Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2024
Sammendrag
No abstract has been registered
Forfattere
Yi Zhang Xingru Yang Yijing Feng Zhiyue Dai Zhangmu Jing Yeqing Li Lu Feng Yanji Hao Shasha Yu Weijin Zhang Yanjuan Lu Chunming Xu Junting PanSammendrag
Exploring the complex mechanism of anaerobic digestion with hydrothermal pretreatment (HTAD) for biomass efficiently and optimising the reaction conditions are critical to improving the performance of methane production. This study used H2O automated machine learning (AutoML) for comprehensive prediction, analysis, and targeted optimization of the HTAD system. An IterativeImputer system for data filling was constructed. The comparison of three basic regressors showed that random forest performed optimally for filling (R2 > 0.95). The gradient boosting machine (GBM) model was searched by H2O AutoML to show optimal performance in prediction (R2 > 0.96). The software was developed based on the GBM model, and two prediction schemes were devised. The generalization error of the software was less than 10%. The Shapley Additive exPlanations value showed that solid to liquid ratio, hydrothermal pretreatment (HT) temperature, and particle size have greater potential for improving cumulative methane production (CMP). A Bayesian-HTAD optimization strategy was devised, using the Bayesian optimization to directionally optimize the reaction conditions, and performing experiments to validate the results. The experimental results showed that the CMP was significantly improved by 51.63%. Compared to the response surface methodology, the Bayesian optimization relatively achieved a 2.21–2.50 times greater effect. Mechanism analyses targeting the experiments showed that HT was conducive to improving the relative abundance of Sphaerochaeta, Methanosaeta, and Methanosarcina. This research achieved accurate prediction and targeted optimization for the HTAD system and proposed multiple filling, prediction, and optimization strategies, which are expected to provide an AutoML optimization paradigm for anaerobic digestion in the future.
Forfattere
Daniel RasseSammendrag
No abstract has been registered
Sammendrag
No abstract has been registered
Sammendrag
No abstract has been registered
Forfattere
Paulina Paluchowska Zhimin Yin Erik Lysøe Simeon Rossmann Mirella Ludwiczewska Marta Janiszewska May Bente Brurberg Jadwiga ŚliwkaSammendrag
No abstract has been registered
Forfattere
Mirella Ludwiczewska Paulina Paluchowska Marta Janiszewska Erik Lysøe Simeon Rossmann Sylwester Sobkowiak Zhimin Yin May Bente Brurberg Jadwiga ŚliwkaSammendrag
No abstract has been registered
Forfattere
Simeon Rossmann Erik Lysøe Monica Skogen Håvard Eikemo Marta Janiszewska Mirella Ludwiczewska Sylwester Sobkowiak Jadwiga Śliwka May Bente BrurbergSammendrag
No abstract has been registered
Forfattere
Muath Alsheik Merethe Bagge May Bente Brurberg Aakash Chawade Timmermann Christiansen Pawel Chrominski Jahn Davik Susann Herzog Liina Jakobson Hans-Arne Krogsti Fredrik Reslow Terje Tähtjärv Ramesh Vetukuri Susanne Windju Nikolai Ødegaard Rodomiro OrtizSammendrag
No abstract has been registered
Forfattere
Knut ØistadSammendrag
No abstract has been registered