Spray cooling control system and control method based on model predictive control algorithm

Abstract

The invention relates to the technical field of spray cooling, in particular to a spray cooling control system and method based on a model predictive control algorithm. The system comprises a spraying device, a sensor, a controller and a calculation unit, the spraying device is used for spraying out the cooling liquid after atomization; the sensor is used for detecting temperature or humidity or speed and the like in the air; the controller is used for controlling the spraying device, and the spraying device is controlled to meet different environmental conditions and load requirements; the calculation unit is used for calculating the cooling requirement. According to the spray cooling control system and control method based on the model predictive control algorithm, the MPC algorithm is adopted, and compared with a traditional PID control method, better control performance and adaptability are achieved; the MPC algorithm can optimize a control strategy in real time, and spray cooling parameters are adjusted by predicting a system model so as to ensure that the system can quickly respond and accurately control the temperature under different working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of spray cooling, and in particular to a spray cooling control system and a control method based on a model predictive control algorithm. Background Art

[0002] Spray cooling technology is a common method for temperature control in hot environments. Existing spray cooling systems are usually based on simple timing or temperature feedback control, which have some obvious disadvantages in practical applications;

[0003] Traditional spray cooling systems often fail to accurately control ambient temperature because they lack flexibility and responsiveness to environmental changes, resulting in excessive or insufficient spraying, which wastes resources or fails to achieve the desired cooling effect;

[0004] Traditional methods usually fail to take into account the complex interrelationships between multiple influencing factors, such as humidity, wind speed, and external temperature changes, resulting in instability of the control system and inconsistent performance under different environmental conditions.

[0005] Existing technical solutions often rely on static preset parameters and cannot adapt to environmental changes or real-time performance requirements. This makes traditional spray cooling systems perform poorly when dealing with dynamic environments and demands.

[0006] To this end, we designed a spray cooling control system and control method based on a model predictive control algorithm to provide another technical solution to the above technical problems. Summary of the invention

[0007] Based on this, it is necessary to provide a spray cooling control system and control method based on a model predictive control algorithm to solve the technical problems raised in the above background technology.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0009] A spray cooling control system and control method based on a model predictive control algorithm, the system comprising a spray device, a sensor, a controller and a computing unit;

[0010] The spray device is used to spray the cooling liquid after atomization;

[0011] The sensor is used to detect the temperature, humidity or speed in the air;

[0012] The controller is used to control the spray device, and the spray device can be controlled to cope with different environmental conditions and load requirements;

[0013] The calculation unit is used to calculate the demand for cooling.

[0014] As a preferred implementation of the spray cooling control system and control method based on the model predictive control algorithm provided by the present invention, the calculation unit includes a spray cooling mathematical model, which is used to model the spray cooling process through environmental conditions, liquid injection characteristics, and heat transfer characteristics.

[0015] As a preferred implementation of the spray cooling control system and control method based on the model predictive control algorithm provided by the present invention, the spray cooling mathematical model is used to define the steam mass fraction on the droplet surface and select the mass transfer number of droplet evaporation in steady-state equilibrium.

[0016] As a preferred implementation of the spray cooling control system and control method based on the model predictive control algorithm provided by the present invention, the controller is an MPC controller, which uses a spray cooling mathematical model for prediction, and adjusts the control strategy in real time according to the prediction results to achieve the best temperature reduction effect.

[0017] It can be seen without a doubt that the above-mentioned technical solution of the present application can definitely solve the technical problem to be solved by the present application.

[0018] At the same time, through the above technical solutions, the present invention has at least the following beneficial effects:

[0019] The present invention provides a spray cooling control system and control method based on a model predictive control algorithm, which adopts an MPC algorithm and has better control performance and adaptability than the traditional PID control method; the MPC algorithm can optimize the control strategy in real time and adjust the spray cooling parameters by predicting the system model to ensure that the system can respond quickly and accurately control the temperature under different working conditions;

[0020] The established mathematical model takes into account multiple key factors, such as ambient temperature, humidity, wind speed, etc., which have a significant impact on the cooling effect; it can more comprehensively analyze and optimize the cooling process to meet the needs of different application scenarios;

[0021] The design and implementation are relatively simple, and it is easy to maintain and operate. It can not only be widely used in meteorology, agriculture, construction and other fields, but also has the feasibility and economy of engineering implementation. It has excellent control performance, comprehensive consideration of multiple factors, efficient energy utilization and simple and easy to use characteristics, which makes it have broad application prospects and technical value in the field of spray cooling. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0023] Figure 1 is a control flow chart of the present invention;

[0024] Figure 2 is a flow chart of the control algorithm of the present invention;

[0025] Figure 3 It is a simulation effect diagram of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0028] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions in the embodiments may be combined with each other.

