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MEC-AD Leveraging Derived Carbon for Energy-Efficient Methane Production:Insights into Electrodes,Accelerants,and Methanogenic Approaches
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作者 ZHANG Xiaoxue WANG Kaijun +5 位作者 GAO Yangyang Yasir Abbas Muhammad Saqlain Jamil PENG Cheng LUO Han yun sining 《硅酸盐学报》 北大核心 2025年第12期3740-3760,共21页
Introduction The generation of biological wastes such as cow dung and aloe vera waste(AVW)causes a serious ecological pollution.The microbial electrolytic cell coupled with anaerobic digestion(MEC-AD)system can make a... Introduction The generation of biological wastes such as cow dung and aloe vera waste(AVW)causes a serious ecological pollution.The microbial electrolytic cell coupled with anaerobic digestion(MEC-AD)system can make a rational utilization of these biodegradable organic wastes,which is of vital importance for alleviating environmental deterioration and reducing resource waste.Electrode materials and accelerants are the two major factors that affect methane production in the MEC-AD system.They affect microbial attachment and electron transfer in the MEC-AD system.Bio-based carbon materials are carbon materials prepared from biomass as raw materials.They have characteristics such as a rich pore structure,good chemical stability,biocompatibility,and controllable surface properties,which can be used as accelerants and electrodes in the MEC-AD system to optimize its performance.This study was to investigate the influence of biomass-derived carbon as an electrode and accelerant on the performance of the MEC-AD system,and the mechanism for increasing the production of biogas and methane was also analyzed,thus providing a basis for the multifunctional application of biomass-derived carbon in the MEC-AD system.Methods A series of experimental methods were adopted to study the MEC-AD system.Two types of bio-based carbon,i.e.,aloe vera waste derived spherical carbon(AVW-SC)and porous carbon(AVW-PC),were synthesized via hydrothermal carbonization.The raw AVW material was washed with water,dried,ground,and subjected to hydrothermal treatment to obtain AVW-SC.After activating AVW-SC with KOH,it was carbonized in a tube furnace to obtain AVW-PC.In the preparation of the electrodes,bio-based carbon(AVW-SC and AVW-PC)was mixed with 5%polytetrafluoroethylene powder in ethanol and deionized water,and then ground in a ball mill for 4 h to form a slurry.The slurry was evenly sprayed on the Ti mesh,dried and sintered in N2 atmosphere at 360℃to obtain Ti-SC and Ti-PC electrodes.Four groups of experiments were conducted to determine the optimal voltage,compare different carbon electrodes,and explore the optimal coating amount.The MEC-AD reactor adopted 500 mL wide-mouthed glass bottles with a working volume of 400 mL.Each MEC-AD system received a co-substrate mixture of cattle dung and aloe vera waste and inoculum of sewage sludge in a mass ratio of 3:7.Afterward,they were placed at(36±1)℃for 35 d.The biogas was collected by a water displacement method.The materials were analyzed by characterization techniques such as X-ray diffraction(XRD)and scanning electron microscopy(SEM),and electrochemical tests were conducted on different electrodes.The composition,pH,TS,VS,TCOD and nutrient content of biogas were analyzed by standard chemical methods.Microbial community analysis was conducted using high-throughput sequencing technology.The modified Gompertz model was adopted to predict the kinetic parameters,and the coulombic efficiency and methane recovery rate were calculated according to a specific formula.Results and discussion The result shows that AVW-SC is spherical and closely aggregated,while AVW-PC has a three-dimensional network structure,with average pore diameter of 9.77 nm.The electron exchange capacity(EEC)of AVW-PC(i.e.,0.75μmol·e-/g)is higher than that of AVW-SC(i.e.,0.15μmol·e-/g),indicating a better electron exchange capacity.These results indicate that AVW-PC provides more substrate and bacteria accumulation sites,and has better electron-donating and electron-accepting ability,thus improving the digestion efficiency.In the MEC-AD system,using Ti mesh as an electrode,the effect of different voltages(i.e.,0,0.4,0.6,0.8 V and 1.2 V)on the system performance is investigated,obtaining the optimum biogas production and organic matter degradation rate at 0.8 V.AVW-SC and AVW-PC are respectively coated on Ti mesh as