CN111338211A - A method and system for optimizing control of waste heat utilization process - Google Patents

Abstract

The invention relates to an optimization control method and system for a cement kiln waste heat utilization process, and relates to the field of optimization of a steam turbine generator unit cold end system and robust control of a grate cooler. Firstly, a dynamic characteristic model of the grate cooler is established, so that the model can be matched with field real-time working condition data, multi-model uncertainty factors are considered through analyzing model characteristics, and a robust controller is designed to inhibit interference. Meanwhile, a turbine cold end system model is established by taking the maximization of the net power increment of the turbine unit as a target, and comprises a cooling tower model, a turbine power increment model and a condenser model, so that the optimal back pressure of the turbine is obtained by adopting an intelligent optimization algorithm, and an operation suggestion of a cooling fan is given, and the purpose of increasing the generated energy is realized.

Description

Optimal control method and system for waste heat utilization process

technical field

The invention relates to the technical field of optimal control of waste heat power generation, in particular to a method and system for optimal control of the waste heat utilization process of a cement kiln with a grate cooler device.

Background technique

In the context of increasing scarcity of natural resources, waste heat power generation systems have become an indispensable part of industrial production, such as dry-process cement production. The waste heat power generation system includes: grate cooler, steam turbine cold end system and boiler system. The grate cooler is the heat source of the waste heat utilization system. The cement clinker falls into the grate bed through the rotary kiln and enters the heat exchange process. This process has the characteristics of large time delay, strong interference and easy change of working conditions. The pressure under the grate is difficult to control and stabilize, resulting in low air temperature and large fluctuation after heat exchange, which seriously reduces the waste heat recovery rate. In addition, the cold end system of the steam turbine is often not operating in the optimal working condition, which also reduces the utilization rate of waste heat and reduces the power generation. Therefore, controlling the grate lower pressure of the grate cooler of the waste heat power generation system and optimizing the operation of the cold end system of the steam turbine will help to improve the utilization rate of waste heat and increase the efficiency of the enterprise.

At present, in the control strategies of waste heat recovery, steam turbine power generation, and boiler steam generation of cement kiln waste heat power generation systems, most of the grate cooler grate pressure control adopts experience-based methods such as fuzzy control and expert system, which are difficult to deal with special emergency conditions. Most of the optimization methods for the operation of the cold end system of the steam turbine are based on the circulating water pump model, and there are few researches on the optimization of the cooling tower model; in comparison, the research on the boiler system is relatively mature, and the model-based or model-free control strategy It has been widely used in actual production and has a good control effect.

Based on the above considerations, the present invention first analyzes the dynamic characteristic data of the grate cooler, and considers the multi-model uncertainty factors based on the established characteristic model, and designs a robust controller to suppress interference. At the same time, aiming at maximizing the net power increase of the steam turbine unit, the cold end system model of the steam turbine is established, including the cooling tower model, the steam turbine power increment model and the condenser model, and then the optimal back pressure of the steam turbine is obtained by using the intelligent optimization algorithm. And give the cooling fan operation suggestions to achieve the purpose of increasing power generation.

SUMMARY OF THE INVENTION

A brief summary of one or more aspects is presented below to provide a basic understanding of the aspects. This summary is not an exhaustive overview of all contemplated aspects and is neither intended to identify key or critical elements of all aspects nor attempt to delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.

An object of the present invention is to provide a method for stabilizing the working condition of a grate cooler by designing a robust controller to suppress disturbances. The method is applicable to include but not limited to cement kiln waste heat power generation systems.

A first aspect of the present invention provides a method for optimizing the working condition parameters of the grate cooler and constructing a robust controller model of the grate cooler system, including:

(1) Collect the process parameters of the grate cooler, select the frequency response data by the spectral analysis method,

(2) Aiming at stabilizing grate down pressure and kiln head cover negative pressure, taking grate speed and exhaust fan speed as decision variables, using robust and decoupling constraints and the frequency response data of the controlled object grate cooler, construct a robust rod controller optimization model,

Preferably (3) solve the optimization model according to the constraints of the optimization model to obtain the optimized grate cooler working condition parameters, where the grate cooler working condition parameters are the grate speed and the rotational speed of the exhaust fan.

所述频率响应数据用G(jω),ω∈Ω表示，所述控制器记为

其中，Ω:R∪{∞}，G(j∞)＝0。In one or more embodiments, the controlled object is described as:

The frequency response data is represented by G(jω), ω∈Ω, and the controller is denoted as

Among them, Ω:R∪{∞}, G(j∞)=0.

In one or more embodiments of the first aspect of the present invention, in step (1), the grate cooler process parameters include one or more parameters selected from the group consisting of: grate down pressure, kiln head hood negative pressure, grate speed and exhaust fan speed.

In one or more embodiments, selecting the frequency response data includes: obtaining the frequency response data of the system by using the excitation signal, or selecting the frequency response data of the system by using spectral analysis by collecting process data in the time domain.

In one or more embodiments, the number of frequency response data ranges from 1000-5000, 1500-4500, 2000-4000, or 2500-3500.

In one or more embodiments, the frequency range of the frequency response data is selected based on a priori knowledge of the plant dynamics. In one or more embodiments, the frequency ranges from 0.01-10, 0.02-9.5, 0.03-9, 0.04-8.5, 0.05-8, 0.1-7.5.

In one or more embodiments, the frequencies of the frequency response data are selected in an equally spaced or logarithmically spaced manner. At this point, the optimization model is transformed into a positive semi-definite programming problem. In one or more embodiments, the optimization model can be solved using general optimization methods, such as convex optimization methods or nonlinear optimization methods.

In one or more embodiments, the plant is a two-in, two-out coupled system.

In one or more embodiments of the first aspect of the present invention, in step (2), the constraints include: minimizing the dynamic deviation of the actual closed-loop system from the desired system, minimizing the magnitude of the sensitivity function, and/or minimizing The relative magnitudes of the off-diagonal and main-diagonal elements of the open-loop transfer function matrix.

In one or more embodiments, step (2) comprises:

其中，q＝1,2；p＝1,2；优化变量控制器参数用向量

表示，基函数向量每个元素如下式所示：Let each element of the 2X2 controller be

Among them, q=1,2; p=1,2; the vector used to optimize the variable controller parameters

Representation, each element of the basis function vector is as follows:

In one or more embodiments, the robust controller optimization model is as follows:

in,

φ ^T =[φ ₀ ,φ ₁ ,…,φ _m-1 ]

Among them, α, β, γ are the system robust performance, closed-loop performance, and decoupling performance indicators; W _1,2 , W _2,1 in the A matrix are weighted sensitivity functions. Preferably, W _1,2 , W _2,1 are low-pass filters whose modulus is less than 1.

In the optimization model (1-A) constraint condition 1, the β of the index value in the A matrix and the optimized ρ are the product relationship, and the constraint condition is nonlinear.

In one or more embodiments of the first aspect of the present invention, the solution of step (3) is an approximate solution using a truncation method. In one or more embodiments, the truncation method includes selecting a finite set of frequency points, Ω _f = {ω ₁ , ω ₂ , . . . , ω _f }, such that feasible solutions satisfy constraints within the set.

In one or more embodiments, the solution of step (3) is transformed into solving a series of convex optimization feasible solution problems using a bisection method.

In one or more embodiments, given β in each iterative solution, if the optimization model has a solution for the given β, then at the next iteration the value of β is decreased, otherwise the value of β is increased.

In one or more embodiments, the solution is terminated when the difference between the two obtained β values is smaller than a predetermined threshold ε.

The invention also discloses a method for controlling the grate pressure of the grate cooler, the method comprising:

(1) Collect the process parameters of the grate cooler, select the frequency response data by the spectral analysis method,

(2) using the model constructed according to the method described in the first aspect of the present invention to optimize the working condition parameters of the grate cooler,

(3) Adjust the grate cooler according to the optimized working condition parameters of the grate cooler, and then control the grate lower pressure to ensure the stability of the negative pressure of the kiln head cover.

