Automatic furnace pressure adjusting and controlling system for closed submerged arc furnace

Abstract

The invention provides a closed submerged arc furnace pressure automatic regulation control system, which relates to the technical field of furnace pressure regulation, and comprises a GPC unit, a crude gas / purified gas frequency converter and a purification system, the GPC unit is used for receiving an input system variable and an interference variable, performing steady-state target adjustment calculation and dynamic optimization according to a set value of the furnace pressure of the closed submerged arc furnace and outputting a control input value, and transmitting the control input value to the crude gas / purified gas frequency converter; the crude gas / purified gas frequency converter is used for receiving a control input value and outputting a control signal of the control input value to an actuating mechanism variable frequency motor of the purification system; and the purification system receives a control signal of the crude gas / purified gas frequency converter, and completes rotation speed adjustment of the crude gas fan and the purified gas fan according to the control signal. The generalized prediction model control algorithm is adopted, stable control over the furnace pressure of the closed submerged arc furnace is achieved, the product quality is guaranteed, energy conservation and environmental protection are achieved, and the overall intelligent level of the submerged arc furnace is improved.

Description

Technical Field

[0001] The invention relates to the technical field of furnace pressure regulation, and in particular to an automatic regulation control system for a closed ore-fired furnace pressure. Background Art

[0002] At present, the design and production capacity of smelting ore-fired furnaces is huge, and the corresponding CO emission is also large. In response to environmental protection and energy-saving requirements, ore-fired furnaces mostly use closed ore-fired furnaces to purify and recycle CO. Correspondingly, one clean gas flue and one waste gas flue are set up to send the gas generated in the closed ore-fired furnace during production into the gas purification system, or to directly discharge the gas when an abnormality occurs in the gas purification system. In the production process of the ore-fired furnace, stable control of the furnace pressure can enhance the stability of the ore-fired furnace. In the production process of the ore-fired furnace, it is very important to realize automatic regulation of the furnace pressure. A typical gas purification process, such as Figure 1 As shown in the figure, the furnace gas purification system is mainly composed of water-cooled pipes, sedimentation coolers, closed buried scraper conveyors, rough air fans, bag dust collectors, clean air fans, etc. Considering that the flue gas temperature is very high during the production process of the ore-fired furnace, in order to prevent the high-temperature flue gas from causing adverse effects on the system equipment, water-cooled sleeves are used in the pipelines before entering the purification system for preliminary cooling, so that the flue gas temperature entering the system is below 500°C. The flue gas enters the purification system through the water-cooled pipeline, and is cooled by the sedimentation chamber and the cooler to control the flue gas temperature of the system below 260°C. Each purification system is equipped with a rough air fan and a clean air fan. After the furnace gas is cooled, it is filtered through the bag dust collector, and the dust concentration is controlled within 30mg / Nm3. The clean furnace gas is sent out of the purification boundary area through the clean air fan, and the filtered dust is transported by the sealed scraper to the purification ash bin for unloading and transportation. During the entire purification process, it is crucial to coordinate the speed control of the raw gas fan and the clean gas fan to ensure the stability of the furnace pressure and realize automatic adjustment. However, in the current actual operation, the automatic adjustment effect of the furnace pressure is not satisfactory. At present, a considerable part of them adopts the cascade PID adjustment of the speed of the raw gas fan and the clean gas fan, but it often causes system oscillation and the furnace pressure is difficult to stabilize; another part directly abandons the adjustment of the clean gas fan and directly uses the raw gas fan for closed-loop adjustment to adjust the furnace pressure to the set range, but the actual use effect is not good.

