Utilization of Biowaste for Sustainable Production of Coal Briquettes

Abstract

In energy scarcity, particularly in Agri-based developing economies, bio-coal briquetting is the most suitable means of meeting sustainable energy needs utilizing agricultural waste. In this study, briquettes were made from an indigenously designed briquetting machine for investigating coal–biomass proportion blend using coal from Dara Adam Khel, Khyber Pakhtunkhwa in Pakistan, and pomegranate/olive waste to analyze their resulting calorific value, strength, and geochemical properties. A central composite design (CCD) and response surface methodology (RSM) were employed to design the experiments and analyze the data. Regression models were developed for each biomass type, demonstrating their adequacy and reliability for further analysis and prediction. Energy Dispersive X-ray Spectroscopy (EDX) analysis provided insights into the elemental composition of the coal briquettes. Mechanical analysis was performed to assess the strength of the briquettes, with varying compositions showing different levels of strength. Optimization using a composite desirability function yielded an optimal calorific value of 6728 kJ/kg. In summary, this study underscores the viability of bio-coal briquetting as a transformative solution to alleviate energy scarcity. Repurposing agricultural waste curtails resource depletion while mitigating waste disposal challenges. The optimized calorific value aligns with eco-friendly energy initiatives, advancing a greener path toward energy security.

1. Introduction

The current Russia and Ukraine war has contributed a lot to the energy crisis, resulting in 78 million–141 million people in a more severe poverty level. These impoverished individuals lack a basic standard of living as they cannot afford heating, cooling, lighting, and energy for electrical appliances [1]. Developing countries like Pakistan also require clean and affordable energy solutions, in which the application of biomass alongside coal can play a pivotal role in addressing the high costs associated with fossil fuels. Pakistan boasts the largest biomass availability, which can be utilized as an alternative source of energy, blending it with coal to create fuels that emit significantly less CO₂ compared to other fossil fuels [2]. All biomass feeds have very low bulk densities ranging from 0.05 to 0.18 g/cc (50 to 180 kg/m³), allowing them to be stored on the ground. However, this necessitates additional measures such as covers to protect them from rain and wind. Briquetting is a technique in which less space, approximately 3–4 sq. meter open space, is needed to store one tone of material. Thus, alternative energy can be stored in less space for 15 to 20 days, depending on its consumption. Only in Brazil, about 330 million tons (Mg) of biomass are generated every year. This cannot be applied as an energy source without a briquetting technique due to poor energy, low density, and heating value, thus resulting in high transportation, handling, and storage costs. Briquetting results in more energy per unit volume while lowering transportation and storage costs [3,4]. Pakistan, situated geographically among Asian nations, is expected to rely on coal for energy security and economic development for the next two decades or even longer. Cost-effective and eco-sustainable designs with thermally resistant and high-strength briquettes represent key areas of study in briquetting technology. Previous studies on bio-coal briquetting have yielded a more successful approach than any other energy project [5].

Organic biomass waste has lower sulphur and nitrogen contents, resulting in lower NO_x and SO_x emissions compared to coal. However, it has lower combustion characteristics, making direct combustion difficult and costly. Nevertheless, this issue has been addressed through the application of bio-coal briquetting techniques [6]. Bio-coal briquettes are a more environmentally friendly and strategic solution to alternative energy sources because they produce CO and H₂S emissions below the threshold values. The practical application of making bio-coal briquettes as a solution to the utilization of rejected coal-mining waste for alternative energy sources has been successful [7]. The environmental aspect and economic analysis of bio-coal briquettes need to be investigated. The use of bio coal briquettes causes coughing among those who are using these bio coal briquettes due to improper design of their kitchens, lack of education, conventional stove coal biofuel combustion, and improper ventilation system [8]. The design of the stoves was improved by providing an upper lid and a galvanized flue gas pipe for smooth ventilation, improving the efficiency of heat exchange. Further investigation is needed for both coal briquettes and improved stoves as they generate more flue gases because they are more efficient than conventional stoves [9].