In one or more embodiments, the grate cooler operating parameters are grate speed and exhaust fan speed.

The invention also discloses a system for adjusting the working condition parameters of the grate cooler and/or controlling the grate pressure of the grate cooler, comprising:

The data acquisition module collects the process parameters of the grate cooler, selects the frequency response data,

The optimization model builds the module, aiming at stabilizing grate down pressure and kiln hood negative pressure, taking grate speed and exhaust fan speed as decision variables, using robust and decoupling constraints and the frequency response data of the controlled object grate cooler, Build a robust controller optimization model,

The data processing module solves the optimization model according to the constraints of the optimization model to obtain the optimized working condition parameters of the grate cooler, adjusts the grate cooler and controls the grate lower pressure according to the parameters.

The invention also discloses a system for adjusting the working condition parameters of the grate cooler and/or controlling the grate pressure of the grate cooler, comprising a computer and a computer program running on the computer, and the computer program runs the first aspect of the present invention on the computer The method and/or the above-mentioned method for controlling the grate pressure of the grate cooler.

The present invention also discloses a computer-readable storage medium storing a computer program, which is characterized in that, after the computer program stored on the storage medium runs, the method described in the first aspect of the present invention and/or the above-mentioned control grate cooler grate is executed. method of downforce.

Another object of the present invention is to provide a method for optimizing working conditions of a steam turbine generator set through a condenser model, a steam turbine power increment model and a cooling tower model. The method is applicable to include but not limited to cement kiln waste heat power generation systems.

A second aspect of the present invention provides a method for optimizing working condition parameters of a steam turbine generator set and constructing a working condition model of the steam turbine generator set, the steam turbine generator set includes a condenser, a steam turbine and a cooling tower, and the method includes the steps:

(1) Collect the process parameters of the steam turbine generator set,

(2) Build a condenser model, a steam turbine power increment model and a cooling tower model, and preprocess the data collected in step 1).

(3) use the model of (2) to construct an optimization model including constraints, and

Preferably (4) the optimization model is solved according to the constraints of the optimization model, so as to obtain the optimized working condition parameters of the steam turbine generator set.

In one or more embodiments, the turbine generator set operating parameter is turbine back pressure.

In one or more embodiments of the second aspect of the present invention, in step (1), the process parameters of the turbo-generator set include one or more parameters selected from the group consisting of: the operation parameters of the condenser, the operation of the circulating water pump parameters, operating parameters of the vacuum pump, and operating parameters of the steam turbine unit.

In one or more embodiments, the turbo-generator set process parameters are collected and stored through the OPC interface of the distributed control system.

In one or more embodiments, turbo-generator set process parameters are collected every 5-30 seconds, preferably every 10, 15, 20, or 25 seconds.

In one or more embodiments, the turbo-generator set process parameters are stored in a database.

In one or more embodiments of the second aspect of the present invention, in step (2), a condenser model is established through mechanism analysis to obtain the relationship between the initial temperature of the circulating water and the condenser pressure.

In one or more embodiments, the condenser model is constructed as follows:

1) The steam temperature at the inlet of the condenser is equal to the saturation temperature t _c corresponding to the pressure p _c of the condenser, which can be expressed by formula (1):

t _c =t _w2 +δt (1)

where t _c is the temperature in the condenser, t _w2 is the temperature of the circulating cooling water coming out of the condenser, δt is the heat transfer end difference of the condenser,

2) When the exhaust steam condenses, the heat transferred to the cooling circulating water is

Q=D _c (h _c -h _n )=1000KA _c Δt=4.187D _w Δt (2)

Wherein D _c , D _w - steam flow and cooling circulating water flow into the steam turbine condenser (t/h);

h _c , h _n — the enthalpy of the condensed water produced by the steam turbine exhaust and cooling (KJ/kg);

K——The total heat transfer coefficient of the condenser (KJ/(m ² hK))

A _c ——the contact area between the outer surface of the cooling water pipe and the steam in the condenser (m ² )

Δt——The average heat transfer temperature difference between cooling circulating water and steam (℃),

3) The logarithmic average temperature difference can be written in the form of formula (3):

4) Combine formulas (2) and (3) to get:

5) After integrating formulas (1)-(4), we can obtain:

6) The relationship between saturated water vapor pressure and temperature is calculated using the Antoine formula:

lgp _s =AB/(C+T _c ) (6)

Where p _s - saturated vapor pressure (mmHg)

T _c —saturation temperature (°C).

In one or more embodiments, for substance water, A=8.10765, B=1750.266, C=235.00 when the temperature is 0-60°C. Therefore, the saturated vapor pressure corresponding to water is:

p _s = 10 ^AB/(C+T) = 10 ^{8.10765-1750.286/(235+Tc)} (7)

In one or more embodiments, T _c is related to t _w2 , D _w , K, and _Ac with a constant turbine steam volume D _c .

In one or more embodiments, the temperature of the circulating water in the condenser is 5-60°C, 10-50°C, preferably 20-40°C.

In one or more embodiments, based on a steam turbine operating characteristic model of an isentropic process, the isentropic efficiency is calculated using the instrument data of the steam turbine, and isentropic results are obtained, thereby evaluating the steam turbine operating state.

In one or more embodiments of the second aspect of the present invention, in step (2), a steam turbine power increment model is established according to the steam turbine data, and the relationship between the steam turbine power increment and the steam turbine back pressure is obtained. In one or more embodiments, the turbine data are turbine back pressure and turbine power rate of change.

In one or more embodiments, the turbine power increment model is constructed as follows:

1) When the load is constant, the relationship between steam turbine power and condenser pressure is shown in the following formula:

Δp=f(p _k ) (8)

Among them, Δp—— is the change rate of steam turbine power

p _k ——Steam turbine exhaust pressure

2) When the actual working load exceeds the rated load, the incremental model under the actual load of the steam turbine can be obtained by polynomial fitting.

In one or more embodiments, the polynomial fitting includes: obtaining a working curve of the steam turbine back pressure and the steam turbine power change rate under rated load, and taking the minimum sum of squares of the difference between the fitted value and the actual value as the objective function, and selecting The order of the fitted polynomial, the objective function is as follows:

为拟合值。where y is the true value,

is the fitted value.

In one or more embodiments, a multi-order fit is used, eg, a 3rd, 4th, 5th, and/or 6th order fit.

In one or more embodiments, using a 4th order fit, equation (8) is shown in equation (10), where p _k ≈ p _s ,

In one or more embodiments of the second aspect of the present invention, in step (2), a BP neural network is used to establish a cooling tower model.

In one or more embodiments, the cooling tower model is a black box model.

In one or more embodiments, the cooling tower model takes circulating water inlet temperature, fan power, circulating water flow and ambient temperature as inputs, and circulating water outlet temperature as output.

In one or more embodiments, the cooling tower model is as follows:

Y=f(X ₁ ,X ₂ ,X ₃ ,..X _n ,X _n+1 ,X _n+2 ,X _n+3 ) (11)

In the formula: Y—— is the temperature of the circulating water leaving the cooling tower

X _1～n ——represent the current of cooling fans 1-n respectively

X _n+1 —— is the temperature of the circulating water entering the cooling tower

X _n+2 —— circulating water flow

X _n+3 - ambient temperature

The power P _f of the cooling tower is as follows:

Among them, P _i represents the power of the ith fan without frequency conversion transformation.

In one or more embodiments, P _f is less than or equal to the rated power.

In one or more embodiments of the second aspect of the present invention, step (3) includes: taking the maximum net power increase of the steam turbine unit as the goal, the condenser pressure as the decision variable, and the power of the cooling tower and the limit back pressure of the steam turbine as constraints Condition, build the optimization model of the working condition of the steam turbine generator set.

