Recent advances in advancement in the application of machine learning to gasification research were outlined.
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Methods for mitigating the black – box nature of some algorithms were proposed.
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Machine learning based studies could foster the development of novel heterogeneous catalysts for biomass gasification.
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Different machine learning algorithms were briefly reviewed.
Abstract
Conventional and hydrothermal gasification are promising thermochemical technologies for the production of syngas from waste biomass. Both gasification processes are complex, with several intermediate reactions occurring simultaneously and at different times. Therefore, traditional modeling approaches, including thermodynamic and kinetic models, process modeling and computation fluid dynamics (CFD), are sometimes used to describe the process and study the influence of process parameters on syngas yield. However, most traditional models are impractical and often challenging to model the input-output relationship. Machine learning (ML) methods provide a promising alternative to traditional modeling approaches. This study outlined the advancement in the application of ML to gasification research. Different ML algorithms, including artificial neural networks, deep learning and support vector machines, are briefly described. Challenges and limitations of ML-based methods for gasification research are also discussed. When implemented effectively, ML – approaches could foster the development of novel heterogeneous catalysts for biomass gasification.
