# Optimization Analysis of Power Coal-Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Authors: Yadong Tao; Wenge Song; Ziqi Zhu; Bin Wang; Ming Shang; Sa Zhang; Chao Lu
Journal: Journal of Sensors
 (2023)

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## Abstract

Research and optimization of the coal-blending system can greatly improve the variety diversity and quality stability of finished coal. However, the existing research on the coal-blending system has some defects, such as fuzzy ash content of coal-blending warehouse products and excessive error of the coal-blending ratio determined by artificial experience, which brings many disadvantages and difficulties to the efficient and stable production of the coal preparation plant. The genetic algorithm and intelligent sensor network are relatively advanced technologies at present. Therefore, the optimization analysis of the coal-blending model and its control system based on the intelligent sensor network and genetic algorithm has become a research hotspot. This paper first introduces the basic theory of the genetic algorithm and intelligent sensor network and their application in coal-blending research, then establishes an optimized dynamic coal-blending model based on the intelligent sensor network and genetic algorithm, and analyzes its application effect. The research results show that the coal quality prediction model can be mined from coal quality data and coal-blending data by using the genetic algorithm and ideas, and the monitoring system based on the intelligent sensor network can monitor the abnormal state anytime and anywhere. Compared with the traditional coal-blending method, the average error is 3.33%, and the accuracy is improved by 4.82%.


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## Body

## 1. Introduction

In power plants and chemical plants, power coal plays an important role as a reductant, heat source, and skeleton. Its quality requirements are strict, mainly including low ash, low sulfur, high strength, and high anisotropy. With the development of blast furnace technology, the requirements for the quality of thermal coal [1] are gradually increasing. Therefore, in order to save energy and meet production needs, various thermal coals are usually used for proportional distribution. However, due to the limitations of mine transportation, transportation, and geographical location, the required thermal coal may not arrive in time, and due to the limitations of thermal coal storage, equipment conditions, and fluctuating combustion conditions of the thermal coal furnace, the thermal coal ratio needs to be adjusted in real time according to the above factors to ensure the stability of thermal coal quality [2, 3].Power coal blending is to mix coal of different qualities in an appropriate proportion, so that various power coal varieties complement each other in quality, so as to mix and match power coal varieties that meet product quality requirements [4]. At present, although some domestic manufacturers have implemented an automatic power coal-blending system for a given power coal-blending ratio, the power coal-blending ratio is usually determined by manual experience, which greatly fluctuates the quality of power coal blending [5]. While making full use of existing resources, ensuring product quality and optimizing the input-output ratio have become an urgent problem [6].The application of precise power coal blending in ports at home and abroad has had successful experience [7]. In 1994, the Netherlands established a power coal-blending system with silo power coal blending as the core, with an annual power coal-blending capacity of 10 million tons. EMO power coal-blending facilities include five power coal silos for mixing 2 to 5 different sources and types of power coal. In about 2 to 3 years, more than 175 different kinds of hybrid coal were sent to #9 generator units in Amercentale, the Netherlands, through this system [8]. Through the EMO power coal-blending system, the Netherlands can save 100 million euros in fuel costs every year. In 2006, China introduced this technology for the first time and successfully completed the power coal wharf in Lianyungang port. The trial products of the Lianyungang CBF project of the China-Hong Kong-India group have been used in some power plants and have been proven to have good application effects by scientific research institutions such as the Thermal Coal Research Institute [9]. The reliability and accuracy of dynamic measurement and the accuracy of online detection of thermal coal are the key factors affecting the accuracy of thermal coal distribution. In the old power coal-blending system, the accuracy of metering and online detection equipment limits the accuracy of power coal blending. With the improvement of the accuracy of measurement and online detection equipment and the application of artificial intelligence technology, accurate coal blending is the inevitable trend of the development of coal-blending technology [10].The basic genetic algorithm and adaptive genetic algorithm have the problems of slow search speed and easily falling into the local optimal solution. The operation rules of the genetic algorithm are probabilistic rather than deterministic [11]. When the crossover and mutation operation of the traditional genetic algorithm is performed with a certain probability, the intersection and mutation location change randomly, and the next generation of individuals also changes randomly. Therefore, its evolution direction is random, uncontrollable, and memoryless. Although real coding solves the problem of accuracy and storage capacity of the genetic algorithm to a great extent, its arithmetic crossover as the main crossover operator is a convex operation [12], so the whole group can only find the optimal value, may inexpertly converge to the local optimal value, and does not solve the lack of memory and randomness of the evolution direction [13]. The main problems of the coal-blending control system in the coal preparation plant are low automation and poor safety and reliability, which are difficult for meeting the needs of coal-blending control. For this reason, many scholars have proposed to use intelligent sensors for coal-blending control research. For example, Zhang et al. [14] aimed at the problem that the short-time coal quantity is too large when the two panels of the main shaft belt conveyor simultaneously discharge coal, resulting in the deviation of the uneven load coefficient. The overall carrying capacity is limited, and local coal overflow, based on the intelligent sensor network technology, optimized the coal-blending control system of the main shaft belt conveyor and collected the sensor data of the coal-blending control system. The real-time blending ratio is calculated and predicted, and the coal release amount in the panel is coordinated to fully optimize the coal-blending system. Mikulandrić et al. [15] established the relationship model between coal flow and belt speed, realized the cooperative and matching operation of coal flow and belt speed based on the intelligent sensor network method, and completed the system simulation. Compared with the traditional control method, it shows that the control effect is better. Wu et al. [16] realized the real-time control and remote control of coal flow in the coal preparation plant by using an intelligent sensor network control system and realized the functions of remote monitoring and fault information recording/query/storage on the upper computer, which improved the intelligent level of the coal preparation plant and reduced the labor intensity of workers. However, there was little research on the matching between coal quantity and belt speed of the belt conveyor.But so far, there are still many problems in the research of the coal-blending system; in particular, there is no mature research work on the intellectualization of the coal-blending system, and the combination of the genetic algorithm and intelligent sensor network will become a hot topic in future research. To improve the quality stability, output, and economic benefits of the Coal Preparation Plant, and achieve the group’s goal of improving quality and efficiency, therefore, it is necessary to research and develop new technologies and equipment in the field of power coal blending to achieve the accuracy of power coal blending.


## 2. GA Model and Intelligent Sensor Network

### 2.1. Genetic Algorithm and Its Application in Coal Research

#### 2.1.1. Principle and Characteristics

There are three methods to solve the typical discrete-time combinatorial optimization problem of assembly line balance in the theoretical circle, namely, the industrial engineering method, mathematical model method, and intelligent optimization algorithm. As a widely used intelligent optimization algorithm, the genetic algorithm combines the theory of “survival of the fittest,” which has the characteristics of adaptability, randomness, and high parallelism. Through the iteration of the population, the advantages and disadvantages of each chromosome are evaluated, and a series of operations such as selection, crossover, and mutation are used for multiple iterations to screen out the optimal chromosome, which is the optimal solution of the problem. The characteristics of genetic algorithm are as follows:(1)
Good at solving hard NP problems with multiple parameters, variables, targets, or regions, but has poor connectivity. Compared with the simulated annealing algorithm, which is good at single-solution search, the genetic algorithm can find the optimal solution of large-scale combinatorial problems faster(2)
Because of its strong scalability, it is easier to mix with other algorithms to generate a hybrid genetic algorithm(3)
There is a “premature” problem. Because the initial population size of the genetic algorithm is limited, through multiple iterations, a large number of chromosomes with a high fitness value are retained in the population, and the evolution speed is reduced. Therefore, the genetic algorithm has the problem of being “premature.” The fundamental reason lies in the similarity of chromosomes and the lack of diversity in the populationAs a random optimization algorithm, the genetic algorithm is different from a simple random search by fully combining all information to standardize the search process. The flow chart of the standard genetic algorithm is shown in Figure1.Step 1.
Input parameters and set the number of iterations,i=0. A set of chromosomes is randomly generated as the initial population (POP), and each chromosome corresponds to a feasible solution.Step 2.
Calculate the fitness values (Fit) of all chromosomes in the population.Step 3.
Determine whether the convergence criteria are met. If it is satisfied, stop calculating the output result. If not, makei=i+1 and proceed to the next step.Step 4.
Select the chromosome according to the fitness function value to generate a selection population,Selecti=1.Step 5.
According to the preset crossover probabilityPd, perform the crossover operation on the selected population Selecti=1 generated in the previous step to generate the crossover population, Pdi+1.Step 6.
Perform the mutation operation on the crossover populationPdi+1 generated in the previous step according to the preset mutation probability Pm to generate a mutant population, Pmi+1.Step 7.
Return to Step2.Figure 1
Flow chart of standard genetic algorithm.


