Resources and Future Availability of Agricultural Biomass for Energy Use in Beijing
Resources and Future Availability of Agricultural Biomass for Energy Use in Beijing
复制标题
北京农业生物质能源资源及未来可利用性
DOI：
10.3390/en12101828
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发表时间：
2019-05
期刊：
影响因子：
3.2
通讯作者：
Johnson Dana M
中科院分区：
文献类型：
--
作者：
Zhang Fengli;Li Chen;Yu Yajie;Johnson Dana M
基于木质纤维素生物质的能源生产的重要性日益增加，迫切需要进行可靠的资源供应评估。本研究对生物质能源资源相对丰富且主要分布在郊区的北京地区农残生物质的可利用性进行了分析和估算。北京考虑的主要农作物类型包括粮食作物（例如玉米、冬小麦、大豆、块茎和水稻）、棉花作物和油料作物（例如花生）。农作物单产估算基于北京市统计局收集的1996年至2017年历史数据。农业残余物的理论和可收集量是根据每种作物的农业产量乘以从文献中收集的具体参数来计算的。对北京当前和近期农作物收获和加工资源中的农业残留物的评估采用了三种先进的建模方法：时间序列分析自回归移动平均（ARMA）模型、最小二乘线性回归和灰色系统灰色模型（GM）（1,1）。结果表明，时间序列模型预测适合短期预测评估；最小二乘拟合结果较为准确，但需要考虑影响农业废弃物产生的因素；灰色系统预测适合趋势预测，但预测精度较低。
The increasing importance of lignocellulosic biomass based energy production has led to an urgent need to conduct a reliable resource supply assessment. This study analyses and estimates the availability of agricultural residue biomass in Beijing, where biomass energy resources are relatively rich and is mainly distributed in the suburbs. The major types of crops considered across Beijing include food crops (e.g., maize, winter wheat, soybean, tubers and rice), cotton crops and oil-bearing crops (e.g., peanuts). The estimates of crop yields are based on historical data between 1996 and 2017 collected from the Beijing Municipal Bureau of Statistics. The theoretical and collectable amount of agricultural residues was calculated on the basis of the agricultural production for each crop, multiplied by specific parameters collected from the literature. The assessment of current and near future agricultural residues from crop harvesting and processing resources in Beijing was performed by employing three advanced modeling methods: the Time Series Analysis Autoregressive moving average (ARMA) model, Least Squares Linear Regression and Gray System Gray Model (GM) (1,1). The results show that the time series model prediction is suitable for short-term prediction evaluation; the least squares fitting result is more accurate but the factors affecting agricultural waste production need to be considered; the gray system prediction is suitable for trend prediction but the prediction accuracy is low.
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影响因子：
15.9
作者：
G. Chinnici;M. D’Amico;M. Rizzo;B. Pecorino
通讯作者：
G. Chinnici;M. D’Amico;M. Rizzo;B. Pecorino
影响因子：
3.1
作者：
Guangling Zhao
通讯作者：
Guangling Zhao
DOI：
--
发表时间：
2010-07
期刊：
Sheng wu gong cheng xue bao = Chinese journal of biotechnology
影响因子：
--
作者：
G. Xie;Xiaoyu Wang;Lantian Ren
通讯作者：
G. Xie;Xiaoyu Wang;Lantian Ren
DOI：
10.1016/j.egypro.2017.12.018
发表时间：
2017-12
期刊：
Energy Procedia
影响因子：
--
作者：
Andile B. Maqhuzu;K. Yoshikawa;F. Takahashi
通讯作者：
Andile B. Maqhuzu;K. Yoshikawa;F. Takahashi
影响因子：
6
作者：
M. Scheftelowitz;R. Becker;D. Thrän
通讯作者：
M. Scheftelowitz;R. Becker;D. Thrän