The studies related to coal briquettes showed that moisture greater than 8% would combust with smoky flame and thus need to be dried. Further, briquettes having more weight than 100 gm show burning and handling problems [10]. Bio-coal briquettes having lower carbon content will generate lower heating value. The problem arises when an inorganic binder is used, resulting in high ash content, thus lowering its calorific value or fixed carbon [11]. Grinding of biomass to the required particle size needs more energy, which increases production cost and ignition time. Many techniques have been used in designing bio-coal briquettes. However, further research is still needed to study the effect of the hardness, elasticity, plasticity, and surface structure of lignite, and the surface physical and chemical properties of the bonding interface on briquette performance [12].

This study aims to examine the utilization of coal briquettes produced through an indigenous coal briquetting machine employing different biomass mixtures. The selected biomasses for this investigation are derived from olive and pomegranate sources. In the role of a binder, polyvinyl acetate (PVA) is employed due to its organic, environmentally friendly attributes, and cost-effectiveness. Experimental runs were planned based on a three-level central composite design (CCD) to guide the fabrication of the coal briquettes. The resulting calorific values for each experimental run were subsequently optimized through the composite desirability function (CDF). Furthermore, a microstructural analysis employing Energy Dispersive Spectroscopy (EDS) was conducted to gain insights into the physical characteristics of the coal briquettes. Lastly, a mechanical assessment was undertaken to gauge the strength properties of the manufactured briquettes.

2. Literature Review

Global energy demands, rising fuel prices, and inflation are exerting high pressure on energy imports from developing economies. There has recently been a surge in interest in coal briquetting technology [13] as a source of residential and industrial fuel [14] due to a surge in petroleum prices. Briquettes are a better fuel than raw coal due to their improved mechanical and thermal properties [15]. Charcoal briquette is a method by which charcoal is converted into a specific shape [16]. Adding certain binders and other additives not only increases the durability and calorific value of the briquettes but also significantly prevents the release of toxic gases into the environment [17]. Briquette can be used for space heating, as well as in residential, commercial, and industrial processes, and also contributes to a reduction in environmental pollution compared to untreated coal and other traditional fuels found in Pakistan, such as wood, dung, and charcoal [18]. Heavy pellets can be easily transported to the market and easily stored and handled at the site of use. Briquettes depend on many factors, such as the type of coal, the size of the coal used to make the briquettes, the type of binder, the curing temperature, etc. [19].

Bio-coal briquettes offer eco-friendly energy solutions for rural households and the catering industry, curbing deforestation, utilizing agricultural waste, and cutting carbon emissions. Their use mitigates issues tied to untreated coal, like flammability, temperature, quality variations, and global warming concerns [20,21]. The type and amount of binder greatly affect the strength and flammability of the briquettes [22]. The use of petroleum products and coal tar as binders makes it possible to obtain high-strength briquettes, but this is unfavorable since they emit toxic gases during combustion and have a number of side effects [23]. On the other hand, natural cellulose binders, such as molasses, sawdust, etc., produce fewer emissions, but their lifespan is relatively lower [24]. Researchers and producers have employed various binding agents such as starch, PVA, molasses fiber, sulphite liquor, bitumen, pitch, dolomite, and others to obtain mechanically strong and high-heating value briquettes from raw coal. There was no specific technique for coal briquettes until 1990, but Gilvari et al. [25] documented four crucial physical parameters for testing coal briquettes, including crushing, impact, abrasion, and water penetration resistance. The quality and strength of coal briquettes are majorly dependent upon the size of raw coal material particles, which was investigated by Flores et al. [26].