#### 2.1.2. Parameter Design

(1) Coding. The goal of the genetic algorithm in population iterative calculation is the chromosome in the initial population, rather than directly solving the problem. Therefore, we need to express the problem we want to solve on the chromosome through coding and decoding.(2) Population Initialization. Population initialization is the initial definition of the number of chromosomes, the number of genes in chromosomes, and the arrangement of genes in chromosomes. If the number of the generated population is small, it is not conducive to the diversity of solutions. If the population is large, the efficiency of the algorithm will be reduced. Therefore, during population initialization, it is necessary to generate a population size matching the size according to the complexity of the problem and the requirements of the solution.(3) Fitness Function. The design of the fitness function is mainly to evaluate the internal chromosomes of the generated initial population. Whether its design is reasonable will affect the generation of the algorithm optimization process and reconciliation. In solving general combinatorial optimization problems, the objective function will be simply transformed to generate a fitness function. If the goal of the problem is to seek the maximum value, then the chromosome with a higher fitness value represents a better solution. On the contrary, the goal of the problem is to seek the minimum value; then, the chromosome with a lower fitness value represents a better solution.(4) Select. The selection operation is to select qualified chromosomes in the generated initial population according to the fitness function evaluation criteria and then use the generated subpopulation as the initial population of the crossover operation.(5) Crossover. The crossover operation is used to select two chromosomes as parents in the population formed after selection operation and exchange some genes in the two chromosomes to form two new offspring chromosomes. The key point is to choose the location and number of intersections in chromosomes and how to exchange genes. At present, there are several commonly used crossing methods, including single-point crossing and double-point crossing.Single-point crossing: as the name suggests, the single-point crossover randomly pairs on the parent chromosome to find a single intersection and then exchanges genes inside the parent chromosome at this intersection. Figure 2 shows the schematic diagram of the single-point crossover operation. The specific exchange process is as follows.Step 1.
Randomly select two chromosomes in the population for pairing.Step 2.
Randomly select an integerr1 on the chromosome and take the gene position represented by this integer as the intersection.Step 3.
After this crossing point, part of the genes of the parent chromosomes is exchanged with each other according to the set crossing probability to generate the offspring chromosomes.Figure 2
Schematic diagram of single-point crossing operation.Double-point intersection: the two-point crossover is used to randomly select two intersections in the parent chromosome and then exchange some genes in the parent chromosome within these two intersections. Figure 3 shows the schematic diagram of the two-point crossover operation. The specific exchange process is as follows.Step 1.
Randomly select two chromosomes in the population for pairing.Step 2.
Randomly select two integersr1 and r2 on the chromosome, and take the gene position represented by the two integers as the intersection.Step 3.
Between these two intersections, some genes of the parent chromosome are exchanged with each other according to the set crossover probability to generate the offspring chromosome.Figure 3
Schematic diagram of two-point crossing operation.(6) Variation. The mutation operation is a method to generate new individuals again after the crossover operation. Combined with the crossover operation, it provides a more diverse solution space for the genetic algorithm. At present, the commonly used variational methods include single-point variational, double-point variational, and multipoint variational.Single-point variation: Figure 4 is the schematic diagram of the single-point mutation operation.Step 1.
Randomly generate an integer to locate the corresponding gene position on the parent chromosome to form a variation point.Step 2.
Select a number within the range of gene values to replace the primordium, and complete the single-point mutation operation to form offspring.Figure 4
Schematic diagram of single-point mutation operation.Multipoint variation: Figure 5 shows the schematic diagram of the multipoint mutation operation.Step 1.
Randomly generate multiple integers to locate the corresponding gene sites on the parent chromosome to form multiple variation points.Step 2.
Select multiple numbers within the range of gene values to replace the primordium, and complete the multipoint mutation operation to form offspring.Figure 5
Schematic diagram of multipoint mutation operation.(7) Termination. Setting the termination condition of the algorithm will make the algorithm stop the cycle immediately after reaching the set goal, which determines that the setting of the termination condition of the algorithm will affect the convergence rate of the algorithm. At present, there are two ways to terminate the algorithm: one is to set the number of iterations of the algorithm in advance, and the other is to judge whether the algorithm has searched for a continuous and invariant optimal solution.


### 2.2. Intelligent Sensor Network and Its Application in Coal Research

The intelligent sensor network structure is composed of the signal acquisition module, signal processing module, fault diagnosis module, power supply module, keyboard input and display module, output and photography module, communication module, and storage module. The processor adopts the STM32F103 series single-chip microcomputer, which adopts the Cortex-M3 architecture, with high cost performance, low power consumption, and flexible configuration.The main module mainly completes the functions of self-test, initialization, channel selection, and calling each module. The signal acquisition module mainly completes the functions of signal amplification, A/D conversion, and data reading. The biggest feature of the intelligent sensor acquisition module is the separation structure design. The signal acquisition module has acquisition slots for current signals, voltage signals, and frequency signals. The slot of the current signal is mainly connected to the current signal output by the current sensor. The standard current of the coal-blending system is 4-20 mA. After the sampling resistance, the current signal is converted into a voltage signal. The amplitude of the voltage signal is small and needs to be processed by the amplifier. The processed voltage signal is a stable voltage signal, which is processed by the A/D analog-to-digital conversion circuit, and finally, the size of the voltage signal is obtained; then, the size of the current signal is calculated. The data signal-processing module mainly completes the functions of data filtering, nonlinear compensation, signal processing, error correction, and search table. The function of the fault diagnosis module is to analyze the signals of each sensor and judge whether the working state is normal. The function of the keyboard input and display module is to query whether the key is pressed. If it is pressed, it will be fed back to the main program module. The main program module will execute or call the corresponding module according to the key to display the data and working status of each sensor. The output, alarm, and photographing module mainly controls the output of analog quantity and alarms and completes photographing tasks. The communication module mainly controls the communication between the RS-232 serial communication interface and the microcomputer. The storage module is mainly used to store the information measured by each sensor and then save the information.After the installation of the intelligent sensor network system is completed, the static calibration and dynamic calibration of the online ash water instrument are carried out first, and then, the original data of the expert automation system is accumulated and stored. The system is commissioned and put into use. After coal blending, all indicators of coal blending basically meet the design requirements. Through the establishment of the intelligent sensor network coal-blending system, the systematic coal-blending calculation of mixed coal can effectively avoid the lag problem of the coal-blending adjustment caused by offline data and reduce the frequency and amplitude of coal quality fluctuation of mixed coal products. After adding the coal-blending mixing device, the full mixing of raw coal, clean coal, and medium coal is realized, and the stable quality of coal-blending products is ensured.


## 2.1. Genetic Algorithm and Its Application in Coal Research

### 2.1.1. Principle and Characteristics

There are three methods to solve the typical discrete-time combinatorial optimization problem of assembly line balance in the theoretical circle, namely, the industrial engineering method, mathematical model method, and intelligent optimization algorithm. As a widely used intelligent optimization algorithm, the genetic algorithm combines the theory of “survival of the fittest,” which has the characteristics of adaptability, randomness, and high parallelism. Through the iteration of the population, the advantages and disadvantages of each chromosome are evaluated, and a series of operations such as selection, crossover, and mutation are used for multiple iterations to screen out the optimal chromosome, which is the optimal solution of the problem. The characteristics of genetic algorithm are as follows:(1)
Good at solving hard NP problems with multiple parameters, variables, targets, or regions, but has poor connectivity. Compared with the simulated annealing algorithm, which is good at single-solution search, the genetic algorithm can find the optimal solution of large-scale combinatorial problems faster(2)
Because of its strong scalability, it is easier to mix with other algorithms to generate a hybrid genetic algorithm(3)
There is a “premature” problem. Because the initial population size of the genetic algorithm is limited, through multiple iterations, a large number of chromosomes with a high fitness value are retained in the population, and the evolution speed is reduced. Therefore, the genetic algorithm has the problem of being “premature.” The fundamental reason lies in the similarity of chromosomes and the lack of diversity in the populationAs a random optimization algorithm, the genetic algorithm is different from a simple random search by fully combining all information to standardize the search process. The flow chart of the standard genetic algorithm is shown in Figure1.Step 1.
Input parameters and set the number of iterations,i=0. A set of chromosomes is randomly generated as the initial population (POP), and each chromosome corresponds to a feasible solution.Step 2.
Calculate the fitness values (Fit) of all chromosomes in the population.Step 3.
Determine whether the convergence criteria are met. If it is satisfied, stop calculating the output result. If not, makei=i+1 and proceed to the next step.Step 4.
Select the chromosome according to the fitness function value to generate a selection population,Selecti=1.Step 5.
According to the preset crossover probabilityPd, perform the crossover operation on the selected population Selecti=1 generated in the previous step to generate the crossover population, Pdi+1.Step 6.
Perform the mutation operation on the crossover populationPdi+1 generated in the previous step according to the preset mutation probability Pm to generate a mutant population, Pmi+1.Step 7.
Return to Step2.Figure 1
Flow chart of standard genetic algorithm.


