CN116483003A

CN116483003A - Control method, device and equipment for coal water slurry manufacturing process

Info

Publication number: CN116483003A
Application number: CN202310319957.1A
Authority: CN
Inventors: 王浩; 孙路滨; 边潇潇; 许中华; 郑梁; 王远辉; 姜良建; 贺慧霞; 张宏科
Original assignee: Wanhua Chemical Group Co Ltd; Wanhua Chemical Ningbo Co Ltd
Current assignee: Wanhua Chemical Group Co Ltd; Wanhua Chemical Ningbo Co Ltd
Priority date: 2023-03-27
Filing date: 2023-03-27
Publication date: 2023-07-25

Abstract

The embodiment of the disclosure provides a control method, a device and equipment for a coal water slurry manufacturing process. The method comprises the following steps: obtaining a current coal slurry interference variable and a current coal slurry controlled variable in the water coal slurry manufacturing process; invoking a target multivariable predictive model trained in advance; inputting the current coal slurry disturbance variable and the current coal slurry controlled variable into the target multivariable predictive model to obtain a current coal slurry operation variable; and regulating and controlling at least part of the first coal slurry parameters according to the current coal slurry controlled variable and the current coal slurry operating variable. In this way, the coal water slurry with the highest concentration can be obtained on the premise of ensuring the safe and stable operation of the coal mill according to the coal quality of raw coal and the real-time operation condition of the coal mill.

Description

Control method, device and equipment for coal water slurry manufacturing process

Technical Field

The disclosure relates to the field of coal water slurry, in particular to the technical field of control of a coal water slurry manufacturing process.

Background

The coal water slurry is the main raw material of the coal water slurry gasification furnace, the coal water slurry preparation device mainly comprises a coal mill, a coal slurry tank, a coal slurry pump and other equipment, as shown in figure 1, raw coal, 1 to more strands of coal grinding water and additives are crushed in the coal mill and mixed to form the coal water slurry with certain concentration requirement, the coal water slurry is buffered in the coal slurry tank and then is conveyed to the coal water slurry gasification furnace by the coal slurry pump to participate in gasification reaction.

Along with the development of coal gasification technology, the coal water slurry preparation process is paid more and more attention in the industry, and how to improve the coal water slurry concentration and the process operation stability of the preparation process becomes a hot spot for research in the industry. The traditional control system of the water-coal-slurry preparation system is simpler, generally only a basic control loop of the coal-slurry flow, the grinding water flow and the additive flow is arranged, personnel adjust the set value of the loop according to production experience and sampling analysis results to control the concentration of the water-coal-slurry, the concentration of the water-coal-slurry lacks an automatic control means, and the personnel operation amount is large.

Therefore, how to automatically and effectively control the water-coal-slurry manufacturing process to ensure that the concentration of the water-coal-slurry is in a proper range is a problem to be solved.

BRIEF SUMMARY OF THE PRESENT DISCLOSURE

The disclosure provides a control method, a device, equipment and a storage medium for a coal water slurry manufacturing process.

According to a first aspect of the present disclosure, a method for controlling a coal water slurry manufacturing process is provided. The method comprises the following steps:

obtaining a current coal slurry interference variable and a current coal slurry controlled variable in a coal water slurry manufacturing process, wherein the current coal slurry controlled variable is used for representing a current actual value of each first coal slurry parameter and a current set value of each second coal slurry parameter in the coal water slurry manufacturing process; wherein, each first coal slurry parameter comprises: raw coal flow, grinding coal flow, additive flow, coal slurry tank liquid level and coal slurry concentration; the second coal slurry parameters include: additive ratio and coal slurry concentration; the current coal slurry interference variable is used for representing each coal slurry interference parameter in the manufacturing process of the coal water slurry;

Invoking a target multivariable predictive model trained in advance;

inputting the current coal slurry disturbance variable and the current coal slurry controlled variable into the target multivariable predictive model to obtain a current coal slurry operation variable, wherein the current coal slurry operation variable is used for representing the current set value of each third coal slurry parameter in the coal water slurry manufacturing process; wherein, each third coal slurry parameter comprises: raw coal flow, water-coal ratio, additive ratio and mill coal water flow;

and regulating and controlling at least part of the first coal slurry parameters according to the current coal slurry controlled variable and the current coal slurry operating variable.

Aspects and any one of the possible implementations as described above, further providing an implementation, the target multivariate predictive model is trained by:

obtaining a historical coal slurry interference variable, a historical coal slurry controlled variable and a historical coal slurry operation variable in the water coal slurry manufacturing process;

training the multi-variable prediction model to be trained by taking the historical coal slurry disturbance variable and the historical coal slurry controlled variable as inputs of the multi-variable prediction model to be trained and taking the historical coal slurry operation variable as outputs of the multi-variable prediction model to be trained so as to determine a first transfer function of the historical coal slurry controlled variable relative to the historical coal slurry operation variable and a second transfer function of the historical coal slurry controlled variable relative to the historical coal slurry disturbance variable in the multi-variable prediction model to be trained;

And constructing the target multivariable predictive model according to the first transfer function, the second transfer function and the multivariable predictive model to be trained.

In accordance with aspects and any of the possible implementations described above, there is further provided an implementation in which, prior to inputting the current slurry disturbance variable and the current slurry controlled variable into the target multi-variable predictive model, the method further comprises:

carrying out validity judgment on the current coal slurry interference variable and the current coal slurry controlled variable;

after the validity judgment is passed, carrying out moving average treatment on the current coal slurry interference variable and the current coal slurry controlled variable;

inputting the current coal slurry disturbance variable and the current coal slurry controlled variable into the target multivariable predictive model to obtain a current coal slurry operating variable, comprising:

and inputting the moving average processing results of the current coal slurry disturbance variable and the current coal slurry controlled variable into the target multivariable predictive model to obtain a current coal slurry operation variable.