[0003] In the field of process control, model predictive control (MPC) is a typical representative of advanced process control (APC). MPC is the most successful control method and technology after PID. It can effectively handle constraints, multivariables, coupling and pure lag. It is widely used in industries such as petroleum, chemical industry, and cement, but it is rarely successfully applied in ore-fired furnace smelting. The MPC multivariable control function uses a model to predict the future behavior of process output under input changes, improves the dynamic performance of process control, can minimize the fluctuation of the control system, and promote the production process to the control target of quality (or economic) constraints that play a key role in the production process, and reduce the variance of the control system. What's more, MPC can smoothly operate the system on certain boundaries of multiple constraints, so that the control system can run at the edge, which is the key to MPC's economic benefits. At the same time, MPC can quickly suppress the influence of interference and can also handle the strong coupling of complex multivariable systems. Because the furnace pressure of the ore-fired furnace is controlled by the strong coupling between the raw gas fan and the clean gas fan at the same time, MPC is very suitable for automatic coordinated control of the raw gas and clean gas fan speeds. Compared with the model algorithm control (MAC) and dynamic matrix control (DMC) in the MPC strategy, the generalized predictive control (GPC) algorithm is proposed for random discrete systems, which does not require high accuracy of the mathematical model and maintains the rolling optimization strategy in the predictive control algorithm. After the MPC was put into use, the furnace pressure of the submerged arc furnace was automatically controlled and adjusted, the operating intensity of the process personnel was greatly reduced, the automation rate was significantly improved, the stability and safety of the device operation were guaranteed, the product quality was guaranteed, the target product yield was improved, the operating cost was reduced, and the economic benefits were significantly improved. Summary of the invention

[0004] According to the above technical problem that the existing closed ore-heating furnace cannot realize stable furnace pressure regulation, a closed ore-heating furnace pressure automatic regulation control system is provided. The present invention adopts a generalized prediction model control algorithm to realize stable control of the closed ore-heating furnace pressure, ensure product quality, save energy and protect the environment, and improve the overall intelligence level of the ore-heating furnace.

[0005] The technical means adopted by the present invention are as follows:

[0006] A closed ore-fired furnace pressure automatic regulation control system, a GPC unit, a raw gas / clean gas frequency converter and a purification system;

[0007] The GPC unit is used to receive input system variables and disturbance variables, and the GPC unit performs steady-state target adjustment calculation and dynamic optimization according to the set value of the closed ore furnace pressure and outputs a control input value, and the control input value is transmitted to the raw gas / clean gas frequency converter;

[0008] The crude gas / clean gas frequency converter is used to receive a control input value and output a control signal of the control input value to the frequency conversion motor of the actuator of the purification system;

[0009] The purification system receives a control signal from a raw air / clean air frequency converter and adjusts the speeds of the raw air fan and the clean air fan according to the control signal.

[0010] Further, the system variables include the deviation between the furnace pressure set value and the real-time value, the raw gas fan inlet temperature, the raw gas fan inlet pressure, the clean gas fan inlet temperature and the clean gas fan inlet pressure;

[0011] The interference variables include flue gas temperature, 2 The content includes H 2 content;

[0012] The control input values ​​include a rough air fan speed offset and a clean air fan speed offset.

[0013] Furthermore, the steady-state target adjustment calculation method of the GPC unit is as follows:

[0014] S1. For objects subject to random interference, the controlled autoregressive integrated moving average model used in minimum variance control is adopted:

[0015] A(q -1 )y(k)=B(q -1 )u(k-1)+C(q -1 )ξ(k) / Δ

[0016] Where: A(q -1 )=1+a 1 q -1 +…+a n q -n , B(q -1 )=b 0 +b 1 q -1 +…+b m q -m , C(q -1 ) takes the value of 1;

[0017] Among them, q -1 is the shift operator, which indicates the corresponding amount of one sampling period backward; Δ = 1-q -1 is a differential operator; ξ(k) is an uncorrelated random noise sequence, which represents the influence of a type of random noise on the system;

[0018] For the closed ore-fired furnace, the raw gas fan speed and clean gas fan speed are selected as the output variable y(k), the furnace pressure, raw gas fan inlet temperature, raw gas fan inlet pressure, clean gas fan inlet temperature, and clean gas fan inlet pressure are selected as the control input variables u(k), and the flue gas temperature, oxygen content in the flue gas, and hydrogen content in the flue gas are selected as the measurable external interference variables d(k);

[0019] S2, control constraints are imposed on the output variable y(k), the control variable u(k) and the control increment Δu(k), y min (k)≤y(k)≤y max (k),u min (k)≤u(k)≤u max (k), Δu min (k)≤Δu(k)≤Δu max (k);

[0020] During the operation of GPC, if it exceeds the set range, GPC will exit the adjustment and switch to manual control mode. After the working conditions return to normal, it will be put into GPC automatic operation mode again.

