet temperature, the air conditioning system reference efficiency, the reference temperature at which the cooling efficiency decreases under standard conditions, and the cooling water temperature. The cooling system-heat exchanger coupling parameters include the hot fluid inlet temperature, the hot fluid outlet temperature, the cold fluid inlet temperature, and the cold fluid outlet temperature. S3: Construct multiphysics coupling models for the various parameters of heat exchange control obtained from S2, and obtain the power system-heat exchanger coupling model, the air conditioning system-heat exchanger coupling model and the cooling system-heat exchanger coupling model respectively; S4: Analyze the multiphysics coupling model obtained in S3, trigger the control mechanism based on the anomaly analysis results, and simultaneously optimize the control through the optimized model to obtain the optimized result; the specific steps are as follows: S4.1: Within the preset monitoring period of the ship's heat exchanger, the multiphysics coupling model is first analyzed to obtain the heat exchanger heat dissipation redundancy anomaly index AR(R). HX ), Air conditioning system cooling efficiency anomaly index AR(η) AC ) and the heat exchanger capacity transfer mismatch index AR(H) coup Then AR(R) HX AR(η) AC ) and AR(H coup Each result is compared with its corresponding threshold. If any abnormal result occurs, a control mechanism is triggered. Abnormal results include AR(R) HX AR(R) is greater than the corresponding threshold. HX ) 0 AR(η) AC ) greater than the corresponding threshold AR(η) AC ) 0 and AR(H coup ) greater than the corresponding threshold AR(H coup ) 0 ; S4.2: Based on the abnormal result triggering control mechanism in S4.1, an optimization model L is constructed using the Lagrangian function for optimization. w1, w2, and w3 represent the corresponding weights, and g j (X) represents the constraint function, g1(X)=T out -T max ≤0,T out T represents the outlet temperature of the heat exchanger. max This represents the maximum permissible temperature, g2(X) = f in -f max ≤0, f in f represents the inlet pressure of the heat exchanger. max This represents the maximum permissible pressure, g3(X) = m c -m max ≤0,m c Indicates cooling water flow rate, m max Indicates the maximum allowed flow rate, γ j Let R denote the Lagrange multiplier of the j-th constraint function. HX η is the heat dissipation redundancy index of the heat exchanger. AC H represents the actual cooling efficiency of the air conditioning system. coup To ensure the matching degree of heat exchanger capacity; S5: Evaluate the results of the control optimization and feed back any abnormal evaluation results to the administrator terminal for human-computer interaction.

2. The method for optimizing the control of a ship heat exchanger according to claim 1, characterized in that: The power system-heat exchanger coupling model is obtained in S3: First, the output power P of the main engine is measured in real time by a power sensor installed on the main engine of the ship. eng Then, the host load P is recorded through sensors and a data acquisition system. eng and heat load Q of heat exchanger HX Historical data were used to fit a linear regression model to obtain the intercept term β0, which represents the base heat load of the heat exchanger when the main unit load is zero, and the slope β1, which is the coupling coefficient; then, a power system-heat exchanger coupling model was constructed: Q HX =β0+β1×P eng +ε, where ε represents the error term; finally, the heat exchanger heat dissipation redundancy index R is obtained. HX R HX =(Q HX,max -Q HX ) / Q HX,max Q HX,max This indicates the amount of heat that the heat exchanger can dissipate under maximum design operating conditions.

3. The method for optimizing the control of a ship heat exchanger according to claim 1, characterized in that: The air conditioning system-heat exchanger coupling model is obtained in S3: First, the heat exchanger outlet temperature T is obtained through sensor technology. HX,out Then, the baseline efficiency η of the air conditioning system is obtained through standard data from the ship's heat exchanger control. AC,0 The reference temperature T for the degradation of cooling efficiency under standard operating conditions base Finally, the coupled model of the air conditioning system and heat exchanger was obtained: η AC The actual cooling efficiency of the air conditioning system is expressed as a decimal, with λ representing the cooling efficiency attenuation coefficient. The cooling water temperature T is recorded using sensors and a data acquisition system. water and the cooling efficiency η of the air conditioning system AC Historical data is input into the cooling efficiency attenuation coefficient λ model: .

4. The method for optimizing the control of a ship heat exchanger according to claim 1, characterized in that: The cooling system-heat exchanger coupling model is obtained in S3: First, the inlet temperature T of the hot fluid is obtained at the inlet and outlet of the heat exchanger using sensor technology. h,in Hot fluid outlet temperature T h,out Cold fluid inlet temperature T c,in and the cold fluid outlet temperature T c,out By using standard data for ship heat exchanger control, the number of heat exchanger zones n is obtained, leading to a cooling system-heat exchanger coupling model. H coup The heat exchanger capacity transfer matching degree is represented by ΔT, where i represents the i-th partition. LM ΔT represents the actual logarithmic mean temperature difference. des ΔT represents the temperature difference under standard operating conditions. LM Including the logarithmic mean temperature difference ΔT between countercurrent hot and cold fluids LM n Logarithmic mean temperature difference ΔT between hot and cold fluids flowing in countercurrent LM b .

5. The method for optimizing the control of a ship heat exchanger according to claim 1, characterized in that: The control optimization in S4 also includes: S4.3: Obtain the required control variables from the ship's heat exchanger control and management database, and use a sequential quadratic programming algorithm to iteratively solve the optimization model L obtained in S4.2, thus obtaining the control variable dataset U that maximizes L. opt , U N This represents the Nth control variable, where N represents the number of control variables. The optimal control parameters are obtained by setting the gradient of the optimization model L to 0; then, based on U... opt Based on the current state, calculate the adjustment amount and generate the corresponding control command.

6. The method for optimizing the control of a ship heat exchanger according to claim 1, characterized in that: S5 includes: real-time monitoring of the heat exchanger heat dissipation redundancy anomaly index AR(R) through data monitoring technology. HX ), Air conditioning system cooling efficiency anomaly index AR(η) AC ) and the heat exchanger capacity transfer mismatch index AR(H) coup If abnormal results occur within M consecutive preset control cycles, the calibration feedback mechanism of the optimization model L will be triggered and sent to the administrator terminal.

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