The aspects and any possible implementation manner described above further provide an implementation manner, where the performing validity judgment on the current coal slurry disturbance variable and the current coal slurry controlled variable includes:

Acquiring a preset validity judgment time domain, a preset minimum allowable deviation sum, a preset maximum allowable deviation sum, a preset minimum confidence value and a preset maximum confidence value which are respectively corresponding to each first coal slurry parameter in the current coal slurry controlled variable and each coal slurry interference parameter of the current coal slurry interference variable, and a value before each time point in the corresponding preset validity judgment time domain;

utilizing the preset validity judgment time domain corresponding to each first coal slurry parameter and each coal slurry interference parameter to carry out summation operation on the difference between the value of each time point and the value before each time point in the corresponding preset validity judgment time domain of each first coal slurry parameter and each coal slurry interference parameter so as to obtain summation results corresponding to each first coal slurry parameter and each coal slurry interference parameter;

judging whether each first coal slurry parameter and each coal slurry interference parameter respectively meet a first preset condition and a second preset condition at the same time, wherein the first preset condition comprises: the corresponding summation result is greater than or equal to the sum of the corresponding preset minimum allowable deviation and less than or equal to the sum of the corresponding preset maximum allowable deviation, and the second preset condition includes: the current actual value of each first coal slurry parameter and each coal slurry interference parameter is larger than or equal to the corresponding preset minimum confidence value and smaller than or equal to the corresponding preset maximum confidence value;

And if the first coal slurry parameters meet the first preset condition and the second preset condition simultaneously, judging that the current coal slurry controlled variable is valid, and if the coal slurry interference parameters meet the first preset condition and the second preset condition simultaneously, judging that the current coal slurry interference variable is valid.

In aspects and any one of the possible implementations described above, there is further provided an implementation, wherein the current set value of each second coal slurry parameter is determined by:

acquiring a current coal mill surface image and a current coal slurry concentration related parameter in the water-coal slurry manufacturing process;

judging the current pulp running degree of the coal mill according to the current coal mill surface image;

calling a preset corresponding relation among the coal slurry concentration related parameter, the slurry running degree and the value of the second coal slurry parameter;

and determining the current set value of each second coal slurry parameter according to the current coal slurry concentration related parameter, the current slurry running degree and the preset corresponding relation.

In the aspects and any possible implementation manner described above, there is further provided an implementation manner, wherein the adjusting at least part of the first coal slurry parameters according to the current coal slurry controlled variable and the current coal slurry operating variable includes:

Calculating a first deviation between the current actual value of the raw coal flow and the current set value of the raw coal flow;

and adjusting the motor frequency of the raw coal weighing device according to the first deviation and a preset adjusting mode of the raw coal weighing device so as to adjust the raw coal flow.

calculating an adjusted coal grinding water flow set value of the coal grinding water loop to be controlled according to the current set value of the raw coal flow, the current set value of the water-to-coal ratio, the total number of all the coal grinding water loops and the current set values of the coal grinding water flows of other coal grinding water loops except the coal grinding water loop to be controlled in all the coal grinding water loops; the coal water flow controllers of all the coal water loops are connected between the raw coal loop and one path of coal water, and the coal water loop to be controlled is any coal water loop in all the coal water loops;

Calculating a second deviation between the adjusted coal grinding water flow set value of the coal grinding water loop to be controlled and the current actual value of the coal grinding water flow of the coal grinding water loop to be controlled;

according to the second deviation and a preset coal grinding water adjusting mode, the opening of the coal grinding water flow controller of the coal grinding water loop to be controlled is adjusted so as to adjust the coal grinding water flow of the coal grinding water loop to be controlled, and therefore the water-coal ratio corresponding to the coal grinding water to be controlled is adjusted.

calculating an additive flow set value according to the current set value of the additive proportion and the current set value of the raw coal flow;

calculating a third deviation between the current actual value of the additive flow rate and the additive flow rate setpoint;

and according to the third deviation, adjusting the opening degree of an additive flow controller to adjust the additive flow, wherein the additive flow controller is connected between the raw coal loop and the additive loop.

According to a second aspect of the present disclosure, a control device for a coal water slurry manufacturing process is provided. The device comprises:

the acquisition module is used for acquiring a current coal slurry interference variable and a current coal slurry controlled variable in the water coal slurry manufacturing process, wherein the current coal slurry controlled variable is used for representing the current actual value of each first coal slurry parameter and the current set value of each second coal slurry parameter in the water coal slurry manufacturing process; wherein, each first coal slurry parameter comprises: raw coal flow, grinding coal flow, additive flow, coal slurry tank liquid level and coal slurry concentration; the second coal slurry parameters include: additive ratio and coal slurry concentration; the current coal slurry interference variable is used for representing each coal slurry interference parameter in the manufacturing process of the coal water slurry;

the calling module is used for calling a target multi-variable prediction model trained in advance;

the processing module is used for inputting the current coal slurry interference variable and the current coal slurry controlled variable into the target multivariable predictive model to obtain a current coal slurry operation variable, wherein the current coal slurry operation variable is used for representing the current set value of each third coal slurry parameter in the water coal slurry manufacturing process; wherein, each third coal slurry parameter comprises: raw coal flow, water-coal ratio, additive ratio and mill coal water flow;

And the regulation and control module is used for regulating and controlling at least part of the first coal slurry parameters according to the current coal slurry controlled variable and the current coal slurry operation variable.

According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory and a processor, the memory having stored thereon a computer program, the processor implementing the method as described above when executing the program.

According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method according to the first aspect of the present disclosure.

According to the method, the current coal slurry disturbance variable and the current coal slurry controlled variable in the water-coal slurry manufacturing process are obtained, the current coal slurry disturbance variable and the current coal slurry controlled variable can be automatically input into the target multivariable prediction model to obtain the current coal slurry operation variable, and then at least part of parameters in each first coal slurry parameter can be automatically regulated and controlled according to the current coal slurry controlled variable and the current coal slurry operation variable, so that the raw coal flow, the additive flow and the coal grinding water flow are regulated, the automatic and reasonable regulation and control of the water-coal ratio are realized, and the water-coal slurry with the highest concentration is obtained on the premise of ensuring the safe and stable operation of the coal mill according to the raw coal quality and the real-time operation condition of the coal mill.