[0021] S3. Assuming the prediction length is j, the output prediction model of the system at time k+j is:

[0022] A(q -1 )y(k+j)=B(q -1 )u(k+j-1)+ξ(k+j) / Δ

[0023] The Diophantine equation is used to obtain the predicted system output value y at time k+j M (k+j), the Diophantine equation is as follows:

[0024] 1=A(q -1 )ΔE j (q -1 )+q -j F j (q -1 )

[0025] In the formula, E j (q -1 ), F j (q -1 ) is composed of A(q -1 ) and the predicted length j is uniquely determined by the polynomial, taking E 1 =1, As E j , F j The initial value of , then the formula can be derived:

[0026]

[0027] E j+1 =E j +e j+1,j q -1 =E j +f j,0 q -1

[0028]

[0029] in,

[0030] S4, let the controlled variable set value at time k be SP, and the actual tracking value is the softened set value w k , the object output expected value adopts the following reference trajectory:

[0031] w(k)=y(k)

[0032] w(k+j)=αw(k+j-1)+(1-α)SP

[0033] Among them, 0<α<1;

[0034] S5. Make the predicted output at time k+j as close as possible to the reference trajectory w(k+j), then the optimization target at time k is:

[0035]

[0036] Where, E is the mathematical expectation; w is the expected value of the output; N1, N2 are the initial and final values ​​of the optimization time domain; NU is the control time domain; λ(j) is the control weighting coefficient, which is generally a constant λ;

[0037] S6. Minimize the quadratic objective function J(k), let The control law is obtained as follows:

[0038] Δu=(G T G+λI) -1 G T (WH)

[0039] Where I is the identity matrix;

[0040]

[0041] Among them, g j,i =g i+1 (i<j) is the step response coefficient; W=[w(k+1)…w(k+N)] T ;

[0042] H=[h 1 …h N ] T ,in:

[0043] h 1 (t) = [G 1 (q -1 )-g 1,0 ]Δu(t)+F 1 y(t)

[0044] h 2 (t) = q[G 2 (q -1 )-q -1 g 2,1 -g 2,0 ]Δu(t)+F 2 y(t)

[0045]

[0046] h N (t) = q N-1 [G N (q -1 )-q -(N-1) g N,N-1 -…-g N,0 ]Δu(t)+F N y(t)

[0047] S7, set g T For (G T G+λI) -1 G T The first line of , then the instantaneous control increment at time k is:

[0048] Δu(k)=g T (WH)

[0049] S8. Apply the control quantity u(k)=u(k-1)+Δu(k) to the system to complete the control of this step.

[0050] Furthermore, in S2, the upper limit of the raw gas fan speed and the clean gas fan speed is set to 50Hz, and the lower limit is set to 10Hz; the upper limit of the furnace pressure is set to 100Pa, and the lower limit is set to -100Pa; the upper limit of the raw gas fan and the clean gas fan inlet temperature is set to 100°C, and no lower limit is set; the upper limit of the raw gas fan and the clean gas fan inlet pressure is set to 1000Pa, and no lower limit is set; the upper limit of the flue gas temperature is set to 800°C, and no lower limit is set; the upper limits of the oxygen content in the flue gas and the hydrogen content in the flue gas are set to 0.5% and 8% respectively, and no lower limit is set.

[0051] Furthermore, the execution cycle of the steady-state target calculation and dynamic optimization of the GPC unit is set to 1 minute; the speed regulation of the raw gas / clean gas inverter uses a continuous PID regulation function block, and the execution cycle is set to 1 second.

[0052] Furthermore, the program execution flow of the GPC unit is as follows:

[0053] T1, initialization procedure;

[0054] T2, according to the latest input and output data, use the recursive least squares method to estimate the model parameters and obtain A(q -1 ) and B(q -1 );

[0055] T3, according to the obtained A(q -1 ), calculate E(q -1 ) and F(q -1 );

[0056] T4, according to B(q -1 )、E(q -1 ) and F(q -1 )Calculate G(q -1 ) element g i , and calculate H;

[0057] T5. Calculate g T , and calculate the control action deviation Δu(k);

[0058] T6. Apply the control quantity u(k)=u(k-1)+Δu(k) to the control system. The output adjustment of this cycle is completed and jump to T1 cycle execution.