Olugbade et al. [27] analyzed the combustion characteristics of briquettes in terms of combustion kinetics. They suggested that the combustion efficiency of the briquettes and the efficiency of the combustion reaction largely depend on the type of binder, the amount of binder, and the addition of the briquettes. Waste is the most widely used binder in the briquetting process. These binders produce solid cakes, but they are hazardous to humans and pollute the environment. Environmentally friendly binders such as molasses, etc., produce granules of relatively low strength. Arafat and Khan [28] concluded that the use of humic acid as a binder can solve these problems concerning the flammable properties of briquettes to some extent. Chen et al. [29] studied the characteristics of various fuels used for combustion. A comparison of different fuels and briquettes was conducted in a typical home oven and identifies that coal briquettes can easily replace traditional wood burning in urban areas [30]. However, the acceptance of briquette combustion in the industrial sector was positively received without much doubt, indicating a slow expansion in this area [31]. Heavy pellets can be easily transported to the market and easily stored and handled at the site of use. Briquettes depend on many factors, such as the type of coal, the size of the coal used to make the briquettes, the type of binder, the curing temperature, etc. [32]. Geng et al. [33] studied the characteristics of various fuels used for combustion. However, the acceptance of briquette combustion in the industrial sector was positively received without much doubt, indicating a slow expansion in this area [34]. Mechanical strength is an important parameter when evaluating briquettes as it affects storage and transportation [35].

Resole was synthesized and used as a binder in coal briquetting and showed greater combustion performance [36]. The manufacturing of smokeless fuel briquettes was reported by Tippayawong et al. [37] utilizing paralyzed coal and biomass, transforming low-rank lignite coal into a high-quality fuel suitable for use in both household and industrial settings. In experimental work, Ayse et al. [38] treated rice straw with a different binding agent and used it as a binder for lignite briquettes. The study revealed that the sodium hydroxide and the solid part of lime have good binding properties in lignite briquettes [39]. Vamuka et al. [40] blended lignite with biomass resources such as pinecone, molasses, sawdust, paper mill waste, and cotton refuse. These combinations were used to make briquettes for use as fuel. Bernard et al. [41] reported on the use of organic binders for briquette production. However, organic binders may lead to hazardous flue emissions. The authors found that using lime as a binder can reduce these hazardous emissions, and the addition of magnesia can capture moisture and sulphur content, potentially resulting in high combustible and environmentally friendly coal briquettes. Guo et al. optimized waste coal briquetting using quadratic orthogonal rotation combination design and regression analysis. Optimal parameters found were 40% upgraded coal, 20% briquetting moisture, 25 MPa pressure, and 12 h drying time. SEM analysis indicated denser lignite briquette surfaces, with a 16.7% reduction in PM2.5 emissions compared to raw coal briquettes [42].

Van der Westhuizen et al. optimized the mechanical properties of coal-fines briquette using steam-exploded sugarcane bagasse (0–18%) as a binder at 24 MPa and 25 °C to 180 °C. Central composite design revealed optimal conditions: 185 °C to 195 °C and 13% bagasse. The resulting briquette had 82% water absorption, 97% abrasion resistance, and compressive strengths of 1205 KPa and 501 KPa (wet) [43]. Bazargan et al. optimized the production of briquettes from palm kernel shell gasification waste. In Scenario 1, they increased the speed, lowered the pressure, and reduced the time, resulting in a production rate over 20 times higher, reduced costs, and extended equipment life. In Scenario 2, they reduced the starch content while maintaining high calorific values, using a minimum pressure of 60 MPa [44]. Wang et al. used grey relational analysis (GRA) and analytical hierarchy process (AHP) to optimize cornstalks biomass briquette production, considering 20+ indices [45]. Florentino et al. studied binder impact on polyaromatic hydrocarbons and greenhouse gases in biomass coal briquette pyrolysis. Biomass inclusion decreased polyaromatic hydrocarbons but increased carbon dioxide; blending with paraffin significantly reduced carbon dioxide concentration [46].

