Operation strategy optimization of desulfurization system based on data mining

Journal Pre-proof

Operation Strategy Optimization of Desulfurization System Based on Data Mining Shan Liu , Li Sun , Senlin Zhu , Jie Li , Xi Chen , Wenqi Zhong PII: DOI: Reference:

S0307-904X(19)30743-7 https://doi.org/10.1016/j.apm.2019.12.004 APM 13190

To appear in:

Applied Mathematical Modelling

Received date: Revised date: Accepted date:

19 April 2019 28 November 2019 3 December 2019

Please cite this article as: Shan Liu , Li Sun , Senlin Zhu , Jie Li , Xi Chen , Wenqi Zhong , Operation Strategy Optimization of Desulfurization System Based on Data Mining, Applied Mathematical Modelling (2019), doi: https://doi.org/10.1016/j.apm.2019.12.004

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.

Highlights 

An optimization method for coal-fired power plant desulfurization systems is proposed.



The optimization method can effectively reduce the desulfurization cost.



An enhanced FCM clustering method is developed.

1

Operation Strategy Optimization of Desulfurization System Based on Data Mining Shan Liu a, Li Sun a, Senlin Zhub, Jie Li c, Xi Chen a, Wenqi Zhong a,*

a

Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education,

School of Energy and Environment, Southeast University, Nanjing 210096, PR China b

c

Datang Environment Industry Group Co., Ltd. Nanjing 211100, PR China Datang Jiangsu Power Generation Co., Ltd. Nanjing 210011, PR China

Corresponding author at School of Energy and Environment, Southeast University, Nanjing 210096, PR China.

E-mail address: [email protected] (W. Zhong)

Abstract：

Desulfurization systems in coal-fired power stations often suffer the problem of high operating costs caused by a rule-of-thumb control strategy, which implies great potential for optimization of the operation. Due to the complex desulfurization mechanism, frequently fluctuating unit load, and severe disturbance, it is challenging to determine the optimal operating parameters based on the traditional mechanistic models, and the operating parameters are closely related to the operational efficiency of the flue gas desulfurization system. In this paper, an operation strategy optimization method for the desulfurization process is proposed based on a data mining framework, which is able to determine online the optimal operating parameter settings from a large amount of historical data. First, Principal Component Analysis (PCA) is used to reduce data redundancy by mapping the data into a new vector space. Based on the new vector space, an enhanced fuzzy C-means clustering (Enhanced-FCM) is developed to cluster the historical data into groups sharing similar characteristics. Taking sulfur dioxide emission concentration as a constraint condition, the system is optimized with economic benefits and desulfurization efficiency as the objective function. When performing optimization, the group that current operating conditions belong to is determined first, then the operating parameters of the best performance are searched within the group and provided as the optimization results. The method is validated and tested based on the data from a wet 3

flue gas desulfurization (WFGD) system of a 1000MWe supercritical coal-fired power plant in China. The results indicate that the proposed operation strategy can appropriately obtain operating parameter settings at different conditions, and effectively reduce the desulfurization cost under the constraint of meeting emission requirements.

Key Words:

Data mining; Enhanced-FCM; Operation strategy optimization; WFGD.

1 Introduction

Fossil fuel combustion accounts for the primary portion of the world‘s power generation, in which coal-fired power generation still contributes to around 40% of global electricity supply[1-2]. Especially in China, the total installed capacity of the power blocks exceeded 1.77 billion kilowatts leading the world by 2017, more than 60% of which is supplied by coal-fired power plants[3]. The excessive utilization of fossil fuels leads to severe air pollution, especially for sulfur dioxide (SO2) emission. Thus, many countries have introduced strict regulations and measures to lower the SO2 emission of power plants as shown in Table 1. Among them, the Chinese government has promulgated a more stringent coal-fired power plant SO2 emission regulation that sulfur dioxide emission limit will be reduced from 100 mg/Nm3 to 35 mg/Nm3 by 2020[4,5]. To achieve this goal, an efficient desulfurization system is required. However, the current desulfurization system operates at a lower level of automation, which brings higher energy consumption to the wet flue gas desulfurization system. Therefore, it is necessary to study the optimization of wet flue gas desulfurization systems. Table 1 Emission limits of SO2 for existing and new power plants in selected countries/regions (mg/m3)[1] Over the past decades, various research studies were conducted to optimize the desulfurization system. Some researchers studied the effect of fume sorbent composition on desulfurization, such as calcium hydroxide, magnesium oxide, iodine,

dilute sodium alkali and manganese hydroxide[6–11]. Other studies focused on the structural properties of absorbers have been carried out as well. Fang et al.[12] studied the effect of different flue gas inlet angles on the performance of the absorber, finding that the downward inclined angle was conducive to extending the residence time of gas. Dou et al.[13] found that the electrostatic spraying absorber worked at a higher efficiency than the uncharged one. However, the fume sorbents mentioned above are relatively expensive which is not appropriate for large scale utilization like wet flue gas desulfurization systems in the coal-fired power plants. The operating control mode for wet flue gas desulfurization systems with inappropriate parameter settings is one of the important reasons for high energy consumption and low efficiency. Wet flue gas desulfurization system optimization methods that are low-cost based on operating parameters have drawn great attention for desulfurization system optimization. Michalski[14] established an aerodynamic characteristics model in spray tower and obtained relationships between pressure drop and liquid resistance time and slurry drop concentration, which provides an effective reference for improving desulfurization efficiency. Zhong et al.[15] studied the influence of different combinations of spray levels on desulfurization efficiency. The results showed that the higher spray level had more influence on desulfurization efficiency than the lower spray level did. Wang and Dai[16] proposed a scheme in which spraying droplets of different diameters in different layers were designed to optimize flow uniformity and spraying column performance. This method provides a

