Optimization of Low-Grade Coal and Refuse-Derived Fuel Blends for Improved Co-Combustion Behavior in Coal-Fired Power Plants

doi:10.21203/rs.3.rs-2730251/v1

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Research Article

Optimization of Low-Grade Coal and Refuse-Derived Fuel Blends for Improved Co-Combustion Behavior in Coal-Fired Power Plants

https://doi.org/10.21203/rs.3.rs-2730251/v1

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published 21 Oct, 2023

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This study aims at utilizing three waste materials, i.e., Biomass-Solid Refuse Fuel (SRF), Tire Derived Fuel (TDF), and Sludge Derived Fuel (SDF), as ecofriendly alternatives to coal-only combustion in co-firing power plants. The contribution of waste materials is limited to ≤ 5% in the composition of the mixed fuel (coal + waste materials). Statistical experimental design and response surface methodology is employed to investigate the effect of mixed fuel composition (SRF, TDF, and SDF) on gross calorific value (GCV) and ash fusion temperature (AFT). A quadratic model is developed and statistically verified to apprehend mixed fuel constituents' individual and combined effects on GCV and AFT. Constrained optimization of fuel blend, i.e., GCV > 1250 kcal/kg and AFT > 1200°C, using the polynomial models projected the fuel-blend containing 95% coal with 3.84% SRF, 0.35% TDF, and 0.81% SDF. The observed GCV of 5307 kcal/kg and AFT of 1225°C for the optimized blend were within 1% of the model predicted values, thereby establishing the robustness of the models. Our findings can foster sustainable economic development and zero CO₂ emission objectives by optimizing the utilization of waste materials without compromising on the GCV and AFT of mixed fuels in coal-fired power plants.

Coal-combustion

Gross calorific value (GCV)

Ash fusion temperature (AFT)

Low-grade coal

Refuse derived fuel

Coal-fired power plants meet approximately 40% of the global energy demand (Sami et al. 2001; Gil and Rubiera 2018; Wang et al. 2021).They are the single most significant contributor to climate change since they contribute approximately 30% to global carbon dioxide (CO₂) emissions (IEA 2019; Iqbal et al. 2019). Besides CO₂ emissions associated with global warming, coal combustion contributes to acidification due to NO_x and SO_x emissions and pulmonary health risks due to particulate matter emissions (Truong et al. 2019; Sajdak et al. 2019). Therefore, developed and developing economies have undertaken various initiatives to achieve zero CO₂ emission objectives by substituting coal, to the extent possible, with selected waste material alternatives (Smith et al. 2019).

The United Nations encourages national governments to generate electricity by burning carbon-free fuels (such as hydrogen and ammonia) to reduce GHG emissions (Dang and Serajuddin 2020). However, until the technology is commercialized, efforts are underway to formulate mixed fuel blends for coal co-firing without compromising the calorific value and residual ash characteristics. In recent years, biomass generated from agriculture, industry, and wastewater treatment plants has received considerable attention as an individual fuel and as a fuel blend to coal (Stelmach and Wasielewski 2008; Yi and Jang 2018a). Municipal solid waste (MSW) is recognized as a widely-available renewable energy resource having carbon neutralities of 50–70% (Saidur et al. 2011; Arteaga-Pérez et al. 2015; Madanayake et al. 2017). Biomass-solid refuse fuel (SRF) - a screened blend of high-quality combustibles - possesses higher calorific value when compared to unsorted waste (Lee et al. 2021) and is prioritized as a fossil fuel substitute (Tyskeng and Finnveden 2010; Yi and Jang 2018b). Similarly, waste tires, which are difficult to dispose-off in landfills due to environmental considerations, can be processed as tire-derived fuel (TDF) with high calorific value (Harding 2002). The co-combustion of waste tires with low-quality coal offers improved ignition characteristics (Zaharia et al. 2009; Pan et al. 2021). Besides, sludge-derived fuel (SDF) is also considered mainly due to the environmental implications of conventional sludge waste disposal practices (Wang et al. 2017; Im and Kim 2017).

