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.

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.

A high conversion ratio DC-DC converter is crucial for fuel cell electric vehicles (FCEV). A fuel cell-based non-isolated high gain integrated DC-DC converter for electric vehicles is proposed in this paper. The system comprises an interleaved boost converter (IBC) at the source end, a switched capacitor cell, coupled inductors, a passive clamp circuit, and a voltage multiplier circuit (VMC). Its significance is to achieve the voltage conversion gain of 12.33 at a conversion ratio of 0.45. The idea is to use a proton exchange membrane fuel cell to power electric vehicles through a high-gain DC-DC converter. The use of an ineffective MPPT can result in lower energy conversion efficiency. Thus, this system incorporates a maximum power point tracking (MPPT) controller based on a neural network, which relies on the radial basis function network (RBFN) algorithm to track the maximum power point of the PEMFC accurately. The comparative study of the fuel cell electric vehicle (FCEV) structure with the RBFN-based MPPT technique was evaluated with that of the fuzzy logic technique using the MATLAB/Simulink platform (R2021b (MATLAB 9.11)). A 1.5 kW experimental prototype is designed with a switching frequency of 10 kHz to validate the design analysis, and its pursuance is compared between RBFN and FLC-based controllers. This manuscript will be a significant contribution towards evidencing a sustainable environment.

The present study deals with the development of a prediction model to investigate the impact of temperature and moisture on the vibration response of a skew laminated composite sandwich (LCS) plate using the artificial neural network (ANN) technique. Firstly, a finite element model is generated to incorporate the hygro-elastic and thermo-elastic characteristics of the LCS plate using first-order shear deformation theory (FSDT). Graphite-epoxy composite laminates are used as the face sheets, and DYAD606 viscoelastic material is used as the core material. Non-linear strain-displacement relations are used to generate the initial stiffness matrix in order to represent the stiffness generated from the uniformly varying temperature and moisture concentrations. The mechanical stiffness matrix is derived using linear strain-displacement associations. Then the results obtained from the numerical model are used to train the ANN. About 11,520 data points were collected from the numerical analysis and were used to train the network using the Levenberg–Marquardt algorithm. The developed ANN model is used to study the influence of various process parameters on the frequency response of the system, and the outcomes are compared with the results obtained from the numerical model. Several numerical examples are presented and conferred to comprehend the influence of temperature and moisture on the LCS plates.

In this experimental study, the performance of the diesel engine was analyzed for biodiesel derived from Calophyllum inophyllum. The impact of the addition of additives such as N-octanol and N-butanol with Calophyllum inophyllum biodiesel has been assessed. Impact of the application of hybrid N-octanol and N-butanol with biodiesel on emission profile used for the engine performance has also been demonstrated. Response surface analysis of alcohol additives-biodiesel blend was performed separately in this study for the engine efficiency and emission profile. A combination of N-octanol and N-butanol presented the highest brake thermal efficiency (BTE) and lowest carbon monoxide (CO) emission among the ternary blends of octanol. N-butanol-biodiesel blend presented the lowest hydrocarbon (HC) emission among the blends of N-butanol. N-octanol with 5 and 10% addition with biodiesel showed the lowest HC emissions among the blends of octanol. The response surface methodology (RSM) optimization revealed that the optimized thermal efficiency and emission were obtained at full load and minimum load, respectively. The addition of N-octanol hindered the emission at all loads, while N-butanol reduced it at higher loads. A strong correlation between the load and alcohol additives on the engine performance and emission profile has been obtained using the RSM optimization approach. The R-squared value obtained from the RSM was 0.92 and emission profile has been characterized.