Lattice Boltzmann model for agrochemical transport in soils

Agrochemical transport in soils is complicated and involves physical, chemical and biochemical reactions; its mathematical modelling remains a challenging task. This paper presents a lattice Boltzmann model to simulate the agrochemical movement. The lattice Boltzmann model is a microscopic and process-based model, simulating the transport process by tracking chemical particles. The model presented in this paper is for one-dimensional vertical leaching and assumes that the chemical particles at the microscopic level move in five directions: one stagnant, two in vertical direction and two in an internal horizontal direction bounded by two reactive walls. Reactions at the walls are assumed to take place at two different rates, one in fast rate where the chemicals in the solution and on the wall are in an instant equilibrium, and the other in slow rate where the mass exchange rate between the chemicals in the solution and on the wall is a first-order kinetic. The reactions on both walls are assumed to occur instantly when the chemical particles moving in the internal direction hit the walls. To test the model, we measured the leaching of atrazine through soil columns in the laboratory. The results simulated with the lattice Boltzmann model are compared with the measured breakthrough curves and the non-equilibrium two-site convection-dispersion model; they all show close agreement. The transport parameters needed in the models are obtained from the measurement of adsorption isotherm of atrazine, bromide leaching in the same soil columns and calibration.     

Comparison of Four Machine Learning Methods for Predicting PM10 Concentrations in Helsinki, Finland

Machine learning methods can offer a practicalalternative to deterministic and statistical methods forpredicting air pollution concentrations. However, for agiven data set, it is often not clear beforehand whichmachine learning method will yield the best predictionperformance. This study compares the variable selection andprediction performance of four machine-learning methods ofdifferent complexity: logistic regression, decision tree,multivariate adaptive regression splines and neuralnetwork. The methods are applied to the task of predictingthe exceedance of the European PM₁₀ daily averageobjective of 50 g m^-3 for a station in Helsinki,Finland. Our study shows that some predictors were selectedby all models but that the different models also pickeddifferent variables. The performance of three of the fourmethods investigated was very similar, however, performanceof the decision tree method was significantly inferior.Performance was sensitive to the learning sample size andtime period used.     

煤矿自制掏槽机与人工掏槽在煤矿密闭施工过程中的应用对比

神东开拓准备中心为克服传统人工掏槽方式的弊端,进一步提高掏槽作业的安全保障水平与作业效率,在充分借鉴传统综掘机破岩机理的基础上,自制出了联巷密闭专用掏槽机,通过对自制掏槽机与人工掏槽在施工工艺、安全保障、效率、效益方面进行对比,结果表明自制掏槽机很大程度地降低了掏槽作业的强度,并将掏槽人工工效提升了2倍,与此同时该掏槽机在现场安全保障方面明显优于传统人工掏槽工艺,因此该机械在神东矿区乃至煤炭行业都具有较好的推广价值。     

基于机器学习的侵财类案件危害程度分析

为了进一步分析侵财类案件的危害程度,以抢劫、抢夺和盗窃3种典型侵财类案件为例,利用ZS市2008—2014年的犯罪数据与统计年鉴数据,提取“发案时间”“发案地域”“选择时机”“选择处所”“选择对象”“人均地区生产总值”“职工月平均工资”7个特征,建立基于多种机器学习分类算法的侵财类案件危害程度预测模型,并进一步开展预测结果的分析研究。研究结果表明:梯度提升决策树（GBDT）算法性能最优,危害程度预测准确率达到了0.88;在抢劫案和抢夺案中,一般和重大的案件容易发生在繁华地带,特大案件容易发生在其他处所;侵财类案件倾向于在工作日的城区中发生,发生的危害程度大多为一般;提出的侵财类案件危害程度预测模型可为侵财类案件的风险评估及警务资源优化配置工作提供方法支持。     

果脯生产废水治理工程设计

介绍了“IC厌氧反应器＋生物接触氧化”工艺处理高浓度果脯生产废水的实例,处理后水中的CODCr、BOD5和SS浓度均低于GB 8978-1996《污水综合排放标准》中的二级标准。该工艺流程重视废水的预处理,运行费用低、管理简单方便、易于操作、无二次污染、投资规模少。实践证明,这一方法用于高浓度果脯生产废水处理是非常成功的,有很高的实用价值。     

基于支持向量机的水环境质量综合评价

提出了基于支持向量机的水环境质量综合评价方法。给出了数据规范化处理及学习样本生成的具体方法,采用“一对一”(1-v-1)的多类别分类算法,使用k-fold交叉验证优化参数。对湖泊水环境富营养化程度的综合评价结果表明,该方法简便、客观,具有较强的实用性。     

U型夹箍自动滚弯成形

介绍了洗衣机U型夹箍自动滚弯成形新工艺。采用钢带经校平、成U形截面、弯圆形、切断 ,压连接端自动连续成形。不但生产效率高 ,产品质量稳定 ,而且大大减轻了工人的劳动强度 ,节省了生产空间。     

太钢热连轧水处理泥系列改造新工艺

新工艺引入“一机两用”和“以废治废”的思想进行系统改造 ,实现改造后污泥处理系统的良性循环 ,根除了污泥外排的局面 ,降低了成本 ,节约了费用 ,创造了良好的社会效益和环境效益     

冲裁模刃口尺寸及公差的自动标注与修改

根据冲裁模刃口尺寸及公差的确定原则和计算公式 ,提出了切实可行的人机交互式的自动标注和修改冲裁模刃口尺寸及公差的方法     

Novel quantitative structure activity relationship models for predicting hexadecane/air partition coefficients of organic compounds

Predicting the logarithm of hexadecane/air partition coefficient (L) for organic compounds is crucial for understanding the environmental behavior and fate of organic compounds and developing prediction models with polyparameter linear free energy relationships. Herein, two quantitative structure activity relationship (QSAR) models were developed with 1272 L values for the organic compounds by using multiple linear regression (MLR) and support vector machine (SVM) algorithms. On the basis of the OECD principles, the goodness of fit, robustness and predictive ability for the developed models were evaluated. The SVM model was first developed, and the predictive capability for the SVM model is slightly better than that for the MLR model. The applicability domain (AD) of these two models has been extended to include more kinds of emerging pollutants, i.e., oraganosilicon compounds. The developed QSAR models can be used for predicting L values of various organic compounds. The van der Waals interactions between the organic compound and the hexadecane have a significant effect on the L value of the compound. These in silico models developed in current study can provide an alternative to experimental method for high-throughput obtaining L values of organic compounds.