In one or more embodiments, the circulating water flow rate remains substantially constant, and the initial temperature of the circulating water is controlled by the air flow of the cooling tower. The circulating water from the condenser after heat exchange directly enters the cooling tower through the pipeline for cooling, and then enters the condenser through the circulating water pump to achieve the purpose of reducing the pressure in the condenser. Among them, the cooling fan controls the air volume to achieve heat exchange, so as to achieve the purpose of reducing the temperature of the circulating water.

In one or more embodiments, the speed of the fan is controlled by the frequency converter to change the air volume of the cooling tower, and finally the temperature of the cooling circulating water is changed.

In one or more embodiments, according to the constraints of the cooling tower power and the ultimate back pressure of the steam turbine, the optimal model for the working conditions of the steam turbine generator set is obtained in combination with equations (7, 10, 11, 12):

Among them, ΔP _net - the net power increase of the steam turbine unit

P _t1 ——The power of the steam turbine after adjustment

P _{t0 ——The} power of the steam turbine before adjustment

P _{f1 ——The} power of the cooling tower fan after adjustment

P _f0 - Adjust the power of the front cooling tower fan

p - turbine back pressure

p _min ——minimum back pressure of steam turbine

p _max —— Maximum back pressure of steam turbine

P _f - power of cooling tower fan

P _fmax - rated power of cooling tower fan.

In one or more embodiments of the second aspect of the present invention, in step (4), an intelligent optimization algorithm is used to solve the optimization model to obtain optimized working condition parameters of the steam turbine generator set. The algorithm is such as whale algorithm, simulated annealing algorithm, differential evolution algorithm and so on. In one or more embodiments, the turbine generator set operating parameter is turbine back pressure.

The invention also discloses a method for improving the net power increase of the steam turbine generator set, the method comprising:

(1) Collect the process parameters of the steam turbine generator set,

(2) Using the model constructed according to the method described in the second aspect of this paper to optimize the working condition parameters of the steam turbine generator set,

(3) Adjust the turbo-generator set according to the optimized working condition parameters of the turbo-generator set, thereby increasing the net power increase of the turbo-generator set.

In one or more embodiments, the steam turbine generator set operating parameter includes steam turbine back pressure.

The invention also discloses a system for adjusting the working condition parameters of the steam turbine generator set and/or improving the net power increase of the steam turbine generator set, comprising:

The data acquisition module collects the process parameters of the steam turbine generator set,

The preprocessing module builds the condenser model, the steam turbine power increment model and the cooling tower model, and preprocesses the data collected by the data acquisition module.

The model building module uses the model of the preprocessing module to build an optimization model including constraints,

The data processing module solves the optimization model according to the constraints of the optimization model to obtain the optimized working condition parameters of the turbo-generator set, adjusts the turbo-generator set according to the working condition parameters, and then increases the net power increase of the turbo-generator set.

The invention also discloses a system for adjusting the working condition parameters of the turbo-generator set and/or improving the net power increase of the turbo-generator set, comprising a computer and a computer program running on the computer, and the computer program runs on the computer. The method described in the aspect and/or the above-mentioned method for increasing the net power increase of a turbo-generator set.

The present invention also discloses a computer-readable storage medium storing a computer program, which is characterized in that, after the computer program stored in the storage medium is executed, the method described in the second aspect of this document and/or the above-mentioned improvement in the cleanliness of the turbo-generator set is executed. method of increasing power.

A third aspect of the present invention provides a method for optimizing parameters of a waste heat power generation system and constructing a working condition model of the waste heat power generation system, wherein the waste heat power generation system includes a grate cooler and a steam turbine generator set, and the steam turbine generator set includes a condenser, A steam turbine and a cooling tower, the parameters of the waste heat power generation system include the operating condition parameters of the grate cooler and the operating condition parameters of the steam turbine generator set, and the method includes the steps:

(1) Collect the process parameters of the grate cooler, select the frequency response data by the spectral analysis method,

(2) Aiming at stabilizing grate down pressure and kiln head cover negative pressure, taking grate speed and exhaust fan speed as decision variables, using robust and decoupling constraints and the frequency response data of the controlled object grate cooler, construct a robust rod controller optimization model,

Preferably (3) solving the robust controller optimization model according to the constraints of the robust controller optimization model to obtain the optimized grate cooler working condition parameters;

(4) Collect the process parameters of the steam turbine generator set,

(5) Constructing a condenser model, a steam turbine power increment model and a cooling tower model, and preprocessing the data collected in step (4),

(6) use the model of (5) to construct an optimization model of the turbo-generator set including the constraints, and

Preferably (7) the optimization model of the turbo-generator set is solved according to the constraints of the optimization model of the turbo-generator set, so as to obtain the optimized working condition parameters of the turbo-generator set.

In one or more embodiments, the grate cooler operating parameters are grate speed and exhaust fan speed.

所述频率响应数据用G(jω),ω∈Ω表示，所述控制器记为

其中，Ω:R∪{∞}，G(j∞)＝0。In one or more embodiments, the controlled object is described as:

The frequency response data is represented by G(jω), ω∈Ω, and the controller is denoted as

Among them, Ω:R∪{∞}, G(j∞)=0.

In one or more embodiments of the third aspect of the present invention, in step (1), the grate cooler process parameters include one or more parameters selected from the group consisting of: grate down pressure, kiln hood negative pressure, grate speed and Exhaust fan speed.

In one or more embodiments, selecting the frequency response data includes: obtaining the frequency response data of the system by using the excitation signal, or selecting the frequency response data of the system by using spectral analysis by collecting process data in the time domain.

In one or more embodiments, the number of frequency response data ranges from 1000-5000, 1500-4500, 2000-4000, or 2500-3500.

In one or more embodiments, the frequency range of the frequency response data is selected based on a priori knowledge of the plant dynamics. In one or more embodiments, the frequency ranges from 0.01-10, 0.02-9.5, 0.03-9, 0.04-8.5, 0.05-8, 0.1-7.5.

In one or more embodiments, the frequencies of the frequency response data are selected in an equally spaced or logarithmically spaced manner.

In one or more embodiments, the plant is a two-in, two-out coupled system.

In one or more embodiments, constraints include: minimizing the dynamic deviation of the actual closed-loop system from the desired system, minimizing the magnitude of the sensitivity function, and/or minimizing the off-diagonal elements of the open-loop transfer function matrix and the principal pair Relative magnitude of corner elements.

In one or more embodiments of the third aspect of the present invention, step (2) comprises:

其中，q＝1,2；p＝1,2；优化变量控制器参数用向量

表示，基函数向量每个元素如下式所示：Let each element of the 2X2 controller be

Among them, q=1,2; p=1,2; the vector used to optimize the variable controller parameters

Representation, each element of the basis function vector is as follows:

In one or more embodiments, the robust controller optimization model is as follows:

in,

φ ^T =[φ ₀ ,φ ₁ ,…,φ _m-1 ]

Among them, α, β, γ are the system robust performance, closed-loop performance, and decoupling performance indicators; W _1,2 , W _2,1 in the A matrix are weighted sensitivity functions. Preferably, W _1,2 , W _2,1 are low-pass filters whose modulus is less than 1.

In the optimization model (1-A) constraint condition 1, the β of the index value in the A matrix and the optimized ρ are the product relationship, and the constraint condition is nonlinear.

In one or more embodiments of the third aspect of the present invention, the solution in step (3) is an approximate solution using a truncation method. In one or more embodiments, the truncation method includes selecting a finite set of frequency points, Ω _f = {ω ₁ , ω ₂ , . . . , ω _f }, such that feasible solutions satisfy constraints within the set.

In one or more embodiments, the solution of step (3) is transformed into solving a series of convex optimization feasible solution problems using a bisection method. In one or more embodiments, the robust controller optimization model is solved using a convex optimization method or a nonlinear optimization method.