[0059] Compared with the prior art, the present invention has the following advantages:

[0060] The present invention is based on a controlled autoregressive integral sliding model in minimum variance control, but has low requirements on the model; the present invention implements a rolling optimization strategy through T1-T6 loop execution, performs rolling calculations in a limited time domain, and replaces the global optimum with local optimization; the present invention uses measured information to estimate model parameters online for feedback correction, thereby enhancing the robustness of control.

[0061] The closed ore-fired furnace pressure automatic regulation and control system developed by the present invention, wherein the key technology can be partially applied, expanded and transplanted to the control systems of DC ore-fired furnaces or other types of industrial furnaces, etc., and this part of the technology is also within the protection scope of this scheme and is subject to the constraints of this technical scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0063] Figure 1 It is the existing furnace gas purification process.

[0064] Figure 2 It is a flow chart of the control scheme of the present invention.

[0065] Figure 3 This is a diagram of the steps for executing the GPC process of the present invention. DETAILED DESCRIPTION

[0066] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0067] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0069] Unless otherwise specifically stated, the relative arrangement of the parts and steps described in these embodiments, the numerical expressions and numerical values ​​do not limit the scope of the present invention. At the same time, it should be clear that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The technology, methods and equipment known to ordinary technicians in the relevant field may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be regarded as part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0070] In the field of process control, model predictive control (MPC) is a typical representative of advanced process control (APC). MPC is the most successful control method and technology after PID. It can effectively handle constraints, multivariables, coupling and pure lag. It is widely used in industries such as petroleum, chemical industry, and cement, but it is rarely successfully applied in ore-fired furnace smelting. The MPC multivariable control function uses a model to predict the future behavior of process output under input changes, improves the dynamic performance of process control, can minimize the fluctuation of the control system, and promote the production process to the control target of quality (or economic) constraints that play a key role in the production process, and reduce the variance of the control system. What's more, MPC can smoothly operate the system on certain boundaries of multiple constraints, so that the control system can run at the edge, which is the key to MPC's economic benefits. At the same time, MPC can quickly suppress the influence of interference and can also handle the strong coupling of complex multivariable systems. Because the furnace pressure of the ore-fired furnace is controlled by the strong coupling between the raw gas fan and the clean gas fan at the same time, MPC is very suitable for automatic coordinated control of the raw gas and clean gas fan speeds. Compared with the model algorithm control (MAC) and dynamic matrix control (DMC) in the MPC strategy, the generalized predictive control (GPC) algorithm is proposed for random discrete systems, which does not require high accuracy of the mathematical model and maintains the rolling optimization strategy in the predictive control algorithm. After the MPC was put into use, the furnace pressure of the submerged arc furnace was automatically controlled and adjusted, the operating intensity of the process personnel was greatly reduced, the automation rate was significantly improved, the stability and safety of the device operation were guaranteed, the product quality was guaranteed, the target product yield was improved, the operating cost was reduced, and the economic benefits were significantly improved.

[0071] Therefore, an automatic adjustment and control system for the furnace pressure of a closed ore-fired furnace was invented. The generalized predictive model control algorithm was used to achieve stable control of the furnace pressure of the closed ore-fired furnace, ensure product quality, save energy and protect the environment, and improve the overall intelligence level of the ore-fired furnace.

[0072] MPC (Model Predictive Control) is the core technology of APC (Advanced Process Control), which is mainly used in process control engineering. MPC uses model control algorithms to predict the future behavior of process outputs when inputs change. We have developed an automatic pressure regulation control system for closed submerged arc furnaces, which uses a generalized predictive model control algorithm to achieve stable automatic pressure regulation control for closed submerged arc furnaces.

