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Biomass supply chain management in North Carolina (part 1): predictive model for cropland conversion to biomass feedstocks

机译:北卡罗莱纳州的生物质供应链管理(第1部分):农田转化为生物质原料的预测模型

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Increased interest in biomass cultivation requires detailed analysis of spatial production potential of possible biorefinery locations, with emphasis on feedstock production cost minimization. Integrated assessment of publicly available spatial data on current crop production, soil type, and yield potential, coupled with techno-economic production cost estimates, can support a functional method for rapid analysis of potential biorefinery sites. A novel predictive model was developed to determine cropland conversion using a probabilistic profit based equation for multiple biomass crops: giant reed, miscanthus, switchgrass, and sorghum (with either canola or barley as a winter crop). The three primary regions of North Carolina (Mountains, Piedmont, and Coastal Plain) were used as a case study and with a single parameter uncertainty analysis was completed. According to the model, the county chosen to represent the Coastal Plain (Duplin County) had the largest potential acreage that would be converted (15,071 ha, 7.1% total land, 9.3% of cropland) primarily to sorghum with canola as a winter crop. Large portions were also predicted to convert to giant reed and switchgrass, depending on the price and yield parameters used. The Piedmont (Granville County, 7697 ha, 5.5% total land, 6.9% cropland) and Mountain (Henderson County, 2117 ha, 2.2% total land, 2.3% cropland) regions were predicted to convert primarily to switchgrass acreage for biomass production, with much less available biomass overall compared to the Coastal Plain. This model provided meaningful insight into regional cropping systems and feedstock availability, allowing for improved business planning in designated regions. Determination of cropland conversion is imperative to develop realistic biomass logistical operations, which in conjunction can assist with rapid determination of profitable biomass availability. After this rapid analysis method is conducted in-depth on-ground biorefinery feasibility analysis can occur, ensuring resource are used only in locations with a high potential for available low cost biomass feedstocks.
机译:对生物质耕种的兴趣日益增加,需要对可能的生物精炼厂位置的空间生产潜力进行详细分析,重点是使原料生产成本最小化。对有关当前作物产量,土壤类型和单产潜力的公开可用空间数据进行综合评估,再加上技术经济生产成本估算,可以为快速分析潜在的生物精炼厂提供支持。开发了一种新颖的预测模型,使用概率利润方程对多种生物量作物(巨型芦苇,桔梗,柳枝switch和高粱(油菜籽或大麦作为冬季作物),使用概率利润方程式来确定农田转化。以北卡罗来纳州的三个主要地区(山脉,皮埃蒙特和沿海平原)为例,并完成了单参数不确定性分析。根据模型,选择代表沿海平原的县(都柏林县)具有最大的潜在耕地面积(15,071公顷,占总土地的7.1%,耕地的9.3%),主要转化为高粱,并以油菜作为冬季作物。据预测,根据所使用的价格和产量参数,大部分也会转化为巨型芦苇和柳枝switch。皮埃蒙特(格兰维尔县,7697公顷,总土地面积5.5%,6.9%耕地)和芒特(亨德森县,2117公顷,总土地2.2%,土地面积2.3%)预计将主要转化为柳枝a的生物量生产面积,与沿海平原相比,总的可用生物量要少得多。该模型为区域种植系统和原料供应提供了有意义的见解,从而可以改善指定区域的业务计划。确定农田转化对于发展现实的生物质物流至关重要,这可以帮助快速确定可获利的生物质。在执行了这种快速分析方法之后,便可以进行深入的地面生物精炼厂可行性分析,从而确保仅将资源用于具有潜力的低成本生物质原料。

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