In one or more embodiments, given β in each iterative solution, if the robust controller optimization model has a solution for the given β, then decrease the value of β at the next iteration, otherwise increase the value of β .

In one or more embodiments, the solution is terminated when the difference between the two obtained β values is smaller than a predetermined threshold ε.

In one or more embodiments of the third aspect of the present invention, the steam turbine generator set operating parameter is steam turbine back pressure.

In one or more embodiments of the third aspect of the present invention, in step (4), the process parameters of the turbo-generator set include one or more parameters selected from the group consisting of: the operation parameters of the condenser, the operation of the circulating water pump parameters, operating parameters of the vacuum pump, and operating parameters of the steam turbine unit.

In one or more embodiments, the turbo-generator set process parameters are collected and stored through the OPC interface of the distributed control system.

In one or more embodiments, turbo-generator set process parameters are collected every 5-30 seconds, preferably every 10, 15, 20, or 25 seconds.

In one or more embodiments, the turbo-generator set process parameters are stored in a database.

In one or more embodiments of the third aspect of the present invention, in step (5), a condenser model is established through mechanism analysis to obtain the relationship between the initial temperature of the circulating water and the condenser pressure.

In one or more embodiments, the condenser model is constructed as follows:

1) The steam temperature at the inlet of the condenser is equal to the saturation temperature t _c corresponding to the pressure p _c of the condenser, which can be expressed by formula (1):

t _c =t _w2 +δt (1)

where t _c is the temperature in the condenser, t _w2 is the temperature of the circulating cooling water coming out of the condenser, δt is the heat transfer end difference of the condenser,

2) When the exhaust steam condenses, the heat transferred to the cooling circulating water is

Q=D _c (h _c -h _n )=1000KA _c Δt=4.187D _w Δt (2)

Wherein D _c , D _w - steam flow and cooling circulating water flow into the steam turbine condenser (t/h);

h _c , h _n — the enthalpy of the condensed water produced by the steam turbine exhaust and cooling (KJ/kg);

K——The total heat transfer coefficient of the condenser (KJ/(m ² hK))

A _c ——the contact area between the outer surface of the cooling water pipe and the steam in the condenser (m ² )

Δt——The average heat transfer temperature difference between cooling circulating water and steam (℃),

3) The logarithmic average temperature difference can be written in the form of formula (3):

4) Combine formulas (2) and (3) to get:

5) After integrating formulas (1)-(4), we can obtain:

6) The relationship between saturated water vapor pressure and temperature is calculated using the Antoine formula:

lgp _s =AB/(C+T _c ) (6)

Where p _s - saturated vapor pressure (mmHg)

T _c —saturation temperature (°C).

In one or more embodiments, for substance water, A=8.10765, B=1750.266, C=235.00 when the temperature is 0-60°C. Therefore, the saturated vapor pressure corresponding to water is:

p _s = 10 ^AB/(C+T) = 10 ^{8.10765-1750.286/(235+Tc)} (7)

In one or more embodiments, T _c is related to t _w2 , D _w , K, and _Ac with a constant turbine steam volume D _c .

In one or more embodiments, the temperature of the circulating water in the condenser is 5-60°C, 10-50°C, preferably 20-40°C.

In one or more embodiments, based on a steam turbine operating characteristic model of an isentropic process, the isentropic efficiency is calculated using the instrument data of the steam turbine, and isentropic results are obtained, thereby evaluating the steam turbine operating state.

In one or more embodiments of the third aspect of the present invention, in step (5), a steam turbine power increment model is established according to the steam turbine data, and the relationship between the steam turbine power increment and the steam turbine back pressure is obtained. In one or more embodiments, the turbine data are turbine back pressure and turbine power rate of change.

In one or more embodiments, the turbine power increment model is constructed as follows:

1) When the load is constant, the relationship between steam turbine power and condenser pressure is shown in the following formula:

Δp=f(p _k ) (8)

Among them, Δp—— is the change rate of steam turbine power

p _k ——Steam turbine exhaust pressure

2) When the actual working load exceeds the rated load, the incremental model under the actual load of the steam turbine can be obtained by polynomial fitting.

In one or more embodiments, the polynomial fitting includes: obtaining a working curve of the steam turbine back pressure and the steam turbine power change rate under rated load, and taking the minimum sum of squares of the difference between the fitted value and the actual value as the objective function, and selecting The order of the fitted polynomial, the objective function is as follows:

为拟合值。where y is the true value,

is the fitted value.

In one or more embodiments, a multi-order fit is used, eg, a 3rd, 4th, 5th, and/or 6th order fit.

In one or more embodiments, using a 4th order fit, equation (8) is shown in equation (10), where p _k ≈ p _s ,

In one or more embodiments of the third aspect of the present invention, in step (5), a BP neural network is used to establish a cooling tower model.

In one or more embodiments, the cooling tower model is a black box model.

In one or more embodiments, the cooling tower model takes circulating water inlet temperature, fan power, circulating water flow and ambient temperature as inputs, and circulating water outlet temperature as output.

In one or more embodiments, the cooling tower model is as follows:

Y=f(X ₁ ,X ₂ ,X ₃ ,..X _n ,X _n+1 ,X _n+2 ,X _n+3 ) (11)

In the formula: Y—— is the temperature of the circulating water leaving the cooling tower

X _1～n ——represent the current of cooling fans 1-n respectively

X _n+1 —— is the temperature of the circulating water entering the cooling tower

X _n+2 —— circulating water flow

X _n+3 - ambient temperature

The power P _f of the cooling tower is as follows:

Among them, P _i represents the power of the ith fan without frequency conversion transformation.

In one or more embodiments, P _f is less than or equal to the rated power.

In one or more embodiments of the third aspect of the present invention, step (6) includes: taking the maximum net power increase of the steam turbine unit as the goal, the condenser pressure as the decision variable, and the power of the cooling tower and the limit back pressure of the steam turbine as constraints Condition, build a steam turbine generator set optimization model.

In one or more embodiments, the circulating water flow rate remains substantially constant, and the initial temperature of the circulating water is controlled by the air volume of the cooling tower. The circulating water from the condenser after heat exchange directly enters the cooling tower through the pipeline for cooling, and then enters the condenser through the circulating water pump to achieve the purpose of reducing the pressure in the condenser. Among them, the cooling fan controls the air volume to achieve heat exchange, so as to achieve the purpose of reducing the temperature of the circulating water.

In one or more embodiments, the speed of the fan is controlled by the frequency converter to change the air volume of the cooling tower, and finally the temperature of the cooling circulating water is changed.

In one or more embodiments, according to the constraints of cooling tower power and steam turbine limit back pressure, combined with equations (7, 10, 11, 12), the steam turbine generator set optimization model is obtained:

(14)

Among them,——net power increase of steam turbine unit

——The power of the steam turbine after adjustment

- Adjust the power of the front steam turbine

- Adjust the power of the cooling tower fan

——Adjust the power of the front cooling tower fan

- Turbine back pressure

——The minimum value of the back pressure of the steam turbine

- Maximum back pressure of steam turbine

- Power of cooling tower fan

- The rated power of the cooling tower fan.

In one or more embodiments of the third aspect of the present invention, in step (7), an intelligent optimization algorithm is used to solve the optimization model of the turbo-generator set to obtain optimized working condition parameters of the turbo-generator set. The algorithm is such as whale algorithm, simulated annealing algorithm, differential evolution algorithm and so on. In one or more embodiments, the steam turbine generator set operating parameter includes steam turbine back pressure.

The invention also discloses a method for improving the utilization rate of waste heat of a waste heat power generation system, wherein the waste heat power generation system includes a grate cooler and a steam turbine generator set, and the method includes:

(1) Collect the process parameters of the grate cooler and the process parameters of the steam turbine generator set,

(2) Using the grate cooler robust controller optimization model and the steam turbine generator set optimization model constructed according to the method described in the third aspect of this paper, the grate cooler working condition parameters and the steam turbine generator set working condition parameters of the waste heat power generation system are analyzed. optimize,

(3) Adjust the waste heat power generation system according to the optimized working condition parameters of the grate cooler and the steam turbine generator set, thereby improving the waste heat utilization rate of the waste heat power generation system.

