An electric dust collector cluster energy-saving optimization operation control method and control system based on distributed model predictive control

Abstract

The present application relates to a kind of based on distributed model predictive control's electric precipitator cluster energy-saving optimization operation control method and control system, based on electric precipitator cluster establishment distributed linear dynamic model, describe the dynamic response relationship between the dust concentration in each electric chamber in electric precipitator cluster and high voltage power supply secondary voltage;According to the outlet concentration information of electric precipitator cluster, design state observer, estimate the dust concentration in each electric chamber;Based on the estimated state of distributed linear dynamic model and state observer, design the controller based on distributed model predictive control, obtain optimal control strategy;Based on method implementation control system.The present application is convenient for control implementation, get rid of the dependence on historical data;Real-time estimation of unobservable electric chamber dust concentration can be implemented in electric precipitator cluster engineering application;Method ensures accurate regulation for each electric chamber, realizes multi-field collaborative dust removal, efficiently and reasonably allocates energy, saves electric precipitator cluster operating cost.

Description

Technical Field

[0001] This invention relates to the technical fields of magnetic or electrostatic separation of solid materials from fluids and high-voltage electric field separation, and particularly to an energy-saving optimized operation control method and control system for electrostatic precipitator clusters based on distributed model predictive control in the field of industrial flue gas dust removal. Background Technology

[0002] Electrostatic precipitators (ESPs) are among the most widely used high-efficiency dust removal devices in the industrial field. With their significant advantages such as high dust removal efficiency, large flue gas handling capacity, and high temperature resistance, they are widely used in dust control in industries such as power, metallurgy, and chemicals, and are core equipment for ensuring that industrial production meets environmental emission standards. However, ESPs involve a complex industrial process characterized by strong nonlinearity, multiple variables, strong coupling, and operational constraints. Traditional control methods, such as PID control, struggle to effectively handle the optimization problem of such a multivariable coupled system, failing to achieve a dynamic optimal balance between ensuring emission compliance and reducing energy consumption.

[0003] To address the aforementioned challenges, distributed model predictive control (DMCC) has emerged as a highly promising technological option. Through online rolling optimization, DMCC can explicitly handle system constraints and optimization objectives. Furthermore, compared to centralized model predictive control, which is computationally intensive and difficult to implement in engineering, DMCC is more suitable for electrostatic precipitator cluster systems with a distributed physical structure consisting of multiple compartments.

[0004] Treating each compartment in an electrostatic precipitator cluster as a subsystem, the core challenge in designing distributed model predictive control (DMCC) lies in effectively coordinating these subsystems to ensure optimal overall system performance and stability under limited information exchange. Current technologies have not effectively addressed this challenge because the primary prerequisite for implementing DMCC is establishing a distributed model that accurately reflects system dynamics and is suitable for distributed control. However, existing data-driven modeling methods, due to their "black box" nature, data dependency, and poor adaptability to operating conditions, struggle to meet the predictive optimization requirements of DMCC for electrostatic precipitator clusters under complex operating conditions. Therefore, establishing a distributed mechanistic model that reflects the operation of an electrostatic precipitator cluster system remains a core issue to be resolved.

[0005] Another key obstacle to implementing distributed model predictive control (DMCC) for electrostatic precipitator (ESP) clusters is the unmeasurable dust concentration within each chamber. ESP cluster DMCC relies on the dust concentration status within each chamber as a feedback signal; however, in industrial settings, only the total outlet dust concentration of the ESP cluster can typically be measured, while the dust concentration status within each chamber cannot be directly measured online. This lack of state information makes state-feedback-based distributed model predictive control impossible. Therefore, how to estimate the dust concentration status within each chamber using the measurable total output concentration of the ESP cluster is one of the core challenges in implementing distributed model predictive control for ESP clusters. Summary of the Invention

[0006] This invention solves the problems existing in the prior art and provides an energy-saving optimization operation control method and control system for electrostatic precipitator clusters based on distributed model predictive control.