### 2.1.2. Parameter Design

(1) Coding. The goal of the genetic algorithm in population iterative calculation is the chromosome in the initial population, rather than directly solving the problem. Therefore, we need to express the problem we want to solve on the chromosome through coding and decoding.(2) Population Initialization. Population initialization is the initial definition of the number of chromosomes, the number of genes in chromosomes, and the arrangement of genes in chromosomes. If the number of the generated population is small, it is not conducive to the diversity of solutions. If the population is large, the efficiency of the algorithm will be reduced. Therefore, during population initialization, it is necessary to generate a population size matching the size according to the complexity of the problem and the requirements of the solution.(3) Fitness Function. The design of the fitness function is mainly to evaluate the internal chromosomes of the generated initial population. Whether its design is reasonable will affect the generation of the algorithm optimization process and reconciliation. In solving general combinatorial optimization problems, the objective function will be simply transformed to generate a fitness function. If the goal of the problem is to seek the maximum value, then the chromosome with a higher fitness value represents a better solution. On the contrary, the goal of the problem is to seek the minimum value; then, the chromosome with a lower fitness value represents a better solution.(4) Select. The selection operation is to select qualified chromosomes in the generated initial population according to the fitness function evaluation criteria and then use the generated subpopulation as the initial population of the crossover operation.(5) Crossover. The crossover operation is used to select two chromosomes as parents in the population formed after selection operation and exchange some genes in the two chromosomes to form two new offspring chromosomes. The key point is to choose the location and number of intersections in chromosomes and how to exchange genes. At present, there are several commonly used crossing methods, including single-point crossing and double-point crossing.Single-point crossing: as the name suggests, the single-point crossover randomly pairs on the parent chromosome to find a single intersection and then exchanges genes inside the parent chromosome at this intersection. Figure 2 shows the schematic diagram of the single-point crossover operation. The specific exchange process is as follows.Step 1.
Randomly select two chromosomes in the population for pairing.Step 2.
Randomly select an integerr1 on the chromosome and take the gene position represented by this integer as the intersection.Step 3.
After this crossing point, part of the genes of the parent chromosomes is exchanged with each other according to the set crossing probability to generate the offspring chromosomes.Figure 2
Schematic diagram of single-point crossing operation.Double-point intersection: the two-point crossover is used to randomly select two intersections in the parent chromosome and then exchange some genes in the parent chromosome within these two intersections. Figure 3 shows the schematic diagram of the two-point crossover operation. The specific exchange process is as follows.Step 1.
Randomly select two chromosomes in the population for pairing.Step 2.
Randomly select two integersr1 and r2 on the chromosome, and take the gene position represented by the two integers as the intersection.Step 3.
Between these two intersections, some genes of the parent chromosome are exchanged with each other according to the set crossover probability to generate the offspring chromosome.Figure 3
Schematic diagram of two-point crossing operation.(6) Variation. The mutation operation is a method to generate new individuals again after the crossover operation. Combined with the crossover operation, it provides a more diverse solution space for the genetic algorithm. At present, the commonly used variational methods include single-point variational, double-point variational, and multipoint variational.Single-point variation: Figure 4 is the schematic diagram of the single-point mutation operation.Step 1.
Randomly generate an integer to locate the corresponding gene position on the parent chromosome to form a variation point.Step 2.
Select a number within the range of gene values to replace the primordium, and complete the single-point mutation operation to form offspring.Figure 4
Schematic diagram of single-point mutation operation.Multipoint variation: Figure 5 shows the schematic diagram of the multipoint mutation operation.Step 1.
Randomly generate multiple integers to locate the corresponding gene sites on the parent chromosome to form multiple variation points.Step 2.
Select multiple numbers within the range of gene values to replace the primordium, and complete the multipoint mutation operation to form offspring.Figure 5
Schematic diagram of multipoint mutation operation.(7) Termination. Setting the termination condition of the algorithm will make the algorithm stop the cycle immediately after reaching the set goal, which determines that the setting of the termination condition of the algorithm will affect the convergence rate of the algorithm. At present, there are two ways to terminate the algorithm: one is to set the number of iterations of the algorithm in advance, and the other is to judge whether the algorithm has searched for a continuous and invariant optimal solution.


## 2.1.1. Principle and Characteristics

There are three methods to solve the typical discrete-time combinatorial optimization problem of assembly line balance in the theoretical circle, namely, the industrial engineering method, mathematical model method, and intelligent optimization algorithm. As a widely used intelligent optimization algorithm, the genetic algorithm combines the theory of “survival of the fittest,” which has the characteristics of adaptability, randomness, and high parallelism. Through the iteration of the population, the advantages and disadvantages of each chromosome are evaluated, and a series of operations such as selection, crossover, and mutation are used for multiple iterations to screen out the optimal chromosome, which is the optimal solution of the problem. The characteristics of genetic algorithm are as follows:(1)
Good at solving hard NP problems with multiple parameters, variables, targets, or regions, but has poor connectivity. Compared with the simulated annealing algorithm, which is good at single-solution search, the genetic algorithm can find the optimal solution of large-scale combinatorial problems faster(2)
Because of its strong scalability, it is easier to mix with other algorithms to generate a hybrid genetic algorithm(3)
There is a “premature” problem. Because the initial population size of the genetic algorithm is limited, through multiple iterations, a large number of chromosomes with a high fitness value are retained in the population, and the evolution speed is reduced. Therefore, the genetic algorithm has the problem of being “premature.” The fundamental reason lies in the similarity of chromosomes and the lack of diversity in the populationAs a random optimization algorithm, the genetic algorithm is different from a simple random search by fully combining all information to standardize the search process. The flow chart of the standard genetic algorithm is shown in Figure1.Step 1.
Input parameters and set the number of iterations,i=0. A set of chromosomes is randomly generated as the initial population (POP), and each chromosome corresponds to a feasible solution.Step 2.
Calculate the fitness values (Fit) of all chromosomes in the population.Step 3.
Determine whether the convergence criteria are met. If it is satisfied, stop calculating the output result. If not, makei=i+1 and proceed to the next step.Step 4.
Select the chromosome according to the fitness function value to generate a selection population,Selecti=1.Step 5.
According to the preset crossover probabilityPd, perform the crossover operation on the selected population Selecti=1 generated in the previous step to generate the crossover population, Pdi+1.Step 6.
Perform the mutation operation on the crossover populationPdi+1 generated in the previous step according to the preset mutation probability Pm to generate a mutant population, Pmi+1.Step 7.
Return to Step2.Figure 1
Flow chart of standard genetic algorithm.


