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There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2. Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.

Multiple machine learning models were developed in this study to optimize biodiesel production from waste cooking oil in a heterogenous catalytic reaction mode. Several input parameters were considered for the model including reaction temperature, reaction time, catalyst loading, methanol/oil molar ratio, whereas the percent of biodiesel production yield was the only output. Three ensemble models were utilized in this study: Boosted Linear Regression, Boosted Multi-layer Perceptron, and Forest of Randomized Tree for optimization of the yield. We then found their optimized configurations for each model, namely hyper-parameters. This critical task is done by running more than 1000 combinations of hyper-parameters. Finally, The R2-Scores for Boosted Linear Regression, Boosted Multi-layer Perceptron, and Forest of Randomized Tree, respectively, were 0.926, 0.998, and 0.992. MAPE criterion revealed that the error rates for boosted linear regression, boosted multi-layer perceptron, and Forest of Randomized Tree was 5.68 × 10-2, 5.20 × 10-2, and 9.83 × 10-2, respectively. Furthermore, utilizing the input vector (X1 = 165, X2 = 5.72, X3 = 5.55, X4 = 13.0), the proposed technique produces an ideal output value of 96.7 % as the optimum yield in catalytic production of biodiesel from waste cooking oil.

This is the first research on the smart control and frequency analysis of a cylindrical sandwich nanoshell in the framework of the numerical-based generalized differential quadrature method (2D-GDQM). The current sandwich smart nanostructure is made of a honeycomb core and piezoelectric face sheets as sensor and actuator (PFSA). For modeling the size-dependent nanoshell, nonlocal stress-strain gradient theory (NSGT) is presented. Also, this nanostructure is under conveying viscous fluid, and the related force is calculated by the modified formulation of Navier–Stokes. Also, the current structure rotates around its axial direction. The stresses and strains are obtained using the higher-order shear deformation theory (HSDT). The external voltage is applied to the sensor layer, and a Proportional-Derivative (PD) controller is used for sensor output control. Governing equations and boundary conditions of the cylindrical sandwich nanoshell are obtained by implementing Hamilton's principle. The results show that the geometry of honeycomb core, PD controller, velocity of fluid flow, length to radius ratio (L/R), and applied voltage and have a significant influence on the frequency characteristics of a cylindrical sandwich nanoshell. Another important consequence is that applying the PD controller leads to an increase in the critical velocity of fluid flow in the smart nanostructure. [ABSTRACT FROM AUTHOR]

Microwave pyrolysis of finely ground jatropha seed biochar was used as bio-filler to develop biocomposites. Effects influencing the mechanical properties of the biocomposites were investigated based on varied material ratio. Ratios by percentage of weight were determined by Doptimal (custom) mixture design using the Stat Ease "Design Expert". The mechanical properties, such as tensile strength, modulus of elasticity, and microhardness, were the dependent variables (response). Bio-filler content was optimised to attain the overall best mechanical properties for the biocomposites. The optimized biocomposite that showcased good tensile strength, modulus of elasticity, and microhardness biocomposite ratio's predicted mechanical properties mean values were tensile strength (9.53 MPa), modulus of elasticity (0.730 GPa), and microhardness (20.4 HV) for polylactic acid and biofiller mixture; and tensile strength (7.92 MPa), modulus of elasticity (0.668 GPa), and microhardness (18.7 HV) for polylactic acid, biofiller, and poly(ethylene-alt-maleic anhydride) mixture. Models generated by the mixture design showcased some degree of noise and error present; however, the outcome through the optimization step was generally reliable for predicting the mechanical properties. Additional data gathered through experimental testing and replicates could improve the reliability of the model. [ABSTRACT FROM AUTHOR]

The morphological and thermal properties of composites containing a bioplastic blend and micro/nano-sized biochar from pyrolyzed jatropha seeds from microwave pyrolyzed jatropha seeds were investigated using scanning electron microscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis, and differential scanning calorimetry. The biocomposite samples exhibited a brittle structure with a slightly ductile chip-like appearance. The Fourier transform infrared spectroscopy results for the PLA/PEMA/BC bio-composites were comparable to the PLA/BC biocomposites. A lower bio-filler content had more pronounced peak intensities than the higher bio-filler content biocomposites. The added PEMA compatibilizer in the PLA/PEMA/BC biocomposite showed more pronounced peaks, which indicated slightly improved bonding/interaction between the bio-filler and the matrix. Overall, increasing bio-filler content did not drastically affect the functional groups of the biocomposites. Thermogravimetric and differential scanning calorimetry analysis showed the developed biocomposites had a slight improvement in thermal stability, in comparison to the PLA sample. Improvements in the thermal stability of the PLA/PEMA/BC biocomposite could be attributed to the additional hydroxyl group, which was due to the added PEMA in the PLA and PLA/BC. According to the results of the analysis of the developed biocomposites, the biocomposites were more brittle and had reasonable thermal stability. [ABSTRACT FROM AUTHOR]