[0007] The technical solution adopted in this invention is an energy-saving optimization operation control method for an electrostatic precipitator cluster based on distributed model predictive control. Based on the electrostatic precipitator cluster, a distributed linear dynamic model is established to describe the dynamic response relationship between the dust concentration inside each electrostatic precipitator chamber and the secondary voltage of the high-voltage power supply. A state observer is designed based on the outlet concentration information of the electrostatic precipitator cluster to estimate the dust concentration inside each electrostatic precipitator chamber.

[0008] Based on the distributed linear dynamic model and the estimated state from the state observer, a controller based on distributed model predictive control is designed to obtain the optimal control strategy.

[0009] Preferably, based on the principle of mass conservation, a nonlinear dynamic model describing any electric cell (p,i) is obtained.

[0010]

[0011] in, Let be the dust concentration in the electrical chamber (p,i). Let P be the flue gas flow rate. Let be the volume of the electric cell (p,i). Concentration at the inlet of each channel, This represents the total area of ​​the dust collection plates inside the electrical chamber. It is the proportionality coefficient acting on the voltage. It is the secondary voltage of the high-voltage power supply, and q is the voltage exponent. This represents the dust concentration in the next higher-level electrical chamber, where t represents the time variable. , m is the total number of channels, and n is the total number of electric field levels.

[0012] Preferably, the above nonlinear dynamic model is linearized to obtain a linear dynamic model;

[0013] Let the steady-state operating point of the electrostatic precipitator be The model is linearized near the steady-state operating point, and state variables are defined. Input variables The continuous-time linear dynamic model of the electrostatic precipitator was obtained.

[0014]

[0015] in, Let be the neighbor set of the electrical cell (p,i). The neighbor set contains only one element, and the corresponding matrix in the state-space equation is denoted by . , , ;

[0016] The continuous-time linear dynamic model of the electrostatic precipitator is discretized to obtain the discrete-time linear dynamic model of the electrostatic precipitator at time k.

[0017] Preferably, the state observer estimates the concentration state of each cell in reverse based on the total concentration output information.

[0018] Preferably, the state observer satisfies the following:

[0019]

[0020] Where A and B are block matrices, For system output, satisfy , , The total flue gas flow rate, Let be the flue gas flow rate of channel p; For state estimation, This represents the actual state. Let k be the total control vector, and k be the time step.

[0021] Preferably, the local optimization problem of the distributed model predictive control is,

[0022]

[0023] Where (s|k) represents the prediction at time k for time k+s. For the stage cost function, For the terminal cost function, For the set of state constraints, To control the set of constraints, It is the terminal constraint set. Let N be the sequence of assumed states of the neighbors, and N be the prediction time domain.

[0024] Preferably, the stage cost function satisfies,

[0025]

[0026] The terminal cost function satisfies,

[0027]

[0028] in, , It is a symmetric positive definite weighted matrix. It is a symmetric positive definite weighted matrix. Let be the dust concentration in the electrical chamber (p,i). Let be the secondary voltage of the cell (p,i).

[0029] Preferably, The state constraints are satisfied.

[0030]

[0031] The control constraints are satisfied.

[0032]

[0033] in, This is the lower limit of the export concentration deviation. This represents the upper limit of the export concentration deviation. This is the lower limit of voltage deviation. This represents the upper limit of voltage deviation.

[0034] Preferably, based on the estimated state of each electrical cell, a non-iterative distributed model predictive control algorithm based on inter-neighbor communication is executed to solve each sub-optimization problem and obtain the corresponding optimal control sequence. And the optimal predicted state sequence; broadcast the optimal predicted state sequence, and the optimal control sequence. The first element acts on the electrostatic precipitator cluster, updates the electrostatic precipitator status, measures the current output of the electrostatic precipitator cluster, and then repeats the process in the next time step.

[0035] An energy-saving optimized operation control system for an electrostatic precipitator cluster based on distributed model predictive control, the control system comprising a processor and a memory:

[0036] The memory is used to store program code and transmit the program code to the processor;

[0037] The processor is used to execute the energy-saving optimized operation control method for electrostatic precipitator clusters based on distributed model predictive control according to the instructions in the program code.