## 2.1.2. Parameter Design

(1) Coding. The goal of the genetic algorithm in population iterative calculation is the chromosome in the initial population, rather than directly solving the problem. Therefore, we need to express the problem we want to solve on the chromosome through coding and decoding.(2) Population Initialization. Population initialization is the initial definition of the number of chromosomes, the number of genes in chromosomes, and the arrangement of genes in chromosomes. If the number of the generated population is small, it is not conducive to the diversity of solutions. If the population is large, the efficiency of the algorithm will be reduced. Therefore, during population initialization, it is necessary to generate a population size matching the size according to the complexity of the problem and the requirements of the solution.(3) Fitness Function. The design of the fitness function is mainly to evaluate the internal chromosomes of the generated initial population. Whether its design is reasonable will affect the generation of the algorithm optimization process and reconciliation. In solving general combinatorial optimization problems, the objective function will be simply transformed to generate a fitness function. If the goal of the problem is to seek the maximum value, then the chromosome with a higher fitness value represents a better solution. On the contrary, the goal of the problem is to seek the minimum value; then, the chromosome with a lower fitness value represents a better solution.(4) Select. The selection operation is to select qualified chromosomes in the generated initial population according to the fitness function evaluation criteria and then use the generated subpopulation as the initial population of the crossover operation.(5) Crossover. The crossover operation is used to select two chromosomes as parents in the population formed after selection operation and exchange some genes in the two chromosomes to form two new offspring chromosomes. The key point is to choose the location and number of intersections in chromosomes and how to exchange genes. At present, there are several commonly used crossing methods, including single-point crossing and double-point crossing.Single-point crossing: as the name suggests, the single-point crossover randomly pairs on the parent chromosome to find a single intersection and then exchanges genes inside the parent chromosome at this intersection. Figure 2 shows the schematic diagram of the single-point crossover operation. The specific exchange process is as follows.Step 1.
Randomly select two chromosomes in the population for pairing.Step 2.
Randomly select an integerr1 on the chromosome and take the gene position represented by this integer as the intersection.Step 3.
After this crossing point, part of the genes of the parent chromosomes is exchanged with each other according to the set crossing probability to generate the offspring chromosomes.Figure 2
Schematic diagram of single-point crossing operation.Double-point intersection: the two-point crossover is used to randomly select two intersections in the parent chromosome and then exchange some genes in the parent chromosome within these two intersections. Figure 3 shows the schematic diagram of the two-point crossover operation. The specific exchange process is as follows.Step 1.
Randomly select two chromosomes in the population for pairing.Step 2.
Randomly select two integersr1 and r2 on the chromosome, and take the gene position represented by the two integers as the intersection.Step 3.
Between these two intersections, some genes of the parent chromosome are exchanged with each other according to the set crossover probability to generate the offspring chromosome.Figure 3
Schematic diagram of two-point crossing operation.(6) Variation. The mutation operation is a method to generate new individuals again after the crossover operation. Combined with the crossover operation, it provides a more diverse solution space for the genetic algorithm. At present, the commonly used variational methods include single-point variational, double-point variational, and multipoint variational.Single-point variation: Figure 4 is the schematic diagram of the single-point mutation operation.Step 1.
Randomly generate an integer to locate the corresponding gene position on the parent chromosome to form a variation point.Step 2.
Select a number within the range of gene values to replace the primordium, and complete the single-point mutation operation to form offspring.Figure 4
Schematic diagram of single-point mutation operation.Multipoint variation: Figure 5 shows the schematic diagram of the multipoint mutation operation.Step 1.
Randomly generate multiple integers to locate the corresponding gene sites on the parent chromosome to form multiple variation points.Step 2.
Select multiple numbers within the range of gene values to replace the primordium, and complete the multipoint mutation operation to form offspring.Figure 5
Schematic diagram of multipoint mutation operation.(7) Termination. Setting the termination condition of the algorithm will make the algorithm stop the cycle immediately after reaching the set goal, which determines that the setting of the termination condition of the algorithm will affect the convergence rate of the algorithm. At present, there are two ways to terminate the algorithm: one is to set the number of iterations of the algorithm in advance, and the other is to judge whether the algorithm has searched for a continuous and invariant optimal solution.


## 2.2. Intelligent Sensor Network and Its Application in Coal Research

The intelligent sensor network structure is composed of the signal acquisition module, signal processing module, fault diagnosis module, power supply module, keyboard input and display module, output and photography module, communication module, and storage module. The processor adopts the STM32F103 series single-chip microcomputer, which adopts the Cortex-M3 architecture, with high cost performance, low power consumption, and flexible configuration.The main module mainly completes the functions of self-test, initialization, channel selection, and calling each module. The signal acquisition module mainly completes the functions of signal amplification, A/D conversion, and data reading. The biggest feature of the intelligent sensor acquisition module is the separation structure design. The signal acquisition module has acquisition slots for current signals, voltage signals, and frequency signals. The slot of the current signal is mainly connected to the current signal output by the current sensor. The standard current of the coal-blending system is 4-20 mA. After the sampling resistance, the current signal is converted into a voltage signal. The amplitude of the voltage signal is small and needs to be processed by the amplifier. The processed voltage signal is a stable voltage signal, which is processed by the A/D analog-to-digital conversion circuit, and finally, the size of the voltage signal is obtained; then, the size of the current signal is calculated. The data signal-processing module mainly completes the functions of data filtering, nonlinear compensation, signal processing, error correction, and search table. The function of the fault diagnosis module is to analyze the signals of each sensor and judge whether the working state is normal. The function of the keyboard input and display module is to query whether the key is pressed. If it is pressed, it will be fed back to the main program module. The main program module will execute or call the corresponding module according to the key to display the data and working status of each sensor. The output, alarm, and photographing module mainly controls the output of analog quantity and alarms and completes photographing tasks. The communication module mainly controls the communication between the RS-232 serial communication interface and the microcomputer. The storage module is mainly used to store the information measured by each sensor and then save the information.After the installation of the intelligent sensor network system is completed, the static calibration and dynamic calibration of the online ash water instrument are carried out first, and then, the original data of the expert automation system is accumulated and stored. The system is commissioned and put into use. After coal blending, all indicators of coal blending basically meet the design requirements. Through the establishment of the intelligent sensor network coal-blending system, the systematic coal-blending calculation of mixed coal can effectively avoid the lag problem of the coal-blending adjustment caused by offline data and reduce the frequency and amplitude of coal quality fluctuation of mixed coal products. After adding the coal-blending mixing device, the full mixing of raw coal, clean coal, and medium coal is realized, and the stable quality of coal-blending products is ensured.


## 3. Coal Power Distribution Model and GA Model

### 3.1. Coal Power Distribution Model

The power coal power distribution model consists of two parts: power coal quality prediction and power coal quality prediction. At present, according to the national standard and the actual production requirements of iron and steel enterprises, the power coal quality is mainly measured by four indicators, namely, crushing strengthM40, abrasion strength M10, sulfur Sb, and ash Ab, which are expressed in the form of inequalities according to different power coal grades:
(1)M40G≤M40≤M40G+MM40,M10G−MM10≤M10≤M10G,Sb≤SbG,Ab≤AbG,where M40G, M10G, SbG, and AbG are the given values of each index of the standard grade.The prediction of power coal quality is a multivariate linear function of the physical mixing process. There aren types of power coal, and the blending ratio Adi, Vdafi, Stdi, and Gi for the ith power coal represents the ash, volatile matter, sulfur, and bond index of the ith power coal, respectively. The definition is as follows:
(2)B=G1G2⋯GnVdaf1Vdaf2⋯Vdaf1Ad1Ad2⋯AdhStd1Std2⋯Stdh,X=x1x2⋮Xn.Then, the fit power coal distribution model is expressed as(3)F¯mixX=BX.Since the power coal distribution process is a physicochemical process, about 65% to 70% of the ash and sulfur of the power coal blend enter the power coal; about 30% to 35% of the sulfur escapes with the barren gas during power coal distribution; and the volatile matter, bonding index, and ash have an impact on the power coal quality indexesM40 and M10, so the process presents a nonlinear function. Let the performance index of mating power coal be Admix,Vdafmix,Stdmix,Gmix, and define D=GmixVdafmixAdmixStdmix as the model input; then, the power coal distribution model is a four-input, four-output structure, set as F¯cokeD. The modeling adopts the improved BP algorithm based on GA; according to the model structure, the BP algorithm adopts a 4-n-4 structure (nis the number of nodes in the implicit layer); in order to avoid falling into the local minimum, the GA is used to optimize its weight coefficients; and it can also realize the online correction of the model according to the change of monitoring information. The GA is used to optimize the weight coefficients in order to avoid local minima and also to achieve online model modification according to the changes of monitoring information. The GA is encoded with real numbers, and the chromosome is composed of BP weights, which are set to W=W1W2⋯W4n, n=10, and the model correction is achieved by improving the arithmetic crossover operator. The arithmetic crossover operatorT is usually taken as a fixed value, but in order to introduce the relevant monitoring information, the variable arithmetic crossover operatorT′ is proposed; set T′=lu, where u is the relevant monitoring information.Combining the above two parts, letFX be a nonlinear function between the power coal-blending ratio and the power coal quality index, then
(4)F¯X=F¯mixXF¯cokeD.