The novelty of this research is to produce coal briquettes using an indigenously developed coal briquetting machine by adding olive and pomegranate biomasses to improve their properties, such as calorific value, and become a good energy source and to minimize ash content. According to the study of Ahmad et al., the Darra Adam Khel coal exhibited a moisture content of approximately 2.22%, an ash content of 18.6%, volatile matter of 6.49%, and a fixed carbon composition of 66.89%. The significant presence of ash content within the coal markedly impacts its operational efficiency in terms of calorific value, which makes it unsuitable for utilization [47]. To the author’s knowledge, no such study has been reported in the literature on the utilization of olive and pomegranate biomass in coal briquette. Further, the systematic approach based on an experimental design using CCD will add novelty to this research.

In a nutshell, the present study will benefit a developing country like Pakistan in several ways. Firstly, the production of improved coal briquettes with enhanced calorific value and reduced ash content can directly contribute to a more efficient and sustainable energy source. Secondly, by incorporating locally available biomass sources such as olive and pomegranate, the research promotes the utilization of indigenous resources. This can lead to economic gains by creating new avenues for agricultural waste management, providing additional income sources for local farmers, and fostering rural development. Finally, the reduction in ash content could positively impact environmental quality by lowering emissions and reducing the environmental footprint associated with traditional coal usage. This aligns with global efforts toward cleaner energy and environmental sustainability.

3. Materials and Methods

In this research, coal from the local region (Darra, Khyber Pakhtunkhwa, Pakistan), known for being one of the largest ores in the province, was collected. The choice of using olive and pomegranate biomass in raw form was deliberate and based on several key advantages. These biomass materials are readily available in our local region, making them a sustainable and cost-effective choice for our study. Furthermore, olive and pomegranate biomass possess unique properties that make them particularly well-suited for coal briquette production. They are known for their high calorific values, which enhance the energy output of the resulting briquettes. Additionally, these biomass types exhibit lower ash content, which positively impacts combustion efficiency and reduces the environmental footprint. Moreover, by incorporating these specific biomass types, we aimed to support local agricultural waste management, promote the utilization of indigenous resources, and contribute to rural development.

Figure 1 illustrates the process flow of coal briquette formation. The raw coal and biomass are first processed in separate roller crushers to reduce the size of the coal particles. The crushed raw coal and biomass are then sent for the screening process, which takes approximately 3 min to screen (200 mesh size). During this process, different-sized particles are separated, and their sizes are measured through sieve analysis using sieve shaker equipment, resulting in particle sizes of 1 mm, 0.5 mm, 0.25 mm, 0.125 mm, and 0.063 mm. In the 3-paddle mixer, the raw coal and biomass of equivalent size are mixed in the presence of PVA binder for 5 min, resulting in a coal paste in the form of a slurry, which is a mixture of coal, biomass, and binder. The mass of the binder (10 gm) is kept constant in the mixture. Once the mixture is prepared, it is sent to the coal briquette machine, where the mixture is pressed with enough force generated by a one-horsepower motor in a hydraulic coal briquette pilot plant to obtain perfect coal briquettes. The briquette machine produces four briquettes in a one-minute cycle. The briquettes obtained are in a wet form and are at risk of breaking, so these wet briquettes are placed in a solar dryer for one hour to dry until they become hard. They are then stored at room temperature.

The machine shown in Figure 2 was fabricated from the scrape to provide an indigenous solution to the local industry. The same machine is used to produce coal briquettes.

3.1. Experimental Design

An experimental design based on response surface methodology (RSM) is employed in the present study. Central composite design (CCD) using RSM is used to investigate and optimize the relationship between input factors and an interest response variable. It is especially useful for investigating nonlinear relationships and determining optimal process conditions. CCD requires selecting a group of experimental runs that cover a wide range of input variable settings [48]. The main goal of a CCD is to fit a mathematical model to the observed response data and use the model to optimize the response variable. This optimization is often performed by finding the combination of input variable settings that maximizes or minimizes the response, depending on the objectives of the study. Typically, the design is analyzed using analysis of variance (ANOVA) and regression analysis, in which a response surface model is fitted to the observed data. This model predicts the response variable as a function of the input factors and provides insight into their relationship. The model can then be used to determine the best input variable settings to maximize or minimize the response or achieve other goals [49].