The high carbon content of coal, when combined with the high volatile matter and low nitrogen content of biomass, allows efficient combustion with minimal NO_x, SO_x, and CO₂ emissions. Moreover, a higher amount of oxygen and hydrogen in biomass implies a faster ignition at a reduced temperature (Zhou et al. 2016; Rokni et al. 2018). The incineration of MSW results in effective energy recovery (Zhou et al. 2014) while discharging solid residues such as bottom ash (Zhang et al. 2010). Therefore, the combustion of MSW with coal represents an environmentally benign approach that will reduce fossil fuel dependency and limit coal usage in the power industry.

The efficient operation of a large-scale co-combustion system depends primarily on the physicochemical characteristics of the fuels, combustion behavior and kinetics of the system, and pollutant emissions from the process (Zhang et al. 2019). In this context, the fuel's gross calorific value (GCV) and the ash fusion temperature (AFT), which indicates the ash quality after combustion, have been considered the two simple indicators for evaluating process performance. While GCV represents a fuel's overall energy content, the AFTs refer to the range of temperatures over which coal ash deposits are formed on the heat-absorbing surfaces of the process equipment. The coal ash deposits cause slagging or fouling to occur, thereby lowering the process efficiency. Therefore, AFTs are routinely used by furnace and boiler operators in power plants for designing, operating, and optimizing coal-based combustion processes (Tambe et al. 2018). Though co-combustion of low-grade coal, SRF, SDF, and TDF have extensively been reported, we couldn't find any scientific study which addresses the interaction among all these fuels and the fuel blend optimization to enhance the overall performance of a co-combustion system - in terms of the GCV and AFT.

This study investigates the co-combustion behavior of SRF, TDF, and SDF as additives to low-grade coal used in thermal power plants. The variations in GCV and AFT are studied with respect to the change in fuel composition. The I-optimal mixture design and response surface methodology (RSM) are used to model the GCV and AFT of the fuel blends. Finally, a numerical optimization is performed to obtain the mixed fuel blends with desirable high GCV and AFT. The statistical quality of the models is assessed and experimentally validated. The results obtained from this study can be considered as a baseline to formulate the low-grade coal-RDF blends with desired co-combustion characteristics in thermal power plants.

2.1. Materials and sample preparation

Low-grade coal (coal) was provided by Coal Indonesia Ltd. (Samarinda, Indonesia). The waste materials for co-combustion were acquired from various sources; Biomass-Solid Refuse Fuel (SRF) from Korea Southern Power Co., Ltd (Busan, South Korea), Tire Derived Fuel (TDF) from Kumho Petrochemical Co. (Seoul, South Korea), Ltd., and Sludge Derived Fuel (SDF) from Jinenertech Co., Ltd. (Seoul, South Korea). The as-received and pulverized samples of coal, SRF, TDF, and SDF are shown in Fig. 1.

The coal, SRF, and SDF were mechanically crushed, while TDF was subjected to cryogenic crushing using liquid nitrogen. The crushed and homogenized fuel samples (mean particle size = 250 µm) were mixed according to pre-determined composition. Finally, the mixed fuels were palletized (average mass = 1 g) using an evacuable pellet press, PIKE Technologies, Inc. (Madison, USA).

2.2. Physico-chemical characterization of materials

The samples were characterized for their GCV, AFT, proximate analysis, and ultimate analysis. The GCV was determined following the ASTM D5865/D5865M-19 method using Parr 6400 automatic isoperibol calorimeter (Parr Instrument Company, Moline, USA) (ASTM 2019). The AFT of the samples was assessed by following ASTM D1857/D1857M-18 method (ASTM 2018a). Briefly, the sample was combusted for 24 h at 750°C, and the residual ash was mechanically mixed, blended with dextrin, and molded into cone-shaped triangular pyramids. Finally, its melting behavior was observed over time by gradually increasing temperature in an electric furnace. The proximate analysis, i.e., moisture, volatile matter (VM), fixed carbon (FC), and ash content of samples were analyzed per ASTM D7582-15 method (ASTM 2016), using a thermogravimetric analyzer (TGA701 Leco Corp., St. Joseph, MI, USA). The ultimate analysis was performed according to the ASTM D5373-21 method using a FLASH 2000 elemental analyzer (Thermo Fisher Scientific, Waltham, MA USA) to determine the quantity of carbon, hydrogen, and nitrogen in the samples (ASTM 2021). The sulfur content was analyzed using 5E-IRS3600 Automatic Infrared Sulfur Analyzer, CKIK (China), following the ASTM D4239-18 method (ASTM 2018b). The oxygen content of the samples was approximated through a pre-calculated mass balance of the samples.