### 3.2. Optimization Calculation of Power Coal Distribution Ratio

The optimal calculation of the power coal distribution ratio is actually a constrained integer programming problem, described as follows:(5)minF¯X:∑i=1nxi=100,where ΔF¯X=G¯−F¯X;F¯Xthe previous prediction model;Gis the average value of the standard grade power coal quality index, i.e.,g40=MM40/2+M40Gwith the rest of the items by analogy, whereMM40is the difference between the highest and lowest limits ofM40; andΔFXis the deviation of the quality index corresponding to the current power coal allocation ratio.The GA is used to optimize the power coal allocation ratio. Since the power coal allocation ratios are expressed as integer percentages, the decimal integer code is chosen, and the adaptation value function is selected based on the power coal power distribution model.(6)F¯fitX=4I−ΔF¯X.Let the initial population number bem; then, the initial population is P0=Xm×n, and the individual chromosome is Xi. In order to improve the efficiency of the search, the initial population is selected empirically under the satisfaction of constraints, such as the proportion of gas and power coal is usually 30%.The crossover operation was performed by arithmetic crossover, and letXi and Xj be the parent individuals and Xi′ and Xj′ be the offspring individuals, then
(7)Xi′=TXi+1−TXj,Xj′=TXj+1−TXi,where T∈0,1 constant. The mutation operation uses nonuniform variation (dynamic); let chromosome Xi=Xi1Xi2⋯Xin, then
(8)Xij=Xij+Δt,UB−Xij,Xij−Δt,UB−Xij,where Δt,V=V⋅1−W1−t/rb, W is the random number on the interval [0,1], T is the maximum number of generations of the algorithm, b is a constant, and UB and LB are the maximum and minimum values, respectively, of Xij. It can be seen that the probability of Δt,V tending to 0 increases when t increases; i.e., the effect of the variation operation decreases. The roulette wheel method is used to select the offspring individuals, and both the offspring individuals and the variant offspring may have individuals that do not satisfy the constraints, and the infeasible solution is restricted by the rejection strategy.Compared with the traditional technology, it can convert more than five different physical quantities and has new functions such as positioning and voice, which is of great significance to promote coal mine safety monitoring and ensure the health of workers. In addition, it is proposed to establish a linear prediction model and a mathematical model of power coal blending according to the principle of power coal blending. Through the elemental analysis and industrial analysis of single coal, the predicted value of each component of mixed coal is obtained. At the same time, the genetic algorithm is used to solve the mathematical model of power coal blending, and the technical measures and schemes for safe, economic, energy-saving, and emission reduction operation of power plants are proposed.


## 3.1. Coal Power Distribution Model

The power coal power distribution model consists of two parts: power coal quality prediction and power coal quality prediction. At present, according to the national standard and the actual production requirements of iron and steel enterprises, the power coal quality is mainly measured by four indicators, namely, crushing strengthM40, abrasion strength M10, sulfur Sb, and ash Ab, which are expressed in the form of inequalities according to different power coal grades:
(1)M40G≤M40≤M40G+MM40,M10G−MM10≤M10≤M10G,Sb≤SbG,Ab≤AbG,where M40G, M10G, SbG, and AbG are the given values of each index of the standard grade.The prediction of power coal quality is a multivariate linear function of the physical mixing process. There aren types of power coal, and the blending ratio Adi, Vdafi, Stdi, and Gi for the ith power coal represents the ash, volatile matter, sulfur, and bond index of the ith power coal, respectively. The definition is as follows:
(2)B=G1G2⋯GnVdaf1Vdaf2⋯Vdaf1Ad1Ad2⋯AdhStd1Std2⋯Stdh,X=x1x2⋮Xn.Then, the fit power coal distribution model is expressed as(3)F¯mixX=BX.Since the power coal distribution process is a physicochemical process, about 65% to 70% of the ash and sulfur of the power coal blend enter the power coal; about 30% to 35% of the sulfur escapes with the barren gas during power coal distribution; and the volatile matter, bonding index, and ash have an impact on the power coal quality indexesM40 and M10, so the process presents a nonlinear function. Let the performance index of mating power coal be Admix,Vdafmix,Stdmix,Gmix, and define D=GmixVdafmixAdmixStdmix as the model input; then, the power coal distribution model is a four-input, four-output structure, set as F¯cokeD. The modeling adopts the improved BP algorithm based on GA; according to the model structure, the BP algorithm adopts a 4-n-4 structure (nis the number of nodes in the implicit layer); in order to avoid falling into the local minimum, the GA is used to optimize its weight coefficients; and it can also realize the online correction of the model according to the change of monitoring information. The GA is used to optimize the weight coefficients in order to avoid local minima and also to achieve online model modification according to the changes of monitoring information. The GA is encoded with real numbers, and the chromosome is composed of BP weights, which are set to W=W1W2⋯W4n, n=10, and the model correction is achieved by improving the arithmetic crossover operator. The arithmetic crossover operatorT is usually taken as a fixed value, but in order to introduce the relevant monitoring information, the variable arithmetic crossover operatorT′ is proposed; set T′=lu, where u is the relevant monitoring information.Combining the above two parts, letFX be a nonlinear function between the power coal-blending ratio and the power coal quality index, then
(4)F¯X=F¯mixXF¯cokeD.


## 3.2. Optimization Calculation of Power Coal Distribution Ratio

The optimal calculation of the power coal distribution ratio is actually a constrained integer programming problem, described as follows:(5)minF¯X:∑i=1nxi=100,where ΔF¯X=G¯−F¯X;F¯Xthe previous prediction model;Gis the average value of the standard grade power coal quality index, i.e.,g40=MM40/2+M40Gwith the rest of the items by analogy, whereMM40is the difference between the highest and lowest limits ofM40; andΔFXis the deviation of the quality index corresponding to the current power coal allocation ratio.The GA is used to optimize the power coal allocation ratio. Since the power coal allocation ratios are expressed as integer percentages, the decimal integer code is chosen, and the adaptation value function is selected based on the power coal power distribution model.(6)F¯fitX=4I−ΔF¯X.Let the initial population number bem; then, the initial population is P0=Xm×n, and the individual chromosome is Xi. In order to improve the efficiency of the search, the initial population is selected empirically under the satisfaction of constraints, such as the proportion of gas and power coal is usually 30%.The crossover operation was performed by arithmetic crossover, and letXi and Xj be the parent individuals and Xi′ and Xj′ be the offspring individuals, then
(7)Xi′=TXi+1−TXj,Xj′=TXj+1−TXi,where T∈0,1 constant. The mutation operation uses nonuniform variation (dynamic); let chromosome Xi=Xi1Xi2⋯Xin, then
(8)Xij=Xij+Δt,UB−Xij,Xij−Δt,UB−Xij,where Δt,V=V⋅1−W1−t/rb, W is the random number on the interval [0,1], T is the maximum number of generations of the algorithm, b is a constant, and UB and LB are the maximum and minimum values, respectively, of Xij. It can be seen that the probability of Δt,V tending to 0 increases when t increases; i.e., the effect of the variation operation decreases. The roulette wheel method is used to select the offspring individuals, and both the offspring individuals and the variant offspring may have individuals that do not satisfy the constraints, and the infeasible solution is restricted by the rejection strategy.Compared with the traditional technology, it can convert more than five different physical quantities and has new functions such as positioning and voice, which is of great significance to promote coal mine safety monitoring and ensure the health of workers. In addition, it is proposed to establish a linear prediction model and a mathematical model of power coal blending according to the principle of power coal blending. Through the elemental analysis and industrial analysis of single coal, the predicted value of each component of mixed coal is obtained. At the same time, the genetic algorithm is used to solve the mathematical model of power coal blending, and the technical measures and schemes for safe, economic, energy-saving, and emission reduction operation of power plants are proposed.


## 4. Model of Coal-Blending System Based on Intelligent Sensor Network and GA

### 4.1. Coal-Blending Prediction Method Based on GA

For the prediction of mixed coal characteristics, the basic idea is to calculate the characteristics of mixed coal by mixing the characteristics of single coal. Wireless sensor networks include many microsatellite mobile sensors; due to the limitations for various reasons, most prediction models of mixed coal characteristics at home and abroad adopt linear addition. Among them, the elemental analysis and industrial analysis (momentum, quantitative value, ash, and volatile matter) of mixed coal are weighted according to the mixing proportion. In wireless sensor networks, the content is first transmitted into the corresponding centralized nodes by wireless messages, and the development from the actual parameters of mixed coal is small. The assignment characteristics, sliding characteristics, and integrity of blended coal vary greatly from the weighted value, which cannot meet the simple weighted relationship [16–18].


#### 4.1.1. Objective Function

(9)minPzhcb=Prl+Phb+Pms+Pfj,where Pzhcb is the comprehensive cost of unit power generation, yuan/kwh; Prl is the fuel cost per unit of power generation, yuan/kwh; Phb is the environmental protection cost per unit of power generation, yuan/kwh; Pms is the wear cost per unit of power generation, yuan/kwh; and Pfj is the power consumption cost per unit of auxiliary power generation, yuan/kwh.