However, it is important to interpret these results cautiously and consider other factors and assumptions of the ANOVA model, i.e., normal distribution of residuals, constant variance (the variability of residuals should be similar among all experimental runs), and independence of residuals (no correlation between model terms). As the biomass type is the categorical variable, separate models are developed for olive and pomegranate. The adequacy of these models is assessed based on the coefficient of determination, i.e., R², adjusted R^2, and predicted R². Its value ranges from 0 to 1, where a value of 1 indicates that the model explains all variation, and a value of 0 indicates that there is no explanation for any variation provided by the model [50].

Levels for Input Variables

In the present study, the input variables considered were coal quantity, biomass quantity, and biomass type, while the response variable was calorific value. To generate the experimental design based on CCD, first, the levels were identified based on literature and preliminary experimental runs.

Table 1. Input variables and their levels.

Input Variables	Units	Symbol	Level 1	Level 2	Level 3
Coal Quantity	gm	CQ	110	145	180
Biomass Quantity	gm	BQ	10	45	80
Biomass Type	-	BT	Olive (O)	Pomegranate (P)

shows the levels set in the present study. The coal quantity and biomass mass quantity are continuous variables, while the biomass type is a categorical variable.

3.2. Calorific Value Measurement

Samples of coal briquettes are prepared according to an experimental plane using CCD based on response surface methodology (RSM) as shown in

Table 2. Calorific values (CV) of coal briquettes.

Experimental Runs	Coal Quantity (gm)	Biomass Quantity (gm)	Biomass Type	Calorific Value (kJ/kg)
1	180	45	P	6694
2	145	45	P	6528
3	110	10	O	6592
4	145	45	O	6210
5	145	45	P	6513
6	145	45	P	6513
7	145	10	O	5982
8	180	10	O	5398
9	180	10	P	6509
10	145	45	O	5982
11	145	45	O	6210
12	180	80	O	5398
13	110	80	O	6590
14	110	45	P	6694
15	110	10	P	6369
16	145	80	P	6548
17	145	10	P	6229
18	145	45	P	6530
19	110	45	O	6710
20	145	80	O	5821
21	110	80	P	6518
22	145	45	O	6240
23	145	45	P	6549
24	145	45	O	6120
25	180	80	P	6619
26	180	45	O	5729

.

4. Results and Discussion

4.1. Analysis of Variance Based on Regression (ANOVA)

The ANOVA results based on central composite design (CCD) were performed at a 95% confidence interval. The p-value less than 0.05 depicts that the modeled terms have a significant effect on calorific value. The results are tabulated in

Table 3. Analysis of variance.

Source	DF	Adj SS	Adj MS	F-Value	p-Value
Model	7	3,497,403	499,629	82.08	0
Linear	3	2,136,389	712,130	116.99	0
Coal Quantity	1	814,323	814,323	133.78	0
Biomass Quantity	1	14,352	14,352	2.36	0.142
Biomass Type	1	1,307,714	1,307,714	214.84	0
Square	2	230,452	115,226	18.93	0
Coal Quantity × Coal Quantity	1	67,953	67,953	11.16	0.004
Biomass Quantity × Biomass Quantity	1	222,804	222,804	36.6	0
2-Way Interaction	2	1,130,562	565,281	92.87	0
Coal Quantity × Biomass Type	1	1,084,805	1,084,805	178.22	0
Biomass Quantity × Biomass Type	1	45,757	45,757	7.52	0.013
Error	18	109,565	6087
Lack-of-Fit	10	64,284	6428	1.14	0.437
Pure Error	8	45,280	5660
Total	25	3,606,968

. It shows that coal quantity, biomass quantity, and biomass type all have a significant effect on calorific value. Coal quantity and biomass quantity exhibit both main and quadratic impacts, while the interactions of biomass type with coal quantity and subsequently with biomass quantity also significantly influence the calorific value.

