CN111859669B - Decomposition furnace temperature control method based on thermal analysis-data driving model - Google Patents

Abstract

The invention discloses a decomposition furnace temperature control method based on a thermal analysis-data driving model, which is applied to a cement sintering system consisting of a cyclone preheater, a decomposition furnace, a rotary kiln and a grate cooler, and comprises the following steps: 1, building a thermal analysis model; 2, establishing a thermal analysis discretization simplified model; 3, establishing a thermal analysis-data driven decomposing furnace outlet temperature model; 4 GPC control of the decomposing furnace outlet temperature is realized based on a thermal analysis-data driving model. The invention can accurately control the outlet temperature of the decomposing furnace, thereby ensuring the quality of the cement burned clinker and the stable operation of the preheating decomposing system.

Description

A Calciner Temperature Control Method Based on Thermal Analysis-Data-Driven Model

technical field

The invention relates to the field of calciner temperature modeling and control, in particular to a calciner temperature control method based on a thermal analysis-data-driven model.

Background technique

Calciner outlet temperature is a very important process parameter. It plays a key role in the quality of cement clinker and the stable operation of the preheating decomposition system. It is very important to establish its mathematical model and optimize its control.

On the one hand, thermal analysis models all require complex principle analysis, fluid dynamics and thermodynamics analysis. This kind of model is based on certain assumptions. It requires a lot of calculations, the amount of calculation is relatively large, and most of the variables are coupled with strong nonlinearity. Some coefficients in the expressions are still difficult to determine. It is difficult to establish an accurate mathematical expression for the outlet temperature of the cement calciner, which cannot meet the actual engineering needs. On the other hand, the cement calciner has abundant online measurement data, and various dynamic and static characteristics of the system can be obtained through online learning and calculation.

At present, because the new dry process cement production system is a complex process industry with time-lag, coupling, interference, and time-varying characteristics, most cement enterprises use conventional manual adjustment methods or experience-based control methods to control temperature, and the energy consumption cost is high.

Contents of the invention

In order to solve the shortcomings of the above-mentioned prior art, the present invention proposes a thermal analysis-data-driven model-based calciner temperature control method in order to accurately control the outlet temperature of the calciner, thereby ensuring the quality of cement clinker and the stable operation of the preheating decomposition system.

The present invention adopts following technical scheme in order to achieve the above-mentioned purpose of the invention:

A thermal analysis-data-driven model-based calciner temperature control method of the present invention is applied to a cement firing system composed of a cyclone preheater, a calciner, a rotary kiln and a grate cooler, and is characterized in that the calciner temperature control method is carried out as follows:

Step 1. Establish a thermal analysis model:

Step 1.1. Utilize formula (1) to establish material balance model:

In formula (1), is the quality of the material in the calciner; F _is the quality of pulverized coal sprayed into the calciner per unit time; F _gf1 is the quality of pulverized coal air supply per unit time; F _g3 is the quality of tertiary air per unit time; is the mass of raw material entering the calciner from the 4th or 5th stage of the cyclone preheater per unit time; /> is the gas mass entering the calciner from the rotary kiln per unit time; /> is the mass of raw material entering the fifth or sixth stage of the cyclone preheater from the calciner per unit time; /> is the mass of gas entering the fifth or sixth stage of the cyclone preheater from the calciner per unit time;

Step 1.2. Utilize formula (2) to establish heat balance model:

In formula (2), Be the heat in the calciner; Q ₁ is the heat that is sprayed into the coal of the calciner per unit time, and has:

Q ₁ =C _c F ₁ T ₁ (3)

In formula (3), _Cc is the specific heat capacity of coal; _T1 is the temperature of pulverized coal;

In formula (2), Q _gf1 is the heat of coal air supply per unit time, and there are:

Q _gf1 = C _gf1 F _gf1 T _gf1 (4)

In formula (4), C _gf1 is the specific heat capacity of coal supply air; T _gf1 is the temperature of coal supply air;

In formula (2), Q _g3 is the heat of the tertiary wind flowing in per unit time, and there are:

Q _g3 ＝C _g3 F _g3 T _g3 (5)

