The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests

Satellite remote sensing has the potential to contribute to plant phenology monitoring at spatial and temporal scales relevant for regional and global scale studies. Historically, temporal composites of satellite data, ranging from 8 days to 16 days, have been used as a starting point for satellite-derived phenology data sets. In this study we assess how the temporal resolution of such composites affects the estimation of the start of season (SOS) by: 1) calibrating a relationship between satellite derived SOS with in situ leaf unfolding (LU) of trembling aspen (Populus tremuloides) across Canada and 2) quantifying the sensitivity of calibrated satellite SOS estimates and trends, over Canadian broadleaf forests, to the temporal resolution of NDVI data. SOS estimates and trends derived from daily NDVI data were compared to SOS estimates and trends derived from multiday NDVI composites that retain the exact date of the maximum NDVI value or that assume the midpoint of the multiday interval as the observation date. In situ observations of LU dates were acquired from the PlantWatch Canada network. A new Canadian database of cloud and snow screened daily 1-km resolution National Oceanic and Atmospheric Administration advanced very high resolution radiometer surface reflectance images was used as input satellite data. The mean absolute errors of SOS dates with respect to in situ LU dates ranged between 13 and 40 days. SOS estimates from NDVI composites that retain the exact date of the maximum NDVI value had smaller errors (~ 13 to 20 days). The sensitivity analysis reinforced these findings: SOS estimates from NDVI composites that use the exact date had smaller absolute deviations from the LU date (0 to − 5 days) than the SOS estimates from NDVI composites that use the midpoint (− 2 to − 27 days). The SOS trends between 1985 and 2007 were not sensitive to the temporal resolution or compositing methods. However, SOS trends at individual ecozones showed significant differences with the SOS trends from daily NDVI data (Taiga plains and the Pacific maritime zones). Overall, our results suggest that satellite based estimates of vegetation green-up dates should preferably use sub-sampled NDVI composites that include the exact observation date of the maximum NDVI to minimize errors in both, SOS estimates and SOS trend analyses. For trend analyses alone, any of the compositing methods could be used, preferably with composite intervals of less than 28 days. This is an important finding, as it suggests that existing long-term 10-day or 15-day NDVI composites could be used for SOS trend analyses over broadleaf forests in Canada or similar areas. Future studies will take advantage of the growing in situ phenology networks to improve the validation of satellite derived green-up dates.     

双氧水环氧化柠檬烯的实验研究与模拟分析

采用PW-Amberlite离子交换树脂为催化剂,以乙腈为溶剂,双氧水与柠檬烯同时进料进行反应制备1,2-环氧柠檬烯。同时考察了反应时间、进料比、反应温度等工艺条件对柠檬烯环氧化生成1,2-环氧柠檬烯选择性的影响。利用化工模拟软件Aspen Plus中的Rbatch模块对整个反应进行模拟。模拟计算值与实验结果吻合良好,表明所建立的Rbatch模型能够很好地描述双氧水环氧化柠檬烯制备1,2-环氧柠檬烯的反应过程。以产品1,2-环氧柠檬烯的收率为考察目标,通过模拟计算并结合实验分析,确定了最佳的工艺参数:反应时间为24h;双氧水与柠檬烯的最佳进料摩尔比是n(双氧水):n(柠檬烯)=2:1;反应温度为33℃。     

橡胶防老剂4010NA的溶剂回收工艺研究

设计了一套完整的橡胶防老剂4010NA生产后的废溶剂回收装置,采用普通精馏工艺分离丙酮、异丙醇和水的混合溶液得到高纯度丙酮;采用共沸精馏工艺,以苯作带水剂,对脱水塔塔顶采出的异丙醇和水的混合物进行分离,得到高纯度异丙醇。利用Aspen Plus模拟软件对各个过程进行模拟,得到各塔的最佳理论板数和每股物料的最佳进料位置,而且模拟得到了每股物料的工艺参数:废溶剂在丙酮脱除塔中的最佳进料位置为第26块理论板,全塔的最佳理论板数为46块,塔顶温度为55.70℃,压力为99.98kPa,回流比为7;水分脱除塔中物料的最佳进料位置为第7块理论板,全塔的最佳理论板数为22块,塔顶温度为79.37℃,压力为100kPa,回流比为6;脱水塔中异丙醇-水混合液的最佳进料位置为第7块理论板,苯的最佳进料位置为第1块理论板,全塔的最佳理论板数为22块,塔顶温度为55.40℃,压力为100kPa,回流比为6。回收处理后,丙酮的含量≥99%,异丙醇含量≥99%,水含量≥99.8%(均为质量分数),达到回收要求。     