The invention also discloses a system for improving the utilization rate of waste heat of a waste heat power generation system. The waste heat power generation system includes a grate cooler and a steam turbine generator set, and the system includes:

The data acquisition module collects the process parameters of the grate cooler and the process parameters of the steam turbine generator set,

The preprocessing module builds the condenser model, the steam turbine power increment model and the cooling tower model described in the third aspect of this paper, and preprocesses the data collected by the data acquisition module.

The model building module is used to construct the robust controller optimization model of the grate cooler described in the third aspect of this article, and the model of the preprocessing module is used to construct the steam turbine generator set optimization model described in the third aspect of this article.

The data processing module solves the optimization model according to the constraints of the two optimization models to obtain the optimized working condition parameters of the grate cooler and the steam turbine generator set, and adjusts the grate cooler and the steam turbine generator set according to these working condition parameters. Then, the utilization rate of waste heat of the waste heat power generation system is improved.

The invention also discloses a system for improving the utilization rate of waste heat of a waste heat power generation system, comprising a computer and a computer program running on the computer. A method of waste heat utilization in a power generation system.

The invention also discloses a computer-readable storage medium storing a computer program, characterized in that, after the computer program stored on the storage medium is executed, the method described in the third aspect herein and/or the above-mentioned method for improving the waste heat of the waste heat power generation system is executed. method of utilization.

The main advantages of the present invention are:

1. Combined with OPC technology, by establishing steam turbine model, condenser model and cooling tower model, the working state of the condenser can be quickly and accurately calculated.

2. Based on the optimization algorithm for optimization, it can quickly and accurately calculate the best working parameters of the condenser, give operation guidance and suggestions, and improve the power generation efficiency.

Description of drawings

Figure 1 shows the flow chart for solving the optimization model of the robust optimal decoupling control of the grate cooler by the dichotomy method.

Detailed ways

The invention includes the optimization of the grate cooler and the optimization of the cold end working condition of the steam turbine generator set, and the two can be used separately or simultaneously.

In the embodiment of the present invention, through the OPC interface of the distributed control system, the real-time data of the production process is read every 10 seconds, and the read data are mainly the operating parameters of the condenser, the operating parameters of the circulating water pump, the operating parameters of the vacuum pump and The operating parameters of the steam turbine unit are stored in the database of the client.

1. The optimization of the grate cooler includes obtaining the robust controller of the grate cooler, that is, the robust optimal decoupling controller of the grate cooler

的频率响应数据，G(jω),ω∈Ω，其中，Ω:R∪{∞}，G(j∞)＝0，寻找一个控制器

使得篦下压力和窑头罩负压稳定在目标值附近，以使得后续发电单元能获得稳定的热源。When the clinker temperature, granulation condition and cooling air volume are relatively stable, maintaining a certain grate lower pressure means that the thickness of the clinker layer on the grate bed can be guaranteed to be stable, so that stable secondary air, tertiary air and use The wind for power generation from waste heat creates conditions for a good and stable calcination process. If the material layer is too thick, it will be difficult for the cooling air to pass through the clinker, resulting in a low temperature of the residual air, affecting the heat exchange effect, not only increasing the content of free calcium oxide in the clinker, but also reducing the power generation. When the material layer is too thin, the cold air quickly penetrates the material layer, the residence time is short, the heat exchange effect is poor, and the power generation is also reduced. Therefore, in actual production, the thickness of the material on the grate bed is often controlled by adjusting the grate speed. In addition, in order to control the negative pressure of the kiln head cover, it is necessary to use the kiln head exhaust fan to balance the air flow in and out of the grate cooler. When there is positive pressure in the kiln head cover, increase the exhaust air volume of the kiln head; otherwise, reduce the exhaust air volume. Since the adjustment of the grate speed and the speed of the exhaust fan affects the air volume inside the grate cooler, the controlled variables of the grate down pressure, the negative pressure of the kiln head cover, and the controlled variables of the grate speed and the speed of the exhaust fan can be regarded as a coupling system with two inputs and two outputs. , taking into account the unmeasurable external disturbance factors such as kiln skin peeling off, clinker granulation characteristics fluctuation, kiln head cover negative pressure fluctuation, etc.

The frequency response data of , G(jω),ω∈Ω, where Ω:R∪{∞}, G(j∞)=0, find a controller

The grate lower pressure and the negative pressure of the kiln head cover are stabilized near the target value, so that the subsequent power generation units can obtain a stable heat source.

1.1 Frequency Domain Data Acquisition

The frequency response data of the system can be obtained directly by using the excitation signal, or the frequency response of the system can be estimated by spectrum analysis by collecting data in the time domain.

1.2 The finite constraint problem

Based on the conditions that the robust decoupling tracking controller needs to meet, the invention uses the frequency response data of the controlled object to construct an optimization model of the controller. Among the constraints of the optimization model, Constraint 1 is proposed based on the tracking performance. By minimizing the dynamic deviation between the actual closed-loop system and the desired system, the final designed closed-loop system can be closer to the desired value; Constraint 2 considers the output Noise suppression reduces the impact of output noise on the closed-loop system by minimizing the amplitude of the sensitivity function; Constraint 3 reduces the coupling by minimizing the relative amplitude of the off-diagonal elements and main diagonal elements of the open-loop transfer function matrix effect on the main circuit.

其中，q＝1,2；p＝1,2；控制器参数用向量

表示，基函数向量每个元素如下式所示：Let each element of the 2X2 controller be

Among them, q=1,2; p=1,2; controller parameters use vector

Representation, each element of the basis function vector is as follows:

Then the robust decoupled tracking controller can be obtained by solving the following convex optimization model:

in,

φ ^T =[φ ₀ ,φ ₁ ,…,φ _m-1 ]

In the optimization model, α, β, γ are the system robust performance, closed-loop performance, and decoupling performance indicators; W _1,2 , W _2,1 are weighted sensitivity functions. In actual design, sufficient frequency point data is helpful to obtain a stable controller and ensure system performance. Typically, the frequency range of interest can be selected based on a priori knowledge of the object's dynamics. In this embodiment, the system usually works in a lower frequency range, so more attention should be paid to the disturbance suppression of the low frequency band.

In the optimization model (1-A), since the constraints need to hold for all ω∈Ω, the number of constraints is infinite. Therefore, the problem is a semi-infinite programming problem, and theoretically there are many different solutions. In practical applications, a simple and effective truncation method is usually used to achieve an approximate solution, that is, a finite set of frequency points is selected, Ω _f ={ω ₁ ,ω ₂ ,...,ω _f }, so that the feasible solution satisfies within the set Restrictions.

The selection of frequency points can be equally spaced or logarithmically spaced, then the optimization model can be transformed into solving a positive semi-definite programming problem. Since the number of constraints is limited, the optimization methods developed at present can effectively solve the optimization model.

1.3 Algorithm steps

In the optimization model (1-A) constraint condition 1, the β of the index value and the optimization variable ρ are the product relationship, and the constraint condition is nonlinear. Therefore, when solving, using the bisection method can transform the solving process into solving a series of convex optimization feasible solution problems. In each iteration, as long as β is given, the optimization model (1-A) is a feasible solution problem satisfying an infinite number of convex constraints. If the optimization model has a solution for a given β, then decrease the value of β in the next iteration, otherwise, increase the value of β. Finally, the algorithm terminates when the difference between the two obtained β values is less than a predetermined threshold ε. As shown in Figure 1, the specific process includes: 1) obtaining frequency response data and initializing; 2) judging whether the index change is greater than the threshold: if the judgment result is "No", the iteration is ended, and the optimal solution is output; if the judgment result is "Yes" "Then solve the optimization model, if there is a solution, use the bisection method to reduce the index value, if there is no solution, use the bisection method to increase the index value, and then execute 2) again until the judgment result is no.

