3D printing has brought significant changes to manufacturing sectors, making it possible to produce intricate, multi-layered designs with greater ease. This study focuses on optimizing the compressive strength (CS) of functionally graded multi-material (PLA/Almond Shell Reinforced PLA) which is fabricated with the aid of the FFF process, a widely used additive manufacturing technique. Six different machine learning models (ML) were utilized to estimate CS using key process parameters, namely print speed (PS), layer height (LH), and printing temperature (PT). Among six ML models, Polynomial Regression (PR) performed best, with an R2 of 0.88 and the lowest error metrics (MAE = 1.38, RMSE = 1.9, MSE = 3.6). SHAP analysis indicated that PS is the most influential parameter, followed by LH. PR predicted optimal parameters (PS = 19 mm/s, LH = 0.1 mm, PT = 216°C) and yielded a predicted CS of 36 MPa, which was experimentally validated as 34.8 MPa with a low error of 3.44%. Also, the PR outperformed the traditional Taguchi method, which predicted a CS of 33.74 MPa, showing a 7.5% improvement and lower error. This demonstrates that PR-based ML optimization offers better accuracy and improved mechanical performance, making these FGMs suitable for various consumer applications.
Accelerating population and modernization has triggered a steady rise in energy demand and a significant rise in household waste, particularly municipal solid waste. In this context, waste-to-energy conversion has emerged as a sustainable solution. This study aims to maximize biofuel production yield using biomass-based banana peel catalyst waste by optimizing process parameters through machine learning models integrated with k-fold cross-validation. The models employed include Polynomial Regression (PR), Decision Tree (DT), Random Forest (RF), and Linear Regression (LR). The three key input variables including reaction temperature (RT), catalyst concentration (CC), and methanol-to-oil molar ratio (MOR) were used to train and test the models, with biodiesel yield as the measured output. Among the models, PR emerged as the best-performing one for predicting biofuel yield, demonstrated by its high R² value of 0.956 and low error metrics (RMSE = 1.54 MSE = 2.39 MAE = 1.43). The best model was determined through balancing bias and variance across k-fold validation iterations, where PR exhibited the highest average R² value of 0.868. Furthermore, the optimized process parameters predicted by PR for maximum biofuel yield were a RT of 59°C, CC of 2.96%, and a MOR of 9.21, resulting in a yield of 95.38%. These findings contribute to advancing large-scale machine learning-driven biofuel optimization, supporting industrial waste-to-energy applications, and fostering sustainable energy development.
Water pollution from the industrial dyes is a serious hazard to ecosystems, and addressing this issue is a significant challenge. To address these issues, we are fabricated BaDyxFe12-xO19 (x = 0.02 to 0.06) by sol-gel auto-ignition (SGA) technique. Several characterizations were used to scrutinize the structural, optical, photocatalytic, and magnetic traits of the produced samples. The X-ray diffraction (XRD) of the sample revels the hexagonal crystal structure. The field emission scanning electron microscopy (FESEM) of both samples reveal the existence of agglomerated grains showing hexagonal shapes. X-ray photoelectron spectroscopy (XPS) analyses confirm the oxidation state of every element present in the synthesized nanomaterials. The specific surface area was found to be 1.069 m2/g for BDF1 and 1.466 m2/g for BDF3. The band gap of the BDF1, BDF2, and BDF3 samples are found 2.16, 2.12, and 1.99 eV. The photocatalytic efficacy of the catalysts was examined by removal of the CR in natural light. A notable degradation efficiency of 89.29% are achieved by the BDF3 catalyst within 90 minutes under natural sunlight irradiation. The results demonstrate a straightforward and efficient approach for producing photocatalytic materials that are highly effective for the elimination of dye pollutants in wastewater treatment.