It should be understood that what is described in this disclosure is not intended to limit the key or critical features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. For a better understanding of the present disclosure, and without limiting the disclosure thereto, the same or similar reference numerals denote the same or similar elements, wherein:

FIG. 1 illustrates a block diagram of a related art coal-water slurry production system;

FIG. 2 illustrates a flow chart of a control method of a coal water slurry manufacturing process according to an embodiment of the present disclosure;

FIG. 3 illustrates a block diagram of a coal water slurry production system, according to an embodiment of the present disclosure;

FIG. 4 illustrates a block diagram of another coal water slurry production system in accordance with an embodiment of the present disclosure;

FIG. 5 shows a block diagram of the smart model server of FIG. 4;

FIG. 6 shows a block diagram of a control device for a coal water slurry manufacturing process, according to an embodiment of the present disclosure;

fig. 7 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to be within the scope of this disclosure.

In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

FIG. 2 illustrates a flow chart of a method 200 for controlling a coal water slurry manufacturing process in accordance with an embodiment of the present disclosure. The method 200 may include:

step 210, acquiring a current coal slurry interference variable and a current coal slurry controlled variable in a coal water slurry manufacturing process, wherein the current coal slurry controlled variable is used for representing a current actual value of each first coal slurry parameter and a current set value of each second coal slurry parameter in the coal water slurry manufacturing process; wherein, each first coal slurry parameter comprises: raw coal flow, grinding coal flow, additive flow, coal slurry tank liquid level and coal slurry concentration; the second coal slurry parameters include: additive ratio and coal slurry concentration; the current coal slurry interference variable is used for representing each coal slurry interference parameter in the manufacturing process of the coal water slurry;

The coal slurry concentration may be the concentration of the coal slurry in the small coal slurry tank and/or the concentration of the coal slurry in the large coal slurry tank as shown in fig. 1 and 3, and since the concentration of the coal slurry in the large coal slurry tank is not well measured, it is preferable that the coal slurry concentration be the concentration of the coal slurry in the small coal slurry tank.

Current coal slurry disturbance variables include, but are not limited to, downstream coal slurry load (i.e., coal slurry flow into the gasifier), small coal slurry pump frequency conversion, and the like.

The additive ratio refers to the quotient of the additive flow and the raw coal flow, and the water-coal ratio refers to the quotient of the ground coal flow and the raw coal flow.

Step 220, invoking a target multi-variable prediction model trained in advance;

step 230, inputting the current coal slurry disturbance variable and the current coal slurry controlled variable into the target multivariable predictive model to obtain a current coal slurry operation variable, wherein the current coal slurry operation variable is used for representing the current set value of each third coal slurry parameter in the coal water slurry manufacturing process; wherein, each third coal slurry parameter comprises: raw coal flow, water-coal ratio, additive ratio and mill coal water flow;

at least part of parameters in the controlled variables are final regulation and control targets so as to ensure that the water-coal ratio is proper, the water-coal ratio is not too high or too low, and the coal slurry concentration is proper; the operation variable is an adjusting means for realizing the controlled variable, namely, the controlled variable can be adjusted through the operation variable; and the disturbance variable is a disturbance parameter that enables adjustment of the controlled variable.

Step 240, adjusting and controlling at least part of the first coal slurry parameters according to the current coal slurry controlled variable and the current coal slurry operation variable.

The current coal slurry disturbance variable and the current coal slurry controlled variable in the water-coal-slurry manufacturing process are obtained, the current coal slurry disturbance variable and the current coal slurry controlled variable can be automatically input into the target multivariable prediction model to obtain the current coal slurry operation variable, and then at least part of parameters in each first coal slurry parameter can be automatically regulated and controlled according to the current coal slurry controlled variable and the current coal slurry operation variable, so that the raw coal flow, the additive flow and the grinding coal water flow can be regulated, the automatic and reasonable regulation and control of the water-coal ratio can be realized, and the water-coal slurry with the highest concentration can be obtained on the premise of ensuring the safe and stable operation of the coal mill according to the raw coal quality and the real-time operation condition of the coal mill.

In some embodiments, the target multivariate predictive model is trained by:

Wherein the first transfer function and the second transfer function include, but are not limited to, a first order transfer function, a second order underdamped transfer function (i.e. a transfer function of an underdamped second order system), a second order overdamped transfer function (i.e. a transfer function of an overdamped second order system), and the like, and preferably the first order transfer function. In general, the first order transfer function is in the form ofWherein G is a gain factor of 0.00001 to 10, preferably 0.001 to 5; d is a lag time of 10 to 2400s, preferably 60 to 1200s; t is a time constant of 10 to 1200s, preferably 60 to 300s.

The multi-variable prediction model to be trained is a prediction model formed by coal slurry disturbance variable, coal slurry controlled variable, coal slurry operation variable and transfer function, and the transfer function is unknown and to be solved, so that the historical coal slurry disturbance variable and the historical coal slurry controlled variable are used as the input of the multi-variable prediction model to be trained, the historical coal slurry operation variable is used as the output of the multi-variable prediction model to be trained, after the multi-variable prediction model to be trained is automatically trained, the first transfer function and the second transfer function can be accurately determined, and then the proper target multi-variable prediction model can be automatically and accurately constructed according to the first transfer function, the second transfer function and the multi-variable prediction model to be trained.

In some embodiments, prior to inputting the current slurry disturbance variable and the current slurry controlled variable into the target multivariate predictive model, the method further comprises:

Before the current coal slurry disturbance variable and the current coal slurry controlled variable are input into a target multivariable predictive model, validity judgment can be carried out to judge whether the current coal slurry disturbance variable and the current coal slurry controlled variable are valid or not, if so, moving average processing is carried out on the current coal slurry disturbance variable and the current coal slurry controlled variable, and therefore a moving average processing result is input into the target multivariable predictive model, and the current coal slurry operation variable is accurately obtained.

In some embodiments, the determining the validity of the current coal slurry disturbance variable and the current coal slurry controlled variable includes:

the first coal slurry parameter or the coal slurry interference parameter is different from the first coal slurry parameter or the coal slurry interference parameter in terms of the preset validity judgment time domain, the sum of preset minimum allowable deviation, the sum of preset maximum allowable deviation, the preset minimum confidence value and the preset maximum confidence value, so that the validity judgment of the parameters can be respectively carried out according to the respective validity judgment parameters.

