No abstract

Global energy consumption has gradually increased as a result of population growth, industrialization, economic development, and rising living standards. Furthermore, as global warming and pollution worsen, the development of renewable energy sources is becoming more essential. Hydrogen is one of the most promising clean and sustainable energy carriers because it emits only water as a byproduct without carbon emission and has the highest energy efficiency. Hydrogen can be produced from a variety of raw resources, including water and biomass. Water electrolysis is one of many hydrogen production technologies that is highly recommended due to its eco-friendliness, high hydrogen generation rate, and high purity. However, in terms of long-term viability and environmental effect, Polymer Electrolyte Membrane water electrolysis has been identified as a potential approach for producing high-purity, high-efficiency hydrogen from renewable energy sources. Furthermore, the hydrogen (H2) and oxygen (O2) produced are directly employed in fuel cells and other industrial uses. As a result, an attempt has been made in this work to investigate hydrogen synthesis and utilization in fuel cell vehicles. Low-temperature combustion technology has recently been applied in engine technology to reduce smoke and NOx emissions at the same time. The advantages and limitations of homogeneous charge compression ignition, partially premixed charge compression ignition, premixed charge compression ignition, and reactivity regulated compression ignition are described separately in low-temperature combustion strategy.

The present study utilized response surface methodology (RSM) and Bayesian neural network (BNN) to predict the characteristics of a diesel engine powered by a blend of biodiesel and diesel fuel. The biodiesel was produced from waste cooking oil using a biocatalyst synthesized from vegetable waste through the wet impregnation technique. A multilevel central composite design was utilized to predict engine characteristics, including brake thermal efficiency (BTE), nitric oxide (NO), unburned hydrocarbons (UBHC), smoke emissions, heat release rate (HRR), and cylinder peak pressure (CGPP). BNN and the logistic–sigmoid activation function were used to train the experimental data in the artificial neural network (ANN) model, and the errors and correlations of the predicted models were calculated. The study revealed that the biocatalyst was capable of producing a maximum yield of 93% at 55 °C under specific reaction conditions, namely a reaction time of 120 min, a stirrer speed of 900 rpm, a catalyst loading of 7 wt.%, and a molar ratio of 1:9. Further, the ANN model was found to exhibit comparably lower prediction errors (0.001–0.0024), lower MAPE errors (3.14–4.6%), and a strong correlation (0.984–0.998) compared to the RSM model. B100-80%-20% was discovered to be the best formulation for emission property, while B100-90%-10% was the best mix for engine performance and combustion at 100% load. In conclusion, this study found that utilizing the synthesized biocatalyst led to attaining a maximum biodiesel yield. Furthermore, the study recommends using ANN and RSM techniques for accurately predicting the characteristics of a diesel engine.