2. The optimization of the cold end condition of the steam turbine generator set includes the construction of the condenser model, the steam turbine power increment model and the cooling tower model.

Cold end equipment of steam turbine generator set: condenser, circulating water pump, cooling tower, vacuum pump and valve, etc. These devices work in coordination with each other to condense the exhausted steam discharged from the steam turbine into liquid water, which affects the working state of the generator set. For example, if the power of the cooling fan of the cooling tower is appropriately increased, and more natural air is used to cool the circulating water, the pressure of the condenser will naturally become lower, and the power generation of the turbine generator will be larger; When the pressure of the steam generator increases, the corresponding work of the steam turbine decreases, so there is a contradictory relationship between the power of the cooling fan of the cooling tower and the power of the steam turbine. However, most of the current technologies and applications change the pressure of the condenser by changing the circulating water flow, thereby changing the power generation of the steam turbine, without involving the cooling tower. .

In the present invention, the steam turbine cold end system model includes: a condenser model, a steam turbine power increment model and a cooling tower model. Since the cooling tower is used to cool the circulating water temperature, the heat exchange effect of the circulating water will be affected by the ambient temperature, and it is difficult to establish an accurate mechanism model. Therefore, the BP neural network is used to establish the black box model of the cooling tower. Water flow and ambient temperature are input, and circulating water outlet temperature is output. The condenser model is established through mechanism analysis, and the relationship between the initial temperature of the circulating water and the condenser pressure is obtained. According to the data in the steam turbine operation manual, establish the relationship between the steam turbine power increment and the steam turbine back pressure. Taking the maximum net power increase of the steam turbine unit as the goal, the condenser pressure as the decision variable, the power of the cooling tower and the limit back pressure of the steam turbine as the constraints, an optimization model is constructed, and the optimal back pressure of the steam turbine is solved by an intelligent optimization algorithm. Finally, under different working conditions, the optimal working condition of the steam turbine unit can be obtained, so that the net power increase of the unit can be maximized, and the purpose of increasing the power generation can be achieved.

2.1 Condenser Model

Based on field data analysis, at the exit of the final stage cylinder of the steam turbine, the condenser pressure p _c is basically the same as the saturated steam pressure p _s , that is, p _c ≈ p _s . Therefore, if the temperature t _c in the condenser is known, the pressure p _c in the condenser can be calculated. Ignoring the energy loss in the process, the steam temperature at the condenser inlet is equal to the saturation temperature t _c corresponding to the condenser pressure p _c , which can be expressed by formula (1):

t _c =t _w2 +δt (1)

Among them, t _w2 is the temperature of the circulating cooling water coming out of the condenser; δt is the heat transfer end difference of the condenser.

When the exhaust steam condenses, the heat transferred to the cooling circulating water is

Q=D _c (h _c -h _n )=1000KA _c Δt=4.187D _w Δt (2)

Wherein D _c , D _w - steam flow and cooling circulating water flow into the steam turbine condenser (t/h);

h _c , h _n — the enthalpy of the condensed water produced by the steam turbine exhaust and cooling (KJ/kg);

K——The total heat transfer coefficient of the condenser (KJ/(m ² hK))

A _c ——the contact area between the outer surface of the cooling water pipe and the steam in the condenser (m ² )

Δt——The average heat transfer temperature difference between cooling circulating water and steam (℃)

Due to the small area of the air cooling area of the steam turbine condenser, it can be assumed that the size of the exhaust steam temperature remains basically unchanged along the entire cooling area, so the logarithmic average temperature difference can be written in the form of formula (3):

Combining the two equations (2) and (3), we get:

Combining the above equations, we get:

The relationship between saturated water vapor pressure and temperature can be calculated using the Antoine formula:

lgp _s =AB/(C+T _c ) (6)

Where p _s - saturated vapor pressure (mmHg)

T _c - saturation temperature (°C)

For substance water, when the temperature is 0～60℃, A=8.10765, B=1750.266, C=235.00. Then the saturated vapor pressure corresponding to water is:

p _s = 10 ^AB/(C+T) = 10 ^{8.10765-1750.286/(235+Tc)} (7)

Under the condition that the steam quantity D _c of the steam turbine is constant, T _c is related to t _w2 , D _w , K, and A _c . In the field, the temperature of the circulating water in the condenser is usually between 20 and 40 °C. Based on the operating characteristic model of the steam turbine based on the isentropic process, as long as the instrument data and other parameters of the steam turbine are given, the isentropic efficiency can be calculated. The isentropic results are convenient for field engineers to evaluate the operating status of the steam turbine.

2.2 Turbine power increment model

When the load is constant, the relationship between steam turbine power and condenser pressure can be expressed by the following formula:

Δp=f(p _k ) (8)

Among them, Δp—— is the change rate of steam turbine power

p _k ——Steam turbine exhaust pressure

In one embodiment, the rated power of the steam turbine is 330 MW, and the rated power of the generator is 360 MW. Therefore, it is necessary to take into account the actual situation of the generator, and fit the “power increment change rate-back pressure” of the steam turbine according to the steam turbine operation manual. Curve equation.

The data points are obtained from the operation manual, the horizontal axis is the turbine back pressure, respectively [4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9], and the vertical axis is the turbine power change rate, respectively is [0.04, 0.0238, 0.0067, -0.0104, -0.0264, -0.0404, -0.0523, -0.062, -0.0697, -0.0762, -0.0826], taking the minimum sum of squares of the difference between the fitted value and the actual value as the objective function, Select the order of the fitted polynomial, and the objective function is as follows:

为拟合值，分别用3阶、4阶、5阶和6阶进行多项式的拟合，不同阶数多项式对应的目标函数f如下表1所示。where y is the true value,

For the fitting value, the 3rd, 4th, 5th and 6th order polynomials are used for fitting respectively. The objective function f corresponding to the polynomials of different orders is shown in Table 1 below.

Table 1 Objective functions of different orders

3rd order 4th order Level 5 6th order f 0.0329 0.0221 0.0352 0.0403

It can be obtained from Table 1 that the square of the difference between the fitting value of the fourth-order polynomial and the actual value is the smallest, and its expression is shown in formula (10).

where p _k ≈ p _s

2.3 Cooling tower model

The flow of circulating cooling water in the cooling tower basically does not change, and the circulating water from the condenser enters the cooling. In one embodiment, cooling is performed by four fans. One of the four fans is used as a backup, and a maximum of three fans can be used. The four fans are of the same model, and the equipment parameters are shown in Table 2. At the same time, the heat exchange effect is also affected by the ambient temperature and humidity. For example, due to the large difference in ambient temperature between noon and night, the heat exchange effect on the circulating water is different under the same air volume. Therefore, the ambient temperature needs to be considered.

Table 2 Cooling fan equipment parameters

The cooling tower is a relatively complex system with many influencing factors, so it is difficult to establish an accurate mechanism model. Therefore, the present invention uses a neural network to establish a "black box" model, and takes the circulating water inlet temperature, fan current, circulating water flow rate and ambient temperature as input, and the circulating water outlet temperature as output. In one embodiment, 200 sets of data are used to build the model, and 110 sets of data are used for model validation. The model of the cooling tower is shown below:

Y=f(X ₁ , X ₂ , X ₃ , X ₄ , X ₅ , X ₆ , X ₇ ) (11)

In the formula: Y—— is the temperature of the circulating water leaving the cooling tower

X _1～4 ——represent the current of cooling fan No. 1-4 respectively

X ₅ - the temperature of the circulating water entering the cooling tower

X ₆ - circulating water flow

X ₇ - ambient temperature

P _f represents the power of the cooling tower, and its expression is as follows:

Among them, P _i represents the power of the ith fan without frequency conversion transformation. In field production, in order to ensure safe production and equipment safety, the motor power cannot exceed the rated power. Therefore, there is a maximum constraint on the cooling tower power P _f .