2.3. Experimental design

The I-optimal mixture experimental design was opted to determine the composition of mixed fuels, which could help assess variability in GCV and AFT with respect to the composition of coal (95–100%), SRF (0–5%), TDF (0–5%), and SDF (0–5%) and their combinations thereof (Myers et al. 2016). These experimental combinations occupy a pre-determined location in the experimental design space leading to the development of a regression model. The experimental design helps reduce the prediction variance integral and minimizes the number of design points required to establish the quadratic model (Zaib et al. 2021). Table 1 shows the composition of the coal and waste materials (SRF, TDF, and SDF), which varied during this study. Table 2 presents the experimental runs performed to obtain the regression models representing GCV and AFT of the mixed fuels. Each experimental run corresponds to a specific design point in the experimental design space. All the experiments were performed in duplicate (at least), and their average values were used.

Table 1

Composition of co-combustion mixture
Materials	Target	Mass (%)
Materials	Target	Lower limits	Upper limit
A: Coal	Minimize	95	100
B: Biomass-Solid Refuse Fuel (SRF)	in range	0	5
C: Tire Derived Fuel (TDF)	in range	0	5
D: Sludge Derived Fuel (SDF)	in range	0	5

Table 2

The experimental design matrix of the fuel mixtures assessed to determine the effects of waste materials (SRF, TDF, and SDF) on GCV and AFT of fuel blends, along with calculated statistical indices.
Run#	Coal	SRF	TDF	SDF	GCV (kcal/kg)		AFT (°C)
	Mass (%)				Exp.	Model	Exp.	Model
1	97.4	2.5	0	0.1	5275.47	5287.37	1202	1201.02
2	100	0	0	0	5288.15	5296.95	1210	1211.15
3	95	2.5	2.5	0.1	5351.16	5380.36	1204	1196.71
4	97.4	2.5	0	0.1	5262.33	5287.37	1202	1201.02
5	95	0	0	5	5293.03	5292.07	1190	1190.97
6	95	5	0	0	5286.29	5270.42	1205	1211.17
7	97.5	0.1	2.5	0	5395.26	5390.89	1194	1196.4
8	97.5	0.1	2.5	0	5414.3	5390.89	1202	1196.4
9	98.1	0.6	0.6	0.7	5370.85	5365.41	1202	1204.45
10	95	0	2.5	2.5	5437.18	5445.17	1182	1186.4
11	95	0	5	0	5482.01	5489.01	1178	1181.9
12	95	0.8	0.9	3.3	5349.86	5349.3	1185	1187.02
13	95	2.5	2.5	0.1	5382.22	5380.36	1196	1196.71
14	96.2	1.2	1.1	1.5	5366.76	5348.43	1194	1198.02
15	97.4	0	0.1	2.5	5342.94	5303.29	1205	1208.74
16	95.6	0.6	3.1	0.7	5393.79	5390.14	1195	1190
17	97.4	0	0.1	2.5	5266.69	5303.29	1214	1208.74
18	95.1	2.4	0	2.5	5231.57	5283.91	1205	1200.58
19	95.1	2.4	0	2.5	5319.03	5283.91	1206	1200.58
20	95.6	3.1	0.7	0.6	5338.09	5308.45	1203	1206.04
	RMSE				5.191		0.893
	SEP%				0.097		0.074
	Bf				1.000		1.000
	Af				1.122		1.122
	MPE%				0.336		0.292

2.4. Statistical evaluation

The fitting and prediction accuracy of the quadratic model for the mixture design was evaluated using root mean square error (RMSE), standard error of prediction (SEP), and model predictive error (MPE) along with the bias (B_f), and accuracy (A_f) factors, as shown in Eqs. 1–5:

$$\text{R}\text{M}\text{S}\text{E}=\sqrt{\frac{\sum _{\text{i}=1}^{\text{N}}({\widehat{{\text{y}}_{\text{i}}}-{\text{y}}_{\text{i}})}^{2}}{\text{N}}}$$

$$\text{S}\text{E}\text{P}\left(\text{%}\right)=\frac{\text{RMSE}}{{\text{y}}_{\text{i}}}\text{ ×100}$$