good solution for system optimization from the perspective of the reaction mechanism. Dou et al.[17] experimentally obtained relationships between SO2 removal efficiency and pH value, droplet diameter and velocity of slurry or flue gas. They developed a model of external mass-transfer, which predicted SO2 removal efficiency more accurately. Ortiz et al.[18] analyzed the effectiveness of operating parameters on SO2 removal efficiency, and the optimum pairs of L/G ratio (liquid-gas ratio) and pH were achieved through an operation cost function. Zhu et al.[19] succeed in predicting the absorption level, and analyzed the relations between absorption height and other parameters. In addition, some studies were focused on the effect of slurry characteristics on desulfurization performance, for example, the effect of H+ on desulfurization performance was studied using numerical simulation and experiments by Gao et al.[20]. Kiil et al.[21] developed a detailed model for a wet flue gas desulfurization pilot plant which could effectively predict the relationships between desulfurization efficiency and other parameters. Warych and Szymanowski[22] developed a detailed process model of the wet limestone flue gas desulfurization system based on cost optimization. However, all the work mentioned above focused on the performance improvement of flue gas desulfurization under steady-state. Kallinikos et al.[23] and Neveux and Moullec[24] focused on the establishment of dynamic models for the flue gas desulfurization system by mechanism analysis and the improvement of its performance. The results show that the dynamic models they developed were able to

simulate the industrial flue gas desulfurization system. Due to the complexity of the desulfurization system, establishing mechanism modeling is complicated. Recently, data mining is popular because of the rapid development of machine learning and artificial intelligence technology[25–28]. Data mining techniques provide an important means of optimizing control as they provide the advantages of less investment, less risk, and high accuracy. Furthermore, with this method, most of the existing desulfurization system equipment would not need to be replaced. Data mining techniques are adopted to optimize the desulfurization system in this paper. The basic idea of the optimization method using data mining is to search and collect operating parameter sets with the best performance under various operating conditions in historical data, then to extract operating parameters according to the current operating conditions. The following problems need to be solved when using data mining techniques: removal of outliers, non-steady state operating data removal, and improving performance of FCM. In this paper, a method for determining the reference parameter settings based on actual operating data is proposed. The method performs multi-parameter synchronous mining on the operating data samples of each operating parameter in each unit load interval, which not only pays attention to desulfurization efficiency but also considers the desulfurization cost. In the data mining strategy, to identify and eliminate anomalous data in the massive operating data-set of the desulfurization system, a data cleaning method is adopted. Second, due to the similar characteristics between many

parameters in the desulfurization operating data, Principal Component Analysis (PCA) is employed to merge different feature parameters and reduce data redundancy to simplify the process of data mining. Then, the Enhanced-FCM proposed in this paper is performed on the new data processed by PCA to cluster massive operating data into groups with similar characteristics. Finally, a multi-objective function based on cost and desulfurization efficiency is defined, and the maximum operating parameter sets of the objective function is selected as the optimal operating parameter settings in each cluster. This paper takes the wet flue gas desulfurization system of a 1000MWe unit in Sanmenxia, Henan Province, China as an example and employs data mining techniques to optimize the operating process. Comparisons of desulfurization costs before and after system optimization are made.

2 Problem Formulation

A schematic diagram of the wet flue gas desulfurization process is shown in Figure 1. The primary reactions of the desulfurization process take place in the absorption tower. The raw flue gas enters the absorption tower from the bottom and then flows upward while mixing with slurry droplets sprayed from the atomizers in countercurrent mode. The SO2 in the flue gas reacts with the CaCO3 in the suspended slurry droplets and the O2 from the blower to form calcium sulfate dihydrate—gypsum. The clean gas after desulfurization flows through the mist eliminator and is discharged into the

atmosphere through the chimney.

Figure 1.

Schematic diagram of the desulfurization process. 1. Desulfurization absorber; 2.Mist eliminator wash water; 3.Flue gas; 4. Concentration meter; 5.Booster fan; 6.Slurry preparation system; 7.Slurry tank; 8.Slurry feed pump; 9.Air; 10.Oxidation fan; 11.Solids removal pump; 12.Recirculating pumps; 13. Concentration meter; 14.Stack; 15. Vacuum belt filter; 16. Hydrocyclone; 17.Gypsum; 18. Filtrate tank.

The principle of the chemical reaction is shown as follows:

SO2  g   H 2O CaCO3 +2H+

H+  HSO3 Ca 2  H 2O  CO2  g 

HSO3  1/ 2O2

Ca 2  SO42  2H 2O

H+  SO42 

CaSO4  2H 2O  s 

(2-1) (2-2) (2-3) (2-4)

Studies have shown that many factors have an effect on the desulfurization efficiency, such as flue gas flowrate, SO2 concentration, slurry flow rate, slurry concentration and its circulation amount, slurry pH value, and oxygen input

amount[15,29–31]. Currently, many advanced control strategies have been introduced into coal-fired power plants, such as fuzzy PID, RBF neural network PID and model predictive control [32–36]. However, many desulfurization systems of many coal-fired power plants in China still rely on the traditional PID control strategy combined with the participation of operators. Due to the changeable operating conditions, it is difficult for operators to determine the operating parameter settings promptly and accurately, which results in excessive energy consumption or SO2 emissions not meeting the regulation. The optimization objective of the wet flue gas desulfurization system is to satisfy SO2 emission regulation while reducing desulfurization cost and improving desulfurization efficiency. This target can be realized by conducting data mining in historical data that contains the empirical knowledge of desulfurization system characteristics. This paper is organized as follows: Section 1 describes previous works. Section 2 elucidates the problems in the desulfurization system. Section 3 presents the method proposed in this paper. Section 4 describes a case study of a desulfurization system based on the framework proposed. Finally, Section 5 presents the conclusions.

