美国城市生活垃圾处理现状及思考

介绍了美国城市垃圾现状,美国环保总局对垃圾管理的政策及美国处理城市生活垃圾的方法。指出了我国处理城市生活垃圾存在的问题并提出了改进建议。     

垃圾填埋场渗滤液回灌法处理技术探讨

回灌法用于垃圾渗滤液的处理已逐渐成为研究的热点。通过对回灌法处理垃圾渗滤液的原理、特点及研究现状和进展的介绍。指出回灌法能有效地去除垃圾渗滤液中的有机物和重金属离子。但仍有待进一步完善和深入研究。     

垃圾渗滤液处理技术的现状及发展趋势

指出了城市垃圾的现状和垃圾渗滤液的特点及处理的意义.结合国内外的研究,分析了垃圾渗滤液处理技术的现状及发展趋势,介绍了多种处理渗滤液方法,并对垃圾渗滤液处理工艺的选取提出了一些建议.     

反渗透膜及其在城市垃圾渗沥液处理中的应用

对反渗透膜的发展过程、主要特性进行了综述,认为反渗透膜的应用处于高速成长期,具有广泛的应用前景.重点介绍了反渗透技术在国内外城市垃圾渗沥液处理中的应用.分析了反渗透技术的特点和优势,提出其在未来城市垃圾渗沥液处理中的发展趋势.     

城市生活垃圾处理现状及发展趋势

针对我国目前城市生活垃圾的现状和特点,探讨了城市垃圾产生量随年度变化的规律,垃圾产生量与人口和GDP的关系,分析了我国主要城市的垃圾组分。简要介绍了处理城市生活垃圾的几种方法和处理技术的发展趋势。     

城市生活垃圾处理现状及发展趋势

针对我国目前城市生活垃圾的现状和特点，探讨了城市垃圾产生量随年度变化的规律，垃圾产生量与人口和GDP的关系，分析了我国主要城市的垃圾组分。简要介绍了处理城市生活垃圾的几种方法和处理技术的发展趋势。     

渣土消纳场再利用工程中生活垃圾的稳定状态评价

采用钻孔取样的方法,利用垃圾气体甲烷浓度、垃圾生化产甲烷能力等两项参数,通过综合指数计算评价方法对大型渣土消纳场内不同深度生活垃圾的稳定状态进行了评价。结果表明,该渣土消纳场内大部分区域生活垃圾处于稳定状态,不会对场地复用造成影响。少数区域生活垃圾含量较高,处于中度不稳定状态,地基处理应适当考虑其影响。     

垃圾填埋场安全隐患处理对策及其工程应用

在我国大城市中,由于垃圾填埋场启用时间早,有相当数量的填埋场在最初的选址、设计和运行管理中不可能按现行的城市生活垃圾卫生填埋技术规范执行.垃圾产生的渗漏液和填埋气体(主要是甲烷气体)给周边企业、社区带来了污染和气体火灾爆炸危险隐患.论述了垃圾填埋场运行管理及封场施工中的安全隐患及处理方法.     

生活垃圾预处理后厌氧发酵产沼气的研究

<正>引言厌氧发酵之后产生的沼气经过净化是一种无污染的气体燃料而且具有很高的利用价值。因此进行生活垃圾预处理之后生成的沼气,不仅能够最大程度地有效减少垃圾对环境的污染,同时还可以综合利用垃圾和沼气,实现其经济效益。这对缓解生活垃圾、能源紧缺等问题具有重要的意义。生活垃圾预处理技术合理利用机械生物预处理技术在应用这项技术的过程中,可以利用现代生物技术来处理城市生活垃圾。垃圾中的玻璃、金属等材料属于可循环利用的范     

基于百年一遇高潮淹没的沿渤海垃圾填埋场环境污染风险评估

为评估沿渤海百年一遇高潮淹没区垃圾填埋场的环境污染风险,采用垃圾填埋场和验潮站的位置数据、百年一遇高潮位数据和GEBCO数字高程模型数据,利用地图制作软件和地理信息系统软件,制作并叠加百年一遇高潮淹没区图和垃圾填埋场分布图,得到百年一遇高潮淹没区内共有15座垃圾填埋场的结果.15个填埋场的环境污染风险评估表明:1)曹妃甸区危废和固废处理处置中心环境污染风险很高,东营市河口区生活垃圾(含石油)填埋场、大韩庄垃圾填埋场和尖子沽生活垃圾填埋场环境污染风险中等,盘锦市生活垃圾填埋场、营口市永远角垃圾填埋场和军粮城简易垃圾填埋场的环境污染风险低,大连市金州西海岸垃圾填埋场、东营市生活垃圾无害化处理场、营口市同方生活垃圾处理厂、唐海无害化生活垃圾填埋场、大港生活垃圾填埋场、东营市河口区生活垃圾综合处理厂、汉沽垃圾填埋场和昌邑市第二生活垃圾场环境污染风险为很低;2)曹妃甸区危废和固废处理处置中心、大韩庄垃圾填埋场和尖子沽生活垃圾填埋场应修建隔离堤进行保护;东营市河口区生活垃圾(含石油)填埋场的垃圾应清运至东营市河口区生活垃圾综合处理厂进行处理;营口市永远角垃圾填埋场封存的垃圾应清运至营口市同方生活垃圾处理厂进行无害化处理.     