[0073] MPC can smoothly operate the system on certain boundaries of multiple constraints, so that the control system runs on the upper or lower limit, which is the key to MPC's economic benefits; MPC can quickly suppress the impact of interference and can handle the strong coupling of complex multivariable systems. MPC uses the model and the measured values ​​of the current process to calculate the future actions of the manipulated variables, and ensures that all inputs and outputs meet the constraints. Then MPC downloads the calculated multiple control input values ​​to the DCS control system, and the regulator in it completes the basic control function. At the same time, the feedback mechanism in MPC can compensate for the prediction error between the model and the process. MPC is a model-based finite time domain (or infinite time domain) open-loop optimal control algorithm. Its essence is to use the process model to predict the future state or output. At each sampling moment, the controller uses the current system state as the initial state, and obtains the control action by minimizing the difference between the future output and the reference trajectory (open-loop optimization problem). The optimization generates a control input sequence, in which the first component will be executed and the other components will be discarded. Therefore, the execution cycle of MPC steady-state target calculation and dynamic optimization is set to 1 minute; while in the DCS control system, the inverter speed regulation uses the continuous PID regulation function block with an execution cycle set to 1 second.

[0074] The generalized predictive control (GPC) MPC strategy is selected, and the controlled autoregressive integrated moving average model (CARIMA) used in minimum variance control is used to describe the object subject to random interference. Compared with the model algorithm control (MAC) and dynamic matrix control (DMC) in the MPC strategy, the generalized predictive control (GPC) algorithm is proposed for random discrete systems, does not require high accuracy of the mathematical model, and maintains the rolling optimization strategy in the predictive control algorithm.

[0075] like Figure 2 and 3 As shown, the present invention provides a closed ore-fired furnace pressure automatic regulation control system, comprising: a GPC unit, a raw gas / clean gas frequency converter and a purification system;

[0076] The GPC unit is adjusted according to the set value of the furnace pressure of the closed ore-fired furnace, with the deviation between the furnace pressure set value and the real-time value, the raw gas fan inlet temperature, the raw gas fan inlet pressure, the clean gas fan inlet temperature, and the clean gas fan inlet pressure as inputs, and the flue gas temperature, O 2 Content, H 2 The content is the interference variable, and the raw air fan speed offset and the clean air fan speed offset are output and transmitted to the raw air / clean air inverter through the DCS control system;

[0077] The steady-state target adjustment calculation method of the GPC unit is as follows:

[0078] S1. For objects subject to random interference, the controlled autoregressive integrated moving average model used in minimum variance control is adopted:

[0079] A(q -1 )y(k)=B(q -1 )u(k-1)+C(q -1 )ξ(k) / Δ

[0080] Where: A(q -1 )=1+a 1 q -1 +…+a n q -n , B(q -1 )=b 0 +b 1 q -1 +…+b m q -m , C(q -1 ) takes the value of 1;

[0081] Among them, q -1 is the shift operator, which indicates the corresponding amount of one sampling period backward; Δ = 1-q -1 is a differential operator; ξ(k) is an uncorrelated random noise sequence, which represents the influence of a type of random noise on the system;

[0082] The control system developed this time can be used for automatic adjustment of the furnace pressure of a closed ore-heating furnace based on model predictive control. For the closed ore-heating furnace, the raw gas fan speed and the clean gas fan speed are selected as the output variable y(k), and the furnace pressure, raw gas fan inlet temperature, raw gas fan inlet pressure, clean gas fan inlet temperature and clean gas fan inlet pressure are selected as the control input variables u(k). The flue gas temperature, oxygen content in the flue gas and hydrogen content in the flue gas are selected as the measurable external interference variables d(k). The speed of the raw gas fan is adjusted by the raw gas fan frequency converter, and the speed of the clean gas fan is adjusted by the clean gas fan frequency converter. In the process of adjustment, the raw gas fan and the clean gas fan are coordinated and controlled to stabilize the furnace pressure of the closed ore-heating furnace.

[0083] S2, control constraints are imposed on the output variable y(k), the control variable u(k) and the control increment Δu(k), y min (k)≤y(k)≤y max (k),u min (k)≤u(k)≤u max (k), Δu min (k)≤Δu(k)≤Δu max (k);

[0084] The upper limit of the raw gas fan speed and the clean gas fan speed is set to 50Hz, and the lower limit is set to 10Hz; the upper limit of the furnace pressure is set to 100Pa, and the lower limit is set to -100Pa; the upper limit of the raw gas fan and the clean gas fan inlet temperature is set to 100°C, and no lower limit is set; the upper limit of the raw gas fan and the clean gas fan inlet pressure is set to 1000Pa, and no lower limit is set; the upper limit of the flue gas temperature is set to 800°C, and no lower limit is set; the upper limits of the oxygen content in the flue gas and the hydrogen content in the flue gas are set to 0.5% and 8% respectively, and no lower limit is set.

