Lu Feng
Forsker
Vedlegg
CVBiografi
Forskningsarbeidene mine tar primært sikte på å styrke anaerob fordøyelsesbasert sirkulær økonomi, rollen til anaerob fordøyelsesprosess på økologisk landbruk, dens effekt på resirkulering og flytting av næringsstoffene, og innvirkning på jords fruktbarhet og klimagassutslipp. For tiden har jeg interesser i å utvikle innovative bioteknologier for å konvertere organiske rester, til bioenergi, organiske syrer, protein, biometanering og syngassfermentering.
Sammendrag
Det er ikke registrert sammendrag
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
Yeqing Li Jinglei Zhang Xinran Wen Mahmoud Mazarji Shuo Chen Qiang Liu Shenggeng Zhao Lu Feng Gang Li Hongjun Zhou Junting PanSammendrag
The use of bio-based composites to enhance the methane production in anaerobic digestion has attracted considerable attention. Nevertheless, the study of electron transfer mechanisms and the applications of biochar/MnO2 (MBC) in complex systems remains largely unexplored. Biochar composited with MnO2 at 10:1 mass ratio (MBC10) increased the content of volatile fatty acids by 9.09 % during acidogenic phase. During the methanogenic experiments using acetate, cumulative methane production (CMP) rose by 5.83 %, and in the methanogenic experiments using food waste, CMP increased by 24.32 %. Microbial community analysis indicated an enrichment of Syntrophomonas, Bacilli, and Methanosaetaceae in the MBC10 group. This enrichment occurred mainly due to the redox capability of MnO2 enhancing MBC capacitance, thereby facilitating microbial electron transfer processes. Additionally, under 2 g/L ammonia nitrogen concentration and 30 g/L organic load, the CMP of MBC10 increased by 12.74 % and 9.44 %, respectively, compared to the BC600 group. This study illuminates MBC's electron transfer mechanisms and applications, facilitating its wider practical adoption and fostering future innovations.
