The size of kesambi leaf powder tested in this study ranged from 12 mesh to 70 mesh, with a tapioca binder ratio of 3–17% to the mass of kesambi leaves in each treatment combination tested. The Response Surface Methodology (RSM) approach was used in the design of this study, which was then evaluated using Central Composite Design (CCD) and Design Expert 13 (DX13) software. This design is an organised, systematic approach to optimising and designing experiments. Interactions according to RSM are presented in Table 1.

Subsequently, tapioca adhesive was mixed with water in a weight/volume ratio 1:8 and boiled until thickened. The prepared binder material was then combined with finely powdered kesambi leaves, and the resulting mixture was moulded into briquettes with a diameter of 4 cm and a height of 8 cm using a moulding tool, as depicted in Figure 1. The produced briquettes were air-dried for 10 days under sunlight. The biobriquettes produced and directed to combustion are presented in Figure 2.

Figure 1.

Briquette moulding equipment

Figure 2.

Briquettes

Various attributes of the biobriquettes were ascertained, including moisture content (MC), evaluated using ASTM D3173 [18] by comparing it with the dry weight of the briquettes. Ash content was determined by subjecting the sample to a 600 °C heat for 2 hours ASTM D1102-84 [19]. Volatile matter (VM) was measured per the standard ASTM D3175 [20] by heating the briquettes to 900 °C for 7 minutes. The fixed carbon (FC) represents the carbon fraction of the briquettes, excluding MC, ash content, and VM, according to ASTM D3172 [21]. Briquette density [g/cm³] was derived by comparing mass and volume [22]. The calorific value [MJ/kg] was assessed using ASTM D5865 [23]. Total phenol [mgGAE/g] was determined via the Folin-Ciocalteu method. The model's suitability was assessed using the lack of fit test and coefficient of determination.

The outcomes of the Anova test shown in Table 4 indicate the significance of the quadratic model in the VM parameter, with particle size and binder ratio treatments yielding F-values surpassing the corresponding P-values.

The model of moisture content (MC, [%]) can be expressed through the following equation:

$MC=4.51+0.16\times A+1.02\times B+0.94\times AB–0.17\times A^2+1.22\times B^2$ (1)

A particle size that is too fine can complicate the mixing and compaction process, resulting in a higher water content in the briquettes. The amount of binder used to produce kesambi leaf briquettes is an important factor in achieving the ideal MC level in the final product. An insufficient binder can cause the briquettes to crumble easily, while an excessive amount will make the briquettes difficult to burn.

Figure 3 and Figure 4 depict the MC response parameter through both the surface and contour plots, showcasing a clear trend. These visualisations indicate that utilising a finer particle size (smaller mesh) and minimising binder usage tend to decrease MC levels. The amount of binder utilised is critical to the MC response calculation. Low binder content can lead to high MC in briquettes, hindering effective burning and producing excessive smoke.

Torrefaction emerges as a viable solution to enhance the attributes of standard briquettes and mitigate moisture absorption during storage, as noted by [24]. Research outcomes indicate that a blend of rice husk and peanut shell briquettes exhibits MC ranging from 3.96% to 5.65% [25].

Figure 3.

The moisture content parameters' contour response

Figure 4.

The moisture content parameters' surface plot

The production of biobriquettes utilising coconut shell charcoal, a biomass composite material, yields an MC of 5.8% [26]. Charcoal briquettes demonstrate greater hydrophobic properties than biomass briquettes (control), implying reduced MC compared to the control sample. Furthermore, higher binder content leads to increased VM and MC, greater charcoal briquette content, and reduced FC [27].

The outcomes of the Anova test are significant, specifically when employing a linear model (without interaction) for the ash content response variable. The treatments involving particle size and binder ratio exhibit an F-value that surpasses the corresponding P-value (Table 5).

The model of ash content [%] can be expressed through the following equation:

$\text{Ash content }=2.52–0.39\times \text{ A }–0.44\times \text{ B}$ (2)

Figure 5 and Figure 6 visually represent the corresponding contour and surface plots.

Figure 5.

The ash parameters' contour response

Figure 6.