[0038] This invention relates to an energy-saving optimization operation control method and control system for an electrostatic precipitator cluster based on distributed model predictive control. Based on the electrostatic precipitator cluster, a distributed linear dynamic model is established to describe the dynamic response relationship between the dust concentration inside each chamber and the secondary voltage of the high-voltage power supply. A state observer is designed based on the outlet concentration information of the electrostatic precipitator cluster to estimate the dust concentration inside each chamber. Based on the distributed linear dynamic model and the estimated state of the state observer, a controller based on distributed model predictive control is designed to obtain the optimal control strategy. The control system is implemented based on this method.

[0039] The beneficial effects of this invention are as follows:

[0040] (1) A mechanism model is established based on the dust removal principle, which has a clear physical meaning and is easier to control and implement, while also getting rid of the dependence on historical data;

[0041] (2) The introduction of the observer can estimate the unmeasurable dust concentration in the electrostatic precipitator in real time, enabling the distributed model predictive control method to be implemented in the engineering application of electrostatic precipitator clusters;

[0042] (3) The use of distributed model predictive control method ensures precise control of each electric chamber, realizes multi-field collaborative dust removal, thereby efficiently and rationally allocates energy and saves the operating cost of the electrostatic precipitator cluster. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the electrostatic precipitator cluster of the present invention;

[0044] Figure 2 This is a flowchart of the method of the present invention;

[0045] Figure 3 This is a flowchart of the observer-based distributed model predictive control algorithm in this invention. Detailed Implementation

[0046] The present invention will be further described in detail below with reference to embodiments, but the scope of protection of the present invention is not limited thereto.

[0047] This invention relates to an energy-saving optimized operation control method for an electrostatic precipitator cluster based on distributed model predictive control. In this embodiment, the electrostatic precipitator cluster is first defined as consisting of m channels and n levels of electric fields, such as... Figure 1As shown, there are m rows of channels parallel to the airflow direction, allowing the flue gas to pass through various electric fields. There are n columns of electric fields perpendicular to the airflow direction, with each column corresponding to a first-level electric field and undertaking the corresponding dust removal task. The entire electrostatic precipitator cluster consists of m rows and n columns of electric chambers, each of which is uniquely determined by a combination of a channel and a first-level electric field. In the electrostatic precipitator, the electric chambers within the same channel are connected in series, while the electric chambers under the same electric field level are connected in parallel.

[0048] In this invention, based on the principle of mass conservation, a nonlinear dynamic response relationship between the concentration inside each electrostatic precipitator chamber and the secondary voltage of the high-voltage power supply is established. This relationship is then linearized near the steady-state operating point to obtain a distributed linear dynamic model that facilitates distributed model predictive control. An internal concentration state observer is designed, utilizing the online measurable total outlet concentration of the cluster to inversely calculate and estimate the dust concentration inside each chamber in real time. This solves the problem of distributed model predictive control of electrostatic precipitator clusters being unable to be implemented due to the lack of state feedback. Finally, based on the dynamic mechanism model and the estimated state of the observer, precise control of the internal concentration of each chamber is achieved, and the controllers of each chamber are effectively coordinated to achieve a dynamic optimal balance between emission compliance and energy saving in the electrostatic precipitator cluster. Figure 2 As shown, the method includes the following steps:

[0049] (1) Based on the electrostatic precipitator cluster, a distributed linear dynamic model is established to describe the dynamic response relationship between the dust concentration inside each electrostatic precipitator chamber and the secondary voltage of the high-voltage power supply in the electrostatic precipitator cluster.

[0050] (2) Design a state observer based on the outlet concentration information of the electrostatic precipitator cluster to estimate the dust concentration inside each electrostatic precipitator chamber;

[0051] (3) Based on the estimated state of the distributed linear dynamic model and the state observer, design a controller based on distributed model predictive control to obtain the optimal control strategy.