3 Proposed Method

An optimization method for the desulfurization process based on data mining is

proposed. The data mining framework will be introduced in detail in this section. Figure 2 shows the schematic of the optimization method.

Figure 2.

Schematic of data mining approach

Many parameters in the desulfurization system are related to the desulfurization efficiency and cost, of which the following parameters are selected for data mining: target SO2 emission concentration (Cs), flue gas flowrate (Fg), inlet SO2 concentration(Ci), slurry flowrate supplied (Fs), slurry pH value, slurry density (ρ), liquid level (h), and the conditions of relevant equipment. The optimal operating parameter settings are obtained by data mining. Then it is possible to adjust the slurry feed flowrate (Fsa), oxidized air flowrate (Coa) and the combination of the slurry recirculating pumps (Eba) to maximize the economic benefits and desulfurization effect. Reasonable setting combinations will be determined by comparing the current operating state (desulfurization cost Ce, desulfurization efficiency η) with the optimal operating condition (desulfurization cost Ce0, desulfurization efficiency η0) when dealing with a new operating condition. The operation optimization method of the desulfurization system based on data mining proposed in this paper mainly includes the following parts: data cleaning, dimensionality reduction, data clustering, and

searching for the optimal parameters.

3.1 Data cleaning

Due to the complexity of the power station system, the desulfurization system Distributed Control System (DCS) database contains parameters categorized by whether the system is in the dynamic state, steady-state, fault state, or start-stop process. Therefore, it is necessary to preprocess operating data to improve the quality of data mining. The preprocessing includes the deletion of bad points and filtration of unsteady-state data. Due to environmental or human interference and equipment fault, the measured data has a large deviation from the actual value. These outliers will harm the mining effect. In this paper, a method named ‗Pauta Criterion‘ is adapted to determine whether the data are outliers. The expression is shown as follows[37]:

  x  x  3 ,

(3-1)

Samples that satisfy the above form are outliers, where x is the average value and

 is the standard deviation of x. Because of the large fluctuations of a dynamic process, the operating parameters cannot truly reflect the operation status of the desulfurization system at this time, so it is necessary to set up a criterion to filter unsteady state operating data. In this paper, a sliding window method is used for steady-state detection: firstly, choose the appropriate window length N, determine the fluctuation of operating data in the

window and delete the non-steady operating data. Then, move the window backward to determine the next set of data until all the data is detected. The criterion is as follows:

v

varmax  varmin  vt varr

(3-2)

where varmax and varmin are the maximum and minimum values of the operating parameters, varr is the rated value of the parameters under the rated condition, and vt is the stability threshold.

3.2 Dimensionality reduction

Most operating parameters of the desulfurization system are not independent, they are affected and restricted by other parameters. Figure 3 shows the curve of unit load and flue gas flowrate, from which it can be seen that the trend of the two parameters is almost the same.

Figure 3. Operating data of unit load and flue gas flowrate Dimensionality reduction is a technique to extract the effective independent

variable space from the original variable space and retain the main characteristics. PCA is one of the common dimensionality reduction technologies, which have been applied successfully to industrial processes[38,39]. Avoiding massive overlap of information is especially important when dealing with the desulfurization system operating data. To reduce redundant information, reduce the workload and improve the effectiveness of data mining, the dimensionality reduction method PCA is used to linearly transform multiple variables to select fewer comprehensive variables. The content of PCA is as follows: A set of data consists of m variables, represented by X1, X2,..., Xm, and these variables form an m-dimensional random vector X=(X1, X2,..., Xm)‘. Linear transformation of the random vector X can be converted into a new comprehensive variable Y and satisfy the following formula: Y1  a11 X 1  a12 X 2 

 a1m X m

Y2  a21 X 1  a22 X 2 

 a2 m X m

 Yk  ak1 X 1  ak 2 X 2 

 akm X m

(3-3)

where Yi and Yj (i≠j) are mutually independent. The new variables Y1, Y2, ..., Yk are the first, second, ... the kth principal component variable respectively and their variances are sorted in descending order. The procedure of the PCA method is to be explained as Procedure 1[40]. Procedure 1 The PCA algorithm

 x11 x12  x1m  x x  x  2m  Input: Dataset X   21 22   X 1 X 2   X m        xn1 xn 2  xnm 

Output: new principal component variables Step1: Normalize the dataset. Z

xij  x j Sj

 i  1, 2

n  j  1, 2

m ,

(3-4)

n

xj 

x i 1

n

ij

,

(3-5)

2 1 n xij  x j  .   n  1 i 1

Sj 

(3-6)

Step2: Calculate the correlation coefficient matrix of variables.  r1 1r 1 2 r m 1    Z Z  r2 1r 2 2 r m 2 , R   n  1      rm1rm 2  rmm  T

(3-7)

where n

rij 

x k 1

x

k i

n 1

k j

i  1 , 2

m  j  1 , 2m  .

(3-8)

Step3: Calculate eigenvalue 𝜆𝑖 of the correlation coefficient matrix in the following characteristic equations.

R  i  0 ,

(3-9)

where

1  2   m  0

.