[0085] During the operation of GPC, if it exceeds the set range, GPC will exit the adjustment and switch to the operator manual control mode. After the working conditions are normal, it can be put into the GPC automatic operation mode again. The whole system is controlled by computer automatically. The system has several switching points. For example, if the inlet temperature of the bag filter is too high, the oxygen content in the gas is too high, and other signals endangering the personal safety of the equipment and personnel, the computer will immediately cut off the purification system and release the furnace gas from the bypass chimney on the closed furnace hood for safety. There is also a dispersion chimney at the rear of the clean air fan, which can be ignited and dispersed when the subsequent system does not need to send gas in.

[0086] S3. Assuming the prediction length is j, the output prediction model of the system at time k+j is:

[0087] A(q -1 )y(k+j)=B(q -1 )u(k+j-1)+ξ(k+j) / Δ

[0088] The Diophantine equation is used to obtain the predicted system output value y at time k+j M (k+j), the Diophantine equation is as follows:

[0089] 1=A(q -1 )ΔE j (q -1 )+q -j F j (q -1 )

[0090] In the formula, E j (q -1 ), F j (q -1 ) is composed of A(q -1 ) and the predicted length j is uniquely determined by the polynomial, taking E 1 =1, As E j , F j The initial value of , then the formula can be derived:

[0091]

[0092] E j+1 =E j +e j+1,j q -1 =E j +f j,0 q -1

[0093]

[0094] in,

[0095] S4, let the controlled variable set value at time k be SP, and the actual tracking value is the softened set value w k , the object output expected value adopts the following reference trajectory:

[0096] w(k)=y(k)

[0097] w(k+j)=αw(k+j-1)+(1-α)SP

[0098] Among them, 0<α<1;

[0099] S5. Make the predicted output at time k+j as close as possible to the reference trajectory w(k+j), then the optimization target at time k is:

[0100]

[0101] Where, E is the mathematical expectation; w is the expected value of the output; N1, N2 are the initial and final values ​​of the optimization time domain; NU is the control time domain; λ(j) is the control weighting coefficient, which is generally a constant λ;

[0102] S6. Minimize the quadratic objective function J(k), let The control law is obtained as follows:

[0103] Δu=(G T G+λI) -1 G T (WH)

[0104] Where I is the identity matrix;

[0105]

[0106] Among them, g j,i =g i+1 (i<j) is the step response coefficient; W=[w(k+1)…w(k+N)] T ;

[0107] H=[h 1 …h N ] T ,in:

[0108] h 1 (t) = [G 1 (q -1 )-g 1,0 ]Δu(t)+F 1 y(t)

[0109] h 2 (t) = q[G 2 (q -1 )-q -1 g 2,1 -g 2,0 ]Δu(t)+F 2 y(t)

[0110]

[0111] h N (t) = q N-1 [G N (q -1 )-q -(N-1) g N,N-1 -…-g N,0 ]Δu(t)+F N y(t)

[0112] S7, set g T For (G T G+λI) -1 G T The first line of , then the instantaneous control increment at time k is:

[0113] Δu(k)=g T (WH)

[0114] S8. Apply the control quantity u(k)=u(k-1)+Δu(k) to the system to complete the control of this step.

[0115] The crude gas / clean gas frequency converter is used to receive a control input value and output a control signal of the control input value to the frequency conversion motor of the actuator of the purification system;

[0116] The purification system receives the control signal of the raw gas / clean gas frequency converter, and adjusts the speed of the raw gas fan and the clean gas fan according to the control signal. The operating parameters are collected and used by other systems.