In formula (5), C _g3 is the specific heat capacity of the tertiary air; T _g3 is the temperature of the tertiary air;

In formula (2), is the heat of raw material entering the calciner from the 4th or 5th stage of the cyclone preheater per unit time, and has:

In formula (6), is the specific heat capacity of CaCO ₃ ; T _C4 is the temperature of the 4th or 5th stage feeding port of the cyclone preheater;

In formula (2), is the heat of gas entering the calciner from the rotary kiln per unit time, and has:

In formula (7), _Cg is the specific heat capacity of gas; is the kiln tail gas temperature;

In formula (2), is the heat of raw meal entering the 5th or 6th stage of the cyclone preheater from the calciner per unit time, and has:

In formula (8), C _cao is the specific heat capacity of CaO; T _A1 is the decomposition furnace temperature;

In formula (2), is the heat of the high-temperature gas entering the fifth or sixth stage of the cyclone preheater from the calciner per unit time, and has:

In formula (2), Q _msf is the heat released by coal combustion in the calciner per unit time, and has:

Q _msf = H _c F ₁ (10)

In formula (10), _Hc is the calorific value of coal;

In formula (2), is the heat consumed by the endothermic decomposition of _CaCO3 in the calciner per unit time, and has:

In formula (11), Mol is CaCO _Molar mass; The heat consumed for the endothermic decomposition of _CaCO3 ;

Step 2. Establish a simplified thermal analysis discretization model:

Step 2.1. Formula (12) is obtained after formula (1) and formula (2) are simplified:

In formula (12), denotes the first parameter, and /> K _T represents the time constant, and /> K ₁ represents the coal feeding amount gain parameter, and/> K ₂ represents the third air temperature gain parameter, and /> K ₃ represents the material gain parameter, and /> K ₄ represents the gas gain parameter of the calciner, and /> K ₅ represents the coal air supply gain parameter, and />

Step 2.2. Carry out Laplace change to formula (12), and ignore The influence of F _gf1 , thus using formula (13) to obtain the simplified model of thermal analysis:

Step 2.3. Discretize formula (13), thereby using formula (14) to establish a thermal analysis discretization model:

In formula (14), a represents the first model parameter, and b represents the second model parameter, and b=K ₁ (1-a), c represents the third model parameter, and c=K ₂ (1-a), d ₁ represents the first delay parameter, and /> and rounded, d ₂ represents the second delay parameter, and /> And rounded up, T _s is the sampling period, τ ₁ is the lag time of coal feed to the calciner, and τ ₂ is the lag time of the third air temperature;

Step 3. Establish thermal analysis-data-driven calciner outlet temperature model:

Step 3.1. Real-time collection of calciner system data where, /> Indicates the outlet temperature value of the calciner at the kth sampling moment, F ₁ (k) represents the coal feed value of the calciner at the kth sampling moment, T _g3 (k) represents the tertiary wind temperature value at the kth sampling moment;

Step 3.2. Using a moving average filter to process the furnace system data Perform preprocessing to obtain filtered furnace system data where, /> Indicates the filtered value of the temperature at the outlet of the decomposition furnace at the kth sampling time, /> Represents the filter value of the calciner coal feed amount at the kth sampling time, /> Indicates the third wind temperature filter value at the kth sampling moment;

Step 3.3. Use the min-max normalization algorithm to filter the furnace system data Carry out regularization processing to obtain the regularized furnace solution system data where, /> Indicates the normalized value of the calciner outlet temperature at the kth sampling moment, /> Indicates the normalized value of the calciner coal feed amount at the kth sampling time, /> Indicates the normalized value of the cubic wind temperature at the kth sampling moment of the kth time;

Step 3.4. Using the mean square error as the error performance criterion, determine two time-delay parameters d ₁ and d ₂ ;

Step 3.5. According to the regularized furnace system data Using the recursive least squares method to identify three parameters a, b, and c in the thermal analysis discretization model;

Step 3.6. Carry out a fitting degree test on the thermal analysis discretization model, if the accuracy requirement is met, it means that the final thermal analysis discretization model is obtained and used as the calciner outlet temperature model, otherwise, return to step 3.1 for sequential execution;

