Influence evaluation of ionic liquids on the alteration of nitrification waste for thermal stability

Nitrification waste (NW) is the processed product of nitro compounds, flammable and explosive, and is prone to accidents due to thermal runaway. Ionic liquids (ILs) are widely used as flame retardants and have a good inhibitory effect. The effect of 1-butyl-3-methylimidazole hexafluorophosphate and 1-butyl-3-methylimidazole tetrafluoroborate as humectants on the thermal stability of NW was investigated in this paper. The microstructure and properties of the ionic liquid hybrid NW were first investigated using scanning electron microscopy. Secondly, thermogravimetric experiments were carried out on the ionic liquid mixed NW by thermogravimetric-infrared spectroscopy to analyze the decomposition law. Finally, six advanced linear regression thermokinetic models were used to calculate its apparent activation energy, pre-exponential factor, and other thermokinetic parameters, and the reaction model was simulated through multiple linear regression. The experimental research results in this paper can provide a reference for the storage and disposal of nitro compounds and their wastes.     

基于HSPF及回归模型的淡水河流域非点源负荷计算

应用分布式水文模型HSPF结合回归模型,对广东省东江流域中淡水河流域的非点源负荷进行计算.研究结果显示,模型较好地再现了悬浮泥沙(SS)、CODCr、NO3--N及TP在2010年内的通量随时间的变化过程.统计结果表明,淡水河流域的SS、CODCr、NO3--N及TP在汛期的通量对年通量的贡献十分显著,分别占年通量的86.81%、77.56%、69.83%和73.08%;NH4+-N和TN的计算结果与实测值之间拟合程度较弱,可能是由于这两类污染物与人类活动的点源排放关系密切,而本研究中所用回归方程只考虑了径流量与污染物通量之间的关系,并没有更多地考虑与人类活动相关的因素.     

北运河武清段水污染时空变异特征

通过对北运河武清段水系(主河道——北运河和龙凤河,北运河支流——柳河、灌溉沟渠)的水质进行监测,采用灰色关联法对水质状况进行评价,并利用多元统计法对污染的时空变异特征进行了分析.结果表明,北运河武清段水系17个观测点中有23.8%的观测点的水质为Ⅲ类水,12.7%为Ⅳ类水,63.5%为Ⅴ类水.水质存在时空变异,夏、冬季节各河道水质差异不显著,春、秋季节差异显著.沟渠水质最差,其总磷(TP)含量和BOD5值明显高于其他河道;TP、硝态氮(NO3--N)、COD、总有机碳(TOC)、温度(T)、溶解氧(DO)和总氮(TN)解释了全部的季节性变异,BOD5、pH、溶解性总固体(TDS)和叶绿素a(Chl-a)的季节变异主要受人为因素影响,氧化还原电位(ORP)、氨氮(NH4+-N)和亚硝态氮(NO2--N)次之.NH4+-N和TN解释了51%的空间变异,主河道的氮污染是由上游工厂和企业排放污水造成的;北运河武清段的主要污染物依次为:有机污染物、氨氮、硝态氮、酸碱废水和磷,污染表现为复合污染,点源仍是最主要的污染源.     

温拌沥青混合料环境与经济效益分析

为了研究温拌沥青混合料相比热拌沥青混合料产生的环境与经济效益,不仅对它们在拌和与摊铺过程中产生的有害气体排放量的检测数据进行了对比分析,还引用计算CO2排放量的回归方程对沥青混合料在不同生产温度时的CO2排放量进行了计算分析。结果表明:在沥青路面工程中应用温拌沥青混合料具有明显的环境与经济效益。在对我国2009年用于公路建设的沥青消耗量带来的燃料消耗与废气排放量进行计算分析后,认为推广应用温拌沥青混合料对国家公路的绿色建设以及经济的可持续发展都有着重要意义。     

潮流场作用下的航标漂移计算方法研究

针对目前航标遥控遥测系统中误报警过多的问题,对当前航标漂移计算方法做出优化。首先采集了航标遥测数据并通过拉依达数学准则对采集数据进行预处理;结合Kmeans算法和ISODATA算法,对预处理数据进行聚类,对比分析计算出的聚类中心,选取精确度更高的聚类中心作为计算航标漂移量的基准点并计算航标漂移距离;采用Person相关性分析方法和回归分析方法,构建潮流场作用下的航标漂移模型。结合航标和潮流场实际数据进行回归分析,确定模型参数,计算均方根误差,对模型进行验证。结果表明,该模型能较好地反映潮流场作用下的航标漂移运动,能有效减少航标漂移误报警,提高航标的管理效率和智能化水平。     