Each time point in the preset validity judgment time domain refers to each second in the preset validity judgment time domain, and if the validity judgment time domain of the controlled variable 1 is 10-600 s, each time point in the preset validity judgment time domain is 10 th second, 11 th second, 12 th second and … … th second.

And if any one of the first coal slurry parameters does not meet the first preset condition and/or does not meet the second preset condition, re-acquiring the any one of the first coal slurry parameters until each of the first coal slurry parameters meets the first preset condition and the second preset condition at the same time.

In the same way as described above,

and if the coal slurry interference parameters meet the first preset condition and the second preset condition at the same time, the current coal slurry interference variable is effective, and if any coal slurry interference parameter in the coal slurry interference parameters does not meet the first preset condition and/or does not meet the second preset condition, the any coal slurry interference parameter is collected again until the coal slurry interference parameters meet the first preset condition and the second preset condition at the same time.

The validity judgment process and the moving average processing process are as follows:

(1) Carrying out validity judgment on the controlled variable 1 to the controlled variable n and the disturbance variable 1 to the disturbance variable i, taking the controlled variable 1 as an example, and judging that the measured value of the controlled variable 1 is valid if the formulas (1) and (2) are simultaneously satisfied; if either of the formulas (1) and (2) is not satisfied, judging that the measured value of the controlled variable 1 is invalid, and stopping the operation of the multivariable predictive model.

A3≤Cy1≤A4 (2)

CV1 _i The first 1 second of the ith second is the controlled variable 1 measurement (CV 1 if i is 10 ₁₀ Controlled variable 1 measurement for 9 th second);

n11, the preset validity of the controlled variable 1 judges the time domain, 10-600 s, preferably 30-180 s; a preset A1, the sum of preset minimum allowable deviation of the controlled variable 1; A1-A4 are preset to be not common to n11, and the values of different variables are different

A2, the sum of preset maximum allowable deviation of the controlled variable 1;

a3, a preset minimum confidence value of the controlled variable 1;

a4, a preset maximum confidence value of the controlled variable 1;

(2) After the validity judgment is passed, moving average processing (namely moving average processing, namely moving average processing for a current time for 10 seconds, and taking n values before 10 seconds as the moving average processing) is carried out on the controlled variables 1 to n and the interference variables 1 to i, wherein the controlled variable 1 is taken as an example, and the calculation formula is as follows:

CV1, the controlled variable 1 moving average value calculation result;

CV1i, the actual value of the controlled variable 1 before the ith second;

n12, the controlled variable 1 moving average calculates the time domain, 5-600 s, preferably 30-180 s;

(3) And communicating the calculation results of the moving average values of the controlled variable 1 to the controlled variable n and the disturbance variable 1 to the disturbance variable i to the multi-variable prediction model to serve as input values.

In some embodiments, the current set point for each of the second slurry parameters is determined by:

the top of the coal mill is provided with a slurry outlet, and if slurry blasting occurs in the slurry outlet, the surface of the coal mill is dyed black, so that whether the slurry blasting and the concrete slurry running degree of the coal mill are judged by judging whether the number of black pigment blocks in the current surface image of the coal mill reaches a certain threshold value or not.

The preset corresponding relation among the coal slurry concentration related parameter, the slurry running degree and the values of the second coal slurry parameters is called, and the current set value of each second coal slurry parameter can be accurately calculated according to the current coal slurry concentration related parameter, the current slurry running degree and the preset corresponding relation.

In some embodiments, the adjusting at least some of the first slurry parameters according to the current slurry controlled variable and the current slurry operating variable includes:

By calculating the first deviation between the current actual value of the raw coal flow and the current set value of the raw coal flow, the motor frequency of the raw coal weighing device can be accurately regulated according to the first deviation and the preset regulation mode of the raw coal weighing device, so that the raw coal flow can be automatically regulated.

The preset adjustment may be PID (Proportional integral derivative).

In general, the motor frequency of the raw coal weighing machine is adjusted by the following formula:

p (t): the controller outputs, namely the output motor frequency;

t: at the current moment, s;

e (t): deviation (namely, first deviation) of actual value and set value of raw coal flow;

and (4) ke: the ratio coefficient is 0.01-10;

Ti: the integral coefficient is 10-5000;

td: the differential coefficient is 0-50.

the calculation formula of the opening of the coal grinding water flow controller of the coal grinding water loop to be controlled is as follows:

FW: the adjusted coal grinding water flow set value (i.e. the adjusted coal grinding water flow set value) of the coal grinding water loop (i.e. the coal grinding water loop to be controlled) can be controlled;

RT1: the water-coal ratio set value (namely the current set value of the water-coal ratio before the adjustment of the coal grinding water loop to be controlled is also the set value of the water-coal ratio given by the target multivariable predictive model);

FC: raw coal flow set point (i.e. the current set point of raw coal flow);

l: the total number of the coal grinding water loops except the controllable coal grinding water loop (namely the number of the other coal grinding water loops except the coal grinding water loop to be controlled in all the coal grinding water loops);

FWi: the setting value of the ith coal water circuit except the controllable coal water circuit (namely the current setting value of the coal water flow of the other coal water circuits except the coal water circuit to be controlled); wherein,,

the controllable coal grinding water loop in the disclosure can be selected from all controllable coal grinding water loops at random, and only one coal grinding water loop is needed to be selected because the water-coal ratio set value is limited.

The coal grinding water circuit is a circuit for providing coal grinding water to a coal mill, as shown in fig. 3, wherein the coal grinding water 1-3 is located, and all the coal grinding water circuits in fig. 3 comprise a coal grinding water circuit 1, a coal grinding water circuit 2 and a coal grinding water circuit 3. The coal grinding water loop to be controlled can be any one of a coal grinding water loop 1, a coal grinding water loop 2 and a coal grinding water loop 3, and a coal grinding water flow controller, namely a proportion-selection controller, is connected between the raw coal loop and the coal grinding water loop.

