A panel study on the relationship between biofuels production and sustainable development

Biomass is one of the renewable energy sources on which policy makers are greatly dependent on since it is a flexible feedstock capable of conversion into electricity, transport liquid fuels and heat by chemical and biological processes on demand. Though numerous publications have examined the relationship of economic growth with renewable energy and other parameters, biomass energy has never been included in these studies. Then, this study examines the causal relationship within a multivariate panel cointegration/error correction framework which combines the cross-section and time series data while allowing for heterogeneity across different provinces. After employing panel data regression model ranging from 2003 through 2012 to examine the relationships of biofuels production with sustainable development in China, the paper concludes that the development of biofuel energy production integrated with the consideration of the improvement of income per capita, and the attraction of more capital investment, does make a significant contribution to economic growth. However, some negative side effects including the increase of greenhouse emissions and the decrease of marginal land still coexist with the economic development. Of course, the importance of these findings lies on their implications and their adoption on strategic policies.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

Effect of alginate on the aggregation kinetics of copper oxide nanoparticles (CuO NPs): bridging interaction and hetero-aggregation induced by Ca<Superscript>2+</Superscript>

The stability of CuO nanoparticles (NPs) is expected to play a key role in the environmental risk assessment of nanotoxicity in aquatic systems. In this study, the effect of alginate (model polysaccharides) on the stability of CuO NPs in various environmentally relevant ionic strength conditions was investigated by using time-resolved dynamic light scattering. Significant aggregation of CuO NPs was observed in the presence of both monovalent and divalent cations. The critical coagulation concentrations (CCC) were 54.5 and 2.9 mM for NaNO₃ and Ca(NO₃)₂, respectively. The presence of alginate slowed nano-CuO aggregation rates over the entire NaNO₃ concentration range due to the combined electrostatic and steric effect. High concentrations of Ca²⁺ (>6 mM) resulted in stronger adsorption of alginate onto CuO NPs; however, enhanced aggregation of CuO NPs occurred simultaneously under the same conditions. Spectroscopic analysis revealed that the bridging interaction of alginate with Ca²⁺ might be an important mechanism for the enhanced aggregation. Furthermore, significant coagulation of the alginate molecules was observed in solutions of high Ca²⁺ concentrations, indicating a hetero-aggregation mechanism between the alginate-covered CuO NPs and the unabsorbed alginate. These results suggested a different aggregation mechanism of NPs might co-exist in aqueous systems enriched with natural organic matter, which should be taken into consideration in future studies.

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Graphical abstract Hetero-aggregation mechanism of CuO nanoparticles and alginate under high concentration of Ca²⁺

     

Rebound effect and its decomposition – an analysis based on energy types in China

Rebound effect derived from energy efficiency improvement has been widely invested. However, most of studies focus on the rebound effect of the energy composite level and neither distinguish nor compare different energy types. We compare the differences in energy saving and energy rebound between primary and secondary energy sources, and further decompose the rebound effect into production rebound part and final demand component. To do so, we add a module for rebound into a comparative state China-CGE model. We design and test two simulation scenarios using the model. In Scenario 1, all production sectors’ energy efficiency of using primary energy increases by 5%. In Scenario 2, all production sectors’ energy efficiency of using secondary energy increases by 5%. The results show that Scenario 2 leads to more GDP growth and more energy saving. Our scenarios show rebound effects range between 9.6% and 27.9%, and in general are higher when energy efficiency of using primary energy sources is improved. Our decomposition analysis shows that improving energy efficiency in production sectors would stimulates energy use of final demand. Indeed, the consumption side has significant contribution to rebound in secondary energy use, especially in crude oil and gas. This study reveals that improving efficiency of using secondary energy is better than improving that of primary energy, both in terms of economic impact and energy rebound. And complementary policies that prevent energy services prices from falling too much can be adopted to reduce rebound. Controlling residential energy use could also be effective in reducing rebound, this has particular implication to economies in which residential energy consumption are far from saturation.     

Automated data analysis to rapidly derive and communicate ecological insights from satellite-tag data: A case study of reintroduced red kites

Analysis of satellite-telemetry data mostly occurs long after it has been collected, due to the time and effort needed to collate and interpret such material. Delayed reporting reduces the usefulness of such data for nature conservation where timely information about animal movements is required. To counter this problem, we present a novel approach which combines automated analysis of satellite-telemetry data with rapid communication of insights derived from such data. A relatively simple algorithm (based on radial and angular velocity calculated from fixes) allowed instantaneous detection of excursions away from settlement areas and automated calculation of home ranges on the remaining data. Automating the detection of both excursions and home-range calculations enabled us to disseminate ecological insights from satellite-tag data instantaneously through a dedicated web portal. The automated analysis, interpretation, and communication of satellite-tag and other ecological data offer clear benefits to nature conservation research and practice.     