Step4: Calculate the feature vector ai corresponding to each eigenvalue λi. The Jacobi-iteration method is used, and the eigenvector satisfies the following formula:

0i  j ai a j    1i  j

(3-10)

The principal component expression can be obtained through the feature vector, and the formula is as follows:  x1    x aim   2       xm 

yi   ai1 , ai 2 ,

(3-11)

Step5: The variance contribution rate of each principal component is as follows:

fi 

i

(3-12)

m

j j 1

where

fi

represents

the

variance

contribution

ratio

of each principal component. To meet the principal component contribution threshold value α, k principal components are selected in descending order of variance contribution ratio: k

f i 1

i



(3-13)

Step6: The objects in the dataset can be evaluated by the integrated values of the determined principal components k

F   fi yi i 1

(3-14)

3.3 Clustering

The key to the operation optimization of the desulfurization system is to give the optimal parameter settings under different operating conditions. Therefore, how to determine the optimal operating parameters is extremely important. Data clustering divides a batch of data into several homogenous groups according to the degree of similarity between them[41]. The operating conditions in the same cluster share the same characteristics, and optimal operating parameters in each cluster can be found according to a multi-objective function. Therefore, the quality of the clustering determines the accuracy of the optimal operating parameters and the degree of optimization of the desulfurization system. A typical clustering technique is the K-means whose advantages are quick and straight forward[42]. K-means method assigns each sample that is to be identified to a specific class strictly. However, most samples cannot be assigned to a particular class. Fuzzy C-means (FCM) is a soft clustering method based on objective function[43], where each sample is not uniquely divided into a particular subgroup, but into different subgroups with different degrees of membership. This technique aims to find the hidden structure of a dataset through the optimization of the objective function[44]. However, FCM is sensitive to the initial cluster centers and cluster number[45], so that it is easy to fall into a local optimal solution. To solve the problems mentioned above, it is essential to find appropriate cluster centers and cluster number. The Enhanced-FCM proposed in this paper combines

K-means and FCM and is more accurate in determining the initial cluster center and cluster number. The clustering method used in this paper can be divided into two steps. In the first step, K-means and silhouette coefficient are combined to determine the initial cluster number by iteration. In the second step, the roulette selection method is introduced to obtain appropriate cluster centers and FCM is used to generate the final clustering effect. The procedure of Enhanced-FCM is to be explained in detail as Procedure 2. Procedure 2. Enhanced-FCM Input: Dataset

X  x1 , x2 ,

xn   Rn

Initiation: fuzzy weighting exponent m, terminal condition  , the maximum number of iterations itermax. Output: optimal cluster number, cluster centers, objection function Step 1: Cluster number k ranges  2, n  and cluster center ci (i=1,2,...,k) is   randomly obtained from the dataset by K-means. All data will be assigned to each group based on distance. Step 2: Calculate the silhouette coefficient corresponding to every cluster number.

S

1 n Sj n j 1

(3-15)

where the silhouette coefficient is as follow:

Sj 

bj  a j max  a j , b j 

,

(3-16)

aj is the average distance between sample xj and other samples in the same cluster.

bj is the minimum average distance between sample xj and other samples in other clusters. Step 3: Obtain the optimal cluster number Kopt and cluster centers C when the silhouette coefficient reaches its maximum. Step 4: Randomly select one of the cluster centers as the initial cluster center c1. Step 5: For each sample xj (j=1,2,..., n) in the dataset, calculate its distance dij from the nearest cluster center ci (i=1,2,..., Kopt-1), and normalize the distance. dij  x

j

, ci



pj 

 x j  ci ,

dij 2

,

n

d i 1

(3-17)

(3-18)

2 ij

Step 6: Calculate the fitness rh of sample xh (h=1,2, ,..., n). h

rh   p j

(3-19)

j 1

Step 7: Randomly generate a number nm (0 nm and rh-1 < nm , then sample xh is selected as a cluster center, otherwise repeat step 7. Step 8: Repeat step 5 and step 7 until Kopt cluster centers are selected. Step 9: Use cluster number Kopt and cluster centers C to calculate the membership matrix. 2

 d  m1 uij    ij  ,   k 1  d kj  K opt

(3-20)

U  uij  ,

(3-21)

Step 10: Update cluster centers.

 u  n

ci 

j 1 n

m

ij

 u  j 1

xj

,

(3-22)

m

ij

C   ci  ,

(3-23)

Calculate objective function Jij. Kopt

m

J  x j , ci     uij  dij 2 ,  m  1 n

(3-24)

i 1 j 1

Step 11: Repeat step 9 and step 10 until the following formula is satisfied. J ij iter 1  J ij iter   ,

(3-25)

Alternatively, the number of iterations reaches itermax. Step 12：Output the clustering results. K-means is one of hard clustering, and FCM is improved based on K-means in combination with fuzzy theory. However, both of them have the disadvantage of being sensitive to initial parameters. Improper initial parameters will affect the final clustering effect. Roulette selection is introduced in this paper to make the initial cluster center distribution more uniform. To illustrate the clustering results difference between K-means, fuzzy C-means, and the Enhanced-FCM, a comparative analysis is performed by taking some operating data of the desulfurization system as an example. The results are shown in Figure 4. Every point in the figure graph corresponds to a historical run data.

Figure 4. Comparisons of clustering effects between different methods The two methods used here belong to density clustering. The different colors in Figure 4 represent different clusters. It can be found that the proposed Enhanced-FCM method has a better clustering effect than FCM. This is because this method provides an appropriate cluster number and initial clusters.

3.4 Searching for optimal parameters

Since reducing the desulfurization cost and reducing the concentration of SO2 are

two contradictory goals, it is necessary to search for optimal operating parameters by a multi-objective method. Therefore, a compound parameterθ is put forward in this paper. The expression is as follows:

 =p  ce  q   so  s  2

(3-26)

where p, q are constants, ρso2 represents the actual SO2 emission concentration, ρs is the maximum allowable SO2 emission concentration, ρso2<ρs, and ce represents the desulfurization relative cost, calculated by the following equation: ce 

c fan  ccycle  cslu mso2

,

(3-27)

where cfan represents the operating cost of oxidation fan, ccycle represents the operating cost of slurry recirculating pumps and cslu represents the cost of limestone slurry consumed during operation of the desulfurization system. cslu contains the operating cost of the wet ball mill and the cost of limestone and water used in the preparation of limestone slurry. Each cluster is traversed separately, and the operating condition corresponding to the minimum of θ is the optimal operating parameters of this cluster. In the actual operating process, the reduction of pumps restarting times should be taken into account.

