Complexity
Volume 2020 (2020), Article ID 2487310, 12 pages
https://doi.org/10.1155/2020/2487310

Research Article

A Complex Evaluation and Optimization Approach for Oxygen-Enriched Combustion Characteristics of Blended Fuels Based on Response Surface Methodology

Y. C. Liu, H. Zhang, Z. M. Luo, S. Qing, A. M. Zhang, and S. P. Yang

State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Department of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China

Correspondence should be addressed to S. Qing; 15087088903@163.com

Received 16 April 2020; Accepted 8 June 2020; Published 15 July 2020

Guest Editor: Kailong Liu

Copyright © 2020 Y. C. Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

With the energy consumption increasing, the coal supply in China has been becoming tight, which has made it difficult for thermal power generation in Yunnan Province. Making full use of local inferior coal and biomass resources in Yunnan can remedy the lack of fuel in power plants. In this paper, an oxygen-rich atmosphere thermogravimetric experiment was performed for a blended sample of Xiaolongtan lignite, Yiliang tobacco rod, and Fuyuan bituminous coal. The combustion characteristics of the mixed fuel under several key operating parameters (i.e., mass ratios, oxygen concentration, and heating rates) were studied. The response surface methodology was used to determine the optimal blending ratio of the three fuels. The results show that the ignition and burnout temperature of coal decrease and the combustion time diminishes with the enrichment of oxygen. The optimal oxygen concentration in the practical application is around 30%. The activation energy and preexponential factor increase with the enlargement of oxygen concentration. Such complex evaluation and optimization approach ensure the stable operation of thermal power plant production.

1. Introduction

Combustion of solid fuel plays an essential role for the social development as it provides the energy needed in the practical operation [1–3]. With the development of the electric power industry, more and more power plants have been developed by using the blended fuels combustion technology in recent years. Hence, both the academic and industrial communities have paid increasing attention to the study of blended fuels. Meanwhile, the optimization of coal blending technology can provide additional benefits such as environmental protection and effective utilization of solid fuel resources. According to the experimental observation with regard to the blended combustion, different types of coal will intensely interact [4]. The ignition temperature of blended coal varies with different amounts of blended coal at low temperatures. Increasing the proportion of high volatile coal improves the ignition, stability, and burnout characteristics of blended coal. Chen et al. [5, 6] studied the mixed combustion characteristics of two kinds of pulverized coals with different ignition characteristics and found that the ignition characteristics of the blended coal are better than those of the single coal. The ignition temperature of blended coal tends to be higher as the proportion of pulverized coal with good ignition characteristics increases. Gao et al. [7] studied the combustion characteristics of blended lignite, bituminous coal, and anthracite by thermogravimetry. It was found that when the low activation energy bituminous coal or lignite is mixed with the anthracite with high activation energy, the ignition characteristic of blended coal is the best. However, when the activation energy of two kinds of coals is similar, the activation energy of blended coal tends to be the coal with relatively high activation energy. From the combustion experiments of blended coal via the simulating combustion conditions, similar conclusions can be drawn [8–10]. The characteristics of blended coal are not a simple linear superposition of single coal. The bigger the difference of coal quality is, the more noticeable this situation will be. Peratla et al. [11] studied the interaction of blended coal and found that the coal quality determines the combustion characteristics of blended coal, which depends on the proportion of combustible coal. Some researchers [12, 13] pointed out that the blended coal is the most critical factor in the coal combustion, and the burnout characteristics rely on the type of coal that burns slowly. At present, the studies for coal blending mainly focus on the blending of different coals while few efforts have been made to study the proportion of biomass smoke rods and coals. In addition, the performance of biomass is different from that of coal because the biomass mainly consists of high volatile substances, which have a low ignition point, rapid burning, and short burnout time. After the biomass is mixed with coal, the blended fuels have complex combustion characteristics. For the superposition of fuels, it needs to consider the coupling and synergy of the mixed fuels, which all depend on the proportion of different combustibles. In other words, the relationship between the coal blending and the main indexes of each coal composition is complexly nonlinear, which can be identified by nonlinear optimization models via response surface method (RSM) and fuzzy logic systems. Moreover, the blending ratio of blended fuels in most of the actual process is ambiguous so that the combustion performance of the blended fuels cannot be guaranteed.

