Slaughterhouse waste is considered to be an emerging issue because of its disposal cost. As an alternative, it would be a great prospect for the bioeconomy society to explore new usages of these leftover materials. As per food safety rules mentioned by EU legislation, all bone waste generated by slaughterhouses ought to be disposed of by rendering. The huge quantity of worldwide bone waste generation (130 billion kilograms per annum) is an environmental burden if not properly managed. The waste animal bones can be efficiently employed as a heterogeneous catalyst to produce biodiesel. This mini review summarized the recent literature reported for biodiesel generation using waste animal bones derived heterogeneous catalyst. It discusses the sources of bone waste, catalyst preparation methods, particularly calcination and its effects, and important characteristics of bones derived catalyst. It suggests that catalysts extracted from waste animal bones have suitable catalytic activity in transesterification of different oil sources to generate a good quality biodiesel.

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 study, engine performance on thermal factors for different biodiesels has been studied and compared with diesel fuel. Biodiesels were produced from Pongamia pinnata (PP), Calophyllum inophyllum (CI), waste cooking oil (WCO), and acid oil. Depending on their free fatty acid content, they were subjected to the transesterification process to produce biodiesel. The main characterizations of density, calorific range, cloud, pour, flash and fire point followed by the viscosity of obtained biodiesels were conducted and compared with mineral diesel. The characterization results presented benefits near to standard diesel fuel. Then the proposed diesel engine was analyzed using four blends of higher concentrations of B50, B65, B80, and B100 to better substitute fuel for mineral diesel. For each blend, different biodiesels were compared, and the relative best performance of the biodiesel is concluded. This diesel engine was tested in terms of BSFC (brake-specific fuel consumption), BTE (brake thermal efficiency), and EGT (exhaust gas temperature) calculated with the obtained results. The B50 blend of acid oil provided the highest BTE compared to other biodiesels at all loads while B50 blend of WCO provided the lowest BSFC compared to other biodiesels, and B50 blends of all biodiesels provided a minimum % of the increase in EGT compared to diesel.

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