分子筛吸附法脱除废水中的聚丙烯酰胺

聚丙烯酰胺(PAM)用于油田驱油产生大量难处理的含聚废水。以分子筛为吸附剂处理含聚废水,研究分子筛类型(Y、Beta和ZSM-5,H型和Na型)和物化性质对其吸附PAM性能的影响,并测定吸附等温线。结果表明,吸附性能顺序为BetaYZSM-5,H型优于Na型。H-Beta对PAM的吸附来自分子筛中阳离子与PAM中阴离子的静电作用,Si—O和Al—OH与PAM中酰胺基的氢键作用。H-Beta开放的通道结构,较高的介孔比例和表面积,较强的酸性和良好的酸中心可接近性使其具有优异的吸附性能。Si O2/Al2O3=26的H-Beta对浓度为200 mg/L的PAM溶液,PAM脱除率可达95.2%。在低PAM平衡浓度时,PAM在H-Beta上的吸附符合Langmuir单层吸附特征,饱和吸附量达70.2 mg/g,在高浓度区域则由于PAM疏水缔合作用加强呈现多层吸附。     

不同培养条件下锰过氧化物酶(MnP)的合成及其对甲基橙的降解

高效、大规模、低成本合成木质素降解酶是直接采用其降解难降解有机污染物所必须解决的问题.对锰过氧化物酶(MnP)降解甲基橙和在非灭菌的反应器中连续合成MnP的可行性进行考察.结果表明,在采用2 mmol H2O2和1.5 mmol MnSO4的降解体系中,获最大脱色效果,且100、200和300 U/L的MnP可在8h内将甲基橙分别脱色18％、23％和35％;在非灭菌的反应器水平上实现了固定化培养的P.chrysosporium连续23 d合成MnP,但MnP酶活仅为2～ 23 U/L,难以酶解甲基橙;然而,在摇瓶培养条件下固定化的P.chrysosporium合成的MnP却能达1 152 U/L.因此,直接采用MnP对污染物进行降解以及在非灭菌的反应器中持续合成MnP是可行的,但就在非灭菌条件下如何提高MnP的合成量还有待开展深入的研究.     

用膨润土处理油田污水的研究进展

介绍了膨润土的基本特性及其存油田污水处理中的研究情况。天然膨润土经改性后,可吸附去除油田污水中的油、烃类衍生物、注聚残留聚合物及表面活性剂;负载金属氧化物的改性膨润土可对光催化氧化产生协同效应,提高光催化氧化油田污水中有机物的能力;在高压下将膨润土制成薄膜,对油田污水中的无机离了有一定的截留去除作用。     

纳米晶粒TiO2多孔微球的制备及其光催化性能的研究进展

介绍了纳米晶粒TiO2多孔微球的主要制备方法、主要参量及表征、光催化性能研究进展,指出了纳米晶粒TiO2多孔微球制备过程的技术关键与基本参量的功能。针对目前存在的问题,提出了今后的主要研究方向,并展望了纳米晶粒TiO2多孔微球在水处理领域的应用前景。     

基于WARMF模型的杭埠-丰乐河流域水文模拟研究

研究了WARMF模型在杭埠-丰乐河流域(巢湖流域最大支流)的水文模拟适应性能并进行了流域水文系统分析.利用AVSWAT2000模型将流域划分为37个子流域,利用流域地貌-土壤分布对应关系、土壤剖面结构、地下水位埋深等条件,确定了子流域的平面分组与剖面土层结构,较大程度上降低了流域模型参数校准的难度与不确定性.利用2000～2003年的水文观测数据,在参数灵敏度分析基础上,对模型水文参数进行了校准与检验.结果表明,WARMF在研究区具有较好的适应性能.WARMF模型与AVSWAT2000模型的水文模拟结果对比表明,WARMF模型具有更好的日拟合性能.基于模型的模拟结果,在空间尺度上定量分析了流域从降水开始到入湖的水循环过程,在时间尺度上分析了年内降雨、径流的分布及其对应关系.流域概化、模型的校准与检验以及流域水文时空变化的系统分析方法等对流域水文、环境的模拟研究与系统分析具有探索意义.