Step 4. Realize GPC control of calciner outlet temperature based on thermal analysis-data-driven model:

According to the calciner outlet temperature model, predict the future output curve of the calciner outlet temperature, and compare it with the expected softening curve of the calciner outlet temperature to obtain a deviation sequence, thereby forming a GPC performance index according to the deviation sequence and the calciner coal feed increment; and constituting the GPC control constraints of the calciner coal feed increment and the calciner coal feed increment;

Performing GPC optimization control on the GPC performance index and the GPC control constraints at the k-th sampling time, so as to obtain the optimal value of the coal feed amount at the k-th sampling time, which is used to control the actual calciner outlet temperature.

Compared with prior art, the beneficial effect of the present invention is:

1. The present invention establishes the thermal analysis model of the calciner according to the law of conservation of mass and heat. Due to the complexity of the system model, the thermal analysis model is rationalized and simplified. On this basis, a data-driven method is used to identify the parameters in the thermal analysis model, which can better describe the mathematical model relationship of the calciner outlet temperature, and has a clear physical meaning. Compared with the traditional thermal analysis model or data-driven model, it is more systematic.

2. The present invention adopts the calciner temperature GPC control method based on the thermal analysis-data-driven model, instead of using the traditional data-driven model, the thermal analysis-data-driven model is used, which reduces the computational complexity while ensuring accuracy.

Description of drawings

Fig. 1 is the operating principle diagram of calciner of the present invention;

Fig. 2 is the original data figure of calciner of the present invention;

Fig. 3 is a principle diagram of the temperature control of the decomposition furnace of the present invention.

Detailed ways

In this embodiment, as shown in Figure 1, the cement firing system is composed of equipment such as cyclone preheater, calciner, rotary kiln and grate cooler. The raw material enters the calciner from the 4th or 5th stage of the cyclone preheater and is mixed with coal powder, coal powder supply air, and tertiary air for combustion. Under the action of the exhaust gas of the rotary kiln, it enters the 5th or 6th stage of the cyclone preheater for separation and then is calcined in the rotary kiln. Clinker calcination transfers about 60% of the required fuel to the calciner, and quickly applies its combustion heat to the carbonate decomposition process. The temperature in the furnace is generally around 890°C, and the carbonate decomposition rate of raw materials entering the kiln reaches 90-95%. Therefore, the outlet temperature of the calciner is a very important process parameter, which plays a key role in the quality of cement clinker.

In concrete implementation, a kind of calciner temperature control method based on thermal analysis-data-driven model is to carry out as follows:

Step 1. Establish a thermal analysis model:

Step 1.1. Utilize formula (1) to establish material balance model:

In formula (1), is the mass of materials in the calciner; F ₁ is the mass of pulverized coal sprayed into the calciner per unit time; F _gf1 is the quality of pulverized coal air supply per unit time; F _g3 is the quality of tertiary air per unit time; It is the mass of raw material entering the calciner from the 4th or 5th stage of the cyclone preheater per unit time; /> is the mass of gas entering the calciner from the rotary kiln per unit time; /> It is the mass of raw material entering the fifth or sixth stage from the calciner to the cyclone preheater per unit time; /> It is the gas quality of the 5th or 6th stage entering the cyclone preheater from the calciner per unit time;

Step 1.2. Utilize formula (2) to establish heat balance model:

In formula (2), is the heat in the calciner; _Q1 is the heat injected into the coal in the calciner per unit time, and has:

Q ₁ =C _c F ₁ T ₁ (3)

In formula (3), _Cc is the specific heat capacity of coal; _T1 is the temperature of pulverized coal;

In formula (2), Q _gf1 is the heat of coal air supply per unit time, and there are:

Q _gf1 = C _gf1 F _gf1 T _gf1 (4)

In formula (4), C _gf1 is the specific heat capacity of coal supply air; T _gf1 is the temperature of coal supply air;

In formula (2), Q _g3 is the heat of the tertiary wind flowing in per unit time, and there are:

Q _g3 ＝C _g3 F _g3 T _g3 (5)

