Plasma incinerator temperature control system and method based on model predictive control

Abstract

The application discloses a model prediction control-based temperature control system and method of a plasma incinerator, and the system comprises a model prediction control module, a chromatographic temperature detector and a plasma torch; the model prediction control module is used for constructing a prediction model and obtaining a control output of a plasma incinerator hearth temperature based on the prediction model; the chromatographic temperature detector is used for measuring a temperature value of the plasma incinerator hearth and feeding back the temperature value to the model prediction control module; the model prediction control module is further used for correcting the control output based on the temperature value to obtain a next-time prediction output of the plasma incinerator hearth temperature; and the plasma torch controls the opening degree based on the control output to complete temperature control. The application can guarantee that the hearth temperature of the plasma incinerator is in a stable state during combustion, effectively suppresses the generation of harmful flue gas, realizes energy saving and emission reduction, and has high practicability.

Description

Technical Field

[0001] This application belongs to the field of plasma incinerator combustion control technology, specifically relating to a plasma incinerator temperature control system and method based on model predictive control. Background Technology

[0002] Plasma technology is an emerging technology for the harmless treatment of pollutants, and plasma incinerators have unique advantages in treating hazardous waste. In the control strategy of plasma combustion, the combustion chamber temperature is a key factor affecting combustion. Accurately measuring and effectively controlling the combustion chamber temperature is crucial for achieving safe and efficient combustion in plasma incinerators.

[0003] When handling hazardous waste, the working environment is harsh, and traditional control methods are unstable, resulting in large temperature fluctuations. This not only wastes energy but also easily generates harmful gases such as dioxins, causing secondary pollution to the environment and posing a risk to the personal safety of hazardous waste handling personnel. Due to the characteristics of plasma incinerators—multiple inputs and outputs, large inertia, and severe hysteresis—and the diverse types and varying calorific values ​​of hazardous waste processed by plasma incinerators, traditional control methods require operators to frequently change control system parameters to meet system requirements. Since parameters differ between different areas, the performance of the furnace depends entirely on the parameter settings, leading to significant manpower consumption and low system performance.

[0004] Model Predictive Control (MPC) is a modern control method applicable to various complex dynamic systems across fields such as mechanical, electrical, chemical, and biological engineering. It is a model-based control method that considers nonlinear and time-varying factors, exhibiting robustness and the ability to handle a certain degree of model uncertainty and measurement noise. MPC can simultaneously handle multiple input and output variables, enabling optimal control of multivariable systems. It considers system constraints, such as input ranges and output limitations, ensuring the system does not exceed specified limits during operation. MPC allows for online optimization, using real-time measurement data and model predictions to continuously improve system performance. Its flexibility, efficiency, and robustness make MPC suitable for optimizing the complex dynamic systems of plasma incinerators, offering broad application prospects. Summary of the Invention

[0005] This application aims to address the shortcomings of existing technologies by proposing a model predictive control-based temperature control system and method for plasma incinerators, in order to achieve stable control of the furnace temperature of plasma incinerators.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] A model predictive control-based temperature control system for a plasma incinerator includes: a model predictive control module, a chromatograph, and a plasma torch.

[0008] The model prediction control module is used to construct a prediction model and obtain the control output of the plasma incineration furnace temperature based on the prediction model.

[0009] The chromatograph is used to measure the temperature value of the plasma incineration furnace and transmit the temperature value back to the model prediction and control module;

[0010] The model prediction control module is also used to correct the control output based on the temperature value to obtain the next moment's predicted output of the plasma incineration furnace temperature.

[0011] The plasma torch controls its opening degree based on the control output, thereby achieving temperature control.

[0012] Preferably, the model prediction control module includes: a model generation unit, a prediction output unit, and an optimization unit;

[0013] The model generation unit is used to collect system data on historical operating conditions of plasma incineration furnace temperature, and to construct the prediction model based on the system data.

[0014] The prediction output unit is used to obtain a prediction output based on the prediction model;

[0015] The optimization unit is used to perform rolling optimization on the predicted output to obtain the control output quantity.