The raw coal loop is a loop for providing raw coal to a coal mill, such as the coal loop shown in fig. 3.

and according to the second deviation and a preset coal grinding water adjusting mode, adjusting the opening of a coal grinding water flow controller (namely a proportion-selection controller in fig. 3) of the coal grinding water circuit to be controlled so as to adjust the coal grinding water flow of the coal grinding water circuit to be controlled, thereby adjusting the water-coal ratio corresponding to the coal grinding water to be controlled.

In general, the coal grinding water flow control loop adopts a PID form, a coal grinding water flow set value is calculated by the water-coal ratio control loop, and the PID controller adjusts the opening of a coal grinding water pipeline regulating valve (namely, the coal grinding water flow controller) according to the deviation (namely, the second deviation) between the set value and the measured value of the coal grinding water flow sensor.

In general, the adjustment formula is:

p (t): the water flow of the coal grinding water after adjustment is output by the controller;

t: at the current moment, s;

e (t): deviation of the actual value and the set value (namely, second deviation) of the coal grinding water flow;

and (4) ke: the ratio coefficient is 0.01-10;

Ti: the integral coefficient is 10-5000;

td: the differential coefficient is 0-50.

According to the current set value of the raw coal flow, the current set value of the water-coal ratio, the total number of all the coal-grinding water loops and the current set value of the coal-grinding water flows of other coal-grinding water loops except the coal-grinding water loop to be controlled in all the coal-grinding water loops, the adjusted coal-grinding water flow set value of the coal-grinding water loop to be controlled can be accurately calculated, then the second deviation of the adjusted coal-grinding water flow set value of the coal-grinding water loop to be controlled and the current actual value of the coal-grinding water flow of the coal-grinding water loop to be controlled is calculated, the opening of the coal-grinding water flow controller of the coal-grinding water loop to be controlled is adjusted according to the second deviation and a preset coal-grinding water adjusting mode, so that the coal-grinding water flow of the coal-grinding water loop to be controlled is adjusted, and the water-coal ratio is the quotient of the coal-grinding water flow of the coal-grinding water loop to be controlled, and the water-grinding water ratio corresponding to the raw coal is adjusted.

typically, the additive ratio control loop takes the form of a ratio, the additive ratio setpoint calculated by the intelligent controller, and adjusts the selected controllable additive loop flow setpoint (i.e., the additive flow setpoint) based on the product of the current setpoint of the additive ratio and the current setpoint of the raw coal flow.

Generally, the additive ratio controller adjusts the formula:

FT＝RT2*FC；

and (3) FT: an additive loop flow set point;

RT2: an additive ratio set point (i.e., the current set point for the additive ratio);

FC: raw coal flow set point (i.e. the current set point of raw coal flow);

According to the third deviation, the opening degree of the additive flow controller is automatically adjusted, the additive flow can be accurately adjusted, and the additive influences the slurry forming performance, so that the slurry forming condition of the water-coal slurry and the concentration of the water-coal slurry are influenced after the additive flow is adjusted.

According to the present disclosure, a control method applied to a water-coal slurry preparation process is provided, the method includes a bottom layer control system and an intelligent control system, as shown in fig. 4, the implementation steps of the method are as follows:

(1) The method comprises the steps of collecting actual values of parameters such as raw coal flow, coal grinding water flow, additive flow, coal slurry tank liquid level and coal slurry concentration by using a tool, obtaining an additive proportion set value and a coal slurry concentration set value (which are all used as controlled variables in the input of a model), and an interference variable (such as coal slurry load required by a downstream gasifier), and communicating the parameters into an intelligent model server connected with a DCS (distributed control system); and the image acquisition camera arranged outside the coal mill shoots the surface image of the coal mill, and the communication enters the intelligent model server.

(2) An intelligent controller for the preparation process of the coal water slurry is arranged in the intelligent model server, raw coal flow, additive flow and water coal proportion set values are calculated according to the deviation between the current running state and the ideal running state of the system, and the calculated result is communicated into a DCS system;

(3) The DCS system is provided with a bottom layer control system in the water-coal slurry preparation process, and the raw coal flow, the grinding coal flow and the additive flow are regulated according to the raw coal flow, the additive set value and the deviation between the actual water-coal ratio and the set value.

Further, in step 2, the intelligent controller for the water-coal-slurry preparation process comprises an advanced control module, a slurry-running visual identification module and a coal-slurry concentration expert control module.

Further, the advanced control module comprises a multivariate prediction model and a data preprocessing program.

The multivariable predictive model is obtained by:

(1) Disturbance variables, operating variables and controlled variables involved in the controller are selected. The disturbance variables comprise but are not limited to downstream coal slurry load and the like, the operation variables comprise but are not limited to raw coal flow set values, water-coal ratio set values and the like, and the controlled variables comprise but are not limited to coal slurry concentration, coal slurry tank liquid level and the like;

(2) Extracting historical operation data of a coal water slurry preparation system, and carrying out model identification on the correlation between each controlled variable and each operating variable and each interference variable to obtain a transfer function between variables in the system;

(3) Constructing a multivariable prediction model according to the transfer function, wherein the calculating formula of the multivariable prediction model is as follows:

CV1, CV2, …, CVn: the 1 st to nth controlled variables in the multivariable predictive model, n being the number of controlled variables;

MV1, MV2, …, MVm: the 1 st to the m-th operation variables in the multivariable predictive model, m being the number of the operation variables;

DV1, DV2, …, DVi: the 1 st to i th disturbance variables in the multivariable predictive model, i is the number of the disturbance variables;

f (11), f (12), …, f (nm): transfer functions of the controlled variables relative to the manipulated variables;

g (11), g (12), …, g (ni): transfer functions of the controlled variables relative to the disturbance variables;

the transfer function form includes, but is not limited to, a first order transfer function, a second order underdamped transfer function, a second order overdamped transfer function, and the like, and is preferably a first order transfer function. In general, the first order transfer function is in the form ofWherein G is a gain factor of 0.00001 to 10, preferably 0.001 to 5; d is a lag time of 10 to 2400s, preferably 60 to 1200s; t is a time constant of 10 to 1200s, preferably 60 to 300s.