[0117] In the actual industrial application process, the execution of the GPC generalized predictive control algorithm is implemented in the intelligent server arranged in the upper network of the DCS control system. The program execution process is as follows: Figure 3 As shown, the specific steps are as follows:

[0118] T1, initialization procedure;

[0119] T2, according to the latest input and output data, use the recursive least squares method to estimate the model parameters and obtain A(q -1 ) and B(q -1 );

[0120] T3, according to the obtained A(q -1 ), calculate E(q -1 ) and F(q -1 );

[0121] T4, according to B(q -1 )、E(q -1 ) and F(q -1 )Calculate G(q -1 ) element g i , and calculate H;

[0122] T5. Calculate g T , and calculate the control action deviation Δu(k);

[0123] T6, the control quantity u(k)=u(k-1)+Δu(k) is applied to the control system, the output adjustment of this cycle is completed, and the process jumps to T1 for loop execution;

[0124] Although the nonlinearity of the closed ore-fired furnace production process is absolute, most large-scale production processes are continuous and stable. Therefore, the steady-state and dynamic characteristics of the process in a certain interval near the operating point show a relatively consistent linear relationship. The corresponding model can more accurately characterize the input-output relationship of the process, so that the steady-state target calculation has a better application effect. As the most effective multivariable control method, the role of MPC is to keep the multivariable control system at the optimal working point, that is, to make the system quickly return to the optimal working point when interference occurs. When operating near the optimal working point, the characteristics reflected by the controlled system are linear. The purpose of effectively controlling the system can be achieved by using this linear multivariable system model. The optimization in MPC does not adopt an unchanging global optimization index, but adopts a rolling optimization strategy in a finite time domain. It is a closed-loop optimization based on feedback correction. This rolling implementation of optimization can cope with uncertainties caused by model mismatch, time variation, interference, etc., timely correct the model and compensate for it, and always establish the new optimization on the actual basis to keep the control actually optimal, that is, to determine the current optimal control strategy by rolling optimization, so that the deviation between the controlled variable and the expected value in the future period of time is minimized.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A closed ore-fired furnace pressure automatic regulation control system, characterized in that: include: GPC unit, crude gas / clean gas frequency converter and purification system; The GPC unit is used to receive input system variables and disturbance variables, and the GPC unit performs steady-state target adjustment calculation and dynamic optimization according to the set value of the closed ore furnace pressure and outputs a control input value, and the control input value is transmitted to the raw gas / clean gas frequency converter; The crude gas / clean gas frequency converter is used to receive a control input value and output a control signal of the control input value to the frequency conversion motor of the actuator of the purification system; The purification system receives a control signal from a raw air / clean air frequency converter and adjusts the speeds of the raw air fan and the clean air fan according to the control signal.

2. The closed ore-fired furnace pressure automatic regulation and control system according to claim 1 is characterized in that: The system variables include the deviation between the furnace pressure set value and the real-time value, the raw gas fan inlet temperature, the raw gas fan inlet pressure, the clean gas fan inlet temperature and the clean gas fan inlet pressure; The interference variables include flue gas temperature, O2 content and H2 content; The control input values ​​include a rough air fan speed offset and a clean air fan speed offset.