[0052] The method is described in detail below with reference to specific embodiments, where m=3 and n=2, representing a cluster of electrostatic precipitators consisting of 3 channels and 2 levels of electric fields. Based on the dust removal principle, a dynamic mechanism model of dust concentration in each electrostatic precipitator with respect to the secondary voltage is established. Any electrostatic precipitator is denoted as (p, i). , .

[0053] (1) Based on the electrostatic precipitator cluster, a distributed linear dynamic model is established to describe the dynamic response relationship between the dust concentration inside each electrostatic precipitator chamber and the secondary voltage of the high-voltage power supply in the electrostatic precipitator cluster.

[0054] Based on the principle of mass conservation, a nonlinear dynamic model describing any electric cell (p,i) is obtained.

[0055] (1)

[0056] in, Let be the dust concentration in the electrical chamber (p,i). Let P be the flue gas flow rate. Let be the volume of the electric cell (p,i). Concentration at the inlet of each channel, This represents the total area of ​​the dust collection plates inside the electrical chamber. It is the proportionality coefficient acting on the voltage. It is the secondary voltage of the high-voltage power supply, and q is the voltage exponent. This represents the dust concentration in the next higher-level electrical chamber, where t represents the time variable. , m is the total number of channels, and n is the total number of electric field levels; here, esp refers to the electrostatic precipitator (ESP), which is distinct from the voltage V.

[0057] The above nonlinear dynamic model is linearized to obtain a linear dynamic model that is easy to implement control.

[0058] Let the steady-state operating point of the electrostatic precipitator be The model is linearized near the steady-state operating point, and state variables are defined. Input variables The continuous-time linear dynamic model of the electrostatic precipitator was obtained.

[0059] (2)

[0060] in, Let be the set of neighbors (electric rooms) of electric room (p,i). Since the dynamics of each electric room are only affected by its immediate upstream electric room, the neighbor set contains only one element, and the corresponding matrix in the state space equation is . , , The subscript 'c' here refers to continuous time.

[0061] The continuous-time linear dynamic model of the electrostatic precipitator is discretized to obtain the discrete-time linear dynamic model of the electrostatic precipitator at time k.

[0062] (3)

[0063] (2) Design a state observer based on the outlet concentration information of the electrostatic precipitator cluster to estimate the dust concentration inside each electrostatic precipitator chamber;

[0064] In this embodiment, the state variables of the 3×2 electric cells are stacked into a column vector in the order of channel first, then electric field, and let... ,in, These represent the dust concentrations in electrical chambers (1,1), (2,1), (3,1), (1,2), (2,2), and (3,2), respectively, corresponding to the total control vector. ,in, Let represent the secondary voltages of cells (1,1), (2,1), (3,1), (1,2), (2,2), and (3,2), respectively. Let T denote the vector transpose. This yields the dynamic model of the overall system.

[0065] (4)

[0066] in, , ,

[0067] ( ), ( ), ( );

[0068] A Luenberger observer is designed to estimate the dust concentration in the internal, unmeasurable electric chambers. The observer gain is calculated using a pole placement method. The state observer back-estimates the concentration state of each electric chamber based on the total concentration output information. After the flue gas exits all channels of the final-stage electric field, it mixes at the outlet manifold, where the dust emission concentration is measured. The observation output is only related to the outlet concentration of the electric chambers in the final-stage electric field, and the output equation is denoted as follows.

[0069] (5)

[0070] in, , The total flue gas flow rate, Let be the flue gas flow rate of channel p;

[0071] Based on this, a Romberg observer is designed to estimate the unmeasurable internal state, i.e., the outlet concentration of each electric cell, so that the state estimation... Converging to the true state The state observer satisfies,

[0072] (6)

[0073] Where A and B are block matrices, For system output, satisfy , , The total flue gas flow rate, Let be the flue gas flow rate of channel p; For state estimation, This represents the actual state. Let k be the total control vector, and k be the time point.

[0074] By appropriately designing the gain L using pole placement, the estimation error can be reduced. It converges quickly to 0.