改性粉煤灰去除硝基苯废水的研究

粉煤灰经过废硫酸改性处理后吸附能力大大提高,用改性粉煤灰对生化后的硝基苯废水进行了实验研究,实验考察了粉煤灰的投加量、pH值、吸附时间对硝基苯去除率的影响,并对机理进行了分析.粉煤灰改性后应用于硝基苯废水处理中达到以废治废,具有一定的经济意义.     

采空场处理与利用

地下采空场既是一种隐患，又是一种资源。介绍了采空场的若干应用；包括水充填、尾砂充填、垃圾充填，对空场处理有借鉴意义。     

化学品污染的成因及预防

世界化学工业迅速发展,各种化学品的产量大幅度增加,新化学品不断涌现。人们在充分利用化学品的同时,也产生了大量的化学废物,其中不乏有毒有害物质。由于毫无控制地随意排放及化学品其他途径的泄放,使环境状况日益恶化,如何认识化学品污染的成因及危害,最大限度地降低化学品污染,加强环境保护力度,已是人们亟待解决的重大问题。     

GIS技术在瓦斯动力灾害预测中的应用

GIS系统是一种重要的空间信息系统,可以采集、存贮、管理、描述和分析与空间和地理分布相关的数据,适时地提供多种空间和地貌的信息.本文结合平顶山矿区的生产实际,将GIS技术引入煤与瓦斯突出区域的预测中,建立相应的信息管理系统,提高了信息处理水平.     

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.     

人工神经网络在煤与瓦斯突出预测中的应用

由于煤与瓦斯突出发生机理的复杂性,传统预测方法的应用受到很大的限制,而人工神经网络理论以其高度非线性映射的特性为解决这一问题提供了新的途径。以突出预测指标为基础,利用多层反向传播神经网络(BP网络)模型实现对突出危险性的预测。实例分析表明,模型精度很高,可用于工作面煤与瓦斯突出危险性的预测。     

On prediction in road safety

Prediction is about potential outcomes: what will happen if and what would have happened if. The first question arises when safety targets are set, the second when the effect of an intervention on safety is to be evaluated. There are many ways to predict. For the same data different prediction methods produce different predictions. What targets are set and what estimates of intervention effect are produced will depend on what method of prediction is chosen. Therefore one has to determine what method tends to predict best. To do so empirically one asks what method would have predicted best had it been applied in the past and then one assumes, inductively, that the same would apply in the future. Quantitative measures of prediction quality are suggested and it is shown how these measures of prediction quality allow one to determine which of two prediction methods should be preferred.The suggested approach was applied to two data sets: The time series of motor vehicle accident fatalities in Province A and in Province B. On the basis of this analysis one may draw tentative conclusions for these jurisdictions and the methods tested; one can say what method seems preferable, what is the average size of bias than needs to be corrected and how accurate is the prediction likely to be. Broader conclusions will emerge once many additional methods of prediction are applied to data from many other jurisdictions and pertaining to a variety of circumstances.     

Emergency resources demand prediction using case-based reasoning

The demand prediction on emergency resources is the premise and basis of optimal allocation of emergency resources. Nowadays, there are only few researches on this aspect in China and abroad. For this reason, the paper aims at the characteristics of emergency resource demand prediction and presents a method for emergency resource demand prediction using case-based reasoning (CBR), which is also a method based on risk analysis. This prediction method cannot only provide a basis for emergency resource reserve and allocation in future, but also provide a method and model support for the emergency resources allocation decision-making system to be constructed in future.     

交通事故预测分析

运用模糊图、灰色系统、非线性回归、随机过程和安全系统工程的理论与方法，进行交通事故预测分析。给出了交通事故预测分析的双向模糊图模型、灰色马尔可夫预测模型及非线性回归模型。模型运行结果可为交通事故的预防和交通安全管理提供科学依据     

自回归模型在井水埋深预测中的应用及改进

用时间序列分析方法建立井水埋深的预测模型时,首先采用差分的方法把季节性时间序列变成平稳时间序列,在此基础上,再用动态数据系统方法的传统F检验定阶法进行分析。由于样本的随机性可能过早地退出对模型的循环检验,从而不能找到合适的预测模型。笔者在用自回归模型建立井水预测模型的基础上,采用了一种改进的建模方法,提高了预测精度,并用实例进行了验证。