CN111338211B

CN111338211B - Waste heat utilization process optimization control method and system

Info

Publication number: CN111338211B
Application number: CN202010160432.4A
Authority: CN
Inventors: 杜文莉; 朱远明; 钟伟民; 钱锋; 赵亮
Original assignee: East China University of Science and Technology
Current assignee: East China University of Science and Technology
Priority date: 2020-03-10
Filing date: 2020-03-10
Publication date: 2022-08-12
Anticipated expiration: 2040-03-10
Also published as: CN111338211A

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

Waste heat utilization process optimization control method and system

Technical Field

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

Background

With the background of increasingly scarce natural resources, cogeneration systems have become an essential part of industrial production (e.g. dry cement production). The waste heat power generation system includes: the grate cooler, the cold end system of the steam turbine and the boiler system. The grate cooler is a heat source of a waste heat utilization system, cement clinker falls onto a grate bed through a rotary kiln and enters a heat exchange process, and the process has the characteristics of large time lag, strong interference, easy change of working conditions and the like. The pressure under the grate is difficult to control stably, so that the air temperature after heat exchange is low and the fluctuation is large, and the waste heat recovery rate is seriously reduced. In addition, the cold end system of the steam turbine does not operate on the optimal working condition many times, the waste heat utilization rate is reduced, and the generated energy is reduced. Therefore, the grate cooler pressure of the waste heat power generation system is controlled, and the cold end system of the steam turbine is operated and optimized, so that the waste heat utilization rate is improved, and the enterprise benefit is increased.

At present, in control strategies aiming at waste heat recovery of a cement kiln waste heat power generation system, steam turbine power generation and boiler steam generation links, most of methods based on experience, such as fuzzy control, expert system and the like, are adopted for pressure control under a grate of a grate cooler, and special sudden working conditions are difficult to deal with; the operation optimization method of the cold end system of the steam turbine is mostly based on a circulating water pump model, and the optimization research aiming at the cooling tower model is less; in contrast, the research on boiler systems is mature, and model-based or model-free control strategies are widely applied to actual production and have good control effects.

Based on the consideration, the dynamic characteristic data of the grate cooler is analyzed, the multi-model uncertainty factor is considered based on the established characteristic model, and the 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.

Disclosure of Invention

The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor 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.

The invention aims to provide a method for stabilizing the working condition of a grate cooler by designing a robust controller to inhibit interference. The method is suitable for the waste heat power generation system including but not limited to the cement kiln.

The invention provides a method for optimizing the operating condition parameters of a grate cooler and constructing a robust controller model of a grate cooler system, which comprises the following steps:

(1) collecting the process parameters of the grate cooler, selecting frequency response data by a spectrum analysis method,

(2) the method is characterized in that a robust controller optimization model is constructed by taking stable grate pressure and kiln hood negative pressure as targets, taking grate speed and exhaust fan rotating speed as decision variables and utilizing robust and decoupling constraint conditions and frequency response data of a controlled object grate cooler,

and preferably (3) solving the optimization model according to the constraint conditions of the optimization model to obtain optimized grate cooler working condition parameters, wherein the grate cooler working condition parameters comprise grate speed and exhaust fan rotating speed.

In one or more embodiments, the controlled object is described as:

the frequency response data is represented by G (j ω), ω ∈ Ω, and the controller is recorded as

Wherein, R { ∞ } is R { ∞, G (j ∞) } is 0.

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

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

In one or more embodiments, the amount of frequency response data ranges from 1000-.

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

In one or more embodiments, the frequencies of the frequency response data are selected in an equally or logarithmically spaced manner. At this time, the optimization model is converted into a semi-positive planning problem. In one or more embodiments, the optimization model may be solved using a general optimization method, such as a convex optimization method or a non-linear optimization method.

In one or more embodiments, the controlled object 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 comprise: 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 off-diagonal elements and main diagonal elements of the open loop transfer function matrix.

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

let the 2X2 controller have each element as

Wherein q is 1, 2; p is 1, 2; vector for optimizing variable controller parameters

Expressed, the basis function vectors are each represented by the following formula:

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

wherein, the first and the second end of the pipe are connected with each other,

φ ^T ＝[φ ₀ ,φ ₁ ,…,φ _m-1 ]

wherein, alpha, beta and gamma are respectively system robust performance, closed-loop performance and decoupling performance indexes; w in A matrix _1,2 ,W _2,1 Is a weighted sensitivity function. Preferably, W _1,2 ,W _2,1 Taken as a low pass filter modulo less than 1.

In the optimization model (1-a) constraint condition 1, β of the index value in the a matrix and ρ of the optimization are a 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 approximated by using a truncation method. In one or more embodiments, the truncation process comprises: selecting a limited set of frequency points, Ω _f ＝{ω ₁ ,ω ₂ ,…,ω _f And } so that the feasible solutions satisfy the constraints within the set.

In one or more embodiments, the solution of step (3) is converted to solve a series of convex optimization feasible solution problems using a dichotomy method.

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

In one or more embodiments, the solution terminates when the difference between two consecutive resulting β values is less than a predetermined threshold ε.

The invention also discloses a method for controlling the pressure below the grate of the grate cooler, which comprises the following steps:

(2) the model constructed by the method according to the first aspect of the invention is adopted to optimize the operating parameters of the grate cooler,

(3) the grate cooler is adjusted according to the optimized working condition parameters of the grate cooler, so that the pressure under the grate is controlled, and the stable negative pressure of a kiln head cover is ensured.

