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.
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Yi Zhang Yun Zhao Yijing Feng Yating Yu Yeqing Li Jian Li Zhonghao Ren Shuo Chen Lu Feng Junting Pan Hongjun Zhou Yongming HanSammendrag
Industrial-scale garage dry fermentation systems are extremely nonlinear, and traditional machine learning algorithms have low prediction accuracy. Therefore, this study presents a novel intelligent system that employs two automated machine learning (AutoML) algorithms (AutoGluon and H2O) for biogas performance prediction and Shapley additive explanation (SHAP) for interpretable analysis, along with multiobjective particle swarm optimization (MOPSO) for early warning guidance of industrial-scale garage dry fermentation. The stacked ensemble models generated by AutoGluon have the highest prediction accuracy for digester and percolate tank biogas performances. Based on the interpretable analysis, the optimal parameter combinations for the digester and percolate tank were determined in order to maximize biogas production and CH4 content. The optimal conditions for the digester involve maintaining a temperature range of 35–38 °C, implementing a daily spray time of approximately 10 min and a pressure of 1000 Pa, and utilizing a feedstock with high total solids content. Additionally, the percolate tank should be maintained at a temperature range of 35–38 °C, with a liquid level of 1500 mm, a pH range of 8.0–8.1, and a total inorganic carbon concentration greater than 13.8 g/L. The software developed based on the intelligent system was successfully validated in production for prediction and early warning, and MOPSO-recommended guidance was provided. In conclusion, the novel intelligent system described in this study could accurately predict biogas performance in industrial-scale garage dry fermentation and guide operating condition optimization, paving the way for the next generation of intelligent industrial systems.
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Yi Zhang Yijing Feng Zhonghao Ren Runguo Zou Li Yeqing Yajing Wang Zhiyang Liu Ziyan Sun Yongming Han Lu Feng Mortaza Aghbashlo Meisam Tabatabaei Pan JuntingSammendrag
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Hao Jiang Shuangqing Wang Baochen Li Lu Feng Limei Zhai Hongjun Zhou Yeqing Li Junting PanSammendrag
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Lu Feng Mihaela Tanase Opedal Francesca Di Bartolomeo Sidsel Markussen Aniko Varnai Svein Jarle HornSammendrag
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Proper treatment of polyvinyl chloride (PVC) waste is challenge as it is not easily degraded and incineration can lead to environmental issue as it will produce toxic chemicals. In this study, a hydrothermal carbonization approach was applied to treat PVC waste. The influence of exogenous additives on dechlorination efficiency of PVC were evaluated. The results showed that, with exogenous additive, substitution, elimination, dehydration and aromatization reaction were enhanced during hydrothermal carbonization. The maximum dechlorination efficiency of 97.50% was achieved with the mass ratio of 1.4% between rice straw and PVC resin at hydrothermal carbonization temperature 240℃ for 120min. The calorific value of hydrothermal charcoal was relatively higher (39.57MJ/kg ± 0.40MJ/kg), indicating a good combustion process. This study presented a novel and sustainable approach, which could convert PVC-waste as a form of solid fuel.
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Lu Feng Lise Bonne Guldberg Michael Jørgen Hansen Chun Ma Rikke Vinther Ohrt Henrik Bjarne MøllerSammendrag
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Lu Feng Lise Bonne Guldberg Michael Jørgen Hansen Chun Ma Rikke Vinther Ohrt Henrik Bjarne MøllerSammendrag
Anaerobic digestion of animal slurry to produce biogas is the dominated treatment approach and a storage period is normally applied prior to digestion. Pre-storage, however, contributes to CH4 emissions and results in loss of biogas potential. Manure management was found to be an efficient approach to reduce not only the on-site CH4 emission but may also have extended influence on CH4 emission/losses for storage and subsequent biogas process, while the connection remains unclear. The objective of this study was therefore to evaluate the impact of slurry management (e.g. removal frequency) on CH4 emission (both on-site and storage process prior to biogas) and biogas yield. An experimental pig house for growing-finishing pigs (30–110 kg) and the relevant CH4 emission was monitored for one year. In addition, the specific CH4 activity (SMA) test was conducted and used as an alternative indicator to reflect the impact. Results showed that the manure management affected both on-site and subsequent methane emission; with increased manure removal frequencies, the methane emission became less dependent on variation of temperatures and the specific methanogenesis activity was significantly lower. The highest SMA (100 mL CH4 gVS-1), for instance, was observed from the slurries with limited emptied times, which was 10 times of that from the slurries being emptied three times a week. These findings could enlighten the development of environmentally friendly strategies for animal slurry management and biogas production.