[0016] Preferably, the workflow of the model generation unit includes:

[0017] Based on the system's unit step response, collect temperature data a1, a2, ..., a3 from P plasma incineration furnaces before the system reaches a stable time. P , where a p Given the sampled values ​​at a stable system moment, based on the principle of system superposition, we obtain:

[0018] y(k)=a1u(kM)+a2u(k-M+1)+…+a M-1 u(k-1)+a M u(k)

[0019] Transforming it into incremental form, we obtain the prediction model:

[0020] Δy(k)=a1Δu(kM)+a2Δu(k-M+1)+…+a M-1 Δu(k-1)+aM Δu(k)

[0021] Where M is the control step size, k is the time, u(k) is the opening degree of the plasma torch at time k, and Δu(k) is the increment of the opening degree of the plasma torch at time k.

[0022] Preferably, the workflow of the prediction output unit includes:

[0023] Based on the prediction model, calculate the prediction output for step P:

[0024]

[0025] Among them, y p y0 is the predicted output of the plasma incineration furnace temperature, while y0 is the original predicted output of the plasma incineration furnace temperature.

[0026] Preferably, the workflow of the optimization unit includes:

[0027] Reference trajectory function for building the model:

[0028] w(k+i)=α i y(k)+(1-α i )y r

[0029] Where α is an important parameter of model predictive control, y(k) is the current output value, and y r This is a set value, where i is a constant, and i = 1, 2, 3, ..., P;

[0030] Based on the predicted output, construct the sequence Y consisting of the first P elements of the initial value of the prediction model. P (k), and construct a reference trajectory sequence W(k) based on the reference trajectory function;

[0031] Based on the sequence Y P The minimum cost function is constructed using W(k) and the reference trajectory sequence W(k):

[0032]

[0033] Wherein, ΔU M (k) represents the control input, and Q and R are the control weighting adjustment matrices;

[0034] The optimal control sequence is obtained based on the minimum cost function:

[0035]

[0036] Where A is the coefficient matrix;

[0037] The first output of the optimal control sequence is taken as the control output.

[0038] Preferably, the model prediction control module further includes a feedback correction unit;

[0039] The feedback correction unit is used to correct the control output based on the temperature value to obtain the predicted output for the next moment.

[0040] Preferably, the workflow of the feedback correction unit includes:

[0041] The system error value is obtained by subtracting the current output value from the predicted output value.

[0042] e(k+1)=y(k+1)-y P (k+1)

[0043] The system error values ​​are weighted and calculated to obtain the corrected prediction output:

[0044] y cor (k+1)=y P (k+1)+He(k+1)

[0045] Where H is the error correction matrix and e is the system error value;

[0046] The corrected prediction output is shifted to obtain the prediction output for the next time step:

[0047] y N (k+1)=Sy cor (k+1)

[0048] Where S is the displacement matrix.

[0049] This application also provides a model predictive control-based method for temperature control of a plasma incinerator, used in the control system of any of the above, comprising the following steps:

[0050] A prediction model is constructed, and the control output of the plasma incineration furnace temperature is obtained based on the prediction model;

[0051] The temperature value of the plasma incineration furnace is measured, and the control output is corrected based on the temperature value to obtain the next moment's predicted output of the plasma incineration furnace temperature.

[0052] The opening degree is controlled based on the control output, thus achieving temperature control.

[0053] Compared with the prior art, the beneficial effects of this application are as follows:

[0054] This application ensures a stable furnace temperature during plasma incineration, effectively suppressing the generation of harmful flue gas and achieving energy conservation and emission reduction, demonstrating high practicality. Real-time monitoring of the furnace temperature via a spectral thermometer feeds back to the feedback correction module in the model predictive controller, enabling timely adjustments to the predicted output value and preventing the generation of dioxins and nitrogen oxides. The largest cost of plasma incineration is electricity consumption; improving energy efficiency is crucial. Parameter configuration via a rolling optimization module controls the plasma torch, preventing system temperature overshoot, saving energy, and extending the lifespan of the plasma torch cathode. Model predictive control utilizes past, present, and future outputs to control the system, fully leveraging system information for effective control of the plasma incineration furnace temperature. Therefore, using model predictive control for furnace temperature tracking significantly reduces power consumption, achieving energy conservation and emission reduction while lowering operating costs. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of this application, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of the system structure according to an embodiment of this application;