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 pressure under the grate of the grate cooler, which comprises the following components:

the data acquisition module is used for acquiring the process parameters of the grate cooler, selecting frequency response data,

an optimization model building module, which takes stable grate pressure and kiln hood negative pressure as targets, takes grate speed and exhaust fan rotating speed as decision variables, and utilizes robust and decoupling constraint conditions and frequency response data of a grate cooler of a controlled object to build a robust controller optimization model,

and the data processing module is used for solving the optimization model according to the constraint condition of the optimization model to obtain the optimized working condition parameters of the grate cooler, adjusting the grate cooler according to the parameters and controlling the pressure under the grate.

The invention also discloses a system for adjusting the working condition parameters of the grate cooler and/or controlling the pressure below the grate of the grate cooler, which comprises a computer and a computer program running on the computer, wherein the computer program runs the method of the first aspect of the invention and/or the method for controlling the pressure below the grate of the grate cooler on the computer.

The invention also discloses a computer readable storage medium storing a computer program, which is characterized in that the computer program stored on the storage medium is operated to execute the method of the first aspect of the invention and/or the method for controlling the grate pressure of the grate cooler.

The invention further aims to provide a method for optimizing the working condition of the steam turbine generator unit through the condenser model, the steam turbine power increment model and the cooling tower model. The method is suitable for the waste heat power generation system including but not limited to the cement kiln.

The second aspect of the present invention provides a method for optimizing operating condition parameters of a steam turbine generator unit and constructing an operating condition model of the steam turbine generator unit, wherein the steam turbine generator unit comprises a condenser, a steam turbine and a cooling tower, and the method comprises the steps of:

(1) collecting the process parameters of the steam turbine generator unit,

(2) constructing a condenser model, a turbine power increment model and a cooling tower model, preprocessing the data acquired in the step 1),

(3) constructing an optimization model containing constraints using the model of (2), and

and preferably (4) solving the optimization model according to the constraint conditions of the optimization model to obtain the optimized working condition parameters of the steam turbine generator unit.

In one or more embodiments, the steam turbine unit operating parameter is turbine backpressure.

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

In one or more embodiments, the steam turbine generator unit process parameters are collected and stored through an OPC interface of the distributed control system.

In one or more embodiments, the steam turbine unit process parameters are collected every 5 to 30 seconds, preferably every 10, 15, 20, or 25 seconds.

In one or more embodiments, the steam turbine generator unit 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 by a mechanistic analysis to obtain a 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 condenser inlet equals the condenser pressure p _c Corresponding saturation temperature t _c Can be represented by the formula (1):

t _c ＝t _w2 +δt (1)

wherein t is _c Is the temperature in the condenser, t _w2 Delta t is the temperature of the circulating cooling water from the condenser, delta t is the heat transfer end difference of the condenser,

2) the heat transferred to the cooling circulation water during the condensation of the discharged steam 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 circulation water flow (t/h) entering the turbine condenser;

h _c 、h _n -the enthalpy (KJ/kg) of the steam turbine exhaust steam and of the condensate produced by cooling;

K-Total Heat transfer coefficient of condenser (KJ/(m) ² hK))

A _c The area (m) of the outer surface of the cooling water pipeline in contact with the steam in the condenser ² )

Delta t-the average heat transfer temperature difference (deg.C) between the chilled circulating water and steam,

3) the logarithmic mean temperature difference can be written in the form of equation (3):

4) combining the two formulas (2) and (3) to obtain:

5) when the formulae (1) to (4) are combined, they can be obtained:

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

lgp _s ＝A-B/(C+T _c ) (6)

wherein p is _s Saturated vapour pressure (mmHg)

T _c -saturation temperature (. degree. C.).

In one or more embodiments, for the substance water, when the temperature is 0 to 60 ℃, a is 8.10765, B is 1750.266, and C is 235.00. Thus, the corresponding saturated vapor pressure of water is:

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

in one or more embodiments, steam flow D is measured at the turbine _c Without change, T _c And t _w2 、D _w 、K、A _c It is related.

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

In one or more embodiments, the steam turbine operating state is evaluated by calculating isentropic efficiency using instrumentation data of the steam turbine based on an isentropic process steam turbine operating characteristic model to obtain an isentropic result.

In one or more embodiments of the second aspect of the present invention, in step (2), a turbine power increase model is built based on the turbine data to obtain a relationship between turbine power increase and turbine back pressure. In one or more embodiments, the turbine data is turbine back pressure and turbine power rate of change.

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

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

Δp＝f(p _k ) (8)

where Δ p-is the rate of change of turbine power

p _k -exhaust pressure of steam turbine

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

In one or more embodiments, the polynomial fitting comprises: obtaining a working curve of the backpressure of the steam turbine and the power change rate of the steam turbine under rated load, selecting the order of a fitting polynomial by taking the minimum sum of squares of the difference between a fitting value and an actual value as a target function, wherein the target function is as follows:

wherein, y is a true value,

are fit values.

In one or more embodiments, a multiple order fit is used, such as a 3 th order, 4 th order, 5 th order, and/or 6 th order fit.

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

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

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

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

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 out of the cooling tower

X _1～n Respectively representing cooling fan currents 1-n

X _n+1 -temperature of circulating water entering cooling tower

X _n+2 -flow of circulating water

X _n+3 -ambient temperature

Power P of cooling tower _f As shown in the following formula:

wherein, P _i Representing the power of the ith fan without frequency conversion modification.

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

In one or more embodiments of the second aspect of the present invention, step (3) comprises: and constructing an optimization model of the working condition of the steam turbine generator unit by taking the maximum net power increase of the steam turbine unit as a target, the pressure of a condenser as a decision variable and the power of a cooling tower and the limit back pressure of a steam turbine as constraint conditions.

