In the present work, a Quantitative Structure -Property Relationship (QSPR) study was performed to predict the Watson characterization factor of hydrocarbon components. A Genetic Algorithm-based Multivariate Linear Regression (GA-MLR) was applied to select the most statistically effective molecular descriptors of the Watson characterization factor. Then, based on the selected molecular descriptors by GA-MLR, a three-layer Feed Forward Neural Network (FFNN) was constructed to predict the Watson characterization factor. The obtained results showed that the constructed FFNN can accurately predict the Watson characterization factor of hydrocarbon components.

No abstract

In the last decades, environmental crises and increasing energy demand have motivated researchers to investigate the practical techniques for the production of clean fuels through renewable energy resources. It is essential to develop technologies to utilize glycerol as a byproduct derived from biodiesel. Glycerol is known as a sustainable and clean source of energy, which can be an alternative resource for the production of value-added chemicals and hydrogen. The hydrogen production via steam reforming (SR) of glycerol using Ni-based catalysts is one of the promising approaches for the entry of the hydrogen economy. The purpose of this review paper is to highlight the recent trends in hydrogen production over Ni-based catalysts using the SR of glycerol. The intrinsic ability of Ni to disperse easily over variable supports makes it a more viable active phase for the SR catalysts. The optimal reaction conditions have been indicated as 650–900 °C, 1 bar, and 15 wt% Ni in catalysts for high glycerol conversion. In this review paper, the effects of various supports, different promoters (K, Ca, Sr, Ce, La, Cr, Fe), and process conditions on the catalytic performance have been summarized and discussed to provide a better comparison for the future works. It was found that Ce, Mg, and La have a significant effect on catalytic performance as promoters. Moreover, SR of glycerol over hydrotalcite and perovskite-based catalysts have been reviewed as they suggest high catalytic performance in SR of glycerol with improved thermal stability and coke resistance. More specifically, the Ni/LaNi0.9Cu0.1O3 synthesized using perovskite-type supports has shown high glycerol conversion and sufficient hydrogen selectivity at low temperatures. On the other hand, hydrotalcite-like catalysts have shown higher catalytic stability due to high thermal stability and low coke formation. It is vital to notice that the primary concern is developing a high-performance catalyst to utilize crude glycerol efficiently.