The data preprocessing module is obtained through the following steps:

A3≤Cy1≤A4 (2)

CV1 _i A controlled variable 1 measurement prior to the ith second;

n11, the validity judgment time domain of the controlled variable 1, 10-600 s, preferably 30-180 s;

a1, the sum of the minimum allowable deviation of the controlled variable 1;

a2, the sum of the maximum allowable deviation of the controlled variable 1;

a3, the minimum confidence value of the controlled variable 1;

a4, the maximum confidence value of the controlled variable 1;

(2) After the validity judgment is passed, moving average processing is carried out on the controlled variables 1 to n and the disturbance variables 1 to i, and the controlled variable 1 is taken as an example, and the calculation formula is as follows:

CV1, the controlled variable 1 moving average value calculation result;

CV1i, the actual value of the controlled variable 1 second before the ith second;

Further, in step 2, the slurry running vision recognition module includes an image processing sub-module, a slurry running judging sub-module and a slurry running database, wherein the image processing sub-module compiles the captured coal mill surface image, converts the captured coal mill surface image into a color pigment proportion value, sends the color pigment proportion value to the slurry running judging sub-module, the slurry running judging sub-module compares the color pigment proportion value with data in the slurry running database, judges and determines the slurry running degree (the surface of the coal mill is black after slurry blasting, so that the slurry running is indicated when the black pigment blocks reach a certain threshold value), and sends the judging result to the coal slurry concentration expert control module. Further, in step 2, the coal slurry concentration expert control module includes an inference engine and a control rule set, where the inference engine selects a corresponding control rule (i.e., determining a current set value of each second coal slurry parameter according to the current coal slurry concentration related parameter, the current slurry running degree, and the preset correspondence) from the control rule set (i.e., a preset correspondence between values of the coal slurry concentration related parameter, the slurry running degree, and the second coal slurry parameter) according to the coal slurry concentration related parameter, and the inference engine, and sends the control rule set as an operation variable control reference (i.e., determining a coal slurry concentration set value and an additive proportion set value). The coal slurry concentration related parameters include, but are not limited to, raw coal internal water content, raw coal element composition, coal mill steel bar running period and slurry running degree, and the parameters are collected together with coal flow.

Further, in step 3, the bottom layer control system of the water-coal slurry preparation process comprises a raw coal flow control loop, a ground coal flow control loop, an additive proportion control loop and a water-coal ratio control loop.

In general, the raw coal flow control loop adopts a PID form, a set value of the raw coal flow is calculated by an intelligent controller, and the PID controller adjusts the frequency conversion of the raw coal weighing device according to the deviation between the set value and the measured value of the raw coal flow sensor.

In the present disclosure, PID refers to Proportional Integral Derivative (PID). PID controllers refer to proportional, integral, derivative controllers, i.e., pro-port, integral, derivative (PID) controllers.

In general, the adjustment formula is:

p (t): a controller output;

t: at the current moment, s;

e (t): deviation between actual value and set value of raw coal flow;

and (4) ke: the ratio coefficient is 0.01-10;

ti: the integral coefficient is 10-5000;

td: the differential coefficient is 0-50.

In general, a coal grinding water flow control loop adopts a PID form, a coal grinding water flow set value is calculated by a water-coal ratio control loop, and a PID controller adjusts the opening of a coal grinding water pipeline regulating valve according to the deviation between the set value and the measured value of a coal grinding water flow sensor.

In general, the adjustment formula is:

p (t): a controller output;

t: at the current moment, s;

e (t): deviation between actual value and set value of water flow of grinding coal;

and (4) ke: the ratio coefficient is 0.01-10;

ti: the integral coefficient is 10-5000;

td: the differential coefficient is 0-50.

Typically, the additive flow control loop takes the form of a PID, and an additive flow set point is calculated by the additive proportional control loop, and the PID controller adjusts the additive line regulator valve opening based on the deviation of the set point (i.e., the additive flow set point) from the additive flow sensor measurement (i.e., the current actual value of the additive flow).

In general, the adjustment formula is:

p (t): a controller output;

t: at the current moment, s;

e (t): deviation of the actual value of the additive flow from the set value (i.e., a third deviation);

and (4) ke: the ratio coefficient is 0.01-10;

ti: the integral coefficient is 10-5000;

td: the differential coefficient is 0-50.

Generally, the water-coal ratio control loop adopts a selection-proportion mode, an operator selects a controllable loop from all the water flow loops of the coal grinding water, and the water-coal ratio control loop adjusts the flow set value of the selected controllable coal grinding water according to the product of the set value and the flow set value of the raw coal according to the water-coal ratio set value calculated by the intelligent controller.

In general, the water-to-coal ratio controller adjustment formula is:

FW: the flow set value of the coal grinding water loop can be controlled;

RT1: setting a water-coal ratio;

FC: setting a raw coal flow rate;

l: the total number of the coal grinding water loops except the controllable coal grinding water loop;

FWi: a coal water grinding loop set point except a controllable coal water grinding loop;

typically, the additive ratio control loop takes the form of a ratio, the additive ratio setpoint calculated by the intelligent controller, and the additive ratio control loop adjusts the selected controllable additive loop flow setpoint by the product of the setpoint and the raw coal flow setpoint.

Generally, the additive ratio controller adjusts the formula:

FT＝RT2*FC；

and (3) FT: an additive loop flow set point;

RT2: setting the proportion of the additive;

FC: setting a raw coal flow rate;

further, the communication network architecture of the intelligent control system in the preparation process of the coal water slurry is shown in fig. 4. The architecture is based on an OPC protocol, and requires at least one DCS server, at least one intelligent control server and at least one OPC server. And the DCS server and the intelligent control server are internally provided with OPC clients, and the intelligent control server is communicated with the DCS through an OPC protocol.