The ash content parameters' surface plot

These graphics show a trend where lower ash content is correlated with finer particle sizes (corresponding to smaller mesh values) and an increased binder ratio. The binder ratio significantly impacts the ash content response, demonstrating its importance. Surprisingly, particle size and binder quantity have no visible effect on the amount of ash in kesambi leaf briquettes. It has been found that using kesambi leaf with a finer particle size (60 mesh) tends to result in less ash production. This feature not only facilitates the attainment of lower moisture contents in the briquettes but also provides an additional benefit of a significant increase in surface area. Moreover, the amount of binder used in the process of creating torrefied kesambi leaf briquettes has a significant effect on the final ash concentration. When there is not enough binder, the briquettes are likelier to break and produce increased ash.

The outcomes of the Anova test in Table 6 show the significance of the quadratic model in the VM parameter, indicating that particle size and binder ratio treatments yield F-values surpassing the corresponding P-values.

The model of volatile matter (VM, [%]) can be expressed through the following equation:

$VM=14.50+0.39\times \text{ A }+0.78\times \text{ B }+0.72\times \text{ AB }+0.45\times {\text{ A}}^2+0.1644\times {\text{ B}}^2$ (3)

When kesambi leaves are torrefied, an increase in particle size causes a decrease in VM. This phenomenon arises from the larger particle size accelerating the breakdown of organic substances in the biomass, producing volatile gases. Subsequently, these gases dissipate, leaving behind a more stable biomass. Intricate changes in VM within kesambi leaf briquettes are also a result of the complex interaction between particle size and binder ratio. Low binder ratios can cause VM to rise, making combustion difficult and producing significant smoke.

Surface plots and contours show the variation in VM in striking detail in Figures 7 and 8. These graphics highlight the tendency for lower binder ratios, smaller mesh values, and finer particle sizes to result in lower VM.

In the production process of torrefied kesambi leaf briquettes, the interaction effect between particle size and the amount of binder significantly influences the volatile matter produced. The VM parameter represents the proportion of combustible elements, making it crucial for determining the heating value of the fuel. The results revealed that particle size impacted VM in burnt kesambi leaf briquettes, with finer particles indicating increased VM in the produced briquettes. Due to their larger specific surface area, finer particles promote enhanced binder absorption and moisture absorption.

Figure 7.

The volatile matter parameters' contour response

Figure 8.

The volatile matter parameters' surface plot

Increased MC and binder ratio in briquettes speed up the breakdown of organic matter, resulting in increased VM. Binder quantity further plays a role in influencing VM within briquettes and kesambi leaves. A lower binder content translates to diminished VM due to decelerated organic matter decomposition. The VM of briquettes produced from tannery solid waste is between 1.61% and 4.56% [28].

The outcomes of the Anova test in Table 7 show the significance of the quadratic model in the FC parameter, with particle size and binder ratio treatments yielding F-values surpassing the corresponding P-values.

The model of fixed carbon (FC, [%])can be expressed through the following equation:

$FC=78.62–0.15\times \text{ A }–1.36\times \text{ B }–1.56\times \text{ AB }–0.27\times {\text{ A}}^2–1.65\times {\text{ B}}^2$ (4)

Figures 9 and Figure 10 visually represent the contour and surface plots associated with the fixed carbon response parameter. Using finer particles in torrefied kesambi leaf briquettes significantly increases the FC content. This pattern emerges because smaller particles facilitate the rapid breakdown of organic matter in biomass, resulting in a more stable fraction of carbon. Additionally, higher binder ratios contribute to higher FC content in kesambi leaf briquettes. The increased binder ratio prolongs the time biomass interacts with larger particle sizes, allowing more organic matter to break down and raising the FC content.

Figure 9.

The fixed carbon parameters' contour response

The results indicate that particle size significantly influences the fixed carbon content in kesambi leaf briquettes. Finer particles contribute to an increase in the fixed carbon content of the resulting briquettes, primarily due to their larger surface area, promoting enhanced binding and water absorption. Moreover, the amount of binder also has a notable impact on the fixed carbon content of kesambi leaf briquettes.

Figure 10.