#### 4.1.2. Constraints

(i)
Calorific value constraint:QA≤fqxi,Qi,Mi,Ai,Vi,Fi≤QB(ii)
Volatile constraint:VA≤fvxi,Mi,Ai,Vi,Fi≤VB(iii)
Ash constraint:AA≤faxi,Mi,Ai,Vi,Fi≤AB(iv)
Sulfur constraint:SA≤fsxi,Si≤SB(v)
Moisture constraints:MA≤fmxi,Mi,Ai,Vi,Fi≤MB(vi)
Slagging characteristic constraints:RA≤frxi,componentanalysisofeachsinglecoalash≤RB(vii)
Burnout characteristic constraint:BcA≤fBcxi,Qi,Mi,Ai,Vi,Fi≤BcB(viii)
Grindability constraints:fhgixi,Mi,Ai,Vi,Fi≥HGIAIn the above list of constraints,xi is the ratio coefficient of single coal; Qi is the calorific value of single coal, %; Mi is the moisture of single coal, %; Ai is the ash content of single coal, %; Vi is the volatile matter of single coal, %; Fi is the moisture content of single coal, %; Bi is the burnout characteristic, %; Ri is the slagging characteristic, %; Si is the sulfur content, %; and fghi is the grindability, %.When using the differential evolution algorithm to solve the optimization problem of the coal-blending scheme, each individual in the main coal-blending program needs to cooperate with the loading program, the population (considering a group of coal-blending proportions) will be mutated and crossed, and the constraints will be considered for the resulting test individuals. If the conditions are met and if the two input proportioning sheets are different, set the frequency of weighing the coal feeder to zero. The next selection operation will be carried out. If not, the coal-blending ratio in the individual will be adjusted according to certain principles to meet the constraints, and the next selection operation will be carried out. Therefore, in the main program, it is necessary to continuously read the stop signal sent by the loading link which will continue to evolve, until the global optimal coal-blending ratio is obtained [19, 20].


### 4.2. Coal-Blending Monitoring System Based on Intelligent Sensor Network

There are many kinds of coal quality indicators, among which ash is an important indicator information collection procedure for evaluating coal quality. The ash is mainly composed of minerals. During combustion, the structural stability of the rock stratum will absorb some heat and discharge it along with the coal gangue after burning, which reduces the calorific value and utilization rate of the coal. In addition, the high ash content in the pulverized coal during boiler combustion will cause slagging on the inner wall of the furnace and lead to equipment wear. Based on the intelligent sensor feedback (the device can accurately measure the uniform coal below 10 mm), an automatic control coal-blending system is built.The general principle of the carbon-mixing system of the coal preparation plant is to minimize the cost, improve the company’s solid cargo interests, and meet the carbon neutralization framework for all indicators required for service analysis and evaluation of the single base coal emission process on the basis of ensuring safety and stability. PLC first sends control signals to the frequency converter. The frequency converter shall control the incoming frequency to control the coal-fired engine to return to the corresponding speed. Motor transportation shall control the reduction equipment, which is the previous monitoring method for coal-fired generators. The reduction equipment shall control the carbon drilling rig. The rotation speed of the coal-fired circuit controls the flow of coal. The network node of the load sensor also estimates the coal flow and sends an analog signal to the PLC controller to close the loop. In addition, due to the different humidity, size, and accuracy of the coal blocks, the carbon output released may vary. However, since the shutter is open and manually set, only the delay time should be considered. By analyzing the coordination process of the two coal bases, the correct transportation speed under the coal base cannot be controlled, and only the slow process between the two coal bases can be considered.


### 4.3. System Implementation Based on Intelligent Sensor Network and GA Algorithm

The coal-blending system is responsible for the coal-blending process flow display, parameter setting, alarm function, database, and trend line record. The loading link is responsible for the loading process flow display, parameter setting, alarm function, database, and trend line record. The monitoring system needs to design an early warning system for new equipment, original equipment, and intermediate parameters, as well as parameter records for loading links, through data interaction with the digital coal yard and real-time production-monitoring system of the power plant; a decision support system for coal blending and combustion of the power plant is developed by using the machine-learning algorithm; and the control system is shown in Figure6.Figure 6
System workflow.


#### 4.3.1. Monitoring System Design

The system provides modularization and plug and play. Through interaction with the digital coal yard data, the BP neural network algorithm is used to establish the prediction model of coal-mixing characteristics, and the differential evolution algorithm is used to solve the optimization problem of the coal-blending scheme. At the same time, through the collection of real-time production data, the online evaluation system of coal blending and burning is established to realize the whole process-blending management of coal storage in the coal yard, blending and burning in the furnace, post blending evaluation, and demand guidance.(1)
Add online ash water detector and belt scale. Three sets of online ash water meters and belt scales are added to the 501 raw coal transfer belt conveyor, the 703 clean coal delivery belt conveyor, and the 507 mixed coal drop belt conveyor. The Df-5756b/C online multipoint ash water meter is selected as the ash water meter, which can realize online detection of moisture and ash content(2)
The existing 507 mixed coal falling belt conveyor will be reformed, the middle part of the belt conveyor will be raised, two redirection rollers will be added, and the coal-blending mixing device will be installed. The mixing device will be customized according to the field control, with a processing capacity of 300 t/h, which can fully mix the raw coal, medium coal, and refined coal(3)
The coal-blending expert automation system adopts the artificial intelligence algorithm to optimize the coal-blending accuracy. It is designed and developed according to the process requirements of coal blending, combined with the user’s coal source and the customer’s coal requirements, and can be expanded according to the user’s functional requirements. Input the coal parameters required by the user, such as coal quantity, ash content, and moisture content, which will be automatically operated by the coal-blending expert automation system. The artificial intelligence algorithm will be used to select the optimal proportioning scheme and adjust the final clean coal-blending amount according to the output and ash content of the final raw coal(4)
The coal-blending database is set to realize the electronic and automatic storage, transmission, and processing of coal-blending data information. The historical data can be viewed in real time on the platform software, the coal quality change can be analyzed, and the coal-blending data report can be automatically generated


#### 4.3.2. Architecture Design

The remote upper computer monitoring system is used to realize that the optimization time is relatively short and can meet the remote monitoring and control of the coal-blending system and the loading link of the coal-blending process. In this design, the information-monitoring platform for safety supervision is carried out on-site, and then, WinCC of Siemens is used as the monitoring software. It is widely used in various industrial systems, pollutant emission, and coal price-monitoring development platforms and integrates information-processing, process management, and local control functions to complete the state monitoring and management of the whole process of enterprise production. The monitoring system is based on the S7-300 PLC controller (Figure7).Figure 7
System architecture.


#### 4.3.3. Monitoring Correction

Based on the fuzzy set theory, the experience and knowledge of people and previous wireless sensor networks are transformed into fuzzy rules, and the control system of the system is constructed by fuzzy inference. The adjustment of the learning rate is controlled by two input variables, and the difference between the two weights is used as the first judgment criterion. The terminal collection facility may use the deviation percentage between the expected output and the actual output as the second judgment criterion. If the topological structure change rate of the weight change network is large and the overall error change rate is not 0, it is judged that the learning rate changes greatly. Use the sensor to correct the measurement controller constant. In order to verify that the control effect of the controller is fed back to the corresponding monitoring results in the case of large delay and model instability, two different controlled objects and three operating procedures are taken as examples to set different delay times for simulation. The discrete PID control, fuzzy PID control, and Darling algorithm are mainly used in the control of the transmission node and coal-blending control system to verify the superiority of this design.


### 4.4. Application Effect

The loading link of the original system has sufficient capacity. The aggregation node notifies the duty room by manually recording the vehicle number, and the duty room cooperates to mobilize wireless sensors. The personnel in the central control room can control the coal preparation system by adjusting the belt. The scheme has a large amount of worker operations, and the ground information-monitoring platform is prone to errors. At the same time, the corresponding loading chain and equipment are also designed for the coal preparation system. In addition, the coal systems of the system cooperate with each other to achieve quantitative and stable loading, which is displayed on the WinCC screen. During the operation, the network has multiple sensor nodes to record the current vehicle number, loading coal type, loading lane, loading ash, preset ash content, carriage load, and dispersion of three products. The data is updated every minute to facilitate operation status and detailed manual retrieval and observation. After the automatic loading system is put into operation, the average vehicle mass is 58 t, the average error is 3.33%, and the accuracy is increased by 4.82%. It can be seen that the improved loading link not only improves the automation level and reduces the labor volume of workers but also improves the loading accuracy and reduces the production cost of the enterprise.


