农村地区秸秆固化成型利用最佳收集半径研究——以成都市为例

实现农村地区大量零碳秸秆资源能源化利用的第一步是需要解决秸秆原料收集半径难以确定等问题。为此,基于“代加工”模式和成都市农村地区秸秆资源量等基础信息,并根据农作物实际的种植时间,建立了适合农村地区的秸秆收集半径计算模型,确定了具有最佳经济性的秸秆最佳收集半径和相应半径下用秸秆颗粒替代煤炭的环境效益。结果表明,成都市秸秆资源丰富,2021年理论秸秆资源总量为389.48万t,其中谷类秸秆和油菜秸秆的资源量最多,分别占总量的51.09%和21.12%。成都市农村地区秸秆最佳收集半径为5.92km,对应的秸秆颗粒生产成本约483.00元·t^(-1),明显低于购买商品化秸秆颗粒(约600元·t^(-1))和燃煤(约950元·t^(-1))的价格,颗粒总产量为33750.17t。秸秆收集半径受单位面积秸秆资源量和运输车辆设计参数的影响最大,而受其他参数的影响较小。若使用秸秆颗粒替代煤炭燃料,将减少41.99%的CO,85.76%的PM2.5,99.44%的SO2排放以及51346t的CO_(2)排放,具有显著的环境效益。

Advanced yolk-shell nanoparticles as nanoreactors for energy conversion

Yolk‐shell structured nanoparticles are of immense scientific and technological interests because of their unique architecture and myriad of applications.This review summarizes recent progresses in the use of yolk‐shell structured nanoparticles as nanoreactors for various chemical reactions.A very brief overview of synthetic strategies is provided with emphasis on recent research progress in the last five years.Catalytic applications of these yolk‐shell structured nanoreactors are then discussed by covering photocatalysis,methane reforming and electrochemical conversion.The state of the art research and perspective in future development are also highlighted.

Analysis of Methodology for the Application of Stratified Random Sampling with Optimum Allocation： The Case Study of Forest Bioenergy

In this paper, analysis of methodology was realized for the application of stratified random sampling with optimum allocation in the case of a subject of research which concerns the rural population and presents high differentiations among the three strata in which this population could be classified. The rural population of Evros Prefecture （Greece） with criterion the mean altitude of settlements was classified in three strata, the mountainous, semi-mountainous and fiat population for the estimation of mean consumption of forest fuelwood for covering of heating and cooking needs in households of these three strata. The analysis of this methodology includes： （1） the determination of total size of sample for entire the rural population and its allocation to the various strata; （2） the investigation of effectiveness of stratification with the technique of analysis of variance （One-Way ANOVA）; （3） the conduct of sampling research with the realization of face-to-face interviews in selected households and （4） the control of forms of the questionnaire and the analysis of data by using the statistical package for social sciences, SPSS for Windows. All data for the analysis of this methodology and its practical application were taken by the pilot sampling which was realized in each stratum. Relative paper was not found by the review of literature.

Development and Commercialization of New Autonomous Low Power Systems for Energy Harvesting

This paper evaluates new technologies for energy harvesting from the environment. We assess the reliability, accuracy, and autonomy of low power electronic devices, their market opportunities, and their ability to enhance sustainable development. For small, portable applications that need long lifetime energy supply systems, size, width, finite energy lifetime, and the need for replacement are important drawbacks. New supply alternatives should be explored that are able to make recharging easier or to remove battery dependence altogether. A new trend in the energy sources for low power applications has emerged in recent years, involving the harvesting of the energy available in the environment to supply power for electronic applications instead of using battery technologies that provide only short, finite amounts of energy. In this paper, we describe an all-purpose module of energy harvesting from mechanical, thermal, solar and RF energy sources developed at the University of Barcelona. We provide proof of concept in order to incorporate this novel energy technology in a wide range of applications and environments.

Status of Renewable Energy Applications in the Malaysian Energy Mix

Demand for power in Malaysia is projected to double the current demand in 2030. The current fuel mix for power generation is heavily dependent on fossil fuels. This has created energy supply and environmental sustainability concerns to the industry. Furthermore, at the recent United Nations Climate Change Conference 2009-15th Conference of Parties （COP15）, Malaysia had given the commitment to voluntary reduction of up to 40% in terms of emissions intensity of GDP by the year 2020 compared to 2005 levels. Renewable energy （RE） provides the best alternative in addressing these issues. In 2001, RE was regarded as the fifth fuel in the new Five Fuel Strategy of the National Energy Policy. Small Renewable Energy Program （SREP） was launched with the main objective of supporting the government＇s strategy to intensify the development of RE as the fifth fuel resource. Biomass, the largest amongst the main RE resources available, can be used for heat and power generation. With optimum utilization, the value ofbiomass resources in Malaysia is estimated to be more than RM 500 billion （USD 156 billion） over the next 20 years. Malaysia has the capacity to use renewable energy resources to address the national energy agenda.

A hybrid dynamic programming-rule based algorithm for real-time energy optimization of plug-in hybrid electric bus

The optimization of the control strategy of a plug-in hybrid electric bus(PHEB) for the repeatedly driven bus route is a key technique to improve the fuel economy. The widely used rule-based(RB) control strategy is lacking in the global optimization property, while the global optimization algorithms have an unacceptable computation complexity for real-time application. Therefore, a novel hybrid dynamic programming-rule based(DPRB) algorithm is brought forward to solve the global energy optimization problem in a real-time controller of PHEB. Firstly, a control grid is built up for a given typical city bus route, according to the station locations and discrete levels of battery state of charge(SOC). Moreover, the decision variables for the energy optimization at each point of the control grid might be deduced from an off-line dynamic programming(DP) with the historical running information of the driving cycle. Meanwhile, the genetic algorithm(GA) is adopted to replace the quantization process of DP permissible control set to reduce the computation burden. Secondly, with the optimized decision variables as control parameters according to the position and battery SOC of a PHEB, a RB control is used as an implementable controller for the energy management. Simulation results demonstrate that the proposed DPRB might distribute electric energy more reasonably throughout the bus route, compared with the optimized RB. The proposed hybrid algorithm might give a practicable solution, which is a tradeoff between the applicability of RB and the global optimization property of DP.

Exergy life cycle assessment model of “CO_2 zero-emission” energy system and application

An exergy life cycle assessment(ELCA) model based on life cycle assessment(LCA) and exergy methodology was developed to assess a 2×300 MW coal-fired power plant,and the results indicated that the exergy input in operation phase of power plant accounts for 99.89% of the total input and only 0.11% in construction and decommission phases. Direct and indirect exergy inputs account for 93.03% and 6.97%,respectively. Compared with coal-fired power generation system before carbon emission reduction,exergy input-output ratio of life cycle "CO2 zero-emission" energy system and exergy efficiency are about 5.563 and 17.97%,respectively,which increases by 62.47% and declines by 11.21% approximately. The model quantifies the energy,resource consumption and pollutant emissions of system life cycle using exergy as the basic physical parameter,which will make the assessment more objective and reasonable.