Motivated by the above requirements, this article will study the optimization problem of the fuels blending via the response surface method, aiming at finding an optimal mix scheme for different fuels with the best combustion performance achieved. The flammability index, comprehensive combustion characteristic index, burnout temperature, and economic costs are chosen to characterize the advantages and disadvantages of different mixing schemes with different parameters. After validated by extensive experiments, the proposed optimization technique is utilized to find the best ratio of different fuels. The experiments are divided into 10 groups, and each group has three kinds of fuels (i.e., lignite of Xiaolongtan, tobacco rod of Yiliang, and bituminous coal of Fuyuan) with different proportions. The advantages and disadvantages of using the response surface method are analyzed, and the different proportional reference assignment schemes are discussed. The analysis and experimental results will shed light on the blending of three kinds of fuels to achieve an excellent combustion efficiency.

This paper is organized as follows: Section 2 gives the problem formulation. Section 3 gives the blended coal studies, which includes raw materials and instruments illustration, thermogravimetric analysis, and chemical structure analysis. Section 4 gives the decision analysis of the response surface method, which includes the optimization of oxygen concentration and heating rate and optimizing the proportion of blended fuel. Section 5 gives the conclusions.

2. Problem Formulation

Combustion of blended fuels should consider its ignition characteristics, volatile precipitation characteristics, slagging, burn-up, and pollutant emissions. However, the blending ratio of blended fuels in most of the actual process is blind and arbitrary so that the combustion performance of the blended fuels cannot be guaranteed. This article aims at discussing the pros and cons of different proportions of blended fuels by using the response surface method, including its flammability index, comprehensive combustion characteristics index, burnout temperature, and economic cost, and finally getting the best ratio of three kinds of fuels blended coal combustion.

Under different O₂/CO₂ atmospheres, thermogravimetric experiments were performed on blending fuels for three samples. The response surface method was used to analyze and optimize the pros and cons of different proportioning schemes to find the optimal ratio. First, the three kinds of fuels are divided into several groups according to different proportions. The different groups include one kind of fuel and a mixture of three kinds of fuel. The air atmosphere is set as 30% O₂ atmosphere, 50% O₂ atmosphere, and the heating rate of 5 K/min, 10 K/min, and 20 K/min, respectively.

3. Blended Fuel Combustion Studies

3.1. Raw Materials and Instruments

Xiaolongtan lignite (X), Yiliang tobacco rod (Y), and Fuyuan bituminous coal (F) are ground by ball mill to obtain the blended fuel with a diameter of 53∼75 μm. The atmosphere in the furnace is air and O₂/CO₂ mixture containing 30% O₂, and the protective gas is high purity argon (i.e., 99.9%).

The experimental instrument is STA 449 F3 Jupiter synchronous thermal analyzer (TGDSC, Germany, NETZSCH) and corundum crucible.

3.2. Thermogravimetric Analysis

Three sample ratios were selected randomly to carry out the experiments. The results showed that the ratio of mixed coal has no influence on the pyrolysis property, while the concentration of O₂ affects this aspect. Taking the X : Y : F mass ratio of 1 : 1:1 and 2 : 2 : 1 in the experiment, the thermogravimetric curves of blended coal samples under different atmospheres with a heating rate of 20 K/min are shown in Figure 1. Higher oxygen concentration in the atmosphere gives rise to a more inclined thermogravimetric curve of blended coal and increases the maximum weight loss rate, indicating that the blended coal segregates in the oxygen-rich atmosphere. With the increase of oxygen concentration, the ignition temperature and burnout temperature of the blended coal decrease dramatically, which indicates that the combustion reaction of blended coal can be completed at a lower temperature and the combustion time can be reduced, which is consistent with the results of references [14, 15].

Figure 1: Effect of oxygen concentration on pyrolysis of blended coal with different mass ratios.