[0057] Figure 2 This is a system control block diagram of an embodiment of this application;

[0058] Figure 3 This is a model prediction control block diagram of an embodiment of this application;

[0059] Figure 4 This is a schematic diagram of the method flow of an embodiment of this application. Detailed Implementation

[0060] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0061] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0062] Example 1

[0063] In this embodiment, as Figure 1 , Figure 2 As shown, the model predictive control-based plasma incinerator temperature control system includes: a model predictive control module, a chromatograph, and a plasma torch;

[0064] The model predictive control module is used to construct a predictive model and obtain the control output for the plasma incineration furnace temperature based on the predictive model. The model predictive control module includes a model generation unit, a prediction output unit, and an optimization unit. The control block diagram of the model predictive control module is shown below. Figure 3 As shown.

[0065] The model generation unit is used to collect system data on historical operating conditions of the plasma incineration furnace temperature and to build a predictive model based on the system data. The workflow of the model generation unit includes: collecting temperature data a1, a2, ..., a3 of the plasma incineration furnace before the system stabilizes, based on the system's unit step response. P , where a p Given the sampled values ​​at a stable system moment, based on the principle of system superposition, we obtain:

[0066] y(k)=a1u(kM)+a2u(k-M+1)+…+a M-1 u(k-1)+a M u(k)

[0067] Transforming it into incremental form, we obtain the prediction model:

[0068] Δy(k)=a1Δu(kM)+a2Δu(k-M+1)+…+a M-1 Δu(k-1)+a M Δu(k)

[0069] Where M is the control step size, k is the time, u(k) is the opening degree of the plasma torch at time k, and Δu(k) is the increment of the opening degree of the plasma torch at time k.

[0070] The prediction output unit is used to obtain the prediction output based on the prediction model; the workflow of the prediction output unit includes: calculating the prediction output for P steps based on the prediction model.

[0071]

[0072] Among them, y p y0 is the predicted output of the plasma incineration furnace, and y0 is the predicted output of the original plasma incineration furnace.

[0073] The optimization unit is used to perform rolling optimization on the predicted output to obtain the control output. The workflow of the optimization unit includes: the objective function of the plasma incinerator temperature control system is to take a desired reference trajectory w(k) that the controlled object tracks at future sampling points, requiring the variance between the reference trajectory and the output value at future sampling points to be minimized, and constructing the reference trajectory function of the model:

[0074] w(k+i)=α i y(k)+(1-α i )y r

[0075] Here, α is a crucial parameter in model predictive control, playing a key role in the dynamic characteristics and robustness of the closed-loop system. The smaller α is, the faster the reference trajectory reaches the setpoint. y(k) is the current output value. r This is a set value, where i is a constant, taking the values ​​i = 1, 2, 3, ..., P;

[0076] The sequence Y, consisting of the first P elements of the initial values ​​of the prediction model constructed based on the predicted output. P (k), constructing a reference trajectory sequence W(k) based on the reference trajectory function;

[0077] To find a set of control variables ΔU M (k) aims to make the predicted output value in the prediction time domain as close to the reference value as possible. Therefore, based on the sequence Y... P The minimum cost function is constructed using W(k) and the reference trajectory sequence W(k):

[0078]

[0079] Wherein, ΔU M (k) represents the control input, and Q and R are the control weighting adjustment matrices;

[0080] The optimal control sequence is obtained based on the minimum cost function:

[0081]

[0082] Where A is the coefficient matrix;

[0083] The first output of the optimal control sequence is used as the control output.

[0084] The chromatograph is used to measure the temperature of the plasma incineration furnace and transmit the temperature value back to the model prediction and control module.

[0085] The model predictive control module also includes a feedback correction unit. Based on the predictive model and rolling optimization, the model predictive controller can already achieve the effect of open-loop control. Ideally, excluding factors such as model mismatch and environmental disturbances, the open-loop controller can perform the control work well. However, that is only under ideal conditions. In real-world environments, the system will inevitably be accompanied by uncertain system disturbances. In such cases, it is necessary to convert open-loop control to closed-loop control to improve the system's disturbance immunity. The feedback correction unit is used to correct the control output based on the temperature value to obtain the predicted output for the next moment. The workflow of the feedback correction unit includes:

[0086] The system error value is obtained by subtracting the current output value from the predicted output value.

