瑞利波预测煤与瓦斯突出

瑞利波勘探是近年来发展起来的一种新型的岩土原位测试勘探方法。笔者重点对瞬态瑞利波相速度的算法进行了研究。提出了对信号进行预先调制 ,随后再对所求得的相位进行修正的方法 ;另为还须考虑其仪器采集信号时的时间差影响。根据以上算法求得的相速度更接近瑞利波真速度 ,从而提高了相速度的分辨率和精确度。把瞬态瑞利波勘探法运用在矿山生产中 ,利用平面瑞利波的频散特性 ,根据瑞利波的传播速度与煤岩的物理力学性质具有相关性 ,提出了一种新型的预测煤与瓦斯突出的方法。为矿井安全生产预测预报瓦斯突出提供了一种新的手段 ,同时对突出煤层掘进前方突出危险区和安全距离的分析判断提供了一种新的方法     

基于灰色GM(1,1)模型的铁岭市工业废水排放量预测

建立铁岭市工业废水排放量预测模型,预测2010～2015年铁岭市工业废水排放量.根据工业废水排放量数据序列特征,将灰色系统理论GM(1 ,1) 模型的建模方法用于构建铁岭市工业废水排放量预测模型,并用GM(1 ,1)残差模型对模型进行修正.利用1999～2007年铁岭市工业废水排放量原始数据与预测数据比较分析,误差较小...     

污水厂出水COD预测模型的对比分析

根据污水厂日报表中的数据分别建立了ARMA、逐步回归分析、基于回归分析的神经网络模型和基于时间序列分析的神经网络模型,通过比较选择了基于时间序列分析的神经网络模型作为对污水厂出水COD的预测模型,其平均预测精度为85%,取得了满意的预测结果,有利于克服根据在线监测调整工艺参数的滞后性的缺陷,保证出水水质.     

On several issues regarding efforts toward a sustainable society

Environmental issues and the future sustainability of society are among the greatest concerns facing society today. How to formulate a pathway toward a sustainable society is a critical question. Several issues associated with this question are presented and discussed. First, a structuring of the issues is presented. The environment can be said to consist of three systems—the natural, social, and human—and their interactions; environmental problems may therefore be defined in terms of perturbations of the interactions among the three systems. A sustainable society can be realized by restoring these interactions. Next, the characteristics of the issues are discussed. Because environmental issues relate to the future, forecasts of the future are essential. Because it is impossible to predict the future with complete accuracy, however, we should develop a method of using information about the future with allowance for error. It should be noted that error characteristics differ according to their time-scale. Third, the relationship between environmental issues and society is discussed. To take collective action on these issues we need society-wide consensus, which requires a reliable and objective platform. Here, more attention must be paid to the distribution of knowledge across society, because scientific knowledge in a modern society tends to be monopolized by research organizations. The role of the media is therefore important. Another important factor is the commitment of the general public; user-friendly ways of galvanizing such commitment should be developed.

基于人工神经网络的松花江朱顺屯断面的水质预测

利用BP神经网络进行短期水质预报并进行水质预测后再评价对环境管理和规划有重要意义。本文建立了朱顺屯断面5个目标水质参数NH3-N、DO、高锰酸盐指数、TN和TP的BPANN水质预测模型,并对实例进行了验证,结果表明建立的推进式ANN可以应用于此类时间序列的环境预测,网络泛化性能好,能够满足实际应用。     

Minimum ignition energy theoretical model for flammable gas based on flame propagation layer by layer