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Yeqing Li Zhangmu Jing Junting Pan Gang Luo Lu Feng Hao Jiang Hongjun Zhou Quan Xu Yanjuan Lu Hongbin LiuSammendrag
Due to the diversity of microbiota and the high complexity of their interactions that mediate biogas production, a detailed understanding of the microbiota is essential for the overall stability and performance of the anaerobic digestion (AD) process. This study evaluated the microbial taxonomy, metabolism, function, and genetic differences in 14 full-scale biogas reactors and laboratory reactors operating under various conditions in China. This is the first known study of the microbial ecology of AD at food waste (FW) at a regional scale based on multi-omics (16S rRNA gene amplicon sequencing, metagenomics, and proteomics). Temperature significantly affected the bacterial and archaeal community structure (R2 = 0.996, P = 0.001; R2 = 0.846, P < 0.002) and total inorganic carbon(TIC) slightly changed the microbial structure (R2 = 0.532, P = 0.005; R2 = 0.349, P = 0.016). The Wood-Ljungdahl coupled with hydrogenotrophic methanogenic pathways were dominant in the thermophilic reactors, where the acs, metF, cooA, mer, mch and ftr genes were 10.1-, 2.8-, 16.2-, 1.74-, 4.15-, 1.04-folds of the mesophilic reactors (P < 0.01). However, acetoclastic and methylotrophic methanogenesis was the primary pathway in the mesophilic reactors, where the ackA, pta, cdh and mta genes were 2.2-, 3.2-, 14.3-, 1.88-folds of the thermophilic group (P < 0.01). Finally, the Wilcoxon rank-sum test was applied to explain the cause of the temperature affecting AD microbial activities. The findings have deepened the understanding of the effect of temperature on AD microbial ecosystems and are expected to guide the construction and management of full-scale FW biogas plants.
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Yi Zhang Linhui Li Zhonghao Ren Yating Yu Yeqing Li Junting Pan Yanjuan Lu Lu Feng Weijin Zhang Yongming HanSammendrag
The parameters from full-scale biogas plants are highly nonlinear and imbalanced, resulting in low prediction accuracy when using traditional machine learning algorithms. In this study, a hybrid extreme learning machine (ELM) model was proposed to improve prediction accuracy by solving imbalanced data. The results showed that the best ELM model had a good prediction for validation data (R2 = 0.972), and the model was developed into the software (prediction error of 2.15 %). Furthermore, two parameters within a certain range (feed volume (FV) = 23–45 m3 and total volatile fatty acids of anaerobic digestion (TVFAAD) = 1750–3000 mg/L) were identified as the most important characteristics that positively affected biogas production. This study combines machine learning with data-balancing techniques and optimization algorithms to achieve accurate predictions of plant biogas production at various loads.
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Yeqing Li Xingru Yang Mingyu Zhu Liming Dong Hao Jiang Quan Xu Hongjun Zhou Yongming Han Lu Feng Chengfei LiSammendrag
The amount of lignocellulose biomass and sludge is enormous, so it is of great significance to find a treatment combining the two substances. Co-hydrothermal carbonization (Co-HTC) has emerged as an efficient approach to dispose sludge. However, the improvement of sludge upgrading and combustion performance remains an important challenge during the Co-HTC of sludge. In this work, the Co-HTC of sludge and Fenton's reagent at different mixing ratios was proposed to achieve sludge reduction. Moreover, the addition of two kinds of biomass improved the adsorption capacity and combustion performance of hydrochars. When sludge and sawdust were the Co-HTC at the mass ratio of 1:3, the liquid phase Pb concentration decreased notably to 18.06%. Furthermore, the adsorption capacity of hydrochars was further improved by modification, which was in accordance with pseudo-second-order kinetics. Particularly, the hydrochars derived from the Co-HTC had higher heating value (HHV) and could be used as a clean fuel. This study proposed a new technical route of combining the HTC with Fenton's reagent and lignocellulose biomass, which could be served as a cleaner and eco-friendly treatment of sludge.
