Herein, raw and alkali-treated hydrochars from biomass waste are prepared as a highly active catalyst for the conversion of waste motor oil into diesel-like fuels. Among all materials, hydrochar obtained at 250 °C and subsequent alkali activation with KOH showed a 600% improvement of the kinetic constant from 0.0088 to 0.0614 m⁻¹. Conversion values at the same conditions were also improved from 66 to 80% regarding thermal and catalytic cracking, respectively. Moreover, the activation energy was also reduced from 293 to 246 kJ mol⁻¹ for thermal and catalytic cracking, respectively. After characterization, the enhanced catalytic activity was correlated to an increased surface area and functionalization due to the alkali activation. Finally, the liquid product characterization demonstrated that catalytic cracking is more effective than thermal cracking for producing hydrocarbons in the diesel range. In particular, hydrochar-based catalysts are suggested to promote the formation of specific hydrocarbons so that the carbon distribution can be tailored by modifying the hydrothermal treatment temperature.

The quality of fuel depends on its chemical composition, which influences engine performance. Gas chromatography, a cornerstone of global oil and fuel R&D, remains crucial for ensuring the quality of petroleum products and regulatory compliance. Scientists use the most accurate analysis (PIONA) as a tool derived from gas chromatography coupled with mass spectrometry to identify and quantify hydrocarbons that influence resistance to detonation, which is determined by the research octane number (RON). This study introduces the “average carbon number (ACN)”, calculated from the molar chemical composition of commercial gasoline samples sold in Ecuador (Extra gasoline and Súper gasoline). A quantitative comparison of the ACN with techniques applied using standardized international procedures reveals that the ACN characterizes gasoline samples by providing insight into the distribution shape of carbon graphs. A comprehensive statistical analysis demonstrates the potential usefulness of ACN in characterizing fuel composition, highlighting its relevance in broader fuel quality assessments without the need for carbon distribution plots.