In one or more embodiments, the flow rate of the circulating water is maintained substantially constant and the initial temperature of the circulating water is controlled by the air flow rate of the cooling tower. Circulating water coming out of the condenser after heat exchange directly enters a cooling tower through a pipeline for cooling and then enters the condenser through a circulating water pump so as to achieve the purpose of reducing the pressure in the condenser. Wherein, the cooling blower controls the amount of wind to realize heat exchange, so as to achieve the purpose of reducing the temperature of the circulating water.

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

In one or more embodiments, the combination of (7,10,11,12) yields an optimized model of the turbo unit operating conditions based on constraints on cooling tower power and turbine ultimate back pressure:

wherein, Δ P _net Net power augmentation of a steam turbine plant

P _t1 -adjusting the power of the rear turbine

P _t0 Adjusting the power of the front turbine

P _f1 -adjusting the power of the cooling tower fan

P _f0 Adjusting the power of the front cooling tower fan

p-turbine back pressure

p _min -minimum value of back pressure of steam turbine

p _max Maximum value of back pressure of the turbine

P _f Power of cooling tower fan

P _fmax -rated power of the cooling tower fan.

In one or more embodiments of the second aspect of the present invention, in step (4), the optimized model is solved by using an intelligent optimization algorithm, so as to obtain optimized operating parameters of the steam turbine generator unit. Such as whale algorithm, simulated annealing algorithm, differential evolution algorithm, etc. In one or more embodiments, the steam turbine unit operating parameter is turbine backpressure.

The invention also discloses a method for improving the net power increase of the steam turbine generator unit, which comprises the following steps:

(1) collecting the process parameters of the steam turbine generator unit,

(2) the steam turbine generator unit operating condition parameters are optimized by using the model constructed according to the method of the second aspect,

(3) and adjusting the steam turbine generator unit according to the optimized working condition parameters of the steam turbine generator unit, and then improving the net power increase of the steam turbine generator unit.

In one or more embodiments, the steam turbine unit operating parameter includes a steam turbine back pressure.

The invention also discloses a system for adjusting the working condition parameters of the steam turbine generator unit and/or improving the net power increase of the steam turbine generator unit, which comprises the following steps:

a data acquisition module for acquiring the process parameters of the steam turbine generator unit,

a preprocessing module for constructing a condenser model, a turbine power increment model and a cooling tower model, preprocessing the data acquired by the data acquisition module,

a model construction module for constructing an optimization model containing constraint conditions by using the model of the preprocessing module,

and the data processing module is used for solving the optimization model according to the constraint conditions of the optimization model so as to obtain the optimized working condition parameters of the steam turbine generator unit, adjusting the steam turbine generator unit according to the working condition parameters and then improving the net increase power of the steam turbine generator unit.

The invention also discloses a system for adjusting the working condition parameters of the steam turbine generator unit and/or improving the net power increase of the steam turbine generator unit, which comprises a computer and a computer program running on the computer, wherein the computer program runs the method of the second aspect of the text on the computer and/or the method for improving the net power increase of the steam turbine generator unit.

The invention also discloses a computer-readable storage medium storing a computer program, wherein the computer program stored on the storage medium is executed to perform the method of the second aspect and/or the method for improving the net power increase of a steam turbine generator unit.

The third aspect of the 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 comprises a grate cooler and a steam turbine generator unit, the steam turbine generator unit comprises a condenser, a steam turbine and a cooling tower, the parameters of the waste heat power generation system comprise the working condition parameters of the grate cooler and the working condition parameters of the steam turbine generator unit, and the method comprises the following steps:

preferably, (3) solving the robust controller optimization model according to the constraint conditions of the robust controller optimization model to obtain optimized grate cooler working condition parameters;

(4) collecting the process parameters of the steam turbine generator unit,

(5) constructing a condenser model, a turbine power increment model and a cooling tower model, preprocessing the data acquired in the step (4),

(6) constructing an optimization model of the turbo generator unit including the constraint conditions by using the model of (5), and

and preferably, (7) solving the optimization model of the steam turbine generator unit according to the constraint conditions of the optimization model of the steam turbine generator unit to obtain the optimized working condition parameters of the steam turbine generator unit.

In one or more embodiments, the controlled object is described as:

Wherein, R { ∞ } is R { ∞, G (j ∞) } is 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 pressure, kiln hood negative pressure, grate speed and exhaust fan rotating speed.

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

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

In one or more embodiments, 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 off-diagonal elements and main diagonal elements of the open loop transfer function matrix.

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

let the 2X2 controller have each element as

φ ^T ＝[φ ₀ ,φ ₁ ,…,φ _m-1 ]

wherein, alpha, beta and gamma are respectively system robust performance, closed loop performance and decoupling performance indexes; w in A matrix _1,2 ,W _2,1 Is a weighted sensitivity function. Preferably, W _1,2 ,W _2,1 Taken as a low pass filter modulo less than 1.

In one or more embodiments of the third aspect of the present invention, the solution in step (3) is approximated by using a truncation method. In one or more embodiments, the truncation process comprises: selecting a limited set of frequency points, Ω _f ＝{ω ₁ ,ω ₂ ,…,ω _f And } so that the feasible solutions satisfy the constraints within the set.

In one or more embodiments, the solution of step (3) is converted to solve a series of convex optimization feasible solution problems using a dichotomy method. In one or more embodiments, the robust controller optimization model is solved using a convex optimization method or a non-linear optimization method.