[0029] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0030] Reference Figure 1-Figure 3 , a spray cooling control system and control method based on a model predictive control algorithm, the system includes a spray device, a sensor, a controller and a computing unit;

[0031] The spray device is used to spray the cooling liquid after atomization;

[0032] The sensor is used to detect the temperature, humidity or speed in the air;

[0033] The controller is used to control the spray device, and the spray device can be controlled to cope with different environmental conditions and load requirements;

[0034] The calculation unit is used to calculate the demand for cooling.

[0035] (1) Mathematical model of spray cooling

[0036] Through in-depth modeling of the spray cooling process, multiple factors including environmental conditions, liquid injection characteristics, heat transfer characteristics, etc. are comprehensively considered. This mathematical model not only takes into account the nonlinear characteristics of the system, but also fully considers the uncertainty factors in actual operation, such as environmental changes and non-uniformity of liquid injection. The following is the detailed derivation process of the spray cooling mathematical model:

[0037] The motion of the droplet obeys the Basset-Boussinesq-Oseen equation. The heat energy exchange between the phases occurs only through convection heat transfer, ignoring other non-steady-state resistance effects. The control equation of a single droplet can be expressed as:

[0038]

[0039]

[0040] Where: X i is the instantaneous position of the droplet; v i is the velocity of the droplet; u i is the carrier gas velocity in the room.

[0041] For the constant droplet temperature model, the vapor mass fraction on the droplet surface is defined, and the mass transfer number for droplet evaporation at steady-state equilibrium is selected:

[0042]

[0043] Where: Y s is the steam mass fraction; Y G is the mass fraction of free steam away from the droplet surface.

[0044] The steam mass fraction under droplet equilibrium state can be expressed as:

[0045]

[0046] According to the Clausius-Clapeyron saturation law, the molar fraction on the droplet surface is:

[0047]

[0048] Where: P sat is the pressure at the droplet position; P G is the gas phase pressure away from the droplet surface; P atm is atmospheric pressure; T B is the normal boiling temperature of the liquid phase; T d is the droplet temperature.

[0049] The mass and temperature control equations of the droplet are expressed as:

[0050]

[0051]

[0052] Where: G Gas is thermal conductivity; C L is the liquid phase heat capacity; is the time derivative of the droplet mass; T G is the indoor gas temperature; H M is the mass transfer potential energy.

[0053] By transforming the single droplet evaporation model, the correlation between the air temperature Ta after spraying and the water consumption Q is obtained as follows:

[0054]

[0055]

[0056] Where: is the derivative of water consumption with respect to time.

[0057] (2) Design of MPC controller

[0058] Spray cooling technology is diverse and complex, and its mathematical model is complex. It is difficult for general control methods to accurately control the spray cooling system. By introducing the model predictive control (MPC) algorithm, accurate control of the spray cooling process can be achieved. MPC is a technology that predicts the future response of the system based on the historical information and future inputs of the controlled object and performs corresponding control. The MPC controller can use the spray cooling mathematical model for prediction and adjust the control strategy in real time according to the prediction results to achieve the best temperature reduction effect. This enables the system to cope with different environmental conditions and load requirements, and improves the robustness and performance of the control system. The following is the working principle and algorithm core of the MPC controller:

[0059] Based on the above-mentioned spray cooling correlation, the state space equation of the system model is as follows:

[0060]

[0061] Where: x(t)∈R n is the system state variable, indicating the air temperature after spraying; u(t)∈R m is the input of the system, representing the water consumption; is the system matrix; is the control matrix.

[0062] The established state space model has a step size of seconds. In order to reduce the complexity of calculation, the model is discretized. The discretized model is shown in the following equation (11).

[0063]

[0064] Where: are the discretized system matrix and control matrix respectively.