[0087] e(k+1)=y(k+1)-y P (k+1)

[0088] The corrected prediction output is obtained by weighting the system error values:

[0089] y cor (k+1)=y P (k+1)+He(k+1)

[0090] Where e is the system error value, which is the difference between the measured value and the predicted output value of the spectrometer, and H is the error correction matrix:

[0091]

[0092] The corrected prediction output is shifted to obtain the prediction output for the next time step:

[0093] y N (k+1)=Sy cor (k+1)

[0094] Where S is the displacement matrix:

[0095]

[0096] The plasma torch controls the opening degree based on the control output, thereby achieving temperature control.

[0097] Example 2

[0098] In this embodiment, as Figure 4 As shown, the temperature control method for a plasma incinerator based on model predictive control includes the following steps:

[0099] S1. Construct a prediction model and obtain the control output of the plasma incineration furnace temperature based on the prediction model.

[0100] Specifically, system data is collected, and a predictive model is built based on the system data: according to the system's unit step response, temperature data a1, a2, ..., a3 are collected for P plasma incineration furnace chambers before the system reaches a steady state. P , where a p Given the sampled values ​​at a stable system moment, based on the principle of system superposition, we obtain:

[0101] y(k)=a1u(kM)+a2u(k-M+1)+…+a M-1 u(k-1)+a M u(k)

[0102] Transforming it into incremental form, we obtain the prediction model:

[0103] Δy(k)=a1Δu(kM)+a2Δu(k-M+1)+…+a M-1 Δu(k-1)+a M Δu(k)

[0104] Where M is the control step size, k is the time, u(k) is the opening degree of the plasma torch at time k, and Δu(k) is the increment of the opening degree of the plasma torch at time k.

[0105] Based on the prediction model, calculate the prediction output for step P:

[0106]

[0107] Among them, y p y0 is the predicted output of the plasma incineration furnace, and y0 is the predicted output of the original plasma incineration furnace.

[0108] The objective function of the plasma incinerator temperature control system is to have the controlled object track a desired reference trajectory w(k) at future sampling points, requiring the variance between the reference trajectory and the output values ​​at future sampling points to be minimized. The reference trajectory function of the model is constructed as follows:

[0109] w(k+i)=α i y(k)+(1-α i )y r

[0110] Here, α is a crucial parameter in model predictive control, playing a key role in the dynamic characteristics and robustness of the closed-loop system. The smaller α is, the faster the reference trajectory reaches the setpoint. y(k) is the current output value. r This is a set value, where i is a constant, taking the values ​​i = 1, 2, 3, ..., P;

[0111] The sequence Y, consisting of the first P elements of the initial values ​​of the prediction model constructed based on the predicted output. P (k), constructing a reference trajectory sequence W(k) based on the reference trajectory function;

[0112] To find a set of control variables ΔU M (k) aims to make the predicted output value in the prediction time domain as close to the reference value as possible. Therefore, based on the sequence Y... P The minimum cost function is constructed using W(k) and the reference trajectory sequence W(k):

[0113]

[0114] Wherein, ΔU M (k) represents the control input, and Q and R are the control weighting adjustment matrices;

[0115] The optimal control sequence is obtained based on the minimum cost function:

[0116]

[0117] Where A is the coefficient matrix;

[0118] The first output of the optimal control sequence is used as the control output.

[0119] S2. Measure the temperature of the plasma incineration furnace, and correct the control output based on the temperature value to obtain the predicted output for the next moment.

[0120] Ideally, excluding factors such as model mismatch and environmental disturbances, an open-loop controller can perform its control function well. However, this is only under ideal conditions. In real-world environments, the system will inevitably experience uncertain system disturbances. In such cases, it is necessary to switch from open-loop control to closed-loop control to improve the system's disturbance immunity. Based on the temperature value, error correction is applied to the control output to obtain the predicted output for the next moment.

[0121] The system error value is obtained by subtracting the current output value from the predicted output value.

