Numerical study of strain development in high-density polyethylene geomembrane liner system in landfills using a new constitutive model for municipal solid waste

Tensile strain development in high-density polyethylene (HDPE) geomembrane (GMB) liner systems in landfills was numerically investigated. A new constitutive model for municipal solid waste (MSW) that incorporates both mechanical creep and biodegradation was employed in the analyses. The MSW constitutive model is a Cam-Clay type of plasticity model and was implemented in the finite difference computer program FLAC?. The influence of the friction angle of the liner system interfaces, the biodegradation of MSW, and the MSW filling rate on tensile strains were investigated. Several design alternatives to reduce the maximum tensile strain under both short- and long-term waste settlement were evaluated. Results of the analyses indicate that landfill geometry, interface friction angles, and short- and long-term waste settlement are key factors in the development of tensile strains. The results show that long-term waste settlement can induce additional tensile strains after waste placement is complete. Using a HDPE GMB with a friction angle on its upper interface that is lower than the friction angle on the underlying interface, increasing the number of benches, and reducing the slope inclination are shown to mitigate the maximum tensile strain caused by waste placement and waste settlement.     

基于现场监测与数值模拟的浅层采空区稳定性分析

隐患采空区是目前影响露天开采矿山安全生产的主要危害源之一。随着台阶开采的不断剥离,露天开采境界内各台阶与地下空区群的隔离层厚度越来越薄,随时有可能发生采空区顶板坍塌事故。考虑到露天矿采空区地质赋存条件和围岩稳固性等特征,以弓长岭露天铁矿浅层采空区为工程背景,运用现场监测和数值模拟相结合的手段综合分析了浅层采空区的稳定性。将液体静力水准地表沉降监测系统的监测数据与FLAC数值模拟结果对比,调整蠕变参数使得数值模拟的蠕变速率与现场监测结果一致,而后据此进行未来结果的预测。最终根据地表沉降数据确定的蠕变参数取值为A=1.0×10^-12、m=1.75、n=0.35。研究表明:静力水准测点地表最大沉降位移为-9.8 mm,蠕变计算结果顶板最大垂直位移约20.4 mm,应力最大值约25 MPa,综合分析显示该采空区较稳定。上述研究提供了一种基于采空区现场监测数据的数值模拟蠕变分析方法,可为类似矿山采空区稳定性分析提供借鉴。     

On the prediction of solubility of alkane in carbon dioxide using the LSSVM algorithm

The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO₂ injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO₂ as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility.     

Modeling dynamic viscosity of n-alkanes using LSSVM technique

One of the important thermophysical properties is viscosity which expresses the resistance of fluid to flow. The least squares support vector machine (LSSVM) algorithm is proposed as a novel method for prediction of dynamic viscosity of different normal alkanes in a wide range of pressure and temperature. As this study is purely computational, 228 experimental data points were gathered from literature for training and validation of the model. The outcomes of the LSSVM algorithm were compared with the actual data with acceptable average absolute relative deviation and the coefficient of determination (R²) of 1.014 and 0.9968, respectively. The comparisons showed that the predicting model has the potential of prediction of n-alkane dynamic viscosity in terms of pressure, temperature, and carbon number of n-alkane, so this strategy can be used as a simple tool for predicting the behavior of reservoir fluids.     

Geosynthetic-sheet pile reinforced foundation for mitigation of earthquake and tsunami induced damage of breakwater

Earthquake and tsunami impose great threats on the stability of a breakwater. Foundation of the breakwater is weakened by these forces, and it may result in collapse of the breakwater. Lateral flow of seabed soils take place beneath the breakwater, and excess pore water pressure is generated in the foundation by an earthquake that precedes tsunami. These factors may lead to excessive settlement and horizontal displacement of the breakwater. Tsunami introduces additional instability to the deformed breakwater. Due to water level difference between seaside and harborside of the breakwater during a tsunami, seepage occurs through its foundation, and it may cause pipping of seabed soils. Tsunami induced scouring of mound is also a big problem for the stability of the breakwater foundation. Finally, these result in failure of the breakwater foundation. Due to failure of its foundation, the breakwater may collapse and cannot block the tsunami. It results in entering of the tsunami in coastal areas. In order to make a breakwater resilient against earthquake and tsunami induced damage, reinforcing countermeasures were developed for foundation of a breakwater. Geogrid, gabions and sheet piles were used for reinforcing a foundation model. The effectiveness of the model is evaluated through physical modeling for mitigating the earthquake and tsunami induced damage. Shaking table tests were performed to determine the effectiveness of the reinforced model under different earthquake loadings. Tsunami overflow test was conducted on the same deformed model in order to see the effects of tsunami on the model. Comparisons were made between the unreinforced and reinforced foundations, and it was observed during the tests that the reinforced foundation performed well in reducing the damage of the breakwater brought by the earthquake and tsunami. Overall, this study is useful for practice engineering, and the reinforced foundation model can be adopted for designing a breakwater foundation to reduce damage triggered by an earthquake and tsunami in the future.     

Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique

In order to ensure safe and sustainable design of geosynthetic-reinforced soil foundation (GRSF), settlement prediction is a challenging task for practising civil/geotechnical engineers. In this paper, a new hybrid technique for predicting the settlement of GRSF has been proposed based on the combination of evolutionary algorithm, that is, grey-wolf optimisation (GWO) and artificial neural network (ANN), abbreviated as ANN-GWO model. For this purpose, the reliable pertinent data were generated through numerical simulations conducted on validated large-scale 3-D finite element model. The predictive power of the model was assessed using various well-established statistical indices, and also validated against several independent scientific studies as reported in literature. Furthermore, the sensitivity analysis was conducted to examine the robustness and reliability of the model. The results as obtained have indicated that the developed hybrid ANN-GWO model can estimate the maximum settlement of GRSF under service loads in a reliable and intelligent way, and thus, can be deployed as a predictive tool for the preliminary design of GRSF. Finally, the model was translated into functional relationship which can be executed without the need of any expensive computer-based program.     

去除水中黄药的试验研究

为给选矿废水中残余黄药的去除提供依据，分别采用自然曝晒法、漂白粉氧化法、酸化分解法、亚铁沉淀法、活性炭吸附法对配制的黄药水溶液进行了黄药降解试验，结果表明，以上诸方法均能较彻底地去除溶液中的黄药，使水中的黄原酸盐浓度达到国家排放标准。其中亚铁沉淀法、漂白粉氧化法药剂来源容易，价格便宜，工艺简单，效果好，较适合处理含黄药选矿废水。     

大型带式输送机动态设计和预测监控探讨

本文着重分析大型带式输送机运行中出现的技术难题与相关设计标准的局限性,提出提高带式输送机运行工况安全可靠性的出路。     

Elman神经网络在动力配煤煤质预测技术中的应用

为了满足动力配煤数字化信息控制系统对配煤煤质在线预测的精度要求，利用Elman 神经网络建立了动力配煤煤质预测模型.该预测模型综合学习了各煤质指标内部以及和配比的关系，能够根据各参配煤种的M_ad,A_ad,V_ad和对应的配比X，直接预测出配煤产品的M_ad,A_ad,V_ad和Q_net,ad . 采用置信区间分析法和相关性分析法，对该预测模型的预测效果进行了检测.检测结果表明，该预测模型具有较高的预测精度和可信度.     

金属矿床开采岩体移动预测系统的开发

分析目前岩体移动预测的各类方法,指出每一类方法适用条件和存在的问题,提出了以工程类比、数值分析与现场监测三者相结合的综合预测理论,进行了"金属矿床开采岩体移动预测辅助系统"的结构设计与开发。