electrodes.The results show that the MEC-AD system with AVW-PC coated Ti mesh as the electrode has a better performance.The electrochemical analysis shows that the electrode coated with AVW-PC has a larger specific capacitance and a smaller charge transfer resistance,indicating that AVW-PC can improve the electrochemical properties and electron transfer ability of MEC-AD system.The influence of coating amount(i.e.,0.025,0.05,0.10,0.15,and 0.20 g)of AVW-PC on the MEC-AD system is investigated.At a coating amount of AVW-PC of 0.1 g,the cumulative biogas production and methane content of the Ti_(0.8)-PC_90.1) group both reach the maximum values.Different doses of AVW-PC(i.e.,0.10%,0.15%,0.20%,and 0.25%)are added as accelerants in Ti_(0.8)-PC_90.1).At the addition amount of AVW-PC of 0.20%,the Ti_(0.8)-PC_90.1)/PC0.2 group performs the optimum biogas production(i.e.,633.63 mL/g VS),methane content(i.e.,65.85%),and total nutrient content of biogas residue(i.e.,42.30 g/kg).In Ti_(0.8)-PC_90.1)/PC0.2,Bacteroidales,Pseudomonadales,Oscillospirales,Methanobacteraceae,Methanospirillaceae,Methanosarcinacea and Methanosaetaceae significantly increase.The increase in microbial diversity promotes interspecific hydrogen transfer(IHT),interspecific acetic transfer(IAT),and direct interspecific electron transfer(DIET),thereby enhancing methanogenic efficiency.Conclusions AVW-SC and AVW-PC were utilized as electrodes and accelerants to enhance methane yield in MEC-AD system.The Ti mesh electrode coated with different concentrations of AVW-PC achieved the optimal biogas production at 0.8 V.Specifically,the Ti_(0.8)-PC_90.1) combination could generate the maximum total amount of biogas and methane proportion.The Ti_(0.8)-PC_90.1)/PC0.2 combination exhibited the optimum performance(i.e.,biogas yield of 633.63 mL/g VS,methane content of 65.85%and total nutritional content of 42.30 g/kg).High abundances of Bacteroidales,Pseudomonadales,Oscillospirales,Methanobacteraceae,Methanospirillaceae,Methanosarcinaceae,and Methanosaetaceae appeared in the Ti_(0.8)-PC_90.1)/PC0.2 group,compared to other groups.In addition,an increased microbial diversity led to an enhanced methane production through processes like DIET.This research could highlight the potential significance of AVW-PC as both electrode and accelerator for increasing methane production and provide a perspective for improving MEC-AD performance through multiple applications of biomass-derived carbon. 展开更多
关键词 aloe vera waste biomass-derived carbon electrode ACCELERANT biogas production
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改性石榴皮生物炭对水中低浓度硝氮的吸附性能研究 被引量:1
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作者 王怡 陈琳风 +6 位作者 王文怀 冯琳琳 柴宝华 范攀 丁卓 云斯宁 徐鸿飞 《西安建筑科技大学学报(自然科学版)》 北大核心 2019年第6期899-904,共6页
以石榴皮为原料在不同条件下制备生物炭,并对其进行盐酸改性,对比改性前后生物炭性质及其对硝氮的吸附效果.SEM、FTIR及等电点测定结果表明,改性后生物炭表面覆盖的颗粒被清除,微孔更清晰且孔径均有所增大;三种改性生物炭均含有-OH官能... 以石榴皮为原料在不同条件下制备生物炭,并对其进行盐酸改性,对比改性前后生物炭性质及其对硝氮的吸附效果.SEM、FTIR及等电点测定结果表明,改性后生物炭表面覆盖的颗粒被清除,微孔更清晰且孔径均有所增大;三种改性生物炭均含有-OH官能团,其中HBC600、HBC700是新增的;HBC600、HBC700和HGBC700等电点分别为9.1、10.1和8.1.未改性生物炭BC600、BC700和GBC700吸附后引起硝氮浓度增高,而改性生物炭HBC600、HBC700和HGBC700均具有较好吸附效果.硝氮初始浓度、吸附时间及生物炭投量均影响生物炭的吸附效果,硝氮初始浓度越低、吸附时间越长、投量越大,三种改性生物炭对硝氮的去除率越高,且相同条件下HBC700对硝氮的吸附效果均最优,最大吸附量可达1.742 mg/g.在硝氮初始浓度9.0 mg/L、生物炭投量8.0 g/L、吸附时间12 h条件下,HBC700吸附去除硝氮的综合效果最佳,去除率为87.6%,且可解吸再利用. 展开更多
关键词 石榴皮 生物炭 改性 低浓度硝氮 吸附
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神经网络短期光伏发电预测的应用研究进展 被引量:60
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作者 贾凌云 云斯宁 +3 位作者 赵泽妮 李红莲 王赏玉 杨柳 《太阳能学报》 EI CAS CSCD 北大核心 2022年第12期88-97,共10页
准确的太阳能发电功率短期预测是保证电力调度和大规模光伏并网的关键。该文对近年来光伏发电功率短期预测研究进展进行综述,并对影响光伏发电功率的各种气象因素进行相关性分析。针对用于光伏发电短期功率预测的人工神经网络模型和深... 准确的太阳能发电功率短期预测是保证电力调度和大规模光伏并网的关键。该文对近年来光伏发电功率短期预测研究进展进行综述,并对影响光伏发电功率的各种气象因素进行相关性分析。针对用于光伏发电短期功率预测的人工神经网络模型和深度学习模型进行总结和评述。太阳辐照度是影响预测模型精度的主要气象参数。在光伏发电功率短期预测中,神经网络及其组合模型均表现出较好的预测精度,但组合模型整体上优于单一预测模型。 展开更多
关键词 光伏发电 神经网络 预测 深度学习 相关性
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基于统计模型的短期风能预测方法研究进展 被引量:32
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作者 赵泽妮 云斯宁 +3 位作者 贾凌云 史加荣 贺宁 杨柳 《太阳能学报》 EI CAS CSCD 北大核心 2022年第11期224-234,共11页
以确定性短期风能预测为出发点,综述常用的4种统计模型的研究进展,包括时间序列方法、人工神经网络、支持向量机和深度学习。针对基础统计模型预测效果不佳的问题,提出各类混合模型。数据预处理、优化算法与基础统计模型之间的组合,或... 以确定性短期风能预测为出发点,综述常用的4种统计模型的研究进展,包括时间序列方法、人工神经网络、支持向量机和深度学习。针对基础统计模型预测效果不佳的问题,提出各类混合模型。数据预处理、优化算法与基础统计模型之间的组合,或人工神经网络与卷积神经网络、循环神经网络等深度学习模型之间的组合,对预测水平都有很好的提升作用。 展开更多
关键词 风力发电 机器学习 预测 数据处理 混合系统
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