[0065] The predicted output of the spray cooling control system in the future k steps can be calculated by the prediction equation. The prediction time domain of the MPC controller is Np, the control time domain is Nc, the current moment is k, and the prediction equation is:

[0066] X(k)=Γx(k)+ΦU(k) (12)

[0067] The system error can be calculated by subtracting the obtained predicted state sequence from the reference input sequence. The error sequence is:

[0068] E(k)=R(k)-X(k) (13)

[0069] Where: Enter the sequence for the reference.

[0070] The model predictive control solution based on the spray cooling model can solve the optimal planning target. Taking the system state, system input and system error value as the objective function, the discrete linear system model predictive control problem with constraints can be described as:

[0071]

[0072]

[0073] Where: Q = diag (q 1 ,q 2 ,…,q n ) is the weight matrix of the state variables; W = diag(w 1 ,w 2 ,…,w n ) is the input weight matrix; F = diag (f 1 ,f 2 ,…,f n ) is the weighting matrix of the system error; u max The maximum water consumption.

[0074] Substituting (12) into (14), we have:

[0075] J(k)=x T (k)Γ T QΓx(k)+2x T (k)Γ T QΦU(k)+U T (k)(Φ T QΦ+W)U(k)+E(k) T FE(k) (16)

[0077] Through the operation of the column matrix, the objective function is converted into a quadratic form to find the extreme value, and the The obtained extreme value solution, that is, the optimal control sequence is:

[0078] U * (k)=-(Φ T QΦ+W) -1 Φ T QΓx(k) (17)

[0079] According to the basic principle of MPC, the first element of the open-loop optimal predictive control sequence is applied to the system, namely:

[0080] u * (k)=-[I p×p ,0,…,0](Φ T QΦ+W) -1 Φ T QΓx(k)=-K MPC x(k) (18)

[0081] Where K MPC To predict the control gain, the expression is:

[0082] K MPC =[I p×p ,0,…,0](Φ T QΦ+W) -1 Φ T QΓ (19)

[0083] Since Q and W are time-independent constants, K MPC Can be calculated offline.

[0084] According to the MPC rolling time domain, the mechanism of repetition is as follows: Figure 2 shown.

[0085] (3) Simulation analysis

[0086] Figure 3 To optimize the algorithm simulation effect diagram, the effectiveness of the technical solution of this system is verified through simulation analysis. The simulation results show that compared with the traditional spray cooling control method, this system can achieve the required temperature reduction more quickly and accurately, and has better stability and adaptability.

[0087] This system technology solution has significant innovation and practicality in the field of spray cooling control. The model-based predictive control algorithm it adopts not only improves the control accuracy, but also improves the robustness and adaptability of the system, making it have a wide range of potential uses in a variety of application scenarios. In addition, the design and implementation of this technical solution comply with the relevant provisions of the Patent Law, providing solid technical support for subsequent patent applications.

[0088] In other embodiments, in addition to the MPC controller, there are other control strategies, such as proportional integral derivative (PID) control and fuzzy logic control, etc. Although these strategies may be effective in some cases, they are generally unable to adapt to complex nonlinear environments and cannot provide similar advanced temperature control performance as in this system.

[0089] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A spray cooling control system and control method based on model predictive control algorithm, characterized in that: The system includes a spray device, a sensor, a controller and a computing unit; The spray device is used to spray the cooling liquid after atomization; The sensor is used to detect the temperature, humidity or speed in the air; The controller is used to control the spray device, and the spray device can be controlled to cope with different environmental conditions and load requirements; The calculation unit is used to calculate the demand for cooling.

2. A spray cooling control system and control method based on model predictive control algorithm according to claim 1, characterized in that: The calculation unit includes a spray cooling mathematical model, which is used to model the spray cooling process through environmental conditions, liquid injection characteristics, and heat transfer characteristics.

3. A spray cooling control system and control method based on model predictive control algorithm according to claim 1, characterized in that: The spray cooling mathematical model is used to define the mass fraction of vapor on the droplet surface and select the mass transfer number of droplet evaporation in steady-state equilibrium.

4. A spray cooling control system and control method based on model predictive control algorithm according to claim 3, characterized in that: The controller is an MPC controller, which uses a spray cooling mathematical model to make predictions and adjusts the control strategy in real time according to the prediction results to achieve the best temperature reduction effect.

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