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Lu FengSammendrag
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Zhanjiang Pei Shujun Liu Zhangmu Jing Yi Zhang Jingtian Wang Jie Liu Yajing Wang Wenyang Guo Yeqing Li Lu Feng Hongjun Zhou Guihua Li Yongming Han Di Liu Junting PanSammendrag
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Yeqing Li Zhenxin Wang Zhuoliang Jiang Lu Feng Junting Pan Mingyu Zhu Chengjie Ma Zhangmu Jing Hao Jiang Hongjun Zhou Hui Sun Hongbin LiuSammendrag
This study evaluated the effects of bio-based carbon materials on methane production by anaerobic digestion. The results showed that biochar and hydrochar can promote cumulative methane yield by 15% to 29%. However, there was no statistical significance (p > 0.05) between hydrochar and biochar produced at different temperature on methane production. 16S rRNA gene sequencing and bioinformatics analysis showed that biochar and hydrochar enriched microorganism that might participate in direct interspecies electron transfer (DIET) such as Pseudomonadaceae, Bacillaceae, and Clostridiaceae. The the surface properties of the modified biochar were characterized with BET, Raman, FTIR and XPS. Bio-based carbon materials with uniform dispersion provided a stable environment for the DIET of microorganisms and electrons are transferred through aromatic functional groups on the surface of materials. This study reveals bio-based carbon materials surface properties on methane production in anaerobic digestion and provides a new approach to recycling spent coffee grounds.
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Yeqing Li Yinjun Liu Ximeng Wang Sen Luo Dongfang Su Hao Jiang Hongjun Zhou Junting Pan Lu FengSammendrag
Syngas from pyrolysis/gasification process is a mixture of CO, CO2 and H2, which could be converted to CH4, so called syngas biomethanation. Its development is obstructed due to the low productivity and CO inhibition. The aim of this study was to demonstrate the feasibility of using syngas as the only carbon source containing high CO concentration (40%) for biomethanation. Lab-scale thermophilic bioreactor inoculated with anaerobic sludge was operated continuously for over 900 h and the shift of microbial structure were investigated. Results showed that thermophilic condition was suitable for syngas biomethanation and the microbes could adapt to high CO concentration. Higher processing capacity of 12.6 m3/m3/d was found and volumetric methane yield of 2.97 m3/m3/d was observed. These findings could strengthen the theoretical basis of syngas biomethanation and support its industrialization in the future.
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Jing Zhang Zilan Du Liqin Fu Yongming Han Wei Zheng Fuhua Yu Huimin Chen Lu Feng Yeqing Li Weiying PingSammendrag
With the development of the world economy and society, the living standards of residents have been improved, along with a large amount of food waste and carbon dioxide (CO2) emissions. In the face of global warming and energy shortages, food waste can be used as high-value bio-energy raw materials which is also an effective way to reduce CO2 emissions. Therefore, this paper proposes a novel anaerobic digestion and CO2 emissions efficiency analysis based on a Slacks-Based Measure integrating Data Envelopment Analysis (SBM-DEA) model to evaluate and optimize the process structure of anaerobic treatment of food waste. The total feed volume and the discharge volume of liquid digestate are taken as inputs, and the total methane (CH4) production volume is taken as the desirable output and CO2 emissions are regarded as the undesirable output to build the biogas production and CO2 emissions evaluation model during the anaerobic digestion process. Finally, the proposed method is used in the actual anaerobic digestion process. The results show that the overall efficiency values in January, April, May, and June in 2020 are higher than those in other months. At the same time, due to the optimal allocation of slack variables of inputs and undesirable outputs, the efficiency values of other inefficient anaerobic digestion days can be improved.
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