含聚油泥处理工艺参数优化及交互作用分析

采用自制的油泥分离剂通过热化学分离法处理聚驱油田现场产生的含聚油泥。采用正交实验得到的最佳工艺参数为:剂泥比2.0 m L/g,反应温度80℃,反应时间30 min,搅拌转速500 r/min,在此工艺条件下原油回收率为92.08%。利用支持向量机运算法(SVM)建立模型,分析了各工艺参数之间的交互作用,得出优化后的含聚油泥处理工艺参数为:剂泥比2.5 m L/g,反应温度80℃,反应时间34 min,搅拌转速530 r/min,理论上的最高原油回收率为94.76%。对于模型优选出的工艺参数进行了5组验证实验,平均原油回收率达94.50%。采用优选工艺参数处理3种不同来源的含聚油泥,原油回收率均高于90%。     

碳交易机制下可再生能源投资价值与投资时机研究

中国承诺2017年启动全国性碳排放交易系统,在碳交易机制下,可再生能源项目可以通过CCER交易获得额外收益。由于碳价格是随着市场条件而随机波动的,碳价波动性使得可再生能源发电项目投资具有了期权性质的权利,即未来不确定性可能包含更高的价值。可再生能源企业在做投资决策时,可以选择立即投资,也可以选择推迟投资,等待更多信息来提高项目收益,而立即投资的项目回报必须足够高以克服等待期权的蕴含价值。为了研究碳价波动下可再生能源项目投资,本文采用实物期权法的三叉树模型,测算了三类可再生能源发电项目投资的npv及其实物期权价值(ROV)。根据延迟实物期权决策规则,三类项目均执行期权延迟投资决策。本文还计算了在无政府补贴和有政府补贴两种情形下,三种可再生能源项目在不同时点的栏杆价格,以确定项目的投资时机。栏杆价格随着政府补贴的增加而逐步下降,说明政府补贴会促进可再生能源项目投资;栏杆价格随着时间的推移而逐步上升,意味着时间跨度越大,不确定性越大,需要更高碳市场价格来确定投资时机。本文对影响可再生能源项目投资因素敏感性进行了分析,结果表明碳价波动率与可再生能源发电项目的栏杆价格呈现正相关的关系,说明碳价波动性增加了企业投资的期权价值,却推迟了企业开展投资的时间。随着中国碳交易体系的不断完善,碳价波动幅度会趋于平稳,从而促进发电企业进行可再生能源发电项目投资。     

近30年来鄱阳湖环湖区土地利用与景观格局变化研究

以1988—2018年7期Landsat遥感卫星影像为数据源,采用土地利用转移矩阵、景观格局指数等方法探究了鄱阳湖环湖区近30年来土地利用与景观格局变化特征.结果表明：(1)近30年来鄱阳湖环湖区建设用地和林地面积显著增加,耕地、草地、水域和未利用地面积减少.(2)建设用地的转入类型以耕地为主,30年间共侵占耕地面积1243.66 km²,占建设用地面积增加量的71.19%.林地的转入类型主要为耕地和草地,其中,耕地转入占比56.95%.耕地的转出类型以建设用地、草地和林地为主,且1999年以后随着城镇化的发展耕地主要转向建设用地.(3)近30年来鄱阳湖环湖区景观总体破碎程度逐渐增大,景观斑块个数共增加63492个,增幅为11.68%.景观连通性降低,各类型土地呈均衡化趋势分布,景观异质性增加.研究结果可为推动鄱阳湖环湖区土地资源保护、生态环境保护和经济协同发展提供参考依据.     

运用价格杠杆，促进环境保护产业的发展

环境保护产业作为“朝阳产业”,正在成为新的经济增长点。应运用价格杠杆,在建设骨干企业集团、健全环境保护产品市场体系、排污收费制度、价格信息服务等方面,推动环保产业的发展     

In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods

There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods.     

基于支持向量机(SVM)的民用飞机重着陆智能诊断研究

针对国内航空公司对于重着陆的判断方法存在的不足,提出采用支持向量机(SVM)建立重着陆的智能诊断模型;分析对重着陆产生影响的相关因素,在力学基础上揭示了重着陆的产生原理;利用快速存取记录器中记录的多个飞行参数的信息,采用B737机型的实际样本数据进行训练和验证。结果表明:该方法能有效判断出是否发生重着陆,其准确率高达92.86%,证明该重着陆智能诊断方法具有较强实际应用价值,为后续研究奠定了基础。     

用于早期火灾分类的非线性决策树支持向量机

对早期火灾信息进行研究,提出了一种基于非线性决策树的支持向量机多类分类模型。该模型利用非线性映射将样本投影到高维特征空间,比较每类样本在高维空间的分布情况,进行聚类构造出一个二叉决策树,使容易区分的类别从根节点逐层分类出来,有效克服了错分积累和避免不可分情况;同时,各个节点采用二值最小二乘小波支持向量机,以获得较高的泛化能力。该文将该模型用于早期火灾分类,并与BP神经网络、K近邻法和决策树方法进行比较,实验结果表明,该模型对早期火灾的识别率更高。     

A Multi‐Scale Analysis of Low‐Rise Apartment Water Demand through Integration of Water Consumption,Land Use,and Demographic Data

Over the past decades, multi‐unit housing developments have been vastly expanded across urban areas due to the population growth. To properly supply water to this growing sector, it is essential to understand the determinants of its water use. However, this task has largely remained unexplored through the empirical study of water demand mainly due to the scarcity of data in this sector. This study integrated apartment water consumption, property characteristics, weather, water pricing, and census microdata to overcome this issue. Using a rich source of GIS‐based urban databases in Auckland, New Zealand, the study developed a large dataset containing the information of 18,000 low‐rise apartments to evaluate the determinants of water use both in the household scale and aggregated scale. The household‐scale demand analysis helped to assess the heterogeneity in responses to the demand drivers specifically water price across different consumer groups, whereas the aggregated analysis revealed the determinants behind the spatial variation in water demand at the census area unit level. Through applying panel data models, the study revealed the household size as the most important determinant of apartment water use in Auckland, where other socioeconomic factors, building features, and water pricing were not significant determinants. This knowledge of determinants of water demand can help water planners to better manage water demand in the compact urban environments.     

基于时变参数模型的中国区域碳排放权价格调控机制研究

区域碳排放权价格的差异制约了中国全国性碳市场的建立,亟需寻求价格调控方式。本文通过Phillips and Sul模型分析了北京、深圳、上海等碳排放权价格收敛性,结果显示单纯市场作用无法形成统一价格,进而运用状态空间方法构建时变参变量模型,从能源价格、经济发展和政策制度等角度,分析了碳排放权价格差异的影响因素组成结构,提出了价格调控机制。认为降低焦炭价格、减缓经济发展速度、提升交易市场活跃程度以及加大违约处罚力度,是缩小中国区域碳排放权价格差异,形成统一价格的有效调控方式,为建立和完善中国统一的碳排放权交易体系提供了决策支持。