3.5 Overall data mining algorithm framework

The data mining framework used for desulfurization system operating optimization is integrated from the previous analysis. The algorithm flow can be described in

Figure 5.

Figure 5. The data mining framework (1) Data cleaning and dimensionality reduction Data processing, which is the first step of data mining, is mainly used for data selecting, deletion and dimensionality reduction. It is necessary to remove unstable data and outliers before analysis while processing system data. Principal Component Analysis is used to reduce data redundancy. (2) Enhanced-FCM The Enhanced-FCM proposed in this paper includes four steps in detail. The first step is to apply K-means to processed data. Then the silhouette coefficient is introduced to verify the validity of cluster results for different cluster numbers. Roulette selection is used for selecting the initial cluster centers. The optimal cluster number and cluster centers are taken as input for FCM which is based on membership function. (3) Searching for optimal operating data

The optimal manipulated operating data that minimizes the objective function is searched in each cluster under different operating conditions. The objective function can be adjusted according to the requirements of different goals.

4. Case study

An actual wet flue gas desulfurization system of a coal-fired power plant of 1000MWe in Sanmenxia, Henan Province, China is taken as an industrial case for system optimization based on the data mining framework proposed. The coal used in this power plant has a wide range of sources and high sulfur content; as a result, the operating conditions of this desulfurization system fluctuate frequently. In the desulfurization system, there are a total of five slurry recirculating pumps (A, B, C, D, and E) with different rated power. The parameters related to the desulfurization efficiency and the desulfurization cost are: unit load L, flue gas flowrate Fg, inlet SO2 concentration Ci, slurry flowrate supplied Fs, pH value of slurry, density ρ of slurry, liquid level h and operation condition of related equipment. In the present case study, 4320 samples that were taken at 5 seconds intervals and exported from the desulfurization system are analyzed in this chapter. Samples and parameters are shown in Figure 6.

Figure 6.

Operational data of power plants

The data were first tested to remove outliers according to equation (3-1). After that, the steady-state data identification is conducted on the processed data. The time for steady-state judgment is set to 20 minutes, and the stability threshold is set to 0.1 in this chapter. The period for steady-state judgment is sequentially shifted backward by 5 minutes, and steady-state assessment is conducted on the data in the new period. The stability of the desulfurization system is judged by equation (3-2). Taking the data of slurry density as an example, a scattergram before and after the data processing is obtained, as shown in Figure 7. It can be found that the noise data is significantly reduced.

Figure 7.

Raw and processed data of slurry density

Before dimensionality reduction, the preprocessed data needed to be standardized first. System parameters such as flue gas flowrate Fg, inlet SO2 concentration Ci, pH value of slurry, density of slurry ρ, liquid level h，unit load L, are taken as the objects for dimensionality reduction. The data is processed using equation (3-4). PCA is performed on the processed data after standardization. The contribution value is set to 0.85, and then a group of 3-dimensional data is obtained after dimensionality reduction. For the data of the desulfurization system varying considerably with the unit load and sulfur dioxide concertation, the operating data is first divided into several groups according to the unit load. The range of the unit load exemplified in this paper is 500-1000MWe. Therefore, operating data is roughly divided into ten groups according to the unit load, which are 500-550MWe, 550-600MWe, ..., 950-1000MWe,

respectively. Then, clustering analysis is conducted on each of the above groups. The relative desulfurization cost is calculated according to equation (3-27), where ρs is set to 35mg/Nm3, p is set to 9, q is set to 1. The optimal operating parameters are sought according to the equation (3-26). The groups are clustered in turn using the Enhanced-FCM clustering method. A total of 70 sets of optimal operating parameters are selected. Take the groups of unit load range of 550-600MWe and 750-800MWe as examples. Figure 8 shows the clustering result of samples in these groups. The samples of the former are divided into 7 clusters, and the samples of the latter are divided into 6 clusters where different colors represent different clusters.

Figure 8.

Clustering results by Enhanced-FCM of 550-600MWe and 750-800MWe

Take the optimization results of the 550-600MW segment as an example (Table 3). The following is a collection of operating conditions with low desulfurization costs on the premise of meeting operation safety. When there is less SO2 entering the absorption tower, the desulfurization relative cost can be reduced by turning on fewer slurry recirculating pumps or reducing the amount of slurry feed: the former reduces the energy consumption of the slurry recirculating pumps, and the latter reduces the energy consumption of the slurry feed pump and the consumption of limestone slurry. As the flow of SO2 increases, switching to a higher recirculating pump is an effective way to reduce relative cost. When the flowrate of SO2 increases to a specific value, switching to a high-lift slurry recirculating pump and correspondingly adjusting the slurry flow rate is a safe and economical solution. Table 2. Optimal operating conditions of unit load during 550-650MW

To verify the effectiveness of the optimization method in reducing the relative desulfurization cost, 150 operating conditions of the desulfurization system are tested. Comparisons of the desulfurization relative costs between the current operation method and optimization method are shown in Figure 9.

Figure 9.

Comparisons of desulfurization relative costs

As shown in Figure 9, the optimization method is better than the current operation mode at controlling the desulfurization relative cost. The desulfurization relative cost reduced by 10.4%-50.5% after adjusted according to the optimal operating parameters. This shows that the desulfurization system has a large room for improvement in cost under the premise of ensuring safe operation.