Figure 2 shows the thermogravimetric curves of blended coal with different mass ratios at the heating rate of 20 K/min. It is noted that the curves have a similar trend. The curve is the most inclined to the high temperature zone with the X : Y : F mass ratio of 0 : 2 : 1 while the curve is the most inclined to the low temperature zone with the X : Y : F mass ratio of 2 : 1 : 1, indicating that the burning speed of blended coal is the fastest. Moreover, the better rate is conducive to its complete combustion [16, 17].

Figure 2: TG curves of mixing coals with different mass ratios.

3.3. Chemical Structure Analysis

Fuel is mainly composed of the carbon skeleton, which is surrounded by various alkyl side chains and oxygen-containing functional group branched chains. The chemical properties of the carbon skeleton are stable at low temperature, while the properties of side chains and branched chains are more active. During the combustion, the groups of side chains and branched chains are easy to participate in the chemical reactions, mainly the oxidation of active functional groups and the precipitation of volatile compounds in coal. The content of aliphatic side chains and oxygen functional groups are higher than that of lignite, which is also an essential factor leading to a better combustion performance of bituminous coal than lignite. Therefore, with the increase of the bituminous coal ratio, the combustion performance of blended fuel is gradually improved [18].

4. Decision Analysis of the Response Surface Method

4.1. Optimization of Oxygen Concentration and Heating Rate

Miscellaneous hybrid design of response surface optimization method in Design-Expert 8.0.6 software was used to design and optimize oxygen concentration and heating rate. The optimal mixing scheme was obtained by taking the combustion characteristic index and burnout temperature as the evaluation indexes. The corresponding response was filled with the calculated results based on experimental data. The design scheme and experimental evaluation indexes are shown in Table 1.

Table 1: Miscellaneous design and evaluating indicator.

Sequence no.
Oxygen concentration, A (%, φ)
Heating rate, B (K/min)
Comprehensive combustion characteristic index, S (×10⁻⁸)
Burnout temperature (°C)

1
50.5
20.0
20.97
516.3

2
50.5
12.5
9.26
472.9

3
80.0
12.5
13.12
419.8

4
80.0
5.0
5.91
406.2

5
50.5
5.0
3.36
422.9

6
50.5
12.5
9.81
483.7

7
80.0
20.0
39.02
427.

8
21.0
20.0
13.84
616.6

9
50.5
12.5
8.87
503.5

10
21.0
5.0
1.92
453.7

11
21.0
12.5
7.33
526.8

4.1.1. Combustion Characteristic Index and Variable Relation Model

Tables 2 and 3 are the variance and evaluation of the combustion characteristic indices fitted by various models, respectively. Table 4 is the confidence degree of the quadratic model. From Table 2, it is noted that the quadratic equation fits best among six models, while the cubic equation fits the worst. From Table 3, the quadratic equation has the best fitting result. Table 4 shows that the quadratic equation has a significant fitting effect and the heating rate has a significant effect on the comprehensive combustion.

Table 2: The variance of comprehensive combustion characteristic index of different models.

Source of variance
Quadratic sum
Degree of freedom
Mean square
F-value
Probability > F
Scheme selection

Average value
1816.17
1
1816.17

Linear model
763.11
2
381.56
21.27
0.0004

2FI
100
1
100
8.12
0.0261

Quadratic equation
83.31
2
41.66
9.48
0.0183
Recommended

Cubic equation
30.17
2
5.09
23.36
0.0041
Poor

Residual deflection
3.62
5
0.72

Grand total
2796.38
13
215.11

Table 3: Comprehensive evaluation of R² of comprehensive combustion characteristic index.

Model
Standard deviation
R²
R² corrected value
R² predicted value
Predicted residual sum of squares
Scheme selection

Linear model
4.21
0.7984
0.7602
0.5012
541.18

2FI
3.18
0.8617
0.8579
0.4631
606.15

Quadratic equation
1.96
0.9366
0.9517
0.6982
327.08
Recommended

Cubic equation
0.77
0.9970
0.9914
0.6771
392.12
Poor

Table 4: The confidence of the two equations for comprehensive combustion characteristic index.

