Traditional Multi-Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or more MCDM techniques to address decision-making problems. The authors attempted to create two novel hybrid MCDM systems in this paper by integrating Additive Ratio ASsessment (ARAS) with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex PRoportional ASsessment (COPRAS). To demonstrate the ability and effectiveness of these two hybrid models i.e., TOPSIS-ARAS and COPRAS-ARAS were applied to solve a real-time robot selection problem with 12 alternative robots and five selection criteria, while evaluating the parametric importance using the CRiteria Importance Through Inter criteria Correlation (CRITIC) objective weighting estimation tool. The rankings of the robot alternatives gained from these two hybrid models were also compared to the obtained results from eight other solo MCDM tools. Although the rankings by the applied methods slightly differ from each other, the final outcomes from all of the adopted techniques are consistent enough to suggest that robot 12 is the best choice followed by robot 11, and robot 4 is the worst one among these 12 alternatives. Spearman Correlation Coefficient (SCC) also reveals that the proposed rankings derived from various methods have a strong ranking relationship with one another. Finally, sensitivity analysis was performed to investigate the effects of weight variation and to validate the robustness of the implemented MCDM approaches.

The objective of this research work is to evaluate the performance, combustion, and exhaust emissions of a variable compression ratio diesel engine utilizing diesel 25% rubber seed biodiesel mixture (B25) blended with 25 ppm and 50 ppm of alumina nanoparticle running with different operating conditions. An ultrasonicator was used to make uniform dispersion of alumina (Al) nanoparticles in the diesel–biodiesel mixture. Biodiesel mixture blended with nanoparticles has physicochemical characteristics that are comparable to ASTM (American Society for Testing and Materials) D6751 limitations. The results revealed that the B25 exhibited a lower cylinder peak pressure and lower HRR (heat release rate) than diesel at maximum power. BTE (brake thermal efficiency) of B25 is 2.2% lower than diesel, whereas BSFC of B25 is increased by 6% in contrast to diesel. Emissions of HC (hydrocarbon), CO (carbon monoxide), and smoke for B25 were diminished, while emissions of NOx (nitrogen oxide) were higher at maximum power. Further, the combustion and performance of diesel engine were improved with the inclusion of alumina nanoparticles to biodiesel blends. In comparison to B25, BTE of B25 with 50% alumina nanoparticles (B25Al50) mixture was enhanced by 4.8%, and the BSFC was diminished by 8.5%, while HC, CO, and smoke were also diminished by 36%, 20%, and 44%, respectively. At peak load, the maximum cylinder pressure and HRR of B25 were improved by 4.2% and 6.7%, respectively, with the presence of 50% alumina nanoparticles in a biodiesel blend (B25Al50).

This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R²), median absolute error (MeAE), mean absolute percentage error (MAPE), and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R² obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R² is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.

In this paper, Al-Fe-Si-Zn-Cu (AA8079) matrix composites with several weight percentages of B₄C (0, 5, 10, and 15) were synthesized by powder metallurgy (PM). The essential amount of powders was milled to yield different compositions such as AA8079, AA8079-5 wt.%B₄C, AA8079-10 wt.%B₄C, and AA8079-15 wt.%B₄C. The influence of powder metallurgy parameters on properties’ density, hardness, and compressive strength was examined. The green compacts were produced at three various pressures: 300 MPa, 400 MPa, and 500 MPa. The fabricated green compacts were sintered at 375 °C, 475 °C, and 575 °C for the time period of 1, 2 and 3 h, respectively. Furthermore, the sintered samples were subjected to X-ray diffraction (XRD) analysis, Energy Dispersive Analysis (EDAX), and Scanning Electron Microscope (SEM) examinations. The SEM examination confirmed the uniform dispersal of B₄C reinforcement with AA8079 matrix. Corrosion behavior of the composites samples was explored. From the studies, it is witnessed that the rise in PM process parameters enhances the density, hardness, compressive strength, and corrosion resistance.