Sustainable Energy from Waste: Experimental Investigation and Optimization of Hybrid Biomass Briquette Production Using a Modified Briquetting Machine

This study investigates the quality performance of hybrid biomass briquettes produced from water hyacinth, sawdust, and rice husk at varying blend ratios using a modified briquetting machine. Fourteen experimental runs were conducted to evaluate the effects of compositional variations on density, shatter index, combustion efficiency, average briquette quality, and desirability. The mixture proportions of water hyacinth (A), sawdust (B), and rice husk (C) ranged between 0.3–0.5kg, 0.2–0.4kg, and 0.1–0.3kg, respectively, ensuring a total blend of 1kg, an equivalence of 100%. The briquette density varied between 0.82 and 0.93 g/cm³, with the highest value (0.93 g/cm³) obtained at the optimal blend ratio 0.5:0.4:0.1 (Run 11), which also demonstrated the best overall performance. The shatter index ranged from 83.1% to 90.2%, while combustion efficiency varied from 75.3% to 85.4%, indicating that higher proportions of water hyacinth and sawdust enhanced both mechanical strength and combustion behavior. The average briquette quality ranged from 36.87% to 43.00%, with the maximum (43.00%) recorded at Run 11, corresponding to the optimal blend ratio of 0.5:0.4:0.1. Similarly, desirability, a composite indicator of performance, ranged from 0.000 to 1.000, with the highest value (1.000) achieved at the same optimal condition. Overall, the results indicate that the optimal briquette formulation for high-quality performance was achieved at 0.5-part water hyacinth, 0.4-part sawdust, and 0.1-part rice husk, yielding superior density (0.93 g/cm³), high shatter resistance (90.1%), efficient combustion (85.4%), and maximum desirability (1.000). This demonstrates the potential of hybrid biomass combinations to produce efficient, sustainable, and durable briquettes suitable for both domestic and industrial energy applications. Statistical analysis further confirmed the model’s reliability and predictive accuracy. The model yielded a mean briquette quality of 43% with a standard deviation of 0.3086, indicating low variability and high consistency in experimental responses. The coefficient of determination (R² = 0.9562) and adjusted R² = 0.9289 show that over 95% of the variability in briquette quality was explained by the model. Additionally, the predicted R² (0.7560) closely aligns with the adjusted R², confirming good model predictability. The adequate precision value (15.3350) far exceeded the threshold of 4.0, indicating a strong signal-to-noise ratio, while the coefficient of variation (C.V.) of 0.7521% confirms high precision and reproducibility of results. The Analysis of Variance (ANOVA) further validated the statistical robustness of the model, showing it was highly significant (F = 34.97, p < 0.0001). The model sum of squares (16.65) accounted for most of the total variation (Cor. total = 17.41), confirming a strong model fit. Among the model terms, the linear mixture component (p = 0.0013) and binary interactions AB (p = 0.0003), AC (p < 0.0001), and BC (p = 0.0050) were all significant, with the AC interaction exerting the greatest influence (F = 129.16). The residual error (0.7618) was minimal, and the lack of fit (F = 0.1236, p = 0.7355) was not significant, confirming that the model adequately represents the experimental data. Collectively, these findings demonstrate that the developed model is statistically sound, reliable, and suitable for predicting and optimizing hybrid biomass briquette quality, providing a strong basis for sustainable bioenergy production.