Factor
Parameter estimation
Degree of freedom
Standard deviation
95% confidence interval lower control limit
95% confidence interval upper control limit
Significant factor

Value
8.97
1.00
0.83
6.73
10.76

Oxygen concentration, A
5.31
1.00
0.77
3.37
7.32
1.12

Heating rate, B
9.95
1.00
0.91
8.26
11.88
1.12

AB
4.83
1.00
1.02
2.26
7.27
1.12

A²
1.47
1.00
1.03
−1.28
4.31
1.03

B²
3.94
1.00
1.15
1.08
7.06
1.03

Figure 3 shows the distribution of studentized residuals for the comprehensive combustion characteristic index fitting model. The dots represent comprehensive combustion characteristic indices. The red values stand for high probability while the blue values represent low probability. It is noted that the residual points are basically distributed near the straight line, indicating that the model has a good applicability. The role of the oxygen concentration and heating rate in the comprehensive combustion characteristic index is shown in Figure 4. As the oxygen concentration increases, the role of the heating rate decreases. As the heating rate increases, the role of the oxygen concentration decreases.

Figure 3: Distribution of the residuals of the combustible index.

Figure 4: Response surface of comprehensive combustion characteristic index and oxygen.

4.1.2. Relation Model between Burnout Temperature and Variables

Tables 5 and 6 present the variance comparisons of burnout temperatures fitted by various models. From the tables, the fitting effect of the quadratic equation is the best. Table 7 shows the confidence of the quadratic equation of the burnout temperature, which shows that the fitting effect of the quadratic model is remarkable, and the heating rate has the greatest influence on burnout temperature.

Table 5: The variance of burnout temperature with different models.

Source of variance
Quadratic sum
Degree of freedom
Mean square
F-value
Probability > F
Scheme selection

Average value
3.173 × 10⁶
1
3.173 × 10⁶

Linear model
33677.22
2
16838.61
32.26
<0.0001

2FI
3987.65
1
3987.65
26.82
0.0004

Quadratic equation
838.42
2
419.21
6.33
0.02271
Recommended

Cubic equation
128.92
2
64.46
1.08
0.37528
Poor

Residual deflection
2.96
5
0.59

Grand total
2786.25
13
214.33

Table 6: Comprehensive evaluation of R² of burnout temperature.

Model
Standard deviation
R²
R² corrected value
R² predicted value
Predicted residual sum of squares
Scheme selection

Linear model
22.88
0.8349
0.8134
0.6525
12953.47

2FI
11.92
0.9437
0.9391
0.9142
3388.57

Quadratic equation
8.02
0.9817
0.9799
0.9547
3446.29
Recommended

Cubic equation
7.34
0.9917
0.9807
0.07831
34337.29
Poor

Table 7: The confidence of the two equations for burnout temperature.

Factor
Parameter estimation
Degree of freedom
Standard deviation
95% confidence interval lower control limit
95% confidence interval upper control limit
Significant factor

Value
492.77
1
3.55
483.48
502.87

Oxygen concentration, A
−60.18
1
3.46
−64.35
−49.38
1.00

Heating rate, B
50.83
1
3.46
41.07
52.77
1.00

AB
−29.98
1
4.26
−42.66
−23.53
1.00

A²
−13.73
1
5.18
−27.81
−2.64
1.12

B²
−4.72
1
5.18
−15.86
6.832
1.12

Figure 5 shows the distribution of residuals of the fitting model in Design-Expert 8.0.6. The ignition represents the burnout temperature. The red values stand for high probability while the blue values represent low probability. It is noted that the residuals are basically distributed near the straight line, which shows that the fitting effect of the model is satisfactory.

Figure 5: Distribution of the residuals of burnout temperature.

The control power of oxygen concentration and heating rate on burnout temperature is shown in Figure 6. With the increase of oxygen concentration, the control power of the heating rate increases. With the increase of heating rate, the control power of oxygen concentration increases. Therefore, in order to get the lowest burnout temperature, the heating rate and oxygen concentration can be reduced simultaneously.

Figure 6: Response surface of the relationship between burnout temperature and oxygen concentration and heating rate.