[0075] (3) Based on the estimated state of the distributed linear dynamic model and the state observer, design a controller based on distributed model predictive control to obtain the optimal control strategy;

[0076] In this invention, based on a linear dynamic model and a state observer, a distributed model predictive controller based on the observer is further designed to achieve precise control of each electrical compartment, so as to effectively coordinate each subsystem, ensure the optimal performance and stability of the overall system, and achieve a dynamic optimal balance between emission compliance and energy consumption reduction.

[0077] First, based on the stringent requirements of distributed model predictive control for online optimization problems, the stage cost function is defined as a quadratic form.

[0078] (7)

[0079] in, , It is a symmetric positive definite weighted matrix;

[0080] To ensure the stability of the closed-loop system and improve control performance, a terminal cost function is introduced, which is designed as follows:

[0081] (8)

[0082] in, It is a symmetric positive definite weighted matrix;

[0083] According to the dust removal efficiency requirements, each electric field stage must undertake a corresponding dust removal task; that is, the dust concentration in each electric field stage should not deviate too much from the expected steady-state operating point concentration. Furthermore, for safety and energy conservation considerations, the discharge voltage of the high-voltage power supply should not deviate too much from the steady-state operating voltage. Therefore, the constraints on the state and input are as follows:

[0084] (9)

[0085] (10)

[0086] in, This is the lower limit of the export concentration deviation. This represents the upper limit of the export concentration deviation. This is the lower limit of voltage deviation. This is the upper limit of voltage deviation;

[0087] Therefore, the local optimization problem of the electric cell (p,i) is defined as follows:

[0088] (11)

[0089] Where (s|k) represents the prediction at time k for time k+s. It is the set of state constraints corresponding to inequality (9). It is the set of control constraints corresponding to inequality (10). It is the terminal constraint set. It is a sequence of hypothetical states of the neighbors, constructed in the form of,

[0090] (12)

[0091] N represents the prediction time domain.

[0092] Based on the estimated states of each electrical cell, a non-iterative distributed model predictive control algorithm based on inter-neighbor communication is executed to solve each sub-optimization problem and obtain the corresponding optimal control sequence. And the optimal predicted state sequence; broadcast the optimal predicted state sequence, and the optimal control sequence. The first element acts on the electrostatic precipitator cluster, updates the electrostatic precipitator status, measures the current output of the electrostatic precipitator cluster, and then repeats the process in the next time step.

[0093] Specifically, based on the established dynamic mechanism model and state observer, the distributed model predictive control algorithm is executed online to solve the optimization problem (11); first, offline initialization settings are performed, including setting the prediction time domain and constructing the initial state sequence, etc., and then at time k, the measured observation output is used. Run the Luneburger observer to obtain the estimated state The local optimization problem is solved based on neighbor information and estimated states. Finally, a hypothesis sequence is constructed and broadcast based on the optimal prediction sequence obtained from the solution, which controls the first component of the sequence. Applying this to the electrostatic precipitator cluster, let k = k + 1, and then proceed to the next time step to repeat the process.

[0094] This invention also relates to an energy-saving optimized operation control system for an electrostatic precipitator cluster based on distributed model predictive control, the control system comprising a processor and a memory:

[0095] The memory is used to store program code and transmit the program code to the processor;

[0096] The processor is used to execute the energy-saving optimized operation control method for electrostatic precipitator clusters based on distributed model predictive control according to the instructions in the program code.

[0097] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0099] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0101] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0102] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for energy-saving optimized operation control of electrostatic precipitator clusters based on distributed model predictive control, characterized in that: Based on the electrostatic precipitator cluster, a distributed linear dynamic model is established to describe the dynamic response relationship between the dust concentration inside each electrostatic precipitator chamber and the secondary voltage of the high-voltage power supply. A state observer is designed based on the outlet concentration information of the electrostatic precipitator cluster to estimate the dust concentration inside each electrostatic precipitator chamber. Based on the distributed linear dynamic model and the estimated state from the state observer, a controller based on distributed model predictive control is designed to obtain the optimal control strategy.

