Algal biomass as a global source of transport fuels: Overview and development perspectives

As a result of the global fuel crisis of the early 1970 s,coupled with concerns for the environment,the use of biofuel has been on the increase in many regions throughout the world.At present,a total of approximately 30 billion(30×109) liters of biofuel are utilized worldwide annually,although most countries rely hugely on the first generation biofuel.The limitations of the first and second generation biofuel gave rise to current interest in algae as a promising alternative to these conventional biofuel sources.Algal biomass could provide a lion’s share of the global transport fuel requirements in future.The present review highlights some important developments in,and potentials of algaculture as a major biomass resource of the future.However,the major constraint to commercial-scale algae farming for energy production is the cost factor,which must be addressed adequately before its potentials can be harnessed.     

硫酸中香蕉皮还原低品位锰矿的浸出行为及动力学模型

Manganese was leached from a low-grade manganese ore (LGMO) using banana peel as the reductant in a dilute sulfuric acid medium. The effects of banana peel amount, H₂SO₄ concentration, reaction temperature, and time on Mn leaching from the complex LGMO were studied. A leaching efficiency of ~98% was achieved at a leaching time of 2 h, banana peel amount of 4 g, leaching temperature of 120°C, manganese ore amount of 5 g, and sulfuric acid concentration of 15vol%. The phase, microstructural, and chemical analyses of LGMO samples before and after the leaching process confirmed the successful leaching of manganese. Furthermore, the leaching process followed the shrinking core model and the leaching rate was controlled by a surface chemical reaction (1 ? (1 ? x)1/3 = kt) mechanism with an apparent activation energy of 40.19 kJ·mol?1.     

Term Structure Forecasting of Government Bond Yields with Latent and Macroeconomic Factors: Do Macroeconomic Factors Imply Better Out‐of‐Sample Forecasts?

This study empirically examines the role of macroeconomic and stock market variables in the dynamic Nelson–Siegel framework with the purpose of fitting and forecasting the term structure of interest rate on the Japanese government bond market. The Nelson–Siegel type models in state‐space framework considerably outperform the benchmark simple time series forecast models such as an AR(1) and a random walk. The yields‐macro model incorporating macroeconomic factors leads to a better in‐sample fit of the term structure than the yields‐only model. The out‐of‐sample predictability of the former for short‐horizon forecasts is superior to the latter for all maturities examined in this study, and for longer horizons the former is still compatible to the latter. Inclusion of macroeconomic factors can dramatically reduce the autocorrelation of forecast errors, which has been a common phenomenon of statistical analysis in previous term structure models. Copyright © 2013 John Wiley & Sons, Ltd.