Bibliometric analysis of artificial intelligence applied to waste-to-syngas

The use of Artificial Intelligence techniques has grown rapidly in the field of waste-to- energy, with gasification processes benefiting from their ability to model and optimize syngas production. However, literature specifically focused on Artificial Intelligence applications in waste-to-syngas systems remains fragmented. This article presents a bibliometric analysis that maps the development of Artificial Intelligence driven research in this domain, focusing mainly on prediction tasks. The analysis is based on data extracted from Scopus and Web of Science, filtered through specific criteria to ensure relevance. Results show a clear rise in publications since 2020, with a dominant contribution from China and a strong focus on hydrogen prediction and neural network models. Despite the progress, this study highlights major gaps in real-time control strategies, data standardization, and industrial-scale implementation. These findings point to emerging opportunities in hybrid modeling, explainable Artificial Intelligence, and underexplored feedstocks should guide future research efforts. • AI research in waste-to-syngas has surged since 2020, led by China. • Most studies focus on predicting hydrogen content through neural networks. • The models are trained dominantly by simulated data, while real experimental data is scarce. • Models validated at an industrial scale are rare. • Hybrid and explainable methods are emerging, but still remain underused.