Further, the software architecture in the intelligent control server is shown in fig. 5. The intelligent control server is divided into a communication layer, a database layer and a calculation layer. The communication layer comprises an OPC client and communicates with the DCS server through an OPC protocol and an OPC server; the database layer comprises a data read-write module and a database, wherein the data read-write module extracts input data of the intelligent control server from the DCS system and the image acquisition camera and stores the extracted input data into the database, and extracts output data of the intelligent control server from the database and writes the output data into the DCS server; the calculation layer comprises an advanced control module, a slurry running visual identification module and a coal slurry concentration expert control module, wherein the three modules can read required data from a database, the slurry running visual identification module calculates and determines the slurry running degree, and the calculation result is transmitted to the coal slurry concentration expert control module; the coal slurry concentration expert control module selects and determines a control rule and transmits a calculation result to the advanced control module; the advanced control module calculates and determines the set value of each operation variable, and transmits the calculated result to the database.

Compared with the traditional control, the method has the following advantages: (1) The key process parameters such as the coal slurry concentration, the liquid level of the coal slurry tank, the water-coal ratio and the like are automatically controlled by using multivariable predictive control, so that the operation stability of the water-coal slurry preparation device is improved, and the labor intensity of personnel is reduced; (2) The coal mill slurry leakage can be found in time through the slurry leakage vision identification module, so that the loss of coal slurry and environmental pollution caused by slurry leakage are reduced; (3) And the optimal coal slurry concentration control index is given according to the coal quality of raw coal and the real-time running condition of the coal mill by the coal slurry concentration expert control module, so that the coal water slurry product with the highest concentration is obtained on the premise of ensuring the safe and stable running of the coal mill, and the running benefit of the device is improved.

Example 1:

the coal gasification device is provided with 3 sets of coal water slurry preparation devices which are made in the same shape and can produce 80m3 coal water slurry at maximum per hour, the operation mode is two-in-one, and the technological process of the device is shown in figure 3. The coal water slurry preparation device mainly comprises a coal mill, a small coal slurry tank, a small coal slurry pump, a large coal slurry tank, a large coal slurry pump and other devices, raw coal, 3 strands of coal grinding water, 1 strand of additive are crushed and mixed in the coal mill to form coal water slurry with the concentration of 57% -62%, the coal water slurry is buffered in the small coal slurry tank and then is conveyed into the large coal slurry tank by the small coal slurry pump, and the coal water slurry in the large coal slurry tank is conveyed to the gasification furnace by the large coal slurry pump. The DCS system shown in fig. 1 is currently provided with 6 control loops, which are respectively a raw coal flow control loop, a ground coal water 1 flow control loop, a ground coal water 2 flow control loop, a ground coal water 3 flow control loop, an additive flow control loop and a small coal slurry tank liquid level control loop.

The system mainly has 3 problems: firstly, the method lacks a means of monitoring and controlling the concentration of coal slurry in real time, only samples and analyzes the coal slurry in a small coal slurry tank and a large coal slurry tank in each shift, the amount of ground coal water is adjusted according to the sampling result, and the fluctuation of the concentration of the coal slurry in the large coal slurry tank can reach about 2%, thereby seriously affecting the benefit of downstream coal gasification reaction; secondly, in order to make the concentration of the coal slurry produced by the device as high as possible, each shift of the device performs dewatering coal ratio operation, and personnel are arranged to perform inspection at regular time, and the water coal ratio is further finely adjusted according to the slurry running condition of the coal mill; thirdly, the large coal slurry tank lacks a self-control loop, and operators need to adjust the load of the coal mill according to the downstream load change and the trend condition of the liquid level of the large coal slurry tank at random so as to maintain the liquid level of the large coal slurry tank stable.

In order to realize safe, stable and economic operation of the coal water slurry preparation device, the scheme is implemented in the chemical plant, and the implementation steps are as follows:

(1) Technological improvements are made to field devices.

The method comprises the steps that a ray type coal slurry concentration online analyzer is added to a small coal slurry pump outlet, an image acquisition camera is added outside a coal mill, and a camera is aligned to the surface of the coal mill;

(2) The bottom layer control system is optimized, and the optimized control loop is shown in fig. 3 and comprises the following contents:

(1) and a liquid level control loop of the small coal slurry pump is canceled.

(2) And adding a water-coal ratio control loop, wherein the loop adopts a selection-proportion mode, an operator selects a controllable loop from all the coal grinding water flow loops, and the flow set value of the selected controllable coal grinding water loop is adjusted according to the product of the set value and the raw coal flow set value. The controller adjusts the formula as follows:

FW＝RT1*FC-FW1-FW2；

FW: the flow set value of the coal grinding water loop can be controlled;

RT1: setting a water-coal ratio;

FC: setting a raw coal flow rate;

FW1: setting values of the coal grinding water loop 1 except the controllable coal grinding water loop;

FW2: setting values of the coal grinding water loop 2 except the controllable coal grinding water loop;

an additive ratio control loop is added, which takes the form of a ratio, and adjusts the selected controllable additive loop flow set point according to the product of the set point and the raw coal flow set point. The controller adjusts the formula as follows:

FT＝RT2*FC；

And (3) FT: an additive loop flow set point;

RT2: setting the proportion of the additive;

FC: setting a raw coal flow rate;

(3) An intelligent control system is constructed, and the intelligent control system comprises an advanced control module, a slurry running visual identification module and a coal slurry concentration expert control module.

(1) The advanced control module includes a multivariate predictive model and a data preprocessing procedure.

Wherein the multivariate predictive model is obtained by:

(1) Disturbance variables, operating variables and controlled variables involved in the controller are selected. The disturbance variables include a downstream coal slurry load (hereinafter referred to as DV 1), a small coal slurry pump variable frequency (hereinafter referred to as DV 2), an additive ratio (hereinafter referred to as DV 3), a raw coal flow rate set point (hereinafter referred to as DV 4), an operating variable includes a raw coal flow rate set point (hereinafter referred to as MV 1), a water-coal ratio set point (hereinafter referred to as MV 2), an additive ratio set point (hereinafter referred to as MV 3), a small coal slurry pump variable frequency (hereinafter referred to as MV 4), and the controlled variables include a large coal slurry tank level (hereinafter referred to as CV 1), a coal slurry concentration (hereinafter referred to as CV 2), an additive ratio (hereinafter referred to as CV 3), and a small coal slurry tank level (hereinafter referred to as CV 4);