$${B}_{f}= {10}^{\left(\sum _{i=1}^{N}\left(log\frac{\widehat{{y}_{i}}}{{y}_{i}}\right)/n\right)}$$

$${A}_{f}= {10}^{\left(\sum _{i=1}^{N}\left|\left(log\frac{\widehat{{y}_{i}}}{{y}_{i}}\right)\right|/n\right)}$$

$$\text{M}\text{P}\text{E}= \frac{100}{n}\sum _{i=1}^{n}\left|\left({y}_{i}- \widehat{{y}_{i}}\right)/\widehat{{y}_{i}}\right|$$

where ${y}_{i}$is the experimentally measured value of the GCV and AFT in concentration, $\widehat{{y}_{i}}$ is the predicted value by the quadratic equation model, $\stackrel{-}{{y}_{i}}$ is the mean value of ${y}_{i}$, n is the number of experimental points.

3.1. Physico-chemical characteristics of materials

The proximate and ultimate analyses of coal, SRF, TDF, and SDF are presented in Fig. 2. The detailed characterization of waste materials can be found in the supplementary information (Table S1-S3). It can be observed that coal comprises 38% volatile matter content, while SRF, TDF, and SDF contain 74%, 65%, and 75.26% volatile matter content, respectively. Thus, the volatile matter content of the co-combustion mixture would improve when these wastes are included with coal co-firing, resulting in better and more stable flame (Mesroghli et al. 2009). Moreover, the lower ash content of SRF (2.6%) and TDF (5.86%) in comparison to coal (8%) suggested a reduction in waste and an increase in the heating value (Buyukada 2016). The ultimate analysis of TDF and SDF suggested higher C content (78.45% and 51.7%, respectively) than coal (55%), indicating a higher heating value. Though SRF has comparatively lower C content (45.16%), higher H and O contents can increase thermal efficiency during combustion. In addition, low S and N content of SRF (0.04%, 0.99%) and SDF (0.56%, 0.64%) suggested that blending of these materials in the co-combustion process could help reduce SO_x and NO_x emissions.

3.2. Model development and analysis

The experimental design matrix of the mixtures (Table 2) was utilized to develop the empirical models for the co-combustion process. The models were assessed based on the sequential model sum of squares, and the quadratic models containing linear and second order interaction terms (from SRF, TDF, and SDF proportion) were selected to represent the GCV and AFT of the mixed fuels. After eliminating the insignificant terms through stepwise regression and ANOVA analysis, the final model for GCV and AFT are shown in Eqs. (6) and (7), respectively (Myers et al. 2016).

$$\text{G}\text{C}\text{V}= 5296.95\text{A}+5270.42\text{B}+5489.01\text{C}+5292.07\text{D}+215.764\text{C}\text{D} +1110.74 \text{A}\text{C}$$

$$\text{A}\text{F}\text{T} = 1211.15\text{A}+1211.17\text{B}+1181.9\text{C}+1190.97\text{D}-42.2634\text{A}\text{B} +33.1815\text{A}\text{D}+102.549\text{B}\text{D}$$

Where A, B, C, and D are mass fractions of coal, SRF, TDF, and SDF, respectively, in the mixed-fuel blend. The positive signs of the terms in the equations depict synergistic effects, while the negative ones represent antagonistic effects (Zaib and Kyung 2022). The regression coefficient (R²) and the standard deviation were considered to evaluate the fitness of the models, as shown in Table 3. The correlation coefficients were obtained as 0.86 and 0.81 for GCV and AFT, respectively. Although higher correlation coefficient value is desirable, R² ≥ 0.8 is deemed acceptable (Bashir et al. 2010; Nassar et al. 2016). The smaller standard deviation and good R² demonstrated a reasonable agreement between the experimental and predicted results (Figs. 3 and 4) (Regti et al. 2017). The plots show the reasonable capacity of the model to fit the experimental data representing GCV and AFT as a function of coal mixed with SRF, TDF, and SDF.