The fixed carbon parameters' surface plot

Increasing the torrefaction particle size inherently increases the fixed carbon content in kesambi leaves. This effect is attributed to the larger particle size, which accelerates the decomposition of organic matter in the biomass, resulting in a more stable FC content. A higher binder ratio also increases FC content in processed kesambi leaves. This dynamic is driven by the higher binder ratio, which extends the duration of interaction between biomass and larger particle sizes, promoting increased decomposition of organic matter and an elevated FC content. The proximate analysis illuminates that sawdust briquettes witness a 38.15% surge in FC content and a substantial 70.69% reduction in MC, fostering an enhanced combustion quality [29].

The binder ratio further influences the FC content response, and the interaction between particle size and binder ratio causes fluctuations in FC content in kesambi leaf briquettes. A lower binder ratio increases the FC content, leading to ignition challenges and increased smoke production.

The outcomes of the Anova test in Table 8 show the significance of the quadratic model in the density parameter, with both particle size and binder ratio treatments yielding F-values that surpass the corresponding P-values.

The model of density [g/cm³] can be expressed through the following equation:

$\text{Density }=0.49+0.038\times \text{ A }+0.027\times \text{ B }+0.014\times \text{ AB }+0.030\times {\text{ A}}^2–0.011\times {\text{ B}}^2$ (5)

Figure 11 and Figure 12 show the contour and surface plots depicting the briquette density response parameter.

Figure 11.

The density parameters' contour response

Figure 12.

The density parameters' surface plot

The interaction between particle size and binder volume influences the density of kesambi leaf briquettes. A low amount of binder can cause briquettes to burn more easily and produce a lot of smoke. Optimal briquette density is important in increasing the calorific value of briquettes, facilitating transportation, and increasing storage effectiveness. In the production scheme of torrefied kesambi leaf briquettes, consideration of the interaction between particle size and binder ratio is the most important thing.

A higher briquette density is achieved through precise control of particle size. The expansion of the surface area of tiny particles enhances water absorption. Briquette density increases with higher moisture content and binder ratios.

Binder quantity further punctuates the ebbs and flows of kesambi leaf briquette density. Limited binder quantities correspond to lower briquette density, as the protracted decomposition of organic matter translates into less compact briquettes. The briquettes' bulk density and calorific value are 0.7−0.8 g/cm³ and 6400−6700 kcal/kg respectively [30].

The outcomes of the Anova test in Table 9 show the significance of the linear model in the calorific value parameter, with both particle size and binder ratio treatments yielding F-values that surpass the corresponding P-values.

The model of calorific value [MJ/kg] can be expressed through the following equation:

$\text{Calorific value }=15.28+0.46\times \text{ A }-0.0268\times \text{ B}$ (6)

The response of calorific value parameters is elucidated through surface plots and contour plots in Figures 13 and 14, with particle size identified as a crucial factor in the heating value response. The calorific value of kesambi leaf briquettes results from the interplay between particle size and binder content. Notably, the combined effect of particle size and binder ratio does not significantly influence the ash content in kesambi leaf briquettes. All research findings indicate that the calorific value of kesambi leaf briquettes is influenced by particle size. A higher calorific value is associated with a finer particle size. This phenomenon can be explained by finer particles offering a larger specific surface area, promoting more efficient chemical reactions during briquetting. Additionally, finer particles can absorb moisture and binders better, enhancing the quality of the produced briquettes.

The calorific value of kesambi leaf briquettes is influenced by the amount of binder used. Opting for a lower binder concentration yields a higher calorific value due to the slower decomposition of organic material in kesambi leaf particles, resulting in denser briquettes.

Empirical evidence from diverse studies underscores the realm of calorific value. Coconut husk waste biobriquettes tout a calorific value of 21065.47 kJ/kg with 60-mesh particle size and 120 bar briquetting pressure, showcasing the dynamic interplay of parameters [31]. Distinctly, rubber seed biomass biobriquettes clinch the zenith with a calorific value of 6836 cal/g from samples with 80-mesh particle size and 250 °C temperature, while the nadir is at 5371 cal/g for 200-mesh particle size and 750 °C [32]. In the gasification of wood sawdust, the highest cold gas efficiency and maximum calorific value are 66.36% and 2.84 MJ/Nm³, respectively [33].