In formula (5), C _g3 is the specific heat capacity of the tertiary air; T _g3 is the temperature of the tertiary air;

In formula (2), It is the heat of the raw material entering the calciner from the 4th or 5th stage of the cyclone preheater per unit time, and has:

In formula (6), is the specific heat capacity of CaCO ₃ ; T _C4 is the temperature of the 4th or 5th stage feeding port of the cyclone preheater;

In formula (2), is the heat of gas entering the calciner from the rotary kiln per unit time, and has:

In formula (7), _Cg is the specific heat capacity of gas; is the kiln tail gas temperature;

In formula (2), is the heat of raw meal entering the 5th or 6th stage of the cyclone preheater from the calciner per unit time, and has:

In formula (8), C _cao is the specific heat capacity of CaO; T _A1 is the decomposition furnace temperature;

In formula (2), is the heat of the high-temperature gas entering the fifth or sixth stage of the cyclone preheater from the calciner per unit time, and has:

In formula (2), Q _msf is the heat released by coal combustion in the calciner per unit time, and has:

Q _msf = H _c F ₁ (10)

In formula (10), _Hc is the calorific value of coal;

In formula (2), is the heat consumed by the endothermic decomposition of _CaCO3 in the calciner per unit time, and has:

In formula (11), Mol is CaCO _Molar mass; The heat consumed for the endothermic decomposition of _CaCO3 ;

Step 2. Establish a simplified thermal analysis discretization model:

Step 2.1. Formula (12) is obtained after formula (1) and formula (2) are simplified:

In formula (12), denotes the first parameter, and /> K _T represents the time constant, and /> K ₁ represents the coal feeding amount gain parameter, and/> K ₂ represents the third air temperature gain parameter, and /> K ₃ represents the material gain parameter, and /> K ₄ represents the gas gain parameter of the calciner, and /> K ₅ represents the coal air supply gain parameter, and />

Step 2.2. Carry out Laplace change to formula (12), and ignore The influence of F _gf1 , thus using formula (13) to obtain the simplified model of thermal analysis:

Step 2.3. Discretize formula (13), thereby using formula (14) to establish a thermal analysis discretization model:

In formula (14), a represents the first model parameter, and b represents the second model parameter, and b=K ₁ (1-a), c represents the third model parameter, and c=K ₂ (1-a), d ₁ represents the first delay parameter, and /> and rounded, d ₂ represents the second delay parameter, and /> And rounded up, T _s is the sampling period, τ ₁ is the lag time of coal feed to the calciner, and τ ₂ is the lag time of the third air temperature;

Step 3. Establish thermal analysis-data-driven calciner outlet temperature model:

Step 3.1. Real-time collection of calciner system data where, /> Indicates the outlet temperature value of the calciner at the kth sampling moment, F ₁ (k) represents the coal feed value of the calciner at the kth sampling moment, and T _g3 (k) represents the tertiary wind temperature value at the kth sampling moment; the collected data is shown in Figure 2, and 700 sets of data from a cement plant under normal working conditions on April 17, 2020 were selected, and the sampling period was 5 seconds.

Step 3.2. There are many internal interference factors in the cement preheating decomposition system, and data acquisition and transmission errors will affect the data quality. These noise errors are all derived from sensor measurement and signal interference. Solving Furnace System Data Using Moving Average Filter Perform preprocessing to obtain filtered furnace system data where, /> Indicates the filtered value of the temperature at the outlet of the decomposition furnace at the kth sampling time, /> Represents the filter value of the calciner coal feed amount at the kth sampling time, /> Indicates the third air temperature filter value at the kth sampling moment.

Step 3.3. Data normalization processing is to make the input and output data better for data analysis, avoiding the loss of information features due to the difference in data dimensional attributes and thus bringing unnecessary impact on the model and identification. After the original data are processed, they are all at the same dimension level, and the information represented by each can be displayed in the model without distinction. Data normalization can also improve the convergence speed and training accuracy of the model. Therefore, the min-max normalization algorithm is used to filter the furnace system data Carry out regularization processing to obtain the regularized furnace solution system data/> where, /> Indicates the normalized value of the calciner outlet temperature at the kth sampling moment, /> Indicates the normalized value of the calciner coal feed amount at the kth sampling time, /> Indicates the normalized value of the cubic wind temperature at the kth sampling moment of the kth time;

Step 3.4. Using the mean square error as the error performance criterion, determine two time-delay parameters d ₁ and d ₂ ; see Table 1 and Table 2.