## 4.1. Coal-Blending Prediction Method Based on GA

For the prediction of mixed coal characteristics, the basic idea is to calculate the characteristics of mixed coal by mixing the characteristics of single coal. Wireless sensor networks include many microsatellite mobile sensors; due to the limitations for various reasons, most prediction models of mixed coal characteristics at home and abroad adopt linear addition. Among them, the elemental analysis and industrial analysis (momentum, quantitative value, ash, and volatile matter) of mixed coal are weighted according to the mixing proportion. In wireless sensor networks, the content is first transmitted into the corresponding centralized nodes by wireless messages, and the development from the actual parameters of mixed coal is small. The assignment characteristics, sliding characteristics, and integrity of blended coal vary greatly from the weighted value, which cannot meet the simple weighted relationship [16–18].


### 4.1.1. Objective Function

(9)minPzhcb=Prl+Phb+Pms+Pfj,where Pzhcb is the comprehensive cost of unit power generation, yuan/kwh; Prl is the fuel cost per unit of power generation, yuan/kwh; Phb is the environmental protection cost per unit of power generation, yuan/kwh; Pms is the wear cost per unit of power generation, yuan/kwh; and Pfj is the power consumption cost per unit of auxiliary power generation, yuan/kwh.


### 4.1.2. Constraints

(i)
Calorific value constraint:QA≤fqxi,Qi,Mi,Ai,Vi,Fi≤QB(ii)
Volatile constraint:VA≤fvxi,Mi,Ai,Vi,Fi≤VB(iii)
Ash constraint:AA≤faxi,Mi,Ai,Vi,Fi≤AB(iv)
Sulfur constraint:SA≤fsxi,Si≤SB(v)
Moisture constraints:MA≤fmxi,Mi,Ai,Vi,Fi≤MB(vi)
Slagging characteristic constraints:RA≤frxi,componentanalysisofeachsinglecoalash≤RB(vii)
Burnout characteristic constraint:BcA≤fBcxi,Qi,Mi,Ai,Vi,Fi≤BcB(viii)
Grindability constraints:fhgixi,Mi,Ai,Vi,Fi≥HGIAIn the above list of constraints,xi is the ratio coefficient of single coal; Qi is the calorific value of single coal, %; Mi is the moisture of single coal, %; Ai is the ash content of single coal, %; Vi is the volatile matter of single coal, %; Fi is the moisture content of single coal, %; Bi is the burnout characteristic, %; Ri is the slagging characteristic, %; Si is the sulfur content, %; and fghi is the grindability, %.When using the differential evolution algorithm to solve the optimization problem of the coal-blending scheme, each individual in the main coal-blending program needs to cooperate with the loading program, the population (considering a group of coal-blending proportions) will be mutated and crossed, and the constraints will be considered for the resulting test individuals. If the conditions are met and if the two input proportioning sheets are different, set the frequency of weighing the coal feeder to zero. The next selection operation will be carried out. If not, the coal-blending ratio in the individual will be adjusted according to certain principles to meet the constraints, and the next selection operation will be carried out. Therefore, in the main program, it is necessary to continuously read the stop signal sent by the loading link which will continue to evolve, until the global optimal coal-blending ratio is obtained [19, 20].


## 4.1.1. Objective Function

(9)minPzhcb=Prl+Phb+Pms+Pfj,where Pzhcb is the comprehensive cost of unit power generation, yuan/kwh; Prl is the fuel cost per unit of power generation, yuan/kwh; Phb is the environmental protection cost per unit of power generation, yuan/kwh; Pms is the wear cost per unit of power generation, yuan/kwh; and Pfj is the power consumption cost per unit of auxiliary power generation, yuan/kwh.


## 4.1.2. Constraints

(i)
Calorific value constraint:QA≤fqxi,Qi,Mi,Ai,Vi,Fi≤QB(ii)
Volatile constraint:VA≤fvxi,Mi,Ai,Vi,Fi≤VB(iii)
Ash constraint:AA≤faxi,Mi,Ai,Vi,Fi≤AB(iv)
Sulfur constraint:SA≤fsxi,Si≤SB(v)
Moisture constraints:MA≤fmxi,Mi,Ai,Vi,Fi≤MB(vi)
Slagging characteristic constraints:RA≤frxi,componentanalysisofeachsinglecoalash≤RB(vii)
Burnout characteristic constraint:BcA≤fBcxi,Qi,Mi,Ai,Vi,Fi≤BcB(viii)
Grindability constraints:fhgixi,Mi,Ai,Vi,Fi≥HGIAIn the above list of constraints,xi is the ratio coefficient of single coal; Qi is the calorific value of single coal, %; Mi is the moisture of single coal, %; Ai is the ash content of single coal, %; Vi is the volatile matter of single coal, %; Fi is the moisture content of single coal, %; Bi is the burnout characteristic, %; Ri is the slagging characteristic, %; Si is the sulfur content, %; and fghi is the grindability, %.When using the differential evolution algorithm to solve the optimization problem of the coal-blending scheme, each individual in the main coal-blending program needs to cooperate with the loading program, the population (considering a group of coal-blending proportions) will be mutated and crossed, and the constraints will be considered for the resulting test individuals. If the conditions are met and if the two input proportioning sheets are different, set the frequency of weighing the coal feeder to zero. The next selection operation will be carried out. If not, the coal-blending ratio in the individual will be adjusted according to certain principles to meet the constraints, and the next selection operation will be carried out. Therefore, in the main program, it is necessary to continuously read the stop signal sent by the loading link which will continue to evolve, until the global optimal coal-blending ratio is obtained [19, 20].


## 4.2. Coal-Blending Monitoring System Based on Intelligent Sensor Network

There are many kinds of coal quality indicators, among which ash is an important indicator information collection procedure for evaluating coal quality. The ash is mainly composed of minerals. During combustion, the structural stability of the rock stratum will absorb some heat and discharge it along with the coal gangue after burning, which reduces the calorific value and utilization rate of the coal. In addition, the high ash content in the pulverized coal during boiler combustion will cause slagging on the inner wall of the furnace and lead to equipment wear. Based on the intelligent sensor feedback (the device can accurately measure the uniform coal below 10 mm), an automatic control coal-blending system is built.The general principle of the carbon-mixing system of the coal preparation plant is to minimize the cost, improve the company’s solid cargo interests, and meet the carbon neutralization framework for all indicators required for service analysis and evaluation of the single base coal emission process on the basis of ensuring safety and stability. PLC first sends control signals to the frequency converter. The frequency converter shall control the incoming frequency to control the coal-fired engine to return to the corresponding speed. Motor transportation shall control the reduction equipment, which is the previous monitoring method for coal-fired generators. The reduction equipment shall control the carbon drilling rig. The rotation speed of the coal-fired circuit controls the flow of coal. The network node of the load sensor also estimates the coal flow and sends an analog signal to the PLC controller to close the loop. In addition, due to the different humidity, size, and accuracy of the coal blocks, the carbon output released may vary. However, since the shutter is open and manually set, only the delay time should be considered. By analyzing the coordination process of the two coal bases, the correct transportation speed under the coal base cannot be controlled, and only the slow process between the two coal bases can be considered.


## 4.3. System Implementation Based on Intelligent Sensor Network and GA Algorithm

The coal-blending system is responsible for the coal-blending process flow display, parameter setting, alarm function, database, and trend line record. The loading link is responsible for the loading process flow display, parameter setting, alarm function, database, and trend line record. The monitoring system needs to design an early warning system for new equipment, original equipment, and intermediate parameters, as well as parameter records for loading links, through data interaction with the digital coal yard and real-time production-monitoring system of the power plant; a decision support system for coal blending and combustion of the power plant is developed by using the machine-learning algorithm; and the control system is shown in Figure6.Figure 6
System workflow.