Figure 13.

The calorific value parameters' contour response

Figure 14.

The calorific value parameters' surface plot

The metamorphosis of candlenut fruit skin waste into briquettes translates into an upgraded biofuel quality, escalating calorific value from 17.00 MJ/kg for raw material to about 18.00 MJ/kg for briquettes [34]. The zenith of quality in mixed rice husk and coffee husk briquettes materialises with a 60-mesh particle size, culminating in a peak calorific value of 17.422 MJ/kg [35]. Mangrove charcoal is very helpful for increasing calorific value since it produces 5919 cal/g of mangrove wood charcoal briquettes [36]. The binder with 15% starch had the highest calorific value, 4678.7 cal/g [37]. For domestic use and small-scale domestic industrial applications, sawdust briquettes with a calorific value of 26918.02 kcal/kg are suitable [38].

The outcomes of the Anova test in Table 10 demonstrate the significance of the quadratic model in the total phenol parameter, as both particle size and binder ratio treatments yield F-values surpassing the corresponding P-values.

The model of total phenol [mgGAE/g] can be expressed through the following equation:

$\text{Total phenol }=0.93–0.28\times \text{ A }–0.17\times \text{ B }–0.0157\times \text{ AB }+0.14\times {\text{ A}}^2–0.016\times {\text{ B}}^2$ (7)

Figures 15 and Figure 16 display the contour plot and response surface of total phenol parameters. Coarser particle sizes, characterised by a larger mesh combined with a small binder ratio, increase the total phenol content. The role of particle size proves to be highly influential in the overall response of phenol. As the empirical data show, particle size effectively regulates the overall phenol concentration in kesambi leaf briquettes.

Figure 15.

The total phenol parameters' contour response

A notable pattern emerges: higher overall phenol content correlates with coarser particle size. This correlation can be explained by the larger pores created by coarse particles, allowing for increased airflow inside the briquettes. Consequently, burning produces more smoke containing phenolic chemicals.

Figure 16.

The total phenol parameters' surface plot

Adjusting the binder amount is an alternative to influence the overall phenol content. Opting for a lower binder quantity may reduce overall phenol concentration. It is attributed to the formation of denser briquettes as the organic content in kesambi leaf particles breaks down more slowly due to the reduced binder.

Table 11 summarises a rubric for assessing particle size and binder ratio optimisation. This rubric is based on the main objective of achieving peak fixed carbon content (FC) and calorific value, which are the most important parameters for maximising briquette calorific value.

Figure 17 shows that the mixture of particle size and binder ratio selected for validation is at a particle size of 60 mesh with a binder ratio of 5%. This pair corresponds to a peak desirability value of 0.93.

Figure 17.

Desirability of the particle size and binder ratio

The resultant response parameters encompass MC at 3.76% ± 0.74; ash content at 2.56% ± 0.32; VM at 14.01% ± 0.37; FC at 79.48% ± 0.58; briquette density at 0.51 g/cm³ ± 0.03; calorific value at 15.76 MJ/kg ± 0.22; and total phenol content at 0.99 mgGAE/g ± 0.11 all aligning with the selected particle size and binder ratio.

Particle size and binder ratio underwent empirical testing, and the laboratory results were compared to discern their accuracy. Table 12 shows data mean values were obtained, and the investigation's results indicate that the calorific value of 15.91 MJ/kg obtained from torrefied kesambi leaves in this study is still less than the 20.80 MJ/kg calorific value obtained from kesambi stems [3]. These response parameter values comfortably fall within the 95% CI and 95% PI ranges.

By confirming that the model's projected range closely matches the real world, confirmatory testing validates the model's accuracy in predicting response parameters like MC, ash content, VM, FC, briquette density, calorific value, and total phenol. As encapsulated in Table 12, the coefficients reveal the mathematical underpinnings of the particle size and binder ratio model response.

It is important to encapsulate that an elevation in particle size, in conjunction with an optimal binder quantum, exerts a dichotomous influence on MC, ash content, VM, FC, briquette density, calorific value, and total phenol.