[0122] e(k+1)=y(k+1)-y P (k+1)

[0123] The corrected prediction output is obtained by weighting the system error values:

[0124] y cor (k+1)=y P (k+1)+He(k+1)

[0125] Where e is the system error value, which is the difference between the measured value and the predicted output value of the spectrometer, and H is the error correction matrix:

[0126]

[0127] The corrected prediction output is shifted to obtain the prediction output for the next time step:

[0128] y N (k+1)=Sy cor (k+1)

[0129] Where S is the displacement matrix:

[0130]

[0131] S3. Control the opening degree based on the control output to complete temperature control.

[0132] The embodiments described above are merely preferred embodiments of this application and are not intended to limit the scope of this application. Any modifications and improvements made to the technical solutions of this application by those skilled in the art without departing from the spirit of this application shall fall within the protection scope defined by the claims of this application.

Claims

1. A plasma incinerator temperature control system based on model predictive control, characterized in that, include: Model predictive control module, chromatograph, and plasma torch; The model prediction control module is used to construct a prediction model and obtain the control output of the plasma incineration furnace temperature based on the prediction model. The chromatograph is used to measure the temperature value of the plasma incineration furnace and transmit the temperature value back to the model prediction and control module; The model prediction control module is also used to correct the control output based on the temperature value to obtain the next moment's predicted output of the plasma incineration furnace temperature. The plasma torch controls its opening degree based on the control output, thereby achieving temperature control. The model prediction control module includes: a model generation unit, a prediction output unit, and an optimization unit; The model generation unit is used to collect system data on historical operating conditions of plasma incineration furnace temperature, and to construct the prediction model based on the system data; The prediction output unit is used to obtain a prediction output based on the prediction model; The optimization unit is used to perform rolling optimization on the predicted output to obtain the control output quantity; The workflow of the model generation unit includes: Based on the system's unit step response, collect P temperature data points from the plasma incineration furnace before the system reaches a stable state. ,in a p is the sampled value at the system's steady-state moment. Based on the principle of system superposition, we obtain: Transforming it into incremental form, we obtain the prediction model: in, M To control the step size, k For a moment, for k The opening degree of the plasma torch at any given time. for k The increment of the plasma torch opening at any given time.

2. The plasma incinerator temperature control system based on model predictive control according to claim 1, characterized in that, The workflow of the prediction output unit includes: Based on the prediction model, calculate the prediction output for step P: in, It is the predicted output of the plasma incineration furnace temperature. It is the predicted output of the original plasma incineration furnace temperature.

3. The plasma incinerator temperature control system based on model predictive control according to claim 2, characterized in that, The workflow of the optimization unit includes: Reference trajectory function for building the model: Among them, α is an important parameter of model predictive control. y ( k ) is the current output value. yr It is a set value. i As a constant, take ; Based on the predicted output, construct a sequence of the first P elements of the initial values ​​for the prediction model. YP ( k Based on the reference trajectory function, a reference trajectory sequence is constructed. W ( k ); Based on the sequence YP ( k ) and the reference trajectory sequence W ( k Construct the minimum cost function: in, Q and R are the control variables, and Q and R are the control weighting adjustment matrices. The optimal control sequence is obtained based on the minimum cost function: in, A It is a coefficient matrix; The first output of the optimal control sequence is taken as the control output.

4. The plasma incinerator temperature control system based on model predictive control according to claim 3, characterized in that, The model prediction control module also includes a feedback correction unit; The feedback correction unit is used to correct the control output based on the temperature value to obtain the predicted output for the next moment.

5. The plasma incinerator temperature control system based on model predictive control according to claim 4, characterized in that, The workflow of the feedback correction unit includes: The system error value is obtained by subtracting the current output value from the predicted output value. The system error values ​​are weighted and calculated to obtain the corrected prediction output: in, H This is the error correction matrix. e This is the system error value; The corrected prediction output is shifted to obtain the prediction output for the next time step: in, S This is the displacement matrix.

6. A model predictive control-based temperature control method for a plasma incinerator, used in the control system of any one of claims 1-5, characterized in that, Includes the following steps: A prediction model is constructed, and the control output of the plasma incineration furnace temperature is obtained based on the prediction model; The temperature value of the plasma incineration furnace is measured, and the control output is corrected based on the temperature value to obtain the next moment's predicted output of the plasma incineration furnace temperature. The opening degree is controlled based on the control output, thus achieving temperature control.

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