### 4.3.1. Monitoring System Design

The system provides modularization and plug and play. Through interaction with the digital coal yard data, the BP neural network algorithm is used to establish the prediction model of coal-mixing characteristics, and the differential evolution algorithm is used to solve the optimization problem of the coal-blending scheme. At the same time, through the collection of real-time production data, the online evaluation system of coal blending and burning is established to realize the whole process-blending management of coal storage in the coal yard, blending and burning in the furnace, post blending evaluation, and demand guidance.(1)
Add online ash water detector and belt scale. Three sets of online ash water meters and belt scales are added to the 501 raw coal transfer belt conveyor, the 703 clean coal delivery belt conveyor, and the 507 mixed coal drop belt conveyor. The Df-5756b/C online multipoint ash water meter is selected as the ash water meter, which can realize online detection of moisture and ash content(2)
The existing 507 mixed coal falling belt conveyor will be reformed, the middle part of the belt conveyor will be raised, two redirection rollers will be added, and the coal-blending mixing device will be installed. The mixing device will be customized according to the field control, with a processing capacity of 300 t/h, which can fully mix the raw coal, medium coal, and refined coal(3)
The coal-blending expert automation system adopts the artificial intelligence algorithm to optimize the coal-blending accuracy. It is designed and developed according to the process requirements of coal blending, combined with the user’s coal source and the customer’s coal requirements, and can be expanded according to the user’s functional requirements. Input the coal parameters required by the user, such as coal quantity, ash content, and moisture content, which will be automatically operated by the coal-blending expert automation system. The artificial intelligence algorithm will be used to select the optimal proportioning scheme and adjust the final clean coal-blending amount according to the output and ash content of the final raw coal(4)
The coal-blending database is set to realize the electronic and automatic storage, transmission, and processing of coal-blending data information. The historical data can be viewed in real time on the platform software, the coal quality change can be analyzed, and the coal-blending data report can be automatically generated


### 4.3.2. Architecture Design

The remote upper computer monitoring system is used to realize that the optimization time is relatively short and can meet the remote monitoring and control of the coal-blending system and the loading link of the coal-blending process. In this design, the information-monitoring platform for safety supervision is carried out on-site, and then, WinCC of Siemens is used as the monitoring software. It is widely used in various industrial systems, pollutant emission, and coal price-monitoring development platforms and integrates information-processing, process management, and local control functions to complete the state monitoring and management of the whole process of enterprise production. The monitoring system is based on the S7-300 PLC controller (Figure7).Figure 7
System architecture.


### 4.3.3. Monitoring Correction

Based on the fuzzy set theory, the experience and knowledge of people and previous wireless sensor networks are transformed into fuzzy rules, and the control system of the system is constructed by fuzzy inference. The adjustment of the learning rate is controlled by two input variables, and the difference between the two weights is used as the first judgment criterion. The terminal collection facility may use the deviation percentage between the expected output and the actual output as the second judgment criterion. If the topological structure change rate of the weight change network is large and the overall error change rate is not 0, it is judged that the learning rate changes greatly. Use the sensor to correct the measurement controller constant. In order to verify that the control effect of the controller is fed back to the corresponding monitoring results in the case of large delay and model instability, two different controlled objects and three operating procedures are taken as examples to set different delay times for simulation. The discrete PID control, fuzzy PID control, and Darling algorithm are mainly used in the control of the transmission node and coal-blending control system to verify the superiority of this design.


## 4.3.1. Monitoring System Design

The system provides modularization and plug and play. Through interaction with the digital coal yard data, the BP neural network algorithm is used to establish the prediction model of coal-mixing characteristics, and the differential evolution algorithm is used to solve the optimization problem of the coal-blending scheme. At the same time, through the collection of real-time production data, the online evaluation system of coal blending and burning is established to realize the whole process-blending management of coal storage in the coal yard, blending and burning in the furnace, post blending evaluation, and demand guidance.(1)
Add online ash water detector and belt scale. Three sets of online ash water meters and belt scales are added to the 501 raw coal transfer belt conveyor, the 703 clean coal delivery belt conveyor, and the 507 mixed coal drop belt conveyor. The Df-5756b/C online multipoint ash water meter is selected as the ash water meter, which can realize online detection of moisture and ash content(2)
The existing 507 mixed coal falling belt conveyor will be reformed, the middle part of the belt conveyor will be raised, two redirection rollers will be added, and the coal-blending mixing device will be installed. The mixing device will be customized according to the field control, with a processing capacity of 300 t/h, which can fully mix the raw coal, medium coal, and refined coal(3)
The coal-blending expert automation system adopts the artificial intelligence algorithm to optimize the coal-blending accuracy. It is designed and developed according to the process requirements of coal blending, combined with the user’s coal source and the customer’s coal requirements, and can be expanded according to the user’s functional requirements. Input the coal parameters required by the user, such as coal quantity, ash content, and moisture content, which will be automatically operated by the coal-blending expert automation system. The artificial intelligence algorithm will be used to select the optimal proportioning scheme and adjust the final clean coal-blending amount according to the output and ash content of the final raw coal(4)
The coal-blending database is set to realize the electronic and automatic storage, transmission, and processing of coal-blending data information. The historical data can be viewed in real time on the platform software, the coal quality change can be analyzed, and the coal-blending data report can be automatically generated


## 4.3.2. Architecture Design

The remote upper computer monitoring system is used to realize that the optimization time is relatively short and can meet the remote monitoring and control of the coal-blending system and the loading link of the coal-blending process. In this design, the information-monitoring platform for safety supervision is carried out on-site, and then, WinCC of Siemens is used as the monitoring software. It is widely used in various industrial systems, pollutant emission, and coal price-monitoring development platforms and integrates information-processing, process management, and local control functions to complete the state monitoring and management of the whole process of enterprise production. The monitoring system is based on the S7-300 PLC controller (Figure7).Figure 7
System architecture.


## 4.3.3. Monitoring Correction

Based on the fuzzy set theory, the experience and knowledge of people and previous wireless sensor networks are transformed into fuzzy rules, and the control system of the system is constructed by fuzzy inference. The adjustment of the learning rate is controlled by two input variables, and the difference between the two weights is used as the first judgment criterion. The terminal collection facility may use the deviation percentage between the expected output and the actual output as the second judgment criterion. If the topological structure change rate of the weight change network is large and the overall error change rate is not 0, it is judged that the learning rate changes greatly. Use the sensor to correct the measurement controller constant. In order to verify that the control effect of the controller is fed back to the corresponding monitoring results in the case of large delay and model instability, two different controlled objects and three operating procedures are taken as examples to set different delay times for simulation. The discrete PID control, fuzzy PID control, and Darling algorithm are mainly used in the control of the transmission node and coal-blending control system to verify the superiority of this design.


## 4.4. Application Effect

The loading link of the original system has sufficient capacity. The aggregation node notifies the duty room by manually recording the vehicle number, and the duty room cooperates to mobilize wireless sensors. The personnel in the central control room can control the coal preparation system by adjusting the belt. The scheme has a large amount of worker operations, and the ground information-monitoring platform is prone to errors. At the same time, the corresponding loading chain and equipment are also designed for the coal preparation system. In addition, the coal systems of the system cooperate with each other to achieve quantitative and stable loading, which is displayed on the WinCC screen. During the operation, the network has multiple sensor nodes to record the current vehicle number, loading coal type, loading lane, loading ash, preset ash content, carriage load, and dispersion of three products. The data is updated every minute to facilitate operation status and detailed manual retrieval and observation. After the automatic loading system is put into operation, the average vehicle mass is 58 t, the average error is 3.33%, and the accuracy is increased by 4.82%. It can be seen that the improved loading link not only improves the automation level and reduces the labor volume of workers but also improves the loading accuracy and reduces the production cost of the enterprise.


## 5. Conclusions

The dynamic coal-blending model is an important research content in the field of coal blending. A dynamic coal-blending model based on the intelligent sensor network and genetic algorithm is established. The system has complete functions and stable operation. The coal-blending effect meets the expected requirements and ensures the stability of the quality of coal-blending products. The main findings are as follows:(1)
The basic theory of the genetic algorithm is introduced, the coal quality prediction model is established, and the constraint conditions of the coal-blending model are established according to the requirements of the boiler for coal quality characteristics. On the premise of satisfying the combustion conditions of the boiler, the mathematical model of coal blending is established to optimize the coal blending(2)
A monitoring system based on the WinCC intelligent sensor network is designed and its communication structure is described. The new system has designed a small alarm program, can accept the abnormal state of the system anytime and anywhere, and can greatly improve the efficiency and accuracy of coal blending(3)
The experimental analysis of the new coal-blending system and the loading link of the coal-blending process shows that compared with the original system, the new system has smaller ash deviation and a more stable proportion. At the same time, the automation degree of the loading link of the coal-blending system is improved. After the automatic loading system was put into operation, the average weight of the car was 58 t, the average error was 3.33%, and the accuracy was improved by 4.82%



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*Source: 3898063-2023-02-27.xml*