Figure 10.

Comparison of the two methods

(a) Flowrate of slurry feed, oxidation fan and slurry recirculating (b) Decomposed costs Table 3. Decomposed cost and flowrate of slurry feed, oxidation fan and slurry recirculating pumps at points A, B, and C The operating conditions of the desulfurization system fluctuate frequently, and the variation range is broad. To ensure that the SO2 emission concentration meets the

regulation, operators tend to increase the slurry feed flowrate and the number of slurry recirculating pumps, where the former has a rapid response to load fluctuation. As can be seen from Figure 10, in the case of point A and B that are adjusted by the optimization method, the amount of slurry feed flowrate is small, the energy consumption of slurry recirculating pumps and oxidation fan is higher. In the case of point C that is adjusted by the optimization method, only the energy consumption of the slurry recirculating pumps is higher than in the current adjustment mode. Table 3 gives the parameters values and decomposed costs of points A, B, and C. All of these operating conditions obtain a reduction in the desulfurization cost. It can be concluded that under the same operating conditions, reducing the amount of slurry feed flowrate or replacing the slurry recirculating pump with the one in the higher position of which the energy is similar, is an effective method to reduce energy consumption and desulfurization cost.

5 Conclusions

In order to optimize the operational efficiency of the desulfurization system, this paper determined the optimal parameter settings of the desulfurization system for field implementation. Based on a large amount of historical data of the coal-fired desulfurization system, an operating optimization strategy based on data mining for the wet flue gas desulfurization system is proposed in this paper. Using the Enhanced-FCM to analyze the operating data of an actual desulfurization system, the

optimal parameter settings of the desulfurization system under various working conditions were determined. The results show that the operating optimization strategy based on data mining can reduce the desulfurization cost and improve the desulfurization efficiency, which can be used to guide the optimization operation.

Acknowledgments

Financial support from the key project of the National Nature Science Foundation of China (project number: 51736002&51806034) and the National key research and development program of China (project number: 2016YFB0600802).

Reference [1] Energy and Air Pollution - World Energy Outlook 2016 Special Report, (2016) 266. [2] L. Sun, Q. Hua, D. Li, L. Pan, Y. Xue, K.Y. Lee, Direct energy balance based active disturbance rejection control for coal-fired power plant, ISA Trans. 70 (2017) 486–493. https://doi.org/10.1016/j.isatra.2017.06.003. [3] L. Sun, Y. Zhang, D. Li, K.Y. Lee, Tuning of Active Disturbance Rejection Control with application to power plant furnace regulation, Control Eng. Pract. 92 (2019) 104122. https://doi.org/10.1016/j.conengprac.2019.104122. [4] Full implementation of the ultra-low emission and energy-saving reconstruction work plan for coal-fired

power

plants,

(n.d.).

http://www.mee.gov.cn/gkml/hbb/bwj/201512/t20151215_319170.htm (accessed September 2, 2019). [5] Emission

standard

of

air

pollutants

for

thermal

power

plants.

(n.d.).

http://kjs.mee.gov.cn/hjbhbz/bzwb/dqhjbh/dqgdwrywrwpfbz/201109/t20110921_217534.shtm l (accessed September 2, 2019). [6] R.-B. Lin, S.-M. Shih, C.-F. Liu, Characteristics and reactivities of Ca(OH)2/silica fume sorbents for low-temperature flue gas desulfurization, Chem. Eng. Sci. 58 (2003) 3659–3668. https://doi.org/10.1016/S0009-2509(03)00222-7. [7] L. Marocco, A. Mora, CFD modeling of the Dry-Sorbent-Injection process for flue gas desulfurization using hydrated lime, Sep. Purif. Technol. 108 (2013) 205–214. https://doi.org/10.1016/j.seppur.2013.02.012. [8] R. del Valle-Zermeño, J. Formosa, J.A. Aparicio, M. Guembe, J.M. Chimenos, Transposition of wet flue gas desulfurization using MgO by-products: From laboratory discontinuous batch reactor

to

pilot

scrubber,

Fuel

Process.

Technol.

138

(2015)

30–36.

https://doi.org/10.1016/j.fuproc.2015.05.002. [9] Z. Zhu, Y. Ma, Z. Qu, L. Fang, W. Zhang, N. Yan, Study on a new wet flue gas desulfurization method based on the Bunsen reaction of sulfur-iodine thermochemical cycle, Fuel. 195 (2017) 33–37. https://doi.org/10.1016/j.fuel.2017.01.045. [10] A. Bandyopadhyaya, M.N. Biswasa, Prediction of the Removal Efficiency of a Novel Two-Stage Hybrid Scrubber for Flue Gas Desulfurization, Chem. Eng. Technol. 29 (2006) 130–145. https://doi.org/10.1002/ceat.200500160. [11] X.-L. Han, X.-X. Chen, Resource-Based Method for Realizing Zero Emission of Sulfur Dioxide from Flue Gas, in: Proc. 3rd 2017 Int. Conf. Sustain. Dev. ICSD 2017, Atlantis Press, Tianjin, China, 2017. https://doi.org/10.2991/icsd-17.2017.13. [12] S.L. Fang, Y.Q. Du, S.Y. Huang, W.Q. Wen, Y. Liu, Numerical Simulation Research for the Optimization of the Wet Flue Gas Desulfurization Tower, Appl. Mech. Mater. 170–173 (2012) 3662–3667. https://doi.org/10.4028/www.scientific.net/AMM.170-173.3662. [13] B. Dou, Y.-C. Byun, J. Hwang, Flue Gas Desulfurization with an Electrostatic Spraying Absorber, Energy Fuels. 22 (2008) 1041–1045. https://doi.org/10.1021/ef700646c. [14] J.A. Michalski, Aerodynamic characteristics of FGD spray towers, Chem. Eng. Technol. 20 (1997) 108–117. https://doi.org/10.1002/ceat.270200208.