To achieve the rapid prediction of minimum ignition energy (MIE) for premixed gases with wide-span equivalence ratios, a theoretical model is developed based on the proposed idea of flame propagation layer by layer. The validity and high accuracy of this model in predicting MIE have been corroborated against experimental data (from literature) and traditional models. In comparison, this model is mainly applicable to uniform premixed flammable mixtures, and the ignition source needs to be regarded as a punctiform energy source. Nevertheless, this model can exhibit higher accuracy (up to 90%) than traditional models when applied to premixed gases with wide-span equivalence ratios, such as C₃H₈-air mixtures with 0.7–1.5 equivalence ratios, CH₄-air mixtures with 0.7–1.25 equivalence ratios, H₂-air mixtures with 0.6–3.15 equivalence ratios et al. Further, the model parameters have been pre-determined using a 20 L spherical closed explosion setup with a high-speed camera, and then the MIE of common flammable gases (CH₄, C₂H₆, C₃H₈, C₄H₁₀, C₂H₄, C₃H₆, C₂H₂, C₃H₄, C₂H₆O, CO and H₂) under stoichiometric or wide-span equivalence ratios has been calculated. Eventually, the influences of model parameters on MIE have been discussed. Results show that MIE is the sum of the energy required for flame propagation during ignition. The increase in exothermic and heat transfer efficiency for fuel molecules can reduce MIE, whereas prolonging the flame induction period can increase MIE.     

江苏中部农业园小麦和土壤镉元素含量关系研究

为研究江苏中部农业园土壤和小麦镉元素含量［ω(Cd_soil)和ω（Cd_wheat）］关系,采集了土壤和小麦样品40组,采用多元线性回归分析方法建立ω(Cd_wheat)的预测模型。结果表明：（1）研究区表层土壤呈中性偏弱酸性,ω(Cd_soil)含量范围为0.083~0.239 mg/kg,平均值为0.152 mg/kg,均低于《土壤环境质量 农用地土壤污染风险管控标准（试行）》（GB 15618—2018）中农用地土壤污染风险筛选值,属于优先保护类土壤;（2）依据《食品安全国家标准 食品中污染物限量》（GB 2762—2017）中ω(Cd_wheat)限定值(0.1 mg/kg),小麦籽实Cd超标率为10%;（3）ω(Cd_wheat)主要受表层ω(Cd_soil)控制,同时受到土壤钼（Mo）、铅（Pb）、砷（As）、钙（Ca）和镉（Cd）等元素有效态影响,另外,还受土壤理化性质（pH值和有机质）的影响。     

BTP-N_2复合气体配比优化及灭火临界条件研究

为了考察一溴三氟丙烯(简称BTP)与氮气(N2)所形成的复合灭火介质的复合比以及不同复合比下的临界灭火条件,开展了以下工作:首先,基于多组分分压原理,探讨了BTP-N2预混技术的可行性;其次,基于燃烧学和气体运动学理论,提出了BTP与N2复合灭火介质的灭火临界条件预测理论模型;再次,依据卤代烷灭火介质的特点和灭火机理,构建了临界灭火试验平台,对不同BTP和N2复合比下的复合气体灭火介质进行灭火临界条件研究;最后,通过实验结果修正和完善预测理论模型。结论:BTP-N2复合灭火介质的灭火临界条件理论值与实验值吻合性较好,因此该预测模型能很好地对复合卤烃灭火介质的临界灭火条件进行估算,为研究卤烃气体与惰性气体复合灭火介质的灭火机理及研发高效清洁灭火介质奠定了基础。     

Spatial prediction of soil contamination based on machine learning: a review

● A review of machine learning (ML) for spatial prediction of soil contamination. ● ML have achieved significant breakthroughs for soil contamination prediction. ● A structured guideline for using ML in soil contamination is proposed. ● The guideline includes variable selection, model evaluation, and interpretation. Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.     

电厂环境噪声模型的研究

为了开展电厂环境噪声预测及评价工作,提出了电厂环境噪声预测数学模型,研制出电算程序。此模型吸取了众多模型的优点,声源模式有点源、分布面源、体源和线源;传播方式分近场和远场;障碍物影响考虑了反射,衍射和衍射的反射。用此模型对耒阳电厂环境噪声进行计算,计算与实测结果表明,此模型精度高,软件实用、方便。