Full-scale translation of methane-enhancement strategies in sewage sludge anaerobic digestion: evidence, constraints, and decision support.

Reported methane gains in sewage-sludge anaerobic digestion do not consistently translate into sustained full-scale benefits. This critical review links process constraints to intervention selection by assessing translational directness and attribution confidence independently, while distinguishing digital prediction from operational deployment. Eighteen full-scale facility cases covering pre-treatment, temperature-driven operation, staged digestion, and co-digestion were evaluated using comparator design, reporting basis, operating duration, and plant-wide burdens. Only five cases provided interpretable intervention-specific baselines or stronger comparators, including one matched parallel comparison. Greater solubilisation, solids throughput, or total gas production did not necessarily demonstrate enhanced sludge-specific methane conversion, and operational gains could be offset by energy, nutrient, solids-handling, or maintenance burdens. Microbial inoculation, conductive materials, magnetic-field assistance, bioelectrochemical integration, and phototrophic coupling were assessed as functionally distinct modules with limited evidence for sustained full-scale benefit. Reported conductive-material benefits did not establish direct interspecies electron transfer as the sole mechanism, while phototrophic methane enrichment did not demonstrate increased methane generation. Among 13 representative digital studies, six used retrospective full-scale records, but none met the defined criteria for prospective deployment. Artificial intelligence and machine learning primarily provided retrospective prediction, monitoring, and offline decision-support evidence rather than verified methane enhancement. The resulting diagnostic and deployment matrices connect process constraints with evidence-compatible interventions, enabling requirements, verification endpoints, and stop criteria. The proposed human-in-the-loop workflow links diagnosis, intervention selection, bounded recommendations, and prospective verification across retention times and disturbances to prioritise sustained plant-wide benefit over peak methane yield or retrospective predictive accuracy.