天然气脱酸气装置动态建模及分析

天然气脱酸气装置是天然气开发过程中重要的组成部分,由于脱酸气装置的来气量经常发生变动,致使装置运行偏离最优工况。本文基于川渝地区某高含硫天然气脱酸气装置,首次应用HYSYS Dynamic流程模拟软件,建立天然气脱酸气装置动态模型,研究脱酸气装置在原料气的进气量在80%～130%负荷变化时,整套装置的动态响应过程,确定了常规PID控制方案对吸收剂循环量控制滞后,造成装置在高负荷下,产品气质量达不到标准;针对动态分析发现的问题,本文提出了一种比例控制方案,依据原料气的处理量的变化比例调节吸收剂循环量,并根据进入再生塔的富吸收液流量比例调节再生塔重沸器的负荷。动态分析表明,在原料气负荷变动条件下,装置可以始终保持产品气质量在要求之内,再生塔负荷也可以根据处理量的变化而自动调节,在低负荷运行时,降低装置的能耗。此比例控制方案大大提高了装置的操作弹性。     

HYSYS过程模拟软件在空分设备操作中的应用

描述了HYSYS过程模拟软件在空分设备操作中的应用,并介绍了软件应用过程中的体会。     

基于环境影响的过程模拟分析

在工艺设计阶段避免和减少污染产生是从源头控制污染产生的有效方法,将环境影响最小集成到化工过程模拟设计中,是近年来清洁生产领域快速发展的一项技术。本文采用废物削减WAR算法来衡量工艺流程的环境影响指标,并将WAR算法与模拟软件Aspen集成,提供在工艺设计阶段的环境分析工具。同时,将集成的环境影响软件应用于乙烷制备乙烯工艺的环境影响分析。     

基于Aspen Plus的燃煤电厂CO_2捕集系统模拟分析

针对乙醇胺溶液(MEA)化学吸收法在实际应用中能耗高的问题,在分析传统MEA法CO_2捕集工艺基础上,提出由菲涅尔式太阳能热泵和燃煤机组烟气余热共同提供再生热量的CO_2捕集技术。利用Aspen Plus软件对新型MEA法CO_2捕集技术模拟分析,获得具有代表性的预测结果。结果表明新型MEA法CO_2捕集工艺可节约63.80%的热能,新型CO_2捕集系统再生塔内的温度分布曲线更为平滑,更有利于CO_2快速分离。     

焦油粗酚的分离与精制流程模拟

基于一种新型的煤焦油粗酚产品分离加工方案,通过Aspen Plus对其进行了流程模拟,并通过对关键因素的灵敏度分析,讨论了相应因素的变化趋势,确定了其最优的加工方案,并给出了下一步工作建议。     

Simulation of 12-bed vacuum pressure-swing adsorption for hydrogen separation from methanol-steam reforming off-gas

This study focuses on analysis of a 12-bed vacuum pressure-swing adsorption (VPSA) process capable of purifying hydrogen from a ternary mixture (H₂/CO₂/CO 75/24/1 mol%) derived from methanol-steam reforming. The process produces 9 kmol H₂/h with less than 2 ppm and 0.2 ppm of CO2 and CO, respectively, to supply a polymer electrolyte membrane fuel cell. The process model is developed in Aspen Adsorption® using the “uni-bed” approach. A parametric study of H₂ purity and recovery with respect to adsorption pressure, adsorbent height, activated carbon:zeolite ratio, feed composition, and number of beds is performed. Results show 12-bed VPSA can meet the H₂ purity goals, with H₂ recovery as high as 75.75%. Adsorption occurs at 7 bar, the column height is 1.2 m, and the adsorbent ratio is 70%:30%. A 4-bed VPSA can achieve the same purity goals as the 12-bed process, but H₂ recovery decreases to 61.34%.     

Economic analysis of two processes for biodiesel production

In this study, two continuous processes for biodiesel production, alkali catalyzed process and solid acid catalyzed process, are discussed with the help of Aspen Plus. By comparing some economic indicators, it can be found that solid acid catalysis could be a good choice for investors in the near future.