4.1.3. Optimizing Process Control Parameters

To make the comprehensive combustion characteristic index the largest and the burnout temperature the smallest, the experiment under certain oxygen concentration and heating rate should be optimized. The range of response index is designed in Numerical Criteria. The analysis via software results of the optimum prediction scheme is shown in Table 8. It is noted that when the oxygen concentration is 80%(φ) and the heating rate is 20 K/min, the expectation is the highest. In this case, the best results can be obtained by combining the combustion characteristic index and burnout temperature.

Table 8: Comprehensive optimization scheme of various indexes of coal under different oxygen concentration and heating rate.

Sequence number
Oxygen concentration (%, φ)
Heating rate (K/min)
Comprehensive combustion characteristic index, S (×10⁻⁸)
Burnout temperature (°C)
Degree of expectation

1
80.00
20.00
35.9821
438.848
0.63

2
74.82
20.00
32.6452
457.646
0.61

3
70.21
20.00
33.0137
470.325
0.53

4
21.00
5.00
3.59823
469.018
0.21

Figures 7 and 8 are the optimized contours and their three-dimensional response surface diagrams according to the relationship between the oxygen concentration and heating rate using the optimized scheme 1. Contours represent the specific values of the significant factors in different intervals of the target value, and their shapes reflect the interaction between the independent variables to control the effect of variables. Figure 7 shows the contours change from elliptic arc to monotonic shape with oxygen concentration when the expectation is 1. The role of heating rate increases first and then decreases when the degree of expectation is less than 1. Meanwhile, the role of heating rate increases gradually with the increase of oxygen concentration. The response surface graph of Figure 8 shows the change of expectation degree with oxygen concentration and heating rate.

Figure 7: The optimized contour map of oxygen concentration and heating rate.

Figure 8: Expected response surface map between desirability of oxygen concentration and heating rate using optimization scheme 1.

4.2. Optimizing the Proportion of Blended Fuel

Design-Expert 8.0.6 software was used to design the scheme and analyze the results of the thermogravimetric experiment of blended fuel. The four evaluation indices, that is, combustibility index, comprehensive combustion characteristic index, burnout temperature, and economic cost, were optimized and the optimal mixing scheme was obtained.

For blended coal of Xiaolongtan lignite, Yiliang tobacco rod, and Fuyuan bituminous, a total of 27 groups of orthogonal experiments with 3 factors and 3 levels are needed. The Design-Expert 8.0.6 Box-Behnken is used to carry out the partial orthogonal experiments. The experimental points are the skeleton points of the experimental model according to the software and model analysis. As shown in Figure 9, there are four repetitive points on each surface of the model skeleton. The designed repetitive experimental group also needs repetition. If the same set of experimental data is used for analysis, the results of the model are inaccurate.

Figure 9: Test point distribution of three-factor BBD experimental design.

According to the design scheme, the corresponding response column is filled according to the calculation results of the experimental data. The designed scheme and the experimental evaluation index are shown in Table 9. It can be seen that the trend of the change of the combustibility index is generally the same as that of the comprehensive combustion characteristic index, and the combustibility index is taken for analysis.

Table 9: Design and results of Box-Behnken orthogonal experiment.

Experiment no.
Mass ratio of X : Y : F
Combustible index (×10⁻⁷)
Comprehensive combustion characteristic index, S (×10⁻¹⁰)
Burnout temperature (°C)
Cost (¥/t)

1
1 : 1 : 1
35.931
3.25673
701
383

2
1 : 1 : 1
34.627
3.10528
700
383

3
1 : 1 : 1
34.153
3.26527
695
383

4
1 : 1 : 1
33.647
3.32489
701
383

5
1 : 1 : 1
35.743
3.25432
703
383

6
2 : 1 : 0
42.584
4.39395
691
296

7
1 : 2 : 2
27.709
2.48589
695
398

8
0 : 1 : 0
11.147
1.23162
707
286

9
2 : 0 : 1
30.328
2.98336
679
363

10
2 : 1 : 2
50.632
5.46327
678
402

11
1 : 0 : 0
11.475
1.18341
668
276

12
1 : 2 : 0
37.871
3.42526
676
265

13
1 : 0 : 2
33.013
3.68168
709
488

14
0 : 2 : 1
27.871
1.99386
716
378

15
2 : 2 : 1
31.987
2.68282
718
351

16
0 : 1 : 2
30.166
2.25216
709
497

17
0 : 0 : 1
4.739
0.41678
742
606

4.2.1. Relation Model between Combustibility Index and Variables

Table 10 shows the flammability index variance of blended coal of different proportions, which can be fitted by various models. From the table, the quadratic model has the best fitting effect, which is recommended for the system. The fitting effect of the cubic equation is the poorest. Table 11 compares the standard deviation, R2, and its correction value, predicted value, and squared sum of predicted residual of four fitting polynomial models. Apparently, the results show that the quadratic equation has the best fitting results.