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

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

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

In one or more embodiments of the third aspect of the present invention, in step (5), a condenser model is established by a mechanistic analysis to obtain a 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:

t _c ＝t _w2 +δt (1)

wherein t is _c Is the temperature in the condenser, t _w2 Delta t is the temperature of the circulating cooling water coming out of the condenserThe difference is that the number of the first and second,

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

K-Total Heat transfer coefficient of the condenser (KJ/(m) ² hK))

4) combining the two formulas (2) and (3) to obtain:

5) when the formulae (1) to (4) are combined, they can be obtained:

lgp _s ＝A-B/(C+T _c ) (6)

wherein p is _s Saturated vapour pressure (mmHg)

T _c -saturation temperature (. degree. C.).

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

In one or more embodiments of the third aspect of the present invention, in step (5), a turbine power increase model is built based on the turbine data to obtain a relationship between turbine power increase and turbine back pressure. In one or more embodiments, the turbine data is turbine back pressure and turbine power rate of change.

Δp＝f(p _k ) (8)

where Δ p-is the rate of change of turbine power

p _k -exhaust pressure of steam turbine

wherein, y is a true value,

are fit values.

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

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

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)

X _1～n Respectively representing cooling fan currents 1-n

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

X _n+2 ——Flow rate of circulating water

X _n+3 -ambient temperature

Power P of cooling tower _f As shown in the following formula:

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

In one or more embodiments of the third aspect of the present invention, step (6) comprises: and constructing an optimized model of the steam turbine generator unit by taking the maximum net power increase of the steam turbine generator unit as a target, the pressure of a condenser as a decision variable and the power of a cooling tower and the limit back pressure of a steam turbine as constraint conditions.

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

(14)

wherein-net power increase of the turboset

-adjusting the power of the rear turbine

Adjusting the power of the front turbine

-adjusting the power of the cooling tower fan

Adjusting the power of the front cooling tower fan

-back pressure of steam turbine

-minimum value of back pressure of steam turbine

Maximum value of back pressure of the turbine

Power of cooling tower fan

-rated power of the cooling tower fan.

In one or more embodiments of the third aspect of the present invention, in step (7), the steam turbine generator unit optimization model is solved by using an intelligent optimization algorithm, so as to obtain optimized steam turbine generator unit operating condition parameters. Such as whale algorithm, simulated annealing algorithm, differential evolution algorithm, etc. In one or more embodiments, the steam turbine unit operating parameter includes a steam turbine back pressure.

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

(1) collecting the process parameters of the grate cooler and the process parameters of the steam turbine generator unit,

(2) optimizing the grate cooler working condition parameters and the steam turbine generator unit working condition parameters of the waste heat power generation system by adopting the grate cooler robust controller optimization model and the steam turbine generator unit optimization model which are constructed according to the method of the third aspect,

(3) and adjusting the waste heat power generation system according to the optimized working condition parameters of the grate cooler and the working condition parameters of the steam turbine generator unit, so that the waste heat utilization rate of the waste heat power generation system is improved.

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

a data acquisition module for acquiring the process parameters of the grate cooler and the process parameters of the steam turbine generator unit,

a preprocessing module, which constructs the condenser model, the turbine power increment model and the cooling tower model of the third aspect of the text, preprocesses the data acquired by the data acquisition module,

a model construction module for constructing the optimization model of the grate cooler robust controller in the third aspect and constructing the optimization model of the steam turbine generator unit in the third aspect by using the model of the preprocessing module,

and the data processing module is used for solving the optimization model according to the constraint conditions of the two optimization models to obtain the optimized grid cooling machine working condition parameters and the optimized steam turbine generator unit working condition parameters, and adjusting the grid cooling machine and the steam turbine generator unit according to the working condition parameters so as to improve the waste heat utilization rate of the waste heat power generation system.

The invention also discloses a system for improving the waste heat utilization rate of the waste heat power generation system, which comprises a computer and a computer program running on the computer, wherein the computer program runs the method in the third aspect of the text on the computer and/or the method for improving the waste heat utilization rate of the waste heat power generation system.

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

The main advantages of the invention are:

1. the working state of the condenser can be rapidly and accurately calculated by establishing a turbine model, a condenser model and a cooling tower model by combining an OPC technology.

2. Optimization is carried out based on an optimization algorithm, the optimal working parameters of the condenser can be rapidly and accurately calculated, an operation guidance suggestion is given, and the power generation efficiency is improved.

Drawings

FIG. 1 shows a flow chart of an optimization model for binary method solving grate cooler robust optimal decoupling control.

Detailed Description

The method comprises the optimization of the grate cooler and the optimization of the cold end working condition of the steam turbine generator unit, wherein the two can be used respectively or simultaneously.

In the embodiment of the invention, the real-time data of the production process is read once every 10 seconds through an OPC interface of a distributed control system, and the read data mainly comprise the operation parameters of a condenser, the operation parameters of a circulating water pump, the operation parameters of a vacuum pump and the operation parameters of a steam turbine set and are stored in a database of a client.

1. The optimization of the grate cooler comprises obtaining a robust controller of the grate cooler, namely a robust optimal decoupling controller of the grate cooler

Under the condition that the clinker temperature, the granulation condition and the cooling air quantity are relatively stable, a certain pressure under the grate is kept, which means that the thickness of a clinker layer on the grate bed can be ensured to be stable, so that stable secondary air, stable tertiary air and air for waste heat power generation can be recycled, and conditions are created for a good and stable calcining process. If the material layer is too thick, cooling air is difficult to penetrate through the clinker, so that the temperature of the residual air is low, the heat exchange effect is influenced, the content of free calcium oxide in the clinker is increased, and the power generation amount is reduced. When the material layer is too thin, cold air quickly penetrates through the material layer, the residence time is short, the heat exchange effect is poor, and the generated energy can be reduced. Therefore, the thickness of the material on the grate bed is often controlled by adjusting the grate speed in practical production. In addition, in order to control the negative pressure of the kiln head cover, the air inlet and outlet amount of the grate cooler needs to be balanced by a kiln head exhaust fan. When the kiln head cover has positive pressure, the exhaust volume of the kiln head is increased; otherwise, the exhaust air volume is reduced. Because the adjustment of the grate speed and the rotating speed of the exhaust fan affects the air quantity in the grate cooler, the pressure under the controlled variable grate, the negative pressure of a kiln hood and the rotating speed of the controlled variable grate and the exhaust fan can be regarded as a two-in two-out coupling system, and the invention takes the factors of kiln skin falling off in the kiln, clinker aggregate characteristic fluctuation, kiln hood negative pressure fluctuation and the like which can not be detected into consideration, constructs a frequency response model based on the two-in two-out relationship, and utilizes the controlled object to construct a frequency response model