3. The closed ore-fired furnace pressure automatic regulation and control system according to claim 1 is characterized in that: The steady-state target adjustment calculation method of the GPC unit is as follows: S1. For objects subject to random interference, the controlled autoregressive integrated moving average model used in minimum variance control is adopted: A(q -1 )y(k)=B(q -1 )u(k-1)+C(q -1 )ξ(k) / Δ Where: A(q -1 )=1+a1q -1 +…+a n q -n , B(q -1 )=b0+b1q -1 +…+b m q -m , C(q -1 ) takes the value of 1; Among them, q -1 is the backward shift operator, which indicates the corresponding amount of moving back one sampling period; Δ = 1-q -1 is a differential operator; ξ(k) is an uncorrelated random noise sequence, which represents the influence of a type of random noise on the system; For the closed ore-fired furnace, the raw gas fan speed and clean gas fan speed are selected as the output variable y(k), the furnace pressure, raw gas fan inlet temperature, raw gas fan inlet pressure, clean gas fan inlet temperature, and clean gas fan inlet pressure are selected as the control input variables u(k), and the flue gas temperature, oxygen content in the flue gas, and hydrogen content in the flue gas are selected as the measurable external interference variables d(k); S2, control constraints are imposed on the output variable y(k), the control variable u(k) and the control increment Δu(k), y min (k)≤y(k)≤y max (k),u min (k)≤u(k)≤u max (k), Δu min (k)≤Δu(k)≤Δu max (k); During the operation of GPC, if it exceeds the set range, GPC will exit the adjustment and switch to manual control mode. After the working conditions return to normal, it will be put into GPC automatic operation mode again. S3. Assuming the prediction length is j, the output prediction model of the system at time k+j is: Tongue -1 )y(k+j)=B(q -1 )u(k+j-1)+ξ(k+j) / Δ The Diophantine equation is used to obtain the predicted system output value y at time k+j M (k+j), the Diophantine equation is as follows: 1=A(q -1 )ΔE j (q -1 )+q -j F j (q -1 ) In the formula, E j (q -1 ), F j (q -1 ) is composed of A(q -1 ) and the predicted length j are uniquely determined by the polynomial, taking E1 = 1, As E j , F j The initial value of , then the formula can be derived: AND j+1 =And j +e j+1,j q -1 =And j +f j,0 q -1 in, S4, let the controlled variable set value at time k be SP, and the actual tracking value is the softened set value w k , the object output expected value adopts the following reference trajectory: w(k)=y(k) w(k+j)=αw(k+j-1)+(1-α)SP Among them, 0<α<1; S5. Make the predicted output at time k+j as close as possible to the reference trajectory w(k+j), then the optimization target at time k is: Where, E is the mathematical expectation; w is the expected value of the output; N1, N2 are the initial and final values ​​of the optimization time domain; NU is the control time domain; λ(j) is the control weighting coefficient, which is generally a constant λ; S6. Minimize the quadratic objective function J(k), let The control law is obtained as follows: Δu=(G T G+λI) -1 G T (W-H) Where I is the identity matrix; Among them, g j,i =g i+1 (i<j) is the step response coefficient; W=[w(k+1)…w(k+N)] T ; H=[h1…h N ] T ,in: h1(t)=[G1(q -1 )-g 1,0 ]Δu(t)+F1y(t) h2(t)=q[G2(q -1 )-q -1 g 2,1 -g 2,0 ]Δu(t)+F2y(t) h N (t)=q N-1 [G N (q -1 )-q -(N-1) g N,N-1 -…-g N,0 ]Δu(t)+F N y(t) S7, set g T For (G T G+λI) -1 G T The first line of , then the instantaneous control increment at time k is: Δu(k)=g T (WH) S8. Apply the control quantity u(k)=u(k-1)+Δu(k) to the system to complete the control of this step.

4. The closed ore-fired furnace pressure automatic regulation and control system according to claim 1 is characterized in that: In S2, the upper limit of the raw gas fan speed and the clean gas fan speed is set to 50Hz, and the lower limit is set to 10Hz; the upper limit of the furnace pressure is set to 100Pa, and the lower limit is set to -100Pa; the upper limit of the raw gas fan and the clean gas fan inlet temperature is set to 100°C, and no lower limit is set; the upper limit of the raw gas fan and the clean gas fan inlet pressure is set to 1000Pa, and no lower limit is set; the upper limit of the flue gas temperature is set to 800°C, and no lower limit is set; the upper limits of the oxygen content in the flue gas and the hydrogen content in the flue gas are set to 0.5% and 8% respectively, and no lower limit is set.

5. The closed ore-fired furnace pressure automatic regulation and control system according to claim 1 is characterized in that: The execution cycle of the steady-state target calculation and dynamic optimization of the GPC unit is set to 1 minute; the speed regulation of the raw gas / clean gas inverter uses a continuous PID regulation function block, and the execution cycle is set to 1 second.

6. The closed ore-fired furnace pressure automatic regulation and control system according to claim 3 is characterized in that: The program execution flow of the GPC unit is as follows: T1, initialization procedure; T2, according to the latest input and output data, use the recursive least squares method to estimate the model parameters and obtain A(q -1 ) and B(q -1 ); T3, according to the obtained A(q -1 ), calculate E(q -1 ) and F(q -1 ); T4, according to B(q -1 )、E(q -1 ) and F(q -1 )Calculate G(q -1 ) element g i , and calculate H; T5. Calculate g T , and calculate the control action deviation Δu(k); T6. Apply the control quantity u(k)=u(k-1)+Δu(k) to the control system. The output adjustment of this cycle is completed and jump to T2 for loop execution.

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