Table 10: The variance of combustibility index using different models.

Source of variance
Quadratic sum
Degree of freedom
Mean square
F-value
Probability > F
Scheme selection

Quadratic sum
15973.686
1
15973.686

Linear model
1298.897
3
432.9657
6.3769
0.0059

2FI
368.196
3
122.732
2.4367
0.1213

Quadratic equation
356.962
3
118.9873
8.896
0.0078
Recommended

Cubic equation
96.981
3
32.327
137.234
0.0002
Poor

Residual deflection
0.924
4
0.231

Grand total
18095.65
17
1064.45

Table 11: Comprehensive evaluation of R² of combustibility index.

Model
Standard deviation
R²
R² corrected value
R² predicted value
Predicted residual sum of squares
Scheme selection

Linear model
7.986
0.599
0.538
0.219
1706.486

2FI
7.038
0.736
0.667
0.081
2003.612

Quadratic equation
3.476
0.946
0.878
0.302
1613.548
Recommended

Cubic equation
0.513
1.000
0.998

Poor

Table 12 shows the results of software analysis on the quadratic equation and the confidence of the influencing factors. It shows that the quadratic equation fitting effect is remarkable, and the interaction between rich source bituminous coal and rich source bituminous coal has the greatest influence on the flammability index.

Table 12: The confidence of the two equations for combustibility index.

Factor
Parameter estimation
Degree of freedom
Standard deviation
95% confidence interval lower control limit
95% confidence interval upper control limit
Significant factor

Value
3.25
1
0.55
2.26
4.58

Xiaolongtan lignite, A
−0.51
1
0.44
−1.32
0.63
1.00

Yiliang tobacco stem, B
−1.86
1
0.44
−2.58
−0.59
1.00

Fuyuan bituminous coal, C
−2.33
1
0.44
−3.02
−1.32
1.00

AB
0.61
1
0.63
−0.87
1.79
1.00

AC
2.52
1
0.63
0.99
3.58
1.00

BC
2.31
1
0.63
0.71
3.27
1.00

A²
0.02
1
0.60
−1.37
1.54
1.01

B²
−0.41
1
0.60
−1.65
1.22
1.01

C²
2.67
1
0.60
1.24
3.76
1.01

4.2.2. Optimizing the Proportion of Blended Fuel

Figure 10 is the distribution of the residual of the flammability index fitting model. The point represents the flammability index. The red values stand for high probability while the blue values represent low probability. It is noted that the residuals are basically distributed near the straight line, which shows that the simulation effect of the model is very good.

Figure 10: Distribution of the residuals of the combustible index.

Figure 11 shows the relationship between the ratio of the other two samples and the flammability index when the ratio of Fuyuan bituminous coal is 50%. When the flammability index is less than 20, the control power of Yiliang tobacco rod decreases with the increase of Xiaolongtan lignite. When the flammability index is 30, the content of Xiaolongtan lignite decreases first and then increases with the increase of Yiliang smoke stem.

Figure 11: Response surface of the relationship between combustible index of mixing fuels and mass ratios of Xiaolongtan lignite and Yiliang tobacco rod.

Figure 12 shows the relationship between the proportion of the other two kinds of coals and the combustibility index when the proportion of Yiliang tobacco rod is 50%. The combustibility index increases with the increase of Xiaolongtan lignite and the combustibility index increases with the increase of rich source bituminous coal. Based on the fitting of experiment and model, the experiment is optimized by using Design-Expert 8.0.6 software, and the combustibility index, burnout temperature, and economic cost are optimized. In the best case, we can find the optimal mixing ratio scheme. Design Criteria of Design-Expert 8.0.6 software are also used to design the range of values of each factor and set the flammability index and combustion characteristic index to be the largest and the burnout temperature and investment cost to be the smallest.