Table 1 Delay parameter d ₁ selection

Table 2 Delay parameter d ₂ selection

In this embodiment, d ₁ =3 and d ₂ =1 are selected.

Step 3.5. According to the regularized furnace system data The three parameters a, b, c in the thermal analysis discretization model were identified by recursive least square method; thus the model parameter values a=0.9889, b=0.6567, c=0.235 were obtained.

Step 3.6. Carry out a fitting degree test on the thermal analysis discretization model, if the accuracy requirement is met, it means that the final thermal analysis discretization model is obtained and used as the calciner outlet temperature model, otherwise, return to step 3.1 and execute in sequence;

Step 4. Realize GPC control of calciner outlet temperature based on thermal analysis-data-driven model:

As shown in Figure 3, according to the calciner outlet temperature model, the future output curve of the calciner outlet temperature is predicted, and the deviation sequence is obtained by comparing with the expected softening curve of the calciner outlet temperature, so that the GPC performance index is formed according to the deviation sequence and the calciner coal feed increment; and the upper and lower limits of the calciner coal feed increment, and the calciner coal feed upper and lower limit constraints constitute the GPC control constraints;

At the kth sampling time, the GPC optimization control is carried out according to the GPC performance index and the GPC control constraints, so as to obtain the optimal value of the coal feeding amount, which is used to control the actual calciner outlet temperature.

Claims (1)

