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.
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.
Sammendrag
Biomethanation represents a promising approach for biomethane production, with biofilm-based processes like trickle bed reactors (TBRs) being among the most efficient solutions. However, maintaining stable performance can be challenging, and both pure and mixed culture approaches have been applied to address this. In this study, inocula enriched with hydrogenotrophic methanogens were introduced to to TBRs as bioaugmentation strategy to assess their impacts on the process performance and microbial community dynamics. Metagenomic analysis revealed a metagenome-assembled genome belonging to the hydrogenotrophic genus Methanobacterium, which became dominant during enrichment and successfully colonized the TBR biofilm after bioaugmentation. The TBRs achieved a biogas production with > 96 % methane. The bioaugmented reactor consumed additional H2. This may be due to microbial species utilizing CO2 and H2 via various CO2 reduction pathways. Overall, implementing bioaugmentation in TBRs showed potential for establishing targeted species, although challenges remain in managing H2 consumption and optimizing microbial interactions.
Forfattere
Getachew Birhanu Abera Aryan Bhusal Nabin Aryal Thea Os Andersen Begum Billgic Linn Solli Svein Jarle Horn Lu FengSammendrag
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