2.4 Optimization model of steam turbine cold end operation

In one embodiment, the circulating water flow rate remains substantially unchanged, and the initial temperature of the circulating water can be controlled by the air volume of the cooling tower. The circulating water from the condenser after heat exchange directly enters the cooling tower through the pipeline for cooling, and then enters the condenser through the circulating water pump to achieve the purpose of reducing the pressure in the condenser. Among them, the cooling fan controls the air volume to achieve heat exchange, so as to achieve the purpose of reducing the temperature of the circulating water.

In general, the lower the pressure in the condenser, the more work the turbine will do when the back pressure of the turbine is not lower than the limit. At the same time, the greater the power of the cooling fan, the lower the pressure in the condenser, without exceeding the cooling fan load. Therefore, there is an optimal state between the two, that is, under different working conditions, different optimal back pressures are required. Through the aforementioned robust control method of the present invention, the heat recovery amount of the grate cooler is stabilized, and a relatively stable steam flow is maintained. Then, the speed of the fan is controlled by the frequency converter to change the air volume of the cooling tower, and finally the temperature of the cooling circulating water is changed.

Considering the steam turbine limit back pressure and the cooling tower rated power constraints, the following optimization model is obtained by combining equations (7, 10, 11, 12):

Among them, ΔP _net - the net power increase of the steam turbine unit

P _t1 ——The power of the steam turbine after adjustment

P _{t0 ——The} power of the steam turbine before adjustment

P _{f1 ——The} power of the cooling tower fan after adjustment

P _f0 - Adjust the power of the front cooling tower fan

p - turbine back pressure

p _min ——minimum back pressure of steam turbine

p _max —— Maximum back pressure of steam turbine

P _f - power of cooling tower fan

P _fmax - rated power of cooling tower fan

Using mature intelligent optimization algorithms to solve optimization models with constraints, such as whale algorithm, simulated annealing algorithm, differential evolution algorithm, etc., the optimal working conditions of the steam turbine unit under different working conditions can be calculated to maximize the net power increase of the unit. .

In addition, the present invention also discloses a system for adjusting the system parameters of the grate cooler and/or controlling the lower pressure of the grate cooler, including a computer and a computer program running on the computer, and the computer program runs on the computer the optimized grate of the foregoing embodiment. A method of chiller system parameters and/or a method of controlling grate cooler grate down pressure. The invention also discloses a system for adjusting the working condition parameters of the turbo-generator set and/or improving the net power increase of the turbo-generator set, comprising a computer and a computer program running on the computer, the computer program running on the computer as described in the foregoing embodiments A method for optimizing the working condition parameters of a turbo-generator set and/or a method for increasing the net power increase of the turbo-generator set. The specific steps of the method are not repeated here.

In addition, the present invention also discloses a computer-readable storage medium storing a computer program, which is characterized in that, after the computer program stored on the storage medium is executed, the method according to the foregoing embodiments is executed. The specific steps of the method are not repeated here.

Although the above-described methods are illustrated and described as a series of acts for simplicity of explanation, it should be understood and appreciated that these methods are not limited by the order of the acts, as some acts may occur in a different order in accordance with one or more embodiments and/or occur concurrently with other actions from or not shown and described herein but understood by those skilled in the art.

Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The various illustrative logic blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented using general purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other Programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein are implemented or performed. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in the ASIC. The ASIC may reside in the user terminal. In the alternative, the processor and storage medium may reside in the user terminal as discrete components.

In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium can be any available medium that can be accessed by a computer. By way of example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, or can be used to carry or store instructions or data structures in the form of Any other medium that conforms to program code and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave , then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc as used herein includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blu-ray disc, where disks are often reproduced magnetically data, and discs reproduce the data optically with a laser. Combinations of the above should also be included within the scope of computer-readable media.

The previous description of the present disclosure is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to the present disclosure will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the present disclosure. Thus, the present disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Example

Example 1

Taking a waste heat power generation 330MW steam turbine unit as the research object, the rated power is 330MW, the limit back pressure is 3.8Pa, and the rated back pressure is 5.2kPa. In working condition 1, the intake air volume is 180t/h, the power of the steam turbine unit is 340MW, the back pressure is 5.12kPa, the optimal back pressure after optimization is 5.0923kPa, the net power increase of the steam turbine unit is 0.0932MW, an increase of 0.027412% ; In working condition 2, the intake air volume is 178t/h, the power of the steam turbine unit is 336MW, the back pressure is 5.104kPa, the optimal back pressure after optimization is 5.125kPa, the net power increase of the steam turbine unit is 0.0425MW, an increase of 0.012649 %; In working condition 3, the intake air volume is 172t/h, the power of the steam turbine unit is 330MW, the back pressure is 5.22kPa, the optimal back pressure after optimization is 5.193kPa, the net power increase of the steam turbine unit is 0.0125MW, which increases the 0.003788%.

Table 3 Cases of optimization results under different working conditions

Claims (10)

1. A method for optimizing operating parameters of a grate cooler and constructing a robust controller model of a grate cooler system, comprising: (1) Collect the process parameters of the grate cooler, select the frequency response data by the spectral analysis method, (2) Aiming at stabilizing grate down pressure and kiln head cover negative pressure, taking grate speed and exhaust fan speed as decision variables, using robust and decoupling constraints and the frequency response data of the controlled object grate cooler, construct a robust rod controller optimization model, Preferably (3) solve the optimization model according to the constraints of the optimization model to obtain the optimized grate cooler working condition parameters, where the grate cooler working condition parameters are the grate speed and the rotational speed of the exhaust fan. 2. The method of claim 1, wherein the method has one or more features selected from the group consisting of: In one or more embodiments, the controlled object is:

The frequency response data is represented by G(jω), ω∈Ω, and the controller is

Among them, Ω:R∪{∞}, G(j∞)=0, In step (1): The grate cooler process parameters include one or more parameters selected from the group consisting of: grate down pressure, kiln hood negative pressure, grate speed and exhaust fan speed, Selecting the frequency response data includes: obtaining the frequency response data of the system by using the excitation signal, or selecting the frequency response data of the system by using spectrum analysis by collecting the process data in the time domain, The number of frequency response data ranges from 1000-5000, 1500-4500, 2000-4000, or 2500-3500, The frequency range of the frequency response data is selected according to the prior knowledge of the dynamic characteristics of the plant, Select the frequency of the frequency response data in an equally spaced or logarithmically spaced manner, In step (2): The controlled object is a coupled system with two inputs and two outputs. Constraints include: minimizing the dynamic deviation of the actual closed-loop system from the desired system, minimizing the magnitude of the sensitivity function, and/or minimizing the relative magnitude of the off-diagonal and main-diagonal elements of the open-loop transfer function matrix, Step (2) includes: making each element of the 2X2 controller be

Among them, q=1,2; p=1,2; the vector used to optimize the variable controller parameters

represents, each element of the basis function vector is as follows: φ ₀ (s)=1,

The robust controller optimization model is as follows:

s.t.Re{AΦρ}＞0 0<α<1, 0＜γ＜1, ω∈Ω,

in,

φ ^T =[φ ₀ ,φ ₁ ,…,φ _m-1 ]

