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Zhen Feng

Researcher at Chinese Academy of Sciences

Publications -  46
Citations -  526

Zhen Feng is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Biogas & Raw material. The author has an hindex of 8, co-authored 46 publications receiving 402 citations.

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Improved biogas production from rice straw by co-digestion with kitchen waste and pig manure.

TL;DR: In order to investigate the effect of feedstock ratios in biogas production, anaerobic co-digestions of rice straw with kitchen waste and pig manure were carried out and the optimal ratio of kitchen waste, pig manure, and rice straw was 0.4:1.
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Factors affecting the performance of a single-chamber microbial fuel cell-type biological oxygen demand sensor.

TL;DR: A simple, low-cost single-chamber microbial fuel cell (SCMFC)-type biochemical oxygen demand (BOD) sensor using carbon felt and activated sludge is developed, and its feasibility in the construction of a real-time BOD measurement system is demonstrated.
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Inhibition Effect of Sodium Concentrations on the Anaerobic Digestion Performance of Sargassum Species

TL;DR: The effect of variable sodium concentrations on the anaerobic digestion performance of Sargassum sp was investigated, and the variation in bacterial and archaeal communities was analyzed by high-throughput gene sequencing Results showed that the maximum methane yield of 29041 ± 3421 mL of CH4 g−1 of volatile solids was obtained at a sodium concentration of 442 g L−1, increased by 381% compared to the control group as mentioned in this paper.
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Optimization of liquid hot water pretreatment on Hybrid Pennisetum anaerobic digestion and its effect on energy efficiency

TL;DR: In this paper, the central composite surface response methodology was used to optimize the process variables (temperature, reaction time and liquid-to-solid ratio) in liquid hot water pretreatment for biomethane production from the anaerobic digestion of Hybrid Pennisetum (a typical energy crop in southern China).
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Biochemical methane potential prediction for mixed feedstocks of straw and manure in anaerobic co-digestion.

TL;DR: In this article, different multivariate regression models were established between BMP and the physicochemical indexes or near-infrared spectroscopy (NIRS), and the results reveal that the predicted accuracy of NIRS model is very high, and meet the requirements of rapid prediction of BMP for co-digestion feedstocks in practical biogas engineering.