Table 3

ANOVA of responses; gross calorific value (GCV) and ash fusion temperature (AFT) of fuel blends.
ANOVA for Reduced Cubic model
Response 1: GCV
Source	SS	d.f.	Mean Square	F-value	p-value
Model	69423.29	5	13884.66	18.04	< 0.0001	significant
Linear Mixture	64190.51	3	21396.84	27.79	< 0.0001
CD	2487.33	1	2487.33	3.23	0.0939
AC(A-C)	3124.8	1	3124.8	4.06	0.0636
Residual	10778.13	14	769.87
Lack of Fit	3296.01	9	366.22	0.2447	0.9675	not significant
Pure Error	7482.12	5	1496.42
Cor Total	80201.42	19
Std. Dev.	27.75		R²	0.8656
Mean	5342.35		Adjusted R²	0.8176
CV %	0.5194		Adeq Precision	14.3834
ANOVA for Reduced Cubic model
Response 2: AFT
Source	SS	df	Mean Square	F-value	p-value
Model	1341.35	6	223.56	9.11	0.0005	Significant
Linear Mixture	1042.12	3	347.37	14.16	0.0002
AB	125.09	1	125.09	5.1	0.0418
AD	78.56	1	78.56	3.2	0.0968
BD(B-D)	36.66	1	36.66	1.49	0.2432
Residual	318.85	13	24.53
Lack of Fit	213.85	8	26.73	1.27	0.4123	Not significant
Pure Error	105	5	21
Cor Total	1660.2	19
Std. Dev.	4.95		R²	0.8079
Mean	1198.7		Adjusted R²	0.7193
CV %	0.4132		Adeq Precision	9.9904

The analysis of variance (ANOVA) was performed to establish the significance of model terms based on F-values and the p-values (Table 3). The reduced quadratic model representing GCV (F-value = 18.04, p-value < 0.0001) and AFT (p-value < 0.05) remain highly significant. All the individual terms (except for interaction between SRF-SDF in AFT analysis) were significant (p value < 0.1). Moreover, insignificant lack-of-fit test for both models suggested no unusually large residuals from model fitting. The regression coefficient (R² = 0.8656) and adjusted R² = 0.8176 were in reasonable agreement, suggesting good model fitness and not overfitting due to additional model terms (Myers et al. 2016).

It is evident from the model that the two-way interaction of TDF with SDF or coal has significant positive interactions on the GCV of the composite mixture (Table 3). Like GCV, the quadratic model for AFT had also been highly significant (p-value = 0.0005), maintaining a non-significant lack-of-fit test (p-value = 0.4123). The difference between R² (0.8079) and adjusted R² (0.7193) could be attributed to the smaller sample size in the context of a number of model terms. The antagonistic interaction between coal and SRF can result in decreased AFT of the co-combustion mixture. Studies have suggested that the ash composition of biomass-derived materials can vary, and biomass fired boiler needs redressal (Li et al. 2013; Niu et al. 2016). A significant synergistic interaction between coal and SDF toward AFT was observed from the model. Apart from this, being particularly rich in SiO₂ and Al₂O₃, sewage sludge also contains a higher amount of phosphorous, calcium, and iron, favoring increased AFT of composite fuel material (Zhang et al. 2013).

The quality of the models was further assessed through the coefficient of variance (CV), adequate precision, RMSE, SEP, bias (Bf), accuracy (Af), and MPE (%) (Myers et al. 2016). A coefficient of variance < 10% suggested the reproducibility of the model. The CV of 0.52% and 0.41% for GCV and AFT, respectively, was observed in this study. The adequate precision ratios of the signals with respect to noise for the GCV and AFT models were 14.4. and 9.9, respectively, which are much higher than the minimum desired value of 4.0 (Anderson and Whitcomb 2016). As shown in Table 2, the RMSE and SEP of 5.19 and 0.097% indicated the suitability of chosen mixture design model. Similarly, very low RMSE (0.89) and SEP (0.0744%) indicated excellent modeling ability for AFT of blended fuel. Furthermore, B_f and A_f were close to unity for GCV and AFT, indicating a good agreement between the experimental and model predicted values. Besides, the MPE (%) of about 0.3362 and 0.2916 indicated better prediction accuracy for both GCV and AFT of blended fuel.