Influence assessment of inlet parameters on thermal risk and productivity: Application to the epoxidation of vegetable oils

The influence of inlet parameters on the production and thermal risk of complex chemical systems can be cumbersome to evaluate. To determine the optimum safe operating conditions, one needs to solve complex differential equations derived from energy and material balances. This robust approach cannot be made on-site, and it is essential to propose simplest tools to evaluate rapidly the performance and safety of some operating conditions. This is the aim of this paper that establishes explicit relationships between the production and thermal risk parameters, and the inlet parameters. In addition, it also proposes a Pareto chart that can be used to make the tradeoff between safety and performance. Such relationships and chart were developed for the production of epoxidized cottonseed oil under isoperibolic and semi-batch mode. The kinetic model developed by Zheng et (Zheng et al., 2016). was used. First, a numerical approach, i.e., least square method, was used to find explicit relationships between thermal risk parameters, production parameters and six inlet parameters. The use of such an approach allows a better understanding of this process. Second, safety and performance indicators are proposed and discussed to evaluate the operating conditions thanks to a simple and intuitive schema. Besides, this approach can be used to find the optimum conditions more rapidly.     

基于未确知测度的岩体稳定性多元联系度评价模型*

为准确评定岩体稳定性等级,引入未确知测度理论、多元联系度理论和同异反确定不确定体系,建立基于未确知测度的岩体稳定性多元联系度评价模型。该模型通过指标组合权重值和单指标测度矩阵,得出多指标综合测度矩阵、同异反联系度矩阵和模糊评价矩阵。并借助多元联系度定量计算待评岩体的评价等级、定性分析及准确预判岩体稳定性的发展趋势,较好地优化置信度评价过程,克服传统模型不能定量表达不确定性因素的缺陷,而且拓展指标隶属等级交叉的特性。以1号岩体为例,研究结果表明:岩体的稳定性等级为Ⅲ级,处于基本稳定状态,但有着向不稳定状态（Ⅳ级）移动的趋势。模型的评价结果与实际结果相吻合,说明应用该模型评判岩体稳定性等级是有效可行的。     

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

● Data acquisition and pre-processing for wastewater treatment were summarized. ● A PSO-SVR model for predicting COD_eff in wastewater was proposed. ● The COD_eff prediction performances of the three models in the paper were compared. ● The COD_eff prediction effects of different models in other studies were discussed. The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination (R²) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R², and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.     

粤北某离子吸附型稀土矿山土壤和地表水重金属分布及风险评价

对粤北某离子吸附型稀土矿24个土壤样品中的As、Cd、Cr、Cu、Mn、Ni、Pb、Zn和Hg,以及15个地表水样品中的As、Cd、Cu、Hg、Mn、Pb和Zn进行了检测,并以多元统计分析与土壤潜在生态风险指数法、地表水健康风险评价模型相结合的方式,研究了重金属的分布特征及风险水平。结果表明:在土壤中,Mn、Zn、Cd和Pb的平均含量均超过了背景值;Mn、Cr、Ni、Cu、Cd和Zn在采区有较明显集聚,As、Pb和Hg的高含量分布相对均匀;Cr、Ni、Cu和Pb含量主要受区域背景影响,Zn、As、Cd和Hg含量与矿区人类活动关系密切,Mn含量受自然和人为因素共同控制;重金属造成的土壤潜在生态风险整体处于轻微水平,Ⅱ采区和Ⅶ采区生态风险较高;Cd和Hg是造成土壤生态危害的主要重金属元素。在地表水中,Mn的平均浓度超过了《地表水环境质量标准》(GB 3838-2002)规定的限值,其余重金属的含量均满足该标准中的Ⅲ类水质要求;重金属浓度在靠近采区及位于河流中下游的位置偏高;Mn、Cd、Pb、Zn和Cu浓度受稀土开采影响较大,As、Hg浓度主要受自然因素影响;重金属产生的健康总风险(9.39×10^-7~1.01×10^-6 a^-1)低于国际辐射防护委员会推荐的参考标准(5×10^-5 a^-1),但儿童通过饮水途径受到的健康风险(1.01×10^-6 a^-1)略超过部分机构的推荐限值;Cd和As是地表水中产生健康风险的主要重金属元素。综上,研究区重金属污染风险管理的主要对象是Cd和Mn。