[15] Y. Zhong, X. Gao, W. Huo, Z. Luo, M. Ni, K. Cen, A model for performance optimization of wet flue gas desulfurization systems of power plants, Fuel Process. Technol. 89 (2008) 1025– 1032. https://doi.org/10.1016/j.fuproc.2008.04.004. [16] P. Wang, G. Dai, Synergistic effect between spraying layers on the performance of the WFGD spray column, Asia-Pac. J. Chem. Eng. 13 (2018) e2266. https://doi.org/10.1002/apj.2266. [17] B. Dou, W. Pan, Q. Jin, W. Wang, Y. Li, Prediction of SO2 removal efficiency for wet Flue Gas

Desulfurization,

Energy

Convers.

Manag.

50

(2009)

2547–2553.

https://doi.org/10.1016/j.enconman.2009.06.012. [18] F.J. Gutiérrez Ortiz, F. Vidal, P. Ollero, L. Salvador, V. Cortés, A. Giménez, Pilot-Plant Technical Assessment of Wet Flue Gas Desulfurization Using Limestone, Ind. Eng. Chem. Res. 45 (2006) 1466–1477. https://doi.org/10.1021/ie051316o. [19] J. Zhu, S. Ye, J. Bai, Z. Wu, Z. Liu, Y. Yang, A concise algorithm for calculating absorption height in spray tower for wet limestone–gypsum flue gas desulfurization, Fuel Process. Technol. 129 (2015) 15–23. https://doi.org/10.1016/j.fuproc.2014.07.002. [20] X. Gao, W. Huo, Z. Luo, K. Cen, CFD simulation with enhancement factor of sulfur dioxide absorption in the spray scrubber, J. Zhejiang Univ.-Sci. A. 9 (2008) 1601–1613. https://doi.org/10.1631/jzus.A0820507. [21] S. Kill, M.L. Michelsen, K. Dam-Johansen, Experimental investigation and modeling of a wet flue gas desulfurization pilot plant, Ind. Eng. Chem. Res. 37 (1998) 2792–2806. https://www.mendeley.com/catalogue/experimental-investigation-modeling-wet-flue-gas-desu lfurization-pilot-plant/ (accessed September 9, 2019). [22] J. Warych, M. Szymanowski, Model of the Wet Limestone Flue Gas Desulfurization Process for

Cost

Optimization,

Ind.

Eng.

Chem.

Res.

40

(2001)

2597–2605.

https://doi.org/10.1021/ie0005708. [23] L.E. Kallinikos, E.I. Farsari, D.N. Spartinos, N.G. Papayannakos, Simulation of the operation of an industrial wet flue gas desulfurization system, Fuel Process. Technol. 91 (2010) 1794– 1802. https://doi.org/10.1016/j.fuproc.2010.07.020. [24] T. Neveux, Y. Le Moullec, Wet Industrial Flue Gas Desulfurization Unit: Model Development and Validation on Industrial Data, Ind. Eng. Chem. Res. 50 (2011) 7579–7592. https://doi.org/10.1021/ie102239q. [25] C. Ning, F. You, Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods, Comput. Chem. Eng. 112 (2018) 190–210. https://doi.org/10.1016/j.compchemeng.2018.02.007. [26] Y. Liu, Z. Ge, Weighted random forests for fault classification in industrial processes with hierarchical

clustering

model

selection,

J.

Process

Control.

64

(2018)

62–70.

https://doi.org/10.1016/j.jprocont.2018.02.005. [27] J. Xu, Y. Gu, D. Chen, Q. Li, Data mining based plant-level load dispatching strategy for the coal-fired power plant coal-saving: A case study, Appl. Therm. Eng. 119 (2017) 553–559. https://doi.org/10.1016/j.applthermaleng.2017.03.094. [28] M.C. Thomas, W. Zhu, J.A. Romagnoli, Data mining and clustering in chemical process databases for monitoring and knowledge discovery, J. Process Control. 67 (2018) 160–175. https://doi.org/10.1016/j.jprocont.2017.02.006.

[29] Optimum Values of Process Parameters of the ―Wet Limestone Flue Gas Desulfurization System‖ - Warych - 2002 - Chemical Engineering & Technology - Wiley Online Library, (n.d.). https://onlinelibrary.wiley.com/doi/pdf/10.1002/1521-4125%28200204%2925%3A4%3C427 %3A%3AAID-CEAT427%3E3.0.CO%3B2-X (accessed October 11, 2019). [30] Drastic SOx Removal and Influences of Various Factors in O2/CO2 Pulverized Coal Combustion System | Energy & Fuels, (n.d.). https://pubs.acs.org/doi/abs/10.1021/ef000171p (accessed October 11, 2019). [31] Optimization of Slurry PH Value for Limestone Wet Desulfurization Technology - IEEE Conference Publication, (n.d.). https://ieeexplore.ieee.org/document/5747877/ (accessed October 11, 2019). [32] Kanimozhi L., Maheswari C., Krishnamurthy K., Implementation of fuzzy PID controller for SO2 emission control process, in: 2015 Int. Conf. Comput. Commun. Technol. ICCCT, 2015: pp. 251–255. https://doi.org/10.1109/ICCCT2.2015.7292755. [33] Z.-K. Yang, C.-Y. Liu, X.-L. Song, Z.-Y. Song, Z.-S. Wang, Application of RBF neural network PID in wet flue gas desulfurization of thermal power plant, in: 2016 Int. Conf. Mach. Learn. Cybern. ICMLC, 2016: pp. 301–306. https://doi.org/10.1109/ICMLC.2016.7860918. [34] X. Liu, P. Guan, C.W. Chan, Nonlinear Multivariable Power Plant Coordinate Control by Constrained Predictive Scheme, IEEE Trans. Control Syst. Technol. 18 (2010) 1116–1125. https://doi.org/10.1109/TCST.2009.2034640. [35] H. Peng, G. Kitagawa, J. Wu, K. Ohtsu, Multivariable RBF-ARX model-based robust MPC approach and application to thermal power plant, Appl. Math. Model. 35 (2011) 3541–3551. https://doi.org/10.1016/j.apm.2011.01.002. [36] A.L.V. Perales, P. Ollero, F.J.G. Ortiz, A. Gomez-Barea, Model Predictive Control of a Wet Limestone Flue Gas Desulfurization Pilot Plant, (n.d.) 7. [37] L. Li, Z. Wen, Z. Wang, Outlier Detection and Correction During the Process of Groundwater Lever Monitoring Base on Pauta Criterion with Self-learning and Smooth Processing, in: L. Zhang, X. Song, Y. Wu (Eds.), Theory Methodol. Tools Appl. Model. Simul. Complex Syst., Springer