G (j ω), ω ∈ Ω, wherein Ω: R { ∞ } and G (j ∞) ═ 0, a controller is sought

The grate pressure and the kiln head cover negative pressure are stabilized near the target value, so that the subsequent power generation unitA stable heat source can be obtained.

1.1 frequency Domain data acquisition

The frequency response data of the system is obtained directly by using the excitation signal or is estimated by collecting data in the time domain and using spectral analysis.

1.2 Limited constraint problem

The method is based on the conditions required to be met by the robust decoupling tracking controller, and utilizes the frequency response data of the controlled object to construct an optimization model of the controller. In the constraint conditions of the optimization model, the constraint condition 1 is proposed based on tracking performance, and the finally designed closed-loop system can be closer to the expected value by minimizing the dynamic deviation of the actual closed-loop system and the expected system; constraint 2 considers the suppression of output noise, and reduces the influence of the output noise on the closed-loop system by minimizing the amplitude of the sensitivity function; constraint 3 reduces the effect of coupling on the main loop by minimizing the relative amplitudes of the off-diagonal elements and the main diagonal elements of the open-loop transfer function matrix.

Let the 2X2 controller have each element as

Wherein q is 1, 2; p is 1, 2; vector for controller parameter

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

wherein the content of the first and second substances,

φ ^T ＝[φ ₀ ,φ ₁ ,…,φ _m-1 ]

in the optimization model, alpha, beta and gamma are respectively system robust performance, closed-loop performance and decoupling performance indexes; w _1,2 ,W _2,1 Is a weighted sensitivity function. In actual design, sufficient frequency point data is helpful for obtaining a stable controller, and system performance is guaranteed. In general, the frequency range of interest may be selected based on a priori knowledge of the dynamic characteristics of the object. In this embodiment, the system generally operates in a lower frequency range, and the disturbance rejection in the lower frequency range needs to be more concerned.

In the optimization model (1-A), since the constraint condition needs to be satisfied for all ω ∈ Ω, the number of constraint conditions is infinite. Thus, the problem is a semi-infinite planning problem, and theoretically there are many different solutions. In practical applications, the approximate solution is usually implemented by using a simple and efficient truncation method, i.e. selecting a limited set of frequency points, Ω _f ＝{ω ₁ ,ω ₂ ,…,ω _f And } causing the feasible solutions to satisfy constraints within the set.

The frequency points may be chosen to be equally spaced or logarithmically spaced, and the optimization model may then be converted to solve a semi-positive planning problem. Due to the limited number of the constraint conditions, the optimization model can be effectively solved by the currently developed and mature optimization method.

1.3 Algorithm Steps

In the optimization model (1-A) constraint condition 1, the index value beta and the optimization variable rho are in a product relation, and the constraint condition is nonlinear. Therefore, when in solving, the dichotomy can convert the solving process into the solving of a series of convex optimization feasible solutions. In each iteration, the optimization model (1-A) is a feasible solution problem which satisfies infinite convex constraint conditions as long as beta is given. If the optimization model has a solution for a given β, then the β value is decreased at the next iteration, otherwise the β value is increased. Finally, the algorithm is terminated when the difference between the two adjacent beta values is less than a predetermined threshold epsilon. As shown in fig. 1, the specific process includes: 1) acquiring frequency response data and initializing; 2) determining whether the index variation is larger than a threshold value: if the judgment result is 'no', ending the iteration and outputting the optimal solution; and if the judgment result is yes, solving the optimization model, if the solution exists, reducing the index value by using a dichotomy, if the solution does not exist, increasing the index value by using the dichotomy, and then executing the step 2) again until the judgment result is no.

2. And the optimization of the cold end working condition of the turbo generator unit comprises the construction of a condenser model, a turbine power increment model and a cooling tower model.

Cold end equipment of the turbo generator set: condenser, circulating water pump, cooling tower, vacuum pump and valve etc.. The devices work in coordination with each other to condense the exhaust steam discharged by the steam turbine into liquid water, thereby affecting the working state of the generator set. For example, the power of a cooling fan of a cooling tower is properly increased, more natural wind is used for cooling circulating water, the pressure of a condenser naturally becomes lower, and the power generation amount of a turbonator is larger; on the contrary, the power of the cooling fan is reduced, the pressure of the condenser is increased, and the work of the corresponding turbine is reduced, so that the power of the cooling fan of the cooling tower and the power of the turbine are in a pair of contradictory relations. The invention provides a method for optimizing a cold end of a turbonator in combination with a cooling tower.