3.3. Effects of mixture composition on GCV and AFT

The main effects of coal, SRF, TDF, and SDF proportion in mixed fuel composition on GCV and AFT are shown in Fig. 3a and Fig. 4a, respectively. The main effect of the variables is measured as a change in GCV to deviation from the reference mixture composition, i.e., coal (96.25%), SRT (1.25%), TDF (1.25%), and SDF (1.25%). In general, the GCV of the mixture decreased with the increase in coal, SRF, and SDF content and increased with TDF. The GCV of the mixture decreased non-linearly from 5354 kcal/kg to 5285 kcal/kg, with an increase of coal content from 96.25–100%. The decrease in GCV with the increase in SRF and SDF was largely linear. The GCV of the SRF and SDF decreased from 5354 kcal/kg to 5274 kcal/kg (~ 1.49%) and 5292 kcal/kg (~ 1.15%), respectively, with the increase of these constituents by 3.75%. Contrary to other constituents of mixed fuel, the increase in TDF by 3.4% (from 1.6–5%) resulted in an increase in GCV of the mixture from 5354 to 5476 kcal/kg.

The combined effects of the SRF, TDF, and SDF on the GCV of mixed fuel containing 95% coal are shown in Fig. 3b. The GCV of the mixed fuel decreased with the increase of SRT and finally fell below 5300 kcal/kg as the SRT content approached 5% (turquoise contour band). Conversely, the GCV increased beyond 5450 kcal/kg with the increase in TDF composition in a mixed fuel. Together, the trace and contour plots postulate the highest GCV of TDF in the mixture, followed by SDF, coal, and SRT, which were in line with the experimental analyses. The order of GCV of the mixed fuel with different components were observed as: TDF (8723 kcal/kg) > SDF (5795 kcal/kg) > coal (4317 kcal/kg) > SRT (4375 kcal/kg).

Figure 4a shows the trace plots representing the individual effects of different mixed fuel components on AFT. An increase in AFT is observed with an increase in coal and SRF contents. The AFT increased with TDF and SDF content in mixture fuel compared to the reference blend, i.e., 96.25% coal and 1.25% each of SRT, TDF, and SDF. It is observed that the AFT of mixed fuel increases linearly from 1198°C to 1211°C with an increase in coal content from 96.25–100%. A similar but non-linear increase in AFT was observed with a change in SRT content from 1.3–5%. However, a decrease in AFT of the mixed fuel from 1198°C to 1182/1191°C was observed, with an increase in TDF/SDF from 1.3–5%.

Figure 4b shows the gradual increase in AFT with an increase in the relative proportion of SRT (red band towards the top of the cone). The mixture of TDF and SDF resulted in an AFT below 1190°C (blue band in contour plot). Furthermore, an increase in AFT to 1190°C is observed with an increase of SDF content up to 5% in the mixture fuel. Therefore, the trace and contour plots suggested that an increase in AFT is observed with the increase in coal and/or SRT, while decrease in AFT was observed with the increase of TDF and SDF in the mixture fuel. A 3.75% increase in coal and SRT increased the AFT by 13°C, while the same increase in TDF and SDF decreased the AFT by 16°C and 7°C, respectively. Hence, at reference mixture composition (96.25% coal, and 1.25% each of SRT, TDF, and SDF), further addition of coal to the mixed fuel decreased the GCV but increased the AFT; further addition of SRT decreased the GCV but increased the AFT; further addition of TDF increased the GCV but decreased the AFT; and further addition of SDF decreased both GCV and AFT. A desirable fuel mixture should possess high GCV and high AFT.(Adeleke et al. 2020) Therefore, numerical optimization was performed to achieve the mixed fuel blends with optimum GCV and AFT. It is noteworthy that co-combustion of food waste digestate with municipal solid waste was conducive to enhancing the combustion performance (Wei et al. 2021).