2. The method for energy-saving optimized operation control of an electrostatic precipitator cluster based on distributed model predictive control according to claim 1, characterized in that: Based on the principle of mass conservation, a nonlinear dynamic model describing any electric cell (p,i) is obtained. ， in, Let be the dust concentration in the electrical chamber (p,i). Let P be the flue gas flow rate. Let be the volume of the electric cell (p,i). Concentration at the inlet of each channel, This represents the total area of ​​the dust collection plates inside the electrical chamber. It is the proportionality coefficient acting on the voltage. It is the secondary voltage of the high-voltage power supply, and q is the voltage exponent. This represents the dust concentration in the next higher-level electrical chamber, where t represents the time variable. , m is the total number of channels, and n is the total number of electric field levels.

3. The method for energy-saving optimized operation control of an electrostatic precipitator cluster based on distributed model predictive control according to claim 2, characterized in that: The above nonlinear dynamic model is linearized to obtain a linear dynamic model. Let the steady-state operating point of the electrostatic precipitator be The model is linearized near the steady-state operating point, and state variables are defined. Input variables The continuous-time linear dynamic model of the electrostatic precipitator was obtained. ， in, Let be the neighbor set of the electrical cell (p,i). The neighbor set contains only one element, and the corresponding matrix in the state-space equation is denoted by . , , ; The continuous-time linear dynamic model of the electrostatic precipitator is discretized to obtain the discrete-time linear dynamic model of the electrostatic precipitator at time k.

4. The method for energy-saving optimized operation control of an electrostatic precipitator cluster based on distributed model predictive control according to claim 1, characterized in that: The state observer estimates the concentration state of each cell in reverse based on the total concentration output information.

5. The method for energy-saving optimized operation control of an electrostatic precipitator cluster based on distributed model predictive control according to claim 4, characterized in that: The state observer satisfies ， Where A and B are block matrices, For system output, satisfy , , The total flue gas flow rate, Let be the flue gas flow rate of channel p; For state estimation, This represents the actual state. Let k be the total control vector, and k be the time step.

6. The method for energy-saving optimized operation control of an electrostatic precipitator cluster based on distributed model predictive control according to claim 5, characterized in that: The local optimization problem of the distributed model predictive control is as follows: ， Where (s|k) represents the prediction at time k for time k+s. For the stage cost function, For the terminal cost function, For the set of state constraints, To control the set of constraints, It is the terminal constraint set. Let N be the sequence of assumed states of the neighbors, and N be the prediction time domain.

7. The method for energy-saving optimized operation control of an electrostatic precipitator cluster based on distributed model predictive control according to claim 6, characterized in that: The stage cost function satisfies, ， The terminal cost function satisfies, ， in, , It is a symmetric positive definite weighted matrix. It is a symmetric positive definite weighted matrix. Let be the dust concentration in the electrical chamber (p,i). Let be the secondary voltage of the cell (p,i).

8. The method for energy-saving optimized operation control of an electrostatic precipitator cluster based on distributed model predictive control according to claim 6, characterized in that: The state constraints are satisfied. ， The control constraints are satisfied. ， in, This is the lower limit of the export concentration deviation. This represents the upper limit of the export concentration deviation. This is the lower limit of voltage deviation. This represents the upper limit of voltage deviation.

9. The method for energy-saving optimized operation control of an electrostatic precipitator cluster based on distributed model predictive control according to claim 6, characterized in that: Based on the estimated states of each electrical cell, a non-iterative distributed model predictive control algorithm based on inter-neighbor communication is executed to solve each sub-optimization problem and obtain the corresponding optimal control sequence. And the optimal predicted state sequence; broadcast the optimal predicted state sequence, and the optimal control sequence. The first element acts on the electrostatic precipitator cluster, updates the electrostatic precipitator status, measures the current output of the electrostatic precipitator cluster, and then repeats the process in the next time step.

10. An energy-saving optimized operation control system for electrostatic precipitator clusters based on distributed model predictive control, characterized in that: The control system includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the energy-saving optimized operation control method for electrostatic precipitator clusters based on distributed model predictive control as described in any one of claims 1 to 9, according to the instructions in the program code.

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