Further, the lack-of-fit is also found significant, so it implies that the model adequately represents the relationship between the model terms and the calorific value. As shown in Figure 3a, it shows that the p-value is expressly greater than 0.05, so it shows that residuals follow normal distribution. Further, the versus fits plot in Figure 3b shows the random distribution of residuals above and below the reference point (i.e., 0) and follows no specific pattern. So, it shows that the constant variance assumption is satisfied. Finally, the versus order in Figure 3c depicts that residuals follow a random time-series pattern, signifying that there is no correlation among them and therefore satisfies the assumption of independence. These results show that the data are reliable and can be used for further analysis and making of prediction models.

Regression models for each biomass type are shown in Equations (1) and (2). R² value obtained is 0.97, indicating that the model is able to explain variation for collected data. However, it is not sufficient to validate the performance of the model because by adding more terms to a model, the R² increases regardless of how much they contribute to explaining a response variable’s variation. Thus, when comparing models with different numbers of terms, R² might not be a reliable measure of model fit. Therefore, in this regard, the adjusted R² is used to address this problem. An adjustment is made to the R² statistic so that it penalizes the addition of unnecessary terms. It considers both the goodness-of-fit and the number of predictors in the model. It ranges from negative infinity to 1. The higher the adjusted R² value, the better the fit, taking into account both the model’s explanatory power and the number of input variables. As a result of this adjustment, irrelevant terms are penalized, resulting in a lower adjusted R-squared value. The R² computed in the present study is 0.96 and is sufficiently higher. Accordingly, it shows that important model terms are retained and are not penalized.

To analyze the prediction accuracy of the model, the Predicted R² is also computed. Predicted R² indicates an explanation for all variation provided by the model for new experimental runs that are within the defined experimental levels but are not included in the experimental design. It varies between negative infinity and 1. The higher the predicted R² value, the better the prediction accuracy of the model. Generally, for good prediction accuracy, the difference between adjusted R² and predicted R² needs to be less than 0.2. The predicted R² obtained is 0.94. Further, the difference between adjusted R² and predicted R² is 0.02. Accordingly, it shows that developed regression models are adequate and can be used for prediction and optimization.

$$\begin{array}{cc}\hfill \mathrm{O}\mathrm{l}\mathrm{i}\mathrm{v}\mathrm{e}=10048& - 42.29 \mathrm{C}\mathrm{o}\mathrm{a}\mathrm{l} \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}+13.98 \mathrm{B}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}\hfill \\ & + 0.0905 \mathrm{C}\mathrm{o}\mathrm{a}\mathrm{l} \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}\times \mathrm{C}\mathrm{o}\mathrm{a}\mathrm{l} \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}\hfill \\ & - 0.1639 \mathrm{B}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}\times \mathrm{B}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}\hfill \end{array}$$

(1)

$$\begin{array}{cc}\hfill \mathrm{P}\mathrm{o}\mathrm{m}\mathrm{e}\mathrm{g}\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{a}\mathrm{t}\mathrm{e}=& 7847-25.11 \mathrm{C}\mathrm{o}\mathrm{a}\mathrm{l} \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}+17.51 \mathrm{B}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}\hfill \\ & + 0.0905 \mathrm{C}\mathrm{o}\mathrm{a}\mathrm{l} \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}\times \mathrm{C}\mathrm{o}\mathrm{a}\mathrm{l} \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}\hfill \\ & - 0.1639 \mathrm{B}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}\times \mathrm{B}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}\hfill \end{array}$$

(2)

