遥感在滑坡调查中的应用

本文介绍了遥感技术在滑坡的现状调查和防治中的应用.提出了航天、航空遥感图像上滑坡现状、规模、易滑地点、稳定性评价等的直接和间接判读标志;并展望了遥感技术在滑坡动态监测中的应用前景.     

Purification of spent cathode carbon from aluminum electrolysis cells by alkali fusion-acid leaching process

Journal of Material Cycles and Waste Management - Spent cathode carbon (SCC), with high recycling potential of carbon, is an unavoidable solid waste in the aluminum electrolysis industry. In this...     

CDM Project Activities in China＇s Iron and Steel Industry：Current Status,Barriers and Development Strategies

China is now facing huge pressure from both the domestic concern of energy security and the global community's call for emission reduction commitment. As one of the major energy consumers and greenhouse gas emitters, China's iron and steel industry has a huge clean development mechanism (CDM) potential. This article both quantitatively and qualitatively analyzes the current status of CDM project activities in the iron and steel industry in China, including characteristics of approved project types, applicable methodologies, and potential technology fields. From the perspective of project implementation, the article summarizes development barriers such as high investment risk, difficulty in project identification, strict requirements on PPDs, long registration waiting time, and etc. Policy suggestions are also put forwarded to help better promote the development of CDM projects in the iron and steel industry.     

湿式静电除尘器在火电厂大型机组中的应用

为了满足最新的GB 13223—2011《火电厂大气污染物排放标准》的烟尘排放要求(一般地区30 mg/m3,重点地区20 mg/m3),需要对现有电除尘器进行改造。湿式静电除尘器可以降低粉尘、液滴和重金属的排放浓度,并同时解决湿法脱硫系统的浆液夹带而产生的石膏雨问题,是目前实现烟尘达标排放的最佳选择方案之一。因此国电集团决定在益阳电厂300MW大型机组的电除尘器烟尘减排技术改造中采用自主研发的湿式静电除尘技术(即柔性电极深度净化技术),该项目作为示范项目,受到集团公司和环保业界的高度关注。调试结果表明:该技术具有适应性强、不受煤种、烟尘特性影响,除尘和去除液滴效率高等优点,具有很广泛的发展前景。     

Modeling the spill in the Songhua River after the explosion in the petrochemical plant in Jilin

An explosion in a petrochemical plant in Jilin in the northeast of China on 13 November 2005 was responsible for the discharge of large quantities of benzene and nitrobenzene into Songhua River. This endangered the water supply of Harbin city and influenced the daily life for millions of people. The dispersion-advection equation was solved analytically and numerically and used to simulate the concentration of benzene and nitrobenzene in the Songhua River after the accident. Both solutions gave practically identical results. The main elimination process for both compounds was volatilization. The model results are quite close to the results obtained by measurements at monitoring stations. Arrival time of the pollutant wave, peak concentrations and end of the pollutant wave at Harbin and along the river were predicted successfully. The peak concentrations of nitrobenzene at Harbin were more than 30 times above the permissible limits for drinking water.     

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov chain Monte Carlo algorithm

● A hydrodynamic-Bayesian inference model was developed for water pollution tracking. ● Model is not stuck in local optimal solutions for high-dimensional problem. ● Model can estimate source parameters accurately with known river water levels. ● Both sudden spill incident and normal sewage inputs into the river can be tracked. ● Model is superior to the traditional approaches based on the test cases. Water quality restoration in rivers requires identification of the locations and discharges of pollution sources, and a reliable mathematical model to accomplish this identification is essential. In this paper, an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge. The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method (MCMC). The methodological framework is tested using a designed case of a sudden wastewater spill incident (i.e., source location, flow rate, and starting and ending times of the discharge) and a real case of multiple sewage inputs into a river (i.e., locations and daily flows of sewage sources). The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites. It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space. In comparison, the inverse models based on the popular random walk Metropolis (RWM) algorithm and microbial genetic algorithm (MGA) do not produce reliable estimates for the two scenarios even after multiple simulation runs, and they fall into locally optimal solutions. Since much more water level data are available than water quality data, the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data, especially for rivers receiving significant sewage discharges.