Reusability of two heterogeneous catalysts in ultrasound (US) assisted biodiesel production was investigated in comparison to each other. An ultrasound (US) generator (200 W, 20 kHz) equipped with a horn type probe (19 mm) was used. Regeneration experiments were planned according to second order central composite design (CCD) method. After the eighth use of the catalysts, biodiesel yield decreased from 99.1% to 90.4% for calcined calcite (CaO) and from 98.8% to 89.8% for calcined dolomite (CaO.MgO). Furthermore, regeneration of spent catalysts by calcination was investigated; optimum temperature and time were found as 750 °C and 90 min, lower than fresh catalyst preparation conditions. The regenerated catalysts were reused in a second process cycle; biodiesel yield was calculated as 97.2% for CaO and 96.5% for CaO.MgO. Finally, the process showed that calcination is an energetically favorable regeneration process of spent catalysts.

This study investigated the production of biodiesel from canola oil, the formulation of sustainable ternary fuel blends with diesel and alcohol (ethanol or propanol), and the experimental and machine learning-based modeling of their physical properties, including density and viscosity over a temperature range of 10 °C to 40 °C. Biodiesel was synthesized via alkali-catalyzed transesterification (6:1 methanol-to-oil molar ratio, 0.5 wt % NaOH of oil) and blended with diesel and alcohols (ethanol and propanol) in varying volume ratios. The experimental results revealed that blend density decreased from 0.8622 g/cm³ at 10 °C to 0.8522 g/cm³ at 40 °C for a blend containing ethanol. Similarly, the viscosity showed a significant reduction with temperature, e.g., the blend exhibited a viscosity decline from 8.5 mPa·s at 10 °C to 7.2 mPa·s at 40 °C. Increasing the alcohol or diesel content further reduced density and viscosity due to the lower intrinsic properties of these components. The machine learning models, Gaussian process regression (GPR), support vector regression (SVR), artificial neural networks (ANN), and decision tree regression (DTR), were applied to predict the properties of these blends. GPR demonstrated the best predictive performance for both density and viscosity. These findings confirm the strong potential of GPR for the accurate and reliable prediction of fuel blend properties, supporting the formulation of alternative fuels optimized for diesel engine performance. These aspects contribute new insights into modelling strategies for sustainable fuel formulations.

In this study, Poly(l-lactic acid) (PLLA) synthesis was studied using direct melt condensation polymerization (DMP) method in an experimental microwave (MW) system where external cooling was applied to keep both temperature (T) and MW power (P) continuous and constant during the polymerization. T, P, reaction time (t), catalyst type, and catalyst amount were considered to determine appropriate process conditions. In addition, some experiments were carried out under the similar process conditions using MW-assisted ring opening polymerization (MW-ROP) and conventional heating (CH) methods. These methods were compared in terms of process performance and various properties of the oligomeric PLLA. According to results, polymerization rate, polymer yield, average-molecular weight, the dispersity, and thermal properties of PLLA products were similar in both MW methods but higher than CH counterparts. Furthermore, low crystallinity of PLLA products synthesized by MW methods showed the effect of MW on the optical purity (L or D) of products. Finally, energy saving was accomplished by 73% and an increase of polymer production rate by 37.7% in MW-DMP method.