4.2. Main Effect and Interaction Plots

The main effect plot suggests that as the quantity of coal increases from 120 gm to 180 gm, the mean CV decreases. This indicates that higher quantities of coal result in lower calorific values, which means that more energy is required to produce the same amount of heat or power. However, the interaction plot reveals an interesting finding related to the biomass type. It shows that the interaction between biomass type and quantity is significant, meaning that the effect of the biomass type on the CV of coal varies depending on the biomass quantity. The mean value of calorific for pomegranate is higher than olive. This is attributed to a higher density of olive that reduces the porosity and increases the moisture content compared to pomegranate biomass. Accordingly, this negatively affects the combustion process when mixed with coal. Porosity has a positive effect on combustion but affects the strength of the coal briquette. Further, pomegranate biomass contains higher levels of volatile organic compounds or lignin, which can contribute to higher calorific values when combined with coal [51,52]. It has lower ash content compared to olive biomass [53,54], and it may result in less interference with the combustion of coal, leading to higher calorific values.

The main effect plot shown in Figure 4 indicates that as the quantity of biomass increases from 10 gm to 54 gm, the mean calorific value increases. This implies that higher quantities of biomass contribute to higher calorific values, indicating that more energy can be obtained from the combustion of the biomass. However, with a further increase in biomass quantity up to 80 gm, the mean calorific value starts to decrease. This pattern suggests that there might be an optimal range or threshold for the biomass quantity, beyond which the excess biomass starts to impact the combustion process negatively, leading to a decrease in the overall calorific value. Additionally, the interaction plot shown in Figure 5 reveals that the interaction between biomass type and quantity is significant. It shows that regardless of the quantity of biomass, the mean calorific value for pomegranate biomass is consistently higher compared to olive biomass. Pomegranate biomass might exhibit more favorable combustion characteristics compared to olive biomass.

4.3. Optimization

A composite desirability function is used to optimize calorific value using Minitab^®19. The desirability values range from 0 (undesirable) to 1 (highly desirable). The objective of optimization is to maximize calorific value. As shown in Figure 6, the response optimizer plot was obtained using Minitab software (Minitab^® 20.4). The optimal setting obtained is coal quantity at 180 gm, biomass quantity at 54 gm, and biomass type pomegranate. The optimized calorific value obtained is 6728 kJ/kg, with the highest desirability function value of 1.

4.4. Energy Dispersive X-ray Spectroscopy (EDS or EDX)

EDX analysis was employed to determine the elemental composition of coal briquettes by manipulating the quantities of coal and pomegranate biomass. The experiment involved three different sample compositions shown in Figure 7 with varying proportions of coal and pomegranate biomass. The results revealed important insights into the mass percentages of various elements in the briquettes. The sample consisting of 110 gm of coal and 80 gm (at experimental run 21, Figure 7a) of pomegranate biomass exhibited the highest mass percentage of carbon at 67.90%. This was followed by oxygen at 26.18%. The remaining ash content contained the following elements: sulphur at 1.81%, iron at 1.53%, lead at 0.95%, silicon at 0.84%, and aluminum at 0.75%. Similarly, the sample comprising 145 gm of coal and 45 gm of pomegranate biomass (at experimental run 18, Figure 7b) displayed a mass percentage of carbon of 66.87%, with oxygen accounting for 26.90%. The remaining ash content contained sulphur at 1.53%, iron at 1.32%, lead at 0.94%, silicon at 1.12%, and aluminum at 1.03%. In contrast, the sample composed of 180 gm of coal and 10 gm of pomegranate biomass (at experimental run 9, Figure 7c) demonstrated the lowest mass percentage of carbon at 66.15%, with oxygen constituting 28.72%. The remaining ash content contained sulphur at 1.22%, iron at 1.10%, lead at 0.59%, silicon at 0.93%, and aluminum at 0.75%. These results provide valuable technical information regarding the elemental composition of coal briquettes. The varying proportions of coal and pomegranate biomass directly influence the carbon and oxygen content in the briquettes. Additionally, the ash content revealed the presence of sulphur, iron, lead, silicon, and aluminum, which may have implications for the quality and potential uses of the briquettes.