Singapore,

Singapore,

2016:

pp.

497–503.

https://doi.org/10.1007/978-981-10-2663-8_51. [38] L.H.C. Braatz E.L. Russell and R.D., Fault Detection and Diagnosis in Industrial Systems, Meas. Sci. Technol. 12 (2001) 1745–1745. https://doi.org/10.1088/0957-0233/12/10/706. [39]S. Joe Qin, Statistical process monitoring: basics and beyond, J. Chemom. 17 (2003) 480–502. https://doi.org/10.1002/cem.800. [40] M. Khanbabaei, F.M. Sobhani, M. Alborzi, R. Radfar, Developing an integrated framework for using data mining techniques and ontology concepts for process improvement, J. Syst. Softw. 137 (2018) 78–95. https://doi.org/10.1016/j.jss.2017.11.019. [41] S. Vega-Pons, J. Ruiz-Shulcloper, A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS,

Int.

J.

Pattern

Recognit.

Artif.

Intell.

25

(2011)

337–372.

https://doi.org/10.1142/S0218001411008683. [42] K.S. Xu, M. Kliger, A.O. Hero III, Adaptive evolutionary clustering, Data Min. Knowl. Discov. 28 (2014) 304–336. https://doi.org/10.1007/s10618-012-0302-x.

[43] T. Goshima, K. Honda, S. Ubukata, A. Notsu, Deterministic annealing process for pLSA-induced fuzzy co-clustering and cluster splitting characteristics, Int. J. Approx. Reason. 95 (2018) 185–193. https://doi.org/10.1016/j.ijar.2018.02.005. [44] L. Duan, F. Yu, L. Zhan, An improved fuzzy C-means clustering algorithm, in: 2016 12th Int. Conf. Nat. Comput. Fuzzy Syst. Knowl. Discov. ICNC-FSKD, 2016: pp. 1199–1204. https://doi.org/10.1109/FSKD.2016.7603349. [45] E. Hadinata, R.W. Sembiring, T.F. Kusumasari, T. Herawan, The Algorithm Expansion for Starting Point Determination Using Clustering Algorithm Method with Fuzzy C-Means, in: T. Herawan, R. Ghazali, N.M. Nawi, M.M. Deris (Eds.), Recent Adv. Soft Comput. Data Min., Springer

International

Publishing,

https://doi.org/10.1007/978-3-319-51281-5_51.

Cham,

2017:

pp.

506–516.

Table 1 Emission limits of SO2 for existing and new power plants in selected countries/regions (mg/m3)[1] Region

Existing

New

boilers

boilers

Industrial Emissions Directive

200-400

150-400

United States

New Source Performance Standards

160-640

160

India

Environment (Protection) Amendment Rules, 2015

200-600

100

Thailand

Royal Thai Government Gazette

700 -1300

180 -360

Korea

Special Measures for Metropolitan Air Quality

286

229

European

Policy

Union

Improvement

Table 2 Optimal operating conditions of unit load during 550-650MW Slurry

L

Fg

Ci

(MW)

(Km3/h)

(mg/m3)

550.14

1561.37

2754.198

9.53

1166.381

5.97

B,D

581.50

1573.81

3456.083

9.62

1173.458

5.545

572.90

1613.96

3682.971

9.58

1148.029

5.822

597.24

1724.87

3475.907

9.36

1146.09

578.38

1627.85

3881.632

9.75

589.89

1678.58

3898.891

557.62

1564.28

4389.562

Fs

θ

ce(CNY/

28.284

5.273

0.534

A,B,D

14.293

4.112

0.348

A,B,E

8.651

3.585

0.292

5.775

A,B,E

20.848

4.093

0.346

1159.334

5.607

A,B,E

24.265

3.918

0.327

10.32

1172.836

5.793

A,B,D

32.862

3.635

0.296

10.35

1146.832

5.188

B,D,E

25.814

3.921

0.326

h (m)

ρ (kg/m3)

pH

circulatio n pumps

(m3/h)

kg)

Table 3. Decomposed cost and flowrate of slurry feed, oxidation fan and slurry recirculating pumps at points A, B, and C ccir

cfan

cslu

ce

(CNY/kg)

(CNY/kg)

(CNY/kg)

(CNY/kg)

41.56

0.258

0.044

0.2225

0.5245

11562.42

41.56

0.258

0.044

0.2225

0.5245

9872.21

12.43

0.233

0.043

0.06

0.336

Qcir(m3/h)

Qfan(m3/h)

A

37761.9

11562.42

B

37761.9

C

37761.9

points

Fs (m3/h)