(3) Constructing a multivariable prediction model according to the transfer function, wherein the controller has the following calculation formula:

the data preprocessing module is obtained through the following steps:

(1) And carrying out validity judgment on CV1, CV2, CV4 and DV1, wherein each variable is required to carry out double judgment on fluctuation degree and confidence interval at the same time, and judging that the measured value of the variable is valid if the judgment is passed. The judgment formulas of the variables are as follows:

20≤CV1≤75

CV1 _i CV1 measurements before the i < th > second;

56≤CV2≤64

CV2 _i CV2 measurements before the i < th > second;

20≤CV4≤80

CV4 _i CV4 measurements before the i < th > second;

40≤DV1≤80

DV1 _i DV1 measurement before the i < th > second;

(2) After the validity judgment is passed, the CV1, CV2, CV4 and DV1 are subjected to moving average processing, and the calculation formula is as follows:

ave_cv1, CV1 moving average calculation;

CV1i, actual CV1 values before the ith second;

ave_cv2, CV2 moving average calculation;

CV2i, actual CV2 values before the ith second;

ave_cv4, CV4 moving average calculation;

CV4i, actual CV4 values before the ith second;

ave_dv1, DV1 moving average calculation;

DV1i, DV1 actual value before the ith second;

(3) And communicating the calculation results of the CV1, CV2, CV4 and DV1 moving average values to a multi-variable prediction model to serve as input values.

(2) The pulp running vision recognition module comprises an image processing sub-module, a pulp running judging sub-module and a pulp running database.

The image processing sub-module compiles the captured coal mill surface image, converts the captured coal mill surface image into a color pigment proportion value, sends the color pigment proportion value to the slurry running judging sub-module, compares the color pigment proportion value with data in a slurry running database, judges and determines slurry running degree, and sends a judging result to the coal slurry concentration expert control module.

The coal slurry concentration expert control module comprises an inference engine and a control rule set, wherein the inference engine selects a corresponding control rule from the control rule set according to the condition of the coal slurry concentration related parameter and sends the control rule to the multi-variable prediction model as an operation variable control reference. The coal slurry concentration related parameters comprise the water content in raw coal, the raw coal element composition (silicon, aluminum, calcium, iron and magnesium), the running period of a coal mill steel rod and the slurry leakage degree.

Thus, the three problems in the process of manufacturing the water-coal-slurry in FIG. 1 are solved, the concentration of the water-coal-slurry can be monitored in real time, and the water-coal ratio can be timely adjusted to the optimal range, so that the water-coal-slurry concentration and the water-coal ratio can be always kept within the optimal range on the premise of ensuring the safe, stable and economic operation of the water-coal-slurry manufacturing system no matter how the coal quality or other parameters are changed.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.

The foregoing is a description of embodiments of the method, and the following further describes embodiments of the present disclosure through examples of apparatus.

Fig. 6 shows a block diagram of a control device 600 for a coal water slurry manufacturing process according to an embodiment of the disclosure. As shown in fig. 6, the apparatus 600 includes:

the obtaining module 610 is configured to obtain a current coal slurry disturbance variable and a current coal slurry controlled variable in a coal water slurry manufacturing process, where the current coal slurry controlled variable is used to characterize a current actual value of each first coal slurry parameter and a current set value of each second coal slurry parameter in the coal water slurry manufacturing process; wherein, each first coal slurry parameter comprises: raw coal flow, grinding coal flow, additive flow, coal slurry tank liquid level and coal slurry concentration; the second coal slurry parameters include: additive ratio and coal slurry concentration; the current coal slurry interference variable is used for representing each coal slurry interference parameter in the manufacturing process of the coal water slurry;

A calling module 620, configured to call a target multivariate prediction model trained in advance;

the processing module 630 is configured to input the current coal slurry disturbance variable and the current coal slurry controlled variable to the target multivariate prediction model, so as to obtain a current coal slurry operation variable, where the current coal slurry operation variable is used to characterize current set values of each third coal slurry parameter in the process of manufacturing the coal water slurry; wherein, each third coal slurry parameter comprises: raw coal flow, water-coal ratio, additive ratio and mill coal water flow;

and the regulation and control module 640 is configured to regulate and control at least some of the first coal slurry parameters according to the current coal slurry controlled variable and the current coal slurry operating variable.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the described modules may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.

The present disclosure also provides, in accordance with embodiments of the present disclosure, an electronic device and a non-transitory computer-readable storage medium storing computer instructions.

Fig. 7 shows a schematic block diagram of an electronic device 700 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

The device 700 includes a computing unit 701 that can perform various suitable actions and processes according to computer programs stored in a Read Only Memory (ROM) 702 or loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 may also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in device 700 are connected to I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, etc.; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, an optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The computing unit 701 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as method 200. For example, in some embodiments, the method 200 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 700 via ROM 702 and/or communication unit 709. One or more of the steps of the method 200 described above may be performed when a computer program is loaded into RAM 703 and executed by the computing unit 701. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the method 200 by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. The control method of the water-coal-slurry manufacturing process is characterized by comprising the following steps:

Invoking a target multivariable predictive model trained in advance;

2. The method of claim 1, wherein the target multivariate predictive model is trained by:

3. The method of claim 1, wherein prior to inputting the current slurry disturbance variable and the current slurry controlled variable into the target multivariate predictive model, the method further comprises:

4. The method of claim 1, wherein the step of determining the position of the substrate comprises,

the effectiveness judgment of the current coal slurry interference variable and the current coal slurry controlled variable comprises the following steps:

5. The method of claim 1, wherein the step of determining the position of the substrate comprises,

the current set value of each second coal slurry parameter is determined by the following steps:

6. The method of claim 1, wherein the step of determining the position of the substrate comprises,

the step of adjusting and controlling at least part of the first coal slurry parameters according to the current coal slurry controlled variable and the current coal slurry operation variable comprises the following steps:

7. The method of claim 1, wherein the step of determining the position of the substrate comprises,

8. The method according to any one of claims 1 to 7, wherein,

9. The control device for the water-coal-slurry manufacturing process is characterized by comprising:

10. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.