Further, the EDX results showed that as the percentage of coal increases and the biomass content decreases, the sulphur content decreases. This suggests that higher coal percentages contribute to reducing sulphur emissions during combustion. Conversely, when the biomass percentage is reduced in coal briquettes, there is an increase in the inherent oxygen content. This finding implies that a decrease in biomass content leads to higher levels of oxygen within the briquettes. This elevated oxygen content may have implications for the combustion process, such as promoting improved flame stability or aiding in the oxidation of other volatile components. Furthermore, the results demonstrate that increasing the biomass content in coal briquettes results in a decrease in the carbon content. This outcome suggests that higher biomass percentages directly impact the carbon composition, potentially due to the lower carbon content inherent in biomass compared to coal. Adjusting the biomass quantity can be an effective means of controlling the carbon content in coal briquettes, which may have significance in various applications, including energy generation and carbon emissions mitigation. These findings underscore the technical implications of manipulating the proportions of coal and biomass in coal briquettes. By optimizing the coal-to-biomass ratio, it is possible to regulate the sulphur content for improved combustion efficiency while also managing the inherent oxygen and carbon content to meet specific requirements in various industrial processes.

4.5. Mechanical Analysis

To evaluate the strength of coal briquettes, a universal testing machine (UH-500KN, Shimadzu, Kyoto, Japan), was employed. Determining the strength is crucial as it directly influences the briquettes’ ability to withstand deformation during transportation and overcome handling challenges. In this investigation, coal briquettes incorporating pomegranate biomass were selected due to their high calorific value. Compression strength was performed to examine the mechanical performance of the coal briquette.

Table 4. Strength analysis of coal briquette with pomegranate biomass.

Sample Number Based on Experimental Run	Coal Quantity (gm)	Biomass Quantity (gm)	Biomass Composition (%)	Compressive Strength (N/mm2)	Standard Deviation
1	180	45	20.00	0.71	0.01
9	180	10	5.26	0.25	0.06
14	110	45	29.03	0.75	0.03
18	145	45	23.68	0.21	0.01
21	110	80	42.11	0.67	0.05
25	180	80	30.77	0.40	0.06

provides an overview of the results, highlighting the strength values of different samples from Table 2. Among the samples tested, sample 14, comprising 110 gm of coal and 45 gm of pomegranate biomass, exhibited the highest ultimate strength, measuring 0.75 (N/mm²). In contrast, sample 18, consisting of 145 gm of coal and 45 gm of pomegranate biomass, displayed the lowest strength at 0.21 (N/mm²).

These findings offer valuable technical insights into the strength characteristics of coal briquettes. The results indicate that the composition, specifically the quantity of coal, significantly affects the compression strength of the briquettes. Understanding these mechanical properties is crucial for optimizing the formulation and manufacturing processes of coal briquettes, ensuring their suitability for transportation and handling applications.

5. Conclusions

• The present study investigated the impact of coal quantity, biomass quantity, and biomass type on coal briquette calorific value using central composite design (CCD) and response surface methodology (RSM).
• It was found that all factors significantly influenced calorific value, with coal and biomass quantity having contrasting effects.
• The study revealed that the pomegranate biomass consistently yielded higher calorific values than olive biomass.
• Nonlinear regression models were developed for each biomass type and have high goodness-of-fit and prediction accuracy.
• The optimized coal briquette formulation (i.e., coal quality of 180 gm, biomass quantity 54 gm, and biomass type pomegranate) using a composite desirability function achieved an optimal calorific value of 6728 kJ/kg.
• Energy Dispersive X-ray Spectroscopy (EDX) was utilized to analyze the elemental composition of coal briquettes, highlighting variations in carbon, oxygen, sulphur, iron, lead, silicon, and aluminum content.
• Mechanical analysis was performed to assess the briquette strength, revealing significant impacts of coal and biomass composition on strength levels.