Among them, α, β, γ are the system robust performance, closed-loop performance, and decoupling performance indicators; W _1,2 , W _2,1 in the A matrix are the weighted sensitivity functions, In the constraint condition 1 of the optimization model (1-A), the β of the index value in the A matrix and the optimized ρ are the product relationship, In step (3): The solution of step (3) is to use the truncation method for approximate solution, The truncation method includes: selecting a limited set of frequency points, Ω _f ={ω ₁ ,ω ₂ ,...,ω _f }, so that the feasible solution satisfies the constraints in this set, The solution of step (3) is transformed into a series of convex optimization feasible solution problems using the bisection method. 3. A method for controlling the grate pressure of a grate cooler, the method comprising: (1) Collect the process parameters of the grate cooler, select the frequency response data by the spectral analysis method, (2) adopting the model constructed according to the method according to any one of claims 1-2 to optimize the grate cooler working condition parameters, (3) Adjust the grate cooler according to the optimized working condition parameters of the grate cooler, and then control the grate lower pressure to ensure the stability of the negative pressure of the kiln head cover. 4. A system for adjusting working condition parameters of a grate cooler and/or controlling the grate pressure of a grate cooler, comprising: The data acquisition module collects the process parameters of the grate cooler, selects the frequency response data, The optimization model builds the module, aiming at stabilizing grate down pressure and kiln hood negative pressure, taking grate speed and exhaust fan speed as decision variables, using robust and decoupling constraints and the frequency response data of the controlled object grate cooler, constructing the robust controller optimization model described in any one of claims 1-2, The data processing module solves the optimization model according to the constraints of the optimization model to obtain the optimized working condition parameters of the grate cooler, adjusts the grate cooler and controls the grate lower pressure according to the parameters. 5. A system for regulating grate cooler working condition parameters and/or controlling grate cooler grate pressure, comprising a computer and a computer program running on the computer, the computer program running any one of claims 1-2 on the computer method described in item. 6. A method for optimizing parameters of a waste heat power generation system and constructing a working condition model of the waste heat power generation system, the waste heat power generation system comprising a grate cooler and a steam turbine generator set, the steam turbine generator set comprising a condenser, a steam turbine and a cooling tower , the parameters of the waste heat power generation system include the working condition parameters of the grate cooler and the working condition parameters of the steam turbine generator set, and the method includes the steps: (1) Collect the process parameters of the grate cooler, select the frequency response data by the spectral analysis method, (2) Aiming at stabilizing grate down pressure and kiln head cover negative pressure, taking grate speed and exhaust fan speed as decision variables, using robust and decoupling constraints and the frequency response data of the controlled object grate cooler, construct a robust rod controller optimization model, Preferably (3) solve the robust controller optimization model according to the constraints of the robust controller optimization model to obtain the optimized grate cooler working condition parameters; (4) Collect the process parameters of the steam turbine generator set, (5) Constructing a condenser model, a steam turbine power increment model and a cooling tower model, and preprocessing the data collected in step (4), (6) use the model of (5) to construct an optimization model of the turbo-generator set including the constraints, and Preferably (7) the optimization model of the turbo-generator set is solved according to the constraints of the optimization model of the turbo-generator set, so as to obtain the optimized working condition parameters of the turbo-generator set. 7. The method of claim 6, wherein the method has one or more characteristics selected from the group consisting of: The operating parameters of the grate cooler are the grate speed and the speed of the exhaust fan. The working condition parameter of the steam turbine generator set is the back pressure of the steam turbine, The controlled object is described as:

The frequency response data is represented by G(jω), ω∈Ω, and the controller is denoted as

Among them, Ω:R∪{∞}, G(j∞)=0, in step (1) The grate cooler process parameters include one or more parameters selected from the group consisting of: grate down pressure, kiln hood negative pressure, grate speed and exhaust fan speed, Selecting the frequency response data includes: obtaining the frequency response data of the system by using the excitation signal, or selecting the frequency response data of the system by using spectrum analysis by collecting the process data in the time domain, The number of frequency response data ranges from 1000-5000, 1500-4500, 2000-4000, or 2500-3500, The frequency range of the frequency response data is selected according to the prior knowledge of the dynamic characteristics of the plant, Select the frequency of the frequency response data in an equally spaced or logarithmically spaced manner, In step (2): The controlled object is a coupled system with two inputs and two outputs. Constraints include: minimizing the dynamic deviation of the actual closed-loop system from the desired system, minimizing the magnitude of the sensitivity function, and/or minimizing the relative magnitude of the off-diagonal and main-diagonal elements of the open-loop transfer function matrix, Step (2) includes: making each element of the 2X2 controller be

Among them, q=1,2; p=1,2; the vector used to optimize the variable controller parameters

represents, each element of the basis function vector is as follows: φ ₀ (s)=1,

The robust controller optimization model is as follows:

s.t.Re{AΦρ}＞0 0<α<1, 0＜γ＜1, ω∈Ω,

in,

φ ^T =[φ ₀ ,φ ₁ ,…,φ _m-1 ]

Among them, α, β, γ are the system robust performance, closed-loop performance, and decoupling performance indicators; W _1,2 , W _2,1 in the A matrix are the weighted sensitivity functions, In the optimization model (1-A) constraint 1, the β of the index value in the A matrix and the optimized ρ are the product relationship, In step (3): The solution of step (3) is to use the truncation method for approximate solution, The truncation method includes: selecting a limited set of frequency points, Ω _f ={ω ₁ ,ω ₂ ,...,ω _f }, so that the feasible solution satisfies the constraints in this set, The solution of step (3) is transformed into a series of convex optimization feasible solution problems by using the bisection method, In step (4), The process parameters of the steam turbine generator set include one or more parameters selected from the following: the operation parameters of the condenser, the operation parameters of the circulating water pump, the operation parameters of the vacuum pump, the operation parameters of the steam turbine set, In step (5), Through the mechanism analysis, the condenser model is established, and the relationship between the initial temperature of the circulating water and the pressure of the condenser is obtained. According to the data of the steam turbine, the power increment model of the steam turbine is established, and the relationship between the steam turbine power increment and the back pressure of the steam turbine is obtained, The cooling tower model is established by BP neural network, In step (6): Step (6) includes: taking the maximum net power increase of the steam turbine unit as the goal, the condenser pressure as the decision variable, the power of the cooling tower and the limit back pressure of the steam turbine as the constraint conditions, and constructing an optimization model of the steam turbine unit, In step (7), Using the intelligent optimization algorithm to solve the optimization model of the turbo-generator set, the optimized working condition parameters of the turbo-generator set are obtained. 8. A method for improving the utilization rate of waste heat of a waste heat power generation system, the waste heat power generation system comprising a grate cooler and a steam turbine generator set, the method comprising: (1) Collect the process parameters of the grate cooler and the process parameters of the steam turbine generator set, (2) adopting the grate cooler robust controller optimization model and the steam turbine generator set optimization model constructed in the method according to any one of claims 6-7 to analyze the grate cooler operating condition parameters and steam generators of the waste heat power generation system The working parameters of the turbo-generator set are optimized, (3) Adjust the waste heat power generation system according to the optimized working condition parameters of the grate cooler and the steam turbine generator set, thereby improving the waste heat utilization rate of the waste heat power generation system. 9. A system for improving the utilization rate of waste heat of a waste heat power generation system, the waste heat power generation system comprising a grate cooler and a steam turbine generator set, the system comprising: The data acquisition module collects the process parameters of the grate cooler and the process parameters of the steam turbine generator set, The preprocessing module, constructing the condenser model, the steam turbine power increment model and the cooling tower model described in any one of claims 6-7, preprocessing the data collected by the data acquisition module, Model building module, builds the grate cooler robust controller optimization model described in any one of claim 6-7, and utilizes the model of preprocessing module to build the steam turbine described in any one of claim 6-7. Turbogenerator set optimization model, The data processing module solves the optimization model according to the constraints of the two optimization models to obtain the optimized working condition parameters of the grate cooler and the steam turbine generator set, and adjusts the grate cooler and the steam turbine generator set according to these working condition parameters. Then, the utilization rate of waste heat of the waste heat power generation system is improved. 10. A system for improving the utilization rate of waste heat of a waste heat power generation system, comprising a computer and a computer program running on the computer, the computer program running the method according to any one of claims 6-7 on the computer.

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