Figure 12: Response surface of the relationship between combustible index of mixing coals and mass ratios of Xiaolongtan lignite and Fuyuan bituminous coal.

Through the analysis, the optimal prediction scheme is shown in Table 13. From the optimization results, scheme 1 is the optimal mixing ratio, which is also suggested by the software. Xiaolongtan lignite: Yiliang tobacco rod: Fuyuan bituminous coal mass ratio is 100 : 55.12 : 1. Under this condition, the model predicts that the combustibility index of blended fuel is 43.3574 × 10⁻⁷, the combustion characteristic index is 4.27 × 10−10, the burnout temperature is 678.112°C, and the cost is 251.688 ¥/t, demonstrating that the expectation is the highest.

Table 13: Comprehensive optimization scheme of each index.

Sequence no.
Mass ratio (%)
Combustible index × 10⁻⁷
Combustion performance index × 10⁻¹⁰
Burnout temperature (°C)
Investment cost (¥/t)
Degree of expectation

Xiaolongtan lignite
Yiliang tobacco stem
Fuyuan bituminous coal

1
100.00
55.12
1.00
43.3574
4.19875
678.112
251.688
0.885

2
100.00
55.64
1.00
43.4866
4.14176
678.332
251.534
0.885

3
99.58
55.12
1.00
43.3305
4.32126
678.862
251.378
0.884

4
99.37
56.37
1.00
43.4927
4.38726
678.375
250.023
0.885

5
98.22
56.81
1.00
43.3993
4.32462
678.562
251.638
0.884

6
97.62
56.13
1.00
43.2784
4.14324
679.012
251.868
0.883

7
96.88
57.66
1.00
43.2642
4.14389
679.326
250.658
0.881

8
95.79
67.86
1.00
43.9873
4.19868
685.465
241.678
0.872

9
100.00
35.87
1.32
39.9855
3.96782
670.483
266.714
0.843

10
100.00
46.02
36.83
43.0117
4.15865
692.821
322.933
0.804

11
100.00
36.33
100.00
46.9865
5.19546
682.757
438.268
0.793

12
100.00
35.05
100.00
48.0963
5.29232
682.962
439.393
0.793

13
99.27
36.78
100.00
47.6752
5.18383
682.878
439.887
0.794

14
100.00
33.89
87.279
45.8892
4.98712
690.009
420.653
0.788

15
100.00
41.02
71.43
44.5671
4.53527
692.656
386.848
0.782

5. Conclusion

In this work, the response surface methodology was used to optimize the oxygen concentration, heating rate, and pulverized coal ratio. The effects of coal ratio on the combustibility index, combustion characteristic index, burnout temperature, and the investment cost of blended coal were analyzed. The following conclusions can be drawn:

(1)In the oxygen-enriched atmosphere, the overall precipitation of mixed coal becomes better. With the increase of oxygen concentration, the combustion rate increases, and the influence of oxygen concentration is greater than that of mixing proportion. Meanwhile, the ignition temperature and burnout temperature of pulverized coal decrease significantly, indicating that the pulverized coal can be burned at the lower temperature and combustion time can be shortened under the oxygen-enriched condition.(2)When the expectation is 1, the role of heating rate increases first and then decreases with the increase of oxygen concentration. When the expectation is less than 1, the control force of heating rate increases gradually with the increase of oxygen concentration.(3)Setting the flammability index and combustion characteristic index to be the largest, the burnout temperature and investment cost will be the smallest. The optimal prediction scheme 1 is the case that the mass ratio of Xiaolongtan lignite: Yiliang tobacco rod: Fuyuan bituminous coal is 100 : 55.12 : 1. Under this condition, the model predicts that the combustibility index of blended fuel is 43.3574 × 10⁻⁷, the combustion characteristic index is 4.27 × 10⁻¹⁰, the burnout temperature is 678.112°C, and the cost is 251.688 ¥/t.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant nos. 51966005 and 51566005.

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