首页|New Machine Learning Study Findings Have Been Reported by In- vestigators at University of Liverpool (Machine Learning-driven Op- timization of Ni-based Catalysts for Catalytic Steam Reforming of Biomass Tar)
New Machine Learning Study Findings Have Been Reported by In- vestigators at University of Liverpool (Machine Learning-driven Op- timization of Ni-based Catalysts for Catalytic Steam Reforming of Biomass Tar)
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Elsevier
2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Machine Learning. According to news originating from Liverpool, United Kingdom, by NewsRx correspondents, research stated, “Biomass gasification is a promising process for producing syngas, which is widely used in various industrial processes. However, the presence of tar in syngas poses a significant challenge to biomass gasification due to the difficulties in its removal and potential downstream issues, such as clogging, slagging, and corrosion.” Funders for this research include European Union (EU), University of Liverpool, China Scholarship Council. Our news journalists obtained a quote from the research from the University of Liverpool, “Extensive efforts have been made to address this challenge through catalytic tar removal using various catalysts, generating a vast amount of experimental data. Processing this large dataset and gaining new insights into process optimization requires the development of efficient data analysis methods. In this study, a comprehensive database was built, encompassing a total of 584 data points and 14 input parameters collected from literature published between 2005 and 2020. Machine learning algorithms were then trained using this dataset to predict and optimize the catalytic steam reforming of biomass tar. The predicted results were found to agree well with the experimental data. The results show that the reaction temperature is the most important process parameter, with the highest relative importance of 0.24, followed by the support (0.16), additive (0.12), nickel (Ni) loading (0.08), and calcination temperature (0.07), among the 14 input parameters. This work has proposed optimal ranges for the reaction temperature (600-700 degrees C), Ni loading (5-15 wt%), and calcination temperature (500-650 degrees C). Furthermore, it was found that a larger specific surface area and higher Ni dispersion are two critical factors for selecting additives and supports.”
LiverpoolUnited KingdomEuropeCyborgsEmerging TechnologiesMachine LearningUniversity of Liverpool
NETL
NSTL
Elsevier