In the invention, the cold end system model of the steam turbine comprises: the system comprises a condenser model, a turbine power increment model and a cooling tower model. Because the cooling tower is used for cooling the circulating water temperature, the heat exchange effect of the circulating water can be influenced by the environmental temperature, and an accurate mechanism model is difficult to establish, therefore, a BP neural network is used for establishing a black box model of the cooling tower, the circulating water inlet temperature, the fan power, the circulating water flow and the environmental temperature are used as input, and the circulating water outlet temperature is used as output. And establishing a condenser model through mechanism analysis to obtain the relation between the initial temperature of the circulating water and the pressure of the condenser. And establishing a relation between the power increment of the steam turbine and the back pressure of the steam turbine according to data on an operation manual of the steam turbine. The method comprises the steps of constructing an optimization model by taking the maximum net power increase of a steam turbine unit as a target, the pressure of a condenser as a decision variable and the power of a cooling tower and the limit back pressure of a steam turbine as constraint conditions, and solving the optimal back pressure of the steam turbine through an intelligent optimization algorithm. Finally, the optimal working condition of the steam turbine unit can be obtained under different working conditions, so that the net power increase of the unit is maximum, and the purpose of increasing the generated energy is achieved.

2.1 condenser model

On the basis of field data analysis, the condenser pressure p at the outlet of the last cylinder of the steam turbine _c With saturated steam pressure p _s Are substantially identical, i.e. p _c ≈p _s . Therefore, if the temperature t in the condenser is known _c The pressure p in the condenser can be calculated _c . Neglecting the energy loss in the process, the steam temperature at the condenser inlet is equal to the condenser pressure p _c Corresponding saturation temperature t _c Can be represented by the formula (1):

t _c ＝t _w2 +δt (1)

wherein t is _w2 The temperature of the circulating cooling water from the condenser; and delta t is the heat transfer end difference of the condenser.

The heat transferred to the cooling circulation water during the condensation of the discharged steam is

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

h _c 、h _n -turbine exhaust steam and cooling productionThe enthalpy value (KJ/kg) of the condensed water of (1);

K-Total Heat transfer coefficient of the condenser (KJ/(m) ² hK))

Δ t-mean temperature difference in Heat transfer between Cooling circulating Water and steam (. degree.C.)

Because the area of the air cooling area of the turbine condenser is small, the magnitude of the exhaust steam temperature can be assumed to be basically constant along the whole cooling area, and therefore, the logarithmic mean temperature difference can be written into a form of an equation (3):

combining the formulas (2) and (3) to obtain:

the above formula is integrated to obtain:

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

lgp _s ＝A-B/(C+T _c ) (6)

wherein p is _s Saturated vapour pressure (mmHg)

T _c Saturation temperature (. degree. C.)

When the temperature of the substance water is 0-60 ℃, A is 8.10765, B is 1750.266, and C is 235.00. The corresponding saturated vapor pressure of water is:

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

in steam turbine steam quantity D _c Without change, T _c And t _w2 、D _w 、K、A _c It is related. In the field, the temperature of circulating water in a condenser is usually 20-40 ℃, and based on an isentropic process steam turbine operation characteristic model, the isentropic efficiency can be calculated as long as instrument data and other parameters of the steam turbine are given, so that an isentropic result is obtained, and a field engineer can conveniently evaluate the operation state of the steam turbine.

2.2 steam turbine Power increment model

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

Δp＝f(p _k ) (8)

where Δ p-is the rate of change of turbine power

p _k -exhaust pressure of steam turbine

In one embodiment, the rated power of the steam turbine is 330MW, and the rated power of the generator is 360MW, so that the "power increment change rate-back pressure" curve equation of the steam turbine is fitted according to the turbine operation manual in consideration of the actual condition of the generator.

Obtaining data points from an operation manual, wherein the horizontal axis is turbine back pressure and is respectively [4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9], the vertical axis is turbine power change rate and is respectively [0.04, 0.0238, 0.0067, -0.0104, -0.0264, -0.0404, -0.0523, -0.062, -0.0697, -0.0762, -0.0826], and the target function is taken as the minimum sum of squares of differences between a fitted value and an actual value, and the order of the fitted polynomial is selected as follows:

wherein, y is a true value,

for the fitting values, 3 rd order, 4 th order, 5 th order and 6 th order polynomials are respectively used for fitting, and the objective functions f corresponding to the polynomials of different orders are shown in the following table 1.

TABLE 1 Objective function of different orders

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

From table 1, the square of the difference between the fitting value of the 4 th order polynomial and the actual value is the smallest, and the expression is shown as equation (10).

Wherein p is _k ≈p _s

2.3 Cooling Tower model

The flow of the circulating cooling water in the cooling tower basically cannot be changed, and the circulating water from the condenser enters the cooling tower. In one embodiment, cooling is performed by four fans. 1 of the four fans is used for standby, and at most, only 3 fans can be started. The four fans are the same in model number, and the equipment parameters are shown in table 2. Meanwhile, the heat exchange effect is also affected by the ambient temperature, the ambient humidity and the like, for example, the heat exchange effect on the circulating water is different in the noon and the evening because the ambient temperature difference is large, and therefore the ambient temperature needs to be considered.

TABLE 2 Cooling Fan Equipment parameters

The cooling tower is a complex system, has more influencing factors, and is difficult to establish an accurate mechanism model. Therefore, the invention adopts a neural network to establish a 'black box' model, and takes the circulating water inlet temperature, the fan current, the circulating water flow and the environment 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 verification. The model of the cooling tower is as follows:

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

X _1～4 Respectively representing cooling fan currents 1-4

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

X ₆ -flow of circulating water

X ₇ -ambient temperature

P _f Representing the power of the cooling tower, the expression of which is as follows:

wherein, P _i Representing the power of the ith fan without frequency conversion modification. In the field production, in order to ensure the safe production and the equipment safety, the power of the motor cannot exceed the rated power, so the power P of the cooling tower _f There is a maximum constraint.

