积极稳妥地开展中水回用

我国是世界上13个贫水国之一,水资源紧缺的问题在一些地区已制约经济和社会的发展。中水主要是指城市污水或生活污水经处理后达到一定的水质标准,可在一定的范围内重复使用的非饮用杂用水。中水的水质介于上水和下水之间,是水资源有效利用的一种形式。开展中水回收工作,已经显现出开源和减轻水污染的双重功能。     

用地下水资料探讨渭河盆地及周边地区地震形势

本文用陕、甘、宁、青的地下水前兆观测资料，运用异常系列方法的思路，研究了四省区中强地震的中期前兆指标，并进一步探讨了渭河盆地及周边的地震形势。     

Recent advances in activity inhibition,recovery and enhancement,and application of the anaerobic ammonium oxidation process北大核心CSCD

Anaerobic ammonium oxidation (Anammox) is a biological process that has the advantages of saving energy and being highly efficient. However, there are some problems with it such as slow proliferation and activity inhibition of Anammox bacteria in practical applications. First, we introduce the inhibitory effects of Anammox by substrate and non-substrate materials, including nitrite, organic and inorganic substances, and others, and elaborated on activity recovery approaches such as controlling the operation parameters and adding sludge. The effects of the addition of inorganic carbon, Anammox intermediates, Fe, and conductive materials on Anammox are then reviewed in detail, and three Anammox metabolic pathways are summarized. Finally, we discuss the problems (including NOB inhibition and low temperature) and countermeasures when Anammox is applied for wastewater treatment as a mainstream technology, and we summarized the measures of NOB washout by free ammonia and free nitrite solutions and methods to cope with low temperatures, including selecting suitable reactor types and culture methods. Meanwhile, we analyzed the interactions among the intermediates and the synergy between Anammox and denitrification and suggest the potential of applying Anammox for flue gas denitrification and anaerobic aromatic hydrocarbon biodegradation. Further studies are suggested regarding multifactor composite inhibition, Anammox physiological characteristics, and intermediate metabolic mechanisms as well as regarding methods for avoiding adverse factors in mainstream wastewater treatment. © 2018 Science Press. All rights reserved.     

Software for prioritizing conservation actions based on probabilistic information

Marxan is the most common decision-support tool used to inform the design of protected-area systems. The original version of Marxan does not consider risk and uncertainty associated with threatening processes affecting protected areas, including uncertainty about the location and condition of species’ populations and habitats now and in the future. We described and examined the functionality of a modified version of Marxan, Marxan with Probability. This software explicitly considers 4 types of uncertainty: probability that a feature exists in a particular place (estimated based on species distribution models or spatially explicit population models); probability that features in a site will be lost in the future due to a threatening process, such as climate change, natural catastrophes, and uncontrolled human interventions; probability that a feature will exist in the future due to natural successional processes, such as a fire or flood; and probability the feature exists but has been degraded by threatening processes, such as overfishing or pollution, and thus cannot contribute to conservation goals. We summarized the results of 5 studies that illustrate how each type of uncertainty can be used to inform protected area design. If there were uncertainty in species or habitat distribution, users could maximize the chance that these features were represented by including uncertainty using Marxan with Probability. Similarly, if threatening processes were considered, users minimized the chance that species or habitats were lost or degraded by using Marxan with Probability. Marxan with Probability opens up substantial new avenues for systematic conservation planning research and application by agencies.     

基于ACE法和模拟退火算法的土层等效参数反演

提出了一种基于ACE法和模拟退火算法的土层参数反演方法:一方面先用均匀试验构造出输入数据与输出数据,再利用ACE非参数回归方法构造两者之间的高效响应面,然后通过响应面关系进行插值运算来得出对应参数的响应值;另一方面根据响应面的关系构造出反演目标函数,利用模拟退火法对目标函数进行寻优计算,从而能避免寻优过程中的目标函数值...     

热电偶自动退火装置的研制

本文简述热电偶自动退火装置研制的意义,退火工作过程的要求和方法,并介绍了研制热电偶自动退火装置的依据,设计原理和特点。     

An improved grey wolf optimizer algorithm for identification and location of gas emission

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.