1. a kind of calciner temperature control method based on thermal analysis-data-driven model is to be applied in the cement firing system that is formed by cyclone preheater, calciner, rotary kiln and grate cooler, it is characterized in that, described calciner temperature control method is to carry out as follows: Step 1. Establish a thermal analysis model: Step 1.1. Utilize formula (1) to establish material balance model: In formula (1), is the quality of the material in the calciner; F _is the quality of pulverized coal sprayed into the calciner per unit time; F _gf1 is the quality of pulverized coal air supply per unit time; F _g3 is the quality of tertiary air per unit time; is the mass of raw material entering the calciner from the 4th or 5th stage of the cyclone preheater per unit time; /> is the gas mass entering the calciner from the rotary kiln per unit time; /> is the mass of raw material entering the fifth or sixth stage of the cyclone preheater from the calciner per unit time; /> is the mass of gas entering the fifth or sixth stage of the cyclone preheater from the calciner per unit time; Step 1.2. Utilize formula (2) to establish heat balance model: In formula (2), Be the heat in the calciner; Q ₁ is the heat that is sprayed into the coal of the calciner per unit time, and has: Q ₁ =C _c F ₁ T ₁ (3) In formula (3), _Cc is the specific heat capacity of coal; _T1 is the temperature of pulverized coal; In formula (2), Q _gf1 is the heat of coal air supply per unit time, and there are: Q _gf1 = C _gf1 F _gf1 T _gf1 (4) In formula (4), C _gf1 is the specific heat capacity of coal supply air; T _gf1 is the temperature of coal supply air; In formula (2), Q _g3 is the heat of the tertiary wind flowing in per unit time, and there are: Q _g3 ＝C _g3 F _g3 T _g3 (5) In formula (5), C _g3 is the specific heat capacity of the tertiary air; T _g3 is the temperature of the tertiary air; In formula (2), is the heat of raw material entering the calciner from the 4th or 5th stage of the cyclone preheater per unit time, and has: In formula (6), is the specific heat capacity of CaCO ₃ ; T _C4 is the temperature of the 4th or 5th stage feeding port of the cyclone preheater; In formula (2), is the heat of gas entering the calciner from the rotary kiln per unit time, and has: In formula (7), _Cg is the specific heat capacity of gas; is the kiln tail gas temperature; In formula (2), is the heat of raw meal entering the 5th or 6th stage of the cyclone preheater from the calciner per unit time, and has: In formula (8), C _cao is the specific heat capacity of CaO; T _A1 is the decomposition furnace temperature; In formula (2), is the heat of the high-temperature gas entering the fifth or sixth stage of the cyclone preheater from the calciner per unit time, and has: In formula (2), Q _msf is the heat released by coal combustion in the calciner per unit time, and has: Q _msf = H _c F ₁ (10) In formula (10), _Hc is the calorific value of coal; In formula (2), is the heat consumed by the endothermic decomposition of _CaCO3 in the calciner per unit time, and has: In formula (11), Mol is CaCO _Molar mass; The heat consumed for the endothermic decomposition of _CaCO3 ; Step 2. Establish a simplified thermal analysis discretization model: Step 2.1. Formula (12) is obtained after formula (1) and formula (2) are simplified: In formula (12), denotes the first parameter, and /> K _T represents the time constant, and K ₁ represents the coal feeding amount gain parameter, and/> K ₂ represents the third air temperature gain parameter, and K ₃ represents the material gain parameter, and /> K ₄ represents the gas gain parameter of the calciner, and /> K ₅ represents the coal air supply gain parameter, and /> Step 2.2. Carry out Laplace change to formula (12), and ignore The influence of F _gf1 , thus using formula (13) to obtain the simplified model of thermal analysis: Step 2.3. Discretize formula (13), thereby using formula (14) to establish a thermal analysis discretization model: In formula (14), a represents the first model parameter, and b represents the second model parameter, and b=K ₁ (1-a), c represents the third model parameter, and c=K ₂ (1-a), d ₁ represents the first delay parameter, and /> and rounded, d ₂ represents the second delay parameter, and /> And rounded up, T _s is the sampling period, τ ₁ is the lag time of coal feed to the calciner, and τ ₂ is the lag time of the third air temperature; Step 3. Establish thermal analysis-data-driven calciner outlet temperature model: Step 3.1. Real-time collection of calciner system data where, /> Indicates the outlet temperature value of the calciner at the kth sampling moment, F ₁ (k) represents the coal feed value of the calciner at the kth sampling moment, T _g3 (k) represents the tertiary wind temperature value at the kth sampling moment; Step 3.2. Using a moving average filter to process the furnace system data Perform preprocessing to obtain filtered furnace system data/> where, /> Indicates the filtered value of the temperature at the outlet of the decomposition furnace at the kth sampling time, /> Represents the filter value of the calciner coal feed amount at the kth sampling time, /> Indicates the third wind temperature filter value at the kth sampling moment; Step 3.3. Use the min-max normalization algorithm to filter the furnace system data Carry out regularization processing to obtain the regularized furnace solution system data where, /> Indicates the normalized value of the calciner outlet temperature at the kth sampling moment, /> Indicates the normalized value of the calciner coal feed amount at the kth sampling time, /> Indicates the normalized value of the cubic wind temperature at the kth sampling moment of the kth time; Step 3.4. Using the mean square error as the error performance criterion, determine two time-delay parameters d ₁ and d ₂ ; Step 3.5. According to the regularized furnace system data Using the recursive least squares method to identify three parameters a, b, and c in the thermal analysis discretization model; Step 3.6. Carry out a fitting degree test on the thermal analysis discretization model, if the accuracy requirement is met, it means that the final thermal analysis discretization model is obtained and used as the calciner outlet temperature model, otherwise, return to step 3.1 for sequential execution; Step 4. Realize GPC control of calciner outlet temperature based on thermal analysis-data-driven model: According to the calciner outlet temperature model, predict the future output curve of the calciner outlet temperature, and compare it with the expected softening curve of the calciner outlet temperature to obtain a deviation sequence, thereby forming a GPC performance index according to the deviation sequence and the calciner coal feed increment; and constituting the GPC control constraints of the calciner coal feed increment and the calciner coal feed increment; Performing GPC optimization control on the GPC performance index and the GPC control constraints at the k-th sampling time, so as to obtain the optimal value of the coal feed amount at the k-th sampling time, which is used to control the actual calciner outlet temperature.