3.4. Optimization

The ultimate objective of the study was to propose a mixed fuel blend with reasonably high GCV and acceptable AFT. The constituents' individual and combined combustion patterns indicated that none of the four constituent materials exhibited high GCV and AFT simultaneously. Coal and SRT showed low GCV and high AFT; TDF possessed high GCV but low AFT, while SDF led low GCV and low AFT. Therefore, numerical optimization was performed to maximize both GCV and AFT provided that the coal consumption above 95% is minimized and the waste materials (SRT, TDF, and/or SDF) are utilized between 0 and 5%. The numerical optimization technique utilizes the desirability function to help estimate optimized mixed fuel composition satisfying the desirable characteristics: GCV > 5200 kcal/kg and AFT > 1200°C (Anderson and Whitcomb 2016; Myers et al. 2016). Figure 5 shows the optimized mixed fuel composition and its predicted GCV and AFT. The red dots indicate the factors, while the blue dots represent the responses. The downward ramp of coal (factor A) indicates the direction of the desirability function towards minimization of factor. Similarly, the upward ramp of GCV and AFT represents the maximization of these responses. It was predicted that mixing 95% coal with 3.84% SRT, 0.35% TDF, and 0.81% SDF would result in GCV of 5294 kcal/kg, and the AFT of the residual ash would be 1214°C. In the confirmation experiment, experiments were conducted at the optimized composition, which demonstrated the GCV of the mixed fuel to be 5307 kcal/kg and AFT as 1215°C (Table 4). Similarly, two other optimized mixed fuel compositions were predicted and experimentally validated. The difference between model-predicted and experimentally observed values was less than 1% in all cases, establishing the robustness and applicability of the models (Anderson and Whitcomb 2016).

Table 4

Optimum mixed fuel compositions predicted by numerical optimization using RSM.
					GCV			AFT
Mixed Fuels	Coal	SRT	TDF	SDF	RSM Predicted	Exp.	Error (%)	RSM Predicted	Exp.	Error (%)
1	95.00	3.84	0.35	0.81	5294	5307	0.24	1214	1225	0.93
2	95.00	4.58	0.43	0.00	5291	5337	0.86	1209	1219	0.85
3	97.50	0.00	0.00	2.50	5289	5313	0.45	1209	1220	0.87

This study adopted a statistical experimental design approach to assess the variability in GCV and AFT of the mixed fuel containing 0 to 5% waste materials such as Biomass-Solid Refuse Fuel (SRF), Tire Derived Fuel (TDF), and Sludge Derived Fuel (SDF). The experimental data was used to establish an empirical model which quantified a relationship between response variables (GCV and AFT) and constituents of the mixed fuel. The ANOVA of the model statistically validated it to navigate the experimental design space. It was observed that increasing TDF content increased the GCV while increasing the coal/SRF/SDF content decreased the GCV. The increase in AFT was directly proportional to coal/ SRT content and inversely proportional to TDF/SDF content. The numerical optimization technique was adopted to ensure that the optimized fuel mixture possesses high GCV (≥ 5200 kcal/kg) and AFT (≥ 1200°C). The optimized mixed fuels exhibited a difference in experimental GCV, and AFT values within 1% to that of model-predicted values. The experimental GCV of all the optimized samples was above 5306 kcal/kg, and the experimental AFT was above 1218°C. The results from this study can help reduce fossil fuel consumption and enhance waste utilization in thermal power plants.

Acknowledgments

SKB, MZ and BM thank Vellore Institute of Technology, Vellore, India, College of Applied Sciences – Sur, University of Technology & Applied Sciences, Oman and Karunya Institute of Technology & Sciences, Coimbatore, India for providing the infrastructural and staff time support to collaborate with researchers from the University of Ulsan, South Korea.

Authors Contributions

Qammer Zaib: Conceptualization, Methodology, Experiment, Software, Validation, Writing - original draft. Sangchul Park: Experiment, Data curation. Shishir Kumar Behera: Writing - review & editing, Biswanath Mahanty: Writing - review & editing, Mohd. Zafar: Software, Writing - review & editing, Hung-suck Park: Conceptualization, Funding acquisition, Supervision, Project administration, Daeseung Kyung: Resources, Writing - review & editing.

Funding

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 23UMRG-B158194-04) and “Regional Innovation Strategy (RIS)” through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE)(2021RIS-003).

Competing Interests

The authors declare no competing interests.

Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Ethical Approval

Not applicable.

Consent to Participate

Not applicable.

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Optimization of Low-Grade Coal and Refuse-Derived Fuel Blends for Improved Co-Combustion Behavior in Coal-Fired Power Plants

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Materials and sample preparation

2.2. Physico-chemical characterization of materials

2.3. Experimental design

2.4. Statistical evaluation

3. Results and discussion

3.1. Physico-chemical characteristics of materials

3.2. Model development and analysis

3.3. Effects of mixture composition on GCV and AFT

3.4. Optimization

4. Conclusions

Declarations

References

Supplementary Tables

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