Linking feedstock characteristics, reactor configuration, and operating conditions to syngas quality in solid oil palm biomass gasification: A systematic review of empty fruit bunch, palm kernel shell, and mesocarp fiber

Gasifying solid oil palm biomass, particularly empty fruit bunches (EFB), palm kernel shells (PKS), and mesocarp fibers (MF), presents a promising thermochemical pathway for syngas production. However, process performance remains sensitive to feedstock variability, tar formation, ash-related operational problems, and differences in reactor and operating conditions. Adhering to PRISMA 2020 guidelines, this Systematic Literature Review (SLR) synthesizes 129 studies published between 2014 and 2025. Because substantial methodological and reporting heterogeneity was identified, the evidence was evaluated through structured descriptive comparison rather than statistical pooling. The objective is to map the interdependencies among feedstock characteristics, reactor configurations, and operating parameters and their reported relationships with syngas composition, gas yield, cold gas efficiency (CGE), carbon conversion efficiency (CCE), and tar-related performance. The synthesis does not identify a universally superior feedstock, reactor type, or operating window. PKS generally showed relatively low ash contents in the compiled studies, whereas EFB presented recurring agglomeration concerns associated with interactions among alkali-rich ash, bed material, temperature, and gasification atmosphere. Fixed- and fluidized-bed systems exhibited different operational advantages, but their reported performance depended strongly on feedstock preparation, gasifying agent, catalyst or bed material, temperature, equivalence ratio, and steam supply. Tar and contaminant levels further determine application-specific downstream cleaning requirements. Circular-economy assessment should therefore extend beyond the use of waste-derived catalysts to include energy and water use, residual-stream management, and environmental performance. Current evidence supports artificial intelligence primarily for prediction and decision support, while autonomous real-time control remains a longer-term research objective requiring pilot-scale validation and reliable process data.