2.4 steam turbine cold end operation optimization model

In one embodiment, the flow rate of the circulating water is kept basically constant, and the initial temperature of the circulating water can be controlled by the air volume of the cooling tower. Circulating water coming out of the condenser after heat exchange directly enters a cooling tower through a pipeline for cooling and then enters the condenser through a circulating water pump so as to achieve the purpose of reducing the pressure in the condenser. Wherein, the cooling blower controls the amount of wind to realize heat exchange, so as to achieve the purpose of reducing the temperature of the circulating water.

Generally, the lower the pressure in the condenser, the more work the turbine will do without going below the turbine's ultimate back pressure. Meanwhile, under the condition that the load of the cooling fan is not exceeded, the higher the power of the cooling fan is, the lower the pressure in the condenser is. Therefore, an optimal state exists between the two, namely different optimal back pressures are needed under different working conditions. The heat recovery amount of the grate cooler is stabilized by the robust control method, and the relatively stable steam flow is maintained. Then, the rotating speed of the fan is controlled through the frequency converter to change the air quantity of the cooling tower, and finally the temperature of the cooling circulating water is changed.

Considering the turbine limit back pressure and the rated power constraint of the cooling tower, the combination formula (7,10,11,12) obtains the following optimization model:

wherein, Δ P _net Net power augmentation of a steam turbine plant

P _t1 -adjusting the power of the rear turbine

P _t0 Adjusting the power of the front turbine

P _f1 -adjusting the power of the cooling tower fan

P _f0 Adjusting the power of the front cooling tower fan

p-turbine back pressure

p _min -minimum value of back pressure of steam turbine

p _max Maximum value of back pressure of the turbine

P _f Power of cooling tower fan

P _fmax Rated power of the cooling tower fan

And solving an optimization model with constraints by using a mature intelligent optimization algorithm, such as a whale algorithm, a simulated annealing algorithm, a differential evolution algorithm and the like, so that the optimal working conditions of the steam turbine set under different working conditions can be calculated, and the net power increment of the steam turbine set is maximum.

In addition, the invention also discloses a system for adjusting the grid cooler system parameters and/or controlling the grid cooler pressure, which comprises a computer and a computer program running on the computer, wherein the computer program runs the method for optimizing the grid cooler system parameters and/or the method for controlling the grid cooler pressure on the computer. The invention also discloses a system for adjusting the working condition parameters of the steam turbine generator unit and/or improving the net power increase of the steam turbine generator unit, which comprises a computer and a computer program running on the computer, wherein the computer program runs the method for optimizing the working condition parameters of the steam turbine generator unit and/or the method for improving the net power increase of the steam turbine generator unit on the computer. The specific steps of the method are not described in detail.

In addition, the invention also discloses a computer readable storage medium storing a computer program, which is characterized in that the computer program stored on the storage medium is executed to execute the method according to the previous embodiment. The specific steps of the method are not described in detail.

While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood by one skilled in the art.

Those of skill would 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 upon the particular 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 logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. 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, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction 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, a removable disk, a 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 integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

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 media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures 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 a 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 (disk) and disc (disc), as used herein, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disks) usually reproduce data magnetically, while discs (discs) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the 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.

Examples

Example 1

A certain waste heat power generation 330MW steam turbine set is used as a research object, the rated power is 330MW, the limit back pressure is 3.8Pa, and the rated back pressure is 5.2 kPa. In the first working condition, the air input is 180t/h, the power of the steam turbine set is 340MW, the backpressure is 5.12kPa, the optimized optimal backpressure is 5.0923kPa, the net power increment of the steam turbine set is 0.0932MW, and the net power increment is improved by 0.027412%; in the second working condition, the air input is 178t/h, the power of the steam turbine set is 336MW, the back pressure is 5.104kPa, the optimized optimal back pressure is 5.125kPa, the net power increment of the steam turbine set is 0.0425MW, and the net power increment is improved by 0.012649%; in the third working condition, the air input is 172t/h, the power of the steam turbine set is 330MW, the backpressure is 5.22kPa, the optimized optimal backpressure is 5.193kPa, the net power increment of the steam turbine set is 0.0125MW, and the improvement is 0.003788%.

TABLE 3 case of optimization results under different conditions

Claims

1. A grate cooler working condition parameter optimization method based on a robust controller model comprises the following steps:

(1) collecting the process parameters of the grate cooler, obtaining the frequency response data of the system by using the excitation signal, or selecting the frequency response data of the system by collecting the process data in the time domain and using spectral analysis,

(3) solving the optimization model according to the constraint condition of the optimization model to obtain optimized grate cooler working condition parameters, wherein the grate cooler working condition parameters are grate speed and exhaust fan rotating speed,

wherein the content of the first and second substances,

the controlled object is as follows:

the frequency response data is represented by G (j omega), omega E omega, and the controller is

Wherein, R { ∞ } is R { ∞, G (j ∞) ═ 0,

in step (1):

the grate cooler process parameters comprise: pressure under the grate, negative pressure of kiln head cover, grate speed and rotation speed of exhaust fan,

the number of frequency response data ranges from 1000- > 5000,

the frequency range of the frequency response data is selected based on a priori knowledge of the dynamic characteristics of the controlled object,

the frequencies of the frequency response data are selected in an equally or logarithmically spaced manner,

in step (2):

the controlled object is a two-in two-out coupled system,

the constraint conditions 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 elements and the main diagonal elements of the open loop transfer function matrix,

the step (2) comprises the following steps: let the 2X2 controller have each element as

Expressed, the basis function vectors are each represented by the following formula: phi is a ₀ (s)＝1,

ξ＞0，

The robust controller optimization model is as follows:

s.t.Re{AΦρ}＞0

0＜α＜1,

0＜γ＜1,

ω∈Ω,

wherein the content of the first and second substances,

φ ^T ＝[φ ₀ ,φ ₁ ,…,φ _m-1 ]

wherein, alpha, beta and gamma are respectively system robust performance, closed-loop performance and decoupling performance indexes; w in A matrix _1,2 ,W _2,1 In order to weight the sensitivity function,

in constraint 1 of the optimization model (1-A), beta of the index value in the A matrix and the optimized rho are in a product relationship,

in step (3):

the solution in the step (3) is approximate solution by a truncation method,

the truncation method comprises the following steps: selecting a limited set of frequency points, Ω _f ＝{ω ₁ ,ω ₂ ,…,ω _f A constraint is satisfied by the feasible solutions within the set,

and (4) converting the solution in the step (3) into a series of convex optimization feasible solution problems by adopting a dichotomy.

