Sustainable Synthesis and Multi-Objective Optimization of Carbon Nanotubes from Algerian Gas Condensate

Carbon nanotubes (CNTs) are advanced nanomaterials with applications in energy storage, catalysis, and high-performance composites. However, their large-scale production remains constrained by the need for low-cost and sustainable carbon sources. In this study, Algerian gas condensate, a hydrocarbon-rich by-product of natural gas extraction, was valorized as a carbon precursor for the synthesis of carbon nanotube materials over a Ni/SiO ₂ catalyst through pyrolysis. Structural characterization by XRD, Raman spectroscopy, and SEM indicated the formation of graphitic tubular carbon with an average diameter of 17 nm, an interlayer spacing of 0.341 nm, and a crystallite size of 7.37 nm, consistent with partially crystalline graphitic structures. Machine-learning models, including Random Forest (RF) and Support Vector Regression (SVR), were used to predict CNT properties with high accuracy (R ² > 0.95), followed by multi-objective optimization to identify favorable synthesis conditions. These results highlight the potential of Algerian gas condensate as a promising carbon feedstock for carbon nanotubes production.