2. A method of controlling grate down pressure, the method comprising:

(1) collecting the process parameters of the grate cooler, obtaining the frequency response data of the system by utilizing the excitation signal, or selecting the frequency response data of the system by collecting the process data in the time domain and utilizing spectral analysis,

(2) the method according to claim 1 is adopted to optimize the operating parameters of the grate cooler,

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

3. A system for adjusting the operating parameters of a grate cooler by the method of claim 1, comprising:

and the data processing module is used for solving the optimization model according to the constraint conditions of the optimization model to obtain the optimized working condition parameters of the grate cooler and adjusting the grate cooler according to the parameters.

4. A system for controlling grate down pressure using the method of claim 1 comprising:

and the data processing module is used for solving the optimization model according to the constraint conditions of the optimization model to obtain the optimized working condition parameters of the grate cooler and controlling the pressure under the grate according to the parameters.

5. A system for adjusting the operating parameters of a grate cooler comprises a computer and a computer program running on the computer, wherein the computer program runs the method in the claim 1 on the computer.

6. A system for controlling grate down pressure comprising a computer and a computer program running on the computer, the computer program running on the computer the method of claim 1.

7. A waste heat power generation system parameter optimization method based on a waste heat power generation system working condition model is disclosed, wherein the waste heat power generation system comprises a grate cooler and a steam turbine generator unit, the steam turbine generator unit comprises a condenser, a steam turbine and a cooling tower, the waste heat power generation system parameters comprise a grate cooler working condition parameter and a steam turbine generator unit working condition parameter, and the method comprises the following steps:

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

(4) collecting the process parameters of the steam turbine generator unit,

(7) solving the optimization model of the turbo generator unit according to the constraint conditions of the optimization model of the turbo generator unit to obtain the optimized working condition parameters of the turbo generator unit,

wherein the content of the first and second substances,

the working condition parameters of the grate cooler are grate speed and exhaust fan rotating speed,

the working condition parameter of the steam turbine generator unit is the back pressure of a steam turbine,

the controlled object is described as:

Wherein, R { ∞ } is R { ∞, G (j ∞) ═ 0,

in step (1)

The grate cooler process parameters comprise: grate pressure, kiln head cover negative pressure, grate speed and exhaust fan rotating speed,

the number of frequency response data ranges from 1000- > 5000,

in step (2):

the controlled object is a two-in two-out coupled system,

the step (2) comprises the following steps: let 2X2 controller each element be

ξ＞0，

The robust controller optimization model is as follows:

s.t.Re{AΦρ}＞0

0＜α＜1,

0＜γ＜1,

ω∈Ω,

wherein the content of the first and second substances,

φ ^T ＝[φ ₀ ,φ ₁ ,…,φ _m-1 ]

in the constraint condition 1 of the optimization model (1-A), beta of index values in an A matrix and optimized rho are in a product relation,

in step (3):

the solution in the step (3) is approximate solution by a truncation method,

the truncation method comprises the following steps: selecting a limited set of frequency points, Ω _f ＝{ω ₁ ,ω ₂ ,…,ω _f -making feasible solutions meet constraints within the set,

converting the solution of the step (3) into a series of convex optimization feasible solution problems by adopting a dichotomy method,

in the step (4), the step (c),

the steam turbine generator unit process parameters include one or more parameters selected from the group consisting of: 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 turbine set,

in the step (5), the step (c),

establishing a condenser model through mechanism analysis to obtain the relation between the initial temperature of the circulating water and the pressure of the condenser,

establishing a turbine power increment model according to turbine data to obtain the relationship between the turbine power increment and the turbine back pressure,

a cooling tower model is established by adopting a BP neural network,

in step (6):

the step (6) comprises the following steps: constructing an optimized model of the steam turbine generator unit by taking the maximum net power increase of the steam turbine generator unit as a target, the pressure of a condenser as a decision variable and the power of a cooling tower and the limit back pressure of a steam turbine as constraint conditions,

in the step (7), the step (c),

and solving an optimization model of the steam turbine generator unit by using an intelligent optimization algorithm to obtain optimized working condition parameters of the steam turbine generator unit.

8. A method for improving the waste heat utilization rate of a waste heat power generation system, wherein the waste heat power generation system comprises a grate cooler and a steam turbine generator unit, and the method comprises the following steps:

(2) the method of claim 7 is adopted to optimize the grate cooler working condition parameters and the steam turbine generator unit working condition parameters of the waste heat power generation system,

9. A system for increasing the waste heat utilization rate of a waste heat power generation system comprising a grate cooler and a steam turbine generator set by using the method of claim 7, the system comprising:

a model construction module for constructing a grate cooler robust controller optimization model and utilizing the model of the pretreatment module to construct a steam turbine generator unit optimization model,

10. A system for increasing the waste heat utilization rate of a waste heat power generation system, comprising a computer and a computer program running on the computer, wherein the computer program runs on the computer the method of claim 7.